Pub Date : 2024-03-11DOI: 10.1109/TEMPR.2024.3375645
Adam Suski;Debabrata Chattopadhyay;Claire Nicolas
Wholesale market design choices continue to be debated after four decades, especially as they are being scrutinized in light of the decarbonization goals. This paper shows how a Nash-Cournot equilibrium model can combine capacity, energy, and ancillary services. The model integrates multi-year capacity expansion with dispatch decisions to capture the gaming behavior of generators in the long term, including entry and short-term capacity withdrawal decisions with and without carbon constraints. The model is deployed for Georgia, a hydro-dominated system in Eastern Europe where a new market will be introduced in 2024. The modeling analysis examines how alternative design options perform to support the country's power sector decarbonization. The results show that, in such a system, the proposed energy-only (EO) marke design performs well, yielding the lowest prices without exacerbating volatility both with and without emission constraints. Although the EO design brings in less capacity, leading to higher expected unserved energy (EUE), it does not breach the incumbent reliability standard, albeit we show that it does expose the system to power shortage in extreme low hydro availability scenarios. On the contrary, the options with a capacity market may lead to significant excess capacity, albeit curbing price volatility as well as EUE. While these findings are specific to Georgia, the modeling framework can be deployed in other systems/countries to evaluate market design proposalst.
批发市场的设计选择在四十年后仍在争论不休，尤其是在考虑到去碳化目标的情况下。本文展示了纳什-库诺均衡模型如何将容量、能源和辅助服务结合起来。该模型将多年产能扩张与调度决策相结合，以捕捉发电商的长期博弈行为，包括有碳约束和无碳约束的进入和短期产能退出决策。该模型针对格鲁吉亚进行了部署，格鲁吉亚是东欧一个以水电为主的系统，将于 2024 年引入一个新的市场。模型分析考察了替代设计方案在支持该国电力行业去碳化方面的表现。结果表明，在这样的系统中，拟议的纯能源市场（EO）设计表现出色，在有排放限制和无排放限制的情况下都能产生最低价格，且不会加剧波动。虽然 EO 设计带来的容量较少，导致预期未服务能源（EUE）较高，但它并没有违反现有的可靠性标准，尽管我们表明，在极端低水力可用性情况下，它确实会使系统面临电力短缺。相反，容量市场方案可能会导致大量容量过剩，尽管会抑制价格波动和 EUE。虽然这些发现是针对格鲁吉亚的，但该建模框架可用于其他系统/国家，以评估市场设计建议。
{"title":"Testing Alternative Electricity Market Design Performances: Methodology and Case Study","authors":"Adam Suski;Debabrata Chattopadhyay;Claire Nicolas","doi":"10.1109/TEMPR.2024.3375645","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3375645","url":null,"abstract":"Wholesale market design choices continue to be debated after four decades, especially as they are being scrutinized in light of the decarbonization goals. This paper shows how a Nash-Cournot equilibrium model can combine capacity, energy, and ancillary services. The model integrates multi-year capacity expansion with dispatch decisions to capture the gaming behavior of generators in the long term, including entry and short-term capacity withdrawal decisions with and without carbon constraints. The model is deployed for Georgia, a hydro-dominated system in Eastern Europe where a new market will be introduced in 2024. The modeling analysis examines how alternative design options perform to support the country's power sector decarbonization. The results show that, in such a system, the proposed energy-only (EO) marke design performs well, yielding the lowest prices without exacerbating volatility both with and without emission constraints. Although the EO design brings in less capacity, leading to higher expected unserved energy (EUE), it does not breach the incumbent reliability standard, albeit we show that it does expose the system to power shortage in extreme low hydro availability scenarios. On the contrary, the options with a capacity market may lead to significant excess capacity, albeit curbing price volatility as well as EUE. While these findings are specific to Georgia, the modeling framework can be deployed in other systems/countries to evaluate market design proposalst.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"407-422"},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174028","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 : 2024-03-05DOI: 10.1109/TEMPR.2024.3372656
Jinhao Li;Changlong Wang;Yanru Zhang;Hao Wang
The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional “black-box” DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation.
