隨著電動車數量持續增加，充電需求逐漸呈現時段集中與區域分布不均等特性，使充電網路營運商在進行充電定價與能源調度時，需同時考量營運收益、電網購電成本與站點負載限制等因素。為提升各充電站的營運效率，本文提出一套基於三層階層式 Stackelberg 賽局之最佳化模型，用以描述充電網路營運商、電動車充電站與電動車使用者之間的互動決策關係。模型中，充電網路營運商負責制定各站點於不同時段之充電價格；充電站則根據使用者需求、時間電價、儲能系統與各站點需求進行能源調度；電動車使用者則依據充電價格、距離成本與充電效益選擇充電站。本文依據國內外實際案場資料去模擬實驗環境，分析儲能系統與能源交換對營運效益的影響，並與最佳化投標策略及多代理人強化學習式時間電價策略進行比較。實驗結果顯示，本文所提出之三層階層式最佳化方法能有效提升充電網路營運商的獲利，並降低部分尖峰時段的電網購電壓力，證明結合動態定價、儲能系統與站點間能源交換機制，能提升公共充電網路之能源管理效率與營運彈性。因此，本文可作為未來多站點電動車充電網路於智慧化定價與能源調度決策上之參考。;As the number of electric vehicles (EVs) continues to grow， charging demand has become increasingly concentrated during specific time periods and unevenly distributed across different regions. Consequently， charging network operators (CNOs) must simultaneously consider operational revenue， electricity procurement costs， and charging station capacity constraints when determining charging prices and energy dispatch strategies. To improve the operational efficiency of multi-charging station networks， this study proposes an optimization framework based on a tri-level hierarchical Stackelberg game to model the interactive decision-making process among the charging network operator， electric vehicle charging stations (EVCSs)， and EV users. In the proposed framework， the charging network operator determines time-varying charging prices for each charging station. Each charging station subsequently performs energy dispatch by considering user charging demand， time-of-use electricity prices， the operation of energy storage systems (ESSs)， and energy exchange among charging stations. Finally， EV users select charging stations according to charging prices， travel distance costs， and charging utility. The simulation environment is constructed based on real-world data collected from domestic and international charging infrastructures to evaluate the impacts of ESS deployment and inter-station energy exchange on operational performance. Furthermore， the proposed approach is compared with an optimal bidding strategy and a multi-agent reinforcement learning-based time-of-use pricing strategy. Simulation results demonstrate that the proposed tri-level hierarchical optimization framework effectively improves the profitability of the charging network operator while reducing electricity procurement from the main grid during peak-demand periods. The results further indicate that integrating dynamic pricing， energy storage systems， and inter-station energy exchange mechanisms can significantly enhance the energy management efficiency and operational flexibility of public EV charging networks. Therefore， the proposed framework provides a practical reference for future intelligent pricing and energy dispatch decision-making in multi-charging station EV charging networks.
