Pub Date : 2025-03-05DOI: 10.1109/TEMPR.2025.3566646
Sara Mollaeivaneghi;Roozbeh Abolpour;Florian Steinke
Multi-location electricity consumers (MLECs) procure electricity for loads at several locations, either from distributed energy resources (DERs) connected behind-the-meter or from the grid. If an MLEC cannot shift its demand between the locations, DERs will often be local monopolists behind the meter and have no incentive to offer the MLEC prices below the grid price. In contrast, if an MLEC can flexibly shift its demand, it may achieve lower procurement costs since the DERs in different locations now compete against each other, at least partially. By modeling a tri-level non-cooperative game between risk-averse DERs with the MLEC as a price-taker, we determine the critical level of MLECs' flexibility required to break DERs' market power. Our theoretical findings, corroborated by empirical simulations, reveal that MLECs with sufficient flexibility can significantly reduce their electricity procurement costs in decentralized energy markets by influencing DERs' pricing strategies.
{"title":"The Impact of Multi-Location Electricity Consumers' Flexibility on Distributed Energy Resources' Pricing Power","authors":"Sara Mollaeivaneghi;Roozbeh Abolpour;Florian Steinke","doi":"10.1109/TEMPR.2025.3566646","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3566646","url":null,"abstract":"Multi-location electricity consumers (MLECs) procure electricity for loads at several locations, either from distributed energy resources (DERs) connected behind-the-meter or from the grid. If an MLEC cannot shift its demand between the locations, DERs will often be local monopolists behind the meter and have no incentive to offer the MLEC prices below the grid price. In contrast, if an MLEC can flexibly shift its demand, it may achieve lower procurement costs since the DERs in different locations now compete against each other, at least partially. By modeling a tri-level non-cooperative game between risk-averse DERs with the MLEC as a price-taker, we determine the critical level of MLECs' flexibility required to break DERs' market power. Our theoretical findings, corroborated by empirical simulations, reveal that MLECs with sufficient flexibility can significantly reduce their electricity procurement costs in decentralized energy markets by influencing DERs' pricing strategies.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"251-259"},"PeriodicalIF":0.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036735","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 : 2025-03-04DOI: 10.1109/TEMPR.2025.3547375
Zhao-Cheng Chen;Chan-Nan Lu
This study introduces a new paradigm for day-ahead flexibility scheduling (DFS) aimed at unlocking the potential of distributed energy resources to enhance operational security in regions with high penetration of intermittent renewables and fluctuating load demands and supports system operators by providing operating reserves through a regional flexibility resources scheduling framework. It incorporates a next-day uncertainty model (NUM) to account for various operational uncertainties and assess flexibility costs. The DFS framework includes optimal day-ahead flexibility scheduling for maintaining regional grid security (DFS-RGS) and providing reserve ancillary services (DFS-RAS). To improve scalability for practical deployment, the NUM utilizes scenario condensation and constraint linearization techniques. Simulation results demonstrate that dispatches from DFS-RGS and DFS-RAS are robust and economically efficient, ensuring secure system operations while aggregating additional power reserves in active distribution networks.
{"title":"An Approach for Scheduling and Unlocking Heterogeneous Distributed Energy Resources as Operating Reserve Assets","authors":"Zhao-Cheng Chen;Chan-Nan Lu","doi":"10.1109/TEMPR.2025.3547375","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3547375","url":null,"abstract":"This study introduces a new paradigm for day-ahead flexibility scheduling (DFS) aimed at unlocking the potential of distributed energy resources to enhance operational security in regions with high penetration of intermittent renewables and fluctuating load demands and supports system operators by providing operating reserves through a regional flexibility resources scheduling framework. It incorporates a next-day uncertainty model (NUM) to account for various operational uncertainties and assess flexibility costs. The DFS framework includes optimal day-ahead flexibility scheduling for maintaining regional grid security (DFS-RGS) and providing reserve ancillary services (DFS-RAS). To improve scalability for practical deployment, the NUM utilizes scenario condensation and constraint linearization techniques. Simulation results demonstrate that dispatches from DFS-RGS and DFS-RAS are robust and economically efficient, ensuring secure system operations while aggregating additional power reserves in active distribution networks.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"274-286"},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036734","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 : 2025-03-02DOI: 10.1109/TEMPR.2025.3564533
Xinhe Chen;Xian Gao;Darice Guittet;Radhakrishna Tumbalam Gooty;Bernard Knueven;John D. Siirola;David C. Miller;Alexander W. Dowling
Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.
