Social Factor in Quadrilemma Optimization of Grid-Connected Industrial Microgrid Sizing Supporting Socio-Economic Development

This paper presents a quadrilemma-based optimization approach for sizing an industrial microgrid, balancing technical, economic, environmental, and social objectives. The proposed method employs a multi-objective Iterative Pareto Fuzzy (IPF) technique, considering four critical factors: maximizing reliability, minimizing overall cost, reducing CO₂ emissions, and maximizing the job creation factor. The sizing optimization is applied to a case study of an industrial microgrid using MATLAB simulations, where photovoltaic (PV), wind turbine (WT), and battery energy storage system (BESS) units are strategically deployed. A comparative analysis with the trilemma approach reveals that the quadrilemma approach determines the optimal solution, achieving a higher reliability improvement of 11.04% while enhancing socio-economic benefits. Although this approach results in increased costs and emissions, the slightly larger PV and BESS capacities, which increased by 23.8% and 6.7%, respectively, contribute to improved scalability and system resilience. The findings demonstrate the effectiveness of the quadrilemma approach in achieving broader sustainability goals and supporting the development of resilient and socioeconomically beneficial industrial microgrid systems.