This paper explores the design, optimisation, and techno-economic assessment of an AC residential Microgrid (MG) for sustainable energy application. The proposed multi objective optimisation strategy aimed to optimise energy management systems integrating renewable energy sources (RES), energy storage systems (ESS), and advanced power management. Using HOMER Pro software for sizing mechanism the study investigates multiple Microgrid frameworks with different tariffs to find the optimal residential MG size determining lowest Net present cost (NPC) with optimal setup. Furthermore, a hybrid optimisation model using the optimal sizing, combining Genetic Algorithm (GA) and Multi-Objective Particle Swarm optimisation (MOPSO), this study enhance energy reliability, reduce greenhouse gas emissions, and ensure cost-effectiveness. A Monte Carlo Simulation (MCL) is incorporated to model uncertainties in the MG PV and load system to test the robustness of the proposed strategy. A comparative analysis of PSO, GA, and Grey Wolf Optimisation (GWO) highlights the superiority of the hybrid GA-MOPSO optimiser in balancing system cost, reliability, and environmental impact. The integrated optimiser outperforms conventional methods by enhancing energy storage efficiency, optimising grid interaction, and reducing dependence on peak-hour supply. The resulting Pareto front demonstrates improved solution diversity and convergence. These findings underscore the effectiveness of integrated evolutionary strategies in advancing cost-efficient and sustainable MG operations.
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