需量反應為目前電力系統需求面管理最受重視議題之一，可透過改變用戶的負載行為提供電力系統供需平衡所需之輔助服務，以及抑低尖峰負載改善負載因數等。根據台灣經濟部能源局統計，2018年工業用戶的用電量占總用電的54.5%，為台灣用電量最大的行業別。鑒於其提供抑低量的潛能，工業用戶成為需量反應的重點實施對象。
本文提出工業用戶參與需量競價措施之中短期電能管理與投標策略，從而獲得需量競價的最大利益。透過倒傳遞類神經網路預測後一個月的負載，以評估未來一個月的工廠排修計畫，以最大化需量競價的抑低潛能。此外，利用歷史得標紀錄與相應的備轉容量率、邊際機組的燃料價格與更新的投標狀態來建構價格適應性機制。依據前述的月前排修計畫與價格分析結果，在考慮機會成本、需量競價執行次數限制與工廠製程規則的情況下，本文利用混合整數線性規劃演算法進行完整的需量競價策略與具有5天動態窗格的每日負載排程。
最後，本文採用台灣鋼鐵廠的實際數據做模擬驗證，分析結果顯示，本文所提出的方法兼顧需量競價的獲利與整體的經濟效益，進而為消費者節省龐大的電費支出。模擬結果中也展現出使用價格適應性機制的效果與考量機會成本的重要性。

Demand Response (DR), as one of the popular issues in today’s power system, focuses on changing electricity user behavior to provide ancillary services for load balance and peak shaving for load factor improvement. Meanwhile, according to the Bureau of Energy in Taiwan, the electricity consumption of industrial customers accounts for 54.5% of the electricity demand in 2018. As the potential it can provide, the industrial customer is one of the best candidates to be encouraged to participate in the DR program.
This thesis proposes a mid and short term energy management and bidding strategy for industrial customers to join in the demand bidding program, and thus gain maximum DR profit. We employ the Back Propagation Neural Network (BPNN) to have the monthly-ahead load forecasting. Afterwards, we arrange the month-ahead maintenance planning based on the load forecasting result to maximize the DR load reduction. The historical bidding experiences, corresponding reserve rates, fuel price of marginal unit, and updated bidding status are used to construct a price adaptive mechanism. On the basis of the price analysis and monthly plan, via mixed integer linear programming (MILP) algorithm, the bidding strategy and scheduling are determined from the daily optimization within the 5-days moving window in consideration of the opportunity cost, DR execution days, and manufacturing process.
The realistic data from a steel company in Taiwan is employed in our case studies. The results illustrate that the economic efficiency of the proposed method by obtaining the DR profit and, therefore, saving the considerable electricity cost for the consumer. The improvement achieved by the proposed price adaptive mechanism and the importance of opportunity cost consideration are demonstrated in the simulation as well.

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