"Investing in Power System Resilience: A mixed methods approach to assessing the tradeoffs of resilience strategies" - Angelena D. Bohman, 2022

As modern American society has become increasingly dependent on the provision of reliable electric power, the importance of enhancing the resilience of the power system has grown. In years to come, stress arising from climate change, and the continuing risks of physical and cyberattacks on the system, will likely make resilience even more important. However, while the topic receives a great deal of attention, there is little rigorous analytical work that has assessed the efficacy and cost-effectiveness of strategies to increase power system resilience.
This research adopts a mixed methods approach to identify and assess strategies for improving the resilience of power systems and thus enhance planning as the power system evolves. Chapter 2 begins by reviewing a range of resilience strategies discussed in the literature that could be deployed on the grid. The literature on this subject is scattered and unsystematic in its treatment of cost and efficacy. This chapter contributes to the resilience literature by developing a comprehensive list of strategies and summarizing what is known about their costs and efficacy.
Chapter 3 has already been published in the journal Risk Analysis. It analyzes and compares a range of individual and collective strategies that could increase the resilience of power supply to residential customers on a distribution feeder by providing contingency power during large power outages of long duration. A typical (but hypothetical) town in the Upper Connecticut River Valley is modelled—we choose this region because it is susceptible to ice storms and hurricanes—to estimate the cost and performance of different resilience strategies, assuming that a large power outage of long duration occurs. This work showed the extent to which collective strategies are cheaper, and therefore highlighted the importance of developing institutional arrangements that make it easier for communities to implement those collective strategies.
Chapters 4 and 5 present the first systematic analysis of empirical data about the efficacy of different resilience strategies. To do this, Chapter 4 focuses on the state of Florida, which has required utilities to make substantial investments in storm hardening to improve the resilience of their systems in the face of tropical cyclones. While these utilities compile and report to the state Public Service Commission a phenomenal amount of data on the cost and performance of their different resilience strategies, almost no analysis has been done on these data to assess whether and to what extent these investments are actually improving the power system’s resilience. After compiling fifteen years’ worth of these data for Florida’s Investor-Owned Utilities, the exploratory data analysis shows that after accounting for tropical cyclone severity, undergrounding power lines, changing pole material to non-wood and installing advanced metering infrastructure across all customers likely improve average tropical cyclone restoration time (CAIDI). Increasing annual tree trimming on the other hand does not seem to have an impact on tropical cyclone CAIDI. However, to continue performing at current levels of reliability, these utilities will have to spend more on the same activities and even more on others to continue to address the impacts of climate change and the uncertainty they induce.
Informed in part by this applied data analysis, in work reported in Chapter 5 utility engineers were interviewed to extract their mental models of power system resilience, as well as their judgments regarding how to enhance resilience most effectively given the range of threats facing their systems. Operators were asked to consider the worst outage their system had experienced and then to elaborate on how implementing resilience strategies could have improved their response to this worst outage without financial barriers. Undergrounding power lines was the preferred strategy by interviewees, but also estimated to be the most expensive. Finally, participants were asked what technologies, policies and resources are especially needed by utilities to enhance their systems’ resilience, and they suggested new rate designs to account for an increase in renewables, distributed energy resources, and electrification as well as defining new policy standards for new loads and distributed generation being added to their systems. They were additionally concerned with supply chain backlogs and having enough money to do the necessary upgrades on an aging system.

"Technology Policy Challenges of Electric and Automated Vehicles" - Aniruddh Mohan, 2022

This body of work covers some important technology policy challenges that will be posed by the deployment of electric and automated vehicles in personal mobility, ridesourcing, and long haul trucking. Substantial electrification of road transport is critical to deep decarbonization. Self-driving technology continues to inch towards wider commercial deployment, despite well-publicized setbacks. Policymaking must grapple with a deeply uncertain technology landscape and trade-offs between multiple societal objectives. I attempt to help inform public policy by carefully navigating the uncertainty present in the deployment of these technologies and highlighting how trade-offs between multiple objectives might materialize. First, I look at the potential energy impact of automation on electric vehicle range. Will electrification and automation of light vehicles develop in sync? I find that automation will incur expected penalties of 10-15% on electric vehicle range, due to the energy consumption of computing and sensors. This suggests that deliberate technology choices towards efficient computing and aerodynamics will be needed to drive electrification of automated vehicles. In long haul trucking, I investigate the potential job impacts from automation of highway driving. I draw on detailed shipment data to show that replacing all highway driving with automated trucking will lead to more than 90% of operator-hours lost to automation. I find limited evidence to support industry claims that increased short haul jobs will compensate for the operator-hours lost on highways. Third, I present a novel agent based model that can answer a series of research questions related to electric vehicle deployment in ridesourcing. Companies such as Uber and Lyft have committed to fully-electrify their fleets by 2030 in the United States. Should policy incentivize larger battery pack sizes or greater investments in dedicated fast-charging infrastructure for ridesourcing? I instantiate the agent based model to study the lifecycle externalities of fully-electric ridesourcing in the city of Chicago, across different levels of battery size and fast-charging infrastructure. I estimate that greenhouse gas externalities are in the range of 10-13¢ per trip, with lower externalities associated with smaller battery packs. Compared to today, fully electrifying ridesourcing can cut greenhouse gas externalities in Chicago by 50-60%. Solely targeting reductions in greenhouse gas emissions, however, comes at the cost of increases in traffic externalities such as congestion.

"Analyzing and Optimizing Shared Mobility Fleet Impacts" - Matthew Bruchon, 2022

Passenger vehicles enable activity, but they generate unpriced negative externalities such as air emissions and traffic. Those externalities constitute a market failure that may justify policy intervention. Passenger vehicle travel, especially within urban areas, is being transformed by vehicle electrification and by shared mobility options offered by ridesourcing services such as Uber and Lyft. These transformations’ impacts on externalities are unclear a priori, as is the role of policy to influence them. To investigate these externalities and what options can address them, I use a mixture of simulation and empirical analysis. One study asks how much an efficiently priced Pigovian tax on unpriced air emissions
externalities would incentivize a ridesourcing service to increase vehicle fleet electrification and reduce its emissions. Applying a mathematical optimization of fleet size and powertrain mix with and without a tax, the study finds the tax increases the optimal usage of battery electric vehicles by 5% to 156% and reduces emissions externalities of the optimal fleet by 10% to 22%. The second study assesses the potential of ridesplitting services—that is, services that combine multiple rides in one car at the same time (e.g., UberPool or Lyft Line)—in a similar manner, asking how much a Pigovian tax on air emissions and traffic (congestion, collisions, and noise) would incentivize increased ridesplitting. Using a dynamic fleet dispatch mathematical optimization problem with and without a tax, the study finds the tax increases use of ridesplitting by 2 percentage points and reduces externalities by 1%. The third study uses the case study of Chicago’s ridesourcing congestion charge policy to ask whether taxes can be practically effective at discouraging solo ridesourcing rides and encouraging ridesplitting. This study conducts an empirical analysis, using a difference-indifferences model to find a downtown zone surcharge reduced total (solo plus ridesplitting) ridesourcing rides by a mean [95% confidence interval] of 8.2% [7.6%, 8.7%], increased ridesplitting opt-in rates by 4.2 [4.0,4.3] percentage points, and increased ridesplitting rides that were successfully matched with another ride by 3.4 [3.3,3.5] percentage points. Special
attention is given to spatially autocorrelated outcomes and potential spillover effects, and the direction of results is robust across modeling choices. To enable these insights, each study considers challenges and applies methods for use of travel data in policy analysis. Specifically, all three apply unsupervised machine learning to make modeling datasets resemble a representative or average set of travel demand, and the first two use supervised learning to estimate varying road network conditions. These steps mitigate avoidable bias in policy findings when using high-volume trip-level travel data. These studies can inform transportation companies, policymakers, and other stakeholders on the market failures involved with passenger vehicle travel and what policies can help correct for those failures. They also help characterize how effective different transformation pathways in passenger transportation (i.e., electrification or shared mobility) are as solutions to those negative impacts.

"Technology transitions inthe electricity and automotive sectors: Embracing political, social, and economic constraints" - Turner Cotterman, 2022

This dissertation is motivated by the urgency to rapidly and deeply reduce global greenhouse gas emissions. We have the set of technologies at our disposal to address this complex environmental objective, but their deployment is complicated by the evolving political, social, and economic landscapes that present challenges as well as opportunities. I focus on two mitigation  technology approaches—low-carbon energy generation and vehicle electrification—with consideration of their broader influences and impacts. In the first study (Chapter 2), I examine how socio-technical constraints affect the most feasible technology pathways for decarbonization. I develop a probabilistic representation of social acceptance characterized by technological risk tolerance and pair it with an energy system optimization model to evaluate techno-economic projections of energy technologies within the context of societal processes. The integration of these two models, demonstrated through an illustrative example of nuclear power in the U.S., finds that overall system costs may increase and select technology availability may decrease due to the presence of societal preferences. This work asserts that quantitative modeling of energy and economic systems can be supported by insights into real-world processes and socio-technical influences. In the second study (Chapter 3), I assess how labor demand (measured in hours) differs
between internal combustion engine vehicle (ICEV) and battery electric vehicle (BEV) manufacturing for powertrain components. I collect detailed data on the production process steps required to build key ICEV and BEV powertrain components and the labor required for each process step from the existing literature and the shop floors of leading automotive manufacturers. I then use this data to build a production process model that determines the labor hours required to produce ICEV and BEV powertrain components in a variety of scenarios subject to different production volumes and labor efficiency levels. I find that BEV powertrains require more labor hours, at least in the short- to medium-term. These results
emphasize the importance of using process step-level information about manufacturing processes and labor requirements to estimate the labor impacts of vehicle electrification. In the third study (Chapter 4), I evaluate how worker skill requirements differ between ICEV and BEV manufacturing, again for powertrain components. I interview ICEV and BEV shop floor workers (i.e., operators, technicians, supervisors) on the labor tasks required for the powertrain production steps from the previous study. I use the O*NET survey instrument and comparative descriptive statistics to evaluate the level of skills required for the two different vehicle technologies. I find that the skill requirements for manufacturing
BEV powertrain components lie within the range of skill requirements for ICEV powertrain components and that production practices used by BEV manufacturers may increase demand for fuller worker skillsets. These studies can support decision-making by energy and automotive firms, policymakers,
organized labor, and other stakeholders and enable more effective strategies for achieving decarbonization and vehicle electrification goals. They also contribute to a more complete understanding of the potential socio-technical constraints facing and impacts by technologies within the ongoing low-carbon transition.

"Hydropower Vulnerability in a Changing Climate: Characterizing Future Risks in the Global South" - Ana Caceres, 2022

Global electricity demand is expected to increase over the following decades, with more than half a billion people worldwide still lacking access to modern electricity services. Additionally, the power sector is one of the largest contributors to increasing GHG atmospheric concentrations, and there is a pressing need to decarbonize the sector. Unfortunately, emerging economies in the Global South, where most of the electrification needs to happen, are some of the most vulnerable to the potential impacts of climate change. Therefore, to achieve the UN’s Sustainable Development Goal 7 of universal electricity access, emerging economies will need to expand their electricity infrastructure while cutting emissions and adapting to climate change. Hydropower may be a low-carbon option to increase supply and decarbonize electricity generation. Unfortunately, climate change can affect hydropower operations through changes in the timing and magnitude of precipitation, rising temperatures, and glacier mass. Evaluating climate impacts on hydropower generally requires detailed local input data and hydrological models, which may not be available in many places of the Global South. Nevertheless, there is a pressing need to understand these impacts for future planning decisions. Research that focuses on developing flexible data requirement tools and models able to use climate projections and remotely sensed datasets for data-scarce regions is needed. Furthermore, identifying climate impacts and their potential risks on hydropower plants is just the first step towards the future adaptation of the hydropower sector. Communicating assessment results to relevant decision-makers will be crucial, yet effective communication tools for climate adaptation are still lacking. The objective of this dissertation was to characterize and understand the impacts of climate change on usable hydropower capacity in the Global South. First, I developed a hydrological model paired with a hydropower operations model to assess usable capacity at the power plant level in data-scarce regions of the world. Then, I used the model to analyze the changes in usable capacity in Brazil, Colombia, and Peru under a multi-model ensemble. Later, I used the same model across five African power pools. I expanded the initial assessment to incorporate changes in variability of hydropower resources in Africa; and generate interconnection scenarios based on the complementarities of these resources. The final piece of this dissertation consisted of creating an interactive analysis tool that includes all previous assessments and incorporated 56 more countries across five regions of the Global South.

"Marching to the beat of an absent drummer: Carbon Dioxide Emissions Reduction in the U.S. Power Sector" - Jeffrey Anderson, 2021

As the call to limit the impact of climate change via decarbonization proliferates through global economies, the demand for power sector low- and zero-carbon generation intensifies. In the United States, there has been much debate on the merits and faults of increasing variable renewable energy, natural gas, and nuclear capacity; and because the depth to which all are willing to decarbonize is contested, direction from an overarching national policy is lacking. The absence of a formulaic policy offers the opportunity to explore fossil-fuel fleet reduction opportunities that do not advocate the wholesale replacement of the fleet with a zero-carbon technology, and allows for flexibility and the utilization of existing incentives to nudge the carbon-emitting fleet to emissions reduction. In this dissertation, I examine the role competing mitigation technologies under these conditions can play in decarbonizing the fossil-fuel fleet. To do so, I develop a tool for least-cost emission reductions, use this tool to quantify uncertainty and determine regret in the complex mitigation decision, and analyze the impact that the U.S. tax code has on this choice. The Obama administration’s Clean Power Plan (CPP) introduced in 2014 was intended to be such a policy to reduce power sector emissions in accordance with global efforts. While the CPP was never enacted, Chapter 2 examines historical and projected power sector emissions to determine if the emission reduction goals laid out in the CPP can still be achieved within the intended timeframe. The analysis demonstrates that marketplace mechanisms are sufficient to achieve the goals, as low natural gas prices propelled initial reductions, and low capital costs for renewable generation are projected to continue to drive emissions to below the 2030 CPP target. However, further value perceived by the market participants along this pathway seems inadequate to achieve the deeper reductions for a low-carbon sector without the guidance of policy tools. As a prelude for how the fossil-fuel fleet might respond to such emission reduction policies, Chapter 3 presents a novel method that uses unique coal-fired electric generating unit (CFEGU) characteristics to evaluate multiple mitigation-technology options under local fuel prices and varying emission reduction targets. This technique produces a least-cost mitigation frontier for nine CFEGU-specific mitigation solutions created within a common assessment framework with which the mitigation options can be ranked to determine the mitigation with the lowest capital cost and levelized cost of electricity to meet CO₂ emission-intensity reduction-targets in accordance with specific policy directives. To demonstrate the application of this tool in assessing uncertainty given the politically turbulent nature of emissions reduction policy, an analysis of mitigation decision-related regret and stranded assets under different reduction targets is presented. In Chapter 4, the above method is expanded with more mitigation options and includes the 2030 projected natural gas combined cycle (NGCC) fleet. Again, absent a national policy for emission reduction and where market forces alone dictate generation, the carbon capture and storage (CCS) incentives in Section 45Q of the U.S. Internal Revenue Code are used for emission reductions and CCS capacity expansion in the coal-fired and natural gas-fired fleets under the current credit structure for immediate storage. The 45Q credit levels and durations are modified to further promote generation from CCS-equipped capacity as a means to nudge deeper reductions in the fossil-fuel fleet and to reduce total system cost for near-zero emissions in the power sector. This incentive is shown to be a possible marketplace tool to induce emission reductions; however, unique credit levels and durations are required for different generation technologies and unit ages to separately achieve the same percent of the projected 2030 net generation from CCS capacity. This objective may be achievable on a bipartisan basis through modification of the existing tax code, and without the legal battles associated with a national policy based on regulations. Therefore, continued emissions reduction may be accomplished without a specific national policy but done so indirectly with tools designed for a more limited scope. However, for the fossil-fleet to attain net-zero emissions, direct air capture and storage is required to augment CCS capacity. Here, each can also benefit from increased federal research, development, and deployment funding for implementation of the current generation technologies, and for innovation in subsequent generations.

"Essays in Environmental, Climate, and Public Health Impacts of Freight Transportation" - Priyank Lathwal, 2021

The freight transportation sector is a growing contributor to global greenhouse gas emissions, and successful emissions mitigation requires studying its impacts in different contexts. However, an evaluation of different decarbonizing strategies across nations is missing in the literature. This dissertation consists of two methodologically distinct but related studies looking at the environmental, climate, and public health impacts of freight transportation. In the second chapter, I look at these impacts in the context of ocean shipping in India and explore the emissions reduction potential of shore power in India. However, given how dirty and emissions-intensive India’s electricity generation currently is, I show that shore power is not a cost-effective strategy to reduce air pollutants and greenhouse gas emissions in India. In the third chapter, I evaluate freight trucking pollution impacts across the contiguous United States and the implications of trucking pollution on minority group populations. Based on my analysis, I find that the environmental and climate social costs due to freight trucking in the US result in ~$17B in environmental damages and ~$25B in climate damages, respectively. Further, more trucking pollution occurs in counties and census tracts with a higher proportion of Black and Hispanic populations.
In a final chapter, I conclude with further discussion of the findings of this work, explain how they are broadly valuable for policymakers, some more general conclusions.

"Challenges in Climate Change Communication on Social Media" - Aman Tyagi, 2021

In today’s fast-paced lifestyle, internet users depend on social media platforms to obtain and debate essential socio-political and economic topics. However, this same vital source su‚ers from various challenges. On social media platforms, such as Twi‹er, users do not necessarily face a lack of information; instead, they are overwhelmed with diverse information sources. ‘ese myriad sources of information on social media can make users unknowingly con€ned to or associated with other users or groups. Moreover, facts or news can be reported in ways that create confusion and a‚ect public sentiment on scienti€c actualities. Such social media challenges can cause a long-lasting impact in reshaping our society, slowing down scienti€c progress, and dampen regulatory endeavors. ‘us, social media’s impact on socio-political and economic topics must be analyzed. In this thesis, I analyze each of these problems using conversations and news articles about one of the most signi€cant challenges our society faces today, i.e., climate change. In my €rst study, I analyze climate change discussions on Twi‹er to study users con€ned to competing belief groups. I classify Twi‹er account users into: (a) users who believe in the anthropogenic cause of climate change (Believers); and (b) users who don’t (Disbelievers). I study the di‚erences in communication topics and network structure in Disbelievers and Believers. I find that both Disbelievers and Believers talk within their group more than with the other group; this is more so the case for Disbelievers than for Believers. In my second study, I develop a framework to quantify hostile communication between Believers and Disbelievers. I show that Disbelievers of climate change are more hostile towards Believers than vice versa. I examined the framing bias of climate change news articles shared on Twi‹er as part of my third study. I €nd that climate change news articles are predominantly framed as related to policy issues in the context of a social group’s traditions, customs, or values. Finally, I explore the spread of conspiracy theories in climate change conversations on Twi‹er. Results suggest that Disbelievers are primarily responsible for sharing messages that contain keywords related to conspiracy theories. Overall, my work in this thesis develops frameworks to analyze social media challenges and contributes to climate change communication research.

"An Interdisciplinary Decision Framework for Risk-Based Nuclear Power Plant Emergency Planning and Protective-Action Strategy Selection" - Adam Stein, 2021

Emergency planning is a required component of licensing for nuclear power plants. A rare opportunity to redefine emergency preparedness has been created by the ongoing work at the Nuclear Regulatory Commission to develop the capability to address small modular reactors and other new technologies and to transition to a risk-informed performance-based regulatory structure. This dissertation develops a new framework for emergency preparedness that can address the characteristics of new reactor technologies while also addressing the limitations of current methods. A review of the literature, current regulations, and methods identifies gaps and limitations. Statistically valid methods are defined to enable new analysis of uncertainty and use cases in limited regulatory validated computer codes. A new interdisciplinary framework for emergency planning is developed to reduce the barriers present in current methods, then a risk-based model that integrates protective action and hazard dispersion models is defined. This integrated model considers the risk caused by multiple hazards, including radiological and transportation hazards. The interdisciplinary and integrated structure of the model provides the opportunity for new measures of effectiveness that provide additional insights beyond existing metrics. The integrated model is used to evaluate emergency response at the Peach Bottom Atomic Power Station as a case study. The key findings of this case study provide insight into effects previously not discussed in nuclear power emergency planning studies. The ability to compare protective actions across multiple metrics allows for risk and consequences-based evaluation and provides more information for decision-makers.When combined dose and non-dose risks are considered, many historically common protective action strategies become inadvisable by creating more combined risk than taking no action. Even small amounts of time between initiating a protective action and the release of radiation can potentially result in a substantial reduction of consequences. The behavior of the population has a large impact on consequences but is not sufficiently captured in prior studies.

"Data and Technology-driven Improvements to Electricity Market Design" - Luke Lavin, 2021

Wholesale electricity market and retail tariff design often uses anachronistic assumptions based on existing technology characteristics or historical computational and data limitations. This dissertation conducts four categories of analysis on how electricity market design can be modernized to increase efficiency and avert roadblocks to economy-wide deep decarbonization. First, electric utilities can capture most of the system benefit of customer-sited energy storage resource (ESR) adoption with critical peak pricing (CPP). CPP is proposed as a simple and Pareto-improving rate design for commercial and industrial customers with ESRs, similar to time-of-use rates for electric vehicle owners. Second, previous research quantifying correlated generator failures in the PJM Interconnection can be incorporated in both resource adequacy and scarcity pricing using an operating reserve demand curve (ORDC). Because correlated failures occur at very high and low temperatures when electricity demand is highest, there are substantial effects on target winter and summer planning reserve margins and increased social welfare from better accounting for generator failure probability when designing ORDCs. Third, as electricity markets evolve toward higher shares of variable, low marginal cost resources with ESRs new rules are needed to ensure resources’ full, competitive participation. A bi-level model implemented on a realistic, high renewables nodal test system highlights strategies ESRs and hybrids can use to raise prices, particularly cross-product and decongestion strategies, and suggests offer uniformity over co-optimized temporal intervals as a remedy. Fourth, metrics like effective load carrying capability (ELCC) are increasingly common for quantifying the system-dependent contribution of variable generation and ESRs to resource adequacy. Extending these methods to a zonal evaluation of resource adequacy using data from the Midcontinent Independent System Operator (MISO) shows transmission and ESRs have a complementarity benefit in zonal resource adequacy that is not realized by variable nor conventional generators, suggesting the importance of increased zonal representation in planning with ESRs.

"Evaluation of the Food-Energy-Water nexus through case studies in the United States and East Africa" - Jorge Izar-Tenorio, 2021

The demand for systems and infrastructure that can equitably and efficiently provide food, energy, and water is central to economic development and sustainable growth. Diverse conditions such as growing population, climate change, and access constraints pose a formidable challenge for industrialized and non-industrialized countries. Industrialized countries’ food and energy systems face the threat of unsustainable practices and competition for resources from multiple productive sectors. Meanwhile, the least developed countries struggle with inadequate access to modern agricultural, water, and energy technologies to provide food efficiently and securely. This thesis aims to identify and quantify food production impacts on energy and water consumption via case studies in the United States and East Africa. In both cases, I use integrated biophysical models to estimate the effects of food production (e.g., chicken broiler meat and irrigated crop yields) on energy and water resources consumption using climatological data inputs. For the case study in East Africa, I also assessed the financial viability of pressurized irrigation on a subnational level. Findings suggest that projected future climate change temperatures by mid-century will increase energy demand for cooling, reduce energy demand for heating, and substantially increase water withdrawals for evaporative cooling for industrial chicken broiler production in the Eastern U.S. The results for the case in East Africa indicate that the techno-economic potential of small-scale pressurized irrigation is highest for horticulture, maize, and potato crops grown with improved seeds and at least moderate fertility levels. My results suggest that food production impacts on energy and water demand are climate and site (or geography) dependent. These factors' relative importance depends on operational practices (e.g., input selection), technology types and costs, and fuel prices.

"The Energy and Environmental Effects of New and Future Mobility: Econometric and Simulation Analysis of Ridesourcing Services Uber and Lyft" - Jacob Ward, 2020

This thesis provides an initial understanding of the potentially fundamental changes to the way passenger vehicle transportation in the United States (U.S.) is changing given the introduction of ridesourcing via transportation network companies (TNCs), like Uber and Lyft, and the effects those changes have on energy and environmental outcomes. First, in a set of two complementary studies, I employ real-world data and econometric modeling to assess the impacts that TNCs have already had on U.S. states and urban areas. In the first study (Chapter 2), I focus on the state level, where relevant data are publicly available to estimate TNC market entry effects on vehicle registrations, gasoline use, vehicle miles traveled, and air pollutant emissions. I find an average decrease in vehicle registrations and no significant effect on other outcomes. In the second study (Chapter 3), I assess TNC effects on vehicle fleet composition (total registrations and fuel economy) and transit ridership at the urban area level and find evidence that TNC entry causes an average 0.7% increase in vehicle registrations and no average effect on overall fleet efficiency or transit ridership. The difference in state- and urban area-level effects on vehicle registrations is due, in part, to heterogeneity in the effects of TNC entry on different cities: I employ heterogeneous treatment effect, clustering, and regression interaction analysis and find significant heterogeneity across urban areas. TNC entry tends to increase vehicle ownership in urban areas with higher initial vehicle ownership and lower population growth rates, TNC entry tends to increase vehicle ownership, increase overall fleet efficiency more in urban areas with lower childless household rates, and increase transit ridership more in urban areas with lower average incomes and childless household rates. Where the first two studies look at aggregate past changes to the transportation system attributable to TNCs from the top down using observable indicators at the aggregate state and urban area levels, a third study in Chapter 4 considers a similar set of outcomes but focused at the vehicle level. I propose and apply a framework to quantify the external costs and benefits of TNC disruption to the transportation energy system by systematically characterizing the avoided cold start emissions and additional non-revenue miles and associated emissions and quantifying the relative size of external benefits and costs from TNC vehicles for several of the largest TNC markets in the U.S. and find that shifting travel from private vehicles to TNCs offers net external air pollutant benefits in some areas while incurring a net external cost in others; however, including externalities associated with additional vehicular travel yields net external costs everywhere. Taken together, these three studies confirm that TNCs have already affected the number and efficiency of vehicles owned and transit ridership rates in the U.S. and that they have done so heterogeneously as a function of preexisting socioeconomic and passenger travel characteristics. And, at the individual TNC trip level, targeted sensitivity and policy analyses to illustrate how transportation and urban planning decisions can increase net external benefits and/or reduce negative external costs.

"Environmental and Economic Prospects of Low-Carbon Vehicles in Support of European Commission 2030 City Logistics Fleet Goals" - Alessandro Giordano, 2020

This thesis explores the operational feasibility, costs and benefits of replacing urban parcel deliveries operated by diesel vans with a diverse set of low-carbon vehicles. The aim is to facilitate the discussion among companies and policy makers on the health, environmental, economic and operational feasibility aspects of low-carbon vehicles, such as BEV vans, electric cargo scooters, and electric and human-powered cargo bicycles; and to produce actionable insights for their decision making on the inclusion of these vehicles in city logistics fleets. The analysis is carried under both private and public perspectives and for six specific European capitals (Berlin, Paris, Rome, Lisbon, Oslo and London), characterized by diverse size, weather, topography, infrastructure, and economic and social conditions. Because of these differences, the insights of this study are valuable to other cities within and outside Europe. The second chapter explores costs and benefits of BEV very large vans compared to their diesel equivalent, performing a life cycle assessment and an annualized cost comparison. Different battery technologies are included in the assessment and the outputs served to model small vans and air pollutant emissions from vehicle productions of low-carbon vehicles based on their weight and battery sizes. The third chapter assesses the effects of temperature on operational feasibility and costs of large BEV (and diesel) vans to make Chapter 2 results more robust. The study finds that the operational costs of diesel and BEV vans due to temperature effect are relatively small when compared to the overall operational costs. Even when including the purchase of dedicated charging stations, large BEV van operational costs remain 40 to 80% lower than for large diesel vans. However, pre-heating large BEV vans can reduce their range limitations in cold cities by 5-10%, 90-95% and 100% for 23.4, 46.8 and 70.2 kWh battery sizes, respectively, while it has a small or no value in warm cities. The fourth chapter then shifts the focus to deliveries performed by small diesel vans and assesses small BEV vans, electric cargo scooters and cargo bicycles’ ability to replace small diesel vans. It also explores the effects of weather and topographic factors, such as temperature, wind and city hilliness, on low-carbon vehicle technologies’ operational feasibility frontiers, expressed in terms of distance and load. Results reveal that the baseline fleet of small diesel vans, and therefore its delivery trips and mileage, can be entirely replaced by 36 kWh small BEV vans, while two-wheeled vehicles have a more limited potential. When multiple cargo bicycles and electric cargo scooters are used to replace diesel van trips, they could replace up to 28-63% of the baseline small diesel vans, with 0.4 average load factor, and 24-62% of the baseline fleet mileage, depending on the characteristics of the city. Across the topographic and weather factors affecting riders’ energy use, “hilliness intensity” and “average wind speed,” are the most relevant ones. The first of the two is predictable, however it could increase energy use considerably. Based on empirical cargo bicycle rides’ data, this study founds this effect varies from 0% in Berlin and London to 37% in Lisbon. Wind speed effect is less predictable daily and its effect on twowheeled vehicles’ energy use varies between 1% and 22%. Hence, electric cargo bicycles in hilly and windy cities like Lisbon would require a set of three 1 kWh batteries to operate the same number of delivery trips that are operationally feasible for 1 kWh electric cargo bicycles in a flat city like Berlin. Furthermore, results reveal that cargo bicycle riders’ “type of diet” is critical to determine whether their deliveries have lower carbon footprint than electric scooters and small BEV vans. When food is considered, human-powered cargo bicycles’ GHG emissions are also larger than for electric cargo bicycle models. In the fifth chapter, private and external costs of different vehicle options are discussed to assess their cost effectiveness and inform the strategies, and policy incentives, delivery companies and European cities will need to achieve increasing levels of the European Commission strategic goal of “CO2-free city logistics,” with and without including cargo vans, by 2030. Results reveal that, low-carbon vehicles are either able to reduce air pollution but not congestion external costs (small BEV vans), or reduce air pollution and congestion external costs, but increase road accident costs (cargo bicycles and electric scooters). The study finds that cities can reduce their city logistics external costs including low-carbon vehicles in their fleets by up to 57% in Berlin, to 45-43% in Paris and Rome, respectively, and 31% in Lisbon, and that these percentages are achievable by prioritizing the inclusion of two-wheeled vehicle options in low-carbon vehicle fleets. In addition, policy makers could award financial or non-financial incentives to low-carbon vehicle options to make them more economically attractive than small diesel vans. These incentives would be justified by external cost savings, which vary across cities and could be up to 500-1,600 EUR/year for small BEV vans, 2,400-6,000 EUR/year for electric cargo scooters and 3,900-7,700 EUR/year for cargo bicycles, allowing low-carbon vehicle options can fully replace small diesel van delivery operations. The study concludes that the European Commission can achieve the 2030 “CO2-free city logistics” goal by a combination of cargo bicycles, electric cargo scooters and BEV vans, and that prioritizing the inclusion of two-wheeled vehicles maximizes cities’ external cost savings. Importantly, future research should include real driving-cycle and monitor operational data, such as load factors and parcel density information, of vehicle technologies in city logistics fleets to reduce energy use uncertainty and improve operational feasibility and external cost estimates.

