本論文提出混合基因演算法與蟻群最佳化方法(Genetic Algorithm-Ant Colony Optimization, GACO)，來求解機組排程問題(Unit Commitment, UC)，並將所求得的結果和文獻方法做一比較。文中應用基因演算法(Genetic Algorithm, GA)所具有的交配與突變，來強化蟻群最佳化(Ant Colony Optimization, ACO)的效能。其應用基因演算法的目的為改善螞蟻搜索的品質，因為經由費洛濃隨機所產生的解，並不能確保其為最佳解，因此藉由基因演算法讓所求得的解自身做優化，進而產生更佳的解，此不但可以增加局部搜尋的能力，還可以快速搜索到所要求的最佳解，並且提早收斂。本論文的另一個目標是研究污染排放限制在發電規劃調度的影響。這個研究目標的構想是來自於減少氣候變遷的負面影響。在實際電力市場結構中，獨立發電業者必須處理幾種複雜的問題。這些問題來自於不可預測的現貨市場價格，機組排程和交易規劃。除了找到最小成本調度及機組排程決策，並同時最大化利潤，發電業者的調度模型必須包括交易決策如現貨市場的買與賣。在本論文中提出結合碳交易及現貨市場的模型，幫助發電業者決定他們的排程可以產生最大的利潤。

In this thesis, the Hybrid Genetic Algorithm-Ant Colony Optimization (GACO) approach is presented to solve the unit commitment problem (UC), and comparison with the results obtained using literature methods. Then this thesis applied the ability of the Genetic Algorithm (GA) operated after Ant Colony Optimization (ACO) can promote the ACO efficiency. The objective of GA is to improve the searching quality of ants by optimizing themselves to generate a better result, because the ants produced randomly by pheromone process are not necessary better. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. The other objective of this thesis is to investigate an influence of emission constraints on generation scheduling. The motivation for this objective comes from the efforts to reduce negative trends in a climate change. In this market structure, the independent power producers have to deal with several complex issues arising from uncertainties in spot market prices, and technical constraints which need to be considered while scheduling generation and trading for the next day. In addition to finding dispatch and unit commitment decisions while maximizing its profit, their scheduling models should include trading decisions like spot-market buy and sell. The model proposed in this thesis build on the combined carbon finance and spot market formulation, and help generators in deciding on when these commitments could be beneficial.

Contents
Chinese Abstract………………………………………………...……I
Abstract..................................................................................................II
Contents…………………………………………………………...…III
List of Tables........................................................................................VI
List of Figures……………………………………………………VIII
Chapter 1 Introduction……………………………………...………….1
1.1 Motivation……………………………………………………1
1.2 Literature Review and Propose of the Thesis………………...3
1.3 Organization of the Thesis……………………………………4
Chapter 2 Carbon Finance……………..………………………...…….5
2.1 Introduction…..………………………………........................5
2.2 Sources of Carbon Finance….….………...………………….5
2.2.1Global warming by CO2…………………..…………….6
2.2.2 The Physical Impacts of Global Warming………..…….9
2.2.3 The Political Context of Global Warming…………….11
2.3 Carbon Finance in Theory and Practice……………………12
2.3.1 Three Mechanisms in the Kyoto Protocol………….…12
2.3.2 The Advantage of Using the Trading Mechanism…….14
2.3.3 The European Union Emission Trading Scheme……...15
2.3.4 National Allocation Plan for the United Kingdom
the U.K……………………………………………….17
2.3.5 Carbon Markets in the United States and Australia…..18
2.3.6 Trading Through the Clean Development Mechanism
in Dutch…………..………………………………….19
Chapter 3 The Implications of Carbon Finance on Power Markets..20
3.1 Introduction…………………………………………………20
3.2 Power Markets…...………...……………………………..…20
3.2.1 Primary Market Structures……………………………21
3.2.2 Risks and Opportunities of Carbon Finance within the
Electric Industry……………………...………………24
3.3 Problem Formulation for Generation Scheduling..…..….26
3.3.1 Traditional Unit Commitment……..…………………27
3.3.2 Problem Formulation for Traditional UC……………28
3.4 Price-Based Unit Commitment with Carbon Trading..…….31
3.4.1 Emission Function...……….….……………………..31
3.4.2 Problem Formulation for Price-Based UC with
Carbon Trading…...……….….……………………..34
Chapter 4 Ant Colony Optimization Plus Genetic Algorithm and
Application Principles…………………...………..........….37
4.1 Introduction…………………………………………………37
4.2 Foundations of Genetic Algorithms………………………...38
4.2.1 Encoding Issue………………………………………..40
4.2.2 Genetic Operators………………….………………….41
4.2.3 Selection………………………………………………44
4.3 Ant Colony Optimization Theory………...…………………46
4.3.1 Double Bridge Experiments……..……………………47
4.3.2 Implementing ACO Algorithms………………………48
4.4 The Performance of ACO plus GA………………………….50
4.4.1 Steps to Solve the Traditional UC by GACO………....53
4.4.2 Steps to Solve the Price-Based UC with Carbon
Trading by GACO…...………...……………………..56
Chapter 5 Simulations and Results………………...…………………60
5.1 Introduction……….……………………………………….60
5.2 The test of Case 1－A Simulation for Traditional UC….....61
5.2.1 A Meaningful Comparison of the Different
Algorithms…………………………………………...62
5.2.2 The Behavior of GACO…………………….………..66
5.2.3 Robustness Test……………………………………....68
5.3 The Simulations for Price-Based UC with Carbon Trading
…………….………………………………………………70
5.3.1 The Test of Case 2—Finding the Bilateral Profits….. 71
5.3.2 The Test of Case 3—Finding the Maximal Profits…...74
Chapter 6 Conclusions…………………….………..………………..85
6.1 Conclusions……………….................................................85
6.2 Prospects for the Future………………...............................86
Refference….…………………………………………………………88

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