Sustainable Hybrid Energy Systems

Carbon Neutral Approaches, Modeling, and Case Studies

Inbunden, Engelska, 2024

Av Jiuping Xu, Fengjuan Wang, China) Xu, Jiuping (Sichuan University, China) Wang, Fengjuan (Sichuan University

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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.

Produktinformation

Utgivningsdatum2024-03-06
Mått170 x 244 x 30 mm
Vikt964 g
FormatInbunden
SpråkEngelska
Antal sidor432
FörlagWiley-VCH Verlag GmbH
ISBN9783527352432

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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.

List of Figures xviiList of Tables xxiiiPreface xxvii1 Introduction 11.1 Background 11.1.1 Global Mission of Achieving Carbon Neutrality 11.1.2 Global Passion for Promoting Energy Transition 31.1.3 Global Status of Developing Hybrid Energy Systems 51.2 Hybrid Energy Systems 81.2.1 Definition 81.2.2 Classification 91.2.3 Advantages 121.3 Chapter Organization 13References 162 Industrial Decarbonization-Oriented Deployment of Hybrid Wind–Solar-Storage System 212.1 Background Review 222.2 Main Issue Description 242.2.1 System Schematic 242.2.2 Decarbonization Datasets 242.2.3 Optimization Scheme 262.3 Mathematical Modeling 272.3.1 Notations 272.3.2 Decarbonized Deployment 292.3.2.1 To Reduce the Total Electricity Utilization Costs 292.3.2.2 To Promote the Installed Capacity of Wind and Solar Power 292.3.2.3 To Accelerate Decarbonization and Control Pollution Emissions 302.3.2.4 Wind Power Output 302.3.2.5 Solar Power Output 312.3.2.6 Operation of Battery Storage System 312.3.2.7 Compensation of Wind, Solar, and Storage Resources 322.3.2.8 Electricity Supply and Demand Balance 322.3.2.9 Available Area for New Energy Installation 332.3.3 Global Model 332.3.4 Model Solving 352.4 Case Study 352.4.1 Case Description 372.4.2 Data Collection 372.4.3 Calculation Results and Analysis 392.4.3.1 Optimal Configurations Results 392.4.3.2 Economic Performance and Self-Sufficiency Ratio 422.4.3.3 Regional Decarbonization Potential 432.5 Comprehensive Discussions 432.5.1 Scenario Simulation 432.5.2 Management Recommendations 44References 453 Sustainable Operation-Oriented Deployment of Hybrid Wind–Solar-Storage System 513.1 Background Review 523.2 Main Issue Description 543.2.1 System Schematic 543.2.2 Operation Strategy 553.2.3 Optimization Scheme 563.3 Mathematical Modeling 573.3.1 Notations 573.3.2 Sustainable Deployment 583.3.2.1 Economic Sustainability: Minimize the Levelized Cost of Electricity 583.3.2.2 Technical Sustainability: Maximize Self-Sufficiency Ratio 603.3.2.3 Environmental Sustainability: Minimize Carbon Emissions 603.3.2.4 Social Sustainability: Maximize Job Creation 603.3.2.5 Output of Solar Power 613.3.2.6 Output of Wind Power 613.3.2.7 Balance of Battery Storage System 623.3.2.8 Balance of Demand and Supply 623.3.2.9 Key Operation Constraints 623.3.3 Global Model 633.3.4 Model Solving 643.4 Case Study 653.4.1 Case Description 653.4.2 Data Collection 663.4.3 Calculation Results and Analysis 673.4.3.1 Results Under Different Scenarios 673.4.4 Results of Energy Balance 693.4.4.1 Influence of Electricity Price 693.4.4.2 Influence of Natural Resources 703.5 Comprehensive Discussion 713.5.1 Related Propositions 713.5.2 Management Recommendations 72References 734 Disaster Resilience-Oriented