台灣在2025 年的能源轉型政策中，積極實現非核家園目標，同時強調全面的再生能源發展以及推動節能、儲能與智慧系統整合等相關措施。2025年再生能源的總裝置容量原訂目標，分別為太陽光電20GW 以及風力6.5GW。隨著再生能源大量的湧入，再生能源的間歇性特性增加排程的不確定性，機組需要快速的升降去滿足系統的波動，進而影響機組的成本和壽命，因此本文在機組排程的過程中，除了考慮經濟性以及環保性，另加以考量整體機組的平均變動率指標。
本文旨在於2025 年環境中，納入夏季與冬季再生能源的發電特性，對電動車用戶進行探討，考慮大規模電動車負載對供應端額外的壓力，最後擬定不同情境之機組淨負載曲線，對台電之火力機組進行排程。本文使用進化退火法來優化機組的開關機狀態，在進行多目標決策的過程中，決策者需要考慮多個互相競爭、矛盾的目標，本文使用的方法為屬於互動式多目標規劃中的逐步法，對發電成本、碳排放、整體機組變動率指標等多項目標來進行規劃。

In Taiwan's 2025 energy transition policy, there is a strong focus on achieving a nuclear-free homeland while promoting extensive green energy development and measures such as energy efficiency, energy storage, and smart system integration. The original renewable energy installation capacity targets for 2025 were 20 GW of solar photovoltaic and 6.5 GW of wind power. As large amounts of renewable energy are connected to the grid, its intermittency will lead to scheduling uncertainty. Units need to rapidly adjust to meet the system's fluctuations, which can impact the cost and lifespan of these units. Therefore, in the unit scheduling process described in this thesis, in addition to considering economic and environmental factors, there is also consideration given to the average variability index of the overall unit.
This thesis aims to consider the characteristics of renewable energy power generation in the environment of 2025, taking into account factors such as peak load and the capacity of renewable energy devices during both summer and winter. The future generation of renewable energy can be represented using historical load data. Furthermore, electric vehicle users will be discussed. The additional pressure on the supply side from large-scale electric vehicle loads will be considered. Ultimately, the unit net load curve is determined for the unit commitment. This thesis uses the GESA method to optimize the operational states of units and utilizes the interactive STEP method to solve multi-objective decision-making problems. During the process of making multi-objective decisions, decision-makers need to consider multiple competing and conflicting objectives. In different scenarios, goals such as power generation costs, carbon emissions, and overall unit variation rate index will be considered for scheduling.

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