The high penetration of renewable energy increases the price volatility between the day-ahead (DA) and real-time (RT) markets, with heightened power system operational risks. Virtual bidding, a rising financial instrument, allows financial entities without energy-generating capacity or demand to arbitrage between the DA and RT markets, which can in turn reduce the market spread between the two markets and thus contain system operation risks. However, in practice, incomplete information often affects the effectiveness of virtual bidding, which poses uncertainties to strategic bidding behaviors, and makes it more challenging to understand the market manipulation. To control such risks, in this paper, we first game theoretically characterize the Nash Equilibrium of virtual bidding with both complete and incomplete information, and evaluate the benefits of virtual bidding for both virtual bidders (VBs) and the system as a whole. Then, we design a joint tax-subsidy mechanism for VBs with truthfulness and individual rationality guarantees against the market manipulation. We also prove that the system average forecast is the key to influencing the virtual bidding equilibrium. Further, we design two information mechanisms to enable VB privacy protection and market risk control separately. Numerical studies based on ISO-NE electricity market data verify our theory.
{"title":"Manipulation-Proof Virtual Bidding Mechanism Design","authors":"Chenbei Lu;Jinhao Liang;Nan Gu;Haoxiang Wang;Chenye Wu","doi":"10.1109/TEMPR.2023.3321649","DOIUrl":"10.1109/TEMPR.2023.3321649","url":null,"abstract":"The high penetration of renewable energy increases the price volatility between the day-ahead (DA) and real-time (RT) markets, with heightened power system operational risks. Virtual bidding, a rising financial instrument, allows financial entities without energy-generating capacity or demand to arbitrage between the DA and RT markets, which can in turn reduce the market spread between the two markets and thus contain system operation risks. However, in practice, incomplete information often affects the effectiveness of virtual bidding, which poses uncertainties to strategic bidding behaviors, and makes it more challenging to understand the market manipulation. To control such risks, in this paper, we first game theoretically characterize the Nash Equilibrium of virtual bidding with both complete and incomplete information, and evaluate the benefits of virtual bidding for both virtual bidders (VBs) and the system as a whole. Then, we design a joint tax-subsidy mechanism for VBs with truthfulness and individual rationality guarantees against the market manipulation. We also prove that the system average forecast is the key to influencing the virtual bidding equilibrium. Further, we design two information mechanisms to enable VB privacy protection and market risk control separately. Numerical studies based on ISO-NE electricity market data verify our theory.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 1","pages":"119-131"},"PeriodicalIF":0.0,"publicationDate":"2023-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135913862","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}
Convex hull pricing provides a potential solution for reducing out-of-market payments in wholesale electricity markets. This article revisits the theoretical construct of convex hull pricing and explores its dependence on the primal formulation of a Unit Commitment (UC) problem. Namely, primal UC formulation practices for speeding up the solution of the scheduling problem, if transferred to the pricing problem, may affect the convex hull prices. A conceptual exposition of the issue is provided along with discussion on two types of such practices commonly observed in electricity markets. Sufficient conditions under which convex hull prices will be preserved by a different UC formulation are also explored. These findings contribute to a better understanding of convex hull pricing and demonstrate the importance of continued theoretical research into the method.
{"title":"On the Primal UC Formulation Dependence of Convex Hull Pricing","authors":"Feng Zhao;Dane Schiro;Jinye Zhao;Tongxin Zheng;Eugene Litvinov","doi":"10.1109/TEMPR.2023.3319159","DOIUrl":"10.1109/TEMPR.2023.3319159","url":null,"abstract":"Convex hull pricing provides a potential solution for reducing out-of-market payments in wholesale electricity markets. This article revisits the theoretical construct of convex hull pricing and explores its dependence on the primal formulation of a Unit Commitment (UC) problem. Namely, primal UC formulation practices for speeding up the solution of the scheduling problem, if transferred to the pricing problem, may affect the convex hull prices. A conceptual exposition of the issue is provided along with discussion on two types of such practices commonly observed in electricity markets. Sufficient conditions under which convex hull prices will be preserved by a different UC formulation are also explored. These findings contribute to a better understanding of convex hull pricing and demonstrate the importance of continued theoretical research into the method.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"227-236"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135800471","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}
The future of electricity markets is envisioned to be heavily based on renewable generation and distributed flexibility. Yet, integrating existing distributed flexibility into market decisions poses a major challenge, given the diversity of consumers' modeling frameworks and controllers. Moreover, in such a system, the market's decisions need to be predictive, adaptive, as well as TSO-DSO coordinated. In this article, we present an iterative market procedure through which, in contrast to traditional electricity markets based on one-off bids, flexible participants can indirectly implement their model by repeatedly responding to tentative pricing signals. This, combined with a scheduling/forecasting grey-box agent introduced on the consumer side, allows for the seamless integration of existing flexible loads' control schemes into a holistic electricity market. The proposed market-operation policy inherently coordinates Transmission and Distribution System Operators' decisions in the presence of uncertain distributed flexibility and renewables' generation. The results demonstrate promising convergence properties and short execution times, which is encouraging towards the scheme's practical applicability.
