Sustainable Hybrid Energy Systems
Discovering comprehensive approaches to build sustainable hybrid energy systems
Hybridization is the eternal theme of human energy utilization. However, it has never been more important than it is now because of the urgency of promoting energy transition and achieving carbon neutrality. Therefore, exploring the design, combustion, operation, and policy challenges of sustainable hybrid energy systems becomes increasingly important.
Sustainable Hybrid Energy Systems: Carbon Neutral Approaches, Modeling, and Case Studies provides a detailed explanation of these aspects. Dividing hybrid energy systems into three categories—co-located, co-combusted, and co-operated, this book emphasizes the deployment optimization, emission quota allocation, scheduling coordination, and renewable portfolio standards implementation of these systems. The results are essential tools for understanding the current and future of multi-input single-output hybrid energy systems.
Sustainable Hybrid Energy Systems readers will also find:
- Clear logical framework that reveals the constitutes of hybrid energy systems.
- Systematic technical scheme for building an economic, environmental, flexible, and resilient future energy system.
- Extensive case studies from single power plant level, multiple power plant level, and grid level.
- Effective guidelines for wider application of the proposed carbon neutral approaches.
Sustainable Hybrid Energy Systems is ideal for power engineers, electrical engineers, scientists in industry, and environmental researchers looking to understand these energy solutions. It will also provide collectible value for libraries.
Jadual kandungan
List of Figures xvii
List of Tables xxiii
Preface xxvii
1 Introduction 1
1.1 Background 1
1.1.1 Global Mission of Achieving Carbon Neutrality 1
1.1.2 Global Passion for Promoting Energy Transition 3
1.1.3 Global Status of Developing Hybrid Energy Systems 5
1.2 Hybrid Energy Systems 8
1.2.1 Definition 8
1.2.2 Classification 9
1.2.3 Advantages 12
1.3 Chapter Organization 13
References 16
2 Industrial Decarbonization-Oriented Deployment of Hybrid Wind–Solar-Storage System 21
2.1 Background Review 22
2.2 Main Issue Description 24
2.2.1 System Schematic 24
2.2.2 Decarbonization Datasets 24
2.2.3 Optimization Scheme 26
2.3 Mathematical Modeling 27
2.3.1 Notations 27
2.3.2 Decarbonized Deployment 29
2.3.2.1 To Reduce the Total Electricity Utilization Costs 29
2.3.2.2 To Promote the Installed Capacity of Wind and Solar Power 29
2.3.2.3 To Accelerate Decarbonization and Control Pollution Emissions 30
2.3.2.4 Wind Power Output 30
2.3.2.5 Solar Power Output 31
2.3.2.6 Operation of Battery Storage System 31
2.3.2.7 Compensation of Wind, Solar, and Storage Resources 32
2.3.2.8 Electricity Supply and Demand Balance 32
2.3.2.9 Available Area for New Energy Installation 33
2.3.3 Global Model 33
2.3.4 Model Solving 35
2.4 Case Study 35
2.4.1 Case Description 37
2.4.2 Data Collection 37
2.4.3 Calculation Results and Analysis 39
2.4.3.1 Optimal Configurations Results 39
2.4.3.2 Economic Performance and Self-Sufficiency Ratio 42
2.4.3.3 Regional Decarbonization Potential 43
2.5 Comprehensive Discussions 43
2.5.1 Scenario Simulation 43
