Pub Date : 2023-04-19DOI: 10.1109/TEMPR.2023.3268440
Mohammad Afkousi-Paqaleh;Mohammad Jafarian;Andrew Keane
The volatility of market prices and the interdependence of multiple markets make it challenging for Distribution System Aggregators (DSAs) to model these prices. In this paper, a novel statistical model based on Gaussian process regression and mutual information screening technique is developed. This model is able to predict different market prices and quantify their uncertainty whilst incorporating the interdependence of different markets. The proposed model is employed to assist DSAs with market price modelling. Price scenarios for various markets generated by this model make it viable to formulate the optimal involvement of DSAs in multiple markets as a stochastic multi-step two-stage problem. Other than providing a set of scenarios that efficaciously model multiple electricity market prices, after the clearing of each market, the proposed model leverages market clearing results to improve the accuracy of price prediction of subsequent markets. Extensive simulation results on large price datasets demonstrate that the proposed methodology will result in a considerable increase in the profit of the DSA compared to state-of-the-art price prediction approaches.
{"title":"Modelling the Interdependence of Multiple Electricity Markets in the Distribution System Aggregator Bidding","authors":"Mohammad Afkousi-Paqaleh;Mohammad Jafarian;Andrew Keane","doi":"10.1109/TEMPR.2023.3268440","DOIUrl":"10.1109/TEMPR.2023.3268440","url":null,"abstract":"The volatility of market prices and the interdependence of multiple markets make it challenging for Distribution System Aggregators (DSAs) to model these prices. In this paper, a novel statistical model based on Gaussian process regression and mutual information screening technique is developed. This model is able to predict different market prices and quantify their uncertainty whilst incorporating the interdependence of different markets. The proposed model is employed to assist DSAs with market price modelling. Price scenarios for various markets generated by this model make it viable to formulate the optimal involvement of DSAs in multiple markets as a stochastic multi-step two-stage problem. Other than providing a set of scenarios that efficaciously model multiple electricity market prices, after the clearing of each market, the proposed model leverages market clearing results to improve the accuracy of price prediction of subsequent markets. Extensive simulation results on large price datasets demonstrate that the proposed methodology will result in a considerable increase in the profit of the DSA compared to state-of-the-art price prediction approaches.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"386-397"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10105510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77542745","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 : 2023-04-19DOI: 10.1109/TEMPR.2023.3268460
Wenqi Wu;Yunpeng Xiao;Xiuli Wang;Jianxue Wang;Xifan Wang
With the ever strengthening interdependence between electricity and natural gas (NG) systems, the outcomes of the two energy markets could be affected by market entities from both markets. The existing studies mainly discuss the strategic behaviors of market entities participating in directly the two energy markets, lacking the concentration on the market entities participating in one single market while they could still pose impacts on the other market via the interdependence between the two markets. This paper studies the operating strategy of liquified NG (LNG) terminal, by describing the problem as a NG market game and an electricity market game. The problem is then reformulated as a bi-level single-leader multi-follower mix-integer programming (MIP) model where the LNG terminal acts as the leader while the NG fired power plants (NGPPs) as the followers. The model could be converted to a single-level one with the value-function method, and the interaction between the leader and followers is addressed with the sample-based algorithm, combined with the diagonalization algorithm to find the equilibrium among followers. The case studies further demonstrate the feasibility and effectiveness of the proposed methodology and illustrate the outcomes of the interdependent electricity and NG markets with the strategic LNG terminal.
{"title":"Operating Strategy of LNG Terminal in Interdependent Electricity and Natural Gas Markets","authors":"Wenqi Wu;Yunpeng Xiao;Xiuli Wang;Jianxue Wang;Xifan Wang","doi":"10.1109/TEMPR.2023.3268460","DOIUrl":"10.1109/TEMPR.2023.3268460","url":null,"abstract":"With the ever strengthening interdependence between electricity and natural gas (NG) systems, the outcomes of the two energy markets could be affected by market entities from both markets. The existing studies mainly discuss the strategic behaviors of market entities participating in directly the two energy markets, lacking the concentration on the market entities participating in one single market while they could still pose impacts on the other market via the interdependence between the two markets. This paper studies the operating strategy of liquified NG (LNG) terminal, by describing the problem as a NG market game and an electricity market game. The problem is then reformulated as a bi-level single-leader multi-follower mix-integer programming (MIP) model where the LNG terminal acts as the leader while the NG fired power plants (NGPPs) as the followers. The model could be converted to a single-level one with the value-function method, and the interaction between the leader and followers is addressed with the sample-based algorithm, combined with the diagonalization algorithm to find the equilibrium among followers. The case studies further demonstrate the feasibility and effectiveness of the proposed methodology and illustrate the outcomes of the interdependent electricity and NG markets with the strategic LNG terminal.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"259-273"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80798902","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}
Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurred battery degradation costs. This paper introduces a novel forecast-informed Model Predictive Control (MPC) methodology in which a strategic and potentially risk-averse BESS performs implicit balancing by taking out-of-balance positions in near-real time. Thereby it anticipates expected imbalance prices in a European-style balancing market, and takes into account state of charge-dependent battery degradation costs. To this end, an attention-based recurrent neural network forecasting technique is leveraged to predict the System Imbalance. The proposed methodology is tested on a real-life case study of the Belgian balancing market. Expected profits of a 2 MW/2 MWh BESS (21,784 €/MW/month) are shown to exceed those of different benchmarks available in the literature, including the profit associated with participating in the day-ahead energy market with perfect price foresight (7,082 €/MW/month). From a system perspective, these implicit balancing actions performed by the BESS owner reduce the system imbalance in 75% of all cases, thus improving the cost-efficiency of power systems.
