The increasing global demand for clean and reliable energy has intensified the need for optimised microgrid systems that integrate multiple renewable energy sources. This study presents a Multi-Objective Optimization of microgrid design with Hybrid Renewable Energy Sources for a Sustainable Environment. The research focuses on developing and analyzing an optimised hybrid energy system that combines solar photovoltaic (PV), wind turbine, battery storage, and optional diesel generation to achieve a balance between economic efficiency, environmental sustainability, and operational reliability. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the optimisation framework was formulated with three key objectives: minimising the Levelized Cost of Energy (LCOE), minimising carbon emissions (CO₂), and minimising the Loss of Load Probability (LOLP). Simulation and optimisation were conducted using MATLAB, HOMER Pro, and Python-based tools. The results revealed that hybrid configurations significantly outperform single-source systems, reducing LCOE by up to 25% and CO₂ emissions by over 60%. The Pareto front analysis demonstrated the trade-offs among cost, reliability, and environmental performance, enabling decision-makers to select optimal system designs based on specific priorities. The study concludes that hybrid renewable micro-grids provide a technically feasible and economically sustainable pathway for decentralised energy generation. The optimised system design not only enhances energy security but also contributes substantially to environmental conservation and the global transition toward sustainable energy development.
Uche-Ibe et al. (Sat,) studied this question.