2.5.2 Management Recommendations 44
References 45
3 Sustainable Operation-Oriented Deployment of Hybrid Wind–Solar-Storage System 51
3.1 Background Review 52
3.2 Main Issue Description 54
3.2.1 System Schematic 54
3.2.2 Operation Strategy 55
3.2.3 Optimization Scheme 56
3.3 Mathematical Modeling 57
3.3.1 Notations 57
3.3.2 Sustainable Deployment 58
3.3.2.1 Economic Sustainability: Minimize the Levelized Cost of Electricity 58
3.3.2.2 Technical Sustainability: Maximize Self-Sufficiency Ratio 60
3.3.2.3 Environmental Sustainability: Minimize Carbon Emissions 60
3.3.2.4 Social Sustainability: Maximize Job Creation 60
3.3.2.5 Output of Solar Power 61
3.3.2.6 Output of Wind Power 61
3.3.2.7 Balance of Battery Storage System 62
3.3.2.8 Balance of Demand and Supply 62
3.3.2.9 Key Operation Constraints 62
3.3.3 Global Model 63
3.3.4 Model Solving 64
3.4 Case Study 65
3.4.1 Case Description 65
3.4.2 Data Collection 66
3.4.3 Calculation Results and Analysis 67
3.4.3.1 Results Under Different Scenarios 67
3.4.4 Results of Energy Balance 69
3.4.4.1 Influence of Electricity Price 69
3.4.4.2 Influence of Natural Resources 70
3.5 Comprehensive Discussion 71
3.5.1 Related Propositions 71
3.5.2 Management Recommendations 72
References 73
4 Disaster Resilience-Oriented Deployment of Hybrid Wind–Solar-Storage-Gas System 79
4.1 Background Review 80
4.2 Main Issue Description 81
4.2.1 System Schematic 81
4.2.2 Resilience Characterization 82
4.2.3 Optimization Scheme 83
4.3 Mathematical Modeling 85
4.3.1 Notations 85
4.3.2 Resilient Deployment 87
4.3.2.1 The Upper-Level Decision Maker: To Maximize the Use of Clean Energy 87
4.3.2.2 To Minimize the Total Annual Power Costs 87
4.3.2.3 To Minimize Carbon Emissions 88
4.3.2.4 To Maximize Power System Resilience 88
4.3.2.5 Clean Energy Use Restrictions 89
4.3.2.6 Installation Area Restriction 89
4.3.2.7 PV Panel Operation 89
4.3.2.8 Energy Storage System Operation 90
4.3.2.9 Battery State Restrictions 90
4.3.2.10 Gas Turbine Operation 90
4.3.2.11 Power Supply and Demand Balance 91
4.3.3 Global Model 91
4.3.4 Model Solving 92
4.4 Case Study 93
4.4.1 Case Description 94
4.4.2 Data Collection 94
4.4.3 Calculation Results and Analysis 95
4.4.3.1 Maximum Resilience Emission Results 97
4.4.3.2 Comparison of Different Scenarios 98
4.4.3.3 Operation Under Normal Modes 98
4.4.3.4 Operation Under Extreme Disasters 101
4.4.3.5 Influence of Changing Market Prices 104
4.5 Comprehensive Discussion 105
4.5.1 Related Propositions 105
4.5.2 Management Recommendations 106
References 107
5 Bi-level Emission Quota Allocation Toward Coal and Biomass Co-combustion 113
5.1 Background Review 113
5.2 Main Issues’ Description 115
5.2.1 System Schematic 116
5.2.2 Uncertain Decision-Making Environment 116
5.2.3 Bi-level Decision-Making Structure 116
5.3 Modeling 118
5.3.1 Notations 118
5.3.2 Perspective from the Local Authority 119
5.3.2.1 To Maximize the Revenue 119
5.3.2.2 To Minimize the Total Carbon Emissions 119
5.3.2.3 Limitations on Each CPP’s Carbon Emissions 119
5.3.2.4 Guarantee of Power Supply 120
5.3.2.5 Gap Between the Assigned Emission Quota and the Actual Emissions 120
5.3.3 Perspective from the CPPs 120
5.3.3.1 To Maximize Profits of Electricity Generation 120
5.3.3.2 Combustion Efficiency 121
