Pub Date : 2024-09-12DOI: 10.1109/TEMPR.2024.3448673
{"title":"IEEE Power & Energy Society Information","authors":"","doi":"10.1109/TEMPR.2024.3448673","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3448673","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10679534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142174026","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 : 2024-09-12DOI: 10.1109/TEMPR.2024.3448669
{"title":"IEEE Transactions on Energy Markets, Policy, and Regulation Information for Authors","authors":"","doi":"10.1109/TEMPR.2024.3448669","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3448669","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10679535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142172633","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 : 2024-09-12DOI: 10.1109/TEMPR.2024.3448671
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Pub Date : 2024-06-13DOI: 10.1109/TEMPR.2024.3404693
{"title":"IEEE Power & Energy Society Information","authors":"","doi":"10.1109/TEMPR.2024.3404693","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3404693","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319607","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 : 2024-06-13DOI: 10.1109/TEMPR.2024.3404689
{"title":"IEEE Transactions on Energy Markets, Policy, and Regulation Information for Authors","authors":"","doi":"10.1109/TEMPR.2024.3404689","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3404689","url":null,"abstract":"","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319681","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 : 2024-06-13DOI: 10.1109/TEMPR.2024.3404695
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Pub Date : 2024-04-10DOI: 10.1109/TEMPR.2024.3387270
Jens Hönen;Sjoerd C. Doumen;Phuong Nguyen;Johann L. Hurink;Bert Zwart;Koen Kok
Local electricity markets (LEMs) have progressed significantly in recent years, but a research gap exists in understanding the influence of human preferences on the effectiveness of LEMs when home energy management systems (HEMSs) are involved. Motivated by this, this work aims to model and integrate human preferences into a HEMS, bridging the gap between end-participant and LEM. A sensitivity analysis of the parameter choices of the HEMS and their impact on the performance and outcomes of a LEM is done. Hereby, a behavior model is used to formulate the preferences and motives of households within a LEM in a bottom-up approach. Various distributed energy resources are modeled and controlled via a HEMS, allowing households to input their preferences and motives to output a tailor-made bidcurve for the LEM. A sensitivity analysis reveals that different preference settings result in different consumption profiles, which to a large extent align with the preferences. In addition, the importance of aligning market mechanisms and steering signals with the participants' goals is highlighted.
{"title":"Modeling and Analyzing the Effect of Human Preferences on a Local Electricity Market","authors":"Jens Hönen;Sjoerd C. Doumen;Phuong Nguyen;Johann L. Hurink;Bert Zwart;Koen Kok","doi":"10.1109/TEMPR.2024.3387270","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3387270","url":null,"abstract":"Local electricity markets (LEMs) have progressed significantly in recent years, but a research gap exists in understanding the influence of human preferences on the effectiveness of LEMs when home energy management systems (HEMSs) are involved. Motivated by this, this work aims to model and integrate human preferences into a HEMS, bridging the gap between end-participant and LEM. A sensitivity analysis of the parameter choices of the HEMS and their impact on the performance and outcomes of a LEM is done. Hereby, a behavior model is used to formulate the preferences and motives of households within a LEM in a bottom-up approach. Various distributed energy resources are modeled and controlled via a HEMS, allowing households to input their preferences and motives to output a tailor-made bidcurve for the LEM. A sensitivity analysis reveals that different preference settings result in different consumption profiles, which to a large extent align with the preferences. In addition, the importance of aligning market mechanisms and steering signals with the participants' goals is highlighted.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"265-275"},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319608","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 : 2024-04-08DOI: 10.1109/TEMPR.2024.3386127
Hanning Mi;Qingxin Li;Ming Shi;Sijie Chen;Yutong Li;Yiyan Li;Zheng Yan
Concept drift means the statistical properties of the variable that a predictor is predicting change over time in unforeseen ways. Existing research solves concept drift in the locational marginal price (LMP) prediction process by updating predictors in online approaches. However, new data is indiscriminately utilized to update predictors in these methods. The new property changes can not be accurately captured when concept drift occurs. This paper proposes a stacking framework for online LMP prediction considering the concept drift phenomenon. Long short-term memory networks and graph attention networks are selected as the base predictors to capture the spatio-temporal dependencies in LMPs. When concept drift occurs, data with drift selected by the adaptive windowing algorithm is used to update the stacked predictor. Numerical results based on real data from Australian Energy Market Operator and Midcontinent Independent System Operator validate the effectiveness of the proposed framework. The comparative experiments prove that attempts to change or simplify the proposed framework can undermine prediction accuracy.