"Effects of climate change on the power system: a case study of the southeast U.S." - Francisco Ralston Fonseca, 2020

The U.S. power sector faces several vulnerabilities due to climate change. On the demand side, increasing temperatures  may result in shifting electricity consumption patterns and increase  need for energy. On the supply side, changes in air temperatures, water avail- ability, and water  temperatures could reduce the capacity and efficiency of thermal units, which currently represent  85% of generating capacity. Previous studies that analyze cli- mate change effects in the power  sector have mostly focused on analyzing these risks separately. Further, studies in the supply  side risks usually looked only at effects of climate change only in existing thermal generators.  However, such studies fail to capture how these demand and supply risks interact with each other  and with the operation of the power grid in general. In order to analyze these risks in more  detail, it is important to integrate them into system-wide  assessments. Such assessments should  take into account the economic  dispatch of the complete generator fleet and future economic decisions to expand this fleet.

This dissertation attempts to understand how climate change will affect the power  sector in the U.S. We implemented an integrated framework  where we use different modeling methods  to represent the different risks the power sector faces due to climate change. We used our modeling  framework in a case study of the SERC Reliability Corporation (SERC), one of eight regional  electric reliability councils under North American Electric Reliability Corporation authority (NERC).

Firstly, we used an econometric model to estimate changes in hourly electricity demand  due to climate change. We used this model to analyze changes hourly electricity demand patterns in  the Tennessee Valley Authority  (TVA)  region for different seasons of the year. Our results  suggest that climate change could result in an average increase in annual electricity   consumption in the TVA region. However, this increase was not uniformly distributed throughout  the year. During summer, total electricity consumption could increase on average by 20% while  during winter it may decrease on average by 6% by the end of the century.

Secondly, we combined the estimates of future hourly electricity demand described in Chapter 2 with simulations of decreases in available capacity of thermal generators due to cli- mate  change. We integrated these simulations  in a capacity expansion (CE) model. This CE model is a  mixed integer linear programming (MILP)  model that we adapted and developed for this study. It  finds the composition of the future generator fleet that minimizes costs subject to the estimated  effects of climate change. We ran this model under different climate change scenarios from 2020 to  2050. Our results showed that by including these effects due to climate change in the decision  making process, the estimated participation of renewables in the generator fleet in 2050 increased  from 24% to over 37–40%. Solar power plants could become more economically attractive. As they have  higher energy output during the summertime, they could help to offset the climate-induced  loss of  thermal capacity during this season because of higher air and water temperatures.

Thirdly, we simulated  the operation of SERC’s power system assuming the different scenarios and generator fleets presented in Chapter 3. To accomplish this, we used a unit commitment  and economic dispatch (UCED) model. The UECD model is a mixed integer linear programming (MILP)   model that we adapted and developed for this study. We used this model to investigate the tradeoffs  between investing or not in the generator fleet assuming different climate change scenarios. Our  results suggest that by not including cli- mate change effects in the planning stage, SERC’s power system could experience loss of  load levels of 12% and overall energy costs could be 260% higher if climate change conditions do materialize by 2050.

 

Correlated generator failures and power system reliability - Sinnott Murphy, 2019

This thesis contributes new knowledge toward understanding the relationship between capacity procurement and power system reliability through rigorous analysis of generator-level availability data. In Chapter 2 I analyze four years of data (2012-2015) from the Generating Availability Data System (GADS) database maintained by the North American Electric Reliability Corporation (NERC) to evaluate key assumptions made by power system planners when determining capacity requirements. Using block subsampling and binomial modeling, I demonstrate that large unavailable capacity events have occurred with much greater frequency than should be expected if current-practice assumptions hold. In Chapter 3 I propose a nonhomogeneous Markov model to explain the observed correlated failures. I use logistic regression to fit a simple model specification that allows generator transition probabilities to depend on ambient temperatures and system load. I fit the model using 23 years of GADS data for the PJM Interconnection (PJM), the largest system operator by generation capacity in North America. Temperature and load are each statistically significant for two-thirds of generators. Temperature dependencies are observed in all generator types, but are most pronounced for diesel and natural gas generators at low temperatures and nuclear generators at high temperatures. The nonhomogeneous Markov model predicts system-level unavailable capacity substantially better than the homogeneous Markov model used currently by industry. In Chapter 4, joint work with Luke Lavin, I quantify the reliability risks implied by temperature dependence in PJM’s generator fleet. We modify an open-source resource adequacy modeling tool to allow generator availability to depend on temperature. We then parameterize the tool for PJM’s system using temperature-dependent forced outage rates developed in Chapter 3. We find that temperature dependence substantially increases capacity requirements to achieve the target level of reliability, though PJM procures still more than our model finds is required. Given the seasonality in temperatures and loads, we also demonstrate that average annual capacity requirements could be significantly reduced were PJM to set separate monthly targets, rather than a single annual target. Finally, we explore the resource adequacy implications of various future generator resource and climate change scenarios for PJM.

Stakeholder Costs and Benefits of Distributed Energy Resources on Distribution Networks  - Jeremy Keen, 2019

Distributed energy resources (DER), such as rooftop solar and combined heat and power (CHP), create a unique opportunity to reduce transmission and distribution network capacity requirements, decrease electrical losses, and potentially improve reliability, resiliency, and other operating metrics. This dissertation examines how DER benefit different stakeholders in the electric power sector: DER owners, ratepayers, utilities, and society. In Chapter 2, we investigate how increasing commercial CHP system peak penetrations may affect net emissions, the distribution network, and total system energy costs. We find that small commercial CHP, due to low and inconsistent heat loads, can increase emissions relative to the bulk grid. We suggest policy options to encourage CHP operation during times of high heat loads. In Chapter 3, we develop metrics based on existing best utility practices that characterize how much solar can reduce peak demand on distribution network feeders. We conclude that solar can act as a capacity resource, but the size of the resource depends on the geographic region. Energy storage or an allowance for occasional overloading within a transformer’s tolerance can increase the capacity resource of solar. Chapter 4 is a value of solar and rate impact study for the Pennsylvania Public Utility Commission (PUC). The Pennsylvania PUC can use it to decide whether the environmental benefits of solar are worth the relatively small rate impact caused by rooftop solar. In Chapter 5, we assess the ability of rooftop solar and storage to reduce peak loads and defer distribution capacity projects in the PECO service territory. We find that targeted placement of solar can increase the total deferral value up to fourfold, but capacity deferral opportunities are rare and large administrative efforts to manage deferral projects, such as markets, are probably not warranted.

Power plant – gas grid dependence   Gerad M. Freeman, 2019

This thesis contributes new knowledge about the effect of the gas grid on the power generation sector and how this effect could inform grid generation resource planning. In chapter 2, I explore how reliability event reporting standards for operators of the natural gas grid compare to the requirements for power generators. Informed by a quantitative comparison of the numerical thresholds of reporting for gas grid and power generator failure events, I recommend a new reporting requirement for the gas grid that will bring it into line with the requirements for gas-fired power plant operators. In chapter 3, I examine why gas-fired power plants in the United States have failed because of fuel shortages. I analyze six years of data from a database of power plant failures called the Generating Availability Data System (GADS). Using pipeline scheduling data, I identify areas of the natural gas grid where enough pipeline space may be available so that increased priority fuel contacts could help mitigate fuel shortages at gas-fired power generators. Chapter 4 examines the economics of distributed fuel storage as a mitigation option for gas shortages at power plants in areas of the U.S. where pipeline space was not historically available. I estimate the additional costs required for New England gas-fired generators to install either distributed compressed natural gas (CNG) storage or oil dual fuel capabilities as fuel security measures at power plant sites. I construct fuel shortage mitigation supply curves using the cost estimates I develop. I also calculate simple payback periods of mitigation options using the cost estimates and foregone energy and capacity revenue stream estimates. I compare the costs of fuel storage options to those of battery storage and demand response incentives.

Integraging climate and health damages for decision-making in the electric power sector - Brian Joseph Sergi, 2019

This dissertation explores the connection between the climate and health impacts of emissions, focusing primarily on the electric power sector. The combustion of fossil fuels is a critical source of carbon dioxide—the principal greenhouse gas driving climate change—and of conventional air pollutants that are detrimental to human health. In this work, we explore the connection between these impacts by examining their role in shaping public support for emissions reductions, by advancing methods for quantifying the health impacts of emissions, and by investigating the benefits of directly linking and co-optimizing for benefits related to these two impacts during the design of policies for emissions reductions. In Chapter 2, we conduct a U.S.-based discrete choice survey to explore the influence of climate and health information on respondents’ support for reducing emissions. We find that, on average, respondents value information on the climate and health impacts of emissions, and are willing to pay more for emissions reductions that target both health and climate benefits simultaneously than they are for scenarios that address only climate or health alone. Respondents also demonstrate that their support for renewable energy sources is largely driven by the perceived health and climate benefits those sources would provide. These findings highlight the importance of communicating these types of benefits when advancing emissions reductions or policies intended to further clean energy. We extend this line of questioning in Chapter 3, in which we conduct a similar survey among residents of ten Chinese cities. In addition to the survey structure from Chapter 2, we use observed air quality data from the locations of the respondents to explore whether air pollution at different time-scales (e.g. hourly, daily, or annual averages) shows any relationship with preferences for emissions reductions. As with the U.S.-based survey, the average respondent demonstrates a willingness to pay more in electricity bills for cleaner energy sources, and in particular sources that are expected to address both health and climate issues. While short-term air quality levels show no relationship with respondents’ support for emissions cuts, respondents in areas of historically worse air quality demonstrate substantially higher willingness to pay for reducing emissions to improve human health, suggesting the importance of awareness of long-term pollution trends to building support for emissions reductions. Having explored how the public interacts with information on the climate and health impacts of emissions, Chapter 4 sets out to evaluate the health effects of air pollution in the U.S. We use an integrated assessment model with reduced complexity air quality modeling and emissions data from 2008, 2011, and 2014 to estimate county-level ambient particulate matter concentrations, population exposure, and finally health consequences, with a focus on how the location of those consequences relate to the origin of emissions. We estimate that total health damages in the U.S. declined from 2008 to 2014, driven largely by the closure of point sources like coal power plants. Despite this, some counties incur increasing per capita health damages over that time period. Though decreasing slightly over time, a large share of health damages continues to be attributable to pollution originating in a different location from where the damages are incurred, implying a sustained need for integrated and transboundary approaches to managing air pollution. Finally, Chapter 5 builds on the previous work by examining how estimates of the health impacts of emissions might be incorporated into the design of policies intended to address climate change for the electric power sector. Using data on the existing fossil fuel fleet and information on the marginal damage of pollution from the analysis in Chapter 4, we investigate how changing the location of power plant retirements and emissions reductions might achieve the same climate goals while maximizing health benefits. We find that using health to inform which plants retire and are replaced by natural gas can increase health benefits by close to one-third while incurring relatively incremental mitigation costs. These gains are in addition to the substantial health benefits achieved by a climate-only approach and are fairly robust to uncertainty and subjective parameter decisions. Policy makers might incorporate these findings by more directly considering the health implications of different pathways for achieving climate targets.

Challenges and Prospects for Data-Driven Climate Change Mitigation - Lynn Helena Kaack, 2019

Successful climate change mitigation will require data-driven decision making, but the field faces a diverse set of challenges. In this dissertation, I provide three examples that illustrate how uncertainty is often not adequately characterized, how missing data can pose a barrier to climate-relevant policy making, and how big data and machine learning could be used to obtain important information. I conclude with a survey and a discussion of how artificial intelligence can be applied to climate change mitigation. In the first chapter, I show how to construct an empirical estimate of the uncertainty of long-term energy forecasts based on past forecast errors, using projections made by the U.S. Energy Information Agency (EIA). This method gives analysts and decision-makers a means to estimate the uncertainty of those forecasts quantitatively. Energy forecasts provide the basis for financial evaluation of energy investments as well as for energy system models. I lay the groundwork for evaluating the performance of these methods in the data-scarce setting of long-term forecasts. The EIA has used my results in their most recent retrospective review. The second chapter is based on a topical review of policies to decarbonize heavy freight transportation by shifting freight from road to rail and water. I find that while the freight sector is responsible for a large share of greenhouse gas (GHG) emissions, a systematic analysis of the potential to decarbonize with modal shift is still missing from the literature. This is partly due to a lack of publicly available, standardized, and updated data. For a global comparison of modal split and trends, I expanded existing databases with national freight activity from 2000-2017. I find that only less than half of the countries in the world provide such information on road freight activity. The third chapter provides an example of how big data and machine learning (ML) could be used to fill in information gaps that inhibit climate policy analysis. I use satellite imagery for truck traffic monitoring in areas where this information is otherwise difficult to obtain. I count the number of freight vehicles visible in the images with deep convolutional neural networks, and estimate the average annual truck traffic on roads from those counts by modeling traffic variation patterns. In a final chapter, I discuss how methods from artificial intelligence can be used to improve socioeconomic, policy, and engineering research for climate change mitigation. I provide a survey of the literature and identify the main barriers and challenges that arise at the intersection of those disciplines. Research in this area demands both careful design of ML algorithms and consideration of domain knowledge. I conclude with proposing a research agenda.

Decisions and Uncertainties in the US Energy System: Electrofuels and Other Applications - Evan D. Sherwin, 2019

Achieving a global warming limit of 2°C is likely only possible if humanity ceases to emit greenhouse gases (GHG) well before the end of this century. This can only be accomplished through, among other things, a massive transformation of a deeply unpredictable global energy system on which billions of people depend. This thesis aims to illustrate three methodologically distinct approaches that could be integrated into a framework for energy decision-making capable of guiding thoughtful and equitable planning for robust reductions in GHG emissions in the face of deep, largely irreducible uncertainty. Although the primary object of study is the US energy system, all three analyses aim to draw generalizable conclusions that are useful in other contexts. Chapter 2 attempts to characterize the predictability and volatility of the US energy system by analyzing errors in past US government projections and historical fluctuations in the price, production, and consumption of key energy quantities. This work finds that the period from 2005-2014 contained a disproportionate number of the largest projection errors and inter-year fluctuations in almost all of the 17 quantities examined. This indicates that the US energy system itself was more volatile and harder to predict in this period than in previous decades. Chapter 3 uses observational residential electricity consumption data to estimate the effect of a low-income electric subsidy on electricity demand, and the externality costs associated with increased electricity generation and higher peak demand. This work finds that the externality costs are on the order of 11% of total subsidy disbursements, with no significant change in this number if intra-day estimates are used instead of time-invariant estimates. Decarbonization of the electric power system will likely eliminate most emissions from power plants, leaving only capacity costs of roughly 5% of subsidy disbursements. Thus, policy makers considering lowincome subsidies as a means of ensuring that low-income households do not disproportionately bear the burden of an energy transition can use such estimates of price responsiveness to estimate any adjustments in peak capacity requirements that may result from increased demand. Chapter 4 uses an optimization-based techno-economic model to characterize the decision space for deep decarbonization of liquid-dependent sectors such as aviation and long-distance road transportation. With today’s technology electrofuels, synthetic hydrocarbons produced using CO2 captured from the atmosphere and hydrogen from electrolysis of water, are likely a more expensive mitigation strategy than continuing to use petroleum-based fuels and offsetting the resulting emissions with direct air capture (DAC) of CO2 with sequestration (DACS). However, if DAC and electrolyzer manufacturers are able to meet near-term cost targets, electrofuels may be competitive with DACS if the cost of petroleum fuels rises substantially or if sequestration costs are higher than anticipated. Several decades into the future, electrofuel costs may fall as low as $2.70 per gallon of gasoline equivalent, potentially achieving cost parity with petroleum fuels. Electrofuel cost is most sensitive to the capital cost the DAC, electrolyzer, and renewable electricity systems, confirming their importance as priorities for research, development, and deployment (RD&D). However, without the operational flexibility afforded by storage or supplementary natural gas or grid electricity interconnections, costs could rise by more than 80%. This points to some less intuitive RD&D priorities, such as metallic phase change materials capable of storing heat above 900°C and low-cost, seasonal CO2 storage. As a whole, this work aims to characterize the depth the uncertainties posed by the task of energy transition while synthesizing insights from analysis of historical data and modeling based on engineering knowledge and expert judgment to gain policy-relevant insights into pathways toward deep decarbonization of the energy system. I hope this represents a small step toward a decision-making paradigm capable of addressing the deep uncertainties we face while using the wealth of data and insight at our disposal to chart a thoughtful course ahead.

Public Understanding of Climate Science, Extreme Weather and Climate Attribution – Rachel L. Dryden Steratore, 2019

Experts in the geophysics community have understood the role of greenhouse gases in shaping the earth's climate for over a century and have grown increasingly confident and concerned about the risks of climate change. Studies conducted since the early 1990s have observed several changes in public understanding of the causes and consequences of climate change. The aim of this thesis is to explore public understanding and perceptions of various aspects of climate science, climate change, and its impacts. This work provides an update on the climate change perceptions literature, identifying new and persistent knowledge gaps, as well as characterizing the belief-driven undercurrent that consistently predicts support for immediate climate action across my studies. In Chapter 2, I employ a local mail-out survey and a national online survey to explore the extent to which people understand the important differences between common air pollution and carbon dioxide (CO2). This work focuses especially on the very different atmospheric residence times of both—and what drives public support to abate climate change now. I find that people do not understand this fundamental difference, dramatically underestimate how long CO2 remains in the atmosphere and continues to change the climate, and the policy implications of long-lived CO2 in the atmosphere. However, this misunderstanding does not deter respondents from showing strong support for immediate climate action. While Chapter 2 focuses on drivers of climate change, in Chapter 3, I evaluate public perceptions of one of the most salient impacts of climate change: an increase in the frequency of extreme weather. In a two-part study comprised of a convenience sample of face-to-face interviews followed by a national study, I assess when, and to what extent, laypeople attribute extreme events to climate change and whether and how their beliefs are predictive of their decision thresholds, sensitivities, and support for immediate climate action using signal detection theory. I find that prior climate change beliefs are significant drivers that influence how people make decisions in attributing extreme events to climate change and in their self-reported support for immediate action in response to climate risks. I also find that simple spinner boards are effective tools in communicating non-stationary processes, such as attribution, to laypeople. In Chapter 4, I focus on public perceptions of what can be done on the part of individuals to reduce CO2 emissions and how laypeople view the efficacy of individual versus collective actions in a two-part study, starting with a convenience sample of face-to-face interviews and followed by a national study. I find that respondents believe that individuals have higher response-efficacy than what is likely to be attainable from individual actions alone, i.e. apart from any broader societal or governmental action. However, respondents view individual and governmental actions as having the same response-efficacy. Finally, Chapter 5 discusses this work’s contribution to the literature and the implications for the development of risk communications revealed in Chapters 2 through 4. Findings in all chapters show strong support for immediate action against climate risks. Climate change beliefs are significant predictors for decision thresholds and sensitivities in identifying hurricane frequencies as evidence of climate change (Chapter 2) and for support for immediate climate action across my studies (Chapters 2 and 3). These, and other findings reported in the thesis, can inform—and offer opportunities for—the development of improved risk communications, as well as alternative decision-making strategies when it comes to long-term risks and educational interventions.

Cumulative Impact and Equity Objectives in Energy Systems Modeling and Policy – Erin Noel Mayfield, 2019

Energy system development is driven by the complexity inherent in physical systems and the influence of a myriad of diverse, interacting stakeholders with heterogeneous preferences. Transforming energy systems entails balancing multiple and often conflicting societal objectives. This thesis presents new modeling approaches for energy systems planning and policy evaluation, with an emphasis on cumulative impacts, equity, and system heterogeneity. The application domain of this thesis is the U.S. natural gas system, although the analytical approaches and insight of this research are intended to extend to the broader domestic and global energy system. Chapter 2 adopts a traditional economic efficiency optimization approach, coupled with methane emissions and abatement cost simulations reflecting system heterogeneity, to evaluate and design system-wide and superemitter policies related to methane abatement in the U.S. transmission and storage system. We find that most emissions, given the existing suite of technologies, have the potential to be abated. We also demonstrate that there are high societal benefits from abatement policies, and minimal (if any) private costs under standard and tax instruments. Superemitter policies, which target the highest emitting facilities, may reduce the private cost burden and achieve high emission reductions, especially if emissions across facilities are highly skewed. However, detection across all facilities is necessary regardless of the policy option, and there are nontrivial societal benefits resulting from abatement of relatively low-emitting sources. Chapters 3 aims to develop and demonstrate a data-driven approach for characterizing systems-level cumulative impacts of current energy systems. Specifically, we comprehensively assess the spatially-and temporally-resolved air, climate, and employment impacts from extraction to end use and over the life of natural gas plays in the Appalachian basin from 2004 to 2016. Our approach highlights the attribution of impacts across the supply chain, the tradeoffs between near- and long-term impacts, and the evolution and accumulation of impacts over time with changing regulation, natural gas activity, and technological and operational efficiencies and practices. We show that short-lived air quality and employment impacts track with the boom-and-bust cycle, while climate impacts persist for generations well beyond the period of natural gas activity. We also find that employment effects are spatially concentrated in rural areas with thin labor markets where development is occurring, and more than half of cumulative premature mortality is within source emissions states. We show that most premature mortality is associated with end uses, while upstream and midstream segments also account for a substantial portion of impacts. With respect to climate change impacts, the magnitude of methane emissions across the supply chain produces temperature impacts nearly equivalent to that of carbon dioxide over a 30-year time horizon, but over longer integration periods, the warming impact from carbon dioxide dominates. We estimate a tax on production of $2 per thousand cubic foot (+172%/-76%) would compensate for cumulative climate and air quality externalities across the supply chain. In Chapter 4, we develop a multiobjective optimization model incorporating cumulative impact objectives to facilitate future energy system planning. We develop natural gas system pathways by optimizing impacts with respect to sequential natural gas decisions regarding the timing and location of infrastructure and activity from extraction to end use. Environmental and employment objectives are conflicting if we follow a natural gas pathway consistent with the status quo; however, a collection siting, emissions abatement, and renewable integration policies may collectively resolve and reverse these conflicts. In Chapter 5, we develop and demonstrate an approach for evaluating the equity state of an energy system. We apply variants of standard methods and present new methods and metrics to quantify spatial, temporal, and distributional equity, leveraging impact estimates of the shale gas boom in the Appalachian basin from Chapter 3. We find that there are high temporal and spatial inequities with respect to cumulative air and employment impacts, and that spatial inequities are constant over time reflecting largely fixed infrastructure and consumption patterns. We also present indicators of temporal climate inequities, estimating that long-term global temperature impacts are 100 times that of near-term impacts. With respect to distributional equity of air quality impacts, we do not observe a disparity in mortality rates across subpopulations on the basis of income and poverty; however, there is a trend of increasing income corresponding to decreasing damages, which demonstrates the higher health burden of lower income communities. With respect to distributional equity of labor markets, we find statistically significant declines in the income disparity and poverty rates in producing counties. Pairwise comparisons of impacts reveal that changes in air and climate impacts are sensitive to changes in employment impacts. In Chapter 6, we develop future natural gas system pathways that optimize for the multiple dimensions of equity. We expand upon the multiobjective optimization model developed in Chapter 4, deriving objectives that instill different normative concepts of spatial, temporal, and distributional equity that apply to air, climate, and employment impacts. We find that there are inherent conflicts between different equity dimensions, as well as, between equity and cumulative impact objectives in a fossil-fuel dominated energy system. However, low-carbon technologies have the potential to reduce inequities.

Pollution externalities and emissions’ consequences of the U.S. electricity sector – Xiaodi Sun, 2019

The electricity sector generates externalities due to pollution that can have damaging impacts on human health, environment, ecology, and climate. This thesis focuses on three problems related to health, environmental and/or climate change externalities associated with the operation of the power sector. In Chapter 1, I estimate the trace elements mass flow rates from U.S. coal-fired power plants, a negative externality that is not well monitored at the plant level, except for gas phase emissions of Hg. I create a generalizable model for stochastically estimating trace element mass flow rates, specifically Hg, Se, As, and Cl, to solid, liquid, and gas phase waste streams of coal-fired power plants and evaluate the accuracy against available data. When compared with measured and reported data on trace element mass flow rates, I find that my model generally overestimates trace element concentrations in coal, leading to overestimation of trace element mass flow rates to the waste streams. The partitioning estimates are consistent for Se, As, and Cl removal from flue gas, but tend to underestimate Hg removal. Model performance would improve with access to more recent measurements of trace element concentrations in the coal blend, where data quality is the weakest. In Chapter 2, I focus on the issue of understanding the emissions of SO2, NOx and CO2 that would result from policies that would lead to an increased usage of coal. I also study the emissions consequences of turning off some of the currently installed air pollution technologies at U.S. coal power plants. While a coal resurgence is unlikely due to other market forces, an increase in coal electricity will cause increases in SO2, NOx and CO2, which have significant human health, environment, and climate consequences. I explore the potential consequences of an increase in coal generation under two bounding scenarios: 1) I assume that environmental regulations are weakened so that coal plants turn off their wet flue gas desulfurization and selective catalytic reduction devices and 2) I assume coal electricity becomes cheaper to operate than natural gas and displaces natural gas electricity. Turning off wet flue gas desulfurization and selective catalytic reactor devices leads to SO2 and NOx that would be twice to three times the emissions observed in 2017. These emissions levels were last observed about 7-10 years ago. A resurgence of coal that would displace natural gas would increase SO2, NOx, and CO2 emissions by 41%, 45%, and 21% compared to 2017 levels. In Chapter 3, I study the potential of deep decarbonization of the Pennsylvania electricity sector, which is an energy policy goal which would push coal out of the fuel mix. The Pennsylvania Department of Environmental Protection aims to reduce greenhouse gas emissions from Pennsylvania to 20% of 2005 levels by 2050. While deep decarbonization is crucial for mitigating the effects of climate change, the infrastructure required to implement deep decarbonization can create significant land and forest impacts that may negatively impact ecology. I model pathways to deep decarbonize Pennsylvania’s electricity sector, quantify the land and forest land use from these pathways, and estimate potential ecological impacts using fragmentation indices. Even if all the coal plants retire, the emissions from current natural gas plants exceed the carbonization goals, suggesting that natural gas cannot be a bridge fuel. If only wind is built, the total land use is 13,300 km2 (Pennsylvania is 119,000 km2), with direct land use and forest land use impacts of 520 km2 and 370 km2, respectively. Solar farms are constructed across Pennsylvania, as there is insufficient land in the southeast where resources are highest, impacting 2400 km2 of forested land. As such, solar contributes to a greater loss of landscape than wind, but wind requires significantly more land allocated to deep decarbonize the Pennsylvania electricity sector. Through these three chapters, I find that energy policy needs to be assessed on a holistic basis by considering all possible cost and benefits of a potential policy. While policy goals, such as decarbonization of the electricity grid, will create obvious net benefits, energy interventions still need to be carefully planned out by decision makers to avoid and minimize other downstream problems. Other policy interventions, such as promoting more coal for the sake of grid reliability, may introduce such significant costs that they should be scrapped entirely.

