Improved Ant Colony Algorithm for Energy Storage Power Optimization in Multi-Power Distribution Networks

Abstract

To address power imbalance, voltage fluctuations, and low new energy consumption rate caused by high penetration of distributed new energy in multi-power distribution networks, this study proposes an energy storage power control method based on an improved ant colony algorithm (ACO). First, A multi-objective optimization model was constructed, considering the reduction of network loss, improvement of new energy consumption, and stability of energy storage SOC. The entropy weight method was used to objectively determine the target weights. Second, to solve the poor accuracy of traditional ACO in continuous control, a "continuous domain discretization" strategy was introduced, and pheromone update rules were optimized to enhance algorithm convergence and precision. Finally, a case study of a 10kV industrial park distribution network is introduced. The research shows that compared with traditional ant colony algorithm and particle swarm algorithm, the improved algorithm reduces voltage fluctuations by 42%, lowers the daily network loss rate to 2.13% (44.2% lower than no energy storage), and increases the photovoltaic consumption rate on clear days to 96.2%. The results of the paper validate the effectiveness of the proposed approach in optimizing the operation of the distribution network and promoting the integration of new energy.