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研究生： Wiwit Marta Pangesty Putri
Wiwit Marta Pangesty Putri
論文名稱： 應用合作賽局於不確定性情境下之電力總體規劃
Cooperative Game Theory by Considering Uncertainty in Electricity Master Plan Development
指導教授： 林希偉
Shi-Woei Lin
Erwin Widodo
Erwin Widodo
口試委員: 葉瑞徽
Ruey Huei Yeh
王孔政
Kung-Jeng Wang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 81
中文關鍵詞： 經濟調度 、改善聚合規則的隨機優化模型 、合作賽局理論 、不確定性
外文關鍵詞： Economic Dispatch, Improved Aggregating-Rule-based Stochastic Optimization (I-ARSO), Cooperative Game Theory, Uncertainties
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    • 相較於傳統的確定性模型，將隨機或不確定性行為納入電力經濟調度問題之建模，不僅更貼近現實世界中的決策，並有助於降低資源調度的成本。此外，若在模型中考量其他再生能源（例如水力發電廠）的應用，可能進一步降低成本。本研究提出一個以促成最低調度成本為目標，結合火力電廠與水力電廠的隨機經濟調度（stochastic economic dispatch, SED）模型，並透過求解合作賽局模型以確認最低投資成本的聯盟。為解決隨機最佳化模型之高運算需求問題，本研究特別採用一個基於改善聚合規則的隨機優化（improved aggregating-rule-based stochastic optimization, I-ARSO）方法，將隨機經濟調度模型拆解為兩個階段。在第一階段，透過蒙地卡羅模擬產生Ｎ個電力需求與發電機的可用性情境，接著運用粒子群優化與人工魚群演算法對電力分配進行最佳化。在第二階段，模擬第一階段得到的每個最佳方案以評估相對應的預期運營成本。最後，透過合作賽局模型決定所有參與者之最佳安排，以確何可以得到包含預期營運成本、固定成本、變動成本、投資成本以及成本分配之最低總成本，。

    Modeling uncertain behavior through stochastic operating strategies in economic dispatch problems may better formulate the nature of real-world decisions and help reduce cost in resource scheduling compared to traditional deterministic approaches. Moreover, the involvement of renewable energy, such as hydro power plant, may further reduce the cost. This study proposes a stochastic economic dispatch (SED) model in thermal and hydro power plants to seek minimum dispatch costs. A cooperative game-theoretic model was also formulated to determine the coalition that will lead to minimum investment cost. In particular, for tackling the issue of high computational requirements when the stochastic optimization problem becomes bigger, SED model in this study was decomposed into two stages based on an improved aggregating-rule-based stochastic optimization (I-ARSO) approach. At the first stage, N Monte Carlo scenarios of power demand and generator availability were generated, and then power dispatch was optimized using the hybrid intelligent algorithm based on particle swarm optimization and artificial fish swarm algorithm. At the second stage, each optimal scenario is simulated to evaluate the corresponding expected operating cost. Finally, cooperative game theory will pick the best arrangement for all players to get the minimum total cost, which includes expected operating cost, fixed cost, variable cost, and investment cost as well as the cost allocation.

    摘要 iv ABSTRACT v ACKNOWLEDGMENT vi TABLE OF CONTENTS vii LIST OF TABLES ix LIST OF FIGURES x CHAPTER 1 INTRODUCTION 1 1.1 Background 1 1.2 Research Objectives 6 1.3 Scopes and Assumption 6 1.4 Research Outlines 6 CHAPTER 2 LITERATURE REVIEW 8 2.1 Economic Dispatch 8 2.2 Game Theory 11 2.3 Stochastic Modeling 13 2.4 Related Research 15 CHAPTER 3 RESEARCH METHODOLOGY 24 3.1 Research Flowchart 24 3.2 Variables and Data 25 3.3 Model Development 27 3.3.1 Mathematical Model Formulation 27 3.3.2 Defining Characteristic Cost Function 28 3.3.3 Utilizing IARSO with Hybrid Intelligent Algorithms (PSO and AFSA) 28 3.3.4 Cost Allocation 36 CHAPTER 4 DATA PROCESSING 40 4.1 Data Collection and Processing 40 4.2 Cost Summary 44 CHAPTER 5 RESULT AND DISCUSSION 45 5.1 Parameter Setting for Hybrid Intelligent Algorithms (PSO and AFSA) 45 5.2 Algorithm Testing on Benchmark Instance 50 5.3 Algorithm Testing on Area 3 (Mahakam System) 53 5.4 Cooperation Evaluation 53 5.5 Cost Allocation 56 CHAPTER 6 CONCLUSION AND RECOMMENDATION 60 6.1 Conclusion 60 6.2 Recommendation for Future Research 61 REFERENCES 62 APPENDIX 69

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