{"title":"Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets","authors":"Jinhao Li;Changlong Wang;Yanru Zhang;Hao Wang","doi":"10.1109/TEMPR.2024.3372656","DOIUrl":"10.1109/TEMPR.2024.3372656","url":null,"abstract":"The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional “black-box” DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"392-406"},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411622","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 : 2024-02-20DOI: 10.1109/TEMPR.2024.3367546
Jolien Despeghel;Johan Driesen
This paper aims to assess the impact of a volumetric and a capacity-based network tariff, as well as the impact of a substantial electricity price increase on the decision of a household to invest in a PV-battery system. Therefore, a convex optimization model is implemented which returns the optimal sizing and operation from the households' perspective by minimizing the equivalent annual cost. Based on the analysis of the optimal PV-battery system for 200 households under four scenarios, this study found that the investment driver of a household changes from minimizing grid withdrawal to maximizing grid feed-in when the feed-in remuneration increases, as well as the maximization of the installed PV capacity. In addition, the price increase leads to a net profit as opposed to a reduced cost. The shift from a volumetric to a capacity-based tariff leads to a smaller gap between the consumers' and prosumers' contribution to the distribution grid costs, increasing fairness. However, the contributions could be insufficient to ensure adequate cost recovery, requiring possible adjustment of the tariff height by the DSO. Finally, policy makers need to be aware that a capacity-based tariff leads to a lower reduction of carbon emissions as opposed to a volumetric tariff.
{"title":"Impact of Network Tariffs and Electricity Prices on the Investment Decisions for PV-Battery Systems","authors":"Jolien Despeghel;Johan Driesen","doi":"10.1109/TEMPR.2024.3367546","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3367546","url":null,"abstract":"This paper aims to assess the impact of a volumetric and a capacity-based network tariff, as well as the impact of a substantial electricity price increase on the decision of a household to invest in a PV-battery system. Therefore, a convex optimization model is implemented which returns the optimal sizing and operation from the households' perspective by minimizing the equivalent annual cost. Based on the analysis of the optimal PV-battery system for 200 households under four scenarios, this study found that the investment driver of a household changes from minimizing grid withdrawal to maximizing grid feed-in when the feed-in remuneration increases, as well as the maximization of the installed PV capacity. In addition, the price increase leads to a net profit as opposed to a reduced cost. The shift from a volumetric to a capacity-based tariff leads to a smaller gap between the consumers' and prosumers' contribution to the distribution grid costs, increasing fairness. However, the contributions could be insufficient to ensure adequate cost recovery, requiring possible adjustment of the tariff height by the DSO. Finally, policy makers need to be aware that a capacity-based tariff leads to a lower reduction of carbon emissions as opposed to a volumetric tariff.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"175-185"},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319612","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}
Electricity retail companies can derive significant benefits from precise recommendations of electricity retail plans (ERPs). However, existing recommendation methods often assume that customers are proficient in evaluating all the attributes of ERPs, and overlook the fact that the accuracy of predicting missing information is closely tied to the objective function of customers’ satisfaction, which degrades the recommendation results significantly. In light of the challenge, an ERP recommendation method based on multigranular hesitant fuzzy sets (MHFSs) and an improved non-negative latent factor model (INLFM) is proposed. First, a quantitative model for customer satisfaction based on MHFSs is established, which provides a foundation for estimating target customers’ satisfaction. Secondly, an INLFM-based prediction model is developed to fill in the missing values of customers’ satisfaction. Additionally, an estimation model for target customer satisfaction based on a customer portrait label system and a dual-layer affinity propagation (DLAP) clustering algorithm is proposed, and a top-H ERPs recommendation method is developed, facilitating precise ERP recommendation tailored to the needs of electricity retail company. Finally, case studies on customers in a high-tech development zone in eastern China show that the proposed method can characterize customers’ satisfaction more accurately and equitably, meanwhile reduce the recommendation deviation effectively.