{"title":"Strategic Implicit Balancing With Energy Storage Systems via Stochastic Model Predictive Control","authors":"Ruben Smets;Kenneth Bruninx;Jérémie Bottieau;Jean-François Toubeau;Erik Delarue","doi":"10.1109/TEMPR.2023.3267552","DOIUrl":"10.1109/TEMPR.2023.3267552","url":null,"abstract":"Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurred battery degradation costs. This paper introduces a novel forecast-informed Model Predictive Control (MPC) methodology in which a strategic and potentially risk-averse BESS performs implicit balancing by taking out-of-balance positions in near-real time. Thereby it anticipates expected imbalance prices in a European-style balancing market, and takes into account state of charge-dependent battery degradation costs. To this end, an attention-based recurrent neural network forecasting technique is leveraged to predict the System Imbalance. The proposed methodology is tested on a real-life case study of the Belgian balancing market. Expected profits of a 2 MW/2 MWh BESS (21,784 €/MW/month) are shown to exceed those of different benchmarks available in the literature, including the profit associated with participating in the day-ahead energy market with perfect price foresight (7,082 €/MW/month). From a system perspective, these implicit balancing actions performed by the BESS owner reduce the system imbalance in 75% of all cases, thus improving the cost-efficiency of power systems.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"373-385"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77941699","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 : 2023-03-31DOI: 10.1109/TEMPR.2023.3263685
Yidan Zhou;Jiale Wang;Yao Zhang;Jingdong Wei;Wei Sun;Jianxue Wang
Peer-to-peer (P2P) energy trading is seen as a promising method for distributed transactive energy (TE) management. Since distribution network is used for electricity transfer, physical constraints should be imposed during the P2P trading. In this paper, we propose a P2P-based distributed TE management approach considering distribution network constraints and product differentiation for P2P energy trading between different buses. A multi-period AC optimal power flow (OPF) model is used to monitor power flow in distribution system, and the exactness of second-order cone relaxation is recovered by the convex-concave procedure, making sure that P2P transactions do not violate network constraints. Moreover, we propose a fully-decentralized variant of alternating direction method of multipliers for clearing P2P transactions in a fully-decentralized manner under AC power flow constraints. Our proposed variant does not need any interactions with the central supervisor in comparison with the traditional one. Finally, numerical results on IEEE 33-bus and 141-bus distribution systems verify the effectiveness of our proposed method in ensuring physical feasibility, lowering data communication and protecting data privacy during P2P energy trading.