Deployment of Hybrid Wind–Solar-Storage-Gas System 794.1 Background Review 804.2 Main Issue Description 814.2.1 System Schematic 814.2.2 Resilience Characterization 824.2.3 Optimization Scheme 834.3 Mathematical Modeling 854.3.1 Notations 854.3.2 Resilient Deployment 874.3.2.1 The Upper-Level Decision Maker: To Maximize the Use of Clean Energy 874.3.2.2 To Minimize the Total Annual Power Costs 874.3.2.3 To Minimize Carbon Emissions 884.3.2.4 To Maximize Power System Resilience 884.3.2.5 Clean Energy Use Restrictions 894.3.2.6 Installation Area Restriction 894.3.2.7 PV Panel Operation 894.3.2.8 Energy Storage System Operation 904.3.2.9 Battery State Restrictions 904.3.2.10 Gas Turbine Operation 904.3.2.11 Power Supply and Demand Balance 914.3.3 Global Model 914.3.4 Model Solving 924.4 Case Study 934.4.1 Case Description 944.4.2 Data Collection 944.4.3 Calculation Results and Analysis 954.4.3.1 Maximum Resilience Emission Results 974.4.3.2 Comparison of Different Scenarios 984.4.3.3 Operation Under Normal Modes 984.4.3.4 Operation Under Extreme Disasters 1014.4.3.5 Influence of Changing Market Prices 1044.5 Comprehensive Discussion 1054.5.1 Related Propositions 1054.5.2 Management Recommendations 106References 1075 Bi-level Emission Quota Allocation Toward Coal and Biomass Co-combustion 1135.1 Background Review 1135.2 Main Issues’ Description 1155.2.1 System Schematic 1165.2.2 Uncertain Decision-Making Environment 1165.2.3 Bi-level Decision-Making Structure 1165.3 Modeling 1185.3.1 Notations 1185.3.2 Perspective from the Local Authority 1195.3.2.1 To Maximize the Revenue 1195.3.2.2 To Minimize the Total Carbon Emissions 1195.3.2.3 Limitations on Each CPP’s Carbon Emissions 1195.3.2.4 Guarantee of Power Supply 1205.3.2.5 Gap Between the Assigned Emission Quota and the Actual Emissions 1205.3.3 Perspective from the CPPs 1205.3.3.1 To Maximize Profits of Electricity Generation 1205.3.3.2 Combustion Efficiency 1215.3.3.3 Fuel Quantity Requirements 1215.3.3.4 Fuel Qualities’ Requirements 1215.3.3.5 Blending Ratio Limitation of Biomass 1225.3.3.6 Responsibility to Ensure Power Supply 1225.3.3.7 Emissions Quota Constraints 1225.3.3.8 Dynamic Fuel Storage 1235.3.3.9 Logistic Constraint on Fuel Storage 1235.3.3.10 Limitation of Warehousing Ability 1235.3.4 Global Model 1235.3.5 Model Solving 1255.4 Case Study 1255.4.1 Case Description 1255.4.2 Data Collection 1285.4.3 Results Under Different Scenarios 1285.5 Discussion 1325.5.1 Propositions and Analyses 1325.5.2 Policy Implications 137References 1396 Bi-Level Emission Quota Allocation Toward Coal and Municipal Solid Waste Co-combustion 1436.1 Background Review 1446.2 Main Issue Description 1456.2.1 System Schematic 1456.2.2 Uncertain Decision-Making 1466.2.3 Bi-Level Relationship 1476.3 Modeling 1486.3.1 Notations 1496.3.2 Modeling Description for Regional Authority 1506.3.2.1 To Maximize Revenue 1506.3.2.2 Emission Quota Limitation 1506.3.2.3 Total Emissions Limitation 1516.3.2.4 Power Supply and Demand Risk 1516.3.3 Modeling Description for Each IPP 1516.3.3.1 To Maximize Profits 1516.3.3.2 Available Capacity Limitations of Power Plants 1526.3.3.3 Quality Requirements of Fuels 1526.3.3.4 Combustion Technical Requirements 1536.3.4 Global Model 1536.3.5 Solution Approach 1546.4 Case Study 1576.4.1 Case Presentation 1576.4.2 Data Collection 