Day-ahead resource scheduling including demand response for electric vehicles

Joao Soares, Hugo Morais, Tiago Sousa, Zita Vale, Pedro Faria

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Abstract

Summary form only given. The energy resource scheduling is becoming increasingly important, as the use of distributed resources is intensified and massive gridable vehicle (V2G) use is envisaged. This paper presents a methodology for day-ahead energy resource scheduling for smart grids considering the intensive use of distributed generation and V2G. The main focus is the comparison of different EV management approaches in the day-ahead energy resources management, namely uncontrolled charging, smart charging, V2G and Demand Response (DR) programs in the V2G approach. Three different DR programs are designed and tested (trip reduce, shifting reduce and reduce+shifting). Other important contribution of the paper is the comparison between deterministic and computational intelligence techniques to reduce the execution time. The proposed scheduling is solved with a modified particle swarm optimization. Mixed integer non-linear programming is also used for comparison purposes. Full ac power flow calculation is included to allow taking into account the network constraints. A case study with a 33-bus distribution network and 2000 V2G resources is used to illustrate the performance of the proposed method.
Original languageEnglish
Title of host publication2014 IEEE PES General Meeting : Conference & Exposition
Number of pages1
PublisherIEEE
Publication date2014
DOIs
Publication statusPublished - 2014
Event2014 IEEE PES General Meeting - National Harbor, Washington, United States
Duration: 27 Jul 201431 Jul 2014

Conference

Conference2014 IEEE PES General Meeting
LocationNational Harbor
CountryUnited States
CityWashington
Period27/07/201431/07/2014

Keywords

  • demand side management
  • distributed power generation
  • electric vehicles
  • energy management systems
  • integer programming
  • nonlinear programming
  • particle swarm optimisation
  • scheduling
  • smart power grids
  • Engineering Profession
  • Power, Energy and Industry Applications
  • 33-bus distribution network
  • computational intelligence techniques
  • day-ahead energy resource scheduling
  • day-ahead energy resources management
  • demand response
  • deterministic techniques
  • distributed generation
  • Distributed power generation
  • distributed resources
  • DR programs
  • Educational institutions
  • Electric vehicles
  • Energy resources
  • EV management approaches
  • full AC power flow calculation
  • Load management
  • massive gridable vehicle
  • mixed integer nonlinear programming
  • modified particle swarm optimization
  • network constraints
  • smart charging
  • Smart grids
  • uncontrolled charging
  • V2G approach

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