电力零售公司可以从电力零售计划（ERP）的精确推荐中获得巨大收益。然而，现有的推荐方法往往假定客户能够熟练地评估ERP的所有属性，而忽略了缺失信息预测的准确性与客户满意度的目标函数密切相关，从而使推荐结果大打折扣。有鉴于此，本文提出了一种基于多粒度犹豫模糊集（MHFS）和改进的非负潜因模型（INLFM）的ERP推荐方法。首先，建立了基于 MHFSs 的客户满意度定量模型，为估算目标客户满意度奠定了基础。其次，建立了基于 INLFM 的预测模型，以填补顾客满意度的缺失值。此外，还提出了基于客户肖像标签系统和双层亲和传播（DLAP）聚类算法的目标客户满意度估算模型，并开发了顶层 H ERP 推荐方法，便于根据电力零售公司的需求进行精确的 ERP 推荐。最后，通过对中国东部某高新技术开发区客户的案例研究表明，所提出的方法能够更准确、更公平地描述客户的满意度，同时有效降低推荐偏差。
{"title":"Electricity Retail Plan Recommendation Method Based on Multigranular Hesitant Fuzzy Sets and an Improved Non-Negative Latent Factor Model","authors":"Yuanqian Ma;Ruinan Zheng;Yuhao Lu;Zhi Zhang;Yunchu Wang;Zhenzhi Lin;Li Yang;Hongle Liang;Peter Xiaoping Liu","doi":"10.1109/TEMPR.2024.3366528","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3366528","url":null,"abstract":"Electricity retail companies can derive significant benefits from precise recommendations of electricity retail plans (ERPs). However, existing recommendation methods often assume that customers are proficient in evaluating all the attributes of ERPs, and overlook the fact that the accuracy of predicting missing information is closely tied to the objective function of customers’ satisfaction, which degrades the recommendation results significantly. In light of the challenge, an ERP recommendation method based on multigranular hesitant fuzzy sets (MHFSs) and an improved non-negative latent factor model (INLFM) is proposed. First, a quantitative model for customer satisfaction based on MHFSs is established, which provides a foundation for estimating target customers’ satisfaction. Secondly, an INLFM-based prediction model is developed to fill in the missing values of customers’ satisfaction. Additionally, an estimation model for target customer satisfaction based on a customer portrait label system and a dual-layer affinity propagation (DLAP) clustering algorithm is proposed, and a top-H ERPs recommendation method is developed, facilitating precise ERP recommendation tailored to the needs of electricity retail company. Finally, case studies on customers in a high-tech development zone in eastern China show that the proposed method can characterize customers’ satisfaction more accurately and equitably, meanwhile reduce the recommendation deviation effectively.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"146-161"},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319683","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 : 2024-02-14DOI: 10.1109/TEMPR.2024.3365977
Elina Spyrou;Ben Hobbs;Deb Chattopadhyay;Neha Mukhi
Computational advances along with the profound impact of uncertainty on power system investments have motivated the creation of power system planning frameworks that handle long-run uncertainty, large number of alternative plans, and multiple objectives. Planning agencies seek guidance to assess such frameworks. This article addresses this need in two ways. First, we augment previously proposed criteria for assessing planning frameworks by including new criteria such as stakeholder acceptance to make the assessments more comprehensive, while enhancing the practical applicability of assessment criteria by offering criterion-specific themes and questions. Second, using the proposed criteria, we compare two widely used but fundamentally distinct frameworks: an ‘agree-on-plans’ framework, Robust Decision Making (RDM), and an ‘agree-on-assumptions’ framework, centered around Stochastic Programming (SP). By comparing for the first time head-to-head the two distinct frameworks for an electricity supply planning problem under uncertainties in Bangladesh, we conclude that RDM relies on a large number of simulations to provide ample information to decision makers and stakeholders, and to facilitate updating of subjective inputs. In contrast, SP is a highly dimensional optimization problem that identifies plans with relatively good probability-weighted performance in a single step, but even with computational advances remains subject to the curse of dimensionality.
{"title":"How to Assess Uncertainty-Aware Frameworks for Power System Planning?","authors":"Elina Spyrou;Ben Hobbs;Deb Chattopadhyay;Neha Mukhi","doi":"10.1109/TEMPR.2024.3365977","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3365977","url":null,"abstract":"Computational advances along with the profound impact of uncertainty on power system investments have motivated the creation of power system planning frameworks that handle long-run uncertainty, large number of alternative plans, and multiple objectives. Planning agencies seek guidance to assess such frameworks. This article addresses this need in two ways. First, we augment previously proposed criteria for assessing planning frameworks by including new criteria such as stakeholder acceptance to make the assessments more comprehensive, while enhancing the practical applicability of assessment criteria by offering criterion-specific themes and questions. Second, using the proposed criteria, we compare two widely used but fundamentally distinct frameworks: an ‘agree-on-plans’ framework, Robust Decision Making (RDM), and an ‘agree-on-assumptions’ framework, centered around Stochastic Programming (SP). By comparing for the first time head-to-head the two distinct frameworks for an electricity supply planning problem under uncertainties in Bangladesh, we conclude that RDM relies on a large number of simulations to provide ample information to decision makers and stakeholders, and to facilitate updating of subjective inputs. In contrast, SP is a highly dimensional optimization problem that identifies plans with relatively good probability-weighted performance in a single step, but even with computational advances remains subject to the curse of dimensionality.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"436-448"},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810690","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}
This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm.