1576.4.3 Calculation Results 1616.4.4 Results of Different Scenarios 1616.4.4.1 S0: Baseline Scenario, α = 1 1616.4.4.2 S1: Initial Curb Scenario, α = 0.9 1636.4.4.3 S2: Moderate Curb Scenario, α = 0.9 1636.4.4.4 S3: Serious Curb Scenario, α = 0.85 1636.4.4.5 S4: Vigorous Curb Scenario, α = 0.8 1646.4.4.6 S5: Maximal Limitation Scenario, α = 0.75 1646.4.5 Scenario Results Comparison 1646.4.5.1 Comparison of Total Carbon Emissions at Each Power Plant 1646.4.5.2 Carbon Emissions from Different Fuels at Each Power Plant 1656.4.5.3 Comparison of Revenue, Costs, and Profits at Each Power Plant 1676.4.5.4 Influence of Subsidy Variation on Profits Trend 1676.5 Comprehensive Discussion 1696.5.1 Policy Implications 1696.5.2 Industrial Management Recommendations 171References 1717 Bi-level Multi-objective Emission Quota Allocation Toward Coal and Sewage Co-combustion 1757.1 Background Review 1767.2 Main Issue Description 1777.2.1 System Schematic 1777.2.2 Uncertain Decision Environment 1777.2.3 Optimization Scheme 1787.3 Modeling 1807.3.1 Notations 1807.3.2 Allocation Scheme for the Authority 1817.3.2.1 Maximizing Economic Benefits 1817.3.2.2 Minimizing Carbon Emission Intensity 1817.3.2.3 Maximizing Sludge Utilization 1827.3.2.4 Benchmark Allocation Method 1827.3.2.5 The Control of Carbon Emission 1827.3.2.6 Power Supply and Demand Balance 1837.3.2.7 Bounds of Quotas 1837.3.3 Strategy for Coal-Fired Plants 1837.3.3.1 Maximizing Profits 1837.3.3.2 Quality Requirements on Fuel 1847.3.3.3 Restrictions on Pollutant Emission 1847.3.3.4 Available Quantities of Fuel 1857.3.4 Global Model 1857.3.5 Model Solving 1857.4 Case Study 1877.4.1 Case Description 1877.4.2 Data Collection 1877.4.3 Calculation Results 1917.4.3.1 Analysis Under Different Objective Weights 1917.4.4 Scenario Analysis 1927.4.4.1 Scenario 1: Results Under Different Levels of Carbon Emission Reductions 1947.4.4.2 Scenario 2: Results Under Different Carbon Emission Intensity Reduction Targets 1957.5 Comprehensive Discussion 1967.5.1 Model Comparison 1977.5.2 Policy Implications 198References 1998 Reliable–Economical Scheduling of Hybrid Solar–Hydro System 2038.1 Background Review 2048.2 Key Problem Statement 2068.2.1 System Description 2068.2.2 Trade-Off Between Reliable and Economical Power Supply 2078.2.3 Handling Renewable Energy Uncertainties 2088.3 Modeling 2098.3.1 Notations 2098.3.2 Hybrid System’s Reliability and Economy Equilibrium 2108.3.2.1 Maximize Power Supply Reliability 2108.3.2.2 Maximize Electricity Sales Revenue 2118.3.3 Constraints of the Hybrid System 2118.3.3.1 Photovoltaic Power Plant’s Output 2118.3.3.2 Accessible Photovoltaic Arrays 2128.3.3.3 Solar Power Output Limitation 2128.3.3.4 Hydro Turbine Output 2138.3.3.5 Limitation on Available Water 2138.3.3.6 Dynamic Water Inventory 2138.3.3.7 Limit on the Ability of Power Transmission 2148.3.3.8 Limit on the Stability of Power Transmission 2148.3.4 Global Model 2148.3.5 Model Solving 2168.4 Case Study 2178.4.1 Case Description 2178.4.2 Data Collection 2198.4.3 Calculation Results 2208.4.3.1 Technical Output Analysis 2238.4.3.2 Power Output Ratio Analysis 2248.4.3.3 Hourly Power Output Analysis 2258.4.3.4 Economic Benefits Analysis 2258.5 Discussion 2288.5.1 Comparative Study 2288.5.2 Related Propositions 2298.5.3 Management Recommendations 231References 2329 