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研究生: 張賀凱
Ho-Kai Chang
論文名稱: 基於發電與負載預測之光儲系統最佳策略規劃
Planning of photovoltaic storage system optimal strategy based on power generation and load forecasting
指導教授: 郭政謙
Cheng-Chien Kuo
口試委員: 張宏展
Hong-Chan Chang
張建國
Chien-Kuo Chang
陳鴻誠
Hung-Cheng Chen
黃維澤
Wei-Tzer Huang
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2023
畢業學年度: 112
語文別: 中文
論文頁數: 66
中文關鍵詞: 電池型儲能系統混合整數線性規劃時間電價發電預測負載預測直流耦合光儲系統
外文關鍵詞: Battery Energy Storage System, Mixed-Integer Linear Programming, Load Prediction, Time-of-Use Electricity Pricing, Power Generation Prediction, Direct Current Coupled PV-Storage System
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  • 隨著全球對氣候變化的關注增加,包括台灣在內的 150 多個國
    家和地區致力於實現 2050 年的淨零排放目標。為此,政府加快能源
    轉型,推廣再生能源及電化學儲能系統。再生能源的間歇性使電力供
    應面臨穩定性挑戰。與此同時,參與 RE100 倡議的台灣企業日益增
    多,它們通過在工廠安裝太陽能光伏和儲能系統來減少碳排放,並提
    高電力使用的穩定性和品質。
    然而,隨著台灣對高壓及特高壓時間電價在尖離峰費率價差越
    差越大的趨勢,將帶動企業對自家廠區電化學儲能應用之光儲市場
    成長。廠區太陽能發電系統滲透率過高,將導致供電變化過大,若儲
    能系統無法即時反應,則須以市電供給負載將導致電費增加也造成
    儲能系統利用率及效益降低。
    本論文對工廠光儲系統做新的規劃,該系統採用直流耦合架構,
    往常多採用交流耦合架構,直流耦合系統與交流耦合系統來說轉換
    效率有一定程度上的提升,加上基於深度學習 Transformer 模型做特
    甄上的擷取搭配 BiLSTM 預測模型開發發電預測及負載預測架構,
    並導入混和整數線性規劃(Mixed-integer linear programming)來做隔日之最佳化儲能系統充放電排程,並在當日做負載移動平均之短期預測來達成儲能系統動態調整以補償預測誤差,比較無此控制策略及
    加入控制策略對運轉中之能源套利之影響。


    Global focus on climate change has led over 150 countries, including Taiwan, to aim for net-zero emissions by 2050, accelerating energy transformation and the adoption of renewable energy and electrochemical storage systems. However, the intermittent nature of renewables challenges power supply stability. In Taiwan, more enterprises are joining the RE100 initiative, integrating solar photovoltaics and storage systems to reduce carbon emissions and enhance power stability.
    Yet, the disparity between peak and off-peak electricity tariffs for high voltages is driving a shift towards battery storage in factory-based photovoltaic systems. High solar power penetration can cause significant power supply fluctuations. Without timely energy storage response, reliance on grid power and costs increase, reducing system efficiency.
    This paper presents a new plan for factory photovoltaic storage systems, favoring direct current (DC) over alternating current (AC) coupling for system efficiency. It integrates a Transformer model(Machine learning model) with a BiLSTM model for predictive power generation and load forecasting. Mixed-integer linear programming optimizes the storage system's daily charge-discharge schedule, and short-term load forecasting dynamically adjusts the system. The thesis evaluates the impact of this strategy on energy arbitrage compared to non-strategic approaches.

    摘要 I Absract II 目錄 III 圖目錄 V 表目錄 VII 第一章 緒論 1 1.1 研究背景與動機 1 1.2 文獻回顧 3 1.3 研究方法 6 1.4 章節概述 9 第二章 AI預測模型介紹 11 2.1 長短期記憶模型 11 2.2 雙向長短期記憶模型 13 2.3 Transformer模型 13 2.4 優化器 16 2.5 損失函數 16 2.5.1 均方誤差損失 17 2.5.2 平均絕對百分比誤差 17 第三章 基於發電及負載預測之日前排程與實時動態控制介紹 18 3.1 背景與系統架構 18 3.2 發電預測 21 3.3 負載預測 25 3.4 混合整數線性規劃之日前排程 28 3.4.1 模擬條件的假設 28 3.4.2 定義決策變數 29 3.4.3 建立目標函數 30 3.4.4 設立約束條件 32 3.4.5 最佳化問題求解 38 3.5 即時動態控制策略 38 第四章 模擬結果與分析 43 4.1 模擬情境說明 43 4.2 模擬結果 44 4.2.1 夏月無儲能系統模擬 45 4.2.2 夏月有儲能系統最佳化排程動態控制模擬 46 4.2.3 非夏月無儲能系統模擬 51 4.2.4 非夏月有儲能系統最佳化排程動態控制模擬 52 4.3 分析 56 第五章 結論與未來展望 62 5.1 結論 62 5.2 未來展望 63 參考文獻 65

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    全文公開日期 2029/01/16 (國家圖書館:臺灣博碩士論文系統)
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