{"title":"A fully distributed model for coordinated operation of distribution generators and electric vehicle aggregators","authors":"Mohammad Sarkhosh, Abbas Fattahi","doi":"10.1016/j.compeleceng.2025.110063","DOIUrl":null,"url":null,"abstract":"<div><div>In today's smart grid, the proliferation of distributed generators (DGs) and electric vehicles (EVs) underscores the importance of coordinating their activities. This coordination aims to leverage these resources to enhance network efficiency and mitigate the risks associated with uncoordinated actions, such as charging during peak times, which can have undesirable consequences on grid stability and reliability. To maintain the privacy of agents and lessen their computational workload, we propose the proximal-tracking distributed optimization algorithm (PTDOA) aimed at minimizing the overall operation cost by coordinating agents, including DGs and electric vehicle aggregators (EVAs). PTDOA enables agents to coordinate and optimize their operations independently. Finally, the proposed approach is evaluated using a 33-bus distribution test network containing EVAs and DGs. The results showcase that the proposed algorithm effectively maximizes agent profits while meeting demand requirements and maintaining bus voltage profiles within standard values.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110063"},"PeriodicalIF":4.0000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Electrical Engineering","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0045790625000060","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
In today's smart grid, the proliferation of distributed generators (DGs) and electric vehicles (EVs) underscores the importance of coordinating their activities. This coordination aims to leverage these resources to enhance network efficiency and mitigate the risks associated with uncoordinated actions, such as charging during peak times, which can have undesirable consequences on grid stability and reliability. To maintain the privacy of agents and lessen their computational workload, we propose the proximal-tracking distributed optimization algorithm (PTDOA) aimed at minimizing the overall operation cost by coordinating agents, including DGs and electric vehicle aggregators (EVAs). PTDOA enables agents to coordinate and optimize their operations independently. Finally, the proposed approach is evaluated using a 33-bus distribution test network containing EVAs and DGs. The results showcase that the proposed algorithm effectively maximizes agent profits while meeting demand requirements and maintaining bus voltage profiles within standard values.
期刊介绍:
The impact of computers has nowhere been more revolutionary than in electrical engineering. The design, analysis, and operation of electrical and electronic systems are now dominated by computers, a transformation that has been motivated by the natural ease of interface between computers and electrical systems, and the promise of spectacular improvements in speed and efficiency.
Published since 1973, Computers & Electrical Engineering provides rapid publication of topical research into the integration of computer technology and computational techniques with electrical and electronic systems. The journal publishes papers featuring novel implementations of computers and computational techniques in areas like signal and image processing, high-performance computing, parallel processing, and communications. Special attention will be paid to papers describing innovative architectures, algorithms, and software tools.