Improving Electricity Access and Reliability using Residential Solar Systems with Battery Storage Systems in Sub-Saharan Africa – Chukwudi K. Udeani, 2019

Poor access and unreliable grid service are amongst the significant challenges facing households in sub-Saharan Africa. In response, these households rely on fossil fuel-based technologies to meet their household needs. Families without grid access rely on kerosene lamps predominantly to meet lighting needs, while grid-connected homes use fossil fuel backup generators. Studies of fossil fuel solutions like kerosene lamps and backup generators show that these technologies impose negative socio-economic impacts on both households and society. As a result, researchers and policymakers have been motivated to explore cleaner and more sustainable technologies like residential solar electric with battery storage systems. This thesis through a quantitative investigation of residential solar systems to analyzes the technical, economic, and environmental merits of residential solar systems in their applicability to address key electricity challenges facing sub-Saharan households. For households in rural areas without access in Kenya, solar home systems provide a cost effective alternative to kerosene lamps as a better lighting alternative, and even better for household economics when the additional services are considered like phone charging. I found was price elasticity of demand to be the key determinant of economic attractiveness for lighting services, which attempts to quantify the value households place on improved lighting service. For grid connected households, findings suggest that residential solar with battery storage electric systems can provide improved household electricity reliability while reducing reliance on backup generators. However, these systems increase grid demand. The main driver of the economic attractiveness of these systems are the household’s reliability needs which is reflected in how many hours the household’s backup generators is available for dispatch in the event of a grid outage as well as discount rate. The study finds that grid availability is also a key determinant of the economic attractiveness of these residential systems, because the quality of grid service in terms of how many hours the grid provides power to the household determines the overall level of backup generator reliance.

Sustainable Energy Transitions in sub-Saharan Africa: Impacts on Air Quality, Economics, and Fuel Consumption – DeVynne Terrell Farquharson, 2019

Historically, the United States, Europe, and China have produced the most greenhouse gas (GHG) emissions, with the largest share (25%) coming from the electricity and heating sector. With declining growth rate projections and access to electricity services all near or at 100%, developed countries have increased their share of sustainable energy sources such as wind and solar power. Unlike the most developed nations, populations are expected to increase drastically throughout the developing world in the 21st century. Recent estimates indicate 97% of the world’s population growth through 2030 (1.3 billion more people) will occur in the developing world. The countries of sub-Saharan Africa alone are projected to add over a billion people through 2050. Such population growth in developing countries will result in growing energy demand and thus growing emissions. The United Nations has underscored the importance of ushering in responsible and equitable energy pathways through their Sustainable Development Goals (SDGs). With a set of goals emphasizing access to affordable and reliable power, access to modern energy services, and reducing poor air quality and GHG emissions, the SDGs aim to improve quality of life across key areas of concern. The aim of this dissertation is to identify and evaluate opportunities for avoiding continued increases in fossil fuel use in sub-Saharan Africa, which would in turn reduce and avoid emissions of greenhouse gases and criteria air pollution and reduce some associated costs. In Chapter 2 we analyze the energy, emissions, and consumer costs of power outages in sub-Saharan African countries. By modeling the fuel mix for the central electricity grid in each country and the diesel fuel needed to produce backup electricity during outages, we estimate the magnitude of these impacts in the region. We show that use of backup generators leads to higher fossil energy consumption (compared to the central grid) in all countries, even countries that already rely on fossil fuels for power generation at centralized plants. Furthermore, for all countries in the analysis, backup diesel generators result in increased mean emissions of at least three of the five pollutants analyzed, compared to the grid. Our analysis highlights the magnitude of potential avoided emissions and economic savings from increased grid reliability, and has implications for achieving Sustainable Development Goals. Increased reliability may not lead to decreases in generator ownership, but it is likely to lead to decreases in generator use, thus avoiding additional emissions and reducing costs for consumers. In Chapter 3 we assess the emissions, health, and economic outcomes of electrifying motorcycle taxis in Kigali, Rwanda. By modeling fleet demand using observed driving distributions, we are able to estimate travel of this unique subset of all motorcycles which form the basis for all estimates. Our analysis reveals that emissions of key pollutants already identified by government officials (NOx, CO, HCs) as well as the greenhouse gas CO2 and health risks from PM2.5 can be drastically reduced via motorcycle fleet electrification. While a reduction of primary and secondary PM2.5 exposure and thus deaths can be achieved with the electrification of motorcycles, such benefits are dependent on the marginal generating unit. Finally, the Levelized Costs of Driving analysis reveals that at least one of the electric motorcycle alternatives presented in this work is cost competitive over a five-year period, and cost competitiveness improves as vehicle life is extended. In Chapter 4 we extend the vehicle electrification analysis to assess the benefits and costs of bus electrification in Rwanda. We employ a Monte Carlo Analysis to assess how the emissions, health impacts, and non-infrastructure costs associated with diesel powered buses compare to those associated with their electric counterparts. We find that mean emissions of CO2, PM2.5, NOx, CO, and HC all decline significantly when travel provided by diesel buses is replaced with electric buses. However, we also observe increased emissions of SO2 with bus electrification due in part to the prevalence of heavy fuel oil and peat electricity generation. Despite this increased SO2, the health analysis reveals that electrification can result in less annual deaths from primary and secondary PM2.5 but not if peat is the marginal electricity generating unit. Our economic analysis shows that electric buses have greater present levelized costs but given a decrease in capital cost and longer lifetime, electric buses can reach parity with the diesel buses. The final analysis in this chapter compares the normalized emissions and costs of conventional motorcycles, electric motorcycles, conventional buses, and electric buses. We find that the electric buses offer the greatest emission reductions (per passenger-kilometer) while the diesel buses offer the cheapest levelized costs. This research highlights the important role public transit electrification could play in achieving the Sustainable Development Goals as countries commit to lower greenhouse gas and harmful air pollutant emissions Finally, in Chapter 5 we discuss the important role developing countries will play in achieving global sustainability as their population, mobility, and electricity usage rise over the coming decades. We discuss the implications that reliable power and electrification efforts could have for the Sustainable Development Goals and urge developed nations to assist developing countries in implementing some of the technologies necessary for their completion. This thesis provides the framework for policy makers throughout SSA to assess the benefits and costs associated with modernizing their electricity systems.

Evaluation of building policies, programs, and potential for energy efficiency in the United States – Oluwatobi Gbemisola Adekanye, 2019

In the United States, buildings i.e. both residential and commercial are responsible for about 40% of total U.S. energy consumption, and as a result, a large amount of greenhouse gas and criteria air pollutants. Energy efficiency has been identified as a low-cost resource of reducing energy use and hence the carbon footprint in the buildings sector. As a result, a myriad of policy actions has been put in place to ensure that energy reduction goals can be achieved through energy efficiency. This dissertation performs a critical examination of some of these programs and policies that have been put in place with the aim of ensuring that their intended efforts are indeed achieved. This work also provides a prospective look into other considerations e.g. the inclusion of broader health and environmental benefits needed to be made when making the decision about building energy efficiency. In Chapter 2, I use a panel data approach to measure the association of policy implementation at different levels of the government with increases in green building adoption. I find that the effectiveness of green building policies is dependent on both the nature of the policy as well as the background federal policy context. I corroborate existing research by finding that local policies especially requirement and density bonuses are essential in driving green building certification. I also highlight the importance of federal policies (e.g. federal funding like the American Recovery and Reinvestment Act – ARRA) and private actions (e.g. through improvements to the building rating system process) in driving green building adoption. These findings highlight that local policy, federal policy, and private actions need to work in tandem to drive green building growth. In Chapter 3, I explore a similar line of questioning, however, focusing on the associations of different energy efficiency programs with reductions in electricity and gas usage. Using the difference-indifference and event history modeling approaches, I find that behavioral programs are associated with the largest increases in energy reductions even when compared to financial incentive programs. I also provide a means of detecting unexpected program impacts (i.e. changes that occur at the same time as the introduction of a new technology leading to biased estimates of program impacts) using electricity and gas usage data. I find gas reductions for some electricity-only programs thereby indicating that energy reductions may have occurred in the absence of the program. I highlight here that energy efficiency programs have the potential to significantly reduce electricity and gas use in buildings. However, the expost evaluation of these programs need to be appropriately measured to ensure that these reductions are indeed associated with policy implementation as significant amounts of money and time is invested in program implementation. While Chapters 2 and 3 focus on the evaluation of past energy efficiency programs and policies, in Chapter 4, I focus on other considerations that need to be made when making the decision about building energy efficiency. Specifically, I focus on the incorporation of other health and climate impacts when addressing the issue of climate change in the building sector. I investigate the energy reductions, greenhouse gas and other air emission reductions, as well as the private net costs and benefits of implementing a myriad of energy efficiency measures using the case study of the state of Pennsylvania. I find significant energy reductions compared to 2017 baseline levels - 36%, 44%, 19%, and 43% reductions of electricity, gas, propane, and fuel oil. More importantly, I estimate significant social benefits of $2.4billion per year and highlight the energy efficiency measures which maximize both the private and social benefits for the state. In Chapter 5, I discuss overarching conclusions and some considerations for policy revealed in Chapters 2 to 4. Findings in Chapters 2 and 3, for example, show the benefits of non-economic programs and incentives in driving building energy efficiency. I corroborate the nascent research on behavioral programs on energy reductions and recommend that utility evaluators examine non-financial program and policy approaches to reducing energy use as it also offers a low-cost alternative to promoting energy use reductions. More specifically, In Chapter 2, I learn that policy actors i.e. local and federal policy makers, as well as private bodies, need to work together in driving green building adoption. However, highlighted is the need for more transparency in ensuring that green building certifications are indeed translating to energy reductions. In Chapter 3, I learn the importance of more robust analyses when using data-driven approaches in the energy measurement and verification process of energy efficiency programs. In Chapter 4, I find that energy efficiency measures which yield the highest private benefits may not necessarily yield the highest social benefits therefore highlighting the need for a more holistic look when making the decision between competing energy efficiency measures.

"Robust Steady-State Analysis of Power Grid using Equivalent Circuit Formulation with Circuit Simulation Methods" - Amritanshu Pandy, 2018

A robust framework for steady-state analysis (power flow and three-phase power flow problem) of transmission as well as distribution networks is essential for operation and planning of the electric power grid. The critical nature of this analysis has led to this problem being one of the most actively researched topics in the energy field in the last few decades. This has produced significant advances in the related technologies; however, the present state-of-the-art methods still lack the general robustness needed to securely and reliably operate as well as plan for the ever-changing power grid. The reasons for this are manifold, but the most important ones are: i) lack of general assurance toward convergence of power flow and three-phase power flow problems to the correct physical solution when a good initial state is not available; ii) the use of disparate formulation and modeling frameworks for transmission and distribution steady-state analyses that has led to the two analyses being modeled and simulated separately.

This thesis addresses the existing limitations in steady-state analysis of power grids to enable a more secure and reliable environment for power grid operation and planning. To that effect, we develop a generic framework based on equivalent circuit formulation that can model both the positive sequence network of the transmission grid and the three-phase network of the distribution grid without loss of generality. Furthermore, we demonstrate that when combined with novel as well as adapted circuit simulation techniques, the framework can robustly solve for the steady-state solution for both these network models (positive sequence and three-phase) by constraining the developed models in their physical space independent of the choice of initial conditions. Importantly, the developed framework treats the transmission grid no differently than the distribution grid and, therefore, allows for any further advances in the field to be directly applicable to the analysis of both. One of which is the ability to jointly simulate the positive sequence network of the transmission grid and three-phase network of the distribution grid robustly.

To validate the applicability of our equivalent circuit formulation to realistic industry sized systems as well to demonstrate the robustness of the developed methods, we simulate large positive-sequence and three-phase networks individually and jointly from arbitrary initial conditions and show convergence to correct physical solution. Examples for positive sequence transmission networks include 75k+ nodes US Eastern Interconnection test cases and for three-phase networks include 8k+ nodes taxonomy distribution test cases.

"Framing a New Nuclear Renaissance Through Environmental Competitiveness, Community Characteristics, and Cost Mitigation Through Passive Safety" - Travis Carless, 2018

The nuclear power sector has a history of challenges with its relative competitiveness against other forms of electricity generation. The availability of low cost low natural gas, the Fukushima accident, and the cancellation of the AP1000 V.C. Summer project has caused a considerable role in ending the short lived “Nuclear Renaissance.” Historically, the nuclear industry has focused on direct cost reduction through construction, increasing installed capacity, and improving efficiencies to capacity factors in the 1990s and 2000s as ways to maintain competitiveness against other forms of energy generation. With renewables serving as an emerging low-carbon competitor, an added focus needs to be placed on indirect methods to increase the competitiveness of nuclear power. This thesis focuses on establishing pathways where nuclear power can be competitive with other forms of electricity generation given its advantages environmentally with Small Modular Reactors (SMRs), socioeconomically with legacy nuclear power plants, and through passive safety with SMRs.

In Chapter 2, I estimate the life cycle GHG emissions and examine the cost of carbon abatement when nuclear is used to replace fossil fuels for the Westinghouse SMR (W-SMR) and AP1000. I created LCA models using past literature and Monte Carlo simulation to estimate the mean (and 90% confidence interval) life cycle GHG emissions of the W-SMR to be 7.4 g of CO2-eq/kwh (4.5 to 11.3 g of CO2-eq/kwh) and the AP1000 to be 7.6 g of CO2-eq/kwh (5.0 to 11.3 g of CO2-eq/kwh). Within the analysis I find that the estimated cost of carbon abatement with an AP1000 against coal and natural gas is $2/tonne of CO2-eq (-$13 to $26/tonne of CO2-eq) and $35/tonne of CO2-eq ($3 to $86/tonne of CO2-eq), respectively. In comparison, a W-SMR the cost of carbon abatement against coal and natural gas is $3/tonne of CO2- eq (-$15 to $28/tonne of CO2-eq) and $37/tonne of CO2-eq (-$1 to $90/tonne of CO2-eq), respectively. I conclude, with the exception of hydropower, the Westinghouse SMR design and the AP1000 have a smaller footprint than all other generation technologies including renewables. Assigning a cost to carbon for natural gas plant or implementing zero-emission incentives can improve the economic competitiveness of nuclear power through environmental competitiveness. The retirement of small and medium-scale coal power plants due the availability of natural gas can provide an opportunity for SMRs to replace that missing capacity. This trade-off between higher costs but lower GHG emissions demonstrates that depending on the value placed on carbon, SMR technology could be economically competitive with fossil fuel technologies Following my environmental competitiveness analysis, I shift towards investigating socioeconomic competitiveness of legacy large scale nuclear power plants compared to baseload coal and natural gas plants.

In Chapter 3, I utilize ANOVA models, Tukey’s, and t-tests to explore the socioeconomic characteristics and disparities that exist within counties and communities that contain baseload power plants. My results indicate, relative to the home counties of nuclear plants, communities closer to nuclear plants have higher home values and incomes than those further away. Conversely, communities near coal and natural gas have incomes and home values that increase with distance from the plant. Communities near coal plants are typically either in less wealthy parts of the county or have a similar socioeconomic makeup as county. It can be suggested that equity issues regarding the community characteristics could be included in the discussion of converting existing power plants to use other fuel sources. Communities near power plants are not created equally and have different needs. While communities near nuclear power plants may benefit from the added tax base and absence of emissions, this is not the case for communities near coal and natural gas. With the impending retirement of large scale coal plants, the conversion of these plants to natural gas or small modular reactors presents an opportunity where negative environmental externalities can be reduced while also retaining some of the economic benefits.

In Chapter 4, I present a model for estimating environmental dose exposure in a post-accident scenario to support scalable emergency planning zones (EPZs). The model includes calculating radionuclide inventory; estimating the impact decontamination factors from the AP1000, NUREG-6189, and EPRI’s Experimental Verification of Post-Accident iPWR Aerosol Behavior test will have on radioactivity within containment; and estimate dose exposure using atmospheric dispersion models. This work aims to compare historical decontamination factors with updated decontamination factors to outline the impact on containment radioactivity and dose exposure relative to the Environmental Protection Agency’s Protective Action Guide (PAG) limits. On average, I have found the AP1000, Surry, and iPWR produces 139, 153, and 104 curies/ft3 75 minutes after a LOCA. The iPWR produces less radioactivity per volume in containment than the AP1000 and Surry 84% and 96% of the time, respectively. The AP1000 produces less radioactivity per volume than Surry 68% of the time. On average, the AP1000, Surry, and iPWR produces 84,000, 106,000, and 7,000 curies/MWth 75 minutes after a LOCA. The lower bound 5 rem PAG limit is never exceeded for and does not exceeds the 1 rem lower PAG limit for whole body exposure at the 5-mile EPZ using the mean value. Considering this analysis uses a simple worst case Gaussian Plume model for atmospheric dispersion, the findings can be used to in conjunction with the State-of-the-Art Reactor Consequence Analyses (SOARCA) to provide accurate and realistic estimates for exposure. I believe this analysis can help to develop a regulatory basis for technology-neutral, risk-based approach to EPZs for iPWRs.

Finally, in Chapter 5 I discuss historical challenges facing the nuclear industry, policy implications, and recommendations. These policy implications and recommendations serve as pathways to frame an new nuclear renaissance. I also recommend future work where I details opportunities for improvements to nuclear competitiveness. Ultimately, this thesis can help policy and decision makers that can improve competitiveness and minimize risk as it relates to the expansion of nuclear power sector.

"Studies in Nuclear Energy: Low Risk and Low Carbon" – Michael J. Ford, 2017

The amount of greenhouse gas emissions mitigation required to prevent the most dramatic climate change scenarios postulated in the 2014 IPCC Synthesis Report is substantial. Prior analyses have examined the potential for nuclear energy to play a role in decarbonizing the energy sector, one of the largest contributors to emissions worldwide. However, advanced, non-light water reactors, while often touted as a viable alternative for development, have languished. Large light water development projects have a repeated history of extended construction timelines, re-work delays, and significant capital risk. With few exceptions, large-scale nuclear projects have demonstrated neither affordability nor economic competitiveness, and are not well suited to nations with smaller energy grids, or to replace fossil generation in the industrial process heat sector. If nuclear power is to play a role in decarbonization, new policy and technical solutions will be needed.

In this manuscript, we examine key aspects of past performance across the nuclear enterprise and explore the future potential of nuclear energy worldwide, focusing on policy and technical solutions that may be needed to move nuclear power forward as a part of a low-carbon energy future. We do so first at a high level, examining the history of nuclear power research and development in the United States, the nation that historically has led the way in the development of this generating technology. A significant portion of our analysis is focused on new developments in this technology – advanced non-light water reactors and small modular reactors. We find that while there are promising technical solutions available, improved funding and focus in research and new models of deployment may be needed if nuclear is to play a continuing or future role. We also find that in examining potential new markets for the technology, a continuing focus on institutional readiness is critical.

"Integrating Demand-Side Resources into the Electric Grid: Economic and Environmental Considerations" – Michael J. Fisher, 2017

Demand-side resources are taking an increasingly prominent role in providing essential grid services once provided by thermal power plants. This thesis considers the economic feasibility and environmental effects of integrating demand-side resources into the electric grid with consideration given to the diversity of market and environmental conditions that can affect their behavior.


Chapter 2 explores the private economics and system-level carbon dioxide reduction when using demand response for spinning reserve. Steady end uses like lighting are more than twice as profitable as seasonal end uses because spinning reserve is needed year-round. Avoided carbon emission damages from using demand response instead of fossil fuel generation for spinning reserve are sufficient to justify incentives for demand response resources.


Chapter 3 quantifies the system-level net emissions rate and private economics of behind-the-meter energy storage. Net emission rates are lower than marginal emission rates for power plants and in-line with estimates of net emission rates from grid-level storage. The economics are favorable for many buildings in regions with high demand charges like California and New York, even without subsidies. Future penetration into regions with average charges like Pennsylvania will depend greatly on installation cost reductions and wholesale prices for ancillary services.


Chapter 4 outlines a novel econometric model to quantify potential revenues from energy storage that reduces demand charges. The model is based on a novel predictive metric that is derived from the building’s load profile. Normalized revenue estimates are independent of the power capacity of the battery holding other performance characteristics equal, which can be used to calculate the profit-maximizing storage size.


Chapter 5 analyzes the economic feasibility of flow batteries in the commercial and industrial market. Flow batteries at a 4-hour duration must be less expensive on a dollar per installed kWh basis, often by 20-30%, to break even with shorter duration li-ion or lead-acid despite allowing for deeper depth of discharge and superior cycle life. These results are robust to assumptions of tariff rates, battery round-trip efficiencies, amount of solar generation and whether the battery can participate in the wholesale energy and ancillary services markets.

"Microgrid Utilities for Rural Electrification in East Africa: Challenges and Opportunities" – Nathaniel J. Williams, 2017

Expanding access to electricity is central to development in East Africa but massive increases in investment are required to achieve universal access. Private sector participation in electrification is essential to meeting electricity access targets. Policy makers have acknowledged that grid extension in many remote rural areas is not as cost effective as decentralized alternatives such as microgrids. Microgrid companies have been unable to scale beyond pilot projects due in part to challenges in raising capital for a business model that is perceived to be risky. This thesis aims to identify and quantify the primary sources of investment risk in microgrid utilities and study ways to mitigate these risks to make these businesses more viable. Two modeling tools have been developed to this end. The Stochastic Techno-Economic Microgrid Model (STEMM) models the technical and financial performance of microgrid utilities using uncertain and dynamic inputs to permit explicit modeling of financial risk. This model is applied in an investment risk assessment and case study in Rwanda. Key findings suggest that the most important drivers of risk are fuel prices, foreign exchange rates, demand for electricity, and price elasticity of demand for electricity. The relative importance of these factors is technology dependent with demand uncertainty figuring stronger for solar and high solar penetration hybrid systems and fuel prices driving risk in diesel power and low solar penetration hybrid systems. Considering uncertainty in system sizing presents a tradeoff whereby a decrease in expected equity return decreases downside risk. High solar penetration systems are also found to be more attractive to lenders. The second modeling tool leverages electricity consumption and demographic data from four microgrids in Tanzania to forecast demand for electricity in newly electrified communities. Using statistical learning techniques, improvements in prediction performance was achieved over the historical mean baseline. I have also identified important predictors in estimating electricity consumption of newly connected customers. These include tariff structures and prices, preconnection sources of electricity and lighting, levels of spending on electricity services and airtime, and pre-connection appliance ownership. Prior exposure to electricity, disposable income, and price are dominant factors in estimating demand.

"Innovation in China’s Renewable Energy Industry" – Long Thanh Lam, 2017

This dissertation includes three studies that examine the remarkable rise of China’s renewable energy industry and its technological contributions to the global industry. China has emerged as the world’s largest carbon emitter by a large margin, and many of its cities experience high levels of air pollution. The Chinese government has turned to wind – and later solar – as alternative power sources to help decarbonize its electricity system and ameliorate increasingly urgent air pollution problems. Through these efforts, China has markedly expanded the share of renewable energy in its energy mix, and in the process absorbed a fair amount of relatively advanced technology, establishing itself as a competitive location to manufacture clean power equipment. In short order China has
bolstered its international standing as a renewable energy powerhouse.

The first study evaluates the question of whether China's wind industry has become an important source of clean energy technology innovation. Results indicate that while China has delivered enormous progress in terms of wind capacity, the outcomes were more limited in terms of innovation and cost competitiveness. Chinese wind turbine manufacturers have secured few international patents and achieved moderate learning rates relative to the global industry’s historical learning rate.

The success of China’s transition to a low-carbon energy system will be key to achieve the global level of emissions reductions needed to avoid large negative consequences from climate change. The second study shows that China made progress in bringing down the levelized cost of wind electricity and cost of carbon mitigation. However, widespread grid-connection issues and wind curtailment rates caused much higher-than-anticipated costs of renewable energy integration.

China has emerged as the global manufacturing center for solar photovoltaic products, and Chinese firms have entered all stages of the supply chain in short order. The third study provides detailed expert assessments of the technological and nontechnological factors that led to the surprised success of China’s silicon photovoltaic industry. Expert judgments suggest that continued declines in in module and system costs and improvements in performance will allow solar photovoltaic to be competitive with fossil fuels in China.

"Economic And Environmental Costs, Benefits, And Trade-Offs Of Low-Carbon Technologies In The Electric Power Sector" – Michael T. Craig, 2017

Motivated by the role of decarbonizing the electric power sector to mitigate climate change, I assess the economic and environmental merits of three key technologies for decarbonizing the electric power sector across four chapters in this thesis. These chapters explore how adding flexibility to power plants equipped with carbon capture and sequestration (CCS) affects system costs and carbon dioxide (CO2) emissions, how grid-scale electricity storage affects system CO2 emissions as a power system decarbonizes, and how distributed solar photovoltaic (distributed PV) electricity generation suppresses wholesale electricity prices. In each chapter, I address these questions through a combination of power system optimization, statistics, and techno-economic analysis, and tie my findings to policy implications.

In Chapter 2, I compare the cost-effectiveness of “flexible” CCS retrofits to other compliance strategies with the U.S. Clean Power Plan (CPP) and a hypothetical stronger CPP. Relative to “normal” CCS, “flexible” CCS retrofits include solvent storage that allows the generator to temporarily eliminate the CCS parasitic load and increase the generator’s net efficiency, capacity, and ramp rate. Using a unit commitment and economic dispatch (UCED) model, I find that flexible CCS achieves more cost-effective emissions reductions than normal CCS under the CPP and stronger CPP, but that flexible CCS is less cost-effective than other compliance strategies under both reduction targets.

In Chapter 3, I conduct a detailed comparison of how flexible versus normal CCS retrofits affect total system costs and CO2 emissions under a moderate and strong CO2 emission limit. Given that a key benefit of flexible CCS relative to normal CCS is increased reserve provision, I break total system costs into generation, reserve, and CCS capital costs. Using a UCED model, I find that flexible CCS retrofits reduce total system costs relative to normal CCS retrofits under both emission limits. Furthermore, 40-80% of these cost reductions come from reserve cost reductions. Accounting for costs and CO2 emissions, though, flexible CCS poses a trade-off to policymakers under the moderate emission limit, as flexible CCS increases system CO2 emissions relative to normal CCS. No such trade-off exists under the stronger emission limit, as flexible CCS reduces system CO2 emissions and costs relative to normal CCS.

In Chapter 4, I quantify how storage affects operational CO2 emissions as a power system decarbonizes under a moderate and strong CO2 emission limit through 2045. In so doing, I aim to better understand how storage transitions from increasing CO2 emissions in historic U.S. systems to enabling deeply decarbonized systems. Additionally, under each target I compare how storage affects CO2 emissions when participating in only energy, only reserve, and energy and reserve markets. Using a capacity expansion (CE) model to forecast fleet changes through 2045 and a UCED model to quantify how storage affects system CO2 emissions, I find that storage quickly transitions from increasing to decreasing CO2 emissions under the moderate and strong emission limits. Whether storage provides only energy, only reserves, or energy and reserves drives large differences in the magnitude, but not the direction, of the effect of storage on CO2 emissions.

In Chapter 5, I quantify a benefit of distributed photovoltaic (PV) generation often overlooked by value of solar studies, namely the market price response. By displacing high-cost marginal generators, distributed PV generation reduces wholesale electricity prices, which in turn reduces utilities’ energy procurement costs. Using 2013 through 2015 data from California including a database of all distributed PV systems in the three California investor owned utilities, we estimate historic hourly distributed PV generation in California, then link that generation to reduced wholesale electricity prices via linear regression. From 2013 through 2015, we find that distributed PV suppressed historic median hourly LMPs by up to $2.7-3.1/MWh, yielding avoided costs of up to $650-730 million. These avoided costs are smaller than but on the order of other avoided costs commonly included in value of solar studies, so merit inclusion in future studies to properly value distributed PV.

"Evaluating Forecasting Performance in the Context of Process-Level Decisions: Methods, Computation Platform, and Studies in Residential Electricity Demand Estimation" – Richard A. Huntsinger, 2017

This dissertation explores how decisions about the forecasting process can affect the evaluation of forecasting performance, in general and in the domain of residential electricity demand estimation. Decisions of interest include those around data sourcing, sampling, clustering, temporal magnification, algorithm selection, testing approach, evaluation metrics, and others.

Models of the forecasting process and analysis methods are formulated in terms of a three-tier decision taxonomy, by which decision effects are exposed through systematic enumeration of the techniques resulting from those decisions. A computation platform based on the models is implemented to compute and visualize the effects. The methods and computation platform are first demonstrated by applying them to 3,003 benchmark datasets to investigate various decisions, including those that could impact the relationship between data entropy and forecastability. Then, they are used to study over 10,624 week-ahead and day-ahead residential electricity demand forecasting techniques, utilizing fine-resolution electricity usage data collected over 18 months on groups of 782 and 223 households by real smart electric grids in Ireland and Australia, respectively.

The main finding from this research is that forecasting performance is highly sensitive to the interaction effects of many decisions. Sampling is found to be an especially effective data strategy, clustering not so, temporal magnification mixed. Other relationships between certain decisions and performance are surfaced, too. While these findings are empirical and specific to one practically scoped investigation, they are potentially generalizable, with implications for residential electricity demand estimation, smart electric grid design, and electricity policy.