{"title":"Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems","authors":"Xinhe Chen;Xian Gao;Darice Guittet;Radhakrishna Tumbalam Gooty;Bernard Knueven;John D. Siirola;David C. Miller;Alexander W. Dowling","doi":"10.1109/TEMPR.2025.3564533","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3564533","url":null,"abstract":"Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"297-308"},"PeriodicalIF":0.0,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036876","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 : 2025-01-31DOI: 10.1109/TEMPR.2025.3537665
Joaquim Dias Garcia;Alexandre Street;Mario Veiga Pereira
Simulating long-term hydrothermal bid-based markets considering strategic agents is a challenging task. The representation of strategic agents considering intertemporal constraints within a stochastic framework brings additional complexity to the already difficult single-period bilevel, thus, non-convex, optimal bidding problem. Thus, we propose a simulation methodology that effectively addresses these challenges for large-scale hydrothermal power systems. We demonstrate the effectiveness of the framework through a case study with real data from the large-scale Brazilian power system. In the case studies, we show the effects of market concentration in power systems and how contracts can be used to mitigate them. In particular, we show how market power might affect the current setting in Brazil. The developed method can strongly benefit policymakers, market monitors, and market designers as simulations can be used to understand existing power systems and experiment with alternative designs.
{"title":"Long-Term Hydrothermal Bid-Based Market Simulator","authors":"Joaquim Dias Garcia;Alexandre Street;Mario Veiga Pereira","doi":"10.1109/TEMPR.2025.3537665","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3537665","url":null,"abstract":"Simulating long-term hydrothermal bid-based markets considering strategic agents is a challenging task. The representation of strategic agents considering intertemporal constraints within a stochastic framework brings additional complexity to the already difficult single-period bilevel, thus, non-convex, optimal bidding problem. Thus, we propose a simulation methodology that effectively addresses these challenges for large-scale hydrothermal power systems. We demonstrate the effectiveness of the framework through a case study with real data from the large-scale Brazilian power system. In the case studies, we show the effects of market concentration in power systems and how contracts can be used to mitigate them. In particular, we show how market power might affect the current setting in Brazil. The developed method can strongly benefit policymakers, market monitors, and market designers as simulations can be used to understand existing power systems and experiment with alternative designs.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 2","pages":"144-156"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281296","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 : 2025-01-31DOI: 10.1109/TEMPR.2025.3536935
Omid Mirzapour;Xinyang Rui;Mostafa Ardakani
To accommodate the increasing share of renewable energy, the transmission network needs to be upgraded. Grid-enhancing technologies (GETs) are attractive alternatives that can replace or complement grid expansion. GETs enhance grid flexibility and increase the transfer capability over the current network. A main challenge facing proliferation of GETs is the lack of proper financial incentives for their adoption and efficient operation. This paper develops a novel Financial Transmission Right (FTR) allocation mechanism to incentivize GETs installation. The model allocates maximum feasible incremental FTRs in the directions requested by investors, while keeping existing FTRs feasible. Using the concept of proxy FTRs, the investors can only obtain incremental FTRs that their investment enables through enhancing the transfer capability. The paper shows that the proposed method is revenue adequate. Two major types of GETs are presented as representative investments that are compatible with the proposed model. These include series flexible ac transmission system (FACTS) devices, such as variable-impedance FACTS and devices based on voltage-sourced converters (VSC), as well as voltage phase controllers. The proposed model is first illustrated on a 3-bus system and then implemented on the IEEE 30-bus system to show its efficiency and scalability.
