This dissertation opens with a discussion of three key transformations of the electricity grid in the 21st century: (1) integration of renewable electricity and electricity storage, (2) growth and increased public popularity of distributed, rather than centralized, electricity supply, and (3) increasing concerns about reliability in modern power systems. These trends broadly motivate a central question that links each chapter of the dissertation: what role can distributed energy play towards providing power locally and mitigating electric system reliability concerns?

My second chapter sets the conceptual stage for the remainder of the dissertation by discussing the microeconomics of power outages. Its general premise is that estimating the benefits of policies that enhance the reliability of the electricity grid is challenging. I outline a microeconomic framework that ties electricity demand to the industry metric meant to encapsulate power outage avoidance benefits, the "value of lost load'' or VoLL. This metric, which I argue is too often presented as a singular point estimate, is used in cost-benefit analyses (CBA) that inform infrastructure investment decisions, and the framework leads to insights about its inherent complexity. I describe the public good properties of electric reliability and review the historical development of VoLL literature, highlighting distinctions of applying the VoLL in CBA versus electricity rationing decision-making. Finally, I outline rarely-implemented revealed preference approaches to VoLL estimation that take advantage of recent adoption of backup power systems like solar-plus-storage and that offer attractive theoretical advantages over traditional estimation methods such as macroeconomic proxies and contingent valuation. I argue that the underlying uncertainty of VoLL estimation suggests that cost-effectiveness analysis should remain a cornerstone of electric reliability decision-making.

My next two chapters then apply different empirical strategies to estimate the VoLL by considering the adoption of backup power technologies. Chapter 3 implements an experimental stated-preference survey. The survey asks whether notification timing of power outages affect consumers willingness-to-pay (WTP) to avoid power outages and compares how ownership of backup power technologies differentiates the results. The setting is the recent major power outages in California, where utilities have begun using targeted demand-side shut offs of the grid to prevent their equipment from sparking wildfires. We find positive and statistically significant WTP to avoid power outages ($86/month or $11/kWh); however, we did not find that advanced notice of a power outage led to statistically significant reduced WTP. Heterogeneity analysis suggested that household wealth is negatively correlated with WTP, though household income was not a strong determinant of our respondents WTP. Customers who owned backup power at the time of the survey reported higher WTP.

Chapter 4 complements the stated-preference survey by describing a revealed preference approach to VoLL estimation. I estimate the causal impact of Pacific Gas & Electric's Power Safety Public Shutoff Program (PSPS) on the adoption of solar and storage technologies. The research design centers around a regression discontinuity that estimates the difference in adoption trends between neighbors who experience differing amounts of power outages and takes advantage of PG&E's differential use of nearby electrical feeder lines to mitigate wildfire risk. The VoLL is then estimated using the incremental costs of such investments and how they compare to a scenario analysis of customer expectations of future power outages. Overall, I calculate a VoLL range of $0.3 - $0.8 per kWh. I conclude this chapter with a discussion of limitations and challenges of applying the mitigation cost framework to estimate the VoLL.

Chapter 5 then aims to understand what the technical limitations are for solar-plus-storage systems to provide residential household electricity during long-duration power outage events in the United States. I develop a methodology to model the performance of behind-the-meter solar-plus-storage systems (PVESS) to provide backup power across a wide range of customer types, geography / climate conditions, and power outage scenarios, considering both whole-building backup and backup of specific critical loads. Such analysis provides new information about the conditions PVESS technologies can be relied on to serve load during long-duration outage events. The results indicate that PVESS can meet a limited set of critical loads that includes refrigeration, nighttime lighting, well-pumps, and internet / computer load for 93% of counties, though this percentage drops to 27% in a critical load scenario that includes heating and cooling. Backup performance is lowest in winter months where electric heat is common (southeast and northwest U.S.) and in summer months in places with large cooling loads (southwest and southeast U.S.). Winter backup performance varies by roughly 20% depending on infiltration rates, while summer performance varies by close to 15% depending on the efficiency of the central air-conditioning system. Differences in temperature set-points correspond to a 40% range in winter backup performance and a 20% range in summer performance. A back-of-the-envelope calculation combines the costs with the performance of PVESS in a set of historical case studies to offer comparisons of what VoLL is needed to justify PVESS investment.

