Optimization Of Renewable Energy Microgrids Using Multi-Objective Genetic Algorithms For Rural Electrification And Climate Resilience

Abstract

The rapid expansion of rural electrification demands sustainable, resilient, and cost-effective energy solutions capable of withstanding climate-induced disruptions. This study presents an optimized framework for renewable energy microgrids using Multi-Objective Genetic Algorithms (MOGAs) to balance key operational trade-offs: cost minimization, reliability enhancement, emission reduction, and climate-resilience improvement. The proposed model integrates photovoltaic systems, wind turbines, micro-hydro units, and battery energy storage, combined with stochastic simulations of rural demand profiles and climate variability. MOGA-based optimization enables simultaneous exploration of diverse design configurations, producing Pareto-optimal microgrid solutions that adapt to local resource availability and environmental stressors. Results demonstrate that MOGA-optimized microgrids can reduce levelized cost of electricity (LCOE) by up to 37%, improve system reliability by 42%, and enhance resilience metrics under extreme weather scenarios. Comparative analysis with single-objective approaches further reveals substantial performance gains in balancing cost, sustainability, and robustness. This research contributes an advanced computational framework to guide policymakers, rural planners, and energy engineers in designing renewable microgrids that support long-term rural development and climate adaptation. The findings highlight the transformative potential of multi-objective evolutionary optimization in accelerating equitable and climate-resilient rural electrification.