{"title":"Optimal management of coupled shared autonomous electric vehicles and power grids: Potential of renewable energy integration","authors":"Xianyi Yang , Adam Abdin , Jakob Puchinger","doi":"10.1016/j.trc.2024.104726","DOIUrl":null,"url":null,"abstract":"<div><p>Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X2400247X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems.
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.