隨著氣候變遷日益嚴重，淨零碳排與再生能源在當今面臨的挑戰下扮演著至關重要的角色，減少碳排放成為確保永續發展的關鍵一環。太陽能光電技術因受惠於無窮的太陽能資源而受到廣泛關注，搭配儲能系統更能有效解決出力波動的問題，進而成為穩定的發電源。光儲結合不僅利於提高再生能源的滲透率，在追求淨零碳排的道路上，太陽能光電與儲能的協同發展將成為實現可持續能源轉型的重要引擎。
本文提出了一個兩階段的電能管理系統，第一階段使用粒子群演算法進行儲能系統容量規劃，第二階段結合模糊控制和長短期記憶模型實現即時最佳化排程，透過太陽能發電量的滾動式預測，有效提高能源使用效率，並減少太陽能棄光量，從而最大化系統的整體收益。在模糊控制中，系統採用了混合整數線性規劃和模糊控制規則的方法。這兩種方法的決策結果結合模糊控制，以應對不同天氣情況帶來的各種變化。
本研究採用實際太陽能案場之資料進行模擬分析，文中包含儲能系統容量規劃及三種不同控制方法之綜合比較，分別為儲能充電優先、太陽能躉售優先及本文所提出之方法。經比較可得本文所提之方法有助於顯著提升系統收益，並適用於各種天氣型態。在儲能系統容量規劃方面，文中根據實際案場特性分析，提出最適化的儲能容量，以優化系統營運效能，從而實現最大總體收益。

As climate change becomes increasingly severe, achieving net-zero carbon emissions and utilizing renewable energy play crucial roles in addressing contemporary challenges, with carbon reduction being a key component of ensuring sustainable development. Solar photovoltaic (PV) technology, benefiting from the inexhaustible solar resources, has garnered widespread attention. When combined with energy storage systems, it effectively mitigates the issue of output fluctuations, thereby becoming a stable power source. The integration of solar PV and storage not only enhances the penetration rate of renewable energy but also becomes an important driver in realizing sustainable energy transformation on the path to net-zero carbon emissions.
This thesis proposes a two-stage energy management system. The first stage involves the use of Particle Swarm Optimization (PSO) for planning the capacity of the energy storage system. The second stage integrates fuzzy control and Long Short-Term Memory (LSTM) models to achieve real-time optimal scheduling. By employing rolling forecasts of solar power generation, the system effectively enhances energy utilization efficiency and reduces solar energy curtailment, thereby maximizing the overall system revenue. In the fuzzy control framework, the system adopts a combination of Mixed-Integer Linear Programming (MILP) and fuzzy control rule based methods. The decisions resulting from these two approaches are integrated into the fuzzy control to handle various changes brought by different weather conditions.
This study conducts a simulation analysis using actual data from a solar power plant. The thesis includes capacity planning for the energy storage system and a comprehensive comparison of three different control methods: storage charging priority, solar power wholesale priority, and the method proposed in this thesis. The comparison indicates that the proposed method significantly improves system revenue and is suitable for various weather patterns. Regarding energy storage system capacity planning, the thesis proposes an optimized storage capacity based on the characteristics of the actual site, aiming to enhance system operational efficiency and achieve maximum overall revenue.

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