{"title":"Integrating Distributed Flexibility Into TSO-DSO Coordinated Electricity Markets","authors":"Georgios Tsaousoglou;Rune Junker;Mohsen Banaei;Seyed Shahabaldin Tohidi;Henrik Madsen","doi":"10.1109/TEMPR.2023.3319673","DOIUrl":"10.1109/TEMPR.2023.3319673","url":null,"abstract":"The future of electricity markets is envisioned to be heavily based on renewable generation and distributed flexibility. Yet, integrating existing distributed flexibility into market decisions poses a major challenge, given the diversity of consumers' modeling frameworks and controllers. Moreover, in such a system, the market's decisions need to be predictive, adaptive, as well as TSO-DSO coordinated. In this article, we present an iterative market procedure through which, in contrast to traditional electricity markets based on one-off bids, flexible participants can indirectly implement their model by repeatedly responding to tentative pricing signals. This, combined with a scheduling/forecasting grey-box agent introduced on the consumer side, allows for the seamless integration of existing flexible loads' control schemes into a holistic electricity market. The proposed market-operation policy inherently coordinates Transmission and Distribution System Operators' decisions in the presence of uncertain distributed flexibility and renewables' generation. The results demonstrate promising convergence properties and short execution times, which is encouraging towards the scheme's practical applicability.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"214-225"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135793977","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 : 2023-09-22DOI: 10.1109/TEMPR.2023.3318197
Robert Ferrando;Laurent Pagnier;Robert Mieth;Zhirui Liang;Yury Dvorkin;Daniel Bienstock;Michael Chertkov
This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market- clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.
{"title":"Physics-Informed Machine Learning for Electricity Markets: A NYISO Case Study","authors":"Robert Ferrando;Laurent Pagnier;Robert Mieth;Zhirui Liang;Yury Dvorkin;Daniel Bienstock;Michael Chertkov","doi":"10.1109/TEMPR.2023.3318197","DOIUrl":"10.1109/TEMPR.2023.3318197","url":null,"abstract":"This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market- clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 1","pages":"40-51"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135599227","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}
Two-stage settlement electricity markets, which include day-ahead and real-time markets, often observe undesirable price manipulation due to the price difference across stages, inadequate competition, and unforeseen circumstances. To mitigate this, some Independent System Operators (ISOs) have proposed system-level market power mitigation (MPM) policies in addition to existing local policies. These system-level policies aim to substitute noncompetitive bids with a default bid based on estimated generator costs. However, without accounting for the conflicting interest of participants, they may lead to unintended consequences when implemented. In this article, we model the competition between generators (bidding supply functions) and loads (bidding quantity) in a two-stage market with a stage-wise MPM policy. An equilibrium analysis shows that a real-time MPM policy leads to equilibrium loss, meaning no stable market outcome (Nash equilibrium) exists. A day-ahead MPM policy leads to Stackelberg-Nash game, with loads acting as leaders and generators as followers. Despite estimation errors, the competitive equilibrium is efficient, while the Nash equilibrium is comparatively robust to price manipulations. Moreover, analysis of inelastic loads shows their tendency to shift allocation and manipulate prices in the market. Numerical studies illustrate the impact of cost estimation errors, heterogeneity in generation cost, and load size on market equilibrium.
{"title":"Market Power Mitigation in Two-Stage Electricity Markets With Supply Function and Quantity Bidding","authors":"Rajni Kant Bansal;Yue Chen;Pengcheng You;Enrique Mallada","doi":"10.1109/TEMPR.2023.3318149","DOIUrl":"10.1109/TEMPR.2023.3318149","url":null,"abstract":"Two-stage settlement electricity markets, which include day-ahead and real-time markets, often observe undesirable price manipulation due to the price difference across stages, inadequate competition, and unforeseen circumstances. To mitigate this, some Independent System Operators (ISOs) have proposed system-level market power mitigation (MPM) policies in addition to existing local policies. These system-level policies aim to substitute noncompetitive bids with a default bid based on estimated generator costs. However, without accounting for the conflicting interest of participants, they may lead to unintended consequences when implemented. In this article, we model the competition between generators (bidding supply functions) and loads (bidding quantity) in a two-stage market with a stage-wise MPM policy. An equilibrium analysis shows that a real-time MPM policy leads to equilibrium loss, meaning no stable market outcome (Nash equilibrium) exists. A day-ahead MPM policy leads to Stackelberg-Nash game, with loads acting as leaders and generators as followers. Despite estimation errors, the competitive equilibrium is efficient, while the Nash equilibrium is comparatively robust to price manipulations. Moreover, analysis of inelastic loads shows their tendency to shift allocation and manipulate prices in the market. Numerical studies illustrate the impact of cost estimation errors, heterogeneity in generation cost, and load size on market equilibrium.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"512-522"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135599254","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 : 2023-09-15DOI: 10.1109/TEMPR.2023.3315953
Brent Eldridge;Bernard Knueven;Jacob Mays
Part 1 of this two-part article describes the impact that uncertainty has on the design and analysis of price formation policies in the non-convex auctions conducted by U.S. wholesale electricity market operators. Using first a toy model and then a large-scale test system, Part 2 demonstrates the difference in prices under the idealized benchmark of ex ante convex hull pricing defined in Part 1 versus existing methods, in particular documenting the potential for suppression of volatility and therefore under-compensation of flexibility by existing methods. The examples highlight that inefficient spot price formation can induce inefficient forward commitments of generators, necessitating out-of-market intervention to restore a reliable and efficient operating plan. Given the potential side effects of existing policies for investment and operation, we suggest two elements in a reoriented approach to the price formation problem: first ensuring that prices exhibit full-strength volatility, and second ensuring that risk-averse market participants have sufficient ability to manage this volatility.