{"title":"A Fully-Decentralized Transactive Energy Management Under Distribution Network Constraints via Peer-to-Peer Energy Trading","authors":"Yidan Zhou;Jiale Wang;Yao Zhang;Jingdong Wei;Wei Sun;Jianxue Wang","doi":"10.1109/TEMPR.2023.3263685","DOIUrl":"10.1109/TEMPR.2023.3263685","url":null,"abstract":"Peer-to-peer (P2P) energy trading is seen as a promising method for distributed transactive energy (TE) management. Since distribution network is used for electricity transfer, physical constraints should be imposed during the P2P trading. In this paper, we propose a P2P-based distributed TE management approach considering distribution network constraints and product differentiation for P2P energy trading between different buses. A multi-period AC optimal power flow (OPF) model is used to monitor power flow in distribution system, and the exactness of second-order cone relaxation is recovered by the convex-concave procedure, making sure that P2P transactions do not violate network constraints. Moreover, we propose a fully-decentralized variant of alternating direction method of multipliers for clearing P2P transactions in a fully-decentralized manner under AC power flow constraints. Our proposed variant does not need any interactions with the central supervisor in comparison with the traditional one. Finally, numerical results on IEEE 33-bus and 141-bus distribution systems verify the effectiveness of our proposed method in ensuring physical feasibility, lowering data communication and protecting data privacy during P2P energy trading.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"297-309"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81851140","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 : 2023-03-30DOI: 10.1109/TEMPR.2023.3263193
Shuying Lai;Jing Qiu;Yuechuan Tao;Junhua Zhao
Pricing strategies can be utilized to manage energy demand-supply imbalance and power distribution network congestion. With the application of smart meter technologies, the real-time consumption behavior of individual households can be studied to enhance the effectiveness of the formulated pricing strategy. Hence, in this paper, a customized pricing strategy based on non-intrusive load monitoring (NILM) techniques is proposed from the perspective of the energy retailer, aiming to incentivize households to change their load consumption patterns. First, the real-time occupancy state of each household is detected to identify the ability of the household to respond to the retail price. Second, the occupancy-aided factorial hidden Markov model (FHMM) is developed to facilitate the retailer to incorporate the real-time consumption behavior of households into pricing strategy formulation. Third, the real-time price elasticity of the consumption of each appliance within each household is mathematically modeled based on the real-time hidden state of each appliance. Numerical results indicate that the proposed pricing strategy is effective in managing the load consumption of households and assisting the retailer in increasing profitability.
{"title":"Customized Pricing Strategy for Households Based on Occupancy-Aided Load Disaggregation","authors":"Shuying Lai;Jing Qiu;Yuechuan Tao;Junhua Zhao","doi":"10.1109/TEMPR.2023.3263193","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3263193","url":null,"abstract":"Pricing strategies can be utilized to manage energy demand-supply imbalance and power distribution network congestion. With the application of smart meter technologies, the real-time consumption behavior of individual households can be studied to enhance the effectiveness of the formulated pricing strategy. Hence, in this paper, a customized pricing strategy based on non-intrusive load monitoring (NILM) techniques is proposed from the perspective of the energy retailer, aiming to incentivize households to change their load consumption patterns. First, the real-time occupancy state of each household is detected to identify the ability of the household to respond to the retail price. Second, the occupancy-aided factorial hidden Markov model (FHMM) is developed to facilitate the retailer to incorporate the real-time consumption behavior of households into pricing strategy formulation. Third, the real-time price elasticity of the consumption of each appliance within each household is mathematically modeled based on the real-time hidden state of each appliance. Numerical results indicate that the proposed pricing strategy is effective in managing the load consumption of households and assisting the retailer in increasing profitability.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 2","pages":"118-130"},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67976968","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 : 2023-03-28DOI: 10.1109/TEMPR.2023.3262581
Aitazaz Ali Raja;Sergio Grammatico
Motivated by the markets operating on fast time scales, we present a framework for online coalitional games with time-varying coalitional values and propose real-time payoff distribution mechanisms. Specifically, we design two online distributed algorithms to track the Shapley value and the core, the two most widely studied payoff distribution criteria in coalitional game theory. We show that the payoff distribution trajectory resulting from our proposed algorithms converges to a neighborhood of the time-varying solutions. We adopt an operator-theoretic perspective to show the convergence of our algorithms. Numerical simulations of a real-time local electricity market and cooperative energy forecasting market illustrate the performance of our algorithms: the difference between online payoffs and static payoffs (Shapley and the core) to the participants is little; online algorithms considerably improve the scalability of the mechanism with respect to the number of market participants.
