Optimal Economic Dispatch Using Mayfly Optimization Algorithm in Sulbagsel Electricity System with Integrated Renewable Energy Sources

This study focuses on optimizing generation costs for thermal power plants in the Southern Sulawesi (Sulbagsel) electricity system by incorporating Renewable Energy Sources (RESs). The Improved Mayfly Algorithm (IMA), inspired by the mating and flight behaviors of adult mayflies and enhanced with Exponent Decreasing Inertia Weight (EDIW) to adjust inertia variations, is applied to minimize generation costs. The effectiveness of the proposed IMA is evaluated through comparisons with other methods, such as the Quadratic Time Optimization (QTO) and the standard MA. Statistical analysis of the benchmarking results demonstrates that IMA outperforms comparable other algorithms. For the first case, mid-day peak load, the optimization results show that QTO reduces costs by 24.24%, MA by 24.25%, and the proposed IMA by 24.28%. In the second case, nighttime peak load, the cost reductions achieved are 25.96% for QTO, 26.28% for MA, and 26.72% for IMA.