Finally, in chapter 6, I explore concerns that the proliferation of PVESS technologies could lead to widespread grid defection. The cost declines of PVESS technologies have led to suggestions that a non-insignificant number of customers would disconnect from utility service and self-supply electricity. Prior research addressing this issue focused on average electricity tariffs, solar profiles, and demand without considering detailed customer heterogeneity. This chapter fills the gap by analyzing how electricity tariffs that shift cost recovery away from variable charges towards fixed charges influence a customer’s decision to disconnect from utility service. A linear optimization model is developed to size an off-grid PVESS. Technology cost and reliability parameters of the optimization model are then adjusted to calculate a range of off-grid costs. Unique rate structure information for over 2,000 utilities in the United States is then used to compare grid costs to off-grid costs. The results show limited ability for solar-plus-storage systems to economically substitute for grid services. However, 1% of households might disconnect from utility service in a scenario that accounts for future off-grid costs and updated tariff designs. We find that 3% of households in the Southwest and California have private economics that favor defection in this scenario, rising to as much as 7% in Hawaii. Grid defection could increase from 1% to 7% of United States households in a slightly reduced reliability scenario. These results indicate that utilities and regulators seeking to limit rooftop solar adoption by lowering variable charges face a possibility that the corresponding increase in fixed charges could lead to inefficient grid defection, though at relatively low penetration rates.

Power system simulation is a vital tool for anticipating, planning for and ultimately address- ing future conditions on the power grid, especially in light of contemporary shifts in power generation, transmission and use that are being driven by a desire to utilize more environ- mentally responsible energy sources. This dissertation leverages power system simulation and engineering-economic analysis to provide initial answers to one open question about future power systems: how will high penetrations of distributed (rooftop) solar power affect the physical and economic operation of distribution feeders? We find that the overall impacts of distributed solar power (both positive and negative) on the feeders we modeled are minor compared to the overall cost of energy, but that there is on average a small net benefit provided by distributed generation. We then describe an effort to make similar analyses more accessible to a non-engineering (high school) audience by developing an educational video game called “Griddle” that is based on the same power system simulation techniques used in the first study. We describe the design and evaluation of Griddle and find that it demonstrates potential to provide students with insights about key power system learning objectives.

This dissertation develops new empirical methods that inform two questions in energy policy: How will locations of photovoltaic (PV) systems affect the the need for flexible resources in power systems? And how much energy is currently being conserved by varying set point schedules in residential households?

Chapters 2

Chapter 2 presents, fits, and validates a model of variability and uncertainty in PV generation that is useful for estimating the future needs of flexibility in power systems. I call this the ``volatility state model'', due to its reliance on latent states that I refer to as volatility states. Specifically, the model (a) accounts for spatial correlation, (b) predicts metrics of variability and uncertainty that are directly relevant to grid operation and planning, and (c) predicts boundaries on distribution tails that are consistent with observed data.

I find that PV variability distributions are roughly Gaussian after conditioning on volatility states, which is helpful for finding the degree of spatial smoothing. I also propose a method for simulating volatility states that results in a very good upper bound for the probability of extreme events. Therefore the model can be used as a tool for planning additional reserve capacity requirements to balance solar variability from yet-to-be-built systems.

Chapter 3, applies the volatility state model to predict the need for reserve generation---load following and regulation---in California under different locational scenarios for PV. I find that clustering PV into small areas exacerbates the need for reserves, resulting primarily from the spatial correlation of hourly forecast errors. The benefits of dispersion diminish, and they can be saturated with a relatively small number of large utility-scale systems: 25, 500 MW systems. However, these systems need to be adequately separated, which has implications for the construction costs of transmission needed to reach them.I also identify trade-offs between locations that minimize variability and uncertainty and locations that maximize energy or capacity value. The largest trade off in California is actually between energy and capacity value: areas of the state with the greatest energy resource tend to be cloudy on summer afternoons, when peak demand---driven by air conditioning---tends to be greatest.

In Chapter 4, I explore the ability of statistical models fit to AMI data---which I refer to as ``utility meter models''---to predict the largest end use of electricity in residential homes: heating, ventilation, and air conditioning (HVAC). Specifically, I evaluate models' abilities to predict of the timing of HVAC use, the efficiency of operation, and the amount of energy consumed. I begin by presenting a general utility meter model; our goals is to create a form that (a) directly relates to physical models of heat dynamics in buildings, and (b) encompasses utility meter models already in the literature. I then fit and validate multiple variations of this model---some similar to those in literature, and some of our own device---using data from air conditioners, thermostats, and residential electricity sub-meters. I test four specific aspects of the general model: whether to use daily or hourly data, whether to allow expected energy use to be discontinuous with respect to outdoor temperature, whether to use binary latent states to classify when HVAC is on, and which probability distribution shape to assume for model disturbances.