{"title":"Rethinking the Price Formation Problem–Part 2: Rewarding Flexibility and Managing Price Risk","authors":"Brent Eldridge;Bernard Knueven;Jacob Mays","doi":"10.1109/TEMPR.2023.3315953","DOIUrl":"10.1109/TEMPR.2023.3315953","url":null,"abstract":"Part 1 of this two-part article describes the impact that uncertainty has on the design and analysis of price formation policies in the non-convex auctions conducted by U.S. wholesale electricity market operators. Using first a toy model and then a large-scale test system, Part 2 demonstrates the difference in prices under the idealized benchmark of \u0000<italic>ex ante convex hull pricing</i>\u0000 defined in Part 1 versus existing methods, in particular documenting the potential for suppression of volatility and therefore under-compensation of flexibility by existing methods. The examples highlight that inefficient spot price formation can induce inefficient forward commitments of generators, necessitating out-of-market intervention to restore a reliable and efficient operating plan. Given the potential side effects of existing policies for investment and operation, we suggest two elements in a reoriented approach to the price formation problem: first ensuring that prices exhibit full-strength volatility, and second ensuring that risk-averse market participants have sufficient ability to manage this volatility.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"490-498"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135495867","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 : 2023-09-15DOI: 10.1109/TEMPR.2023.3315956
Brent Eldridge;Bernard Knueven;Jacob Mays
Operators of organized wholesale electricity markets attempt to form prices in such a way that the private incentives of market participants are consistent with a socially optimal commitment and dispatch schedule. In the U.S. context, several competing price formation schemes have been proposed to address the non-convex production cost functions characteristic of most generation technologies. This paper considers how the design and analysis of price formation policies for non-convex markets are affected by the uncertainty inherent in electricity demand and supply. We argue that by excluding uncertainty, the analytical framework underlying existing policies mischaracterizes the incentives of market participants, leading to inefficient price formation and poor incentives for flexibility. We establish favorable theoretical properties of a new construct, ex ante convex hull pricing , and demonstrate the difference between this idealized benchmark and existing methods on a large-scale test system. Given increased operational uncertainty with a transition to wind and solar generation, distortions caused by poor incentives for flexibility are likely to grow without improved price formation in organized wholesale markets.
{"title":"Rethinking the Price Formation Problem–Part 1: Participant Incentives Under Uncertainty","authors":"Brent Eldridge;Bernard Knueven;Jacob Mays","doi":"10.1109/TEMPR.2023.3315956","DOIUrl":"10.1109/TEMPR.2023.3315956","url":null,"abstract":"Operators of organized wholesale electricity markets attempt to form prices in such a way that the private incentives of market participants are consistent with a socially optimal commitment and dispatch schedule. In the U.S. context, several competing price formation schemes have been proposed to address the non-convex production cost functions characteristic of most generation technologies. This paper considers how the design and analysis of price formation policies for non-convex markets are affected by the uncertainty inherent in electricity demand and supply. We argue that by excluding uncertainty, the analytical framework underlying existing policies mischaracterizes the incentives of market participants, leading to inefficient price formation and poor incentives for flexibility. We establish favorable theoretical properties of a new construct, \u0000<italic>ex ante convex hull pricing</i>\u0000, and demonstrate the difference between this idealized benchmark and existing methods on a large-scale test system. Given increased operational uncertainty with a transition to wind and solar generation, distortions caused by poor incentives for flexibility are likely to grow without improved price formation in organized wholesale markets.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"480-489"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135495624","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 : 2023-09-13DOI: 10.1109/TEMPR.2023.3308657
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Pub Date : 2023-09-13DOI: 10.1109/TEMPR.2023.3308661
{"title":"IEEE Power & Energy Society Information","authors":"","doi":"10.1109/TEMPR.2023.3308661","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3308661","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9794458/10250976/10250977.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50329230","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 : 2023-09-13DOI: 10.1109/TEMPR.2023.3308659
{"title":"IEEE Transactions on Energy Markets, Policy, and Regulation Information for Authors","authors":"","doi":"10.1109/TEMPR.2023.3308659","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3308659","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 3","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9794458/10250976/10251023.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50290971","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}
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