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系統識別號 U0026-0308201614223400
論文名稱(中文) 具不確定性分散式發電之智慧電網下的需求端管理: 以二階段穩健最佳化方法求解
論文名稱(英文) Demand Side Management for Smart Grids with Distributed Uncertain Generation Using Two-Stage Robust Optimization
校院名稱 成功大學
系所名稱(中) 資訊管理研究所
系所名稱(英) Institute of Information Management
學年度 104
學期 2
出版年 105
研究生(中文) 徐宇鋒
研究生(英文) U-Fong Choi
學號 R76044124
學位類別 碩士
語文別 英文
論文頁數 52頁
口試委員 指導教授-劉任修
口試委員-翁慈宗
口試委員-王惠嘉
口試委員-蔡青志
中文關鍵字 需求端管理  即時計價  穩健最佳化  智慧電網 
英文關鍵字 Demand Side Management  Real-time pricing  Robust Optimization  Smart Grids 
學科別分類
中文摘要 本論文主要探討在即時電價下的電力成本最小化問題。與過去的研究不同,在本研究的問題中所有工作皆為不可中斷。另外,未來的智慧電網是有能力整合分散式電力資源以及電力儲存設備到電力系統中,因此本研究假設所有使用者都裝配太陽能發電板以及電池。使用者可以向電網購買電力去滿足他們的電力需求,除此之外,他們可以使用太陽能發電電量以及電池電力,或在尖峰用電時段把多餘的電力賣給電網以降低使用者自身的電費。由於太陽能的不確定性所引起的困難,傳統的最佳化方法不能夠有效產出電力成本最少化問題的可靠的答案。
本研究改為使用穩健最佳化方法進行求解,將問題建構成一個二階段穩健最佳化的模型並使用限制式及變數產生演算法(column-and-constraint generation algorithm) 求解。本研究另外提出一套名為穩健需求端管理演算法(robust demand side management algorithm)。新的演算法由兩部份組成:第一部分為用於產生所有使用者的用電電器排程;第二部分為基於動態規劃的演算發,用於決定如何使用太陽能發電板及電池。根據實驗結果顯示,新演算法的收斂速度較限制式及變數產生演算法快。藉由新演算法可以有效解決具有不確定再生能源的工作排程問題,最小化每一個使用者的用電成本及降低電力系統的peak-to-average ratio (PAR)。
英文摘要 In this thesis, we investigate the problem of energy cost minimization under the real-time pricing model. The jobs in our problem are non-interruptible, which is different from those examined in prior studies. Moreover, the future smart grid is capable of integrating distributed energy resources and storage equipment into the energy system. We assume that each user is equipped with a photovoltaic panel and a battery. Users can purchase energy from the grid to fulfill their energy demands. Moreover, they can use the renewable energy produced from the photovoltaic panel and the energy drawn from the battery or sell it back to the grid during peak hours in order to lower their electricity bills. Because of the difficulty caused by intermittent renewable energy sources, conventional optimization techniques cannot produce a reliable solution to the energy cost minimization problem. We use a robust optimization approach to solve the problem, and the problem is formulated as a two-stage robust optimization model. We apply a column-and-constraint generation (C&CG) algorithm to obtain the solution to the problem. We also propose a new algorithm called the robust demand side management (RDSM) algorithm. The new algorithm consists of two portions: The first portion is a heuristic-based algorithm and is used to produce the appliance schedules for all users. The second portion is based on dynamic programming and is used to utilize the photovoltaic panel and the battery. According to the simulation results, the proposed new algorithm can produce a solution with faster convergence as compared with the C&CG algorithm. It can effectively handle the scheduling problem with uncertain renewable energy, minimize the energy cost for each user and lower the peak-to-average ratio (PAR) of the energy system.
論文目次 Table of Contents
Chinese Abstract i
Abstract ii
Acknowledgements iii
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Research Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Chapter 2 Related Works 6
2.1 Demand Side Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Energy Trading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Renewable Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Robust Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Chapter 3 Research Methodology 13
3.1 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 Scheduling Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Dynamic Programming for Battery Management . . . . . . . . . . . . . . . 21
3.4 Demand Side Management Algorithm . . . . . . . . . . . . . . . . . . . . . 25
3.5 Robust Demand Side Management . . . . . . . . . . . . . . . . . . . . . . . 26
Chapter 4 Experiment 34
4.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2 Algorithm Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Chapter 5 Conclusion and Future Work 48
References 50
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