Reliable–Economical Equilibrium-Based Short-Term Scheduling of Hybrid Solar–Wind–Gas System 2379.1 Background Review 2389.2 Key Problem Statement 2399.2.1 System Description 2409.2.2 Resolving Renewable Energy Uncertainties 2409.2.3 Achieving Reliable-Economical Equilibrium 2429.3 Modeling 2439.3.1 Notations 2439.3.2 To Guarantee Economic Benefits and Reliability 2449.3.2.1 To Maximize Total Income 2449.3.2.2 To Minimize the Deviation of Power Supply and Demand 2459.3.3 Constraints of System Components 2459.3.3.1 Output of Solar Power Plants 2459.3.3.2 Solar Power Output Limitation 2469.3.3.3 Power Output of Wind Farm 2469.3.3.4 Wind Power Output Limitation 2469.3.3.5 Output of Natural Gas Power Plants 2479.3.3.6 Operation Limitations of Natural Gas Turbines 2479.3.3.7 System Spinning Reserve 2479.3.4 Global Model 2479.3.5 Mathematical Solving 2499.3.5.1 Transforming the Multi-Objective Model Using ε-Constraint Method 2499.3.5.2 Select the Optimal Solution Using Fuzzy Satisfying Method 2499.4 Case Study 2509.4.1 Case Description 2509.4.2 Data Collection 2519.5 Calculation Results and Analysis 2559.5.1 Optimal Solutions 2559.5.2 Economic Benefits Analysis 2559.5.3 System Reliability Analysis 2579.6 Comprehensive Discussion 2609.6.1 Related Propositions 2609.6.2 Comparative Study 2629.6.3 Management Recommendations 263References 26410 Reliable–Economical–Social Equilibrium-Based Scheduling of Hybrid Solar–Wind–Hydro System 26910.1 Background Review 26910.2 Key Problem Statement 27110.2.1 System Description 27110.2.2 Multi-objective Decision-Making Problem 27210.2.3 Seasonal and Daily Uncertainties 27310.3 Modeling 27410.3.1 Notations 27410.3.2 Four Main Goals Considered for the Hybrid System 27510.3.2.1 Maximizing Complementary Rate 27510.3.2.2 Maximizing Power Supply Reliability 27610.3.2.3 Minimizing New Energy Curtailments 27710.3.2.4 Maximizing Yearly Power Supply Profits 27710.3.3 Constraints of the Hybrid System 27710.3.3.1 New Energy Output Limitation 27710.3.3.2 Hydropower Output Limitation 27810.3.3.3 Water Flow Limitation 27810.3.3.4 Water Volume Limitation 27810.3.3.5 Transmission Capacity Limitation 27910.3.4 Global Model 27910.3.5 Model Solving 28010.4 Case Study 28110.4.1 Case Description 28110.4.2 Data Collection 28310.5 Results 28410.5.1 Complementary Rates of New Energies 28610.5.2 Results Under Different Reliability and Complementarity Rates 28810.5.3 Results Under Different New Energy Curtailment Rates 29010.5.4 Comparison of Different Systems 29310.6 Discussion 29310.6.1 Core Findings 29510.6.2 Management Recommendations 296References 29711 Optimal RPS Implementation Strategy Considering Both Power Suppliers and Users 30111.1 Background Review 30111.2 Key Problem Statement 30311.2.1 Decision Process Description 30311.2.2 Power User Classifications 30411.2.3 Multi-Objectives of Demand and Supply Sides 30411.3 Modeling 30511.3.1 Assumptions 30511.3.2 Notations 30511.3.3 Objectives 30611.3.3.1 To Minimize the Electricity Tariff Variations 30711.3.3.2 To Minimize Total Costs 30711.3.3.3 To Maximize RPS 30811.3.4 Provincial Power Constraints 30811.3.4.1 Power Generation and Consumption Balance 30811.3.4.2 Power Sale Limitations 30911.3.4.3 Power Transmission Limitations 30911.3.4.4 RPS and Non-hydro RPS Target Limitations 30911.3.5 Global Model 31011.3.6 Model Solving 31111.4 Case