Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Hossein Jokar, Taher Niknam, Moslem Dehghani, Ehsan Sheybani (), Motahareh Pourbehzadi and Giti Javidi
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Hossein Jokar: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
Taher Niknam: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
Moslem Dehghani: Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran
Ehsan Sheybani: School of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USA
Motahareh Pourbehzadi: School of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USA
Giti Javidi: School of Information Systems and Management, Muma College of Business, University of South Florida, Tampa, FL 33620, USA

Energies, 2023, vol. 17, issue 1, 1-23

Abstract: Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

Keywords: microgrid; energy management; plug-in hybrid electric vehicles; hydrogen storage; meerkat optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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