Optimal Scheduling of Mobile Energy Storage via Dynamic Path Guidance

This study presents an innovative optimization framework for mobile energy storage systems (MESS) that integrates dynamic path planning with multi-objective energy scheduling. By transforming real-world road networks into time-weighted graph models and employing an enhanced Dijkstra algorithm, the framework enables rapid and efficient MESS scheduling in response to dynamic grid demands. The optimization model aims to maximize economic benefits through peak-valley electricity price arbitrage while minimizing system voltage deviations, thereby enhancing the operational efficiency and reliability of distribution networks. A case study using renewable energy outputs, typical winter load data, and real traffic networks from a Chinese city was conducted to validate the proposed method. The results demonstrate that the MESS optimization strategy effectively smoothens the net load curve and significantly reduces voltage fluctuations, improving grid stability. Specifically, the net revenue of MESS within a scheduling cycle was 203.78 yuan, with arbitrage income accounting for 78.9% of the total revenue. Voltage fluctuations were reduced from 0.2165 to 0.1691, highlighting the framework's effectiveness in enhancing grid stability and economic efficiency. Additionally, the optimized path planning reduced driving costs to 21.94 yuan and minimized energy losses caused by traffic congestion, further improving scheduling efficiency. The study concludes that the proposed MESS optimization framework offers a robust solution for enhancing grid flexibility and reliability in the context of high renewable energy penetration. Future research will focus on refining path planning algorithms and exploring the application of MESS in grids with even higher penetration levels of renewable energy, aiming to further advance the integration of energy and transportation systems in smart cities.