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研究生: 陳品蓉
Chen, Pin-Jung
論文名稱: 改良式黏菌演算法應用於微電網之能源管理系統
Improved Slime Mould Algorithm Applied to Energy Management System in Microgrid
指導教授: 陳瑄易
Chen, Syuan-Yi
口試委員: 李政道
Lee, Jeng-Dao
談光雄
Tan, Kuang-Hsiung
藍建武
Lan, Chien-Wu
陳瑄易
Chen, Syuan-Yi
口試日期: 2024/01/10
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: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 130
中文關鍵詞: 基本規則庫控制策略全域搜索法控制策略黏菌演算法控制策略能量管理系統微電網直流-直流轉換器
英文關鍵詞: Rule-based control strategy, Global search algorithm, Slime mould algorithm, Energy management System, Microgrid, DC-DC converter
DOI URL: http://doi.org/10.6345/NTNU202400180
論文種類: 學術論文
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  • 本研究旨在為智慧家庭發展一整合太陽能發電、市電及儲能系統之微電網能量管理系統(Energy Management System, EMS),透過設計最佳化多能源系統能量管理技術,合理分配不同能源之間的功率流向,同時確保儲能系統進行必要的儲能與釋能,以降低總體用電成本。研究中建構一個包含太陽能、市電和儲能系統的微電網模型,並提出多種控制策略,包括基本規則庫、全域搜索法、黏菌演算法以及本研究所提出的改良式黏菌演算法,該策略將螺旋搜索策略整合至黏菌演算法中,以降低演算法在搜索過程中陷入局部最佳解的機率,同時提高搜索速度。透過本研究提出的控制策略,能夠針對在不同需求功率和儲能電池狀態下,計算出最佳功率分配比例,實現能量管理最佳化。
    本研究以個人電腦及Matlab/Simulink發展控制策略,透過個人電腦計算最佳能量分配比例後,輸出控制訊號以對各組直流-直流轉換器進行電壓與電流控制,使各模組輸出功率可被主動式分配,實現最佳化能量管理目標。實驗結果表明,改良式黏菌演算法控制策略能夠有效地最佳化分配能源,以達到降低用電成本的目的。以夏日情境為例,使用全域搜索法控制策略能比基本規則庫控制策略之用電成本改善幅度11.826%;使用黏菌演算法控制策略改善幅度10.210%;而使用改良式黏菌演算法控制策略改善幅度10.945%。由上述說明可知,本研究提出的改良式黏菌演算法能實現理想的微電網能量管理目標。

    The purpose of this study is to develop an Energy Management System (EMS) for smart homes that integrates solar power generation, utility power, and energy storage systems into a microgrid. By designing of optimized multi-energy system energy management techniques, the objective is to rationally allocate power flows among different energy sources while ensuring necessary energy storage and discharge, ultimately reducing the overall electricity costs. In this study, a microgrid model with multiple energy systems was established, including solar power generation, utility power, and energy storage systems. Several control strategies were proposed, including Rule-Based (RB), Global Search Algorithm (GSA), Slime Mould Algorithm (SMA) and Improved Slime Mould Algorithm (ISMA) introduced in this study. This strategy integrates a spiral search strategy into the slime mould algorithm to reduce the likelihood of the algorithm getting stuck in local optimal solutions during the search process. Simultaneously, it shortens the required search time, addressing the issue of excessive search time in the global search algorithm. Through the control strategy proposed in this study, optimal power distribution ratios can be calculated for different power demand scenarios and energy storage battery states, achieving energy management optimization.
    This study developed control strategies using a personal computer and Matlab/Simulink. After calculating the optimal energy distribution ratios on the personal computer, commands for controlling voltage and current were generated to regulate each group of DC-DC converters. This enables the active allocation of power output from each module, achieving the goal of optimized energy management. The experimental results of the study indicate that the ISMA control strategy effectively optimizes energy distribution to achieve the goal of reducing electricity costs. Specifically, in the case of a summer scenario, the use of the GSA control strategy improves electricity costs by 11.826% compared to the RB control strategy. The SMA control strategy improves costs by 10.210%, and the ISMA control strategy achieves an improvement of 10.945%. Overall, the results suggest that the ISMA proposed in this study approaches the results obtained by the GSA more closely.

    摘要 i ABSTRACT ii 誌謝 iv 目錄 v 表目錄 viii 圖目錄 ix 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻探討 3 1.3 研究目的 7 1.4 研究架構 8 第二章 智慧家庭微電網系統介紹 9 2.1 電力轉換器系統架構 9 2.1.1 降壓型直流-直流轉換器 10 2.1.2 升壓型直流-直流轉換器 12 2.1.3 直流-交流轉換器 15 2.2 儲能系統 17 2.2.1 鋰電池之等效電路模型 19 2.2.2 鋰電池之放電模式 20 2.2.3 鋰電池之充電模式 21 2.3 太陽能發電系統 23 2.3.1 太陽能發電模型 23 2.3.2 太陽能系統運作架構 26 2.4 電費計算方式 28 2.4.1 市電成本 28 2.4.2 電池隱藏成本 30 第三章 市電端功率分析與控制 32 3.1 交流系統向量分析 32 3.1.1 靜止座標軸系統轉換 34 3.1.2 同步旋轉座標軸系統轉換 35 3.2 市電端功率計算與控制 37 第四章 最佳化能量管理策略 40 4.1 基本規則庫控制策略 40 4.2 全域搜索法 44 4.3 黏菌演算法 48 4.3.1 黏菌演算法之數學模型 49 4.3.2 黏菌演算法之能量管理策略 55 4.4 改良式黏菌演算法 58 第五章 最佳化能量管理策略模擬與討論 63 5.1 模擬參數設定 63 5.2 基本規則庫控制策略之模擬結果 66 5.3 全域搜索法控制策略之模擬結果 70 5.4 黏菌演算法控制策略之模擬結果 74 5.5 改良式黏菌演算法控制策略之模擬結果 77 5.6 模擬結果之電費比較 81 第六章 實驗平台介紹與結果討論 88 6.1 實驗平台說明 88 6.2 實驗平台建模 97 6.3 實驗結果 101 6.3.1 市電端功能測試 101 6.3.2 基本規則庫控制策略之實驗結果 103 6.3.3 全域搜索法控制策略之實驗結果 107 6.3.4 黏菌演算法控制策略之實驗結果 110 6.3.5 改良式黏菌演算法控制策略之實驗結果 113 6.4 實驗結果之電價比較 115 第七章 結論與未來展望 122 7.1 結論 122 7.2 未來展望 123 參考文獻 124 自傳 129 學術成就 130

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