{"title":"Online Coalitional Games for Real-Time Payoff Distribution With Applications to Energy Markets","authors":"Aitazaz Ali Raja;Sergio Grammatico","doi":"10.1109/TEMPR.2023.3262581","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3262581","url":null,"abstract":"Motivated by the markets operating on fast time scales, we present a framework for online coalitional games with time-varying coalitional values and propose real-time payoff distribution mechanisms. Specifically, we design two online distributed algorithms to track the Shapley value and the core, the two most widely studied payoff distribution criteria in coalitional game theory. We show that the payoff distribution trajectory resulting from our proposed algorithms converges to a neighborhood of the time-varying solutions. We adopt an operator-theoretic perspective to show the convergence of our algorithms. Numerical simulations of a real-time local electricity market and cooperative energy forecasting market illustrate the performance of our algorithms: the difference between online payoffs and static payoffs (Shapley and the core) to the participants is little; online algorithms considerably improve the scalability of the mechanism with respect to the number of market participants.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 2","pages":"97-106"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67817732","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 : 2023-03-27DOI: 10.1109/TEMPR.2023.3262127
Steffen Kaminski;Kenneth Bruninx;Erik Delarue
While capacity remuneration mechanisms are emerging around the globe, a variety of capacity demand curves is being used. Poorly tuned capacity demand curves can, however, lead to expensive over- or underprocurement of capacity. This work provides a methodology to directly compare different capacity demand curve design methodologies, accurately reflecting the net cost of new entry in the definition of the demand curves. To this end, a long-run equilibrium with energy-only and capacity markets with risk-averse market participants is considered. We find that sloped capacity demand curves, setting the net cost of new entry at the reliability target, and inelastic capacity demands, that are set to the reliability target, perform ideally assuming perfect foresight. Considering erroneous assumptions on the value-of-lost-load and risk-aversion, the results obtained using these sloped capacity demand curves are more sensitive to uncertainties than those using inelastic capacity targets. In contrast, conservatively tuned marginal reliability impact-based curves perform suboptimal assuming perfect foresight, but are less sensitive to erroneous parameterization.
{"title":"A Comparative Study of Capacity Market Demand Curve Designs Considering Risk-Averse Market Participants","authors":"Steffen Kaminski;Kenneth Bruninx;Erik Delarue","doi":"10.1109/TEMPR.2023.3262127","DOIUrl":"10.1109/TEMPR.2023.3262127","url":null,"abstract":"While capacity remuneration mechanisms are emerging around the globe, a variety of capacity demand curves is being used. Poorly tuned capacity demand curves can, however, lead to expensive over- or underprocurement of capacity. This work provides a methodology to directly compare different capacity demand curve design methodologies, accurately reflecting the net cost of new entry in the definition of the demand curves. To this end, a long-run equilibrium with energy-only and capacity markets with risk-averse market participants is considered. We find that sloped capacity demand curves, setting the net cost of new entry at the reliability target, and inelastic capacity demands, that are set to the reliability target, perform ideally assuming perfect foresight. Considering erroneous assumptions on the value-of-lost-load and risk-aversion, the results obtained using these sloped capacity demand curves are more sensitive to uncertainties than those using inelastic capacity targets. In contrast, conservatively tuned marginal reliability impact-based curves perform suboptimal assuming perfect foresight, but are less sensitive to erroneous parameterization.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"286-296"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91047824","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 : 2023-03-23DOI: 10.1109/TEMPR.2023.3259028
Georgios Tsaousoglou
Modern power systems face important demand uncertainties due to increasing penetration of behind-the-meter renewable generation. System operators need to account for such uncertainties when solving the unit commitment and economic dispatch problem. The research literature has proposed advanced methods for decision making under uncertainty but, in practice, actual system operators put more trust in the tried-and-true approach of dealing with future uncertainty by committing reserves. In this paper, the unit commitment and economic dispatch problem is formulated for a system with high penetration of storage and the inadequacy of methods based on the traditional notion of reserves is exposed. Namely, in contrast to a generator, a storage unit can provide reserve capacity in a number of timeslots but it cannot provide an analogous reserve activation in all of those timeslots due to the battery's energy being depleted. After discussing two plausible but inadequate approaches, a new, generalized notion of reserves is proposed, which addresses these issues while not abandoning the practical, reserve-based approach for the operator's problem, thus making the best of both worlds. The proposed scheme enables storage units to provide reserves, without putting the system at risk of energy scarcity, which is shown to result in substantial cost savings.