{"title":"Deregulating Grid-Enhancing Technologies Through Financial Transmission Rights","authors":"Omid Mirzapour;Xinyang Rui;Mostafa Ardakani","doi":"10.1109/TEMPR.2025.3536935","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3536935","url":null,"abstract":"To accommodate the increasing share of renewable energy, the transmission network needs to be upgraded. Grid-enhancing technologies (GETs) are attractive alternatives that can replace or complement grid expansion. GETs enhance grid flexibility and increase the transfer capability over the current network. A main challenge facing proliferation of GETs is the lack of proper financial incentives for their adoption and efficient operation. This paper develops a novel Financial Transmission Right (FTR) allocation mechanism to incentivize GETs installation. The model allocates maximum feasible incremental FTRs in the directions requested by investors, while keeping existing FTRs feasible. Using the concept of proxy FTRs, the investors can only obtain incremental FTRs that their investment enables through enhancing the transfer capability. The paper shows that the proposed method is revenue adequate. Two major types of GETs are presented as representative investments that are compatible with the proposed model. These include series flexible ac transmission system (FACTS) devices, such as variable-impedance FACTS and devices based on voltage-sourced converters (VSC), as well as voltage phase controllers. The proposed model is first illustrated on a 3-bus system and then implemented on the IEEE 30-bus system to show its efficiency and scalability.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 2","pages":"182-193"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144281207","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 : 2025-01-20DOI: 10.1109/TEMPR.2025.3529689
Elina Spyrou;Qiwei Zhang;Robin B. Hytowitz;Benjamin F. Hobbs;Siddharth Tyagi;Mengmeng Cai;Michael Blonsky
The presence of variable renewable energy resources with uncertain outputs in day-ahead electricity markets results in additional balancing needs in real-time. Addressing those needs cost-effectively and reliably within a competitive market with unbundled products is challenging as both the demand for and the availability of flexibility depends on day-ahead energy schedules. Existing approaches for reserve procurement usually rely either on oversimplified demand curves that do not consider how system conditions that particular day affect the value of flexibility, or on bilateral trading of hedging instruments that are not co-optimized with day-ahead schedules. This article presents and analyzes a new product, ‘Flexibility Options’, which system operators could consider to address these two limitations. The demand for this product is endogenously determined in the day-ahead market and it is met cost-effectively by considering real-time supply curves for product providers, which are co-optimized with the energy supply. As we illustrate with numerical examples and mathematical analysis, the product addresses the hedging needs of participants with imbalances cost-effectively, provides a less intermittent revenue stream for participants with flexible outputs, promotes value-driven pricing of flexibility, and ensures that the system operator is revenue-neutral. This article provides a comprehensive design that can be further tested and applied in large-scale systems.
{"title":"Flexibility Options: A Proposed Product for Managing Imbalance Risk","authors":"Elina Spyrou;Qiwei Zhang;Robin B. Hytowitz;Benjamin F. Hobbs;Siddharth Tyagi;Mengmeng Cai;Michael Blonsky","doi":"10.1109/TEMPR.2025.3529689","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3529689","url":null,"abstract":"The presence of variable renewable energy resources with uncertain outputs in day-ahead electricity markets results in additional balancing needs in real-time. Addressing those needs cost-effectively and reliably within a competitive market with unbundled products is challenging as both the demand for and the availability of flexibility depends on day-ahead energy schedules. Existing approaches for reserve procurement usually rely either on oversimplified demand curves that do not consider how system conditions that particular day affect the value of flexibility, or on bilateral trading of hedging instruments that are not co-optimized with day-ahead schedules. This article presents and analyzes a new product, ‘Flexibility Options’, which system operators could consider to address these two limitations. The demand for this product is endogenously determined in the day-ahead market and it is met cost-effectively by considering real-time supply curves for product providers, which are co-optimized with the energy supply. As we illustrate with numerical examples and mathematical analysis, the product addresses the hedging needs of participants with imbalances cost-effectively, provides a less intermittent revenue stream for participants with flexible outputs, promotes value-driven pricing of flexibility, and ensures that the system operator is revenue-neutral. This article provides a comprehensive design that can be further tested and applied in large-scale systems.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 3","pages":"260-273"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036979","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 : 2025-01-17DOI: 10.1109/TEMPR.2025.3530266
Vladimir Dvorkin
In two-stage electricity markets, renewable power producers enter the day-ahead market with a forecast of future power generation and then reconcile any forecast deviation in the real-time market at a penalty. The choice of the forecast model is thus an important strategy decision for renewable power producers as it affects financial performance. In electricity markets with large shares of renewable generation, the choice of the forecast model impacts not only individual performance but also outcomes for other producers. In this paper, we argue for the existence of a competitive regression equilibrium in two-stage electricity markets in terms of the parameters of private forecast models informing the participation strategies of renewable power producers. In our model, renewables optimize the forecast against the day-ahead and real-time prices, thereby maximizing the average profits across the day-ahead and real-time markets. By doing so, they also implicitly enhance the temporal cost coordination of day-ahead and real-time markets. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of regression equilibrium in energy-only markets. We also devise two methods to compute regression equilibrium: centralized optimization and a decentralized ADMM-based algorithm.
