Tao Xu, H. Hou, Qingyong Zhang, Jianjian Wang, Peng Liu, A. Tang
{"title":"Generation and Load Integrated Optimal Scheduling Incorporating Distributed Energy Storage and Adjustable Load","authors":"Tao Xu, H. Hou, Qingyong Zhang, Jianjian Wang, Peng Liu, A. Tang","doi":"10.1109/INDIN45582.2020.9442106","DOIUrl":null,"url":null,"abstract":"The rapid development of renewable energy and adjustable load has brought challenges to the safety and economic operation of power system. In this paper, we propose a generation and load integrated optimal scheduling strategy. The power generation side considers the wind-photovoltaic hybrid power system with battery energy storage system. The user side considers electric vehicles and the adjustable load such as transferable load and interruptible load to participate in scheduling. A scheduling strategy model is established to optimize both the benefits of power generation side and the user side. The multi-objective particle swarm optimization algorithm is used to solve the model. Simulation results based on historical data of a particular region (105.0° E, 35.40° N) show the feasibility of the proposed optimal scheduling strategy.","PeriodicalId":185948,"journal":{"name":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","volume":"79 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 18th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN45582.2020.9442106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid development of renewable energy and adjustable load has brought challenges to the safety and economic operation of power system. In this paper, we propose a generation and load integrated optimal scheduling strategy. The power generation side considers the wind-photovoltaic hybrid power system with battery energy storage system. The user side considers electric vehicles and the adjustable load such as transferable load and interruptible load to participate in scheduling. A scheduling strategy model is established to optimize both the benefits of power generation side and the user side. The multi-objective particle swarm optimization algorithm is used to solve the model. Simulation results based on historical data of a particular region (105.0° E, 35.40° N) show the feasibility of the proposed optimal scheduling strategy.