Pub Date : 2023-12-13DOI: 10.1109/TEMPR.2023.3333483
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Pub Date : 2023-12-13DOI: 10.1109/TEMPR.2023.3333481
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Pub Date : 2023-12-01DOI: 10.1109/TEMPR.2023.3274227
Jacob Mays
Inefficient coordination between decentralized generation investment and centralized transmission planning is a significant barrier to achieving rapid decarbonization in liberalized electricity markets. While the optimal configuration of the transmission grid depends on the relative social costs of competing technologies, existing processes have not led to transmission expansion consistent with declines in the cost of wind and solar combined with increased estimates of the social costs of traditional thermal resources. This paper describes the negative feedback loop preventing efficient interconnection of new resources in U.S. markets, its connection to conceptual flaws in current resource adequacy constructs, and the ways in which it protects incumbent generators. To help resolve these issues, the paper recommends a shift to a “connect and manage” approach and outlines a straw proposal for a new financial right connected with transmission service. From a generator perspective, the effect of the proposed reforms is to trade highly uncertain network upgrade and congestion costs for a fixed interconnection fee. From a transmission planning perspective, the goal is to improve the quality of information about new generation included in forward-looking planning processes. Simulation on a stylized two-node system demonstrates the potential of the approach to facilitate a transition to clean technologies.
{"title":"Generator Interconnection, Network Expansion, and Energy Transition","authors":"Jacob Mays","doi":"10.1109/TEMPR.2023.3274227","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3274227","url":null,"abstract":"Inefficient coordination between decentralized generation investment and centralized transmission planning is a significant barrier to achieving rapid decarbonization in liberalized electricity markets. While the optimal configuration of the transmission grid depends on the relative social costs of competing technologies, existing processes have not led to transmission expansion consistent with declines in the cost of wind and solar combined with increased estimates of the social costs of traditional thermal resources. This paper describes the negative feedback loop preventing efficient interconnection of new resources in U.S. markets, its connection to conceptual flaws in current resource adequacy constructs, and the ways in which it protects incumbent generators. To help resolve these issues, the paper recommends a shift to a “connect and manage” approach and outlines a straw proposal for a new financial right connected with transmission service. From a generator perspective, the effect of the proposed reforms is to trade highly uncertain network upgrade and congestion costs for a fixed interconnection fee. From a transmission planning perspective, the goal is to improve the quality of information about new generation included in forward-looking planning processes. Simulation on a stylized two-node system demonstrates the potential of the approach to facilitate a transition to clean technologies.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"410-419"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138633884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1109/TEMPR.2023.3338568
Sofia Taylor;Aditya Rangarajan;Noah Rhodes;Jonathan Snodgrass;Bernard C. Lesieutre;Line A. Roald
This paper presents the California Test System (CATS), a synthetic transmission grid in California that can be used by the public for power systems policy research without revealing any critical energy information. The proposed synthetic grid combines publicly available geographic data of California's electric infrastructure, such as the actual locations of transmission corridors, with invented topology and transmission line parameters that are “realistic but not real”. The result is a power grid test system that is suitable for power flow and policy analyses with geo-referenced applications, including studies related to weather, topography, and socio-economic considerations. The methods used to develop and evaluate the CATS grid are documented in detail in this report.