{"title":"Regression Equilibrium in Electricity Markets","authors":"Vladimir Dvorkin","doi":"10.1109/TEMPR.2025.3530266","DOIUrl":"https://doi.org/10.1109/TEMPR.2025.3530266","url":null,"abstract":"In two-stage electricity markets, renewable power producers enter the day-ahead market with a forecast of future power generation and then reconcile any forecast deviation in the real-time market at a penalty. The choice of the forecast model is thus an important strategy decision for renewable power producers as it affects financial performance. In electricity markets with large shares of renewable generation, the choice of the forecast model impacts not only individual performance but also outcomes for other producers. In this paper, we argue for the existence of a competitive regression equilibrium in two-stage electricity markets in terms of the parameters of private forecast models informing the participation strategies of renewable power producers. In our model, renewables optimize the forecast against the day-ahead and real-time prices, thereby maximizing the average profits across the day-ahead and real-time markets. By doing so, they also implicitly enhance the temporal cost coordination of day-ahead and real-time markets. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of regression equilibrium in energy-only markets. We also devise two methods to compute regression equilibrium: centralized optimization and a decentralized ADMM-based algorithm.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 1","pages":"121-132"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621542","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 : 2024-12-17DOI: 10.1109/TEMPR.2024.3519796
{"title":"2024 Index IEEE Transactions on Energy Markets, Policy and Regulation Vol. 2","authors":"","doi":"10.1109/TEMPR.2024.3519796","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3519796","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 4","pages":"583-595"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10805490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844458","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 : 2024-12-16DOI: 10.1109/TEMPR.2024.3518624
Xin Lu;Jing Qiu;Yi Yang;Chenxi Zhang;Jiafeng Lin;Sihai An
Day-ahead electricity price prediction is crucial for market participants to make optimal trading decisions. The implementation of the five-minute settlement (5MS) process in the Australian National Electricity Market (NEM) on October 1, 2021, reduced the settlement interval from 30 minutes to 5 minutes. This change has led to more frequent adjustments in pricing, allowing for a more accurate reflection of real-time supply and demand conditions. However, this increased frequency has significantly heightened the complexity of price fluctuations in the wholesale market. Consequently, conventional machine learning and deep learning methods struggle to provide accurate predictions at this higher resolution. Since electricity prices are fundamentally determined by the supply-demand balance and the bidding behaviors of market participants, this work introduces individual participant's bidding behaviors into the prediction model. We fine-tune a pre-trained Large Language Model (LLM) to create bidding behavior agents, which forecasts day-ahead bidding behaviors. Moreover, market sentiment plays a significant role in electricity price volatility, yet it remains challenging to quantify and assess its impact. To address this, we employ a pre-trained LLM to analyze online resources, incorporating market sentiment into the price prediction model. Additionally, to enhance the accuracy of spike predictions, we improve the conditional time series generative adversarial network (CTSGAN) model by utilizing a spike confusion matrix and further strengthen the model by integrating bidding behavior and market sentiment as inputs. Case studies demonstrate that the proposed model significantly improves both electricity price and spike prediction accuracy, offering a robust tool for market participants to navigate the complexities of the modern electricity market.