I find a large benefit to combining models fit to daily and hourly data;

models perform best when days that are classified as without HVAC energy use cannot contain hours that are classified with HVAC. I also find that the distribution shape assumed for model disturbances greatly affects model classifications and parameters; where kernel density estimates for these distributions outperform the traditional normal distributions. Finally, I find that applying a post-fitting process that disaggregates model residuals---attributing part to HVAC and part to other end uses---increases estimations of cooling energy use.

Concluding chapter 4, I attempt to recover indoor temperature dynamics in homes metered by AMI. Though our models do not estimate these dynamics endogenously, I can infer them using out model outputs: the building is likely heating when estimated cooling energy use is less than that required to maintain a steady state, and vice versa. I find that I can infer intra-day changes in temperature well, and inter-day changes in temperature weakly.

Chapter 4 lays the groundwork for a future study, in which I hope to estimate the thermostat set point schedules of a large sample of households metered by AMI. This future work will provide an important empirical estimate of the energy currently being saved as a result of variable set point schedules.

It will also provide an important behavioral baseline for the current practices of consumers; the results of energy efficiency or demand response programs should be measured against this baseline.

Decarbonizing its electricity sector through ambitious targets for wind and solar is India’s major strategy for mitigating its rapidly growing carbon emissions. In this dissertation, I explore the economic, social, and environmental impacts of wind and solar generation on India’s future low-carbon electricity system, and strategies to mitigate those impacts. In the first part, I apply the Multi-criteria Analysis for Planning Renewable Energy (MapRE) approach to identify and comprehensively value high-quality wind, solar photovoltaic, and concentrated solar power resources across India in order to support multi-criteria prioritization of development areas through planning processes. In the second part, I use high spatial and temporal resolution models to simulate operations of different electricity system futures for India. In analyzing India’s 2022 system, I find that the targets of 100 GW solar and 60 GW wind set by the Government of India that are likely to generate 22% of total annual electricity, can be integrated with very small curtailment (approximately 1%). Further, I find that flexibility strategies that include increasing the size of the balancing area (moving from state level to regionally coordinated scheduling and dispatch), lowering the minimum generation levels of thermal plants, and increasing inter-regional transmission capacity are the most effective in decreasing production costs and renewable energy curtailment. In the final part of this dissertation, I examined the effects of different mixes and targets of wind and solar installed capacities on overall system cost and avoided emissions in 2030. I find that the value of renewable energy decreases with increasing penetration across all mixes of wind and solar, with value of solar decreasing faster with higher penetration than wind. In India, the limited correlation of wind and solar generation profiles with load leads to a relatively small conventional generation capacity being avoided by renewable energy. The data sets, models, and tools developed through these analyses can be used to evaluate future low carbon electricity systems, and develop strategies and policies to ensure that integration of wind and solar is cost-effective and socially and environmentally sustainable.

This dissertation investigates options that exist to reduce emissions from residential space and water heating over the next few decades. There are four main research questions that I aim to answer:

1. What is the most promising route to decarbonizing residential space and water heating?

2. If heating becomes electrified, what new electric loads should we expect?

3. How might the building stock transition to electrified heating, and how can this transition occur at minimum cost?

4. What policy changes are necessary in California to encourage electrification?

These research questions are tackled one at a time, in each of the main chapters of the dissertation. In Chapter One I look specifically at California and build the case for why energy efficiency with electrification of heating is the most likely path to achieve the large carbon emission reduction needed from this sector. I examine alternative decarbonization strategies, such as solar thermal, biogas, synthetic natural gas, and electrification and show why electrification is likely to be the most promising path. I evaluated these options across the dimensions of scale, cost, and suitability. I find that electrification has the potential to serve all heating loads, while the other options may serve only 2-70% of loads. I also expect that electrification could reduce emissions from this sector at less than 1/2 the cost of other options. While electrification may be the most promising path in California, it is not necessarily the most promising path in all regions. The benefits of electrification and its limitations are discussed.

