結合動態無線與固定充電之電動車路徑與車輛對電網排程

Abstract

電動車（EV）的快速普及，為交通系統與電力電網帶來了顯著的挑戰與機會。為有效管理EV移動性、動態無線充電、固定式充電基礎設施，以及車輛對電網（V2G）運作之間的複雜交互，本研究提出一套完整的二階段最佳化架構。第一階段利用螞蟻族群最佳化演算法（ACO），考量用戶限制、即時電價及充電設施的時空分布，生成高效的EV路徑與充電規劃。第二階段則設計一種創新的啟發式負載平衡排程器（HLBS），可動態協調EV放電行為，以緩解區域負載不均並提升電網頻率穩定性。於台灣實際大型道路網進行的1,500筆EV行程模擬結果顯示，本方法能顯著降低總能源成本，將電網負載變異度減少約70%，並在頻率穩定性上優於現有基準方法。所提出之架構能有效結合多元EV用戶需求與電網管理策略，為未來智慧城市與智慧電網應用提供具規模化潛力的解決方案。

The rapid adoption of electric vehicles (EVs) introduces significant challenges and opportunities for both transportation systems and electricity grids. To effectively manage the complex interactions between EV mobility, dynamic wireless charging, stationary charging infrastructure, and vehicle-to-grid (V2G) operations, we propose a comprehensive two-stage optimization framework. The first stage employs Ant Colony Optimization (ACO) to generate efficient EV routing and charging plans, considering user constraints, real-time pricing, and the spatial-temporal distribution of charging infrastructure. In the second stage, a novel Heuristic Load-Balancing Scheduler (HLBS) dynamically coordinates EV discharging actions, mitigating regional load imbalances and enhancing grid frequency stability. Evaluations conducted on a realistic, large-scale Taiwan road network involving 1,500 EV trips demonstrate that our approach significantly reduces total energy costs, decreases grid load variance by approximately 70%, and ensures superior frequency stability compared to baseline methods. The proposed framework effectively integrates diverse EV user needs with robust grid management strategies, offering scalable solutions for future smart city and smart grid implementations.