Probabilistic Optimal Bi-level Scheduling of a Multi-Microgrid System with Electric Vehicles

Mohammad Mirzaei, Reza Keypour, Mehdi Savaghebi, Keyvan Golalipour*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

In this paper, an efficient energy management system (EMS) is proposed for optimal operation of multiple electrically coupled microgrids (MGs). A new bi-level EMS is employed as an enhanced technique to coordinate vehicle-to-grid (V2G) operation of electric vehicles (EVs) with a stochastic framework in a multi-microgrid system. Hierarchical EMS helps the system to preserve the privacy of each MG. The EV scheduling and demand response programs have been integrated simultaneously in the optimization strategy to reduce the load demand of the peak hours and reshape the load profile. Uncertainties related to the system load demand, renewable generations, EV fleet behavior and energy price are considered. The proposed stochastic system is solved by adaptive particle swarm optimization algorithm. Numerical studies on a two electrically coupled industrial and residential MGs test system verify the efficiency of proposed EMS for cost reduction of the system and optimal operation of V2G.

Original languageEnglish
JournalJournal of Electrical Engineering & Technology
Volume15
Issue number6
Pages (from-to)2421-2436
ISSN1975-0102
DOIs
Publication statusPublished - Nov 2020

Keywords

  • Adaptive particle swarm optimization
  • Bi-level stochastic programming
  • Demand response
  • Multi-microgrid system
  • Optimal scheduling
  • Plug-in electric vehicles

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