{"title":"A Stacking Framework for Online Locational Marginal Price Prediction Considering Concept Drift","authors":"Hanning Mi;Qingxin Li;Ming Shi;Sijie Chen;Yutong Li;Yiyan Li;Zheng Yan","doi":"10.1109/TEMPR.2024.3386127","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3386127","url":null,"abstract":"Concept drift means the statistical properties of the variable that a predictor is predicting change over time in unforeseen ways. Existing research solves concept drift in the locational marginal price (LMP) prediction process by updating predictors in online approaches. However, new data is indiscriminately utilized to update predictors in these methods. The new property changes can not be accurately captured when concept drift occurs. This paper proposes a stacking framework for online LMP prediction considering the concept drift phenomenon. Long short-term memory networks and graph attention networks are selected as the base predictors to capture the spatio-temporal dependencies in LMPs. When concept drift occurs, data with drift selected by the adaptive windowing algorithm is used to update the stacked predictor. Numerical results based on real data from Australian Energy Market Operator and Midcontinent Independent System Operator validate the effectiveness of the proposed framework. The comparative experiments prove that attempts to change or simplify the proposed framework can undermine prediction accuracy.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"254-264"},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319611","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 : 2024-04-03DOI: 10.1109/TEMPR.2024.3384833
Shilpa Bindal;Abhijit R. Abhyankar
Optimal scheduling of smart inverter-assisted distributed energy resources (DERs) benefits power system operation and planning entities. The comprehensive evaluation and fair allocation of these co-existing benefits should be the prime criteria for designing a compensation scheme. This work focuses on classifying, quantifying, and allocating multiple benefits extracted through phase-specific optimal power injection in distribution networks. This paper proposes a fair and self-sufficient compensation scheme that stacks uniquely assigned benefit components among the DERs. Using a rectangular branch-current component-based approach, this paper proposes a method to quantify the cooperative optimal power dispatch benefits, broadly classified as capacity deferral and operational benefits. In addition, this work employs the unitary participation-based Aumann Shapley value method to allocate the joint-coalitional benefits fairly among various DERs. By implementing the proposed scheme on the IEEE European distribution network for a long time horizon, various attributes of the proposed scheme are analyzed. The proposed compensation scheme provides the distribution system operator with a financial tool to stimulate DERs' coordination in the optimal power delivery action.
智能逆变器辅助分布式能源资源（DER）的优化调度有利于电力系统的运行和规划。全面评估和公平分配这些并存效益应成为设计补偿方案的首要标准。这项工作的重点是对配电网中通过特定相位优化功率注入获得的多重效益进行分类、量化和分配。本文提出了一种公平、自给自足的补偿方案，在 DER 之间堆叠唯一分配的收益成分。本文采用基于矩形分支电流分量的方法，提出了一种量化合作优化电力调度效益的方法，大致分为容量递延效益和运行效益。此外，本文还采用了基于单元参与的 Aumann Shapley 值方法，在各种 DER 之间公平分配联合-联盟效益。通过在 IEEE 欧洲配电网络上长期实施拟议方案，分析了拟议方案的各种属性。建议的补偿方案为配电系统运营商提供了一种金融工具，以激励 DERs 在最优电力输送行动中进行协调。
{"title":"A Fair and Self-Sufficient Value Stack-Based Compensation Scheme for DERs Enhancing Network Performance","authors":"Shilpa Bindal;Abhijit R. Abhyankar","doi":"10.1109/TEMPR.2024.3384833","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3384833","url":null,"abstract":"Optimal scheduling of smart inverter-assisted distributed energy resources (DERs) benefits power system operation and planning entities. The comprehensive evaluation and fair allocation of these co-existing benefits should be the prime criteria for designing a compensation scheme. This work focuses on classifying, quantifying, and allocating multiple benefits extracted through phase-specific optimal power injection in distribution networks. This paper proposes a fair and self-sufficient compensation scheme that stacks uniquely assigned benefit components among the DERs. Using a rectangular branch-current component-based approach, this paper proposes a method to quantify the cooperative optimal power dispatch benefits, broadly classified as capacity deferral and operational benefits. In addition, this work employs the unitary participation-based Aumann Shapley value method to allocate the joint-coalitional benefits fairly among various DERs. By implementing the proposed scheme on the IEEE European distribution network for a long time horizon, various attributes of the proposed scheme are analyzed. The proposed compensation scheme provides the distribution system operator with a financial tool to stimulate DERs' coordination in the optimal power delivery action.