"Transmission and interconnection planning in power systems: Contributions to investment under uncertainty and cross-border cost allocation" – Manuel Valentim Miranda de Loureiro, 2017

Electricity transmission network investments are playing a key role in the integration process of power systems in the European Union. Given the magnitude of investment costs, their irreversibility, and their impact in the overall development of a region, accounting
for the role of uncertainties as well as the involvement of multiple parties in the decision process allows for improved and more robust investment decisions. Even though the creation of this internal energy market requires attention to flexibility and strategic decision-making, existing literature and practitioners have not given proper attention to these topics.

Using portfolios of real options, we present two stochastic mixed integer linear programming models for transmission network expansion planning. We study the importance of explicitly addressing uncertainties, the option to postpone decisions and other sources of flexibility in the design of transmission networks. In a case study based on the Azores archipelago we show how renewables penetration can increase by introducing contingency planning into the decision process considering generation capacity uncertainty.

We also present a two-party Nash-Coase bargaining transmission capacity investment model. We illustrate optimal fair share cost allocation policies with a case study based on the Iberian market.

Lastly, we develop a new model that considers both interconnection expansion planning under uncertainty and cross-border cost allocation based on portfolios of real options and Nash-Coase bargaining. The model is illustrated using Iberian transmission and market data.

"Ensuring the Reliable Operation of the Power Grid: State-Based and Distributed Approaches to Scheduling Energy and Contingency Reserves" – Jose Fernando Prada, 2017

Keeping a contingency reserve in power systems is necessary to preserve the security of real-time operations. This work studies two different approaches to the optimal allocation of energy and reserves in the day-ahead generation scheduling process.

Part I presents a stochastic security-constrained unit commitment model to co-optimize energy and the locational reserves required to respond to a set of uncertain generation contingencies, using a novel state-based formulation. The model is applied in an offer-based electricity market to allocate contingency reserves throughout the power grid, in order to comply with the N-1 security criterion under transmission congestion.

The objective is to minimize expected dispatch and reserve costs, together with post contingency corrective redispatch costs, modeling the probability of generation failure and associated post contingency states. The characteristics of the scheduling problem are exploited to formulate a computationally efficient method, consistent with established operational practices.

We simulated the distribution of locational contingency reserves on the IEEE RTS96 system and compared the results with the conventional deterministic method. We found that assigning locational spinning reserves can guarantee an N-1 secure dispatch accounting for transmission congestion at a reasonable extra cost. The simulations also showed little value of allocating downward reserves but sizable operating savings from co-optimizing locational nonspinning reserves. Overall, the results indicate the computational tractability of the proposed method.

Part II presents a distributed generation scheduling model to optimally allocate energy and spinning reserves among competing generators in a day-ahead market. The model is based on the coordination between individual generators and a market entity.

The proposed method uses forecasting, augmented pricing and locational signals to induce efficient commitment of generators based on firm posted prices. It is price-based but does not rely on multiple iterations, minimizes information exchange and simplifies the market clearing process.

Simulations of the distributed method performed on a six-bus test system showed that, using an appropriate set of prices, it is possible to emulate the results of a conventional centralized solution, without need of providing make-whole payments to generators. Likewise, they showed that the distributed method can accommodate transactions with different products and complex security constraints.

"Enabling the future grid: an analysis of operational and flexibility issues in the Indian power grid" – Hameed Safiullah, 2016

The Indian power system is expected to integrate large amounts of renewable energy resources in the near future. However, the characteristics of renewable energy resources differ greatly from conventional energy resources. Integrating large quantities of renewable resources therefore warrants enhancements and modifications to current practices as the current Indian power system lacks sufficient operational services that protect the grid against contingencies. This dissertation aims to analyze the operational and flexibility needs of the Indian grid to accommodate diverse and new energy sources.

The first part of the dissertation analyzes the operational issues in the current power system. The Indian power system is restricted to a few services to support grid operation, which is primarily balancing demand and supply, in real-time. The different enhancements to the current balancing mechanism have varying impacts on the demand and supply balance, and this is reflected in the grid frequency. Therefore, the grid frequency under different balancing mechanism is modeled to understand the impacts. The results indicate that improving primary frequency response from generators along with a revision of the current prices is the most effective strategy.

Next, given the current conditions that exist in the grid, a feasible load balancing mechanism is analyzed to understand the related benefits and costs. While the first part of the dissertation analyzes grid level impacts of different balancing mechanisms, the second part explores a service that should be implemented by the electric system operator to support grid balancing. The results indicate that the proposed mechanism is beneficial in reducing real-time emergency events by 55% at a power purchase cost increase of 3.5%.

In addition to services, the system operators and regulators must ensure that there are sufficient flexible resources that can support the variability and uncertainty in the grid. The third part of the dissertation analyzes the impact of different generation scenarios on power system operation and reliability. The section highlights the need for flexible resources to counter the uncertainty and variability of renewable energy resources. In essence, the dissertation aims to encourage a rigorous approach to planning and policy making with regards to renewable energy integration.

"Case-Studies in the Economics of Ancillary Services of Power Systems in Support of High Wind Penetrations" – Todd Ryan, 2016

This thesis analyzes two potential means of mitigating the cost increase of ancillary services that is expected with the decarbonization of the U.S. electricity network. The first method, balancing area consolidation, addresses this cost rise by reducing the demand for ancillary services. This research quantifies the economic benefit of consolidation in the frequency regulation market by estimating the resulting reductions in frequency regulation requirements and cost. The results show that this policy leads to a reduction in frequency regulation cost of approximately $0.1 per MWh of total load. These results do not significantly change with the inclusion of 20% wind, suggesting that in the near term, wind’s interaction in the frequency regulation market is not a prime motivation for consolidation. This analysis does not consider all the benefits or costs of BA consolidation, and is not meant as an assessment of net-benefits. Though the results show consolidation could lead to an increase in emissions of some air pollutants, which suggest that there may be significant trade-offs associated with the decision to consolidate balancing areas.

The second means of addressing the expected increase in ancillary services costs is to increase the supply of ancillary services by leveraging residential demand response. We developed methods that optimally schedule ancillary service capacity on demand response resources while accounting for the risk of customer response fatigue. The model is used to test the efficacy of hourly caps on demand response penetration in ancillary service markets. The results show that residential demand response could provide a significant portion of the total ancillary service requirements attributable to residential loads: between 50% and 75%. Hourly caps on demand response participation are shown to be economically inefficient. With a 25% market cap, residential demand response is scheduled to provide 25% of the hourly total market value, while the risked-based optimization schedules residential demand response to provide 82% of the total value. Methods like the ones presented in this paper, that can appropriately weight the benefits and risks of committing residential demand response will be critical to efficiently and effectively use this resource for ancillary services.

"Quantitative Modeling Under Uncertainty to Inform Effective Energy and Environmental Policies" – Leslie Abrahams, 2016

Natural gas production in the United States has increased significantly over the past decade. This is largely due to advancements in hydraulic fracturing, horizontal drilling, and seismic monitoring capabilities that have enabled extraction of natural gas from shale resources to be economically viable. While natural gas is an important global energy resource and may result in fewer emissions than coal for electricity generation, it is important to recognize that extraction of natural gas has the potential to cause local and regional environmental damages. Successfully managing these risks is critical in order to ensure natural gas consumption has a net positive environmental footprint. The second and third chapters of this dissertation use quantitative modeling to assess how policies can address and mitigate potential environmental impacts in a cost-effective manner. Specifically, this work focuses on minimizing incremental fragmentation in critical core forest ecosystems resulting from natural gas infrastructure and on managing wastewater byproducts from natural gas extraction.

The second chapter finds that in the case study of a core forest region in Bradford County, Pennsylvania, the number of core patches of forest, an indicator of fragmentation, could as much as double throughout the life time of the Marcellus Shale play (from 80 to 160 core patches) without any regulatory intervention. However, through unitization and collaborative planning, and by designating that gathering pipelines must follow the route of pre-existing roads in forests whenever possible, natural gas infrastructure can be developed in a manner that would both prevent incremental fragmentation from occurring and reduce pipeline construction costs for operators as a result of reduced infrastructure redundancies. The third chapter finds that approximately 1.3 million gallons of wastewater, called produced water, are generated by each well. Across Pennsylvania, 67% of the time Class II disposal is the least cost option, 25% of the time CWT is the least cost option, and 8% of the time on-site treatment is the least cost option. The corresponding average costs are $5.80/bbl ($0.015/Mcf), $7.80/bbl ($0.020/Mcf), and $8.40/bbl ($0.021/Mcf), respectively. In addition to cost, however, there are several technical, ecological, regulatory, and logistical issues that also affect the relative feasibility of these three produced water management strategies. If regulators could capture producers willingness to pay to dispose of water rather than treat the water, that money could be invested in treating other water quality issues in Pennsylvania such as coal mine drainage, which can be treated for $0.064/bbl on average, or agricultural runoff, which could be prevented at an average cost of $0.08/bbl.

The last two chapters in this dissertation explore how quantitative modeling can inform policy making on a national and global scale. Chapter 4 does this by characterizing the life cycle greenhouse gas impact of United States natural gas exports. This study finds that mean landed (pre-combustion) life cycle GHGs for exported U.S. LNG after regasification at the importing country were found to be 37 g CO2-equiv/MJ with a range of 27 to 50. The net global impact of these emissions depends on the global warming potential time scale, methane leakage rate, end use, and the fuel it displaces. On a 100 year time scale, life cycle emissions from exported LNG were found on average to be 655 g CO2-equiv/kWh for electricity generation, a 45% reduction over life cycle emissions from coal consumption. However, because of the spatial shift in emissions generation, although there is a global GHG benefit to US natural gas exports, the United States should consider the implications of this given that emissions calculations are based on CO2-eq emitted within a country’s borders rather than based on the net global impact of those emissions. The fifth chapter continues to explore international trade policy by focusing on the global crude trade as a case study. This chapter considers how shifting trade patterns can influence global costs and greenhouse gas emissions using a linear optimization model. The baseline 2014 crude trade system had a global cost of $3T and resulted in 16.5 Gt of CO2. Minimizing by cost would save $6T and increase emissions by 4 Gt CO2, while minimizing by emissions would increase cost by $0.5T and decrease emissions by 5.4 Gt. This chapter then explores the interaction between climate policies including carbon accounting methods, a designated global carbon cap, and unilateral country specific emissions allocations. There is a 40% higher allowable consumption under a strict global carbon cap without country-specific emissions allocations (1100 Mmt) than with country-specific emissions caps (770 Mmt). These results illustrate cooperative international climate policy could be more cost-effective in mitigating carbon emissions than countries acting individually.

"The Future of Low Carbon Electric Power Generation: An Assessment of Economic Viability & Water Impacts under Climate Change and Mitigation Policies" – Shuchi Talati, 2016

As the electric power generation sector transitions towards low-carbon technologies under climate change and mitigation policies, technology choices and water use will shift alongside it. With the implementation of climate regulations, the viability of different technologies will begin to change as decreased emissions begin to be incentivized. This thesis addresses how proposed climate regulations necessitating use of carbon capture and storage (CCS) will affect water use from new fossil-fuel fired power generation as well as how climate changes and policies could affect water use from the electricity generation sector on the whole in the long term. This thesis also addresses the economic viability of existing coal-fired power plants using carbon capture and storage (CCS) retrofits under the impending market structure of the finalized Clean Power Plan.

Chapter 1 examines the water use impacts of the proposed New Source Performance Standards for CO2 emissions new fossil fuel-fired electricity generation units proposed by the U.S. Environmental Protection Agency in September 2013. To meet the emissions requirements of this regulation, coal-fired units will require use of CCS at 40% capture, increasing water use by approximately 30%, though added water use varies with plant and CCS designs. More stringent standards could require CCS at natural gas combined cycle (NGCC) plants as well. When examined over a range of emission standards, new NGCC plants consume roughly 60 to 70% less water than coal-fired plants.

Chapter 2 quantifies plant and regional shifts in water consumption from the energy generation sector in light of ambient climate changes and potential regulation shifts from climate mitigation policies on a 100-year planning horizon in the Southwest. Employing an integrated modeling framework, feedbacks between climate change, air temperature and humidity, and consequent power plant water requirements are assessed. These direct impacts of climate change on water consumption by 2095 range from a 3%-7% increase over scenarios that do not incorporate ambient air impacts. Adaptation strategies to lower water use include the use of advanced cooling technologies and greater dependence on solar and wind. Water consumption may be reduced by 50% in 2095 from the reference from an increase in dry cooling shares to 35- 40%. This reduction could also be achieved through solar and wind power generation constituting 60% of the grid, necessitating a 250% in technology learning rates.

Chapter 3 analyzes the economic feasibility of retrofitting carbon capture and storage (CCS) to existing coal-fired electricity generating units (EGUs) in Texas for compliance with the Clean Power Plan's rate-based emission standards under an emission trading scheme. Using a database of 18 technologically capable EGUs in Texas, CCS retrofits are modeled under a range of scenarios. Through an emission rate credit (ERC) marketplace, units enlisting the use of 90% capture of CO2 would prove to be more profitable than existing units at average prices of $27.8 per MWh under the final state standard. The combination of ERC trading and CO2 utilization can greatly reinforce economic incentives and market demands for CCS to accelerate large-scale deployment, even under scenarios with high retrofit costs. This chapter additionally compares the costs of electricity generation between CCS retrofits and renewable technology under the trading scheme, finding that EGUs retrofitted with CCS may not only be competitive with wind and solar, but more profitable under certain market conditions.

"Computational Models for Renewable Energy Target Achievement & Policy Analysis" – Kristen R. Schell, 2016

To date, over 84% of countries worldwide have renewable energy targets (RET), requiring that a certain amount of electricity be produced from renewable sources by a target date. Despite the worldwide prevalence of these policies, little research has been conducted on ex-ante RET policy analysis. In an effort to move toward evidence-based policymaking, this thesis develops computational models to assess the tradeoffs associated with alternatives for both RET achievement and RET policy formulation, including the option of creating renewable energy credit (REC) markets to facilitate meeting an RET goal. A mixed integer linear program (MILP), a probabilistic cost prediction model and a mixed complementarity problem (MCP) serve as the theoretical bases for the RET alternative and policy formulation analyses. From these models it was found, inter alia, that RET goals set too low run the risk of creating technological lock-in and could inhibit achievement of higher goals; probabilistic cost predictions give decision-makers essential risk information, when cost estimation is an integral part of alternatives assessment; and though REC markets may facilitate RET achievement, including REC markets in an RET policy formulation may not result in the lowest possible greenhouse gas emissions (GHG).

"Hydropower Development in the Brazilian Amazon" – Felipe Aguiar Marcondes de Faria, 2016

Brazil plans to meet the majority of its growing electricity demand with new hydropower plants located in the Amazon basin. The government’s energy policy forecasts the construction of 55 GW of installed capacity by 2028, with total investments in the range of 100 and 200 billion reais (30 to 60 billion dollars), and the creation 9,000 km2 of artificial reservoirs. However, the construction and operation of large hydropower plants may affect the environment, the local economy, and the population surrounding those projects. Considering the magnitude of the investments and the potential impacts for the Amazon basin, it is crucial to apply policy analysis techniques to support informed decisions about whether the construction of large hydropower plants in the Amazon is the best alternative to supply the additional electricity that Brazil needs, taking into account economic, social, and environmental costs and benefits. Here, I apply three different quantitative policy analysis techniques to assess three major questions related to the construction of hydropower plants in the Amazon region. First, I study the greenhouse gas emissions from hydropower reservoirs in the Amazon. Second, I explore the local socio-economic impacts of building hydropower plants. Finally, I investigate alternative electricity sources that could replace Amazon hydropower reservoirs by modeling the Brazilian electricity network under five capacity expansion scenarios.

"Powering the Information Age: Metrics, Social Cost Optimization Strategies, and Indirect Effects Related to Data Center Energy Use" – Nathaniel Charles Horner, 2016

This dissertation contains three studies examining aspects of energy use by data centers and other information and communication technology (ICT) infrastructure necessary to support the electronic services that now form such a pervasive aspect of daily life. The energy consumption of ICT in general and data centers in particular has been of growing interest to both industry and the public, with continued calls for increased efficiency and greater focus on environmental impacts.

The first study examines the metrics used to assess data center energy performance and finds that power usage effectiveness (PUE), the de facto industry standard, only accounts for one of four critical aspects of data center energy performance. PUE measures the overhead of the facility infrastructure but does not consider the efficiency of the IT equipment, its utilization, or the emissions profile of the power source. As a result, PUE corresponds poorly with energy and carbon efficiency, as demonstrated using a small set of empirical data center energy use measurements.

The second study lays out a taxonomy of indirect energy impacts to help assess whether ICT’s direct energy consumption is offset by its energy benefits, and concludes that ICT likely has a large potential net energy benefit, but that there is no consensus on the sign or magnitude of actual savings, which are largely dependent upon implementation details.

The third study estimates the potential of dynamic load shifting in a content distribution network to reduce both private costs and emissions-related externalities associated with electricity consumption. Utilizing variable marginal retail prices based on wholesale electricity markets and marginal damages estimated from emissions data in a cost-minimization model, the analysis finds that load shifting can either reduce data center power bills by approximately 25%–33% or avoid 30%–40% of public damages, while a range of joint cost minimization strategies enables simultaneous reduction of both private and public costs. The vast majority of these savings can be achieved even under existing bandwidth and network distance constraints, although current industry trends towards virtualization, energy efficiency, and green powermay make load shifting less appealing.

"Economics of Behind-the-Meter Solar PV and Energy Storage" – Shelly C. Hagerman, 2016

In this thesis, I present three research papers that focus on the economics of behind-the-meter technologies for residential, commercial, and industrial customers. Each of these papers takes the perspective of the customer, where the value of the technology comes from reducing their electricity bill.

In Chapter 2, I assess whether solar photovoltaics are economically viable without subsidies for residential customers across the United States. I calculate the break-even electricity prices and installation costs and find that, at a state level, solar PV is only currently economically attractive in Hawaii without the use of subsidies. In order for widespread adoption of solar PV, I illustrate how the availability of favorable financing terms, installation costs at or below $1.5/W, and the
continuance of net energy metering policies are each critical.

In Chapter 3, I create a case study to better understand solar PV economics for commercial and industrial customers, who collectively account for the majority of annual electricity sales in the United States. While residential customers are billed based on the total amount of energy they consume, commercial and industrial customers are also billed according to their greatest 15-minute energy use in a month with a demand charge. I analyze the net present value of a solar
PV investment using both simulated and measured load and solar data for a variety of commercial customers in North and South Carolina. I identify key factors that influence economic viability and find that solar PV is not presently economically viable for these customers without subsidies, but will be once installation costs drop to below $1.25/W.

In Chapter 4, I evaluate the economics of using energy storage to further reduce demand charges for each of the customers examined in Chapter 3. Using a “black-box” approach, I apply several generic energy storage technical attributes of a high-energy lithium-ion battery to assess the ideal performance and maximum economic benefit of energy storage. I find that batteries with lower capacities are most profitable for the commercial and industrial customers examined using an optimistic algorithm, but require further cost reductions using a pessimistic algorithm.

"Optimal Locations for Siting Wind Energy Projects: Technical Challenges, Economics, and Public Preferences" – Julian V. Lamy, 2016

Increasing the percentage of wind power in the United States electricity generation mix would facilitate the transition towards a more sustainable, low-pollution, and environmentally-conscious electricity grid. However, this effort is not without cost. Wind power generation is time-variable and typically not synchronized with electricity demand (i.e., load). In addition, the highest-output wind resources are often located in remote locations, necessitating transmission investment between generation sites and load. Furthermore, negative public perceptions of wind projects could prevent widespread wind development, especially for projects close to densely-populated communities. The work presented in my dissertation seeks to understand where it’s best to locate wind energy projects while considering these various factors.

First, in Chapter 2, I examine whether energy storage technologies, such as grid-scale batteries, could help reduce the transmission upgrade costs incurred when siting wind projects in distant locations. For a case study of a hypothetical 200 MW wind project in North Dakota that delivers power to Illinois, I present an optimization model that estimates the optimal size of transmission and energy storage capacity that yields the lowest average cost of generation and transmission ($/MWh). I find that for this application of storage to be economical, energy storage costs would have to be $100/kWh or lower, which is well below current costs for available technologies. I conclude that there are likely better ways to use energy storage than for accessing distant wind projects.

Following from this work, in Chapter 3, I present an optimization model to estimate the economics of accessing high quality wind resources in remote areas to comply with renewable energy policy targets. I include temporal aspects of wind power (variability costs and correlation to market prices) as well as total wind power produced from different farms. I assess the goal of providing 40 TWh of new wind generation in the Midwestern transmission system (MISO) while minimizing system costs. Results show that building wind farms in North/South Dakota (windiest states) compared to Illinois (less windy, but close to population centers) would only be economical if the incremental transmission costs to access them were below $360/kW of wind capacity (break-even value). Historically, the incremental transmission costs for wind development in North/South Dakota compared to in Illinois are about twice this value. However, the break-even incremental transmission cost for wind farms in Minnesota/Iowa (also windy states) is $250/kW, which is consistent with historical costs. I conclude that for the case in MISO, building wind projects in more distant locations (i.e., Minnesota/Iowa) is most economical.

My two final chapters use semi-structured interviews (Chapter 4) and conjoint-based surveys (Chapter 5) to understand public perceptions and preferences for different wind project siting characteristics such as the distance between the project and a person’s home (i.e., “not-in-my-backyard” or NIMBY) and offshore vs. onshore locations. The semi-structured interviews, conducted with members of a community in Massachusetts, revealed that economic benefit to the community is the most important factor driving perceptions about projects, along with aesthetics, noise impacts, environmental benefits, hazard to wildlife, and safety concerns. In Chapter 5, I show the results from the conjoint survey. The study’s sample included participants from a coastal community in Massachusetts and a U.S.-wide sample from Amazon’s Mechanical Turk. Results show that participants in the U.S.-wide sample perceived a small reduction in utility, equivalent to $1 per month, for living within 1 mile of a project. Surprisingly, I find no evidence of this effect for participants in the coastal community. The most important characteristic to both samples was the economic benefits from the project – both to their community through increased tax revenue, and to individuals through reduced monthly energy bills. Further, participants in both samples preferred onshore to offshore projects, but that preference was much stronger in the coastal community. I also find that participants from the coastal community preferred expanding an existing wind projects rather than building an entirely new one, whereas those in the U.S.-wide sample were indifferent, and equally supportive of the two options. These differences are likely driven by the prior positive experience the coastal community has had with an existing onshore wind project as well as their strong cultural identity that favors ocean views. I conclude that preference for increased distance from a wind project (NIMBY) is likely small or non-existent and that offshore wind projects within 5 miles from shore could cause large welfare losses to coastal communities.

Finally, in Chapter 6, I provide a discussion and policy recommendations from my work. Importantly, I recommend that future research should combine the various topics throughout my chapters (i.e., transmission requirements, hourly power production, variability impacts to the grid, and public preferences) into a comprehensive model that identifies optimal locations for wind projects across the United States.

"Oxyfuel Carbon Capture For Pulverized Coal: Techno-Economic Model Creation and Evaluation Amongst Alternatives" – Kyle James Borgert, 2015

Today, and for the foreseeable future, coal and other fossil fuels will provide a major portion of the energy services demanded by both developed and developing countries around the word. In order to reduce the emissions of carbon dioxide associated with combustion of coal for electricity generation, a wide range of carbon capture technologies are being developed. This thesis models the oxyfuel carbon capture process for pulverized coal and presents performance and cost estimates of this system in comparison to other low-carbon fossil fuel generators.

Detailed process models for oxygen production, flue gas treatment, and carbon dioxide purification have been developed along with the calculation strategies necessary to employ these components in alternative oxyfuel system configurations for different types of coal-fired power plants. These new oxyfuel process models have been implemented in the widely-used Integrated Environmental Control Model (IECM) to facilitate systematic comparisons with other low-carbon options employing fossil fuels.

Assumptions about uncertainties in the performance characteristics of gas separation processes and flue gas duct sealing technology, as well as plant utilization and financing parameters, were found to produce a wide range of cost estimates for oxyfuel systems. In case studies of a new 500 MW power plant burning sub-bituminous Powder River Basin coal, the estimated levelized cost of electricity (LCOE) 95% confidence interval (CI) was 86 to 150 [$/MWh] for an oxyfuel system producing a high-purity [99.5 mol% CO2] carbon dioxide product while capturing 90% of the flue gas carbon dioxide. For a CoCapture oxyfuel system capturing 100% of the flue gas CO2 together with all other flue gas constituents, the estimated LCOE 95% CI was 90 to 153 [$/MWh] (all costs in constant 2012 US Dollars).

Using the IECM, an oxyfuel system for CO2 capture also was compared under uncertainty to an existing amine-based post-combustion capture system for a new 500 MW power plant, with both systems capturing 90% of the CO2 and producing a high-purity stream for pipeline transport to a geological sequestration site. The resulting distribution for the cost of CO2 avoided showed the oxyfuel-based system had a 95% CI of 44 to 126 [$/tonne CO2] while the amine-based system cost 95% CI ranged from 50 to 133 [$/tonne CO2]. The oxyfuel cost distribution had a longer tail toward more expensive configurations but over 70% of the distribution showed the oxyfuel-based system to be ~10[$/tonne CO2] lower in cost compared to the amine-based capture system.

An evaluation of several low-carbon generation options fueled by coal and natural gas further considered both direct and indirect greenhouse gas emissions. This analysis showed oxyfuel to be economically competitive with all capture system considered, and also indicated oxyfuel to be the preferred carbon capture technology for minimizing overall carbon intensity. Combined, these results suggest that oxyfuel is a promising carbon capture technology, and the only one which offers the unique ability to capture all the combustion gases to become a truly zero emission coal plant. Realization of the latter option, however, is contingent on the development of new regulatory policies for underground injection of mixed flue gas streams that is outside the scope of this thesis.

Contact: Kyle Borgert
kborgert@alumni.cmu.edu

"Low Carbon Policy and Technology in the Power Sector: Evaluating Economic and Environmental Effects" – David Luke Oates, 2015

In this thesis, I present four research papers related by their focus on environmental and economic effects of low-carbon policies and technologies in electric power. The papers address a number of issues related to the operation and design of CCS-equipped plants with solvent storage and bypass, the effect of Renewable Portfolio Standards (RPS) on cycling of coal-fired power plants, and the EPA’s proposed CO2 emissions rule for existing power plants.

In Chapter 2, I present results from a study of the design and operation of power plants equipped with CCS with flue gas bypass and solvent storage. I considered whether flue gas bypass and solvent storage could be used to increase the profitability of plants with CCS. Using a price- taker profit maximization model, I evaluated the increase in NPV at a pulverized coal (PC) plant with an amine-based capture system, a PC plant with an ammonia-based capture system, and a natural gas combined-cycle plant with an amine-based capture system when these plants were equipped with an optimally sized solvent storage vessel and regenerator. I found that while flue gas bypass and solvent storage increased profitability at low CO2 prices, they ceased to do so at CO2 prices high enough for the overall plant to become NPV-positive.

In Chapter 3, I present results from a Unit Commitment and Economic Dispatch model of the PJM West power system. I quantify the increase in cycling of coal-fired power plants that results when complying with a 20% RPS using wind power, accounting for cycling costs not usually included in power plant bids. I find that while additional cycling does increase cycling-related production costs and emissions of CO2, SO2, and NOX, these increases are small compared to the overall reductions in production costs and air emissions that occur with high levels of wind.

In proposing its existing power plant CO2 emissions standard, the Environmental Protection Agency determined that significant energy efficiency would be available to aid in compliance. In Chapter 4, I use an expanded version of the model of Chapter 3 to evaluate compliance with the standard with and without this energy efficiency, as well as under several other scenarios. I find that emissions of CO2, SO2, and NOX are relatively insensitive to the amount of energy efficiency available, but that production costs increase significantly when complying without efficiency.

In complying with the EPA’s proposed existing power plant CO2 emissions standard, states will have the choice of whether to comply individually or in cooperation with other states, as well as the choice of whether to comply with a rate-based standard or a mass-based standard. In Chapter 5, I present results from a linear dispatch model of the power system in the continental U.S. I find that cooperative compliance reduces total costs, but that certain states will prefer not to cooperate. I also find that compliance with a mass-based standard increases electricity prices by a larger margin than does compliance with a rate-based standard, with implications for the distribution of surplus changes between producers and consumers.

Contact: David Luke Oates
dloates@cmu.edu

"Reducing Pollution from Aviation and Oceanshipping" – Parth Trilochan Vaishnav, 2015

International aviation and ocean shipping are significant and potentially fast growing sources of greenhouse gas emissions. Both sectors also contribute to poor local and regional air quality. This thesis analyzes three interventions aimed at reducing air emissions from airplanes and ships. The first is the use of tugs, or an electric motor embedded in the landing gear, to propel the aircraft on the ground. If airlines were to tow all large narrowbody aircraft on domestic service from the gate to the edge of the runway before take off at 41 of the 50 busiest airports in the U.S., CO2 emissions would fall by 0.5 million tonnes annually. In addition, the switch would produce $150 million in annual air quality benefits from reduced emissions of particulate matter, hydrocarbons and the oxides of nitrogen. Using embedded electric motors to taxi large narrowbody aircraft would cut CO2 emissions by nearly 2 million tonnes per year. The second intervention is the market based mechanism, designed to cap CO2 emissions from international aviation at 2020 levels, currently being designed at ICAO. An analysis of an early draft of this mechanism suggests that it would require airlines to offset an average of 270 million tonnes in CO2 emissions during each of the years between 2021 and 2035 when it will be active. The analysis suggests that the current proposal is complex, and poorly specified. We recommend that the mechanism be made much simpler: for example, by simply determining an airline’s offset obligations on the basis of its carbon footprint in that year. Finally, we study the costs and benefits of a more widespread use of grid electricity to energize berthed vessels. We use mixed-integer linear programming to identify combinations of ports and vessels where using shore power would produce the greatest benefit to society. We conclude that the practice could reduce CO2 emissions by 0.2 million tonnes per year and yield air quality improvements worth $80-200 million per year at no net cost to society.