In Chapter Two, I estimate what new electric loads might look like if existing natural gas space and water heating transition to electric heat pumps. In order for electrification to gain support from policymakers, system operators, and utilities we need to better understand what impacts electrification of space and water heating would have on the grid. The electricity grid needs to be prepared for the additional load, and in order to do that we need to better understand the characteristics of new heating loads. I present a new method for estimating hourly residential space heating and water heating demand using hourly electricity consumption data (smart meter data) and daily natural gas data. This estimate was done using a dataset of 30,000 customer accounts in Northern California. I applied linear regression at both the individual house level and to hourly, climate-band-averaged whole-home electricity consumption, climate-band-averaged whole-home gas consumption, and outdoor air temperature data to determine both the hours when heating is more active and the outdoor temperature dependence of that consumption. This varying temperature responsiveness allowed me to assign varying amounts of space heating load to different hours. I then scaled up the results to the entire utility service area to show when and where electric heating will impact peak demand. About 1/2 of the residential space and water heating gas use could be electrified without any impact on peak demand. I also find that electrification of space and water heating would increase the load factor by at least 5%--and even more if heating loads are controllable. While electrification of heating would have little impact on peak demand on a systemwide basis (until very high penetration), at the distribution level electrifying heating loads may have an impact on peak demand for feeders that are mostly residential.

In Chapter Three I show how California could deploy hot water heaters to meet different emissions targets at lowest cost. I describe several scenarios and show what the lowest cost pathway would be as emissions are constrained. Different water heating technologies are considered, such as gas tank, gas tankless, electric resistance, and electric heat pump, and high efficiency electric heat pump with CO2 refrigerant. Emissions from natural gas leakage and refrigerant leakage are both considered. I have developed a linear program that minimizes total present operating and capital cost of statewide residential water heating. Relative to the lowest cost case, adding cumulative emissions targets can lower emissions from 71% to 77% without early retirement of water heating appliances. In order to meet a 90% reduction goal from the sector in 2050 (while minimizing cumulative emissions), heat pump water heaters need to have full market share in new construction immediately unless efficiency standards are increased, and most scenarios suggest that the lowest cost pathway include a transition to electric water heating that should have already occurred. Heat pumps need to begin replacing existing gas water heaters by the early 2030s at the latest, while most scenarios suggest that this transition should have already happened to minimize cost. Given projections for gas and electricity prices and costs of water heating equipment, an emissions target of a 90% reduction in 2050 relative to 2010 emissions could be met at a cost of $97-153/ton CO2 relative to the unconstrained, lowest cost case. Delaying action beyond 2017 makes the cumulative emissions target unreachable in two scenarios, while a third scenario allows delay until 2029, at a carbon cost of over $200/ton CO2.

Finally, in Chapter Four I examine potential policy changes that could be made to encourage a transition to electric space and water heating. Current energy policies and economics give an advantage to natural gas appliances over electric appliances. Simultaneously, California's climate policy is aiming for very large reductions in emissions, which will either be impossible or costly without a phase out of many natural gas end uses. Aligning energy and climate policy is possible, but will require several changes. Some potential suggestions are offered in this chapter mostly related to changes to the building energy code. In addition to changes to building codes, other options are also possible such as redesigning electricity rates that properly reward flexible loads. Specific legislation may also be required to jump start a transition to electric heating. Such policies have been put in place in the past to support other technologies that may have even less climate benefit per dollar.

Energy efficiency (EE) and demand response (DR) programs are designed to reduce energy consumption, mitigate grid capacity constraints, support intermittent renewable energy integration, and reduce pollution for less than the cost of additional generation. However, the savings and flexibility achieved by EE and DR programs are contingent on programs finding and enrolling customers well matched to program objectives. Many EE programs currently rely on broad but shallow savings from prescriptive measures, but growing interest in deeper and more reliable savings is leading to increased attention for program planning and targeting using empirical criteria. Through data gathered by smart meters and software designed to manage and analyze large data sets, the tools required to cost effectively characterize, target, and change patterns of energy demand are beginning to emerge. This dissertation consists of three chapters on the analysis of meter data and one on the policy implications of these new capabilities.

Chapter 2 analyzes hourly electricity and daily natural gas smart meter readings from 30,000 residential customers of Pacific Gas and Electric (PG&E). Meter data is used to derive distributions of previously unobserved characteristics of the housing stock. We show that the targeting of EE and DR programs would be improved through selection of households based on their positions within these distributions. It follows that every utility (or public utility commission) with sufficient metering infrastructure could apply similar techniques to improve the targeting, implementation, and evaluation of their energy efficiency and demand response programs.

Chapter 3 uses regression models of patterns in daily household electricity consumption to estimate the physical and operational characteristics of homes. We apply semi-physical regressors designed to capture patterns in the space-heating and cooling, scheduling, and occupancy of homes. When applied to data from approximately 160,000 PG&E customers, this approach supports an evaluation of competing regression model formulations and provides distributions of model coefficients used to evaluate patterns of domestic energy use, including annual and system peak coincident air conditioning loads, cooling set points, day of week scheduling, and lighting energy.

