Multi-objective optimization of renewable energy systems using genetic algorithms: A case study

Abstract

This study optimizes a renewable energy system using a genetic algorithm (GA) in a multi-objective context, focusing on cost minimization, efficiency maximization, and emission reduction. The optimized system consists of a combination of solar PV, wind turbine, and energy storage battery. Simulation results show that the optimal population size is 100 to 150 individuals, with a crossover probability of 0.8 to 0.9 and a mutation probability of 0.01 to 0.05. In the cost minimization scenario, the Pareto optimal solution has a total cost of $150,000, an energy efficiency of 85%, and an emission reduction of 10%. The emission reduction scenario results in a total cost of $170,000, an efficiency of 88%, and an emission reduction of 25%. The compromise scenario costs $160,000, an efficiency of 90%, and an emission reduction of 20%. The trade-off analysis shows that emission reduction often incurs higher costs but provides long-term benefits. This study shows that GA effectively finds optimal solutions in multi-objective problems despite data assumptions and computational time limitations. These findings provide a solid foundation for designing efficient, sustainable, and environmentally friendly renewable energy systems.