{"title":"A Strengthened Primal-Dual Decomposition Algorithm for Solving Electricity Market Pricing With Revenue-Adequacy and FFR Constraints","authors":"Hamed Goudarzi;Mohammad Reza Hesamzadeh;Derek Bunn;Mahmud Fotuhi-Firuzabad;Mohammad Shahidehpour","doi":"10.1109/TEMPR.2024.3363371","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3363371","url":null,"abstract":"This paper develops a new decomposition algorithm for solving Electricity Market Pricing (EMP) problem, taking into account both revenue-adequacy and Fast Frequency Reserve (FFR) constraints. Due to revenue-adequacy constraint, a bilevel model of the EMP problem is introduced (BL-EMP). The upper level of the BL-EMP model represents the non-convex unit commitment (UC) decisions as well as the revenue-adequacy constraints of the market participants (generators, loads, and battery-storage owner). The lower level is a convex economic dispatch model with FFR constraint. To tackle the computational complexity of the considered BL-EMP model, this paper develops, tests, and proposes a Strengthened Primal-Dual Decomposition (SPDD) algorithm, which takes benefits from both Benders-like and Lagrange Dual-like algorithms. The new SPDD algorithm has a series of interesting computational properties, which are theoretically discussed in the paper. The SPDD algorithm has better computational performance than standard Benders decomposition algorithm and it also does not need tuning of the Big-M (or disjunctive) parameters for solving the proposed BL-EMP problem. Results from the modified IEEE 24-bus, the IEEE 118-bus, and the IEEE 300-bus system show the superiority of proposed SPDD algorithm over the classic Benders algorithm.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"379-391"},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142172632","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 : 2024-02-05DOI: 10.1109/TEMPR.2024.3361873
Wenqian Jiang;Chenye Wu
Prior sample-based mechanisms rely predominately on empirical validations for their efficiency, with little attention to how finite samples theoretically impact decision-making. Additionally, differentially private noise injection before data publication further complicates the understanding of the samples' impact. To this end, taking electricity procurement as an example, we seek to theoretically quantify the impact of authentic and privacy-preserving samples on decision-making. Specifically, based on the customized sample average approximation procurement solution, we derive the minimum number of samples to guarantee near-optimal decisions. Numerical studies validate the theoretical bounds by comparing them to empirical observations. Our analysis offers practical insights into effective demand forecast mechanism design and efficient sample collection.
{"title":"Optimal Electricity Procurement Enabled by Privacy-Preserving Samples","authors":"Wenqian Jiang;Chenye Wu","doi":"10.1109/TEMPR.2024.3361873","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3361873","url":null,"abstract":"Prior sample-based mechanisms rely predominately on empirical validations for their efficiency, with little attention to how finite samples theoretically impact decision-making. Additionally, differentially private noise injection before data publication further complicates the understanding of the samples' impact. To this end, taking electricity procurement as an example, we seek to theoretically quantify the impact of authentic and privacy-preserving samples on decision-making. Specifically, based on the customized sample average approximation procurement solution, we derive the minimum number of samples to guarantee near-optimal decisions. Numerical studies validate the theoretical bounds by comparing them to empirical observations. Our analysis offers practical insights into effective demand forecast mechanism design and efficient sample collection.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"339-349"},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171567","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 : 2024-02-01DOI: 10.1109/TEMPR.2024.3360996
Carmen Bas Domenech;James Naughton;Shariq Riaz;Pierluigi Mancarella
Distributed energy markets (DEM) have emerged to efficiently integrate distributed energy resources (DER) in power systems, applying the principles from wholesale energy markets and generating distribution locational marginal prices (DLMPs). In this paper we introduce a novel DLMP decomposition that provides fundamental insights into the relationship between DLMPs and wholesale market prices, active and reactive power injections, thermal and voltage constraints, and losses. The DLMP decomposition is based on an exact second order cone optimal power flow (SOC-OPF) using an iterative algorithm to accurately and efficiently compute DLMP, including instances with negative prices, when generally SOC-OPF is not exact. The salient features of the proposed DLMP decomposition are demonstrated in different DEM applications using a 2-bus canonical example and a 34-bus medium voltage test network. By revealing the underlying drivers of electricity prices at distribution level, the proposed DLMP decomposition initiates crucial discussions on DEM, setting the foundations for further DEM developments.
