Pub Date : 2020-12-14DOI: 10.1002/9781119534938.ch7
W. Jiang
{"title":"Distributed State Estimation","authors":"W. Jiang","doi":"10.1002/9781119534938.ch7","DOIUrl":"https://doi.org/10.1002/9781119534938.ch7","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122475818","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 : 2020-12-14DOI: 10.1002/9781119534938.ch9
Melissa M. Gibbons, W. Klug
{"title":"Discussion and Future Work","authors":"Melissa M. Gibbons, W. Klug","doi":"10.1002/9781119534938.ch9","DOIUrl":"https://doi.org/10.1002/9781119534938.ch9","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134210815","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 : 2020-12-14DOI: 10.1002/9781119534938.ch8
{"title":"Hardware‐Based Algorithms Evaluation","authors":"","doi":"10.1002/9781119534938.ch8","DOIUrl":"https://doi.org/10.1002/9781119534938.ch8","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122636846","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 : 2020-12-14DOI: 10.1002/9781119534938.index
{"title":"Index","authors":"","doi":"10.1002/9781119534938.index","DOIUrl":"https://doi.org/10.1002/9781119534938.index","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"47 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123804708","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 : 2020-12-14DOI: 10.1002/9781119534938.ch6
{"title":"Distributed Social Welfare Optimization","authors":"","doi":"10.1002/9781119534938.ch6","DOIUrl":"https://doi.org/10.1002/9781119534938.ch6","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127892423","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 : 2020-12-14DOI: 10.1002/9781119534938.ch5
{"title":"Distributed Demand‐Side Management","authors":"","doi":"10.1002/9781119534938.ch5","DOIUrl":"https://doi.org/10.1002/9781119534938.ch5","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129831271","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 : 2016-10-31DOI: 10.1002/9781119083016.oth
Hong Chen
{"title":"IEEE Press Series on Power Engineering","authors":"Hong Chen","doi":"10.1002/9781119083016.oth","DOIUrl":"https://doi.org/10.1002/9781119083016.oth","url":null,"abstract":"","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123814216","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 : 1900-01-01DOI: 10.1002/9781119534938.ch3
Yinliang Xu, Wei Zhang, Wenxin Liu, Wenbin Yu
This chapter discusses three distributed control methods/solutions for active power control, where the applications of these solutions differ depending on the control targets, control objectives, and available resources. The first control solution introduced is subgradient‐based active power sharing, which aims at maintaining the supply‐demand balance in a microgrid. The second control method is used for the economic dispatch (ED) of the smart grids, which is not limited to the microgrid. The last control solution aims at integrating the ED with the frequency control. Focusing on the self‐contained, medium voltage, and small‐scale microgrid, which consists of multiple renewable energy sources, the chapter aims to maintain the supply‐demand balance of active and reactive power and regulate the voltage magnitude and system frequency to the desired values. A multi‐agent system‐based fully distributed control approach for renewable generators’ controller consisting of two control levels is proposed.
{"title":"Distributed Active Power Control","authors":"Yinliang Xu, Wei Zhang, Wenxin Liu, Wenbin Yu","doi":"10.1002/9781119534938.ch3","DOIUrl":"https://doi.org/10.1002/9781119534938.ch3","url":null,"abstract":"This chapter discusses three distributed control methods/solutions for active power control, where the applications of these solutions differ depending on the control targets, control objectives, and available resources. The first control solution introduced is subgradient‐based active power sharing, which aims at maintaining the supply‐demand balance in a microgrid. The second control method is used for the economic dispatch (ED) of the smart grids, which is not limited to the microgrid. The last control solution aims at integrating the ED with the frequency control. Focusing on the self‐contained, medium voltage, and small‐scale microgrid, which consists of multiple renewable energy sources, the chapter aims to maintain the supply‐demand balance of active and reactive power and regulate the voltage magnitude and system frequency to the desired values. A multi‐agent system‐based fully distributed control approach for renewable generators’ controller consisting of two control levels is proposed.","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"11 18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358979","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 : 1900-01-01DOI: 10.1002/9781119534938.ch4
Yinliang Xu, Wei Zhang, Wenxin Liu, Wenbin Yu
This chapter discusses two types of the reactive power control methods. The first method is based on the well‐known artificial intelligence algorithm, Q‐learning algorithm. The second method is based on the distributed sub‐gradient algorithm. In reinforcement learning method, two agents exchange information with each other only when their own buses are electrically coupled. In order to reach the goal of minimizing the active power loss and satisfy operational constraints at the same time, a distributed Q‐learning algorithm is implemented. Q‐learning algorithm circumvents the dilemma to analyze complicated power system models. Multi‐agent system‐based distributed solution for optimal reactive power control, which is based on a distributed sub‐gradient algorithm, is appropriate for distributed computing.
{"title":"Distributed Reactive Power Control","authors":"Yinliang Xu, Wei Zhang, Wenxin Liu, Wenbin Yu","doi":"10.1002/9781119534938.ch4","DOIUrl":"https://doi.org/10.1002/9781119534938.ch4","url":null,"abstract":"This chapter discusses two types of the reactive power control methods. The first method is based on the well‐known artificial intelligence algorithm, Q‐learning algorithm. The second method is based on the distributed sub‐gradient algorithm. In reinforcement learning method, two agents exchange information with each other only when their own buses are electrically coupled. In order to reach the goal of minimizing the active power loss and satisfy operational constraints at the same time, a distributed Q‐learning algorithm is implemented. Q‐learning algorithm circumvents the dilemma to analyze complicated power system models. Multi‐agent system‐based distributed solution for optimal reactive power control, which is based on a distributed sub‐gradient algorithm, is appropriate for distributed computing.","PeriodicalId":110907,"journal":{"name":"Distributed Energy Management of Electrical Power Systems","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123397867","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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