5.3.3.3 Fuel Quantity Requirements 121
5.3.3.4 Fuel Qualities’ Requirements 121
5.3.3.5 Blending Ratio Limitation of Biomass 122
5.3.3.6 Responsibility to Ensure Power Supply 122
5.3.3.7 Emissions Quota Constraints 122
5.3.3.8 Dynamic Fuel Storage 123
5.3.3.9 Logistic Constraint on Fuel Storage 123
5.3.3.10 Limitation of Warehousing Ability 123
5.3.4 Global Model 123
5.3.5 Model Solving 125
5.4 Case Study 125
5.4.1 Case Description 125
5.4.2 Data Collection 128
5.4.3 Results Under Different Scenarios 128
5.5 Discussion 132
5.5.1 Propositions and Analyses 132
5.5.2 Policy Implications 137
References 139
6 Bi-Level Emission Quota Allocation Toward Coal and Municipal Solid Waste Co-combustion 143
6.1 Background Review 144
6.2 Main Issue Description 145
6.2.1 System Schematic 145
6.2.2 Uncertain Decision-Making 146
6.2.3 Bi-Level Relationship 147
6.3 Modeling 148
6.3.1 Notations 149
6.3.2 Modeling Description for Regional Authority 150
6.3.2.1 To Maximize Revenue 150
6.3.2.2 Emission Quota Limitation 150
6.3.2.3 Total Emissions Limitation 151
6.3.2.4 Power Supply and Demand Risk 151
6.3.3 Modeling Description for Each IPP 151
6.3.3.1 To Maximize Profits 151
6.3.3.2 Available Capacity Limitations of Power Plants 152
6.3.3.3 Quality Requirements of Fuels 152
6.3.3.4 Combustion Technical Requirements 153
6.3.4 Global Model 153
6.3.5 Solution Approach 154
6.4 Case Study 157
6.4.1 Case Presentation 157
6.4.2 Data Collection 157
6.4.3 Calculation Results 161
6.4.4 Results of Different Scenarios 161
6.4.4.1 S0: Baseline Scenario, α = 1 161
6.4.4.2 S1: Initial Curb Scenario, α = 0.9 163
6.4.4.3 S2: Moderate Curb Scenario, α = 0.9 163
6.4.4.4 S3: Serious Curb Scenario, α = 0.85 163
6.4.4.5 S4: Vigorous Curb Scenario, α = 0.8 164
6.4.4.6 S5: Maximal Limitation Scenario, α = 0.75 164
6.4.5 Scenario Results Comparison 164
6.4.5.1 Comparison of Total Carbon Emissions at Each Power Plant 164
6.4.5.2 Carbon Emissions from Different Fuels at Each Power Plant 165
6.4.5.3 Comparison of Revenue, Costs, and Profits at Each Power Plant 167
6.4.5.4 Influence of Subsidy Variation on Profits Trend 167
6.5 Comprehensive Discussion 169
6.5.1 Policy Implications 169
6.5.2 Industrial Management Recommendations 171
References 171
7 Bi-level Multi-objective Emission Quota Allocation Toward Coal and Sewage Co-combustion 175
7.1 Background Review 176
7.2 Main Issue Description 177
7.2.1 System Schematic 177
7.2.2 Uncertain Decision Environment 177
7.2.3 Optimization Scheme 178
7.3 Modeling 180
7.3.1 Notations 180
7.3.2 Allocation Scheme for the Authority 181
7.3.2.1 Maximizing Economic Benefits 181
7.3.2.2 Minimizing Carbon Emission Intensity 181
7.3.2.3 Maximizing Sludge Utilization 182
7.3.2.4 Benchmark Allocation Method 182
7.3.2.5 The Control of Carbon Emission 182
7.3.2.6 Power Supply and Demand Balance 183
7.3.2.7 Bounds of Quotas 183
7.3.3 Strategy for Coal-Fired Plants 183
7.3.3.1 Maximizing Profits 183
7.3.3.2 Quality Requirements on Fuel 184
7.3.3.3 Restrictions on Pollutant Emission 184
7.3.3.4 Available Quantities of Fuel 185
7.3.4 Global Model 185
7.3.5 Model Solving 185
7.4 Case Study 187
7.4.1 Case Description 187
7.4.2 Data Collection 187
7.4.3 Calculation Results 191
7.4.3.1 Analysis Under Different Objective Weights 191
