以OPAL-RT硬體迴圈實現基於深度強化學習演算法與負載預測模型之微電網經濟調度

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姓名

黃育城(Yu-Cheng Huang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以OPAL-RT硬體迴圈實現基於深度強化學習演算法與負載預測模型之微電網經濟調度
(Implementation of Microgrid Economic Dispatch Based on Deep Reinforcement Learning Algorithms and Load Forecasting Model Using OPAL-RT Hardware in the Loop)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

本論文對於微電網的能源管理最佳化進行了深入的分析和探討，介紹了微電網的結構和調度模型，並提出了一種結合深度確定性策略梯度(Deep Deterministic Policy Gradient, DDPG)與長短期記憶(Long Short-Term Memory, LSTM)負載預測模型的經濟調度方法，旨在實現微電網的最優調度控制。首先，利用LSTM網路來預測微電網的負載信息，以確定發電機輸出與儲能系統的充放電策略；其次，利用DDPG來實現微電網的最佳化經濟調度，為了驗證結果是否符合電力潮流限制，採用位於台灣澎湖群島的七美島微電網模型進行研究，並將所提出的方法與基於經驗的能量管理系統、牛頓結合粒子群體法和深度Q網路(Deep Q Network, DQN)進行比較；最後，通過使用OPAL-RT即時模擬器和浮點數位訊號處理器構建的硬體迴圈(Hardware In the Loop, HIL)系統，充分驗證和展現所提出方法的有效性。

摘要(英)

This study presents an in-depth analysis and exploration of energy management optimization in microgrid. It introduces the structure and scheduling model of microgrid and proposes an economic dispatch method that combines Deep Deterministic Policy Gradient (DDPG) and Long Short-Term Memory (LSTM) load forecasting model to achieve optimal dispatch control in microgrid. Firstly, the LSTM network is utilized to predict the load information in microgrid, determining the output of power generator and the charging/discharging control strategy of a battery energy storage system. Secondly, DDPG is employed to optimize the economic dispatch of the microgrid. To verify the results against power flow constraints, a study is conducted using Cimei Island microgrid model located in the Penghu Islands, Taiwan. The proposed method is compared with experience-based energy management systems, Newton-particle swarm optimization, and Deep Q-Network (DQN). Finally, the effectiveness of the proposed method is fully validated and demonstrated through the Hardware In the Loop (HIL) system, which is built using OPAL-RT real-time simulator with floating-point digital signal processor.

關鍵字(中)

★ 經濟調度
★ 微電網
★ 負載預測
★ 長短期記憶
★ 深度強化學習
★ 確定性策略梯度
★ 能源管理系統
★ 牛頓粒子群優化
★ 深度Q網路

關鍵字(英)

★ Economic dispatch
★ microgrid
★ load forecasting
★ long short-term memory
★ deep reinforcement learning
★ deep deterministic policy gradient, energy management system
★ energy management system
★ Newton-particle swarm optimization
★ deep Q-network

論文目次

摘要 I
Abstract II
目錄 III
圖目錄 VII
表目錄 XI
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 本文貢獻 4
1.4 論文大綱 5
第二章微電網架構與分散式電源控制 6
2.1 微電網簡介 6
2.1.1 IEEE 1547-2003規範 6
2.1.2 七美島微電網之研究架構 7
2.2 微電網控制策略 11
2.3 定功率控制 11
2.4 分級控制 12
2.5 三相座標軸轉換 13
2.5.1 靜止坐標軸 15
2.5.2 同步旋轉座標軸 17
2.6 變流器之實虛功控制與電流控制 18
2.7 分散式電源及控制介紹 19
2.7.1 柴油發電機及燃氣發電機 19
2.7.2 儲能系統 21
第三章公共電業與輔助服務 24
3.1 時間電價 24
3.2 售購電運作方式 26
3.3 輔助服務介紹 27
3.3.1 調頻備轉服務 29
3.3.2 增強型動態調頻備轉 31
3.3.3 即時備轉 32
3.3.4 補充備轉 33
第四章經濟調度模型及深度強化學習演算法 34
4.1 問題描述 34
4.2 演算法介紹 38
4.2.1 基於經驗的EMS[72] 38
4.2.2 Newton-PSO[72] 40
4.2.3 深度學習 41
4.2.3.1 前饋神經網路(Feedforward Neural Network) 41
4.2.3.2 卷積神經網路（Convolutional Neural Network, CNN） 42
4.2.3.3 遞迴神經網路 (Recurrent Neural Network, RNN) 43
4.2.3.4 LSTM 44
4.2.4 強化學習 47
4.2.4.1 Model-based與Model-free 47
4.2.4.2 On-policy與Off-policy 47
4.2.4.3 Value-based與Policy-based 48
4.2.5 深度強化學習 48
4.2.5.1 馬可夫決策過程(Markov Decision Process, MDP) 48
4.2.5.2 DQN 49
4.2.5.3 DDPG 52
第五章模擬結果 55
5.1 情境一之模擬結果 55
5.2 情境二之模擬結果 60
5.3 情境三之模擬結果 65
第六章硬體迴圈規劃與實驗結果 70
6.1 簡介 70
6.2 硬體迴圈規劃 71
6.2.1 即時模擬介紹 71
6.2.2 系統架構 73
6.2.3 系統硬體 74
6.2.3.1 OP4510 74
6.2.3.2 TMS320F28335 76
6.2.3.3 SN65HVD230 78
6.2.4 RT-LAB軟體 79
6.2.4.1 模型分割與命名 81
6.2.4.2 OpComm [79] 82
6.2.4.3 OpIPSocketCtrl [79] 82
6.2.4.4 OpAsyncRecv [79] 83
6.2.4.5 OpAsyncSend [79] 83
6.2.4.6 電路硬體處理器 83
6.2.5 通訊方式 84
6.2.5.1 Ethernet 84
6.2.5.2 CANbus 86
6.3 實驗結果 86
6.3.1 情境一之實驗結果 86
6.3.2 情境二之實驗結果 89
6.3.3 情境三之實驗結果 92
第七章結論與未來展望 96
7.1 結論 96
7.2 未來展望 96
參考文獻 97
作者簡歷 107

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指導教授

林法正(Faa-Jeng Lin)

審核日期

2023-8-3

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