Contact: Parth Trilochan Vaishnav
parthv@cmu.edu

"Electric Vehicles and the Grid: Interactions and Environmental and Health Impacts" – Allison E. Weis, 2015

The societal benefit of electric vehicles depends heavily on how they interact with the electric power system. In this thesis, I investigate the impact of electric vehicles based on this interaction in order to determine the possible benefits of controlling electric vehicle charging and how they compare to other vehicle options based on optimization models of electricity systems. I estimate the cost reductions from controlled charging of electric vehicles in the New York power system both with and without a high wind penetration and with and without the need for capacity expansion. In this power system, controlled charging can reduce the generation costs associated with charging the vehicles in half, with slightly higher cost reductions in high wind scenarios. I also estimate the cost reductions along with the changes in carbon and criteria air pollutant emissions due to controlled charging in the PJM power system. I examine both current and future grid scenarios, several plug-in vehicles types, and a high wind penetration scenario. Again I find that controlled charging can significantly reduce the costs of charging the vehicles, on the order of 30% of the generation costs to meet the charging demand. However, the environmental and health damages from the emissions cause total social costs to be higher with controlled charging in most cases. Finally, using the charging emissions from PJM, I evaluate the lifecycle emissions of plug-in, hybrid, and conventional vehicles in this region to determine which has lower environmental and health damages. I find that given the representative vehicles studied, plug-in electric vehicles have higher lifecycle damages than hybrids in PJM in 2010 but have lower lifecycle damages in a forecasted 2018 PJM power system.

Contact: Allison E. Weis
aeweis@andrew.cmu.edu

"Analysis of Selected Regulatory Interventions to Improve Energy Efficiency" – Russell M. Meyer, 2014

This dissertation includes three studies of public policy designed to improve energy efficiency in the United States. In an ex ante study of two residential lighting demand-side efficiency programs, I find that despite considerable uncertainty in the achieved energy savings it is unlikely that these programs are not cost-effective. Several recommendations are made to improve the reporting of these programs that would enable more learning from past activities and thus more cost-effective efficiency investments in the future. In an ex post study of a separate demand-side efficiency program I find that participation in the program is associated with a subsequent increase in household energy consumption. The likely reason for this counterintuitive finding is that consumers are using the rebate as an equipment subsidy to consume additional energy services rather than as an equipment replacement program to consume a constant level of energy services. The contradiction of the findings of these two studies highlights the need for ex post analyses of demand-side efficiency programs as a critical component of program design in order to ensure that anticipated benefits are being realized in practice. Finally, I create a model of fuel consumption by light-duty vehicles in the United States in order to generate a projection of fuel demand in the context of demographic changes and increasing fuel economy standards. I find that long-term trends in population growth are more than offset by increasing fuel efficiency, assuming that these standards are met.

Contact: Russell M. Meyer
russellmeyer@cmu.edu

"Retrospective and prospective analysis of policy incentives for wind power in Portugal" – Ivonne A Pena Cabra, 2014

Concerns over climate change impacts, goals to increase environmental sustainability, and questions about the reliability of fuel supply have led several countries to pursue the goal of increasing the share of renewable energy sources in their electricity grid. Portugal is one of the leading countries for wind electricity generation. Wind diffusion in Portugal started in the early 2000’s and in 2013 wind electricity generation accounted for more than 24% (REN 2013b). The large share of wind in Portuguese electricity production is a consequence of European Union (E.U.) mandates and national policies, mainly feed-in tariffs. Discussions on the appropriate policy design and level of incentive to promote renewable energy adoption and meet further renewable capacity goals are ongoing in Portugal, namely in what concerns the level and duration of feed-in tariffs that should be provided to independent power producers. This, in turn, raises the question of whether the past feed-in tariff levels were well designed to achieve the goals of a larger penetration of renewables in the Portuguese grid. The policies to induce wind adoption have led to a growth in wind installed capacity and share of electricity generated by wind in Portugal from less than 1% in 2000 to approximately 24% in 2013, but questions arise on their cost-effectiveness and whether alternative policy designs would have led to the same goal.

"Development of Geostatistical Models to Estimate CO2 Storage Resource in Sedimentary Geologic Formations" – Olga Popova, 2014

Carbon capture and sequestration (CCS) is a technology that provides a near-term solution to reduce anthropogenic CO2 emissions to the atmosphere and reduce our impact on the climate system. Assessments of carbon sequestration resources that have been made for North America using existing methodologies likely underestimate uncertainty and variability in the reservoir parameters. This thesis describes a geostatistical model developed to estimate the CO2 storage resource in sedimentary formations. The proposed stochastic model accounts for the spatial distribution of reservoir properties and is implemented to a case study of the Oriskany Formation of the Appalachian sedimentary basin. The developed model allows for estimation of the CO2 sequestration resource of a storage formation with subsequent uncertainty analysis. Since the model is flexible with respect to changing input parameters and assumptions it can be parameterized to calculate the CO2 storage resource of any porous subsurface unit.

The thesis continues with evaluation of the cost of CO2 injection and storage for the Oriskany Formation utilizing storage resource estimates generated by our geostatistical model. Our results indicate that the cost of sequestering CO2 has significant spatial variation due to heterogeneity of formation properties and site geology. We identify the low-cost areas within the Oriskany footprint. In general, these areas correspond to the deepest portions of the Appalachian basin and could be considered as potential CO2 injection sites for CCS industrial scale projects.

Overall, we conclude that significant improvement can be made by integrating basin geology and spatial heterogeneity of formation petrophysical properties into CCS cost assessments, and that should be a focus of future research efforts. This will allow for more accurate cost estimates for the entire CCS system and identify areas of sedimentary basins with optimal conditions for CO2 injection and storage. To mitigate the effects of climate change, the U.S. will need a widespread deployment of low-carbon electricity generating technologies including natural gas and coal with CCS. More precise CO2 storage resource and CCS cost estimates will provide better recommendations for government and industry leaders and inform their decisions on what greenhouse gas mitigation measures are the best fit for their regions.

"Access to Electricity in Rural India - Tradeoffs and Interventions for Meaningful Electrification" – Santosh Maddur Harish, 2014

This thesis investigates the engineering economics of interventions to reduce consumer inconvenience due to unreliable electricity supply in rural India. The work introduces and applies a novel approach to estimate interruption costs as loss in consumer surplus due to restricted consumption of electricity services.

Chapter 2 reports an assessment that compares grid extension with distributed generation (DG) alternatives, based on the subsidies they will necessitate, and costs of service interruptions that are appropriate in the rural Indian context. Despite the inclusion of interruption costs, standalone DG does not appear to be competitive with grid extension at distances of less than 17 km. However, backing up unreliable grid service with local DG plants is attractive when reliability is very poor, even in previously electrified villages. Introduction of energy efficient lighting changes these economics, and the threshold for acceptable grid unreliability significantly reduces.

Chapter 3 analyzes supply rostering (alternatively, “load shedding”) in metropolitan, small town and rural feeders in and around Bangalore city. The inequity in load shedding is analyzed through transfers due to differential tariffs between the urban and rural residential consumers, and the relief provided to BESCOM, through avoided procurement of additional supply from generators, because rural and small town feeders are load shed higher than Bangalore city. The values of the load shedding transfers are estimated to be in the range of Rs. 120-380/consumer-year from the rural consumers, and Rs. 220-370/consumer-year from the small town consumers. The metropolitan consumers are found to be net beneficiaries. The viability of using smart meters to provide current limited but uninterrupted supply is investigated as one alternative to outright blackouts.

Chapter 4 develops a broader theoretical framework that can be used to model consumer demand for electricity services with unreliable supply and adaptation. Demand for energy ‘services’ is modeled by incorporating time of use, duration and deferability. Supply reliability is disaggregated into its constituent dimensions– mean and variance of supply availability in times of high demand, and supply predictability, and their respective impacts on consumer welfare are discussed. Primary data collected from Karnataka inform the discussion, especially with backup adoption. New consumer-oriented reliability indices are proposed.

"Exploring the Deployment Potential of Small Modular Reactors" – Ahmed Abdulla, 2014

This thesis reports the results of several investigations into the viability of an emergent technology. Due to the lack of data in such cases, and the sensitivity surrounding nuclear power, exploring the potential of small modular reactors (SMRs) proved challenging. Moreover, these reactors come in a wide range of sizes and can employ a number of technologies, which made investigating the category as a whole difficult.

We started by looking at a subset of SMRs that were the most promising candidates for near to mid-term deployment: integral light water SMRs. We conducted a technically detailed elicitation of expert assessments of their capital costs and construction duration, focusing on five reactor deployment scenarios that involved a large reactor and two light water SMRs. Consistent with the uncertainty introduced by past cost overruns and construction delays, median estimates of the cost of new large plants varied by more than a factor of 2.5. Expert judgments about likely SMR costs displayed an even wider range. There was consensus that an SMR plant’s construction duration would be shorter than a large reactor’s. Experts identified more affordable unit cost, factory fabrication, and shorter construction schedules as factors that may make light water SMRs economically viable, though these reactors do not constitute a paradigm shift when it comes to nuclear power’s safety and security.

Using these expert assessments of cost and construction duration, we calculated levelized cost of electricity values for four of the five scenarios. For the large plant, median levelized cost estimates ranged from $56 to $120 per MWh. Median estimates of levelized cost ranged from $77 to $240 per MWh for a 45MWe SMR, and from $65 to $120 per MWh for a 225MWe unit. We concluded that controlling construction duration is important, though not as important a factor in the analysis as capital cost, and, given the price of electricity in some parts of the U.S., it is possible to construct an argument for deploying SMRs in certain locations.
We then decided to investigate the technical and institutional barriers hampering the development and deployment of a subset of six SMRs, including two light water designs and four non-light water advanced designs. We organized an invitational workshop that became an integrated assessment of various designs and of the institutional innovations required to bring SMRs to market.

Some valuable insights were gleaned from the workshop: there is consensus that many of the challenges facing advanced SMRs are rooted in institutional biases in favor of the light water economy, as opposed to technical ones. The institutional factors that are judged to pose the greatest challenge to the mass deployment of SMRs are: the lack of a greenhouse gas control regime; political and financial instability; public concerns about nuclear safety and waste; and inadequate national and international institutions.

When asked what factors most help promote SMR adoption in OECD and developing countries, economic factors dominate the list of characteristics that most contribute to their promotion in OECD countries but, when it comes to developing countries, institutional factors are regarded as being of highest import. Safety of design and safety in operation are judged the most important characteristic on both lists.

Contact: Ahmed Y. Abdulla
aya1@cmu.edu

"Reducing Carbon Intensity in Restructured Markets: Challenges and Potential Solutions" – Roger Lueken" 2014

The U.S. electric power sector is in the early stages of transitioning from a reliance on carbon intensive generation sources to a system based on low-carbon sources. In this thesis, I present analyses of four different aspects of this transition, with an emphasis on the PJM Interconnection.

The effects of bulk electricity storage on the PJM market
I analyze the value of three storage technologies in the PJM day-ahead energy market, using a reduced-form unit commitment model with 2010 data. I find that large-scale storage would increase overall social welfare in PJM. However, the annualized capital costs of storage would exceed social welfare gains. Consumers would save up to $4 billion annually, largely at the expense of generator surplus. Storage modestly increases emissions of CO2 and other pollutants.

The external costs and benefits of wind energy in PJM
Large deployments of wind create external costs and benefits that are not fully captured in power purchase agreements. I find that wind’s external costs in the PJM market are uncertain but significant when compared to levelized PPA prices. Pollution reduction benefits are very uncertain but exceed external costs with high probability.

The climate and health effects of a USA switch from coal to gas electricity generation
I analyze the emission benefits created by a hypothetical scenario in which all U.S. coal plants are switched to natural gas plants in 2016. The net effect on warming is unclear; results are highly sensitive to the rate of fugitive methane emissions and the efficiency of replacement gas plants. However, the human health benefits of such a switch are substantial. The costs of building and operating new gas plants likely exceed the health benefits.

Robust resource adequacy planning in the face of coal retirements
Over the next decade, many U.S. coal-fired power plants are expected to retire, posing a challenge to system planners. I investigate the resource adequacy requirements of the PJM Interconnection, and how procuring less capacity may affect reliability. I find that PJM’s 2010 reserve margin of 20.5% was sufficient to achieve the stated reliability standard with 90% confidence. PJM could reduce reserve margins to 13% and still achieve levels of reliability accepted by other power systems.

Contact: Roger Lueken
rlueken@andrew.cmu.edu

"Cost Effectiveness of CO2 Mitigation Technologies and Policies in the Electricity Sector" – Jared Moore, 2014

In order to find politically feasible ways to reduce greenhouse gas emission emissions, governments must examine how policies affect a variety of stakeholders. The costs and benefits of low carbon technology options are unique and affect different market participants in different ways. In this thesis, we examine the cost effectiveness of carbon mitigation technologies and policies from the social perspective and from the perspective of consumers.

In Chapter 2, we perform an engineering-economic analysis of hybridizing concentrating solar thermal power with fossil fuel. We examine the cost effectiveness of substituting the solar power for new coal or gas and find the cost of mitigation to be approximately ~$130/tCO2 to ~$300/tCO2.

In Chapter 3, we quantify some externalized social costs and benefits of wind energy. We estimate the costs due to variability and transmission unique to wind to have an expected value of ~$20/MWh.

In Chapter 4, we quantify the cost effectiveness of a renewable portfolio standard and a carbon price from the perspective of consumers in restructured markets. We find that both that the RPS can be more cost effective than a carbon price for consumers under certain circumstances: continued excess supply of capacity, retention of nuclear generators, and high natural gas prices.

In Chapter 5, we examine the implications of lowering electricity sector CO2 emissions in PJM through a Low Carbon Capacity Standard (LCCS). We estimate that an LCCS would supply the same amount of energy (105,000 GWh) as the RPS’s in PJM and an additional ~10 GW of capacity. We find that the LCCS could be more cost effective for consumers than an RPS if it lowered capacity prices.

Contact: Jared Moore
jaredmoo@andrew.cmu.edu

"Assessing the Costs and Risks of Novel Wind Turbine Applications" – Stephen Rose, 2013

This thesis addresses the cost-effectiveness of curtailing a wind farm to regulate the electrical grid frequency and the hurricane risk to offshore wind farms in the eastern United States. Additionally, this thesis presents a new method to generate long periods of non-stationary wind speed time series data sampled at high rates by combining measured and simulated data.

Paper 1 calculates the cost of curtailing the power output of a wind farm to provide a reserve of power to regulate the electrical grid frequency, as required by grid operators in several countries with high wind-power penetrations. The simulations in Paper 1 show that it is most efficient to curtail a few turbines deeply rather than curtail all turbines in a wind farm equally. Compared to regulation prices in the Texas (ERCOT) market in 2007-2009, a curtailed wind farm would be cost-competitive with conventional generators less than 1% of the time.
Paper 2 supports the simulations in Paper 1 by developing a method to combine long periods of low-frequency wind speed data with realistic simulated high-frequency turbulence. The combined time series of wind speeds retains the non-stationary characteristics of wind speed, such as diurnal variations, the passing of weather fronts, and seasonal variations, but gives a much higher sampling rate.

Papers 3 and 4 estimate the hurricane risks to current designs of offshore wind turbines in the U.S. Paper 3 develops analytical probability distributions based on historical hurricane records to predict the distribution of damages to a single wind farm in a given location. Paper 4 uses simulated hurricanes with realistic statistical properties to estimate the correlated risks to all the wind farms in a region and estimate the distribution of aggregate losses over different periods. Both papers find hurricane risks are small for current turbine designs in New England and the Mid-Atlantic, but the vi risks in the Gulf of Mexico and the Southeast are significant enough to warrant new, stronger designs. Hurricane risks could be reduced almost an order of magnitude by ensuring that turbines can continue yawing to track the wind direction even if grid power is lost.

Contact: Stephen Rose
srose@andrew.cmu.edu

"Facilitating the Development and Integration of Low-Carbon Energy Technologies" – Emily Fertig, 2013

Climate change mitigation will require extensive decarbonization of the electricity sector. This thesis addresses both large-scale wind integration (Papers 1 - 3) and development of new energy technologies (Paper 4) in service of this goal.

Compressed air energy storage (CAES) could be paired with a wind farm to provide rm, dispatchable baseload power, or serve as a peaking plant and capture upswings in electricity prices. Paper 1 presents a rm-level engineering-economic analysis of a wind/CAES system with a wind farm in central Texas, load in either Dallas or Houston, and a CAES plant whose location is pro t-optimized. Of a range of market scenarios considered, the CAES plant is found to be pro table only given the existence of large and infrequent price spikes. Social bene ts of wind/CAES include avoided construction of new generation capacity, improved air quality during peak demand, and increased economic surplus, but may not outweigh the private cost of the CAES system nor justify a subsidy.

Like CAES, pumped hydropower storage (PHS) ramps quickly enough to smooth wind power and could pro t from arbitrage on short-term price uctuations exacerbated by large-scale wind. Germany has aggressive plans for wind power expansion, and Paper 2 analyzes an investment opportunity in a PHS facility in Norway that practices arbitrage in the German spot market. Price forecasts given increased wind capacity are used to calculate pro t-maximizing production schedules and annual revenue streams. Real options theory is used to value the investment opportunity, since unlike net present value, it accounts for uncertainty and intertemporal choice. Results show that the optimal investment strategy under the base scenario is to wait approximately eight years then i Abstract ii invest in the largest available plant.

Paper 3 examines long-distance interconnection as an alternate method of wind power smoothing. Frequency-domain analysis indicates that interconnection of aggregate regional wind plants across much of the western and mid-western U.S. would not result in signi cantly greater smoothing than interconnection within a single region. Time-domain analysis shows that interconnection across regions reduces the magnitude of low-probability step changes and doubles rm power output (capacity available at least 92 % of the time) compared with a single region. An approximate cost analysis indicates that despite these bene ts, balancing wind and providing rm power with local natural gas turbines would be more cost-e ective than with transmission interconnection.

Papers 1 and 3 demonstrate the need for further RD&D (research, development, and deployment) of low-carbon energy technologies. Energy technology development is highly uncertain but most often modeled as deterministic, which neglects the ability both to adapt RD&D strategy to changing conditions and to invest in initially high-cost technologies with small breakthrough probabilities. Paper 4 develops an analytical stochastic dynamic programming framework in which RD&D spending decreases the expected value of the stochastic cost of a technology. Results for a two-factor cost model (which separates RD&D into R&D and learning-by-doing) applied to carbon capture and sequestration (CCS) indicate that given 15 years until large-scale deployment, investment in the RD&D program is optimal over a very broad range of initial mitigation costs ($10{$380/tCO2). While the NPV of the program is zero if initial mitigation cost is $100/tCO2, under uncertainty the program is worth about $7 billion. If initial mitigation cost is high, the program is worth most if cost reductions exogenous to the program (e.g. due to private sector activity) are also high. Factors that promote R&D spending over learning-by-doing include more imminent deployment, high initial cost, lower exogenous cost reductions, and lower program funds available.

Contact: Emily Fertig
emily.fertig@gmail.com

"How the Timing of Climate Change Policy Affects Infrastructure Turnover in the Electricity Sector: Engineering, Economic and Policy Considerations" – Catherine Finlay Izard, 2013

The electricity sector is responsible for producing 35% of US greenhouse gas (GHG) emissions. Estimates suggest that ideally, the electricity sector would be responsible for approximately 85% of emissions abatement associated with climate polices such as America’s Clean Energy and Security Act (ACES). This is equivalent to ~50% cumulative emissions reductions below projected cumulative business-as-usual (BAU) emissions.

Achieving these levels of emissions reductions will require dramatic changes in the US electricity generating infrastructure: almost all of the fossil-generation fleet will need to be replaced with low-carbon sources and society is likely to have to maintain a high build rate of new capacity for decades. Unfortunately, the inertia in the electricity sector means that there may be physical constraints to the rate at which new electricity generating capacity can be built. Because the build rate of new electricity generating capacity may be limited, the timing of regulation is critical—the longer the U.S. waits to start reducing GHG emissions, the faster the turnover in the electricity sector must occur in order to meet the same target. There is a real, and thus far unexplored, possibility that the U.S. could delay climate change policy implementation for long enough that it becomes infeasible to attain the necessary rate of turnover in the electricity sector.

This dissertation investigates the relationship between climate policy timing and infrastructure turnover in the electricity sector. The goal of the dissertation is to answer the question: How long can we wait before constraints on infrastructure turnover in the electricity sector make achieving our climate goals impossible?

Using the Infrastructure Flow Assessment Model, which was developed in this work, this dissertation shows that delaying climate change policy increases average retirements rates by 200-400%, increases average construction rates by 25-85% and increases maximum construction rates by 50-300%. It also shows that delaying climate policy has little effect on the age of retired plants or the stranded costs associated with premature retirement. In order for the electricity sector to reduce emissions to a level required by ACES while limiting construction rates to within achievable levels, it is necessary to start immediately. Delaying the process of decarbonization means that more abatement will be necessary from other sectors or geoengineering. By not starting emissions abatement early, therefore, the US forfeits its most accessible abatement potential and increases the challenge of climate change mitigation unnecessarily.

"Virtual Home-Auditing: A Statistical Investigation Using Publically Available Data on Gainesville, FL, Building Stock" – Enes Hoşgör, 2013

Energy efficiency (EE) and energy conservation today are recognized as the low-hanging fruit of energy sources. However, the potential benefits of energy efficiency are often unrealized due to market failures and market barriers. The overarching objective behind my work is to merge publicly available data, e.g., property tax dataset for physical properties of households and voter registration data set for demographic household properties, to build statistically significant insight on energy efficiency and consumption for a group of households (n=7,091) in Gainesville, FL. This will explore and try to verify the concept of an energy efficiency reservoir. Absence of data is one of the biggest barriers to information flow and efficiency deployment that I aim to overcome in my thesis. The generated insight will be provided to different efficiency stakeholders, e.g., electric utilities, homeowners, contractors, home energy performance product providers, for them to implement their investment strategies in an informed manner.

"Managing Wind-based Electricity Generation and Storage" – Helen Zhou, 2012

Among the many issues that profoundly affect the world economy every day, energy is one of the most prominent. Countries such as the U.S. strive to reduce reliance on the import of fossil fuels, and to meet increasing electricity demand without harming the environment.

Two of the most promising solutions for the energy issue are to rely on renewable energy, and to develop efficient electricity storage. Renewable energy—such as wind energy and solar energy—is free, abundant, and most importantly, does not exacerbate the global warming problem. However, most renewable energy is inherently intermittent and variable, and thus can benefit greatly from coupling with electricity storage, such as grid-level industrial batteries. Grid storage can also help match the supply and demand of an entire electricity market. In addition, electricity storage such as car batteries can help reduce dependence on oil, as it can enable the development of Plug-in Hybrid Electric Vehicles, and Battery Electric Vehicles. This thesis focuses on understanding how to manage renewable energy and electricity storage properly together, and electricity storage alone.

In Chapter 2, I study how to manage renewable energy, specifically wind energy. Managing wind energy is conceptually straightforward: generate and sell as much electricity as possible when prices are positive, and do nothing otherwise. However, this leads to curtailment when wind energy exceeds the transmission capacity, and possible revenue dilution when current prices are low but are expected to increase in the future. Electricity storage is being considered as a means to alleviate these problems, and also enables buying electricity from the market for later resale. But the presence of storage complicates the management of electricity generation from wind, and the value of storage for a wind-based generator is not entirely understood.

I demonstrate that for such a combined generation and storage system the optimal policy does not have any apparent structure, and that using overly simple policies can be considerably suboptimal. I thus develop and analyze a triple-threshold policy that I show to be nearoptimal. Using a financial engineering price model and calibrating it to data from the New York Independent System Operator, I show that storage can substantially increase the monetary value of a wind farm: If transmission capacity is tight, the majority of this value arises from reducing curtailment and time-shifting generation; if transmission capacity is abundant this value stems primarily from time-shifting generation and arbitrage. In addition, I find that while more storage capacity always increases the average energy sold to the market, it may actually decrease the average wind energy sold when transmission capacity is abundant.

In Chapter 3, I examine how electricity storage can be used to help match electricity supply and demand. Conventional wisdom suggests that when supply exceeds demand, any electricity surpluses should be stored for future resale. However, because electricity prices can be negative, another potential strategy of dealing with surpluses is to destroy them. Using real data, I find that for a merchant who trades electricity in a market, the strategy of destroying surpluses is potentially more valuable than the conventional strategy of storing surpluses.

In Chapter 4, I study how the operation and valuation of electricity storage facilities can be affected by their physical characteristics and operating dynamics. Examples are the degradation of energy capacity over time and the variation of round-trip efficiency at different charging/discharging rates. These dynamics are often ignored in the literature, thus it has not been established whether it is important to model these characteristics. Specifically, it remains an open question whether modeling these dynamics might materially change the prescribed operating policy and the resulting valuation of a storage facility. I answer this question using a representative setting, in which a battery is utilized to trade electricity in an energy arbitrage market.

Using engineering models, I capture energy capacity degradation and efficiency variation explicitly, evaluating three types of batteries: lead acid, lithium-ion, and Aqueous Hybrid Ion— a new commercial battery technology. I calibrate the model for each battery to manufacturers’ data and value these batteries using the same calibrated financial engineering price model as in Chapter 2. My analysis shows that: (a) it is quite suboptimal to operate each battery as if it did not degrade, particularly for lead acid and lithium-ion; (b) reducing degradation and efficiency variation have a complimentary effect: the value of reducing both together is greater than the sum of the value of reducing one individually; and (c) decreasing degradation may have a bigger effect than decreasing efficiency variation.

Contact: Helen Zhou Yangfang
Assistant Professor of Operations Management
Lee Kong Chian School of Business, Singapore Management University
helenzhou@smu.edu.sg

"Managing Wind Power Forecast Uncertainty in Electric Grid" – Brandon Mauch, 2012

Electricity generated from wind power is both variable and uncertain. Wind forecasts provide valuable information for wind farm management, but they are not perfect. Chapter 2 presents a model of a wind farm with compressed air energy storage (CAES) participating freely in the day-ahead electricity market without the benefit of a renewable portfolio standard or production tax credit. CAES is used to reduce the risk of committing uncertain quantities of wind energy and to shift dispatch of wind generation to high price periods. Using wind forecast data and market prices from 2006 – 2009, we find that the annual income for the modeled wind-CAES system would not cover annualized capital costs. We also estimate market prices with a carbon price of $20 and $50 per tonne CO2 and find that the revenue would still not cover the capital costs. The implied cost per tonne of avoided CO2 to make a wind-CAES profitable from trading on the day-ahead market is roughly $100, with large variability due to electric power prices.

Wind power forecast errors for aggregated wind farms are often modeled with Gaussian distributions. However, data from several studies have shown this to be inaccurate. Further, the distribution of wind power forecast errors largely depends on the wind power forecast value. The few papers that account for this dependence bin the wind forecast data and fit parametric distributions to the actual wind power in each bin. A method to model wind power forecast uncertainty as a single closed-form solution using a logit transformation of historical wind power forecast and actual wind power data is presented in Chapter 3. Once transformed, the data become close to jointly normally distributed. We show the process of calculating confidence intervals for wind power forecast errors using the jointly normally distributed logit transformed data. This method has the advantage of fitting the entire dataset with five parameters while also providing the ability to make calculations conditioned on the value of the wind power forecast.

The model present in Chapter 3 is applied in Chapter 4 to calculate increases in net load uncertainty introduced from day-ahead wind power forecasts. Our analyses uses data from two different electric grids in the U.S. having similar levels of installed wind capacity with large differences in wind and load forecast accuracy due to geographic characteristics. A probabilistic method to calculate the dispatchable generation capacity required to balance day-ahead wind and load forecast errors for a given level of reliability is presented. Using empirical data we show that the capacity requirements for 95% day-ahead reliability range from 2100 MW to 5600 MW for ERCOT and 1900 MW to 4500 MW for MISO, depending on the amount of wind and load forecast for the next day. We briefly discuss the additional requirements for higher reliability levels and the effect of correlated wind and load forecast errors. Additionally, we show that each MW of additional wind power capacity in ERCOT must be matched by a 0.30 MW day-ahead dispatchable generation capacity to cover 95% of day-ahead uncertainty. Due to the lower wind forecast uncertainty in MISO, the value drops to 0.13 MW of dispatchable capacity for each MW of additional wind capacity.

