Abstract: This paper addresses the multi-objective optimization problem for microgrid design, integrating various renewable energy sources and energy storage systems. The proposed framework aims to optimize the sizing of generation plants (solar, onshore wind, offshore wind) and battery storage, while simultaneously minimizing investment costs and managing the probability of power excess or deficit within the microgrid. To handle the inherent variability of wind energy, a symbolic regression method is employed for wind speed forecasting. The optimization task is tackled using two prominent metaheuristic algorithms: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-objective Particle Swarm Optimization (MOPSO) algorithm. The study details the mathematical models for all microgrid components, including their associated investment, maintenance, and replacement costs. A comparative analysis of NSGA-II and MOPSO is presented, evaluating their performance in achieving optimal Pareto fronts regarding investment costs and power balance, highlighting the trade-offs between initial investment and operational efficiency for sustainable microgrid solutions.

Keywords: Microgrids, Multi-objective Optimization, NSGA-II, MOPSO, Renewable Energy, Solar Power, Wind Power, Battery Storage, Symbolic Regression, Energy Management, Design Optimization

Pages: 63-72

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