全球綠能發展趨勢下，氣候變遷和減少碳排成為各國政府和企業的首要任務。隨著再生能源技術的進步，儲能系統在穩定能源供應、提升能源利用效率以及促進能源轉型等方面扮演著關鍵角色。共享儲能為一種新興的儲能應用模式，能夠有效降低建置及維護成本，提升資源利用率。
本研究旨在研究社區型微電網錶後儲能共享方案暨容量規劃方法，並提出基於Stackelberg賽局理論的雙層兩階段最佳化模型，決策儲能系統建置容量及租賃定價，實現最小化用戶用電成本和最大化營運商利潤。下層模型使用兩階段混合整數非線性規劃模型 (Mixed Integer Nonlinear Programming)進行儲能容量決策，其中第一階段通過分散式排程，模擬共享儲能用戶的充放電調度，而第二階段則根據第一階段的結果決策總建置容量及場域契約容量。上層模型利用差分進化演算法 (Differential Evolution)最佳化共享儲能的租賃定價，考慮營運商的投資回報與用戶的用電成本。
實際案例分析結果驗證，與用戶自行建置儲能相比，租賃共享儲能可以節省更多用電成本。而微電網營運商建置共享儲能的投資報酬率，於用戶參與及未參與即時備轉的案例中，分別為10.6%及13.1%。儲能價格靈敏度分析結果顯示，儲能價格下降有助於提供更低的租賃價格，從而刺激更多用戶參與共享儲能，進一步提高經濟效益。即時備轉容量結清價格靈敏度分析結果則對參與用戶的成本節約影響較顯著。

Under the global trend of green energy development, climate change and carbon emission reduction have become top priorities for governments and enterprises. With the advancement of renewable energy technologies, energy storage systems play a crucial role in stabilizing energy supply, improving energy efficiency, and promoting energy transition. Co-shared energy storage is an emerging application model that can effectively reduce construction and maintenance costs and enhance resource utilization.
This study aims to explore community-based microgrid behind-the-meter co-shared energy storage solutions and capacity planning methods. It proposes a bi-level, two-stage optimization model based on Stackelberg game theory to decide the storage system's capacity and rental pricing, aiming to minimize user electricity costs and maximize operator profits. The lower-level model uses a two-stage Mixed Integer Nonlinear Programming model for storage capacity decision-making. In the first stage, decentralized scheduling simulates the charging and discharging schedules of co-shared storage users. The second stage decides the total construction capacity and site contract capacity based on the results from the first stage. The upper-level model utilizes a Differential Evolution algorithm to optimize the rental pricing of co-shared storage, considering the operator's return on investment and the users' electricity costs.
The results of the case study verify that compared to users building their own storage systems, renting co-shared storage can save more on electricity costs and improve the operator's return on investment. The energy storage price sensitivity analysis shows that a decrease in energy storage prices helps provide lower rental prices, stimulating more users to participate in co-shared energy storage, further enhancing economic benefits. The real-time reserve capacity clearing price sensitivity analysis shows a more significant impact on cost savings for participating users.

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