{"title":"California Test System (CATS): A Geographically Accurate Test System Based on the California Grid","authors":"Sofia Taylor;Aditya Rangarajan;Noah Rhodes;Jonathan Snodgrass;Bernard C. Lesieutre;Line A. Roald","doi":"10.1109/TEMPR.2023.3338568","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3338568","url":null,"abstract":"This paper presents the California Test System (CATS), a synthetic transmission grid in California that can be used by the public for power systems policy research without revealing any critical energy information. The proposed synthetic grid combines publicly available geographic data of California's electric infrastructure, such as the actual locations of transmission corridors, with invented topology and transmission line parameters that are “realistic but not real”. The result is a power grid test system that is suitable for power flow and policy analyses with geo-referenced applications, including studies related to weather, topography, and socio-economic considerations. The methods used to develop and evaluate the CATS grid are documented in detail in this report.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 1","pages":"107-118"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140135279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1109/TEMPR.2023.3327903
Mohamed Awadalla;François Bouffard
The daily operation of real-world power systems and their underlying markets relies on the timely solution of the unit commitment problem. However, given its computational complexity, several optimization-based methods have been proposed to lighten its problem formulation by removing redundant line flow constraints. These approaches often ignore the spatial couplings of renewable generation and demand, which have an inherent impact of market outcomes. Moreover, the elimination procedures primarily focus on the feasible region and exclude how the problem's objective function plays a role here. To address these pitfalls, we move to rule out redundant and inactive constraints over a tight linear programming relaxation of the original unit commitment feasibility region by adding valid inequality constraints. We extend the optimization-based approach called umbrella constraint discovery through the enforcement of a consistency logic on the set of constraints by adding the proposed inequality constraints to the formulation. Hence, we reduce the conservativeness of the screening approach using the available historical data and thus lead to a tighter unit commitment formulation. Numerical tests are performed on standard test networks to substantiate the effectiveness of the proposed approach.
{"title":"Tight and Compact Data-Driven Linear Relaxations for Constraint Screening in Unit Commitment","authors":"Mohamed Awadalla;François Bouffard","doi":"10.1109/TEMPR.2023.3327903","DOIUrl":"10.1109/TEMPR.2023.3327903","url":null,"abstract":"The daily operation of real-world power systems and their underlying markets relies on the timely solution of the unit commitment problem. However, given its computational complexity, several optimization-based methods have been proposed to lighten its problem formulation by removing redundant line flow constraints. These approaches often ignore the spatial couplings of renewable generation and demand, which have an inherent impact of market outcomes. Moreover, the elimination procedures primarily focus on the feasible region and exclude how the problem's objective function plays a role here. To address these pitfalls, we move to rule out \u0000<italic>redundant</i>\u0000 and \u0000<italic>inactive</i>\u0000 constraints over a tight linear programming relaxation of the original unit commitment feasibility region by adding valid inequality constraints. We extend the optimization-based approach called \u0000<italic>umbrella constraint discovery</i>\u0000 through the enforcement of a consistency logic on the set of constraints by adding the proposed inequality constraints to the formulation. Hence, we reduce the conservativeness of the screening approach using the available historical data and thus lead to a tighter unit commitment formulation. Numerical tests are performed on standard test networks to substantiate the effectiveness of the proposed approach.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 1","pages":"63-78"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135213256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1109/TEMPR.2023.3324630
Eo Jin Choi;Ji Woo Lee;Dam Kim;Gab-Su Seo;Seung Wan Kim
In recent years, there has been a global acceleration in the adoption of distributed energy resources (DERs), due to their potential to decrease net demand and minimize costs associated with transmission and distribution networks. In practice, however, many of them are not situated in load areas, but in remote areas for return of investment, i.e., mostly characterized by high solar radiation, abundant wind resources, and relatively low land-use fees. As a result, the locational mismatches can lead to excessive network construction, significant congestion, and loss costs. To achieve cost-effective grid operation and planning results, it is crucial to locate DERs considering their system level impacts. Since the locational benefits of DERs are not fully assessed for and reflected in their field deployment process today, DERs are not induced to the appropriate sites. To fill this gap, this study quantifies the benefits of diverse DER deployment scenarios using Monte Carlo simulations and provides policy recommendations for utilities and authorities. To estimate the benefits, we conducted a long-term analysis using the transmission expansion planning approach and a short-term analysis based on the optimal power flow methodology. The proposed analysis reveals that the upper 10% scenario of the experimental group with better DER locations can achieve 27% cost reduction than that of the control group. The noteworthy improvement of the well-located scenario for the same amount of DER deployment accounts for a benefit of $1519M in the Korean power system case study.