分布式能源市场（DEM）的出现是为了将分布式能源资源（DER）有效地整合到电力系统中，它应用了能源批发市场的原理并产生了分布式边际价格（DLMPs）。在本文中，我们介绍了一种新颖的 DLMP 分解方法，该方法提供了关于 DLMP 与批发市场价格、有功和无功功率注入、热约束、电压约束和损耗之间关系的基本见解。DLMP 分解基于精确的二阶锥形最优功率流 (SOC-OPF)，使用迭代算法精确高效地计算 DLMP，包括负价格的情况，而一般情况下 SOC-OPF 并不精确。在不同的 DEM 应用中，使用 2 总线典型示例和 34 总线中压测试网络演示了所提出的 DLMP 分解的显著特点。拟议的 DLMP 分解揭示了配电网电价的基本驱动因素，从而引发了有关 DEM 的重要讨论，为进一步开发 DEM 奠定了基础。
{"title":"Towards Distributed Energy Markets: Accurate and Intuitive DLMP Decomposition","authors":"Carmen Bas Domenech;James Naughton;Shariq Riaz;Pierluigi Mancarella","doi":"10.1109/TEMPR.2024.3360996","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3360996","url":null,"abstract":"Distributed energy markets (DEM) have emerged to efficiently integrate distributed energy resources (DER) in power systems, applying the principles from wholesale energy markets and generating distribution locational marginal prices (DLMPs). In this paper we introduce a novel DLMP decomposition that provides fundamental insights into the relationship between DLMPs and wholesale market prices, active and reactive power injections, thermal and voltage constraints, and losses. The DLMP decomposition is based on an exact second order cone optimal power flow (SOC-OPF) using an iterative algorithm to accurately and efficiently compute DLMP, including instances with negative prices, when generally SOC-OPF is not exact. The salient features of the proposed DLMP decomposition are demonstrated in different DEM applications using a 2-bus canonical example and a 34-bus medium voltage test network. By revealing the underlying drivers of electricity prices at distribution level, the proposed DLMP decomposition initiates crucial discussions on DEM, setting the foundations for further DEM developments.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"240-253"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319682","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 : 2024-01-31DOI: 10.1109/TEMPR.2024.3360475
Jacques Cartuyvels;Gilles Bertrand;Anthony Papavasiliou
The next phase of electricity market integration in Europe will see the introduction of pan-European balancing platforms, MARI and PICASSO, for the trading of manual and automatic frequency restoration reserve. This article provides an analytical framework for the study of pricing asymmetries between European member states in this context. The pricing asymmetries are due to balancing incentive components and consist of the unilateral introduction by a member state of either (i) an adder on the imbalance price and balancing price, (ii) an adder on the imbalance price solely, or (iii) the introduction of a real-time price for the trading of real-time balancing capacity. Our analytical framework allows us to characterize the optimal bidding strategy of flexible assets under the different designs and to derive the resulting equilibria. Our analysis demonstrates that adders without the trading of balancing capacity create inefficiencies by distorting the merit order and tend to be detrimental to the member state that introduces it.
