{"title":"基于云边缘协同的电动汽车充放电优化调度策略","authors":"Jing Zhang, Q. Jiang, Aiqiang Pan, Taoyong Li, Zhe Liu, Yuanxing Zhang, Linru Jiang, Xiangpeng Zhan","doi":"10.1109/AEEES51875.2021.9403163","DOIUrl":null,"url":null,"abstract":"This paper proposes a decentralized scheduling method for electric vehicles charge and discharge management based on cloud-edge collaboration so as to protect users' privacy. Firstly, as a cloud computing center, distribution system operator solves an optimal power flow model based on second-order cone programming in order to minimize power costs. Secondly, as an edge computing unit, charging station solves an energy management model based on mixed-integer linear programming in order to track scheduling instructions of the distribution system operator. Finally, charging stations return benders cut constraints to distribution system operator to revise energy plan. And the scheduling instructions are updated iteratively to ensure the feasibility and optimality of the energy plan. The simulation is carried out in IEEE 33-bus test system. And the results show that the proposed cloud-edge collaborative strategy can reduce memory use, protect users' privacy as well as reducing power costs.","PeriodicalId":356667,"journal":{"name":"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"An Optimal Dispatching Strategy for Charging and Discharging of Electric Vehicles Based on Cloud-Edge Collaboration\",\"authors\":\"Jing Zhang, Q. Jiang, Aiqiang Pan, Taoyong Li, Zhe Liu, Yuanxing Zhang, Linru Jiang, Xiangpeng Zhan\",\"doi\":\"10.1109/AEEES51875.2021.9403163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a decentralized scheduling method for electric vehicles charge and discharge management based on cloud-edge collaboration so as to protect users' privacy. Firstly, as a cloud computing center, distribution system operator solves an optimal power flow model based on second-order cone programming in order to minimize power costs. Secondly, as an edge computing unit, charging station solves an energy management model based on mixed-integer linear programming in order to track scheduling instructions of the distribution system operator. Finally, charging stations return benders cut constraints to distribution system operator to revise energy plan. And the scheduling instructions are updated iteratively to ensure the feasibility and optimality of the energy plan. The simulation is carried out in IEEE 33-bus test system. And the results show that the proposed cloud-edge collaborative strategy can reduce memory use, protect users' privacy as well as reducing power costs.\",\"PeriodicalId\":356667,\"journal\":{\"name\":\"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-03-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEEES51875.2021.9403163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 3rd Asia Energy and Electrical Engineering Symposium (AEEES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEEES51875.2021.9403163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Optimal Dispatching Strategy for Charging and Discharging of Electric Vehicles Based on Cloud-Edge Collaboration
This paper proposes a decentralized scheduling method for electric vehicles charge and discharge management based on cloud-edge collaboration so as to protect users' privacy. Firstly, as a cloud computing center, distribution system operator solves an optimal power flow model based on second-order cone programming in order to minimize power costs. Secondly, as an edge computing unit, charging station solves an energy management model based on mixed-integer linear programming in order to track scheduling instructions of the distribution system operator. Finally, charging stations return benders cut constraints to distribution system operator to revise energy plan. And the scheduling instructions are updated iteratively to ensure the feasibility and optimality of the energy plan. The simulation is carried out in IEEE 33-bus test system. And the results show that the proposed cloud-edge collaborative strategy can reduce memory use, protect users' privacy as well as reducing power costs.