本文首先分析了工業、商業和居民負荷的需求響應能力，建立了其需求響應模型，基於模糊C均值聚類對不同的需求響應資源進行聚類，將分類結果作為定量分析每類需求響應資源可挖掘潛力的指標；接下來，對負荷的構成進行分析，並考慮了溫度因素對負荷的影響，建立了基於BP神經網絡的日前負荷預測模型，為制定合理的需求響應措施奠定基礎；接下來，在負荷需求響應模型的基礎上，建立考慮需求響應差異性的大規模風電消納模型，提出基於差分進化粒子群算法的模型求解方法；最後在一個含大規模風電的10機系統中，對考慮需求響應差異性的大規模風電消納模型進行求解，提出日前風電消納策略，求解結果表明，本策略可有效提高風電消納水平、減少火電機組啟停次數，對電力系統運行經濟性起到了積極的作用。

First of all this thesis analysis the ability of demand response(DR) of the industrial, commercial and residential load, and build demand response models of them, and cluster different demand response resources based on fuzzy C-means algorithm, make the cluster results as targets to quantitative analysis the potential that can be mined of every kind of demand response. Second this thesis analysis the constituent of the loads, and consider the influence of temperature, and build a prediction model of load day-ahead based on BP(back propagation) neural network, and it lays a foundation for making proper measures of demand response. Next build a large scale wind power absorption model considering demand response diversity, and propose a solution method for the model based on differential evolution particle swarm optimization. Last solve a large scale wind power absorption model considering demand response diversity in a 10-motor system possesses large scale wind power, offer a strategy to one-day-before wind power absorption, and the results of the solution shows the proposed strategy can heighten the effects of wind power absorption, reduce the generator’s start/stop count, and make positive contribution to Power system operation economy.

ABSTRACT II
摘要…………………………………………………………………………….III
ACKNOWLEDGEMENTS IV
LIST OF ILLUSTRATIONS VII
LIST OF TABLES VIII
1. INTRODUCTION 1
1.1 Research background and purpose 1
1.1.1 Research background 1
1.1.2 Research purposes 5
1.2 Contribution of this thesis 6
1.3 Thesis structure 7
2. CLASSIFICATION METHOD OF LOADS BASED ON DEMAND RESPONSE DIVERSITY 8
2.1 Classification of demand response features of various loads 8
2.1.1 Analysis of demand response features of industrial loads 8
2.1.2 Models of demand response of loads 9
2.1.3 Classification method of demand response resources based on fuzzy C-means algorithm 10
2.2 Models of demand response of loads 11
2.2.1 Price elasticity matrix model 12
2.2.2 Consumer psychology model 14
2.3 Classification method of demand response resources based on fuzzy C-means algorithm 16
2.3.1 Fuzzy C-means algorithm 17
2.3.2 Classification method of demand response resources 18
2.4 Analysis of examples 19
2.5 Brief summary 22
3.PREDICTION MODEL OF LOAD ONE-DAY-BEFORE CONSIDERING TEMPERATURE FACTOR 24
3.1 Analysis of the constituent of the loads 24
3.2 BP neural network algorithm 25
3.2.1 Structure of BP neural network 25
3.2.2 Correction of weight and threshold value of BP neural network 27
3.2.3 Steps of BP neural network algorithm 28
3.3 Prediction model of load one-day-before based on BP neural network 30
3.4 Analysis of examples 31
3.5 Brief summary 34
4. MODEL OF LARGE SCALE WIND POWER ABSORPTION CONSIDERING DEMAND RESPONSE DIVERSITY 35
4.1 Model building 35
4.1.1 Target function 35
4.1.2 constraint condition 37
4.1.3 description of variables need to be optimized 39
4.2 Solution method for the model based on differential evolution particle swarm optimization (DEPSO) 40
4.2.1 Basic construction and calculation procedure of DEPSO 40
4.2.2 Method to solve model 45
4.3 Analysis of examples 47
4.3.1 Example introduction 47
4.3.2 Solution results 52
4.4 Brief summary 60
5. SUMMARY AND PROSPECT 62
5.1 Summary 62
5.2 Prospect 63
6. REFERENCES 64

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