Direct load control (DLC) has received a lot of attention lately as an enabler of wind power. One major benefit of DLC is the added flexibility it brings to the grid. Utilities in some parts of the U.S. can bid the load reduction from DLC into energy markets. Forecasts of the resource available for DLC assist in determining load reduction quantities to offer. In Chapter 5, we present a censored regression model to forecast load from residential air conditioners using historical load data, hour of the day, and ambient temperature. We tested the forecast model with hourly data from 467 air conditioners located in three different utilities. We used two months of data to train the model and then ran day-ahead forecasts over a six week period. Mean square errors ranged from 4% to 8% of mean air conditioner load. This method produced accurate forecasts with much lower data requirements than physics based forecast models.

Contact: Brandon Mauch
Utility Regulation Engineer
Iowa Utilities Board
1375 E. Court Ave.
Des Moines, IA 50319
brandon.mauch@iub.iowa.gov

"Topics in Residential Electric Demand Response" – Shira R. Horowitz, 2012

Demand response and dynamic pricing are touted as ways to empower consumers, save consumers money, and capitalize on the "smart grid" and expensive advanced meter infrastructure. In this work, I attempt to show that demand response and dynamic pricing are more nuanced. Dynamic pricing is very appealing in theory but the reality of it is less clear. Customers do not always respond to prices. Price differentials are not always large enough for customers to save money. Quantifying energy that was not used is difficult.

In chapter 2, I go into more detail on the potential benefits of demand response. I include a literature review of residential dynamic pilots and tariffs to see if there is evidence that consumers respond to dynamic rates, and assess the conditions that lead to a response.

Chapter 3 explores equity issues with dynamic pricing. Flat rates have an inherent cross-subsidy built in because more peaky customers (who use proportionally more power when marginal price is high) and less peaky customers pay the same rates, regardless of the cost they impose on the system. A switch to dynamic pricing would remove this cross subsidy and have a significant distributional impact. I analyze this distributional impact under different levels of elasticity and capacity savings.

Chapter 4 is an econometric analysis of the Commonwealth Edison RTP tariff. I show that it is extremely difficult to find the small signal of consumer response to price in all of the noise of everyday residential electricity usage.

Chapter 5 looks at methods for forecasting, measuring, and verifying demand response in direct load control of air-conditioners. Forecasting is important for system planning. Measurement and verification are necessary to ensure that payments are fair. I have developed a new, censored regression based model for forecasting the available direct load control resource. This forecast can be used for measurement and verification to determine AC load in the counterfactual where DLC is not applied. This method is more accurate than the typical moving averages used by most ISO's, and is simple, easy, and cheap to implement.

Contact: Shira Horowitz
PJM Interconnection, Inc.
horows@pjm.com

"Integrating Variable Renewables into the Electric Grid: An Evaluation of Challenges and Potential Solutions" – Colleen A. Lueken, 2012

Renewable energy poses a challenge to electricity grid operators due to its variability and intermittency. In this thesis I quantify the cost of variability of different renewable energy technologies and then explore the use of reconfigurable distribution grids and pumped hydro electricity storage to integrate renewable energy into the electricity grid.

Cost of Variability
I calculate the cost of variability of solar thermal, solar photovoltaic, and wind by summing the costs of ancillary services and the energy required to compensate for variability and intermittency. I also calculate the cost of variability per unit of displaced CO2 emissions. The costs of variability are dependent on technology type. Variability cost for solar PV is $8-11/MWh, for solar thermal it is $5/MWh, and for wind it is around $4/MWh. Variability adds ~$15/tonne CO2 to the cost of abatement for solar thermal power, $25 for wind, and $33-$40 for PV.

Distribution Grid Reconfiguration
A reconfigurable network can change its topology by opening and closing switches on power lines. I show that reconfiguration allows a grid operator to reduce operational losses as well as accept more intermittent renewable generation than a static configuration can. Net present value analysis of automated switch technology shows that the return on investment is negative for this test network when considering loss reduction, but that the return is positive under certain conditions when reconfiguration is used to minimize curtailment of a renewable energy resource.

Pumped Hydro Storage in Portugal
Portugal is planning to build five new pumped hydro storage facilities to balance its growing wind capacity. I calculate the arbitrage potential of the storage capacity from the perspective of an independent storage owner, a thermal fleet owner, and a consumer-oriented storage owner. This research quantifies the effect storage ownership has on CO2 emissions, consumer electricity expenditure, and thermal generator profits. I find that in the Portuguese electricity market, an independent storage owner could not recoup its investment in storage using arbitrage only, but a thermal fleet owner or consumer-oriented owner may get a positive return on investment.

Contact: Colleen Lueken
cahorin@gmail.com

"Energy Storage on the Grid and the Short-term Variability of Wind" – Eric Hittinger, 2012

Wind generation presents variability on every time scale, which must be accommodated by the electric grid. Limited quantities of wind power can be successfully integrated by the current generation and demand-side response mix but, as deployment of variable resources increases, the resulting variability becomes increasingly difficult and costly to mitigate. In Chapter 2, we model a co-located power generation/energy storage block composed of wind generation, a gas turbine, and fast-ramping energy storage. A scenario analysis identifies system configurations that can generate power with 30% of energy from wind, a variability of less than 0.5% of the desired power level, and an average cost around $70/MWh.

While energy storage technologies have existed for decades, fast-ramping grid-level storage is still an immature industry and is experiencing relatively rapid improvements in performance and cost across a variety of technologies. Decreased capital cost, increased power capability, and increased efficiency all would improve the value of an energy storage technology and each has cost implications that vary by application, but there has not yet been an investigation of the marginal rate of technical substitution between storage properties. The analysis in chapter 3 uses engineering-economic models of four emerging fast-ramping energy storage technologies to determine which storage properties have the greatest effect on cost-ofservice. We find that capital cost of storage is consistently important, and identify applications for which power/energy limitations are important.

In some systems with a large amount of wind power, the costs of wind integration have become significant and market rules have been slowly changing in order to internalize or control the variability of wind generation. Chapter 4 examines several potential market strategies for mitigating the effects of wind variability and estimate the effect that each strategy would have on the operation and profitability of wind farms. We find that market scenarios using existing v price signals to motivate wind to reduce variability allow wind generators to participate in variability reduction when the market conditions are favorable, and can reduce short-term (30- minute) fluctuations while having little effect on wind farm revenue.

Contact: Eric Hittinger
Assistant Professor of Public Policy
Rochester Institute of Technology
Office: Eastman 1-1313
92 Lomb Memorial Drive
Rochester, NY 14623-5604
eshgpt@rit.edu

"Does Tropical Cyclone Modification Make Sense? A Decision Analytic Perspective" – Kelly Klima, 2012

Recent dramatic increases in damages caused by tropical cyclones (TCs) and improved understanding of TC physics have led the Department of Homeland Security to fund research on intentional hurricane modification. Here I present a decision analytic assessment of whether hurricane modification is potentially cost effective in South Florida.

First, for a single storm I compare hardening buildings to lowering the wind speed of a TC by reducing sea surface temperatures with wind-wave pumps. I find that if it were feasible and properly implemented, modification could reduce net wind losses from an intense storm more than hardening structures. However, hardening provides "fail safe" protection for average storms that might not be achieved if the only option were modification. The effect of natural variability is larger than that of either strategy.

Second, for multiple storms over a given return period, I investigate TC wind and storm surge damage reduction by hardening buildings and by wind-wave pumps. The coastal areas examined experience more surge damages for short return periods, and more wind damages for long periods. Surge damages are best reduced through a surge barrier. Wind damages are best reduced by a portfolio of techniques including wind-wave pumps, assuming they work and are correctly deployed. Damages in areas outside of the floodplain will likely be dominated by wind damages, and hence a similar portfolio will likely be best in these areas.

Since hurricane modification might become a feasible strategy for reducing hurricane damages, to facilitate an informed and constructive discourse on implementation, policy makers need to understand how people perceive hurricane modification. Therefore using the mental models approach, I identified Florida residents’ perceptions of hurricane modification techniques. First, hurricane modification was perceived as a relatively ineffective strategy for vi damage reduction. Second, hurricane modification was expected to lead to changes in path, but not necessarily strength. Third, reported anger at hurricane modification was weaker when path was unaltered and the damages equal to or less than projected. Fourth, individuals who recognized the uncertainty inherent in hurricane prediction reported more anger at scientists across modification scenarios.

Contact: Kelly Klima
Climate Adaptation Policy Advisor
Center for Clean Air Policy
750 First Street NE
Washington, D.C. 20002
kklima@ccap.org

"Evaluating Interventions in the U.S. Electricity System: Assessments of Energy Efficiency, Renewable Energy, and Small-Scale Cogeneration" – Kyle Siler-Evans, 2012

There is growing interest in reducing the environmental and human-health impacts resulting from electricity generation. Renewable energy, energy efficiency, and energy conservation are all commonly suggested solutions. Such interventions may provide health and environmental benefits by displacing emissions from conventional power plants. However, the generation mix varies considerably from region to region and emissions vary by the type and age of a generator. Thus, the benefits of an intervention will depend on the specific generators that are displaced, which vary depending on the timing and location of the intervention.

Marginal emissions factors (MEFs) give a consistent measure of the avoided emissions per megawatt-hour of displaced electricity, which can be used to evaluate the change in emissions resulting from a variety of interventions. This thesis presents the first systematic calculation of MEFs for the U.S. electricity system. Using regressions of hourly generation and emissions data from 2006 through 2011, I estimate regional MEFs for CO2, NOx, and SO2, as well as the share of marginal generation from coal-, gas-, and oil-fired generators. This work highlights significant regional differences in the emissions benefits of displacing a unit of electricity: compared to the West, displacing one megawatt-hour of electricity in the Midwest is expected to avoid roughly 70% more CO2, 12 times more SO2, and 3 times more NOx emissions.

I go on to explore regional variations in the performance of wind turbines and solar panels, where performance is measured relative to three objectives: energy production, avoided CO2 emissions, and avoided health and environmental iii damages from criteria pollutants. For 22 regions of the United States, I use regressions of historic emissions and generation data to estimate marginal impact factors, a measure of the avoided health and environmental damages per megawatthour of displaced electricity. Marginal impact factors are used to evaluate the effects of an additional wind turbine or solar panel in the U.S. electricity system. I find that the most attractive sites for renewables depend strongly on one’s objective. A solar panel in Iowa displaces 20% more CO2 emissions than a panel in Arizona, though energy production from the Iowa panel is 25% less. Similarly, despite a modest wind resource, a wind turbine in West Virginia is expected to displace 7 times more health and environmental damages than a wind turbine in Oklahoma.

Finally, I shift focus and explore the economics of small-scale cogeneration, which has long been recognized as a more efficient alternative to central-station power. Although the benefits of distributed cogeneration are widely cited, adoption has been slow in the U.S. Adoption could be encouraged by making cogeneration more economically attractive, either by increasing the expected returns or decreasing the risks of such investments. I present a case study of a 300-kilowatt cogeneration unit and evaluate the expected returns from: demand response, capacity markets, regulation markets, accelerated depreciation, a price on CO2 emissions, and net metering. In addition, I explore the effectiveness of feed-in tariffs at mitigating the energy-price risks to cogeneration projects.

Contact: Kyle Siler-Evans
Carnegie Mellon University
ksilerevans@gmail.com

"Plug-In Hybrid Electric Vehicles: Battery Degradation, Grid Support, Emissions, and Battery Size Tradeoffs" – Scott B. Peterson, 2012

Plug-in hybrid electric vehicles (PHEVs) may become a substantial part of the transportation fleet on time scales of a decade or two. This dissertation investigates battery degradation, and how the introduction of PHEVs may influence the electricity grid, emissions, and petroleum use in the US. It examines the effects of combined driving and vehicle-to-grid (V2G) usage on the lifetime performance of relevant commercial Li-ion cells. The loss of battery capacity was quantified as a function of driving days as well as a function of integrated capacity and energy processed by the cells. The cells tested showed promising capacity fade performance: more than 95% of the original cell capacity remains after thousands of driving days worth of use. Statistical analyses indicate that rapid vehicle motive cycling degraded the cells more than slower, V2G galvanostatic cycling. These data are used to examine the potential economic implications of using vehicle batteries to store grid electricity generated at off-peak hours for off-vehicle use during peak hours. The maximum annual profit with perfect market information and no battery degradation cost ranged from ~US$140 to $250 in the three cities. If the measured battery degradation is applied, however, the maximum annual profit decreases to ~$10–120. The dissertation details the increase in electric grid load and emissions due to vehicle battery charging in PJM and NYISO with the current generation mix, the current mix with a $50/tonne CO2 price, and this case but with existing coal generators retrofitted with 80% CO2 capture. It also models emissions using natural gas or wind+gas. PHEV fleet percentages between 0.4 and 50% are examined. When compared to 2020 CAFE standards, net CO2 emissions in New York are reduced by switching from gasoline to electricity; coal-heavy PJM shows somewhat smaller benefits unless coal units are fitted with CCS or replaced with lower CO2 generation. NOX is reduced in both RTOs, but there is upward pressure on SO2 emissions or allowance prices under a cap. Finally the dissertation compares increasing the all-electric range (AER) of PHEVs to installing charging infrastructure. Fuel use was modeled using the National Household Travel Survey and Greenhouse Gasses, Regulated Emissions, and Energy Use in Transportation model. It was found that increasing AER of plug-in hybrids was a more cost effective solution to reducing gasoline consumption than installing charging infrastructure. Comparison of results to current subsidy structure shows various options to improve future PHEV or other vehicle subsidy programs.

Contact: Scott Peterson
Carnegie Mellon University
sxotty@gmail.com

"Energy Efficiency and Rebound Effects in the United States: Implications for Renewables Investment and Emissions Abatement" – Brinda Ann Thomas, 2012

By lowering the energy required to provide a service, energy efficiency can help society consume less energy, emit less CO2e and other air pollutants, while maintaining quality of life. In this work, I examine a key benefit of energy efficiency, reducing renewables investment costs, and a side-effect, expanding energy service demand, also known as the rebound effect.

First, I assess the economics of an energy efficiency intervention, using dedicated direct current (DC) circuits to operate lighting in commercial buildings. I find that using DC circuits in grid-connected PV-powered LED lighting systems can lower the total unsubsidized capital costs by 4% to 21% and levelized annual costs by 2% to 21% compared to AC grid-connected PV LEDs providing the same level of lighting service. I also explore the barriers and limitations of DC circuits in commercial buildings.

Second, I examine the rebound effect from residential energy efficiency investments through a model in which households re-spend energy expenditure savings from an efficiency investment on more of the energy service (direct rebound) or on other goods and services (indirect rebound).

Using U.S. household expenditure data and environmentally-extended input-output analysis, I find indirect rebound effects in CO2e emissions of 5-15%, depending on the fuel saved and assuming a 10% direct rebound.

Third, I examine the variation in the indirect rebound from electricity efficiency across U.S. states due to differences in electric grid mix, fuel prices, household income, and spending patterns. I find that the CO2e direct and indirect rebound effects vary across states between 6-40%, when including full supply chain emissions, and between 4-30% when including only combustion and electricity emissions.

I conclude that energy efficiency can provide significant benefits for reducing energy expenditures, CO2e and other pollutants, and renewables investment costs under policy mandates, even after accounting for the rebound effect. While the CO2e rebound effect is currently modest in the U.S., there are some exceptions that may be relevant for energy efficiency policy assessments. In addition, more data collection and measurements of direct rebound effects are needed, especially in developing countries where the demand for energy services has not fully been met.

"Characterizing Impacts and Implications of Proposals for Solar Radiation Management, a Form of Climate Engineering" – Katharine L. Ricke, 2011

Even under optimistic emissions scenarios, rising concentrations of greenhouse gases in the atmosphere will result in significant increases in global mean temperatures and associated effects for the foreseeable future. Concerns that mitigation may be too slow in coming have lead to renewed dialogue within the scientific community regarding potential strategies for counteracting global warming through geoengineering, defined as “the deliberate large-scale intervention in the Earth’s climate system, in order to moderate global warming.

The geoengineering schemes that are considered most feasible today involve planetary albedo modification, or “solar radiation management” (SRM). This thesis addresses several outstanding questions regarding uncertainty in global and regional effects of SRM activities. The technical components of this work are centered on two modeling experiments which use a coupled atmosphere-ocean general circulation model (AOGCM) implemented through climateprediction.net. Drawing upon knowledge gained through these experiments and interaction with the broader research community, I explore the international relations implications of SRM and the global governance issues associated with it.

The first experiment explored regional differences in climate modified by SRM using a large-ensemble modeling experiment that examines the effects of global temperature stabilization scenarios. Our results confirm other research that shows a world with SRM would generally have less extreme temperature and precipitation anomalies than one with unmitigated greenhouse gas emissions and no SRM, but illustrate the physical unfeasibility of simultaneously stabilizing global precipitation and temperature as long as greenhouse gases continue to rise. Over time, simulated temperature and precipitation in large regions such as China and India vary significantly with different SRM trajectories and diverge from historic baselines in different ways. Hence the use of SRM to stabilize climate in all regions simultaneously may not be possible. Regional diversity in the response to different levels of SRM could complicate what is already a very challenging problem of global governance, and could make consensus about the “optimal” level of geo-engineering difficult, if not impossible, to achieve.

The second experiment modeled SRM using a perturbed physics ensemble with a wide range of temperature responses and climate sensitivities, all of which are consistent with observed recent warming. The analysis shows that the efficacy and distribution of effects of SRM varies with the temperature response of the model. Models that produce more global warming are also generally more sensitive to SRM, so the amount of modification of the Earth’s energy balance needed to meet any given climate stabilization criteria appear to be relatively insensitive to climate sensitivity. While in the more sensitive models, SRM is generally less successful in returning regional climates to their unperturbed states the longer it is used to compensate for rising greenhouse gases, it is also where SRM is most effective relative to a no SRM alternative.

SRM does not prevent further acidification of the oceans and this fact, coupled with the fact that SRM can only slow, never halt, changes to regional climate states, makes SRM untenable as a long-term solution to the problems caused by rising GHGs in the atmosphere. Much more research on SRM is needed before any conclusions on whether or not to deploy it are reached, but this work suggests that regional inequities in climate response are probably not the main impediment to its effective implementation. While SRM can never perfectly correct for regional climate change, these experiments suggest that it generally reduces (rather than exacerbates) changes to regional temperature and precipitation and greatly reduces the rate of temperature change. Considering the slow progress society has made towards reducing emissions, however, it is important to consider the potential benefits SRM technologies may confer in reducing impacts to buy time for both mitigation and adaptation.

"Climate Implications of Biomass Appropriation: Integrating Bioenergy and Animal Feeding Systems" – Kyle W. Meisterling, 2011

Through land use and biomass utilization, humans are dominant forces in the planetary biosphere and carbon and nitrogen cycles. Economic subsidies and policy mandates for producing biomass- sourced fuels and electricity could increase further the human appropriation of planetary net primary productivity. After reviewing the magnitude of organic byproducts available as feedstock, and presenting a model of the climate impact of organic waste management, this dissertation focuses on the climate impact of the main biomass consumers in the United States: livestock, including beef and dairy cattle, chickens (for meat and eggs), pigs and turkeys.

Existing estimates of feed consumption by livestock are synthesized, showing that beef cattle in particular are large consumers of cellulosic biomass in the form of hay and grazed roughage. I then determine the extent to which harvesting energy from animal manure can reduce and offset the greenhouse gas (GHG) emissions from producing animal products. Finally, a life cycle assessment (LCA) of an integrated animal product and bioenergy facility is presented. Biomass flows and global warming potential (GWP) are modeled for two systems: one where the animal production and bioenergy facilities are distinct and one where the facilities are integrated. The animal production system includes a mix of animals. Such a system may be able to more efficiently utilize byproducts from each system, but increasing the concentration of animals and manure nutrients may make such a system difficult to implement.

"Environmental and Economic Implications of Thermal Energy Storage for Concentrated Solar Power Plants" – Sharon J. Wagner, 2011

In response to renewable energy policies and incentives, the parabolic trough (PT) concentrated solar power (CSP) industry has recently experienced much national and global growth. Although thermal energy storage (TES) is a commercial technology that can extend PT power plant operational hours beyond sunny periods, currently no United States PT plants use this technology. This study examines the question of whether renewable energy policies should include provisions to encourage TES for CSP. This question is examined through an analysis of the economic and environmental tradeoffs associated with the choice of a backup system for a PT plant.

An engineering-economic model has been created to quantify the levelized cost of electricity (LCOE) and expected annual profit of a parabolic trough plant with three different backup configurations: 1) no storage/ no natural gas backup (except for heat transfer fluid freeze protection); 2) 1-12 hours of two-tank indirect molten salt TES; and 3) natural gas backup (in the form of a heat transfer fluid heater) to match each TES capacity. An overview of the key inputs and outputs related to this model is presented in Figure 1, which displays an iterative process used to select the solar field area that minimizes the LCOE for each plant configuration modeled. The results of this model show that TES can increase the unsubsidized, pre-tax LCOE of a PT power plant by 4-26% compared to no storage/backup. TES and NG backup also have similar LCOE values across most storage/backup capacities. Under current federal incentives and with fixed electricity pricing, TES can increase the expected annual profit compared to no storage/backup and compared to NG backup.

In addition, a life cycle assessment was conducted to compare the cumulative energy demand, greenhouse gas (GHG) emissions, water and land use associated with different storage/backup configurations. The results of this analysis show that TES can reduce life cycle GHG emissions up to 7% compared to no storage/backup and up to 210% compared to NG backup. Life cycle water use decreases with increasing storage capacity/NG backup. In each case, TES and NG have similar life cycle water use values. On-site water use increases slightly with TES/NG backup. Life cycle land use is similar for the three plant configurations overall.

A sensitivity analysis was conducted to understand how key model inputs affect economic and environmental results. This analysis revealed opportunities for a multi-variable optimization that incorporates variations in heat transfer fluid mass flow rates along with changing storage capacities and solar field areas. When these changes are incorporated, TES can lower the LCOE of a parabolic trough plant compared to the no-storage alternative and NG backup alternative. A further analysis was conducted to examine the tradeoffs between the economic and environmental implications of TES. The analysis included comparisons between environmental and economic results; a multi-criteria decision analysis that considers a range of preferences for different attributes; and a calculation of the carbon price required for each configuration modeled in the study to be competitive with new coal-fired electricity generation. The results of the decision analysis show that under most preference scenarios examined, no storage/backup is preferred over any of the TES/NG capacities. If some backup system is required, TES is preferred over natural gas backup, except where economic outcomes are strongly preferred. A carbon price of $150-$240/tonne CO2eq is required for PT to be competitive with coal-fired generation, depending on the TES configuration. The study concludes with recommendations for research to further refine and extend the current analysis.

"Geologic CO2 Sequestration and Subsurface Property Rights: A Legal and Economic Analysis" – Robert Lee Gresham, 2010

Carbon dioxide emissions (CO2) from the combustion of fossil fuels must be reduced on a large scale to mitigate the effects of global climate change. Carbon capture and sequestration (CCS) has the potential to allow the continued use of fossil fuels with little or no emissions until alternative, low-to-zero emission sources of energy are more widely deployed. This thesis considers the legal and economic implications of securing the right to use geologic pore space-the microscopic space in subsurface rock matrixes-in an effort to sequester CO2 deep underground to mitigate climate change. The findings and conclusions drawn in this thesis are intended to help guide discussion, research, and decision-making processes undertaken by policymakers and industry leaders with respect to the commercial-scale deployment of CCS. Prior to the commencement of sequestration, a project developer/operator must have authorization to access and use pore space to avoid liability for subsurface trespass. This authorization can be acquired via bilateral contract, where monetary compensation is remitted to the property owner in exchange for the right to use pore space.

However, the question remains open as to whether the use of pore space for geologic CO2 sequestration (GCS) is a trespass requiring compensation under the law. In fact, there is ample legal precedent in the context of underground injection activities such as enhanced hydrocarbon recovery, fluid waste disposal, and freshwater storage to support the supposition that the invasion of pore space by injected is compensable only when substantial harm or interference with an existing or non-speculative, investment-backed future use of the subsurface results from the injection of such fluids. This thesis shows that if CCS is widely deployed, the cost of electricity and power plant profitability could be adversely affected by a legal requirement that pore space owners must be compensated for GCS in all circumstances.

Moreover, absent unrealistically high electricity prices or some form of sequestration subsidy, pore space has no net-positive, intrinsic economic value to electric generators that can be passed along to property owners. Therefore, while paying property owners to use of pore space for geologic CO2 sequestration may very well foster public acceptance and appease staunch private property rights advocates, there is no demonstrable legal or economic rationale for compensating property owners who have no current or nonspeculative, investment-backed future use of the subsurface where pore space targeted for sequestration is located. A pragmatic and equitable solution for constraining the potential negative economic effects associated with acquiring pore space rights would be for state or federal legislatures, or courts, to limit required compensation to only those instances where the injection and migration of CO2 materially impairs current or non-speculative, investment backed future uses of the subsurface. Future work should include a detailed analysis of takings law and the anticipated long-term constitutional and economic implications of various approaches to pore space property rights governance before new CCS-specific laws are enacted. The models presented in this thesis should also be applied to additional site specific geologic data for saline aquifer sequestration targets. Additionally, the implications of GCS paired with enhanced oil recovery (EOR) on power plant economics should be studied.

Contact: Robert Lee Gresham
rgresham@andrew.cmu.edu

"Wind Power Variability, Its Cost, and Effect on Power Plant Emissions" – Warren Katzenstein, 2010

The recent growth in wind power is transforming the operation of electricity systems by introducing variability into utilities’ generator assets. System operators are not experienced in utilizing significant sources of variable power to meet their loads and have struggled at times to keep their systems stable. As a result, system operators are learning in real-time how to incorporate wind power and its variability. This thesis is meant to help system operators have a better understanding of wind power variability and its implications for their electricity system.

Characterizing Wind Power Variability
We present the first frequency-dependent analyses of the geographic smoothing of wind power's variability, analyzing the interconnected measured output of 20 wind plants in Texas. Reductions in variability occur at frequencies corresponding to times shorter than ~24 hours and are quantified by measuring the departure from a Kolmogorov spectrum. At a frequency of 2.8x10-4 Hz (corresponding to 1 hour), an 87% reduction of the variability of a single wind plant is obtained by interconnecting 4 wind plants. Interconnecting the remaining 16 wind plants produces only an additional 8% reduction. We use step-change analyses and correlation coefficients to compare our results with previous studies, finding that wind power ramps up faster than it ramps down for each of the step change intervals analyzed and that correlation between the power output of wind plants 200 km away is half that of co-located wind plants. To examine variability at very low frequencies, we estimate yearly wind energy production in the Great Plains region of the United States from automated wind observations at airports covering 36 years. The estimated wind power has significant inter-annual variability and the severity of wind drought years is estimated to be about half that observed nationally for hydroelectric power.

Estimating the Cost of Wind Power Variability
We develop a metric to quantify the sub-hourly variability cost of individual wind plants and show its use in valuing reductions in wind power variability. Our method partitions wind energy into hourly and sub-hourly components and uses corresponding market prices to determine the cost of variability. The metric is applicable to variability at all time scales faster than hourly, and can be applied to long-period forecast errors. We use publically available data at 15 minute time resolution to apply the method to ERCOT, the largest wind power production region in the United States. The range of variability costs arising from 15 minute to 1 hour variations (termed load following) for 20 wind plants in ERCOT was $6.79 to 11.5 per MWh (mean of $8.73 ±$1.26 per MWh) in 2008 and $3.16 to 5.12 per MWh (mean of $3.90 ±$0.52 per MWh) in 2009. Load following variability costs decrease as wind plant capacity factors increase, indicating wind plants sited in locations with good wind resources cost a system less to integrate. Twenty interconnected wind plants have a variability cost of $4.35 per MWh in 2008. The marginal benefit of interconnecting another wind plant diminishes rapidly: it is less than $3.43 per MWh for systems with 2 wind plants already interconnected, less than $0.7 per MWh for 4-7 wind plants, and less than $0.2 per MWh for 8 or more wind plants. This method can be used to value the installation of storage and other techniques to mitigate wind variability.

Estimating How Wind Power Variability Affects Power Plant Emissions
Renewables portfolio standards (RPS) encourage large scale deployment of wind and solar electric power, whose power output varies rapidly even when several sites are added together. In many locations, natural gas generators are the lowest cost resource available to compensate for this variability, and must ramp up and down quickly to keep the grid stable, affecting their emissions of NOx and CO2. We model a wind or solar photovoltaic plus gas system using measured 1-minute time resolved emissions and heat rate data from two types of natural gas generators, and power data from four wind plants and one solar plant. Over a wide range of renewable penetration, we find CO2 emissions achieve ~80% of the emissions reductions expected if the power fluctuations caused no additional emissions. Pairing multiple turbines with a wind plant achieves ~77 to 95% of the emissions reductions expected. Using steam injection, gas generators achieve only 30-50% of expected NOx emissions reductions, and with dry control NOx emissions increase substantially. We quantify the interaction between state RPSs and constraints such as the NOx Clean Air Interstate Rule (CAIR), finding that states with substantial RPSs could see upward pressure on CAIR NOx permit prices, if the gas turbines we modeled are representative of the plants used to mitigate wind and solar power variability.

