全新电力市场大数据分析陈启鑫 等9787030715166

Contents

1 Introduction to Power Market Data 1

1.1 Overview of Electricity Markets 1

1.2 Organization and Data Disclosure of Electricity Market 4

1.2.1 Transaction Data 5

1.2.2 Price Data 7

1.. Supply and Demand Data 7

1.2.4 System Oraio Data 8

1.2.5 Forecast Data 8

1.2.6 Confidential Data 9

1.3 Conclusions 9

References 9

PartⅠ Load Modeling and Forecasting

2 Load Forecasting with Smart Meter Data 13

2.1 Introduction 13

2.2 Framework 14

. Ensemble Learning for Probabilistic Forecasting 16

..1 ntile Regression Averaging 17

..2 Factor ntile Regression Averaging 18

.. LASSO ntile Regression Averaging 18

..4 ntile Gradient Boosting Regression Tree 19

..5 Rolling Window-Based Forecasting 20

2.4 Case Study 20

2.4.1 Experimental Setups 2

2.4.2 Evaluation Criteria 21

2.4.3 Experimental Results 22

2.5 Conclusions 24

References 24

3 Load Data Cleaning and Forecasting 27

3.1 Introduction 27

3.2 Characteristics of Load Profiles 29

3.2.1 Low-Rank Property of Load Profiles 29

3.2.2 Bad Data in Load Profiles 30

3.3 Methodology 31

3.3.1 Framework 31

3.3.2 Singular Value Thresholding (SVT) 32

3.3.3 ntile RF Regression 34

3.3.4 Load Forecasting 35

3.4 Evaluation Criteria 35

3.4.1 Data Cleaning-Based Criteria 35

3.4.2 Load Forecasting-Based Criteria 35

3.5 Case Study 36

3.5.1 Result of Data Cleaning 36

3.5.2 Da Aea Point Forecast 37

3.5.3 Da Aea Probabilistic Forecast 38

3.6 Conclusions 40

References 40

4 Monthly Electricity Consumption Forecasting 43

4.1 Introduction 43

4.2 Framework 46

4.2.1 Data Collection and Treatment 46

4.2.2 SVECM Forecasting 47

4.. Self-adaptive Screening 48

4.2.4 Novelty and Characteristics of SAS-SVECM 48

4.3 Data Collection and Treatment 48

4.3.1 Data Collection and Tests 49

4.3.2 Seasonal Adjustments Based on X-12-ARIMA 49

4.4 SVECM Forecasting 49

4.4.1 VECM Forecasting 49

4.4.2 Time Series Extrapolation Forecasting 52

4.5 Self-adaptive Screening 53

4.5.1 Influential EEF Identification 53

4.5.2 Influential EEF Grouping 53

4.5.3 Forecasting Performance Evaluation Considering Different EEF Groups 55

4.6 Case Study 56

4.6.1 Basic Data and Tests 56

4.6.2 Electricity Consumption Forecasting Performance Without SAS 58

4.6.3 EC Forecasting Performance with SAS 61

4.6.4 SAS-SVECM Forecasting Comparisons with Other Forecasting Methods 65

4.7 Conclusions 67

References 67

5 Probabilistic Load Forecasting 71

5.1 Introduction 71

5.2 Data and Model 73

5.2.1 Load Dataset Exploration 73

5.2.2 Linear Regression Model Considering Recency-Effects 73

5.3 Pre-Lasso Based Feature Selection 76

5.4 Sparse Penalized ntile Regression (ntile-Lasso) 77

5.4.1 Problem Formulation 77

5.4.2 ADMM Algorithm 78

5.5 Implementation 80

5.6 Case Study 81

5.6.1 Experiment Setups 81

5.6.2 Results 82

5.7 Concluding Remarks 86

References 86

Part Ⅱ Electricity Price Modeling and Forecasting

6 Subspace Characteristics of LMP Data 91

6.1 Introduction 91

6.2 Model and Distribution of LMP 93

6.3 Methodology 

6.3.1 Problem Formulation 96

6.3.2 Basic Framework 97

6.3.3 Principal Component Analysis 98

6.3.4 Recursive Basis Search (Bottom-Up) 98

6.3.5 Hyperplane Detection (Top-down) 100

6.3.6 Short Summary 103

6.4 Case Study 103

6.4.1 Case 1: IEEE 30-Bus System 104

6.4.2 Case 2: IEEE 118-Bus System 106

6.4.3 Case 3: Illinois 200-Bus System 106

6.4.4 Case 4: Southwest Power Pool (SPP) 107

6.4.5 Time Consumption 108

6.5 Discussion and Conclusion 110

6.5.1 Discussion on Potential Applications 110

6.5.2 Conclusion 110

References 111

7 Da-Aea Electricity Price Forecasting 113

7.1 Introduction 113

7.2 Problem Formulation 116

7.2.1 Decoition of LMP 116

7.2.2 Short-Term Forecast for Each Component 117

7.. Summation and Stacking of Individual Forecasts 118

7.3 Methodology 119

7.3.1 Framework 119

7.3.2 Feature Engineering 121

7.3.3 Regression Model Selection and Parameter Tuning 122

7.3.4 Model Stacking with Robust Regression 1

7.3.5 Metrics 124

7.4 Case Study 124

7.4.1 Model Selection Results 125

7.4.2 Componential Results 126

7.4.3 Stacking Results (Overall Improvements) 128

7.4.4 Error Distribution Analysis 129

7.5 Conclusion 132

References 132

8 Economic Impact of Price Forecasting Error 135

8.1 Introduction 135

8.2 General Bidding Models 137

8.2.1 Deterministic Bidding Model 138

8.2.2 Stochastic Bidding Model 139

8.3 Methodology an Faework 141

8.3.1 Forecasting Error Modeling 141

