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研究生： 董堃堉
Dong, Kun-Yu
論文名稱： 錶後分散式電力資源之需量反應與空調舒適度整合控制
Behind-the-meter Distributed Energy Resources for Integrated Demand Response and Air-conditioning Comfort Control
指導教授： 楊宏澤
Yang, Hong-Tzer
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 82
中文關鍵詞： 電能管理系統 、增強式學習 、聚合空調控制 、需量反應 、舒適度 、儲能系統 、雙層式最佳化排程
外文關鍵詞： Energy Management System (EMS), Reinforcement Learning (RL), Heating, Ventilation and Air Conditioning (HVAC) Control, Demand Response (DR), Thermal Comfort, Battery Energy Storage System (BESS), Two-Stage Optimal Scheduling
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    • 隨著能源需求不斷增加，需求側管理和需量反應成為當前研究的熱點。在需量反應的背景下，高耗能的空調系統成為一個關鍵因素。因此，探討結合空調控制和整體系統資源的電能管理系統具有重要意義。
    本研究以中小型商辦大樓為研究對象，提出了一套結合空調系統控制的電能管理系統。該系統綜合考慮多種資源，並能靈活應對商辦大樓參與或不參與台電公司需量競價措施的不同操作情境。在參與需量反應情境中，該系統通過聚合整棟商辦大樓的空調系統和已建置的儲能系統進行聯合調度，旨在降低整體大樓用電成本，同時考慮室內人員的舒適度；在不參與需量反應情境中，該系統亦能管理電動車充電，並操作儲能系統以進行時間電價套利，以實現整體大樓用電成本最小化。
    模擬結果顯示，與傳統的規則控制方法相比，無論是否參與需量反應，該系統能夠分別節省 1.71% 和 4.39% 的總成本。此外，透過聚合空調系統參與需量反應，該系統在舒適度表現上優於傳統控制方法1%，證明了本文提出方法之有效性。未來這套系統可推廣至各級商辦大樓或園區，以利於擴大需量反應規模，進而提升電網強健性。

    Energy Transition has become a global challenge due to the ever-increasing demand for clean energy. In this process, demand-side management (DSM) and demand response (DR) have become the focal points of current research. To attend DR, those energy-intensive factors such as heating, ventilation, and air-conditioning (HVAC) system become crucial to building energy savings. Therefore, exploring a power management system that integrates air-conditioning control and overall system resources is of great significance.
    This study takes commercial buildings as the research object and proposes an energy management system (EMS) combined with air-conditioning system control. The system can simultaneously consider the operating scenarios of whether the commercial building participates in Taipower's demand bidding markets. In the scenario of participation in DR, the proposed strategy conducts joint scheduling by aggregating the air-conditioning system of the entire commercial building and the built battery energy storage system (BESS), aiming to reduce the overall building electricity cost while considering the comfort of indoor personnel. In the scenario without engaging in DR, the proposed EMS can also manage the charging of EVs and operate the BESS for time-of-use (TOU) electricity price arbitrage to minimize the overall building electricity cost.
    The simulation results show that the proposed EMS, whether it participates in DR or not, can save either 1.71% or 4.39% of the total cost compared with the traditional rule-based control method. In addition, by participating in the DR through the aggregated air-conditioning system, the system outperforms the traditional control method by 1% in terms of comfort performance, which validates the effectiveness of the method proposed in this paper. In the future, this system can be expanded to encompass commercial buildings or parks at all levels, thereby increasing the scale of DR and enhancing the robustness of the power grid.

    摘要 I EXTENDED ABSTRACT II 誌謝 VII 目錄 VIII 圖目錄 XII 表目錄 XV 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 2 1.3 研究方法與貢獻 5 1.4 論文架構 7 第二章 結合空調控制之即時電能管理系統架構說明 8 2.1 簡介 8 2.2 商辦大樓電能管理系統架構 8 2.3 鋰電池儲能系統 9 2.4 空調系統 10 2.5 電動車充電樁 12 2.6 電力市場 14 2.6.1 時間電價方案 14 2.6.2 契約容量 16 2.6.3 需量反應 17 第三章 結合空調控制之即時電能管理策略 19 3.1 簡介 19 3.2 雙層式電能管理系統排程方法 20 3.2.1 全系統之運作流程 20 3.2.2 非需量反應時段運作流程 22 3.2.3 需量反應時段上層之運作流程 24 3.2.4 需量反應時段下層之運作流程 26 3.3 電能管理系統最佳化問題描述 28 3.3.1.非需量反應時段之最佳化目標函數 28 3.3.2.需量反應時段之上層最佳化目標函數 29 3.3.3.系統限制式 37 3.4 需量反應時段之下層空調控溫增強式學習控制排程方法 41 3.5.1 增強式學習(Reinforcement Learning, RL) 41 3.5.2 Double Deep Q-Network (DDQN) 42 3.5.3 空調可用量分配 45 3.5.4 馬可夫決策過程 46 第四章 系統模擬與結果分析 49 4.1 簡介 49 4.2 系統相關參數設定 49 4.2.1 商辦大樓系統與模型相關參數設定 49 4.2.2 商辦大樓電動車進離場時間與電量分佈 53 4.2.3 商辦大樓負載、空調負載用電與再生能源發電 56 4.2.4 時間電價相關參數設定 58 4.3 情境說明與模擬結果分析 59 4.3.1 兩段式電價與三段式電價模擬結果分析 59 4.3.2 未參與需量反應之模擬結果分析 61 4.3.3 參與需量反應之模擬結果分析 66 4.3.4 夏季代表日之總成本分析 72 第五章 結論與未來研究方向 74 5.1 結論 74 5.2 未來研究方向 75 參考文獻 77

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