Contact: Warren Katzenstein
Associate
The Brattle Group
44 Brattle Street
Cambridge, MA 02138
wkatzenstein@gmail.com

"Essays on Power Systems Economics" – Sompop Pattanariyankool, 2010

We explore the optimal size of the transmission line from distant wind farms, modeling the tradeoff between transmission cost and benefit from delivered wind power.

We also examine the benefit of connecting a second wind farm, requiring additional transmission, in order to increase output smoothness. Since a wind farm has a low capacity factor, the transmission line would not be heavily loaded, on average; depending on the time profile of generation, for wind farms with capacity factor of 29-34%, profit is maximized for a line that is about ¾ of the nameplate capacity of the wind farm.

Although wind generation is inexpensive at a good site, transmitting wind power over 1,000 miles (about the distance from Wyoming to Los Angeles) doubles the delivered cost of power. As the price for power rises, the optimal capacity of transmission increases. Connecting wind farms lowers delivered cost when the wind farms are close, despite the high correlation of output over time. Imposing a penalty for failing to deliver minimum contracted supply leads to connecting more distant wind farms.

Chapter 2: The optimal baseload generation portfolio under CO2 regulation and fuel price uncertainties We solve for the power generation portfolio that minimizes cost and variability among existing and near-term baseload technologies under scenarios that vary the carbon tax, fuel prices, capital cost and CO2 capture cost. The variability of fuel prices and uncertainty of CO2 regulation favor technologies with low variable cost and low CO2 emission. The qualitative results are expected; stringent CO2 regulation cost leads to more technology with little carbon emissions, such as nuclear and IGCC CCS, while penalizing coal. However, the variability of fuel prices and the correlation among fuel prices are the principal attributes shaping the optimal portfolio mix. We also model a Bayesian approach that allows the planner to express his belief on the future cost of power generation technology.

Contact: Sompop Pattanariyankool
Ministry of Energy, Thailand
sompop.pattana@gmail.com

"Techno-economic Evaluation of Coal-to-Liquids (CTL) Plants and Their Effects on Environment and Resources" – Hari Chandan Mantripragada, 2010

Coal-to-liquids (CTL) process involves gasification of coal to produce syngas which is then catalytically converted into liquid fuels in a Fischer-Tropsch (FT) reactor. Two general configurations of CTL plants are possible – liquids-only and co-production. In the liquids-only configuration the unconverted syngas from the FT reactor is recycled to the reactor to increase the productivity of the liquids. In the co-production configuration, the unconverted syngas from the FT reactor, instead of being recycled, is combusted in a gas turbine steam turbine combined cycle power plant to generate electricity. The by- product electricity can be sold to the grid.

In this thesis, techno-economic models are developed to evaluate the performance and costs of CTL plants using different component technologies and process configurations and under different carbon constraints. The results are used to study the implications of large-scale deployment of CTL plants on the environment and resource consumption, particularly in terms of:

Emissions of CO2
Consumption of resources such as coal, water and land
Economic benefits/costs of transport fuels derived from coal
It was found that, depending on various factors, the costs of liquid product from both liquids-only and co-production plants are in the range of $40 - $100/barrel. CTL plants are highly capital intensive, with the capital cost component accounting for about half the total product cost. Carbon capture and sequestration (CCS) is cheaper than paying a CO2 price of more than $12/tonne for liquids-only plant and more than $30/tonne for co- production plants

Co-production plants, with or without CCS consume less coal and emit less CO2 than separate production of liquids and power. Co-production CTL plants with CCS, supplying 20% of petroleum demand, can meet around 30% of US electricity demand (energy basis) and by displacing conventional coal power plants, have the potential of reducing the US CO2 emissions by 9% from the 2008 emissions.

In summary it can be said that CTL has significant scope for producing domestic liquid fuels from the abundant coal resources. The commercialization of the technology depends on how the investors and regulatory agencies deal with the economic and environmental risks associated with CTL.

"A Life Cycle Approach to Technology, Infrastructure, and Climate Policy Decision Making: Transitioning to Plug-in Hybrid Electric Vehicles and Low-Carbon Electricity" – Costa Samaras, 2009

In order to mitigate the most severe effects of climate change, large global reductions in the current levels of anthropogenic greenhouse gas (GHG) emissions are required in this century to stabilize atmospheric carbon dioxide (CO2) concentrations at less than double pre-industrial levels. The Intergovernmental Panel on Climate Change (IPCC) fourth assessment report states that GHG emissions should be reduced to 50-80% of 2000 levels by 2050 to increase the likelihood of stabilizing atmospheric CO2 concentrations. In order to achieve the large GHG reductions by 2050 recommended by the IPCC, a fundamental shift and evolution will be required in the energy system. Because the electric power and transportation sectors represent the largest GHG emissions sources in the United States, a unique opportunity for coupling these systems via electrified transportation could achieve synergistic environmental (GHG emissions reductions) and energy security (petroleum displacement) benefits. Plug-in hybrid electric vehicles (PHEVs), which use electricity from the grid to power a portion of travel, could play a major role in reducing greenhouse gas emissions from the transport sector.

However, this thesis finds that life cycle GHG emissions from PHEVs depend on the electricity source that is used to charge the battery, so meaningful GHG emissions reductions with PHEVs are conditional on low-carbon electricity sources. Power plants and their associated GHGs are long-lived, and this work argues that decisions made regarding new electricity supplies within the next ten years will affect the potential of PHEVs to play a role in a low-carbon future in the coming decades. This thesis investigates the life cycle engineering, economic, and policy decisions involved in transitioning to PHEVs and low-carbon electricity.

The government has a vast array of policy options to promote low-carbon technologies, some of which have proven to be more successful than others. This thesis uses life cycle assessment to evaluate options and opportunities for large GHG reductions from plug-in hybrids. After the options and uncertainties are framed, engineering economic analysis is used to evaluate the policy actions required for adoption of PHEVs at scale and the implications for low-carbon electricity investments. A logistic PHEV adoption model is constructed to parameterize implications for low-carbon electricity infrastructure investments and climate policy. This thesis concludes with an examination of what lessons can be learned for climate, innovation, and low-carbon energy policies from the evolution of wind power from an emerging alternative energy technology to a utilityscale power source. Policies to promote PHEVs and other emerging energy technologies can take lessons learned from the successes and challenges of wind power’s development to optimize low-carbon energy policy and R&D programs going forward.

The need for integrated climate policy, energy policy, sustainability, and urban mobility solutions will accelerate in the next two decades as concerns regarding GHG emissions and petroleum resources continue to be environmental and economic priorities. To assist in informing the discussions on climate policy and low-carbon energy R&D, this research and its methods will provide stakeholders in government and industry with plug-in hybrid and energy policy choices based on life cycle assessment, engineering economics, and systems analysis.

Contact: Dr. Costa Samaras
RAND Corporation
4750 Fifth Avenue, Suite 600
Pittsburgh, PA 15213
tel: 412.683.2300
csamaras@rand.org

"Integrating Comprehensive Air Quality Modeling with Policy Analysis: Applications for Distributed Electricity Generation" – Elisabeth Gilmore, 2009

Small scale and located close to the point of demand, distributed electricity generation (DG) could reduce the cost of electricity, improve grid reliability and support renewable technologies. These facilities also shift the magnitude, timing and location of air quality emissions. The costs from adverse human health effects caused by changes in air quality may outweigh any benefits. In this work, I evaluate the air quality, human health effects and costs for two DG applications. I transform the emissions into ambient concentrations using a chemical transport model, the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx), and dispersion plumes. I then translate the concentrations into health effects with concentration-response functions. Finally, I express the health effects as a social cost reflecting the “willingness to pay” to avoid these effects.

First, I investigate using installed backup generators instead of a more expensive peaking turbine for meeting peak electricity demand. Many of generators are uncontrolled diesel engines which have a high social cost. Adding a diesel particulate filter with exhaust gas recirculation to reduce fine particulate matter and nitrogen oxides can mitigate these costs. This result holds in four urban centers over a range of specified health endpoints and when accounting of uncertainty in the representation of the formation of secondary PM2.5 in PMCAMx. I conclude that properly controlled generators can be employed for meeting peak electricity demand without substantial harm to human health.

Second, I evaluate the changes in the net and distribution of social cost from integrating a utility-scale battery into the New York State electricity grid. Located in New York City, the battery would discharge when electricity prices are high and charge with cheaper generation during off peak hours. For most types of charging plants, I calculate a net social benefit from displacing dirtier fuel oil peaking plants, but a net social cost from displacing natural gas peaking plants. In the short term, the upstate population experiences a social cost from the charging plant. In the long term, however, the battery may support renewable generation such as night time wind power resulting in benefits locally and statewide.

Contact: Dr. Elisabeth Gilmore
eagilmor@andrew.cmu.edu

"Energy Efficiency in the U.S. Residential Sector: An Engineering and Economic Assessment of Opportunities for Large Energy Savings and Greenhouse Gas Emissions Reductions" – Inês Margarida Lima de Azevedo, 2009

Addressing the issue of climate change mitigation will be one of the most daunting tasks of our generation. A large set of strategies for carbon mitigation are needed on a global scale to reduce greenhouse gas (GHG) emissions by 80% below 1990 levels by 2050, in order to avoid global irreversible consequences of climate change. In light of possible near-term GHG regulations, the US Government is now paying more attention to various options for carbon mitigation. Energy efficiency and conservation is a very promising part of a portfolio of strategies. Today, US residential buildings sector account for nearly 17% of US GHG emissions and several new technologies and energy efficiency measures offer potential for large energy savings. While energy efficiency options are currently being deployed or considered as a means of reducing carbon emissions, there is still large uncertainty about the effect of such measures on overall carbon savings.

The first part of this thesis provides an assessment, at the national level, of the energy efficiency potential in the residential sector. I estimate the 2009 energy efficiency potential for the residential sector and its costs under several different scenarios. These include assuming that consumers bear the costs of new technologies, assuming that utilities are incentivized to promote energy efficiency, and estimating the societal costs and benefits of energy efficiency.

Throughout this work, I build the argument that energy efficiency policies cannot consider efficiency gains in energy, electricity or carbon dioxide alone. Instead, the effects of each of these three indicators should be considered in energy efficiency assessments.

I conclude that there is a large potential for energy efficiency in the U.S. residential sector, but large investments are needed realize this potential, since consumers are unlikely to voluntary adopt the most efficient end-use devices.

The second part of this thesis deals with a detailed assessment of the potential for whitelight LEDs for energy and carbon dioxide savings in the U.S. commercial and residential sectors. Lighting constitutes more than 20% of total U.S. electricity consumption, a similar fraction in the E.U., and an even a larger fraction in many developing countries.

Because many current lighting technologies are highly inefficient, improved technologies for lighting hold great potential for energy savings and for reducing associated greenhouse gas emissions. Solid-state lighting shows great promise as a source of efficient, affordable, color-balanced white light.

Indeed, assuming market discount rates, engineering-economic analysis demonstrates that white solid-state lighting already has a lower levelized annual cost (LAC) than incandescent bulbs. The LAC for white solid-state lighting will be lower than that of the most efficient fluorescent bulbs by the end of this decade. However, a large literature indicates that households do not make their decisions in terms of simple expected economic value.
After a review of the technology, I compare the electricity consumption, carbon emissions and cost-effectiveness of current lighting technologies, accounting for expected performance evolution through 2015. I then simulate the lighting electricity consumption and implicit greenhouse gases emissions for the U.S. residential and commercial sectors through 2015 under different policy scenarios: voluntary solid-state lighting adoption, implementation of lighting standards in new construction and rebate programs or equivalent subsidies. Finally, I provide a measure of cost-effectiveness for solid-state lighting in the context of other climate change abatement policies.

Contact: Inês Margarida Lima de Azevedo
iazevedo@cmu.edu

"Coal Supply and Cost Under Technological and Environmental Uncertainty" – Melissa Chan, 2009

This thesis estimates available coal resources, recoverability, mining costs, environmental impacts, and environmental control costs for the United States under technological and environmental uncertainty. It argues for a comprehensive, well-planned research program that will resolve resource uncertainty, and innovate new technologies to improve recovery and environmental performance. A stochastic process and cost (constant 2005$) model for longwall, continuous, and surface mines based on current technology and mining practice data was constructed. It estimates production and cost ranges within 5 – 11 percent of 2006 prices and production rates. The model was applied to the National Coal Resource Assessment. Assuming the cheapest mining method is chosen to extract coal, 250 – 320 billion tons are recoverable. Two-thirds to all coal resource can be mined at a cost less than $4/mmBTU. If U.S. coal demand substantially increases, as projected by alternate Energy Information Administration (EIA), resources might not last more than 100 years. By scheduling cost to meet EIA projected demand, estimated cost uncertainty increases over time. It costs less than $15/ton to mine in the first 10 years of a 100 year time period, $10-$30/ton in the following 50 years, and $15-$90/ton thereafter.

Environmental impacts assessed are subsidence from underground mines, surface mine pit area, erosion, acid mine drainage, air pollutant and methane emissions. The analysis reveals that environmental impacts are significant and increasing as coal demand increases. Control technologies recommended to reduce these impacts are backfilling underground mines, surface pit reclamation, substitution of robotic underground mining systems for surface pit mining, soil replacement for erosion, placing barriers between exposed coal and the elements to avoid acid formation, and coalbed methane development to avoid methane emissions during mining. The costs to apply these technologies to meet more stringent environmental regulation scenarios are estimated. The results show that the cost of meeting these regulatory scenarios could increase mining costs two to six times the business as usual cost, which could significantly affect the cost of coal-powered electricity generation.

This thesis provides a first estimate of resource availability, mining cost, and environmental impact assessment and cost analysis. Available resource is not completely reported, so the available estimate is lower than actual resource. Mining costs are optimized, so provide a low estimate of potential costs. Environmental impact estimates are on the high end of potential impact that may be incurred because it is assumed that impact is unavoidable. Control costs vary. Estimated cost to control subsidence and surface mine pit impacts are suitable estimates of the cost to reduce land impacts. Erosion control and robotic mining system costs are lower, and methane and acid mine drainage control costs are higher, than they may be in the case that these impacts must be reduced.

Contact Melissa Chan
Kennedy School of Government
Harvard University
79 JFK Street
Cambridge MA 02138
melissa_chan@ksg.harvard.edu

"Meeting Electric Peak on the Demand Side: Wholesale and Retail Market Impacts of Real-Time Pricing and Peak Load Management Policy" – Kathleen Spees, 2008

Traditionally, the participation of customers in the electric market has been weak or non-existent. Almost all customers have paid a flat rate for power without variations based on the time of their consumption, so these customers have had no incentive to reduce their usage during times of capacity shortage and very high wholesale prices. Perhaps even more importantly, customers have not participated in forward decisions about whether it would be better to build additional capacity at very high cost or to commit to peak load reductions during a few peak hours each year. In this thesis I present the status of efforts to incorporate customer decisions into the electric market place and calculate the possible system benefits.

In Part I I discuss recent activities relating to demand response and demand-side management. Although interest in demand response is growing among policy-makers and industry participants, the process of making this possible will be a complicated navigation among the incentives of involved parties and the jurisdictions of state and federal regulators. One of the key problems in developing a coordinated policy is that the wholesale markets covering generation and transmission are under the jurisdiction of the federal government represented by the Federal Energy Regulatory Commission while electric distribution and retail markets are under the jurisdiction of the state, represented by state public utility commissions (PUC).

In Part II I investigate the value to the system of reducing peak demand and compare this value to the current costs of peak load reductions. Peak load reductions are currently being achieved at $21/kW∙y, or less than one fourth of the $94/kW∙y it costs to build new capacity. Similarly, energy efficiency is being achieved at $29/MWh, or roughly one third of the $92/MWh retail price for electricity. At current rates, peak load could be cost-effectively reduced by some 17%, although I expect that at greater levels of peak reductions the marginal cost of achieving more reductions will increase, it is clear that significant peak load reductions can be achieved cost-effectively.

I further investigate the value to the system of shifting the burden of uncertainty in peak load on to customers and the utilities acting on their behalves who have the most ability to determine what peak load will be. The traditional means of accounting for uncertainty in peak load has been to build enough excess capacity that the chance of shortages is low. I calculate that a right-sizing peak capacity to the best estimate of peak load would reduce the amount cost of supplying capacity by 8.5% below the current level.

In Part III I investigate the short-run economic impacts of a policy change from flat-rate retail electric pricing to real-time prices (RTP) or time-of-use (TOU) prices. If retail prices reflected hourly wholesale market prices, customers would shift consumption away from peak hours and installed capacity could drop. I use hourly price and load data from Pennsylvania-New Jersey-Maryland Regional Transmission Organization (RTO) to estimate consumer and producer savings from a change toward RTP or TOU. Surprisingly, neither RTP nor TOU has much effect on average price under plausible short-term consumer responses. Consumer plus producer surplus rises 2.8%-4.4% with RTP and 0.6%-1.0% with TOU. Peak capacity savings are seven times larger with RTP. Peak load drops by 10.4%-17.7% with RTP and only 1.1%-2.4% with TOU. Half of all possible customer savings from load shifting are obtained by shifting only 1.7% of all MWh to another time of day, indicating that only the largest customers need be responsive to get the majority of the short-run savings.

Placing customers on an RTP can benefit them through lower average rates for energy and capacity, but the advanced metering infrastructure (AMI) required to make RTP and customer response possible is a large investment. In Part IV I determine how many customers can be cost-effectively placed on RTP from the perspective of a PUC. I calculate that for wide scale implementation of AMI, all customers above 2.5 kW in coincident peak load (about 40% of all customers, representing all industrial, all commercial, and large residential customers) could be cost-effectively placed on RTP if there are no benefits to the AMI other than demand response from RTP. For the customers below size 0.31-0.73 kW (the smallest 10%-20% of customers, representing small residential loads), installing an AMI is not cost effective even under the most favorable assumptions about other AMI benefits and highly responsive customers. For intermediate-size customers the investment would be justified in some cases but not others.

Contact: Kathleen Spees
Kathleen.Spees@brattle.com

"Limiting the Financial Risks of Electricity Generation Capital Investments Under Carbon Constraints: Applications and Opportunities for Public Policies and Private Investments" – Adam Newcomer, 2008

Increasing demand for electricity and an aging fleet of generators are the principal drivers behind an increasing need for a large amount of capital investments in the US electric power sector in the near term. The decisions (or lack thereof) by firms, regulators and policy makers in response to this challenge have long lasting consequences, incur large economic and environmental risks, and must be made despite large uncertainties about the future operating and business environment. Capital investment decisions are complex: rates of return are not guaranteed; significant uncertainties about future environmental legislation and regulations exist at both the state and national levels - particularly about carbon dioxide emissions; there is an increasing number of shareholder mandates requiring public utilities to reduce their exposure to potentially large losses from stricter environmental regulations; and there are significant concerns about electricity and fuel price levels, supplies, and security.

Large scale, low carbon electricity generation facilities using coal, such as integrated gasification combined cycle (IGCC) facilities coupled with carbon capture and sequestration (CCS) technologies, have been technically proven but are unprofitable in the current regulatory and business environment where there is no explicit or implicit price on carbon dioxide emissions.

The paper examines two separate scenarios that are actively discussed by policy and decision makers at corporate, state and national levels: a future US electricity system where coal plays a role; and one where the role of coal is limited or nonexistent. The thesis intends to provide guidance for firms and policy makers and outline applications and opportunities for public policies and for private investment decisions to limit financial risks of electricity generation capital investments under carbon constraints.

Contact: Adam Newcomer
Exelon Power Team
10 South Dearborn Street
Chicago, IL 60603
Adam.Newcomer@exeloncorp.com

"Emerging Electric Energy Storage Technologies and Demand Response in Deregulated Electricity Markets" – Rahul S. Walawalkar, 2008

Unlike markets for storable commodities, electricity markets depend on the real-time balance of supply and demand. Although much of the present-day grid operates effectively without storage, cost-effective ways of storing electrical energy can help make the grid more efficient and reliable. I have investigated the economics of two emerging electric energy storage (EES) technologies: sodium sulfur (NaS) batteries and flywheels in the electricity markets operated by the New York Independent System Operator (NYISO) and the PJM Interconnection (PJM). The analysis indicates that there is a strong economic case for flywheel installations in both the PJM and NYISO markets for providing regulation services. The economic case for NaS batteries for energy arbitrage is weak in both NYISO and PJM. Some of the uncertainties regarding regulation market rules are one of the reasons for lack of investment in flywheels. On the other hand, some market participants have already made investments in NaS batteries due to anticipated system upgrade deferral benefits. Capital cost reduction and efficiency are important factors that will influence the economics of NaS batteries for energy arbitrage in deregulated electricity markets.

I have also analyzed the economic demand response program offered by PJM.

PJM's program provided subsidies to customers who reduced load in response to price signals before 2008. The program incorporated a "trigger point", set at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved may introduce distortions into the market. I have simulated demand-side bidding into the PJM market, and compare the economic welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, I estimate that the social welfare gains exceeded the subsidies during 2006. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment.

Contact: Rahul S. Walawalkar
Customized Energy Solutions Ltd.
100 North 17th Street, 14th Floor
Philadelphia, PA 19103 USA
rahul@walawalkar.com

"The Economics of CO2 Transport by Pipeline and Storage in Saline Aquifers and Oil Reservoirs" – Sean T. McCoy 2008

Large reductions in carbon dioxide (CO2) emissions are needed to mitigate the impacts of climate change. One method of achieving such reductions is CO2 capture and storage (CCS). CCS requires the capture of carbon dioxide (CO2) at a large industrial facility, such as a power plant, and its transport to a geological storage site where CO2 is sequestered. If implemented, CCS could allow fossil fuels to be used with little or no CO2 emissions until alternative energy sources are more widely deployed. Large volumes of CO2 are most efficiently transported by pipeline and stored either in deep saline aquifers or in oil reservoirs, where CO2 is used for enhanced oil recovery (EOR). This thesis describes a suite of models developed to estimate the project-specific cost of CO2 transport and storage. Engineering-economic models of pipeline CO2 transport, CO2-flood EOR, and aquifer storage were developed for this purpose. The models incorporate a probabilistic analysis capability that is used to quantify the sensitivity of transport and storage cost to variability and uncertainty in the model input parameters. The cost of CO2 pipeline transport is shown to be sensitive to the region of construction, in addition to factors such as the length and design capacity of the pipeline. The cost of CO2 storage in saline aquifers is shown to be most sensitive to factors affecting site characterization cost. For EOR projects, CO2 storage has traditionally been a secondary effect of oil recovery; thus, a levelized cost of CO2 storage cannot be defined. Instead EOR projects were evaluated based on the breakeven price of CO2 (i.e., the price of CO2 at which the project net present value is zero). The breakeven CO2 price is shown to be most sensitive to oil prices, losses of CO2 outside the productive zone of the reservoir, and reservoir pressure. Future research should include collection and aggregation of more specific data characterizing possible sites for aquifer storage and applications of these models to this data. The implications of alternative regulations and requirements for site characterization should also be studied to more fully assess cost impacts.

Contact: Sean T. McCoy
Department of Engineering & Public Policy Carnegie Mellon University
stmccoy@andrew.cmu.edu

"A Life Cycle Comparison of Coal and Natural Gas for Electricity Generation and the Production of Transportation Fuels" – Paulina Jaramillo, 2007

Demand for electricity is expected to increase in the next 25 years. Currently, 50% of the electricity generated in the U.S. is produced using coal. Although natural gas has traditionally been used by the commercial, industrial and residential sector, demand for natural gas for electricity generation has increased in the past decade and this growth is expected to continue in the next 25 years. Since demand is growing but North American supply is expected to remain constant, alternative sources of natural gas will need to be developed. LNG has been identified as one alternative, and plans to increase imports of this fuel are underway. In addition, synthetic natural gas could be produced from coal to meet some of the increasing demand for natural gas.

The demand for natural gas by the transportation sector is currently negligible, but worldwide interest on natural gas-derived transportation fuels (such as natural gas based Fischer-Tropsh Liquids and Compressed Natural Gas) is increasing. The U.S. could either produce these fuels internally, requiring larger imports of LNG, or import them from natural gas-rich countries. Alternatively, the U.S. could produce transportation fuels from coal. Although non-existent in 2005, by 2030 coal-to-liquid-fuel producers are expected to consume as much coal as coke plants. Thus, the production of transportation fuels is an additional end-use where coal and natural gas could compete as the fuel of choice.

The goal of this research is to compare coal and natural gas for use by the electric power sector and for the production of transportation fuels in the next 25 years. This comparison concentrates on the life cycle GHG emissions of these fuels. In addition to comparing natural gas and coal to determine which fuel is better suited for each end-use, a comparison of each end-use will also be performed in order to help determine which is a better use of each fuel.

Two main results arise from this research. First, it was found that in a future where advanced power plant technologies with carbon capture and sequestration are used, coal and globally sourced natural gas could have very similar life cycle GHG emissions. This begs the question of whether investing billions of dollars in LNG/SNG infrastructure will lock us into an undesirable energy path that could make future energy decisions costlier than ever expected and increase the environmental burden from our energy infrastructure. Second, it was found that the use of transportation fuels derived from coal and natural gas will not help the U.S. reduce the GHG emissions associated with the life cycle of transportation fuels, and in a worse case scenario, the use of these alternative fuels could in fact increase these GHG emissions. In addition, it was found that there is high uncertainty associated with the energy security benefits that could be associated with the consumption of transportation fuels derived from coal.

Contact: Paulina Jaramillo
Department of Civil and Environmental Engineering
Carnegie Mellon University
pjaramil@andrew.cmu.edu

"A Decentralized Approach to Reducing the Social Costs of Cascading Failures" – Paul Hines, 2007

Large cascading failures in electrical power networks come with enormous social costs. These can be direct financial costs, such as the loss of refrigerated foods in grocery stores, or more indirect social costs, such as the traffic congestion that results from the failure of traffic signals. While engineers and policy makers have made numerous technical and organizational changes to reduce the frequency and impact of large cascading failures, the existing data, as described in Chapter 2 of this work, indicate that the overall frequency and impact of large electrical blackouts in the United States are not decreasing. Motivated by the cascading failure problem, this thesis describes a new method for Distributed Model Predictive Control and a power systems application. The central goal of the method, when applied to power systems, is to reduce the social costs of cascading failures by making small, targeted reductions in load and generation and changes to generator voltage set points. Unlike some existing schemes that operate from centrally located control centers, the method is operated by software agents located at substations distributed throughout the power network. The resulting multi-agent control system is a new approach to decentralized control, combining Distributed Model Predictive Control and Reciprocal Altruism.

Experimental results indicate that this scheme can in fact decrease the average size, and thus social costs, of cascading failures. Over 100 randomly generated disturbances to a model of the IEEE 300 bus test network, the method resulted in nearly an order of magnitude decrease in average event size (measured in cost) relative to cascading failure simulations without remedial control actions. Additionally, the communication requirements for the method are measured, and found to be within the bandwidth capabilities of current communications technology (on the order of 100kB/second). Experiments on several resistor networks with varying structures, including a random graph, a scale-free network and a power grid indicate that the effectiveness of decentralized control schemes, like the method proposed here, is a function of the structure of the network that is to be controlled.

Contact: Paul Hines
Assistant Professor
School of Engineering
301 Votey Hall
University of Vermont
33 Colchester Ave.
Burlington, VT 05405
phines@cems.uvm.edu

"An Electricity-focused Economic Input-output Model: Life-cycle Assessment and Policy Implications of Future Electricity Generation Scenarios" – Joe Marriott, 2007

The electricity industry is extremely important to both our economy and our environment: we would like to examine the economic, environmental and policy implications of both future electricity technologies and the interaction of this industry with the rest of the economy. However, the tools which currently exist to analyze the potential impacts are either too complex or too aggregated to provide this type of information.

Because of its importance, and the surprising lack of associated detail in the inputoutput model of the U.S. economy, the power generation sector is an excellent candidate for disaggregation. This work builds upon an existing economic inputoutput tool, by adding detail about the electricity industry, specifically by differentiating among the various functions of the sector, and the different means of generating power.

We build a flexible framework for creating new industry sectors, supply chains and emission factors for the generation, transmission and distribution portions of the electricity industry. In addition, a systematic method for creating updated state level and sector generation mixes is developed.

The results of the analysis show that the generation assets in a region have a large impact on the environmental impacts associated with electricity consumption, and that interstate trading tends to make the differences smaller. The results also show that most sector mixes are very close to the U.S. average due to geographic dispersion of industries, but that some sectors are different, and they tend to be important raw material extraction or primary manufacturing industries. Further, in scenarios of the present and future, for electricity and for particular products, results show environmental impacts split up by generation type, and with full supply chain detail. For analyses of the current electricity system and products, economic and environmental results match well with external verification sources, but for analyses of the future, there is significant uncertainty. Future work in this area must address the inherent uncertainty of using an economic model to generate emissions values, although the framework of the model allows for infinite expansion and adjustment of assumptions.

