Probabilistic Optimal Bi-level Scheduling of a Multi-Microgrid System with Electric Vehicles
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ORIGINAL ARTICLE
Probabilistic Optimal Bi‑level Scheduling of a Multi‑Microgrid System with Electric Vehicles Mohammad Mirzaei1 · Reza Keypour1 · Mehdi Savaghebi2 · Keyvan Golalipour3 Received: 6 December 2019 / Revised: 21 March 2020 / Accepted: 3 August 2020 © The Korean Institute of Electrical Engineers 2020
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. Keywords Bi-level stochastic programming · Plug-in electric vehicles · Multi-microgrid system · Adaptive particle swarm optimization · Optimal scheduling · Demand response Abbreviations Acronyms APSO Adaptive particle swarm optimization CEMS Central energy management system DR Demand response DRP Demand response program EMS Energy management system ELS Elitist learning strategy EV Electric vehicle * Keyvan Golalipour [email protected] Mohammad Mirzaei [email protected] Reza Keypour [email protected] Mehdi Savaghebi [email protected] 1
Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
2
Electrical Engineering Section, Mads Clausen Institute, University of Southern Denmark, Odense, Denmark
3
Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
ICA Imperialist competitive algorithm FC Fuel cell MG Microgrid MMG Multi-microgrid MT Microturbine O&M Operation and maintenance PDF Probability density function PV Photovoltaics SoC State of charge V2G Vehicle to grid WT Wind turbine Indices/Sets i, j Index for particles in PSO d Index for dimensions in PSO k Index for EV number m, n Index for MG s Index for generation sources t, ta, tb Index for time Parameters B Price bid of system components Bold, Bnew Electricity price before/after DRP c1, c2 Acceleration coefficients in PSO C Cost of system components CEV Cost of V2G program of EVs
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CO&M Cost of O&M of microsources disi Mean distance of particle i in APSO EV Energy of EV Etrip Energy consumed by EV in the trips EiniV, EfinV Initial/final Energy of EV at the beginning and end of the day E(ta, tb) Self (ta = tb) or cross elasticity f
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