8.3.2 Multiparametric Linear Programming (MPLP)Theory 141

8.3.3 Error Impact Formulation 142

8.3.4 Overall Framework 144

8.4 Case Study 145

8.4.1 Measurement of STPF Error Level 145

8.4.2 Case 1: LSE with Demand Response Programs 147

8.4.3 Case 2: LSE with ESS 148

8.4.4 Case 3: Stochastic LSE Bidding Model 151

8.4.5 Time&es;Cnumption 153

8.5 Conclusions and Future Work 153

References 153

9 LMP Forecasting and FTR Speculation 155

9.1 Introduction 155

9.2 Stochastic Optimization Model 158

9.2.1 Model of FTR Portfolio Construction Problem 158

9.2.2 Scenario-Based Stochastic Optimization Model 159

Contents

9.3 Data-Dnven Framework 160

9.4 Methodology 161

9.4.1 Clustering 161

9.4.2 Mid-Term Probabilistic Forecasting 164

9.4.3 Copulas for Dependence Modeling 165

9.4.4 Training and Evaluation Timeline 166

9.4.5 Scenario Generation 167

9.5 Case Study 167

9.5.1 Data Description 167

9.5.2 Comparison Methods 168

9.5.3 Statistical Validation of ntile Regression 169

9.5.4 Scenario lity Evaluation 169

9.5.5 Impact of Node Reduction with Clustering 171

9.5.6 Revenue and Risk Estimation 171

9.5.7 Sensitivity Analysis on the Number of Clusters 175

9.6 Conclusion 177

References 177

Part Ⅲ Market Bidding Behavior Analysis

10 Pattern Extraction for Bidding Behaviors 183

10.1 Introduction 183

10.2 Assutin and Propose Faework 186

10.2.1 Model Assutin 186

10.2.2 Bidding Data Format 187

10.. Data-Driven Analysis Framework 188

10.3 Data Standardization Processing 188

10.3.1 Filtering Available Capacities 188

10.3.2 Sampling Bidding Curves 189

10.3.3 Unifying Data Length 189

10.3.4 Clipping Extreme Prices 191

10.4 Adaptive Clustering of Bidding Behaviors 191

10.4.1 Distance Measurement 192

10.4.2 K-Medoids Clustering 192

10.4.3 Adaptive Clustering Procedure 192

10.4.4 Clustering Algorithm 193

10.5 AEM Data Description 194

10.5.1 Description of Market Participants 194

10.5.2 Description of Bidding Data 195

10.6 Bidding Pattern Analysis 195

10.6.1 Parameter Setting 196

10.6.2 Bidding Patterns of DUs by Fuel Type 197

10.6.3 Comparison of Similar DUs 201

10.6.4 Discussion 203

10.7 Feature Analysis on Bids 203

10.7.1 Discrete Aggregation Feature 204

10.7.2 Probability Distribution Feature 205

10.7.3 Time Distribution Feature 206

10.8 Conclusions 206

References 208

11 Aggregated Supply Curves Forecasting 211

11.1 Introduction 211

11.2 Market an Faework 214

11.2.1 Market Descriptions 214

11.2.2 Forecasting Framework 215

11.3 Data Integration and Feature Extraction 216

11.3.1 Data Integration 216

11.3.2 Feature Extraction 219

11.4 ASC Forecasting 221

11.4.1 LSTM Model 221

11.4.2 Influencing Factors 222

11.4.3 Training and Forecasting 2

11.4.4 Evaluation Criteria 2

11.5 Case Study 224

11.5.1 Dataset Description 224

11.5.2 Feature Extraction 224

11.5.3 ASC Forecasting 227

11.5.4 Calculation Information 4

11.5.5 Methods Comparison 4

11.6 Conclusion 5

References 

12 Learning Individual Offering Strategy 

12.1 Introduction 

12.2 Data-Driven Market Simulation Framework 242

12.2.1 Market Assutin 242

12.2.2 Offering Data Clustering and Indexing 243

1. Individual Offering Strategy Learning 245

1..1 MFNN Model Structure 246

1..2 MFNN Model Inputs 247

1.. MFNN Model Training 248

1..4 DNN-Based Model Structure 249

12.4 Market Clearing Simulation 249

12.5 Case Study 251

12.5.1 Basic Data 251

12.5.2 Individual Offering Behavior Forecasting 253

12.5.3 Market Simulation 254

12.5.4 Comparison with Current Price Forecasting Methods 259

12.5.5 Calculation Efficiency 260

12.6 Conclusions 260

References 261

13 Reward Function Identification of GENCOs 265

13.1 Introduction 265

13.2 Assutin an Faework 267

13.2.1 Market Assutin 267

13.2.2 Data-Driven Framework 267

13.3 Bidding Decision Process Formulation 269

13.3.1 Markov Decision Process in Wholesale Markets 269

13.3.2 Reinforcement Learning Process 270

13.3.3 Bidding Data Integration 270

13.4 Reward Function Identification 271

13.4.1 Deep Inverse Reinforcement Learning Algorithm 271

13.4.2 Discretization Methods for States and Actions 273

13.5 Bidding Behavior Simulation 273

13.5.1 DN-Based Bidding Simulation Model 273

13.5.2 Value Function and -Network 274

13.6 Case Study 275

13.6.1 Dataset Description 275

13.6.2 Parameter Setting 276

13.6.3 Reward Function Identification 276

13.6.4 Bidding Behavior Simulation 281

13.7 Conclusions 282

References 283