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研究生: 張志宏
Chang, Chih-Hung
論文名稱: 應用於微電網之電能管理策略
Designs of Power Management Strategy for Microgrid Application
指導教授: 陳瑄易
Chen, Syuan-Yi
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 109
中文關鍵詞: 能量管理策略微電網最小等效能耗策略適應性人工蜂群演算法
英文關鍵詞: Energy Management System, microgrid, Equivalent Consumption Minimization Strategy, Adaptive Artificial Bee Colony Algorithm
DOI URL: http://doi.org/10.6345/NTNU202001482
論文種類: 學術論文
相關次數: 點閱:187下載:23
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  • 本研究之目標為針對智慧家庭,發展整合太陽能發電、市電及儲能系統之微電網系統,透過設計多能源系統最佳化能量管理技術(Energy Management System, EMS),適當調度各電源之間之功率流向,並對儲能系統進行必要之儲能與釋能,以降低整體用電成本。在此研究中,首先發展基於規則控制策略(Rule-Based Control Strategy, RBCS )於微電網系統中,以達到節省電能消耗、降低碳排放量與減少用戶電費支出等目的。然而,由於實際微電網系統在運作時,家用負載、太陽能發電功率、儲電量與即時電價等各項數值均會隨時間變化而縝密變動,且RBCS之切換條件無法兼顧所有可能性。因此,為提高整體用電成本最小化之目標,本研究進一步以最小等效能耗策略(Equivalent Consumption Minimization Strategy, ECMS) 設計多能源之電能管理策略,因應不同再生能源發電量、即時電價與負載需求進行功率分配最佳化,將能源更有效率地使用,進而達到電價最小化之目標。礙於最小等效能耗策略搜尋時間過於冗長,最終提出適應性人工蜂群演算法(Adaptive Artificial Bee Colony Algorithm, AABC)設計多能源之電能管理策略來降低搜索時間,實驗結果表明以月計電費夏日時段為例,使用AABC之控制策略比RBCS之控制策略能省下9.8%的電費;使用ECMS之控制策略比RBCS之控制策略一個月能省下11.2%的電費。

    In the light of this, the objective of this thesis is to develop a smart home which is based on photovoltaic, mains supply and battery in three kinds of microgrid. This thesis presents a Rule-Based Control Strategy (RBCS) to conserve electricity and reduce carbon emissions of Home Energy Management System (HEMS). Nevertheless, considering about the real load, solar power and electricity rates situation will be different with estimation. Consequently, the objective is to minimize electricity payment when satisfying conditions. Further more, the standard way to solve this kind of problem is Equivalent Consumption Minimization Strategy (ECMS) and Adaptive Artificial Bee Colony Algorithm (AABC).Simulation result demonstrate RBCS electricity rates is 9.8% worth more than EMS(AABC) for a month. Then RBCS electricity rates is 11.2% worth more than EMS (ECMS) for a month.

    摘要 i ABSTRACT ii 誌謝 iii 目錄 iv 表目錄 vi 圖目錄 vii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻探討 2 1.3 研究目的 6 1.4 研究架構 7 第二章 多能源管理系統介紹 8 2.1 鋰電池與電池管理系統 8 2.2 電能轉換裝置 12 2.3 太陽能介紹 18 2.4 電費計算方式 22 第三章 能源管理策略設計 25 3.1 蓄電池建構 25 3.2 類神經網路預測太陽能功率 30 3.3 基於規則能量控制策略 36 3.4 最小等效能耗策略 41 3.5 適應性人工蜂群演算法 47 第四章 微電網系統架構 53 4.1 微電網系統架構 53 4.2 系統硬體介紹 54 4.3 系統軟體介紹 60 第五章 PSIM 平台建模 65 5.1 PSIM 雙向直流/直流轉換模組模擬軟體設計 65 5.2 PSIM太陽能發電系統暨MPPT模擬軟體設計 66 5.3 PSIM三進一出能量管理系統模擬軟體設計 68 5.4 PSIM基於規則能量控制策略模擬 72 第六章 實驗結果與討論 79 6.1 實驗平台說明 79 6.2 基於規則能量控制策略實驗 80 6.3 最小等效能耗策略實驗 89 6.4 適應性人工蜂群演算法實驗 93 6.5 實驗結果與驗證 97 第七章 結論與未來展望 102 7.1 結論 102 7.2 未來展望 102 參考文獻 104

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