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  • 标题:Multi-objective Particle Swarm Optimization to Solve Energy Scheduling with Vehicle-to-Grid in Office Buildings Considering Uncertainties
  • 本地全文:下载
  • 作者:Nuno Borges ; João Soares ; Zita Vale
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2017
  • 卷号:50
  • 期号:1
  • 页码:3356-3361
  • DOI:10.1016/j.ifacol.2017.08.523
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis paper presents a Multi-Objective Particle Swarm Optimization (MOPSO) methodology to solve the problem of energy resource management in buildings with a penetration of Distributed Generation (DG) and Electric Vehicles (EVs). The proposed methodology consists in a multi-objective function, in which it is intended to maximize the profit and minimize CO2emissions. This methodology considers the uncertainties associated with the production of electricity by the photovoltaic and wind energy sources. This uncertainty is modeled with the use of a robust optimization approach in the metaheuristic. A case study is presented using a real building facility from Portugal, in order to verify the feasibility of the implemented robust MOPSO.
  • 关键词:KeywordsElectric VehiclesEnergy Resources ManagementMulti-Objective OptimizationRobust OptimizationUncertainty
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