Contact: Joe Marriott
jmm185@pitt.edu

 

"U.S. Biomass Energy: An Assessment of Costs & Infrastructure for Alternative Uses of Biomass Energy Crops as an Energy Feedstock" – William Morrow, 2006

Reduction of the negative environmental and human health externalities resulting from both the electricity and transportation sectors can be achieved through technologies such as clean coal, natural gas, nuclear, hydro, wind, and solar photovoltaic technologies for electricity; reformulated gasoline and other fossil fuels, hydrogen, and electrical options for transportation. Negative externalities can also be reduced through demand reductions and efficiency improvements in both sectors. However, most of these options come with cost increases for two primary reasons: (1) most environmental and human health consequences have historically and are currently excluded from energy prices; (2) fossil energy markets have been optimizing costs for over 100 years and thus have achieved dramatic cost savings over time. Comparing the benefits and costs of alternatives requires understanding of the tradeoffs associated with competing technology and lifestyle choices.

Bioenergy advocates propose its use as an alternative energy resource for electricity generation and transportation fuel production, primarily focusing on ethanol. These advocates argue that bioenergy offers environmental and economic benefits over current fossil energy use in each of these two sectors as well as in the U.S. agriculture sector. However, estimates of bioenergy resource reveal that bioenergy is only capable of offsetting a portion of current fossil consumption in each sector. As bioenergy is proposed as a large-scale feedstock within the United States, a question of “best use” of bioenergy becomes important. Unfortunately, bioenergy research has offered very few comparisons of these two alternative uses. This thesis helps fill this gap.

This thesis compares the economics of bioenergy utilization by a method for estimating total financial costs for each proposed bioenergy use. Locations for potential feedstocks and bio-processing facilities (co-firing switchgrass and coal in existing coal fired power plants and new ethanol refineries) are estimated and linear programs are developed to estimate large-scale transportation infrastructure costs for each sector. Each linear program minimizes required bioenergy distribution and infrastructure costs. Truck and rail are the only two transportation modes allowed as they are the most likely bioenergy transportation modes. Switchgrass is chosen as a single bioenergy feedstock. All resulting costs are presented in units which reflect current energy markets price norms (¢/kWh, $/gal). The use of a common metric, carbon-dioxide emissions, allows a comparison of the two proposed uses. Additional analysis is provided to address aspects of each proposed use which are not reflected by a carbon-dioxide reduction metric. Using switchgrass as an electricity generation feedstock offers more than twice the amount of carbon-dioxide emission reductions as using switchgrass as an ethanol feedstock (370 versus 160 million short tons per year respectively; representing 14% and 12% of electricity and transportation sector annual CO2 emissions). Total costs, including capital, labor, feedstock, and transportation, is more certain for electricity production than for ethanol; 20 - 45 $/ton CO2 mitigated versus free - 80 $/ton CO2 mitigated respectively. In both cases, mitigation cost is a variable of fossil energy costs. Coal price are very stable as compared to crude oil prices and therefore, more risk is inherent in ethanol economics than in electricity economics.

Additional analysis comparing life-cycle benefits and burdens though full-cost accounting methods also favors bioenergy for electricity production. Agricultural impacts are neutral, while criteria pollutants increase with ethanol use and decrease with bioenergy electricity production. Moreover, ethanol use could cause an increase in groundwater toxicity, a risk that is not associated with electricity production. Considering other available alternative technologies, switchgrass co-firing in existing coal power plants is the least costs retrofitting option available to existing coal fired power plants wishing to lower their carbon emissions. Plug hybrids offer increased system efficiencies over current gasoline-propulsion systems, thereby lowering criteria pollutants and greenhouse gas emissions all at a cost less than or comparable to ethanol. However, shifting transportation energy demands into the United States’ antiquated electrical grid will require large-scale electricity infrastructure investments. The economic impact of a large-scale transfer of energy from petroleum to electricity should be a topic of future research.

Contact: William R. Morrow, III, Ph.D., P.E.
Senior Consultant
Energy and Environmental Economics, Inc.
101 Montgomery Street, Suite 1600
San Francisco, CA 94104
415-391-5100
415-391-6500 (fax)
bill@ethree.com

"Capturing CO2 From Ambient Air: A Feasibility Assessment" – Joshuah K. Stolaroff, 2006

In order to mitigate climate change, deep reductions in CO2 emissions will be required in the coming decades. Carbon capture and storage will likely play an important role in these reductions. As a compliment to capturing CO2 from point sources, CO2 can be captured from ambient air ("air capture"), offsetting emissions from distributed sources or reducing atmospheric concentrations when emissions have already been constrained. In this thesis, we show that CO2 capture from air is physically and thermodynamically feasible, discuss the various routes available, and explain why NaOH solution is a viable sorbent for largescale capture. An example system using NaOH spray is presented. With experimental data and a variety of numerical techniques, the use of NaOH spray for air capture is assessed and an example contacting system developed. The cost and energy requirements of the example contacting system are estimated. Contactor estimates are combined with estimates from industry and other research to estimate the cost of a complete air capture system. We find that the cost of capturing CO2 with the complete system would fall between 80 and 250 $/t-CO2, and improvements are suggested which reduce the upper-bound cost to 130 $/t-CO2. Even at the high calculated cost, air capture has implications for climate policy, however dedicated engineering and technological innovation have potential to produce much lower-cost systems.

Contact: Joshuah K. Stolaroff
Center for Program Analysis
Office of Solid Waste and Emergency Response
Environmental Protection Agency
1200 Pennsylvania Ave NW
Mailcode: 5101T
Washington, DC 20460
stolaroff.joshuah@epa.gov

"Valuing Risk-Reduction: Three applications in the Electricity Industry" – Dalia Patiño Echeverri, 2006

This dissertation is motivated by the belief that it is possible for regulators to attenuate some of the uncertainties that surround the operation of electricity markets, and therefore understanding the sources, implications and costs of these uncertainties can help shape policies in the field. At least in some cases, the quantification of the effects of uncertainty can serve as an incentive for industry participants and regulators to make a common front against unnecessary costs.Options theory and the method of risk-neutral valuation provide a framework to quantify the value of hedging against uncertainty. By incorporating options theory –widely used in the financial world- this thesis contributes a framework to quantify the risks and value accordingly the instruments or strategies that provide hedging. Having an idea of what the fair cost of hedging is, we will have better tools to identify inefficiencies and opportunities for regulation improvement.

This dissertation looks at three cases of uncertainty in the electricity industry, related to generation, transmission and ancillary services, and proposes a method to quantify the cost of this uncertainty and use this value to inform policy making. In the three cases, there is a strategy or contract that can be seen as a hedging instrument and valued as such. In the ambit of electricity transmission, Financial Transmission Rights (FTRs) can be seen as hedging instruments that provide protection against highly volatile transmission congestion costs. An FTR is essentially a contract that allows (or obligates) the holder to get the monetary difference between the marginal price of electricity at the point where it is withdrawn to the marginal price electricity at its source. In the ambit of electricity generation, the investment in environmental-control-devices or cleaner generation technologies can be seen as protection against the risk of not being able to comply with potential stringent air-emission regulations. In the ambit of ancillary services, the provision of reliability-support resources can be seen as reduction of the risk of not being able to deal with contingencies that treat the instantaneous balance between supply and demand.

Contact: Dalia Patiño Echeverri
Assistant Professor
Nicholas School of the Environment and Earth Sciences
Box 90328
Duke University
Durham, NC 27708
919.613.8000

"Electric Power Micro-grids: Opportunities and Challenges for an Emerging Distributed Energy Architecture" – Douglas E. King, 2006

Distributed energy resources (DERs) have become the focus of considerable research and investigation, as well as commercial interest in the U.S. and around the world. Despite a significant body of research that explores the potential benefits associated with DERs, several factors have combined to make progress toward serious adoption in the US very slow. These include: technical challenges; the absence of suppliers who can provide "turn-key" systems; real and perceived risks associated with the large-scale integration of DERs; the reluctance of legacy utilities to allow new entrants into markets in which, up until now, they have enjoyed a monopoly; and general deliberation and caution on the part of state utility regulators.

One emerging concept that holds considerable potential for improving the value of DERs is the micro-grid architecture, which builds on conventional continuous-use DER applications by aggregating and interconnecting small groups of customers onto a local grid. Some of the advantages of this kind of aggregation parallel the advantages of the centralized grid system - better resource utilization, increased redundancy and system robustness, and possible economies of scale. Other advantages include: increased levels of reliability, much greater net energy-use efficiency through the use of combined-heat-and-power applications, and increased customer choice and flexibility. Although progress has been made by both the regulatory and business community that has led to limited growth of conventional continuous-use DER applications, the micro-grid concept has yet to attract much commercial attention in the U.S.

Chapter 2 presents the results of the micro-grid customer engineering-economic model (MCEEM), developed by the author. In some cases, micro-grids can be good investments with current utility rate structures, reducing net present energy costs over a 25-year period by 5-10% in many of the cases studied and by over 20% in the best cases. The economic value of a micro-grid is shown to be greater for customers that have a value for highly-reliable electric power supply. The cost of natural gas and electricity is a significant factor in estimating the value of micro-grids, and continually rising natural gas prices may decrease their value, but other factors are also shown to be very significant. A sensitivity analysis reveals that the choice of micro-grid customer mix also has a large impact on system economics, whereas climate plays only a modest role. Economies of scale are shown to be fairly modest for the scenarios studied, but micro-grids do show clear benefits over traditional single customer distributed generation (DG). If performance goals of current United States Department of Energy (US DOE) research programs for IC engines and micro-turbines are met, rates of return for micro-grid investments increase 10-20%.

In Chapter 3, the regulatory environment for micro-grids is examined using results from a survey of state regulatory officials conducted in Fall 2004. Only 17 of 27 participating states indicated that the installation and operation of a micro-grid is probably or definitely legal, and only under certain circumstances and subject to varying stipulations that make for an unattractive market environment. Among those 17 states, only 4 indicated that existing laws for the interconnection and operation of DERs would apply to micro-grid systems. No states have clear guidance for the regulatory oversight of micro-grid systems once they are installed, and most respondents indicated that such oversight would be conducted on a case-by-case basis. A series of recommendations for regulatory change are provided that could reduce uncertainty and lead to a much more hospitable environment for microgrid market development.

Chapter 4 addresses the question of how electric utilities can best recover net costs from customer generators. The problem of tariff design for customer-generators is introduced, with an overview of the competing goals of utility tariffs and the various mechanisms (i.e. tariff components) for cost recovery. The various costs and benefits that customer-generators can impose on electric utilities are discussed, along with a framework for how both benefits and costs can and should be quantified and incorporated into the rate-setting process. Results from the MCEEM are presented that demonstrate how well (or poorly) different tariff components achieve the goals of a utility tariff, and the implications of these results are discussed. Standby rates are shown to increase customer peak period consumption by customer-generators, and represent a poor choice for cost-recovery in most cases. Increased demand charges are shown to be the best option for cost-recovery by utilities in most cases.

Chapter 5 examines the argument that a market based on DERs will have higher rates of innovation and new technology adoption than conventional, centralized supply. Data from the electricity industry are provided that demonstrate historically low rates of innovation and adoption. The characteristics that distinguish DERs from centralized supply - small size, dispersed resources, and modular design - are described, and relevant literature from the fields of economics and management science is discussed.

This literature provides theoretical support for the claim that DERs will encourage greater innovative activity, but the claim is not tested empirically.

Contact: Douglas E. King
Building Knowledge, Inc.
425 Orange Street, Apt. 401
Oakland, CA 94610
douglaseking@gmail.com

"Network Topologies and Transmission Investment Under Electric-Industry Restructuring" – Seth Adam Blumsack, 2006

A number of factors, including the U.S. blackout of August, 2003, have convinced even some skeptics that the North American power grid is under increasing stress, and that restructuring has failed to attract sufficient transmission investment in areas controlled by regional transmission organizations (RTOs). The architects of electricity restructuring hoped that the energy markets run by RTOs would encourage a vibrant non-utility transmission segment of the industry. Analyses by Hogan (1992) and Bushnell and Stoft (1996) suggest compensating transmission investors by awarding them financial rights to a portion of the congestion rent along a given network path. An allocation of these financial rights that respects the physical constraints of the network will yield the proper incentives for market-based transmission planning.

This thesis addresses several issues in transmission planning and investment in the restructured electricity industry. In particular, the thesis exploits topological structures common in actual power networks to highlight some problems with market-based transmission planning.

The topological analysis of the power grid focuses on identifying and analyzing Wheatstone structures embedded in larger systems. In other networks (such as water or gas pipes, the internet, and even crowd control), the Wheatstone network is associated with the Braess Paradox, a phenomenon where adding links to a network increases congestion throughout the network. This thesis provides the first quantitative analysis of how the presence of a Wheatstone structure can affect the flow of power through electric networks, and develops a fast heuristic algorithm to identify embedded Wheatstone structures, which are quite common in real networks.

In power systems that use locational pricing signals to manage congestion and promote investment, the presence of an embedded Wheatstone network drives a wedge between the price signal and the underlying physical state of the grid. Locational prices fail to identify the active system constraint; simply upgrading the transmission line with the highest congestion price fails to relieve physical congestion in the system. The thesis derives conditions under which this phenomenon occurs. One consequence is that even if financial congestion contracts are allocated according to the method suggested by Hogan (1992), investors can still profit from exploiting the Braess Paradox – that is, by constructing transmission lines that cause congestion rather than relieving congestion.

Wheatstone networks can cause congestion, but they may be justified on the grounds that they increase the reliability of the network, helping to reduce the frequency of blackouts. Models of market-based transmission investment labor under the assumption that congestion and reliability are independent attributes in power networks. New transmission links can be justified as providing either a reliability benefit or an economic (congestion-relief) benefit. The cost of investments made for reliability should be socialized, while market incentives will provide for economic investments. This thesis provides the first quantitative assessment of the claim that reliability and congestion are independent. The thesis develops metrics to decompose a line’s reliability benefit from its impact on network congestion, and applies these metrics to four embedded Wheatstone sub-networks in the IEEE 118-bus test system. While it is possible to account separately for a transmission line’s effect on system reliability and congestion, the two are almost never independent quantities. Further, the benefit of a particular transmission line to the network varies highly with the level of demand and the topological state of the rest of the system.

From a policy standpoint, the analysis of Wheatstone networks in this thesis suggests that the debate over transmission investment, at least in areas that have undertaken restructuring, has been misguided. The principal problem is not with non-utility transmission, but in the way that RTOs have proposed to compensate non-utility transmission investments. RTOs should stop trying to attract transmission investment by offering financial contracts based on locational spot-market prices. RTOs and their regulators also need to realize that the network benefit of a given transmission project depends critically on identifying the relevant range of demand and the state of the system, both at the time of construction and into the future. Under restructuring, the transmission planning problem has been cast as a problem of encouraging competition under peak demand conditions. It should be re-cast as a problem in risk management. The question of who (utilities, non-utility transmission companies, or RTOs) should bear the responsibility for transmission investment is a matter of who can manage this risk at the lowest cost.

Contact: Seth Blumsack
Assistant Professor of Energy Policy and Economics
Department of Energy and Mineral Engineering
Penn State Institute for Energy and the Environment
The Pennsylvania State University
University Park, PA 16802
blumsack@psu.edu

 

"A Technical and Economic Assessment of CO2 Capture Technology for IGCC Power Plants" – Chao Chen, 2005

As an emerging technology for electric power generation, Integrated Gasification Combined Cycle (IGCC) power plants are of increasing interest because of their potential advantage for CO2 capture in addition to conventional pollution control. To further explore this technology, this thesis develops a general modeling framework to provide tools for assessing gasification-based energy conversion systems with various CO2 capture options on a systematic and consistent basis.

Many factors influence the performance and cost of an IGCC power plant.
Simulation studies of an oxygen-blown Texaco quench gasifier system with a water gas shift (WGS) reactor and Selexol CO2 capture unit indicated that the CO2 avoidance cost is lowest when the CO2 removal efficiency is in the range of 85%-90%. The overall cost of IGCC systems with and without CO2 and storage varied significantly with coal quality and plant size (among other factors). For low rank coals (sub-bituminous and lignite) costs increased significantly relative to the nominal case with bituminous coal. It was also found that larger IGCC plants have slightly higher thermal efficiency and lower capital cost. Without incentive financing, however, an IGCC power plant without CO2 capture was found to be less competitive (more costly) than PC and NGCC power plants in terms of both the total capital requirement and cost of electricity production. However, IGCC plants with CO2 capture were competitive with PC and NGCC capture plants without incentive financing.

This thesis also provides an overview of available options and decisions factors for using IGCC technology to repower aging PC power plants. Studies in this thesis show that IGCC repowering is less capital intensive than greenfield plants, but the feasibility of repowering is very site-specific.

Under suitable conditions, IGCC repowering may be an economically attractive option for existing PC plants.
This thesis also attempts to characterize key uncertainties affecting the performance and cost of IGCC systems with CO2 capture through data mining and Monte Carlo simulation. Most of the capital cost uncertainty in an IGCC capture plant comes from the IGCC process, rather than the CO2 capture process. Considering the historical variability of capacity factor and coal price for large U.S. coal plants, the COE of an IGCC capture plant may be higher than the expected value based on typical deterministic assumptions.

This thesis also presents preliminary evaluations of IGCC systems using two advanced technologies, the Ion Transport Membrane (ITM) system for oxygen production and the GE H-class gas turbine system for power generation. Study results show that these two technologies can significantly improve the competitiveness of IGCC systems and will influence the application of IGCC technologies in the near future.

Contact: Chao Chen
Chao.Chen@WorleyParsons.com

"Future Electricity Generation: An Economic and Environmental Life Cycle Perspective on Near-, Mid- and Long-Term Technology Options and Policy Implications" – Joule Andrea Bergerson, 2005

The U.S. electricity industry is currently experiencing and adapting to enormous change including concerns related to security, reliability, increasing demand, aging infrastructure, competition and environmental impacts. Decisions that are made over the next decade will be critical in determining how economically and environmentally sustainable the industry will be in the next 50 to 100 years. For this reason, it is imperative to look at investment and policy decisions from a holistic perspective, i.e., considering various time horizons, the technical constraints within the system and the environmental impacts of each technology and policy option from an economic and environmental life cycle perspective.

This thesis evaluates the cost and environmental tradeoffs of current and future electricity generation options from a life cycle perspective. Policy and technology options are considered for each critical time horizon (near-, mid-, and long-term). The framework developed for this analysis is a hybrid life cycle analysis which integrates several models and frameworks including process and input-output life cycle analysis, an integrated environmental control model, social costing, forecasting and future energy scenario analysis.

The near-term analysis shows that several recent LCA studies of electricity options have contributed to our understanding of the technologies available and their relative environmental impacts. Several promising options could satisfy our electricity demands. Other options remain unproven or too costly to encourage investment in the near term but show promise for future use (e.g. photovoltaic, fuel cells). Public concerns could impede the use of some desirable technologies (e.g. hydro, nuclear). Finally, less tangible issues such as intermittency of some renewable technologies, social equity and visual and land use impacts, while difficult to quantify, must be considered in the investment decision process.

Coal is a particularly important fuel to consider in the U.S. and is the main focus of this thesis. A hybrid life cycle analysis including the use of process level data, Economic Input-Output Life Cycle Assessment (EIOLCA) and the Integrated Environmental Control Model (IECM) quantify a range of potential impacts for new power plants. This method provides a more complete and consistent basis for comparing different technologies. While Integrated Coal Gasification Combined Cycle (IGCC) technology has clear environmental benefits for bituminous coals over conventional pulverized coal plants, the advantages are less clear for the lower ranked coals at present. Near-term implementation of this technology is hampered by concerns about its reliability and performance. A full scale U.S. installation of this technology would settle the performance concerns while more stringent emissions standards would increase its value. In the mid-term analysis, this thesis explores alternative methods for transport of coal energy. A hybrid life cycle analysis is critical for evaluating the cost, efficiency and environmental tradeoffs of the entire system. If a small amount of additional coal is to be shipped, current rail infrastructure should be used where possible. If entirely new infrastructure is required, the mine mouth generation options are cheaper but have increased environmental impact due to the increased generation required to compensate for transmission line losses. Gasifying the coal to produce methane also shows promise in terms of lowering environmental emissions.

The long-term analysis focuses on the implications of a high coal use future. This scenario analysis focuses on life cycle issues and considers various generation and control technologies. When advanced technologies such as gasification with carbon capture and sequestration are used, emissions during generation decrease to a level where environmental discharges from extraction, processing and transportation become the dominant concern. The location of coal, coal composition and mining method are important in determining the overall impacts.

Coal is an inherently dirty fuel. However, for the next half century, coal is likely to play a major role in electricity generation. In deciding how much coal to use, the U.S. must understand the cost and environmental implications of the technologies available, including the whole life cycle of the fuel and the facilities used from extraction, transport, generation, and use or disposal of by products.

Contact: Dr. Joule A. Bergerson
University of Calgary
Chemical and Petroleum Engineering
2500 University Drive NW, Room 602
Earth Sciences Building
Calgary, Alberta T2N 1N4
Canada
jbergers@ucalgary.ca

"Mapping Alternatives: Facilitating Citizen Participation in Development Planning and Environmental Decision Making" – Shalini P. Vajjhala 2005

Recent decades have seen a growing international awareness of the need for major development projects in tandem with a call for more environmentally sensitive decision making; however, many technical infrastructure projects currently face widespread difficulty associated with facilities siting. This rising difficulty is due to a variety of causes, including public opposition and not-in-my-backyard (NIMBY) protests. Efforts to mitigate public opposition have focused on improving citizen participation, but many participatory programs have still resulted in opposition and project delays. Taken as a whole, there is a growing need for 1) better characterizations of siting difficulty and the relative role of public opposition and 2) new strategies for facilitating timely, inclusive, and effective public participation.

The five main chapters of this dissertation bring together these interrelated problems. Each chapter consists of a stand-alone paper that together offer an integrated view of participatory development planning and environmental decision-making. Chapter 1 presents an introduction that connects the fields of planning and participation. Chapters 2 and 3 develop a policy-level quantitative evaluation of facilities siting difficulty and its major causes, including public opposition, based on a case study of electric transmission line siting. Next Chapter 4 proposes a conceptual framework of the basic components of participatory processes to link these agency-level analyses on siting difficulty and public opposition to local level participation. Chapters 5 and 6 then provide a counterpart to this top-down view through a series of community-level mapping studies to understand local priorities, perceptions, and preferences for “the backyard.” These studies further evaluate a combination of community mapping and Geographic Information Systems (GIS) as a new tool for facilitating participation. Finally, Chapter 7 concludes with a discussion of additional applications of the proposed mapping methods and avenues for future research.

Major results from all chapters include a state-level quantitative model for predicting siting difficulty and its dominant causes across the U.S. Results of siting analyses in Chapter 2 and 3 reveal large variations in state-level transmission line siting difficulty and demand. These variations have the potential to negatively impact the long-term success of current policy proposals such as Regional Transmission Organizations (RTOs) and federal eminent domain authority. Furthermore, perceptions of siting difficulty and siting constraints, including public opposition, vary significantly among stakeholders associated with different phases of project timelines. In spite of these variations, public opposition is identified as the dominant constraint on transmission siting from both qualitative survey results and related quantitative assessments.

These results bring the focus to the role of citizen participation as a means of addressing public concerns and improving siting decisions. Toward this end, the studies in Chapters 5 and 6 offer a complement to these agency-level findings. The results from these chapters provide strong support for the proposed combination of participatory mapping and GIS as an effective tool for 1) facilitating project information exchange, 2) enabling broader feedback and stakeholder communication, and 3) supporting participatory decision-making in development planning. Finally, Chapter 7 extends the proposed methods and findings to an ongoing transport planning project in Lesotho, Southern Africa.

Taken as a whole, this dissertation examines a sequence of important and interconnected issues: the need for new infrastructures, the causes of siting difficulty, the related call for participation, and strategies for improving public involvement. The integration of the top-down and bottom-up evaluations within this research provides a necessary transition from designing and informing effective policies to coordinating and implementing locally relevant solutions.

Contact: Dr. Shalini Vajjhala
Resources for the Future
1616 P Street, NW
Washington, DC 20036-1400
shalini@rff.org

"The Economics and Environmental Impacts of Large-Scale Wind Power in a Carbon Constrained World" – Joseph DeCarolis, 2004

Serious climate change mitigation aimed at stabilizing atmospheric concentrations of CO2 will require a radical shift to a decarbonized energy supply. The electric power sector will be a primary target for deep reductions in CO2 emissions because electric power plants are among the largest and most manageable point sources of emissions. With respect to new capacity, wind power is currently one of the most inexpensive ways to produce electricity without CO2 emissions and it may have a significant role to play in a carbon constrained world. Yet most research in the wind industry remains focused on near term issues, while energy system models that focus on century-long time horizons undervalue wind by imposing exogenous limits on growth. This thesis fills a critical gap in the literature by taking a closer look at the cost and environmental impacts of large-scale wind.

Estimates of the average cost of wind generation – now roughly 4¢/kWh – do not address the costs arising from the spatial distribution and intermittency of wind. Even when wind serves an infinitesimal fraction of demand, its intermittency imposes costs beyond the average cost of delivered wind power. This thesis develops a theoretical framework for assessing the intermittency cost of wind. In addition, an economic characterization of a wind system is provided in which long-distance electricity transmission, storage, and gas turbines are used to supplement variable wind power output to meet a time-varying load. With somewhat optimistic assumptions about the cost of wind turbines, the use of wind to serve 50% of demand adds ~1-2¢/kWh to the cost of electricity, a cost comparable to that of other largescale low carbon technologies.

This thesis also explores the environmental impacts posed by large-scale wind. Though avian mortality and noise caused controversy in the early years of wind iv development, improved technology and exhaustive siting assessments have minimized their impact. The aesthetic valuation of wind farms can be improved significantly with better design, siting, construction, and maintenance procedures, but opposition may increase as wind is developed on a large scale. Finally, this thesis summarizes collaborative work utilizing general circulation models to determine whether wind turbines have an impact of climate. The results suggest that the climatic impact is non-negligible at continental scales, but further research is warranted.

Contact: Dr. Joseph DeCarolis
Atmospheric Protection Branch
Office of Research and Development
U.S. Environmental Protection Agency
Mail Drop E305-02
109 TW Alexander Drive
Research Triangle Park, NC 27711
decarolis.joseph@epa.gov

"Two Essays on Problems of Deregulated Electricity Markets" – Dmitri Perekhodtsev, 2004

1. The data from California energy crisis of 2000 suggests that the largest departures of observed electricity prices from the estimates of the competitive price occur when demand approaches market capacity. This paper studies models of unilateral and collusive market power applicable to electricity markets. Both suggest a unique mechanism explaining the increase of the price-cost margin with demand. The empirical test of these models provides more evidence for unilateral market power than for behavior suggesting tacit collusion.
   
   2. In order to preserve the stability of electricity supply, electric generators have to provide ancillary services in addition to energy production. Hydro generators are believed to be the most efficient source of ancillary services because of their good dynamic flexibility. This paper studies optimal operation decisions for river dams and pumped storage facilities operating in markets for energy and ancillary services as well as the change in the water shadow price in presence of ancillary services markets. The analysis is applied to valuation of the ancillary services provided by hydro resources in the Tennessee Valley Authority. A simulation of ancillary services markets shows that TVA’s hydro resources providing ancillary services can allow for substantial savings in total costs of energy provision. Optimal hydro scheduling in markets for energy and ancillary services increases the value of TVA’s hydro resources by 9% on average and up to 26% for particular units. As a result of hydro participation in ancillary services markets water shadow prices of river dams drop significantly allowing for tightening hydro constraints in favor of other water uses.
   
   Contact: Dr. Dmitri Perekhodtsev
   LECG
   66 Avenue de New York
   75116 Paris
   France
   DPerekhodtsev@lecg.com

"Electric Power Systems Under Stress: An Evaluation of Centralized Versus Distributed System Architectures" – Hisham Zerriffi, 2004

The issue of electric power systems under persistent and high stress conditions and possible changes to electric power systems to deal with this issue is the subject of this dissertation. The stresses considered here are not the single event type of disruptions that occur as a result of a hurricane or other extreme weather event or the large blackouts that result from a particular set of circumstances. Instead the focus is on conditions that cause systematic and long-term performance degradation of the system such as underinvestment in infrastructure, poor maintenance, and military conflict.

While it has long been recognized that persistent stresses such as conflict and war can have a large impact on electric power systems, there has been few systematic analyses of the problem. The first goal of this research was to model and quantify the reliability and economic differences between centralized and distributed energy systems for providing electricity and heat, particularly under stress conditions. This goal was met through the development of Monte Carlo reliability simulations, applied to different system network topologies. The results of those models show significant potential improvements in energy delivery with distributed systems.

The second goal was to determine the impact of heterogeneity of local loads on the desired level of decentralization of the system and the impact of decentralization on the network requirements. This goal was met through a combination of Monte Carlo simulations applied to systems with differentiated and non-coincident loads and an optimal power flow applied to a more realistic network topology. The results of those models show the potential for improvements when loads are non-coincident and micro-grids can share power as well as the fact that the power sharing may be largely limited to local clusters of micro-grids. This research also showed the need for incorporation of stress in power systems modeling and a method for characterizing stress.

Contact: Hisham Zeriffi
Assistant Professor
Ivan Head South/North Research Chair
Liu Institute for Global Issues
University of British Columbia
6476 NW Marine Dr.
Vancouver BC V6T 1Z2
Canada
hzerriffi@exchange.ubc.ca