再生能源高佔比下之強健最佳機組排程｜國立清華大學博碩士論文庫

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研究生：	黃強 Huang, Chiang
論文名稱：	再生能源高佔比下之強健最佳機組排程 Robust Unit Commitment with High Penetration Levels of Renewable Energy
指導教授：	朱家齊 Chu, Chia-Chi
口試委員:	洪穎怡 Hong, Ying-Yi 吳有基 Wu, Yu-Chi 連國龍 Lian, Kuo-Lung
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 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：	英文
論文頁數：	72
中文關鍵詞：	再生能源、鴨子曲線、機組排程、強健最佳化、強健機組排程
外文關鍵詞：	Renewable energy, Duck curve, Unit commitment, Robust optimization, Robust unit commitment
相關次數：	點閱：2 下載：0
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

因環保意識抬頭，綠色能源佔比增加，淨負載曲線變為鴨子曲線，如何克服鴨子曲線與負載需求的爬升率，成為重要的議題。本論文以 2025年台灣能源計畫為例，探討風力以外之再生能源發電曲線固定時，用抽蓄機組來解決鴨子曲線於負載爬升時的機組排程問題，並使用混合整數線性規劃來求得最佳解。
再者考慮風力的不穩定性，使用兩種強健最佳化建置機組排程，分別為靜態強健最佳化與適應強健最佳化。兩種都使用不確定集合來表示風力不穩定的輸出。靜態強健最佳化模型為單一層的最佳化，在處理風力不穩定與其他限制條件時，讓可行解範圍較無彈性，使問題容易得到無解。適應強健最佳化模型改善了靜態強健最佳化模型，創建第二層在不穩定風力後可做的決策，使可行解範圍更為彈性，以處理更為不穩定的風力。我們使用新的 MATLAB 模組 RSOME 外掛上 gurobi 商業化套件模擬台灣電力系統並以現實台灣的電力與水文系統進行測試及分析，比較與討論加入與未加入強健最佳化的機組排程模型，並呈現模擬成果。
本論文以機組的六階段線性成本模型，使求解與非線性成本模型相比更加快速，並提出兩種應用於台灣電力系統的強健最佳化模型來處理在不同的不穩定閾度下的風力，同時搭配抽蓄機組與線路限制，完成安全約束機組排程。

As environmental awareness rises, the proportion of renewable energy increases. The net load curve becomes a duck curve. How to overcome the ramping rate of the load demand of the duck curve around 3-5 pm has become an important issue.This thesis explores the use of pumped hydro storage units to solve the unit commitment problem of load climbing of the duck curve under fixed renewable energy output, and uses mixed-integer linear programming to obtain the optimal solution.
Furthermore, considering the uncertainty of the wind power, two for-mulations of robust optimization unit commitment are used, namely the static robust optimization and the adaptive robust optimization. Both for-mulations use the uncertainty set to represent uncertain wind power output. The static robust optimization is a single-stage optimization. When dealing with uncertain of wind power and other constraints, the range of feasible solutions is relatively inflexible, so that the problem is not easily solved. Adaptive robust optimization improves the static robust optimization, cre-ating a second-stage of decisions that can be made after uncertain wind power made, making the range of feasible solutions more flexible to handle wind power. We use the new MATLAB module whose name is ”RSOME” to plug in the Gurobi commercial kit to simulate the Taiwan power system and test and analyze it with real Taiwan power system and hydro-logical systems, compare and discuss the schedule formulations with and without the addition of robust optimization, and present simulation results.
This thesis uses the six-segment linear cost model of the unit to make the solution faster than the nonlinear cost model, and proposes two robust optimization formulations applied to the Taiwan power system to deal with wind power generations at different uncertainty budget period, At the same time, with PHS units and network constraints, complete security-constrained unit commitment.

Abstract in Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract in English . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 System Descriptions of Taiwan Power Systems . . . . . . 3
1.3 Duck Curve in Future of Taiwan . . . . . . . . . . . . . . 4
1.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Robust Unit Commitment . . . . . . . . . . . . . . . . . . . . . 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Formulation of Classical Unit Commitment Problems . . 10
2.2.1 Objective Function . . . . . . . . . . . . . . . . . 10
2.2.2 Constraints . . . . . . . . . . . . . . . . . . . . . 10
2.3 Overview of Robust Optimization . . . . . . . . . . . . . 15
2.3.1 Compact Formulations . . . . . . . . . . . . . . . 15
2.3.2 Static Robust Optimization . . . . . . . . . . . . . 15
2.3.3 Adaptive Robust Optimization . . . . . . . . . . . 21
2.4 summary . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3 Case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Deterministic UC Without Uncertain Data in Taiwan Power
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1 Case 1A: Unit commitment in summer of 2025 . . 31
3.2.2 Case 1B: Unit commitment in Winter of 2025 . . 36
3.3 Static RUC with uncertain data in Taiwan Power System . 42
3.3.1 Case 2A: Static robust unit commitment in summer
of 2025 . . . . . . . . . . . . . . . . . . . . . . . 44
3.3.2 Case 2B: Static robust unit commitment in winter
of 2025 . . . . . . . . . . . . . . . . . . . . . . . 48
3.4 Adaptive RUC with uncertain data in Taiwan power system 52
3.4.1 Case 3A: Adaptive robust unit commitment in summer
of 2025 . . . . . . . . . . . . . . . . . . . . . 52
3.4.2 Case 3B: Adaptive robust unit commitment in winter
of 2025 . . . . . . . . . . . . . . . . . . . . . 56
3.5 summary . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 63
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Appendix A:Application of RSOME Solving UC Problem . . . . . 68
A.1 Syntax of RSOME when Solving UC Problem . . . . . . . 68
A.2 Example of RSOME when Solving UC Problem . . . . . 69
A.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 72
                                

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