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系統識別號 U0026-1607201815222800
論文名稱(中文) 以資料包絡分析法與隨機混合互補問題求解納許均衡於雙階燃煤電力市場
論文名稱(英文) Data Envelopment Analysis and Stochastic Mixed Complementarity Problem for Solving Nash Equilibrium in Two-level Coal-fired Electricity Market
校院名稱 成功大學
系所名稱(中) 製造資訊與系統研究所
系所名稱(英) Institute of Manufacturing Information and Systems
學年度 106
學期 2
出版年 107
研究生(中文) 曾蓁宜
研究生(英文) Chin-Yi Tseng
學號 P96054032
學位類別 碩士
語文別 英文
論文頁數 142頁
口試委員 指導教授-李家岩
口試委員-高強
口試委員-王逸琳
口試委員-楊大和
中文關鍵字 資料包絡分析法  混合互補問題  納許均衡  兩階電力市場  燃煤發電廠  效率評估  隨機規劃  穩健最佳化 
英文關鍵字 Data envelopment analysis  Mixed complementarity problem  Nash equilibrium  Two-level electricity power market  Coal-fired power plant  Efficiency measurement  Stochastic programming  Robust optimization 
學科別分類
中文摘要 在電力自由化的趨勢下,電力市場變得更為競爭。實際上,由於電力系統的能源特徵,只要市場中存在一些限制,如地理位置所造成的運輸限制或考量政治和經濟因素,即使市場存在多家發電企業,仍可運行市場力量。因此,對於一些像中國這樣的國家,發電廠與輸電系統營運企業所組成的市場結構仍屬於不完全競爭,並可能導致生產低效率。
本研究連結上游的燃煤電廠與下游的電網營運企業,並研究納許均衡,其概念表明市場參與者在理性低效率時仍可以為電力產業帶來更多利潤。我們提出以資料包絡分析法和混合互補問題在基於估計之生產可能集合下找出兩階電力市場中的納許均衡。根據市場力量運作的假設不同,我們探討兩種確定型模型,並且應用方向距離函數來估計朝向納許均衡的方向以評估各個決策單位的效率。此外,為了瞭解價格參數的影響,本研究採用了敏感度分析和用於不確定性的最佳化方法,隨機規劃與穩健最佳化被分別用於發展隨機混合互補問題以求解不確定參數下的納許均衡。實證研究以中國北部與東北地區之燃煤電力市場為例,以驗證本研究所提出之模型。
本研究結果顯示兩階電力市場中的最佳均衡解,而基於納許均衡解之效率分析則為提高兩階電力市場系統中各個決策單位之生產力提出了管理上的洞察。對參數影響的討論則是說明上游市場中的價格對均衡解會有顯著的影響,而透過分析由隨機混合互補問題所求得之納許均衡解,說明了均衡模型考量參數不確定的必要性。
英文摘要 Under the trend of electricity liberalization, the electricity market becomes more competitive. In fact, due to the energy-type features of electricity power systems, even the market with several generation firms can exercise the market power if there are some limitations, such as transportation restrictions caused by geographical locations or political and economic factors. Thus, for some countries like China, the market structures of electricity generation plants and transmission system operators (TSO) are still imperfectly competitive which may lead to productive inefficiency.
This study connects coal-fired plants in upstream market and grid operators in downstream market together, and then investigate the Nash equilibrium which claims “rational inefficiency” but bring more profits for power industries. We propose the data envelopment analysis (DEA) and mixed complementarity problem (MiCP) to identify the Nash equilibrium in the estimated production possibility set of the two-level electricity market. Two cases of deterministic models based on different assumptions about operation of market power are discussed. Then the directional distance function approach is applied to estimate the direction toward the Nash equilibrium and measure the efficiency. Additionally, to understand the impacts of price parameters on the equilibrium results, the sensitivity analysis and optimization approaches for uncertainty are employed. This study develops the stochastic mixed complementarity problem for solving the Nash equilibrium with stochastic programming and robust optimization respectively. An empirical study of the electricity market in both north and northeast regions of China is conducted to validate the proposed models.
The results show the optimal equilibrium solution in the two-level electricity market, and the efficiency analysis with respect to the Nash solution provides the managerial insights to drive the productivity in the two-level electricity market system. The discussion for the parameter reveals that the price in the upstream market have significant influence to the equilibrium solution. And the analysis of Nash solutions generated from stochastic mixed complementarity problem illustrates the necessity to model with uncertain parameters.
論文目次 中文摘要 I
Abstract II
Table of Contents IV
List of Tables VII
List of Figures X
Terminology and Notations XIII
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Problem Description and Research Purposes 5
1.3 Research Overview 8
Chapter 2 Literature Review 9
2.1 Data Envelopment Analysis 9
2.1.1 Production Possibility Set 11
2.1.2 Efficiency Measurement 14
2.2 Equilibrium Model 16
2.2.1 Equilibrium Model and Mixed Complementarity Problem 17
2.2.2 Nash Equilibrium Model with the Production Possibility Set 24
2.3 Uncertainty in Optimization 26
2.3.1 Stochastic Programming 27
2.3.2 Robust Optimization 29
2.4 Conclusion and Discussion 31
Chapter 3 Deterministic Nash Equilibrium Model 33
3.1 Research Framework 33
3.2 Problem Definition and Structuring 35
3.3 Data Collection 38
3.4 Deterministic Nash Equilibrium Model 41
3.4.1 Case 1: Grid companies have the market power 42
3.4.2 Case 2: Both have the same market power 48
3.5 Empirical Study 53
3.6 Efficiency Analysis 60
3.7 Summary and Discussion 64
Chapter 4 Stochastic Nash Equilibrium Model 66
4.1 Sensitivity Analysis 66
4.1.1 Sensitivity Analysis for the Parameters of Contract Price 67
4.1.2 Sensitivity Analysis for the Parameter of Selling Price 73
4.2 Stochastic Programming 81
4.2.1 Stochastic Nash Equilibrium Model with Uncertain Contract Price 81
4.2.2 Stochastic Nash Equilibrium Model with Uncertain Selling Price 90
4.2.3 Empirical Study: Stochastic Nash Equilibrium Model 95
4.3 Robust Optimization 109
4.3.1 Robust Optimization for the Nash Equilibrium Model with Uncertain Contract Price 110
4.3.2 Empirical Study: Robust Optimization Model 120
4.4 Summary and Discussion 125
Chapter 5 Conclusion and Future Research 129
5.1 Conclusion 129
5.2 Contribution of This Study 130
5.3 Future Work 132
References 134
Appendix 140
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