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 3","pages":"423-435"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173943","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 : 2024-04-01DOI: 10.1109/TEMPR.2024.3383369
Arnav Gautam;Destenie Nock;Amritanshu Pandey
Sustained power outages are growing in scale and number primarily due to i) the increasing number and intensity of disasters and ii) decarbonization- and electrification-related grid changes. Outage mitigation technologies (e.g., backup diesel generators, and solar panels) increasingly provide vital electricity access during disasters. However, their adoption is inequitable due to individual- or community-level barriers and historic underinvestment in certain communities. We postulate that community-based Resilience Hubs (RHs), which are being increasingly deployed to provide on-site services during disasters, can be expanded to address this inequity by supplying backup power to vulnerable communities through islanded operations. To that end, we present Grid-Aware Tradeoff Analysis (GATA) framework to identify the best backup power systems for expanded RHs. To include technical, economic, and social facets in the framework, we will use three-phase power flow (TPF) and multi-criteria decision analysis (MCDA). TPF will enforce the electrical feasibility of islanded RH operation, and MCDA will quantify the economic, environmental, and equity-weighted outage mitigation performance. As a use case for GATA, we will evaluate multiple representative RHs in Richmond, California, and highlight the non-dominated systems for the electrically feasible RHs. We show the value of GATA's detailed grid simulation, its ability to quantify tradeoffs across scenarios, and its possible extensions.
{"title":"Grid-Aware Tradeoff Analysis for Outage Mitigation Microgrids at Emerging Resilience Hubs","authors":"Arnav Gautam;Destenie Nock;Amritanshu Pandey","doi":"10.1109/TEMPR.2024.3383369","DOIUrl":"https://doi.org/10.1109/TEMPR.2024.3383369","url":null,"abstract":"Sustained power outages are growing in scale and number primarily due to i) the increasing number and intensity of disasters and ii) decarbonization- and electrification-related grid changes. Outage mitigation technologies (e.g., backup diesel generators, and solar panels) increasingly provide vital electricity access during disasters. However, their adoption is inequitable due to individual- or community-level barriers and historic underinvestment in certain communities. We postulate that community-based Resilience Hubs (RHs), which are being increasingly deployed to provide on-site services during disasters, can be expanded to address this inequity by supplying backup power to vulnerable communities through islanded operations. To that end, we present Grid-Aware Tradeoff Analysis (GATA) framework to identify the best backup power systems for expanded RHs. To include technical, economic, and social facets in the framework, we will use three-phase power flow (TPF) and multi-criteria decision analysis (MCDA). TPF will enforce the electrical feasibility of islanded RH operation, and MCDA will quantify the economic, environmental, and equity-weighted outage mitigation performance. As a use case for GATA, we will evaluate multiple representative RHs in Richmond, California, and highlight the non-dominated systems for the electrically feasible RHs. We show the value of GATA's detailed grid simulation, its ability to quantify tradeoffs across scenarios, and its possible extensions.","PeriodicalId":100639,"journal":{"name":"IEEE Transactions on Energy Markets, Policy and Regulation","volume":"2 2","pages":"186-199"},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319609","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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