近年来，由于分布式能源资源（DERs）具有减少净需求和最大限度降低输配电网络相关成本的潜力，全球加速了对这些资源的采用。然而，在实践中，许多分布式能源资源并不位于负荷区，而是位于偏远地区以获取投资回报，这些地区大多具有太阳辐射强、风力资源丰富、土地使用费相对较低的特点。因此，区位错配会导致过度的电网建设、严重的拥堵和损失成本。为了实现具有成本效益的电网运行和规划结果，在确定 DER 的位置时必须考虑其对系统的影响。由于目前在现场部署过程中并未充分评估和反映 DER 的选址优势，因此并未将 DER 引导到合适的地点。为了填补这一空白，本研究使用蒙特卡洛模拟法量化了不同 DER 部署方案的效益，并为公用事业部门和当局提供了政策建议。为了估算收益，我们使用输电扩展规划方法进行了长期分析，并根据最优电力流方法进行了短期分析。分析结果表明，与对照组相比，具有较好 DER 位置的实验组的上 10% 方案可降低 27% 的成本。在韩国电力系统案例研究中，在部署相同数量 DER 的情况下，位置优越方案的显著改善带来了 1.51 亿美元的收益。
{"title":"Quantifying Benefit of Well-Located Distributed Energy Resources","authors":"Eo Jin Choi;Ji Woo Lee;Dam Kim;Gab-Su Seo;Seung Wan Kim","doi":"10.1109/TEMPR.2023.3324630","DOIUrl":"10.1109/TEMPR.2023.3324630","url":null,"abstract":"In recent years, there has been a global acceleration in the adoption of distributed energy resources (DERs), due to their potential to decrease net demand and minimize costs associated with transmission and distribution networks. In practice, however, many of them are not situated in load areas, but in remote areas for return of investment, i.e., mostly characterized by high solar radiation, abundant wind resources, and relatively low land-use fees. As a result, the locational mismatches can lead to excessive network construction, significant congestion, and loss costs. To achieve cost-effective grid operation and planning results, it is crucial to locate DERs considering their system level impacts. Since the locational benefits of DERs are not fully assessed for and reflected in their field deployment process today, DERs are not induced to the appropriate sites. To fill this gap, this study quantifies the benefits of diverse DER deployment scenarios using Monte Carlo simulations and provides policy recommendations for utilities and authorities. To estimate the benefits, we conducted a long-term analysis using the transmission expansion planning approach and a short-term analysis based on the optimal power flow methodology. The proposed analysis reveals that the upper 10% scenario of the experimental group with better DER locations can achieve 27% cost reduction than that of the control group. The noteworthy improvement of the well-located scenario for the same amount of DER deployment accounts for a benefit of $1519M in the Korean power system case study.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 1","pages":"92-106"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10286304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1109/TEMPR.2023.3324920
Qianli Ma;Wei Wei;Shengwei Mei
The growing penetration of renewable energy in modern power systems requires energy storage to take on more responsibilities in multiple regulation services. Battery energy storage system (BESS) possesses fast response capability and is suitable to shave peak demand and provide frequency support. This article studies coordinated bidding strategies of BESS in frequency regulation and energy markets. Challenge arises from the fact that frequency control and energy arbitrage actions are taken in different timescales, and the capacity used in either market affects the available capacity and revenue in the other one. This article proposes a two-timescale decision framework, offering the hourly base-power bid in the energy market and capacity bid in the frequency regulation market, as well as real-time responses to the automatic generation control (AGC) signal every few seconds. In the fine timescale, we employ a threshold policy to generate AGC response accounting for battery lifespan. In the coarse timescale, we establish a stochastic dynamic programming model and optimize the bidding policy without exact forecasts of market prices. To solve the stochastic dynamic programming model online, a simulation-based policy improvement method is developed to approximate the state-action value function using a heuristic base policy. The performance improvement property brought by simulation is theoretically proven. We carry out comprehensive case studies to validate the effectiveness of the proposed method and analyze the economic impact of electricity prices and battery $E/P$ ratio. Empirical tests show that with an $E/P$ ratio of 3 $sim$ 5, the BESS gains a higher net revenue across the lifespan.