Chapter 4 presents a method for estimating the hourly timing of occupant driven loads based on smart meter data. The residuals of a predictive regression model are assumed to include occupant activities because occupant controlled energy use is not fully determined by externally observable factors. Occupant activity timing is converted into empirical distributions of the probability of such events by hour-of-day or day-of-week. With estimates calculated for approximately 25,000 PG&E customers and grouped using K-means clustering, prevailing patterns are interpreted as the result of occupant lifestyles — with applications in efficiency and demand response program targeting.

Drawing upon the applications developed in the preceding chapters, Chapter 5 discusses the potential for using smart meter data to support public interest utility programs in the context of ongoing concerns over public disclosure and privacy concerns, including malicious use by bad actors and inappropriate commercial use. We propose differentiated levels of access to meter data, with access to data mediated by delegated analysis, which allows stakeholders to receive the outputs of approved algorithms without requiring direct access to sensitive data. Such a system would provide privacy protection and oversight without foreclosing on creative and innovative uses of meter data.

Customers are adopting new technologies, fundamentally changing their relationship with the electrical grid. These technologies, known as distributed energy resources (DERs), allow customers to control their consumption. They give customers the flexibility to purchase energy when prices are lower and sometimes sell electricity back to the grid when prices are high. While DERs can benefit the individual customer and potentially contribute to decarbonization, they also require electrical utility companies to change how they operate the grid, especially in the distribution system. This dissertation examines the relationship between DER operations and the electrical distribution system. We first explore how “business as usual” approaches to DERs could negatively impact the grid. Specifically, how current electricity tariffs provide an incentive structure for customers to optimize their DERs and how their optimized consumption could lead to congestion issues on the distribution system. Then, we explore data-driven tools for addressing this congestion. We collect data from simulated smart meters and develop a voltage estimator to manage congestion through a centralized optimized DER dispatch approach. Finally, we propose an electricity tariff design that provides the benefits of centralized congestion management, but instead through price signals sent to customers.

In Chapter 1, we explore the relationship between current electricity tariffs and future DER operations. We model commercial customers optimizing their electric vehicle (EV) charging under different Pacific Gas and Electric (PG&E) tariffs, including a new tariff (BEV tariff) with a novel subscription-based power charge. We model customers optimizing their load across different seasons to capture the prices in each tariff and compare the total annual costs. Once we calculate the optimal charging strategy for each customer, we model them in an open-source grid simulation software using a feeder model based on a PG

In Chapter 2, we explore ways to add a voltage constraint to the same EV charging coordination problem as Chapter 1 for distribution system operators (DSOs) to perform direct, centralized control of DERs. This chapter presents a novel three-phase data-driven linear voltage magnitude estimator based on past smart meter and substation data. This estimator is trained offline solely on data readily available for the distribution system operator and reduces the size of the voltage constraint by only estimating voltage at customer service connections. We show that our voltage estimator can prevent undervoltage in the EV charging coordination problem and is faster and more accurate than three-phase linearized voltage approximations, requiring less computational memory and no knowledge of the distribution system network connectivity.

In Chapter 3, we examine price-signal control methods for congestion management of DERs. We describe a new pricing scheme for day-ahead congestion management of DERs, referred to as Load Responsive Prices (LRPs). Using the LRP method, we can determine the optimal load profile of customers from offline analyses and then use day-ahead prices (potentially taken from the wholesale market) to calculate prices that customers can independently optimize for without additional communication from the DSO beyond the prices themselves. We find that customers optimizing using LRPs consume the same way as customers following direct load control methods but without knowing the load levels the system operator requires. We end the chapter with a discussion of various implementation considerations. We discuss ways to reduce customer cost increases, methods for addressing incorrect forecasts, and other important issues for practitioners.

We conclude with a summary of findings, policy recommendations, and future research directions.

Existing and emerging stressors are forcing monumental changes in the operation and planning of electric power systems. These stressors include increasing integration of renewable energy, rapidly growing demands due to electrified transportation and heating, and widespread climate change impacts. At the same time, policymakers, regulators, and the public are holding electric power systems accountable for meeting electricity demand safely, reliably, and cost-effectively, for operating sustainably, and for serving customers equitably. In this context, planning for the future requires increasing attention on accounting for the technical characteristics of the electricity system alongside policy goals. This dissertation is structured around bridging technical and policy considerations in two key areas of power system planning: (1) integrating new technologies and loads on electric distribution grids, and (2) adapting to climate change.