7.4.4 Scenario Analysis 192
7.4.4.1 Scenario 1: Results Under Different Levels of Carbon Emission Reductions 194
7.4.4.2 Scenario 2: Results Under Different Carbon Emission Intensity Reduction Targets 195
7.5 Comprehensive Discussion 196
7.5.1 Model Comparison 197
7.5.2 Policy Implications 198
References 199
8 Reliable–Economical Scheduling of Hybrid Solar–Hydro System 203
8.1 Background Review 204
8.2 Key Problem Statement 206
8.2.1 System Description 206
8.2.2 Trade-Off Between Reliable and Economical Power Supply 207
8.2.3 Handling Renewable Energy Uncertainties 208
8.3 Modeling 209
8.3.1 Notations 209
8.3.2 Hybrid System’s Reliability and Economy Equilibrium 210
8.3.2.1 Maximize Power Supply Reliability 210
8.3.2.2 Maximize Electricity Sales Revenue 211
8.3.3 Constraints of the Hybrid System 211
8.3.3.1 Photovoltaic Power Plant’s Output 211
8.3.3.2 Accessible Photovoltaic Arrays 212
8.3.3.3 Solar Power Output Limitation 212
8.3.3.4 Hydro Turbine Output 213
8.3.3.5 Limitation on Available Water 213
8.3.3.6 Dynamic Water Inventory 213
8.3.3.7 Limit on the Ability of Power Transmission 214
8.3.3.8 Limit on the Stability of Power Transmission 214
8.3.4 Global Model 214
8.3.5 Model Solving 216
8.4 Case Study 217
8.4.1 Case Description 217
8.4.2 Data Collection 219
8.4.3 Calculation Results 220
8.4.3.1 Technical Output Analysis 223
8.4.3.2 Power Output Ratio Analysis 224
8.4.3.3 Hourly Power Output Analysis 225
8.4.3.4 Economic Benefits Analysis 225
8.5 Discussion 228
8.5.1 Comparative Study 228
8.5.2 Related Propositions 229
8.5.3 Management Recommendations 231
References 232
9 Reliable–Economical Equilibrium-Based Short-Term Scheduling of Hybrid Solar–Wind–Gas System 237
9.1 Background Review 238
9.2 Key Problem Statement 239
9.2.1 System Description 240
9.2.2 Resolving Renewable Energy Uncertainties 240
9.2.3 Achieving Reliable-Economical Equilibrium 242
9.3 Modeling 243
9.3.1 Notations 243
9.3.2 To Guarantee Economic Benefits and Reliability 244
9.3.2.1 To Maximize Total Income 244
9.3.2.2 To Minimize the Deviation of Power Supply and Demand 245
9.3.3 Constraints of System Components 245
9.3.3.1 Output of Solar Power Plants 245
9.3.3.2 Solar Power Output Limitation 246
9.3.3.3 Power Output of Wind Farm 246
9.3.3.4 Wind Power Output Limitation 246
9.3.3.5 Output of Natural Gas Power Plants 247
9.3.3.6 Operation Limitations of Natural Gas Turbines 247
9.3.3.7 System Spinning Reserve 247
9.3.4 Global Model 247
9.3.5 Mathematical Solving 249
9.3.5.1 Transforming the Multi-Objective Model Using ε-Constraint Method 249
9.3.5.2 Select the Optimal Solution Using Fuzzy Satisfying Method 249
9.4 Case Study 250
9.4.1 Case Description 250
9.4.2 Data Collection 251
9.5 Calculation Results and Analysis 255
9.5.1 Optimal Solutions 255
9.5.2 Economic Benefits Analysis 255
9.5.3 System Reliability Analysis 257
9.6 Comprehensive Discussion 260
9.6.1 Related Propositions 260
9.6.2 Comparative Study 262
9.6.3 Management Recommendations 263
References 264
10 Reliable–Economical–Social Equilibrium-Based Scheduling of Hybrid Solar–Wind–Hydro System 269
10.1 Background Review 269
10.2 Key Problem Statement 271
10.2.1 System Description 271
10.2.2 Multi-objective Decision-Making Problem 272
10.2.3 Seasonal and Daily Uncertainties 273