{"title":"A Two-Timescale Operation Strategy for Battery Storage in Joint Frequency and Energy Markets","authors":"Qianli Ma;Wei Wei;Shengwei Mei","doi":"10.1109/TEMPR.2023.3324920","DOIUrl":"10.1109/TEMPR.2023.3324920","url":null,"abstract":"The growing penetration of renewable energy in modern power systems requires energy storage to take on more responsibilities in multiple regulation services. Battery energy storage system (BESS) possesses fast response capability and is suitable to shave peak demand and provide frequency support. This article studies coordinated bidding strategies of BESS in frequency regulation and energy markets. Challenge arises from the fact that frequency control and energy arbitrage actions are taken in different timescales, and the capacity used in either market affects the available capacity and revenue in the other one. This article proposes a two-timescale decision framework, offering the hourly base-power bid in the energy market and capacity bid in the frequency regulation market, as well as real-time responses to the automatic generation control (AGC) signal every few seconds. In the fine timescale, we employ a threshold policy to generate AGC response accounting for battery lifespan. In the coarse timescale, we establish a stochastic dynamic programming model and optimize the bidding policy without exact forecasts of market prices. To solve the stochastic dynamic programming model online, a simulation-based policy improvement method is developed to approximate the state-action value function using a heuristic base policy. The performance improvement property brought by simulation is theoretically proven. We carry out comprehensive case studies to validate the effectiveness of the proposed method and analyze the economic impact of electricity prices and battery \u0000<inline-formula><tex-math>$E/P$</tex-math></inline-formula>\u0000 ratio. Empirical tests show that with an \u0000<inline-formula><tex-math>$E/P$</tex-math></inline-formula>\u0000 ratio of 3\u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00005, the BESS gains a higher net revenue across the lifespan.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"200-213"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136373023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1109/TEMPR.2023.3324798
Nam Trong Dinh;Sahand Karimi-Arpanahi;S. Ali Pourmousavi;Mingyu Guo;Jon Anders Reichert Liisberg
Extreme peak power demand is a major factor behind high electricity prices, despite occurring only for a few hours annually. This peak demand drives the need for costly upgrades for the network asset, which is ultimately passed on to the end-users through higher electricity network tariffs. To alleviate this issue, we propose a solution for cost-effective peak demand reduction in a local neighbourhood using prosumer-centric flexibility and community battery storage (CBS). Accordingly, we present a CBS sizing framework for peak demand reduction considering receding horizon operation and a bilevel program in which a profit-making entity (leader) operates the CBS and dynamically sets mark-up prices. Through the dynamic mark-up and real-time wholesale market prices, the CBS operator can harness the demand-side flexibility provided by the load-shifting behaviour of the local prosumers (followers). To this end, we develop a realistic price-responsive model that adjusts prosumers' behaviour with respect to fluctuations of dynamic prices while considering prosumers' discomfort caused by load shifting. The simulation results based on real-world data show that adopting the proposed framework and the price-responsive model not only increases the CBS owner's profit but also reduces peak demand and prosumers' electricity bills by 38% and 24%, respectively.