{"title":"A New Notion of Reserve for Power Systems With High Penetration of Storage and Flexible Demand","authors":"Georgios Tsaousoglou","doi":"10.1109/TEMPR.2023.3259028","DOIUrl":"https://doi.org/10.1109/TEMPR.2023.3259028","url":null,"abstract":"Modern power systems face important demand uncertainties due to increasing penetration of behind-the-meter renewable generation. System operators need to account for such uncertainties when solving the unit commitment and economic dispatch problem. The research literature has proposed advanced methods for decision making under uncertainty but, in practice, actual system operators put more trust in the tried-and-true approach of dealing with future uncertainty by committing reserves. In this paper, the unit commitment and economic dispatch problem is formulated for a system with high penetration of storage and the inadequacy of methods based on the traditional notion of reserves is exposed. Namely, in contrast to a generator, a storage unit can provide reserve capacity in a number of timeslots but it cannot provide an analogous reserve activation in all of those timeslots due to the battery's energy being depleted. After discussing two plausible but inadequate approaches, a new, generalized notion of reserves is proposed, which addresses these issues while not abandoning the practical, reserve-based approach for the operator's problem, thus making the best of both worlds. The proposed scheme enables storage units to provide reserves, without putting the system at risk of energy scarcity, which is shown to result in substantial cost savings.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 2","pages":"131-144"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67817735","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 : 2023-03-23DOI: 10.1109/TEMPR.2023.3279129
Sarineh Hacopian Dolatabadi;Mohammad Amin Latify;Hamidreza Karshenas;Alimorad Sharifi;Nikos D. Hatziargyriou
Economic efficiency is the main goal of all markets, including the electricity market. Technical and pecuniary restrictions, known as externalities, caused by the technical limits of generation units are inherent in electricity markets and can significantly affect their economic efficiency. In this article, an efficient service-based method is proposed aimed at internalizing negative externalities in the electricity markets. The method employs services procured by energy storage systems and responsive demand to internalize the generation sector's technical externalities. For the optimal capacity and price of the proposed service, bi-level optimization and an innovative discriminatory pricing scheme are applied. In this way, the first-best optimal market solution can be reached by internalizing the negative externalities in the sub-optimal second-best solution due to the generators' operational constraints. Numerical case studies verify the effectiveness of the proposed scheme, where considering nearly 5% responsive demand and storage capacity around 5% of peak load, it is possible to achieve economic efficiency by reducing the total cost. In real power systems, this cost reduction can result in millions of dollars in annual operational cost savings.
{"title":"Dealing With Inefficiencies of Electricity Markets by Internalization of Negative Externalities of the Operational Restrictions of Generating Units","authors":"Sarineh Hacopian Dolatabadi;Mohammad Amin Latify;Hamidreza Karshenas;Alimorad Sharifi;Nikos D. Hatziargyriou","doi":"10.1109/TEMPR.2023.3279129","DOIUrl":"10.1109/TEMPR.2023.3279129","url":null,"abstract":"Economic efficiency is the main goal of all markets, including the electricity market. Technical and pecuniary restrictions, known as externalities, caused by the technical limits of generation units are inherent in electricity markets and can significantly affect their economic efficiency. In this article, an efficient service-based method is proposed aimed at internalizing negative externalities in the electricity markets. The method employs services procured by energy storage systems and responsive demand to internalize the generation sector's technical externalities. For the optimal capacity and price of the proposed service, bi-level optimization and an innovative discriminatory pricing scheme are applied. In this way, the first-best optimal market solution can be reached by internalizing the negative externalities in the sub-optimal second-best solution due to the generators' operational constraints. Numerical case studies verify the effectiveness of the proposed scheme, where considering nearly 5% responsive demand and storage capacity around 5% of peak load, it is possible to achieve economic efficiency by reducing the total cost. In real power systems, this cost reduction can result in millions of dollars in annual operational cost savings.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"420-429"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77169035","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 : 2023-03-19DOI: 10.1109/TEMPR.2023.3277844
Tomas Valencia Zuluaga;Shmuel S. Oren
We propose a high-level stochastic steady-state model to analyze the value of co-located energy storage systems for wind power producers that participate in an electricity market through forward contracts and use storage to unlock access to capacity payments. In particular, we try to find optimal storage and contract sizing, as well as stationary operating policies for profit maximization in the long-run. We propose a stylized model calibrated to actual wind power production that allows us to obtain limiting distributions of battery storage levels, assess the value of storage size and perform a sensitivity analysis on key parameters such as contract prices, capacity payments and storage efficiency. We develop the case with contracts of constant price, outline how this model can be extended to a variable-price setting and discuss potential challenges in that avenue.
{"title":"Data-Driven Sizing of Co-Located Storage for Uncertain Renewable Energy","authors":"Tomas Valencia Zuluaga;Shmuel S. Oren","doi":"10.1109/TEMPR.2023.3277844","DOIUrl":"10.1109/TEMPR.2023.3277844","url":null,"abstract":"We propose a high-level stochastic steady-state model to analyze the value of co-located energy storage systems for wind power producers that participate in an electricity market through forward contracts and use storage to unlock access to capacity payments. In particular, we try to find optimal storage and contract sizing, as well as stationary operating policies for profit maximization in the long-run. We propose a stylized model calibrated to actual wind power production that allows us to obtain limiting distributions of battery storage levels, assess the value of storage size and perform a sensitivity analysis on key parameters such as contract prices, capacity payments and storage efficiency. We develop the case with contracts of constant price, outline how this model can be extended to a variable-price setting and discuss potential challenges in that avenue.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"1 4","pages":"348-359"},"PeriodicalIF":0.0,"publicationDate":"2023-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81110991","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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