A. Berrueta, A. M. Miguel García, Í. de la Parra, P. Sanchis, A. Ursúa
{"title":"可再生能源发电厂锂离子电池能量管理算法的关键比较","authors":"A. Berrueta, A. M. Miguel García, Í. de la Parra, P. Sanchis, A. Ursúa","doi":"10.1109/EEEIC.2019.8783864","DOIUrl":null,"url":null,"abstract":"Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. A good energy management strategy is required in order to increase the profitability of an energy system using a Li-ion battery for storage. The vast number of management algorithms that has been proposed to optimize the achieved profit, with diverse computational power requirements and using models with different complexity, raise doubts about the suitability of an algorithm and the required computation power for a particular application. The performance of three energy management algorithms based on linear, quadratic, and dynamic programming are compared in this work. A realistic scenario of a medium-sized PV plant with a constraint of peak shaving is used for this comparison. The results achieved by the three algorithms are compared and the grounds of the differences are analyzed. Among the three compared algorithms, the quadratic one seems to be the most suitable for renewableenergy applications, given the undue simplification of the battery aging required by the linear algorithm and the discretization and computational power required by a dynamic algorithm.","PeriodicalId":422977,"journal":{"name":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Critical comparison of energy management algorithms for lithium-ion batteries in renewable power plants\",\"authors\":\"A. Berrueta, A. M. Miguel García, Í. de la Parra, P. Sanchis, A. Ursúa\",\"doi\":\"10.1109/EEEIC.2019.8783864\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. A good energy management strategy is required in order to increase the profitability of an energy system using a Li-ion battery for storage. The vast number of management algorithms that has been proposed to optimize the achieved profit, with diverse computational power requirements and using models with different complexity, raise doubts about the suitability of an algorithm and the required computation power for a particular application. The performance of three energy management algorithms based on linear, quadratic, and dynamic programming are compared in this work. A realistic scenario of a medium-sized PV plant with a constraint of peak shaving is used for this comparison. The results achieved by the three algorithms are compared and the grounds of the differences are analyzed. Among the three compared algorithms, the quadratic one seems to be the most suitable for renewableenergy applications, given the undue simplification of the battery aging required by the linear algorithm and the discretization and computational power required by a dynamic algorithm.\",\"PeriodicalId\":422977,\"journal\":{\"name\":\"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EEEIC.2019.8783864\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EEEIC.2019.8783864","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Critical comparison of energy management algorithms for lithium-ion batteries in renewable power plants
Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. A good energy management strategy is required in order to increase the profitability of an energy system using a Li-ion battery for storage. The vast number of management algorithms that has been proposed to optimize the achieved profit, with diverse computational power requirements and using models with different complexity, raise doubts about the suitability of an algorithm and the required computation power for a particular application. The performance of three energy management algorithms based on linear, quadratic, and dynamic programming are compared in this work. A realistic scenario of a medium-sized PV plant with a constraint of peak shaving is used for this comparison. The results achieved by the three algorithms are compared and the grounds of the differences are analyzed. Among the three compared algorithms, the quadratic one seems to be the most suitable for renewableenergy applications, given the undue simplification of the battery aging required by the linear algorithm and the discretization and computational power required by a dynamic algorithm.