{"title":"Cost-Effective Community Battery Sizing and Operation Within a Local Market Framework","authors":"Nam Trong Dinh;Sahand Karimi-Arpanahi;S. Ali Pourmousavi;Mingyu Guo;Jon Anders Reichert Liisberg","doi":"10.1109/TEMPR.2023.3324798","DOIUrl":"10.1109/TEMPR.2023.3324798","url":null,"abstract":"Extreme peak power demand is a major factor behind high electricity prices, despite occurring only for a few hours annually. This peak demand drives the need for costly upgrades for the network asset, which is ultimately passed on to the end-users through higher electricity network tariffs. To alleviate this issue, we propose a solution for cost-effective peak demand reduction in a local neighbourhood using prosumer-centric flexibility and community battery storage (CBS). Accordingly, we present a CBS sizing framework for peak demand reduction considering receding horizon operation and a bilevel program in which a profit-making entity (leader) operates the CBS and dynamically sets mark-up prices. Through the dynamic mark-up and real-time wholesale market prices, the CBS operator can harness the demand-side flexibility provided by the load-shifting behaviour of the local prosumers (followers). To this end, we develop a realistic price-responsive model that adjusts prosumers' behaviour with respect to fluctuations of dynamic prices while considering prosumers' discomfort caused by load shifting. The simulation results based on real-world data show that adopting the proposed framework and the price-responsive model not only increases the CBS owner's profit but also reduces peak demand and prosumers' electricity bills by 38% and 24%, respectively.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"536-548"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136374260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-12DOI: 10.1109/TEMPR.2023.3323944
Sambuddha Chakrabarti;Hosna Khajeh;Thomas R Nudell;Mohammad Reza Hesamzadeh;Ross Baldick
This article considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this article introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC's goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies.
{"title":"Transmission Investment Coordination Using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle","authors":"Sambuddha Chakrabarti;Hosna Khajeh;Thomas R Nudell;Mohammad Reza Hesamzadeh;Ross Baldick","doi":"10.1109/TEMPR.2023.3323944","DOIUrl":"10.1109/TEMPR.2023.3323944","url":null,"abstract":"This article considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this article introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC's goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 1","pages":"52-62"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136303221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-06DOI: 10.1109/TEMPR.2023.3322531
Jacob Mays;Jesse D. Jenkins
This article considers the evolution of electricity market design as systems shift toward carbon-free technologies. Growth in wind and solar generation is likely to lead to increased price volatility on diurnal and seasonal timescales. In the standard risk-neutral optimization framework, volatility does not pose any theoretical issues for market design. Because revenue volatility has the potential to lead to a higher cost of capital for investments in competitive markets, however, many observers have questioned the viability of competitive models for resource adequacy as wind and solar grow in importance. To assess the role of risk management in overall market performance, we construct a stochastic equilibrium model incorporating financial entities as hedge providers for investors in generation capacity. Unlike in the standard optimization framework, the cost of capital in the equilibrium framework is endogenously determined by interannual revenue volatility and the risk measures used by market participants. Surprisingly, exploratory numerical tests suggest that overall investment risk may be lower in systems dominated by variable renewables due to reduced exposure to fuel price uncertainty. However, changes in investment risk are not uniform across resource types, and increased risk for peaking and backup resources contributes to lower reliability in the modeled future systems.
{"title":"Financial Risk and Resource Adequacy in Markets With High Renewable Penetration","authors":"Jacob Mays;Jesse D. Jenkins","doi":"10.1109/TEMPR.2023.3322531","DOIUrl":"10.1109/TEMPR.2023.3322531","url":null,"abstract":"This article considers the evolution of electricity market design as systems shift toward carbon-free technologies. Growth in wind and solar generation is likely to lead to increased price volatility on diurnal and seasonal timescales. In the standard risk-neutral optimization framework, volatility does not pose any theoretical issues for market design. Because revenue volatility has the potential to lead to a higher cost of capital for investments in competitive markets, however, many observers have questioned the viability of competitive models for resource adequacy as wind and solar grow in importance. To assess the role of risk management in overall market performance, we construct a stochastic equilibrium model incorporating financial entities as hedge providers for investors in generation capacity. Unlike in the standard optimization framework, the cost of capital in the equilibrium framework is endogenously determined by interannual revenue volatility and the risk measures used by market participants. Surprisingly, exploratory numerical tests suggest that overall investment risk may be lower in systems dominated by variable renewables due to reduced exposure to fuel price uncertainty. However, changes in investment risk are not uniform across resource types, and increased risk for peaking and backup resources contributes to lower reliability in the modeled future systems.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"523-535"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136007780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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