Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

Joao Soares, Zita Vale, Bruno Canizes, Hugo Morais

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.
Original languageEnglish
Title of host publicationproceedings of 2013 IEEE Computational Intelligence Applications in Smart Grid
PublisherIEEE Computer Society Press
Publication date2013
ISBN (Print)9781467360029
DOIs
Publication statusPublished - 2013
Event2013 CIASG: 2013 IEEE Computational Intelligence Applications in Smart Grid - Grand Copthorne Waterfront Hotel, Singapore, Singapore
Duration: 15 Jan 201319 Apr 2013

Conference

Conference2013 CIASG
LocationGrand Copthorne Waterfront Hotel
Country/TerritorySingapore
CitySingapore
Period15/01/201319/04/2013

Keywords

  • distributed power generation
  • load flow
  • Pareto optimisation
  • particle swarm optimisation
  • power generation scheduling
  • smart power grids
  • Bioengineering
  • Power, Energy and Industry Applications

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