An Intelligent Generation Method for Transformer Overload Mitigation Based on Spatiotemporal Optimization

Distribution transformers often face heavy load and overload issues under high load conditions, which significantly impact the reliability and economic efficiency of the system. To address this issue, this paper proposes a comprehensive mitigation strategy based on load transfer, mobile energy storage scheduling, and transformer capacity expansion. Through simulation analysis, the effectiveness of the proposed method is validated. The results show that the strategy significantly improves load balancing among transformers, reduces heavy load and overload occurrences, and effectively lowers mitigation costs. The mitigation measures introduce fixed energy storage, load transfer, and transformer expansion, which, while generating operational costs, substantially reduce penalty costs associated with heavy load and overload, leading to a significant reduction in total costs. Additionally, the proposed method demonstrates high flexibility, enabling quick responses to load fluctuations and enhancing the system's emergency response capability. The proposed mitigation strategy has broad application potential in optimizing transformer load distribution, improving economic efficiency, and strengthening emergency response capacity.