10.3 Modeling 274
10.3.1 Notations 274
10.3.2 Four Main Goals Considered for the Hybrid System 275
10.3.2.1 Maximizing Complementary Rate 275
10.3.2.2 Maximizing Power Supply Reliability 276
10.3.2.3 Minimizing New Energy Curtailments 277
10.3.2.4 Maximizing Yearly Power Supply Profits 277
10.3.3 Constraints of the Hybrid System 277
10.3.3.1 New Energy Output Limitation 277
10.3.3.2 Hydropower Output Limitation 278
10.3.3.3 Water Flow Limitation 278
10.3.3.4 Water Volume Limitation 278
10.3.3.5 Transmission Capacity Limitation 279
10.3.4 Global Model 279
10.3.5 Model Solving 280
10.4 Case Study 281
10.4.1 Case Description 281
10.4.2 Data Collection 283
10.5 Results 284
10.5.1 Complementary Rates of New Energies 286
10.5.2 Results Under Different Reliability and Complementarity Rates 288
10.5.3 Results Under Different New Energy Curtailment Rates 290
10.5.4 Comparison of Different Systems 293
10.6 Discussion 293
10.6.1 Core Findings 295
10.6.2 Management Recommendations 296
References 297
11 Optimal RPS Implementation Strategy Considering Both Power Suppliers and Users 301
11.1 Background Review 301
11.2 Key Problem Statement 303
11.2.1 Decision Process Description 303
11.2.2 Power User Classifications 304
11.2.3 Multi-Objectives of Demand and Supply Sides 304
11.3 Modeling 305
11.3.1 Assumptions 305
11.3.2 Notations 305
11.3.3 Objectives 306
11.3.3.1 To Minimize the Electricity Tariff Variations 307
11.3.3.2 To Minimize Total Costs 307
11.3.3.3 To Maximize RPS 308
11.3.4 Provincial Power Constraints 308
11.3.4.1 Power Generation and Consumption Balance 308
11.3.4.2 Power Sale Limitations 309
11.3.4.3 Power Transmission Limitations 309
11.3.4.4 RPS and Non-hydro RPS Target Limitations 309
11.3.5 Global Model 310
11.3.6 Model Solving 311
11.4 Case Study 314
11.4.1 Case Description 314
11.4.2 Data Collection 314
11.4.3 Calculation Results 317
11.4.3.1 Details of Power Consumption for Three Groups of Users 317
11.4.3.2 Details of Guangdong Province’s Power Schedule 318
11.4.3.3 Three Key Findings From the Results Analysis 318
11.5 Discussion 319
11.5.1 Comparison With the Existing Schedule 319
11.5.1.1 Comparison of Power Tariff and Policy Acceptance 320
11.5.1.2 Generation Costs and CO 2 Emissions 320
11.5.2 Scenario Analysis 322
11.5.2.1 RE Consumption Proportion Results 323
11.5.2.2 Power Tariff Results 324
11.5.2.3 Generation Cost and CO 2 Emission Results 325
11.5.3 Key Finding 326
References 327
12 Optimal RPS Implementation Strategy Considering Equity and Economy Equilibrium 331
12.1 Introduction 331
12.2 Key Problem Statement 333
12.2.1 Bi-Level Relationship 333
12.2.2 Equity and Economy Trade-Off 334
12.3 Modeling 335
12.3.1 Notations 335
12.3.2 Central Government’s Equity Concern 336
12.3.2.1 Equitable Allocation 336
12.3.2.2 Renewable Energy Consumption Ratio 337
12.3.3 The Provincial Government’s Economic Concern 337
12.3.3.1 The Balance of Renewable Electricity Generation and Trading 338
12.3.3.2 The Balance of Power Supply and Demand 338
12.3.3.3 Limitation of Generation Capacity 338
12.3.3.4 Limitation of Transmission Capacity 339
12.3.4 Global Model 339
12.3.5 Model-Solving Approach 340
12.4 Case Study 341
12.4.1 Case Description 341
12.4.2 Data Collection 341