{"title":"Large Language Model-Based Bidding Behavior Agent and Market Sentiment Agent-Assisted Electricity Price Prediction","authors":"Xin Lu;Jing Qiu;Yi Yang;Chenxi Zhang;Jiafeng Lin;Sihai An","doi":"10.1109/TEMPR.2024.3518624","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3518624","url":null,"abstract":"Day-ahead electricity price prediction is crucial for market participants to make optimal trading decisions. The implementation of the five-minute settlement (5MS) process in the Australian National Electricity Market (NEM) on October 1, 2021, reduced the settlement interval from 30 minutes to 5 minutes. This change has led to more frequent adjustments in pricing, allowing for a more accurate reflection of real-time supply and demand conditions. However, this increased frequency has significantly heightened the complexity of price fluctuations in the wholesale market. Consequently, conventional machine learning and deep learning methods struggle to provide accurate predictions at this higher resolution. Since electricity prices are fundamentally determined by the supply-demand balance and the bidding behaviors of market participants, this work introduces individual participant's bidding behaviors into the prediction model. We fine-tune a pre-trained Large Language Model (LLM) to create bidding behavior agents, which forecasts day-ahead bidding behaviors. Moreover, market sentiment plays a significant role in electricity price volatility, yet it remains challenging to quantify and assess its impact. To address this, we employ a pre-trained LLM to analyze online resources, incorporating market sentiment into the price prediction model. Additionally, to enhance the accuracy of spike predictions, we improve the conditional time series generative adversarial network (CTSGAN) model by utilizing a spike confusion matrix and further strengthen the model by integrating bidding behavior and market sentiment as inputs. Case studies demonstrate that the proposed model significantly improves both electricity price and spike prediction accuracy, offering a robust tool for market participants to navigate the complexities of the modern electricity market.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 2","pages":"223-235"},"PeriodicalIF":0.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279071","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}
Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare events. Efficiently maintaining the reliability of renewable-dominant power systems during extreme weather events requires co-optimizing system resources, while differentiating between large/rare and small/frequent deviations from forecast conditions. To address this gap in both research and practice, we propose managing the uncertainties associated with extreme weather events through an additional reserve service, termed extreme reserve. The procurement of extreme reserve is co-optimized with energy and regular reserve using a large deviation theory chance-constrained (LDT-CC) model, where LDT offers a mathematical framework to quantify the increased uncertainty during extreme events. To mitigate the high additional costs associated with reserve scheduling under the LDT-CC model, we also propose an LDT model based on weighted chance constraints (LDT-WCC). This model prepares the power system for extreme events at a lower cost, making it a less conservative alternative to the LDT-CC model. The proposed market design leads to a competitive equilibrium while ensuring cost recovery. Numerical experiments on an illustrative system and a modified 8-zone ISO New England system highlight the advantages of the proposed market design.
{"title":"Electricity Market-Clearing With Extreme Events","authors":"Tomás Tapia;Zhirui Liang;Charalambos Konstantinou;Yury Dvorkin","doi":"10.1109/TEMPR.2024.3517474","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3517474","url":null,"abstract":"Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare events. Efficiently maintaining the reliability of renewable-dominant power systems during extreme weather events requires co-optimizing system resources, while differentiating between large/rare and small/frequent deviations from forecast conditions. To address this gap in both research and practice, we propose managing the uncertainties associated with extreme weather events through an additional reserve service, termed extreme reserve. The procurement of extreme reserve is co-optimized with energy and regular reserve using a large deviation theory chance-constrained (LDT-CC) model, where LDT offers a mathematical framework to quantify the increased uncertainty during extreme events. To mitigate the high additional costs associated with reserve scheduling under the LDT-CC model, we also propose an LDT model based on weighted chance constraints (LDT-WCC). This model prepares the power system for extreme events at a lower cost, making it a less conservative alternative to the LDT-CC model. The proposed market design leads to a competitive equilibrium while ensuring cost recovery. Numerical experiments on an illustrative system and a modified 8-zone ISO New England system highlight the advantages of the proposed market design.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"3 2","pages":"194-208"},"PeriodicalIF":0.0,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279074","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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