Multi-Objective Optimization of a Hybrid Energy Storage System for Community Loads Using a Decomposition-Based Evolutionary Algorithm

Abstract

The inherent intermittency of renewable energy sources and the mismatch between generation and community load demand pose significant challenges to the reliability of microgrids. To address these issues, this paper proposes a robust multi-objective optimization framework for the capacity sizing of a Hybrid Renewable Energy System (HRES) integrating Wind, Photovoltaic (PV), Battery Energy Storage System (BESS), and a Power-to-Heat coupling subsystem. A rule-based Energy Management Strategy (EMS) is developed to coordinate the electrical and thermal power flows, prioritizing renewable utilization and storage efficiency. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is employed to solve the sizing problem, simultaneously minimizing the Net Present Cost (NPC) and the Loss of Energy Ratio (LER). A case study based on real-world hourly meteorological data and a large-scale community load profile demonstrates the proposed approach. The simulation results demonstrate that the MOEA/D algorithm effectively identifies a set of Pareto-optimal solutions, revealing the trade-off between economic investment and system reliability. The optimal compromise solution suggests a PV-dominated configuration (924 MW) supported by a hybrid storage system comprising 2478 MWh of battery and 585 MWh of thermal storage. This configuration achieves a high reliability with an LER of 1.92% and a lifecycle cost of $2.40 billion, proving the techno-economic feasibility of the proposed electric-thermal coupling scheme for community energy supply.