{"title":"配电网络和计算动力网络协调下的电动汽车联合能源-计算管理","authors":"Weifeng Zhong;Wei Su;Xumin Huang;Jiawen Kang;Chau Yuen;Ruilong Deng;Yan Zhang;Shengli Xie","doi":"10.1109/TSG.2024.3498945","DOIUrl":null,"url":null,"abstract":"This paper explores the integration of electric vehicles (EVs) into the power distribution network (PDN) and computing power network (CPN), leveraging EVs’ inherent energy storage and computing resources. A conceptual hub called a charging and computing station (CCS) is introduced, enabling parked EVs to interact with the PDN and CPN simultaneously. The CPN is composed of the EVs and edge servers, whose computing resources are collectively utilized for processing computation tasks from various applications. The EVs and edge servers in CCSs consume energy at different nodes of the PDN. A two-stage framework is proposed for joint energy and computation management in the EV-PDN-CPN coordination. In Stage 1, a day-ahead system cost minimization problem is formulated with decisions on EV charging/discharging energy scheduling and computation task reallocation among edge servers. A fast algorithm based on the convex-concave procedure is developed to solve the Stage-1 problem whose nonconvexity stems from network constraints of both the PDN and CPN. In Stage 2, EV computing resources are utilized to achieve real-time task offloading, coping with the prediction errors of computation tasks in Stage 1 and minimizing the use of extra energy and computing resources. A linear search algorithm is proposed to solve the nonconvex Stage-2 problem. Results show that the proposed algorithms are more computationally efficient than off-the-shelf solvers, and the proposed EV-PDN-CPN coordination model can save 4.7% and 91.9% of costs in the two stages, respectively, compared to uncoordinated models.","PeriodicalId":13331,"journal":{"name":"IEEE Transactions on Smart Grid","volume":"16 2","pages":"1549-1561"},"PeriodicalIF":8.6000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint Energy-Computation Management for Electric Vehicles Under Coordination of Power Distribution Networks and Computing Power Networks\",\"authors\":\"Weifeng Zhong;Wei Su;Xumin Huang;Jiawen Kang;Chau Yuen;Ruilong Deng;Yan Zhang;Shengli Xie\",\"doi\":\"10.1109/TSG.2024.3498945\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores the integration of electric vehicles (EVs) into the power distribution network (PDN) and computing power network (CPN), leveraging EVs’ inherent energy storage and computing resources. A conceptual hub called a charging and computing station (CCS) is introduced, enabling parked EVs to interact with the PDN and CPN simultaneously. The CPN is composed of the EVs and edge servers, whose computing resources are collectively utilized for processing computation tasks from various applications. The EVs and edge servers in CCSs consume energy at different nodes of the PDN. A two-stage framework is proposed for joint energy and computation management in the EV-PDN-CPN coordination. In Stage 1, a day-ahead system cost minimization problem is formulated with decisions on EV charging/discharging energy scheduling and computation task reallocation among edge servers. A fast algorithm based on the convex-concave procedure is developed to solve the Stage-1 problem whose nonconvexity stems from network constraints of both the PDN and CPN. In Stage 2, EV computing resources are utilized to achieve real-time task offloading, coping with the prediction errors of computation tasks in Stage 1 and minimizing the use of extra energy and computing resources. A linear search algorithm is proposed to solve the nonconvex Stage-2 problem. Results show that the proposed algorithms are more computationally efficient than off-the-shelf solvers, and the proposed EV-PDN-CPN coordination model can save 4.7% and 91.9% of costs in the two stages, respectively, compared to uncoordinated models.\",\"PeriodicalId\":13331,\"journal\":{\"name\":\"IEEE Transactions on Smart Grid\",\"volume\":\"16 2\",\"pages\":\"1549-1561\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Smart Grid\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10753461/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Smart Grid","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10753461/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Joint Energy-Computation Management for Electric Vehicles Under Coordination of Power Distribution Networks and Computing Power Networks
This paper explores the integration of electric vehicles (EVs) into the power distribution network (PDN) and computing power network (CPN), leveraging EVs’ inherent energy storage and computing resources. A conceptual hub called a charging and computing station (CCS) is introduced, enabling parked EVs to interact with the PDN and CPN simultaneously. The CPN is composed of the EVs and edge servers, whose computing resources are collectively utilized for processing computation tasks from various applications. The EVs and edge servers in CCSs consume energy at different nodes of the PDN. A two-stage framework is proposed for joint energy and computation management in the EV-PDN-CPN coordination. In Stage 1, a day-ahead system cost minimization problem is formulated with decisions on EV charging/discharging energy scheduling and computation task reallocation among edge servers. A fast algorithm based on the convex-concave procedure is developed to solve the Stage-1 problem whose nonconvexity stems from network constraints of both the PDN and CPN. In Stage 2, EV computing resources are utilized to achieve real-time task offloading, coping with the prediction errors of computation tasks in Stage 1 and minimizing the use of extra energy and computing resources. A linear search algorithm is proposed to solve the nonconvex Stage-2 problem. Results show that the proposed algorithms are more computationally efficient than off-the-shelf solvers, and the proposed EV-PDN-CPN coordination model can save 4.7% and 91.9% of costs in the two stages, respectively, compared to uncoordinated models.
期刊介绍:
The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.