Application Study of the Improved ACO Algorithm in the Optimized Dispatch of Microgrids with Energy Storage

To address the economic dispatch problem of microgrids with energy storage, this paper proposes an improved Ant Colony Optimization (ACO) algorithm to enhance the system’s economic performance and stability under scenarios with renewable energy fluctuations and load uncertainty. A multi-energy coordinated dispatch model is established, incorporating micro gas turbines, photovoltaic generation, energy storage systems, and grid power purchases, while comprehensively considering constraints such as power balance, equipment output limits, and the state of charge of the energy storage system. Through adaptive parameter adjustment, elite ant strategy, dynamic penalty factors, and local search mechanisms, the improved ACO algorithm significantly enhances global search capability and convergence speed. Experimental results show that, in the eil51 path optimization benchmark, the optimal path length of the proposed algorithm is over 25% shorter than that of the traditional ACO, and the solution stability is significantly improved. In microgrid dispatch applications, the total system operating cost is reduced by approximately 14.2%, storage utilization is increased by about 12%, and the peak grid purchasing power is reduced by around 17%. Compared to the traditional ACO, the improved algorithm achieves better equipment output coordination, cost optimization, and constraint satisfaction, with smoother operation curves and more reasonable utilization of the energy storage system, leading to notable improvements in system economy and engineering feasibility. This study provides an efficient theoretical approach and practical engineering solution for optimized dispatch of microgrids with high renewable energy penetration.