在欧洲电力市场一体化的下一阶段，将引入泛欧平衡平台 MARI 和 PICASSO，用于人工和自动频率恢复储备的交易。本文为在此背景下研究欧洲成员国之间的定价不对称问题提供了一个分析框架。定价不对称是由平衡激励因素造成的，包括成员国单方面引入 (i) 不平衡价格和平衡价格的加成，(ii) 仅不平衡价格的加成，或 (iii) 为实时平衡容量交易引入实时价格。我们的分析框架允许我们描述不同设计下灵活资产的最优投标策略，并推导出由此产生的均衡。我们的分析表明，不进行平衡容量交易的附加机制会扭曲绩优顺序，从而造成效率低下，而且往往不利于引入附加机制的成员国。
{"title":"Market Equilibria in Cross-Border Balancing Platforms","authors":"Jacques Cartuyvels;Gilles Bertrand;Anthony Papavasiliou","doi":"10.1109/TEMPR.2024.3360475","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3360475","url":null,"abstract":"The next phase of electricity market integration in Europe will see the introduction of pan-European balancing platforms, MARI and PICASSO, for the trading of manual and automatic frequency restoration reserve. This article provides an analytical framework for the study of pricing asymmetries between European member states in this context. The pricing asymmetries are due to balancing incentive components and consist of the unilateral introduction by a member state of either (i) an adder on the imbalance price and balancing price, (ii) an adder on the imbalance price solely, or (iii) the introduction of a real-time price for the trading of real-time balancing capacity. Our analytical framework allows us to characterize the optimal bidding strategy of flexible assets under the different designs and to derive the resulting equilibria. Our analysis demonstrates that adders without the trading of balancing capacity create inefficiencies by distorting the merit order and tend to be detrimental to the member state that introduces it.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"162-174"},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10417720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319680","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 : 2024-01-17DOI: 10.1109/TEMPR.2024.3355291
Carlos Matamala;Luis Badesa;Rodrigo Moreno;Goran Strbac
The reduction in system inertia is creating an important market for frequency-containment Ancillary Services (AS) such as enhanced frequency response (e.g., provided by battery storage), traditional primary frequency response and inertia itself. This market presents an important difference with the energy market: while the need for energy production is driven by the demand from consumers, frequency-containment AS are procured because of the need to deal with the largest generation/demand loss in the system (or smaller losses that could potentially compromise frequency stability). Thus, a question that arises is: who should pay for frequency-containment AS? In this work, we propose a cost-allocation methodology based on the nucleolus concept, in order to distribute the total payments for frequency-containment AS among all generators or loads that create the need for these services. It is shown that this method complies with necessary properties for the AS market, such as avoidance of cross-subsidies and maintaining players in this cooperative game. Finally, we demonstrate its practical applicability through a case study for the Great Britain power system, while comparing its performance with two alternative mechanisms, namely proportional and Shapley value cost allocation.
系统惯性的减少为频率控制辅助服务（AS）创造了一个重要的市场，如增强频率响应（如电池储能提供的频率响应）、传统的一次频率响应和惯性本身。这个市场与能源市场有一个重要区别：能源生产的需求是由消费者的需求驱动的，而频率控制辅助服务的采购则是因为需要处理系统中最大的发电/需求损失（或有可能影响频率稳定的较小损失）。因此，出现的一个问题是：谁应该为频率控制自动系统付费？在这项工作中，我们提出了一种基于核子概念的成本分配方法，以便将频率控制 AS 的总支付额分配给所有需要这些服务的发电机或负载。结果表明，该方法符合自动服务市场的必要属性，如避免交叉补贴和维持合作博弈中的参与者。最后，我们通过对大不列颠电力系统的案例研究证明了该方法的实际适用性，同时将其性能与两种替代机制（即比例成本分配和沙普利价值成本分配）进行了比较。
{"title":"Cost Allocation for Inertia and Frequency Response Ancillary Services","authors":"Carlos Matamala;Luis Badesa;Rodrigo Moreno;Goran Strbac","doi":"10.1109/TEMPR.2024.3355291","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3355291","url":null,"abstract":"The reduction in system inertia is creating an important market for frequency-containment Ancillary Services (AS) such as enhanced frequency response (e.g., provided by battery storage), traditional primary frequency response and inertia itself. This market presents an important difference with the energy market: while the need for energy production is driven by the demand from consumers, frequency-containment AS are procured because of the need to deal with the largest generation/demand loss in the system (or smaller losses that could potentially compromise frequency stability). Thus, a question that arises is: who should pay for frequency-containment AS? In this work, we propose a cost-allocation methodology based on the nucleolus concept, in order to distribute the total payments for frequency-containment AS among all generators or loads that create the need for these services. It is shown that this method complies with necessary properties for the AS market, such as avoidance of cross-subsidies and maintaining players in this cooperative game. Finally, we demonstrate its practical applicability through a case study for the Great Britain power system, while comparing its performance with two alternative mechanisms, namely proportional and Shapley value cost allocation.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"328-338"},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171568","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：