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研究生： 米蘭諾
Melanio Pech Jr.
論文名稱： 利用具鄰居影響的粒子群聚方法來解決最小能量損失的最佳能源派遣問題
Optimal Dispatch with Minimal Power Transmission Loss Using a Novel Neighbor-Influenced Particle Swarm
指導教授： 蘇豐文
Soo, Von-Wun
口試委員: 周志遠
Chou, Jerry
蔡孟伸
Tsai, Men-Shen
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年： 2012
畢業學年度： 100
語文別： 英文
論文頁數： 65
中文關鍵詞： 粒子群優化 、優化最佳能源派遣 、電力系統
外文關鍵詞： particle swarm optimization, optimal economic dispatch, electric power system
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    • 粒子群優化是模擬動物個體合作行為的一種進化式智慧型演算法用來解決找尋最佳解的問題。其演算法主要利用每一個粒子依據其之前的經驗法則在所要解決的問題範圍內不斷的自我移動並搜尋。傳統的粒子群優化方式缺乏考慮於局部空間中粒子與粒子的互動與經驗法則交流，因此我們利用每個粒子其周圍的粒子來改善粒子群優化的演算法。此篇論文把改善後的粒子群優化方法利用在電力網路中經濟調度的問題藉以達成電力供給與需求的平衡。從實驗結果得知我們改善後的粒子群優化方式成功的找尋到問題的最佳解並且降低計算量，此一成果將可以進一步運用於大型且非線性的複雜問題。
    關鍵字: 粒子群，優化最佳能源派遣， 電力系統

    As a swarm intelligent technique, particle swam optimization (PSO) is inherently an evolutionary algorithm that simulates the animal collective behaviors. Since each individual has a different experience based on its position, each particle produces its own movement based on these experiences. The original PSO lacks deeper interaction between individuals at a local level. This implementation of PSO will explore the influence that individuals in its neighborhood have on its search and how it can improve on the capabilities of PSO. Therefore, in this study, a variation will be added to the current PSO technique and applied to Economic Dispatch (ED) problem.
    ED is an integral component in planning and operation of power systems, because it results in the most cost-efficient method of power delivery while serving the demand. In solving ED there are also other considerations such as power loss that play a large role in cost. More recently, evolutionary algorithms have replaced conventional methods of solving these power system problems. They require less computation and are also very useful in large non-linear problems.

    中文摘要 I Abstract II Acknowledgments III Table of Contents IV Table of Figures VIII List of Tables IX Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Particle Swarm Optimization 2 1.1.1.1 Terms used in PSO 2 1.1.2 Optimal Power Flow 4 1.1.3 Economic Dispatch 6 1.2 Motivation 6 1.3 Contribution 7 1.4 Problem Overview 7 1.5 Thesis Organization 8 Chapter 2 Related Work 9 2.1 PSO for Applications in Power Systems 9 2.1.1 The Basic Concept 10 2.1.2 PSO Parameters 11 2.1.3 PSO Algorithm 12 2.1.4 PSO Termination Criterion 13 2.2 PSO Variants and Modifications 13 2.2.1 Binary and Discrete PSO 14 2.2.2 Hybrids 14 2.2.3 Neighborhood 14 2.3 PSO Applications in Economic Dispatch 15 Chapter 3 Problem formulation 18 3.1 Constraints 19 3.2 Optimal Economic Dispatch 20 3.2.1 Objective function 20 3.2.1.1 Incremental generation costs 21 3.2.2 Power Loss 22 Chapter 4 Methodology 24 4.1 Methodology Overview 24 4.2 Symbols and Definitions 26 4.2.1 Notation 26 4.3 Research Steps 26 4.3.1 Steps for PSO 27 4.3.1.1 Update Velocity and position 29 4.3.1.2 Constraint violation 29 4.3.2 PSO Parameter Selection 30 4.3.3 PSO Parameters 31 4.3.4 PSO applied to ED with Loss Minimization 32 4.3.4.1 Fitness function 33 4.3.4.2 Power Mismatch Criteria 33 4.3.5 Generating Feasible Initial Solutions 33 4.4 PSO algorithm Definitions 33 4.4.1 Off the Shelf PSO 34 4.4.2 Aggregating PSO 35 4.4.3 Aggregating PSO with Repulsion Shields 36 4.4.4 Repulsive PSO with Repulsion Shields 37 Chapter 5 Results and Discussion 38 5.1 Data Formats 39 5.2 Benchmark 39 5.2.1 Benchmark Summary 44 5.3 Case Studies 45 5.3.1 Case I 45 5.3.2 Case II 46 5.3.3 Case III 47 5.3.4 Case IV 48 5.3.5 Case V 50 5.4 Mismatch Tolerance 51 5.5 Comparison of results 51 5.6 Summary and conclusion for cases 55 Chapter 6 Conclusion and Future Work 56 6.1 Conclusion 56 6.2 Future Work 57 Reference 58 Chapter 7 Appendix 62 7.1 6 Bus System 62 7.2 IEEE 30 Bus System 63

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