{"title":"移动储能路径和车到网的时空聚类方法","authors":"Xinjiang Chen , Jiayang Yao , Guannan He","doi":"10.1016/j.etran.2025.100478","DOIUrl":null,"url":null,"abstract":"<div><div>Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes encompasses high-dimensional spatiotemporal decision variables, making it challenging for general commercial solvers to solve efficiently. To address this challenge, we develop an improved time–space network-based model that uses feasible spatiotemporal arcs to represent the routing schemes for MES throughout the entire scheduling period. Furthermore, we propose an adaptive spatiotemporal clustering algorithm based on time–space network aggregation-split to solve the model quickly. In the aggregation phase, given the lower bound of cluster quantities, nodes with closely related spatiotemporal distances are clustered into one representative node. During the split phase, we design a spatiotemporal envelope method to identify nodes with potential arbitrage opportunities in each cluster and classify them into a separate cluster. We apply the proposed model and algorithm to the energy arbitrage of MES within the California power grid. The results reveal that, compared to the commercial solver, the proposed algorithm significantly reduces the average time overhead by 92.7%, while only sacrificing 0.9% in optimality in more than 300 daily scheduling cases.</div></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":"26 ","pages":"Article 100478"},"PeriodicalIF":17.0000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A spatiotemporal clustering method for mobile energy storage routing and vehicle-to-grid\",\"authors\":\"Xinjiang Chen , Jiayang Yao , Guannan He\",\"doi\":\"10.1016/j.etran.2025.100478\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes encompasses high-dimensional spatiotemporal decision variables, making it challenging for general commercial solvers to solve efficiently. To address this challenge, we develop an improved time–space network-based model that uses feasible spatiotemporal arcs to represent the routing schemes for MES throughout the entire scheduling period. Furthermore, we propose an adaptive spatiotemporal clustering algorithm based on time–space network aggregation-split to solve the model quickly. In the aggregation phase, given the lower bound of cluster quantities, nodes with closely related spatiotemporal distances are clustered into one representative node. During the split phase, we design a spatiotemporal envelope method to identify nodes with potential arbitrage opportunities in each cluster and classify them into a separate cluster. We apply the proposed model and algorithm to the energy arbitrage of MES within the California power grid. The results reveal that, compared to the commercial solver, the proposed algorithm significantly reduces the average time overhead by 92.7%, while only sacrificing 0.9% in optimality in more than 300 daily scheduling cases.</div></div>\",\"PeriodicalId\":36355,\"journal\":{\"name\":\"Etransportation\",\"volume\":\"26 \",\"pages\":\"Article 100478\"},\"PeriodicalIF\":17.0000,\"publicationDate\":\"2025-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Etransportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590116825000852\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116825000852","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
A spatiotemporal clustering method for mobile energy storage routing and vehicle-to-grid
Mobile Energy Storage (MES) has proven effective in integrating renewable energy and alleviating grid congestion due to its flexible deployment. However, in MES routing and Vehicle-to-Grid applications (such as energy arbitrage), the large-scale routing problem involving multiple vehicles and nodes encompasses high-dimensional spatiotemporal decision variables, making it challenging for general commercial solvers to solve efficiently. To address this challenge, we develop an improved time–space network-based model that uses feasible spatiotemporal arcs to represent the routing schemes for MES throughout the entire scheduling period. Furthermore, we propose an adaptive spatiotemporal clustering algorithm based on time–space network aggregation-split to solve the model quickly. In the aggregation phase, given the lower bound of cluster quantities, nodes with closely related spatiotemporal distances are clustered into one representative node. During the split phase, we design a spatiotemporal envelope method to identify nodes with potential arbitrage opportunities in each cluster and classify them into a separate cluster. We apply the proposed model and algorithm to the energy arbitrage of MES within the California power grid. The results reveal that, compared to the commercial solver, the proposed algorithm significantly reduces the average time overhead by 92.7%, while only sacrificing 0.9% in optimality in more than 300 daily scheduling cases.
期刊介绍:
eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation.
The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment.
Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.