Security energy management of microgrids including PHEV subject to high penetration of renewable energy sources

• Published in: Expert systems with applications Vol. 268; p. 126207
• Main Authors: Li, Guangming, Li, Bing, Zuo, Congrui, Mao, Liuming, Huang, Bo, Wang, Xuantao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.04.2025
• Subjects: Distributed generation integration; Hybrid microgrid optimization; Plug-in electric vehicles; Proton exchange membrane fuel cell; Stochastic model for energy optimization; PEV; RES; DG; MILP; RCU; Stochastic model for energy optimization; GHG; CHP; CDR; DFR; PHH; UG; SPV; Hybrid microgrid optimization; Proton exchange membrane fuel cell; MG; Distributed generation integration; WT; EC; HMG; MT; EM; EMS; DAG; ET; Plug-in electric vehicles; DER; EV; PEMFC; DoD; C/D; HA; TOU; BESS; FC
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.126207

This research optimizes the operability of a reconfigurable hybrid microgrid (HMG) with significant integration of distributed generation (DG) units and plug-in hybrid vehicles (PHVs). The study aims to minimize the overall operating costs, considering DG unit operation, grid power transactions, startup/shutdown costs, reconfiguration switching costs, and plug-in electric vehicle (PEV) costs. Key contributions include integrating a proton exchange membrane fuel cell (PEMFC) plant with high thermal energy recovery capabilities to form a combined heat and power (CHP) system for meeting thermal load demands. Additionally, the model leverages the vehicle-to-grid (V2G) mode for PEVs to enhance economic efficiency. The model addresses uncertainties in solar photovoltaic (SPV) and wind turbine (WT) generation, power exchange prices, fuel prices, PEMFC operating temperature, hydrogen selling prices, and O2 and H2 pressure in the FC units. A multi-layer stochastic model with probability density functions (PDFs) handles these uncertainties, while a novel nature-inspired horse optimization algorithm (HOA) efficiently explores the solution space and proposes optimal set points. Battery degradation costs are incorporated to enhance battery lifetime and state of health while avoiding deep discharges. The proposed model is implemented on a blockchain-based smart platform using the directed acyclic graph (DAG) method for secure data flow and energy transactions between and within microgrids (MGs). Extensive numerical analysis demonstrates the model’s effectiveness in improving the economic and operational efficiency of hybrid AC-DC microgrids, integrating fuel cells, reconfiguration strategies, electric vehicles, and robust security measures.