因應全球暖化、環境汙染及全球電力需求日益增加等因素，再生能源發電系統被廣為使用。有鑑於再生能源發電不確定性影響調頻備轉容量規劃，進而限制再生能源的發展及降低電力系統可靠與安全。適當的備轉容量規劃有助於最小化規劃成本、維持系統穩定，並解決上述可能發生的問題。
本文提出一日前調頻備轉容量規劃之最佳化方法，其中分為上限及下限備轉容量規劃，並透過分析N-1事故及負載、再生能源發電不確定性造成之供需不平衡，考慮成本最小化下搭配日前輔助服務市場及需量反應市場，得出合適的備轉容量。本文所提之備轉容量規劃以粒子群演算法為基礎進行經濟調度，得到最佳化機組排程結果。為了使規劃策略具準確性和可用性，本方法建立太陽能、風力發電及負載之隨機模型模擬預測誤差及天氣對再生能源發電與用電量之影響；同時亦建立需量反應模型模擬價格、自我參與動機對參與量之影響。上述之隨機模型透過蒙地卡羅法模擬所有可能發生的情境並使用機會約束規劃法分析各種信任區間所對應的最佳調頻備轉容量。
本論文為驗證所提備轉容量規劃之可行性，使用具高再生能源滲透率之IEEE 30-bus系統進行測試。模擬結果顯示所提日前頻率調整備轉容量規劃方法具有良好的計算效率及經濟效益，未來應可進一步應用於大型系統之電力調度中心，以獲得有效之規劃結果。

Renewable energy is commonly used nowadays not only to fulfill the increasing power demand but also to reduce global warming and environmental pollution. However, the uncertain characteristics of renewable energy heavily affects the capacity planning of spinning reserve, limits the development of renewable resources, and most importantly, reduces the reliability and security of power system. Therefore, appropriate planning of reserve capacity is needed to solve these problems while maintaining cost minimization and power system stability.
This thesis proposes a day-ahead planning of spinning reserve, categorized into two parts, considering the upper and lower bound of reserve capacity separately. Planning has the N-1 criterion, and uncertainty of load and renewable energy system (RES) generation discussed. The optimal reserve capacities are determined comprehensively by modeling the price of day-ahead ancillary service market and demand response (DR) market. An economic dispatch (ED) method based on particle swarm optimization (PSO) is implemented to schedule the intra-regional generators and the interchange flow from connected grid. In order to achieve the accuracy and applicability of this algorithm, the stochastic models of photovoltaic (PV), wind turbine (WT), and load are established by forming the forecasting errors and weather effect. Meanwhile, a DR model is built to study the influence of price and self-motivation on the participated quantities. In order to consider all possible scenarios and maintain the balance between cost and risk, Monte-Carlo simulation (MCS) and chance constrained programming (CCP) are implemented to determine the feasible capacity for spinning reserve under different confidence levels.
To validate the proposed reserve-capacity planning, this method is tested in a modified IEEE 30-bus system with high renewable energy penetration. The result shows a day-ahead arrangement of spinning reserve with good efficiency and economy. This method can be applied to a larger realistic system in the future and is believed to perform well with high applicability.

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