12.4.3 Calculation Results 345
12.4.3.1 Generation and Trading Results for Individual Provinces 345
12.4.3.2 The Minimum and Maximum RPS that can be Achieved for Individual Provinces 347
12.4.3.3 Results of Central Government Considering Allocation Equity 348
12.5 Discussions 348
12.5.1 Trade-offs Between Equity and Economy 348
12.5.1.1 Comparison of Integrated Scores 351
12.5.1.2 Comparison of Maximum Equity Parameter 351
12.5.1.3 Comparison of the Cost-Change Rate 351
12.5.1.4 Comparison of Generation Strategy 354
12.5.2 Key Findings 354
References 355
13 Optimal RPS Implementation Strategy Considering Emission Trade and Green Certificate Trade 359
13.1 Introduction 359
13.2 Key Problem Statement 361
13.2.1 Integration of the TGC and CET Policies 361
13.2.2 Interaction of Power Generation and Trading 363
13.3 Modeling 364
13.3.1 Assumptions 364
13.3.2 Notations 365
13.3.3 Power Generation and Trading Objectives 366
13.3.3.1 Economic Performance 366
13.3.3.2 Environmental Protection 367
13.3.4 Generation and Trading Constraints 367
13.3.4.1 Renewable Power Generation Capacity Limitation 367
13.3.4.2 Traditional Power Generation Capacity Limitation 367
13.3.4.3 Power Demand and Supply Balance 368
13.3.4.4 Power Transmission Limitation 368
13.3.4.5 Power Trading Constraints 368
13.3.4.6 TGC Trading Constraints 368
13.3.4.7 CET Trading Constraints 368
13.3.4.8 RPS-Bundled TGC Consumption 369
13.3.4.9 CET Quota Constraints 369
13.3.5 Global Model 369
13.3.6 Model Solving 370
13.3.6.1 Model Transformation Process 371
13.3.6.2 Applying Fuzzy Satisfying Approach to Select the Optimal Solution 372
13.4 Case Study 372
13.4.1 Case Description 372
13.4.2 Data Collection 374
13.4.2.1 Technical Generation Parameters 374
13.4.2.2 Policy-Related Parameters 375
13.4.3 Calculation Results and Analysis 375
13.4.3.1 Results of Power Generation and Trading 375
13.4.3.2 Results of Economic–Environmental Trade-Offs 378
13.5 Discussion 378
13.5.1 Scenario Analyses 378
13.5.1.1 Scenario Settings 379
13.5.1.2 Economic and Environmental Trade-Offs Under Different Scenario 379
13.5.1.3 Power Generation Results Under Different Scenarios 380
13.5.1.4 Power Trading Results Under Different Scenarios 380
13.5.2 Key Findings 381
References 382
14 Emerging Hybrid Energy Storage Systems 387
References 394
Index 395
Mengenai Pengarang
Jiuping Xu, Professor, holds doctoral degrees in applied mathematics and physical chemistry, is Director of the Institute of New Energy and Low-Carbon Technology, Sichuan University, China. He is Academician of International Academy for Systems and Cybernetic Sciences, Honorary Academician of Academy of Sciences of Moldova, and Academician of Mongolian National Academy of Sciences. He is President of International Society for Management Science and Engineering Management. He is also the creator of the decision and technology innovation paradigm named the ‘Theory-Spectrum-Model-Grou-Algorithm Cluster’.
Fengjuan Wang, holds a doctoral degree in management science and engineering, is an assistant professor at the Institute of New Energy and Low-Carbon Technology, Sichuan University. Her research is focused on the optimization of hybrid energy systems.
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