Chapters 1 and 2 of this dissertation focus on electric distribution systems. These are comprised of the substations, circuits, and associated electrical equipment that serve homes, businesses, and industry. This infrastructure will need to accommodate the increasing deployment of new technologies, such as distributed solar arrays and electric vehicles, as well as growing demands for electricity to serve new loads. However, its ability to do so may come into conflict with ambitious policy goals for the deployment of distributed energy resources and emissions reductions through electrification. The State of California forms a ripe case study for a granular analysis of distribution system limits: the state is a leader in deploying these new technologies and has set ambitious climate goals that will rely in part on electrifying buildings and transportation. It has also led efforts in requiring its largest utilities to publish granular data on their electric distribution systems.

In Chapter 1, I consider the limits of the distribution circuits operated by California's two largest electric utilities, Pacific Gas & Electric (PG&E) and Southern California Edison, to accommodate distributed solar and new loads. I find that distribution circuit limits will broadly constrain households' ability to adopt new technologies. Further, I find that distribution system limits will exacerbate existing inequities in who can benefit from these technologies. Meeting policy goals related to equitable access to distributed resources and their benefits will therefore require careful consideration of the value these technologies may bring in the context of distribution system constraints.

In Chapter 2, I consider how new electricity demands due to vehicle and building electrification may stress PG&E's distribution grid. Specifically, I evaluate circuit and substation limits for new load in concert with projections of hourly demand patterns due to electrification. I find that the rate of circuit upgrades that will be needed to meet increasing demands due to electrification may outpace what is possible with conventional grid investments. Enabling rapid electrification to occur will require, therefore, planning ahead for grid upgrades as well as evaluating alternatives to conventional investments.

Chapters 3 and 4 of this dissertation focus on climate change adaptation for electricity systems. Climate change will have widespread impacts on electricity demand, generating resources, and electric lines and other equipment. Planning for these impacts requires evaluating these impacts in the context of the planning approaches that are currently used in the electricity sector.

In Chapter 3, I focus on understanding how climate data may be used in electricity sector planning. I use California as a case study, which enables a nuanced exploration of the many data streams, inputs, and actors involved in today's electricity sector planning processes. I find that existing planning approaches will require significant restructuring to incorporate climate data that must be used to plan for the future.

In Chapter 4, I move beyond California to consider how critical infrastructure planning processes, with electricity as a case study, might be able to appropriately incorporate the significant uncertainties that climate change will bring. I conduct a review of literature in decision science, electric industry practice, and electricity sector planning under climate change. Through these reviews, I identify key insights from decision science for managing deep uncertainty and evaluate how they might be incorporated into electricity sector planning.

I conclude with a summary of findings, recommendations for policymakers, and areas for future research.

Electric power systems are currently facing substantial changes in their operating frameworks on a wide variety of fronts. With increasing deployment of grid-connected wind and solar generators, power system operators are concerned that the challenge of following variable generator outputs may degrade system reliability. Additionally, as temperature changes associated with climate change increase, the magnitudes and frequencies of peak loads will correspondingly grow, thus compounding the variability problems introduced by intermittent generation. The fossil fuels that are currently used to run most of the system are changing in cost as new methods are developed to extract them, even as new policies are being proposed that restrict or tax carbon emissions. Additionally, much of the current electricity system infrastructure is nearing the end of its life, and will soon need to be decommissioned or replaced.

Energy storage has been proposed in several different venues as a solution to many of these problems. If energy storage can be deployed appropriately, it is possible that variability could be reduced, fossil fuel dependency could be lessened, and additional investments in new plants to handle increased demand could be avoided. Though these benefits could be large, the benefits to adding storage to the current grid have not been fully characterized.

We seek to better characterize the potential for storage to change overall grid operations. To do this, we use a 240-bus model based on the US Western Interconnection. In this model, we first optimally locate storage devices in the network and then dispatch them along with conventional power generation units by using a unit commitment model with DC load flow. Storage in the model can provide frequency regulation, load following, and arbitrage for each hour of a study year, and we investigate a range of scenarios for fuel price and renewables penetration. We use this model to investigate the demand curves for added storage and added TCL aggregations that function as thermal storage, as well as the extent to which carbon taxes may have an effect on the overall benefits that storage may provide. We find that storage and demand response are most valuable operationally and economically when they are providing high power services like frequency regulation and demand response. We also show that the relationship between carbon taxes and the benefits that energy storage resources could provide is not linear.