Study 31411.4.1 Case Description 31411.4.2 Data Collection 31411.4.3 Calculation Results 31711.4.3.1 Details of Power Consumption for Three Groups of Users 31711.4.3.2 Details of Guangdong Province’s Power Schedule 31811.4.3.3 Three Key Findings From the Results Analysis 31811.5 Discussion 31911.5.1 Comparison With the Existing Schedule 31911.5.1.1 Comparison of Power Tariff and Policy Acceptance 32011.5.1.2 Generation Costs and CO 2 Emissions 32011.5.2 Scenario Analysis 32211.5.2.1 RE Consumption Proportion Results 32311.5.2.2 Power Tariff Results 32411.5.2.3 Generation Cost and CO 2 Emission Results 32511.5.3 Key Finding 326References 32712 Optimal RPS Implementation Strategy Considering Equity and Economy Equilibrium 33112.1 Introduction 33112.2 Key Problem Statement 33312.2.1 Bi-Level Relationship 33312.2.2 Equity and Economy Trade-Off 33412.3 Modeling 33512.3.1 Notations 33512.3.2 Central Government’s Equity Concern 33612.3.2.1 Equitable Allocation 33612.3.2.2 Renewable Energy Consumption Ratio 33712.3.3 The Provincial Government’s Economic Concern 33712.3.3.1 The Balance of Renewable Electricity Generation and Trading 33812.3.3.2 The Balance of Power Supply and Demand 33812.3.3.3 Limitation of Generation Capacity 33812.3.3.4 Limitation of Transmission Capacity 33912.3.4 Global Model 33912.3.5 Model-Solving Approach 34012.4 Case Study 34112.4.1 Case Description 34112.4.2 Data Collection 34112.4.3 Calculation Results 34512.4.3.1 Generation and Trading Results for Individual Provinces 34512.4.3.2 The Minimum and Maximum RPS that can be Achieved for Individual Provinces 34712.4.3.3 Results of Central Government Considering Allocation Equity 34812.5 Discussions 34812.5.1 Trade-offs Between Equity and Economy 34812.5.1.1 Comparison of Integrated Scores 35112.5.1.2 Comparison of Maximum Equity Parameter 35112.5.1.3 Comparison of the Cost-Change Rate 35112.5.1.4 Comparison of Generation Strategy 35412.5.2 Key Findings 354References 35513 Optimal RPS Implementation Strategy Considering Emission Trade and Green Certificate Trade 35913.1 Introduction 35913.2 Key Problem Statement 36113.2.1 Integration of the TGC and CET Policies 36113.2.2 Interaction of Power Generation and Trading 36313.3 Modeling 36413.3.1 Assumptions 36413.3.2 Notations 36513.3.3 Power Generation and Trading Objectives 36613.3.3.1 Economic Performance 36613.3.3.2 Environmental Protection 36713.3.4 Generation and Trading Constraints 36713.3.4.1 Renewable Power Generation Capacity Limitation 36713.3.4.2 Traditional Power Generation Capacity Limitation 36713.3.4.3 Power Demand and Supply Balance 36813.3.4.4 Power Transmission Limitation 36813.3.4.5 Power Trading Constraints 36813.3.4.6 TGC Trading Constraints 36813.3.4.7 CET Trading Constraints 36813.3.4.8 RPS-Bundled TGC Consumption 36913.3.4.9 CET Quota Constraints 36913.3.5 Global Model 36913.3.6 Model Solving 37013.3.6.1 Model Transformation Process 37113.3.6.2 Applying Fuzzy Satisfying Approach to Select the Optimal Solution 37213.4 Case Study 37213.4.1 Case Description 37213.4.2 Data Collection 37413.4.2.1 Technical Generation Parameters 37413.4.2.2 Policy-Related Parameters 37513.4.3 Calculation Results and Analysis 37513.4.3.1 Results of Power Generation and Trading 37513.4.3.2 Results of Economic–Environmental Trade-Offs 37813.5 Discussion 37813.5.1 Scenario Analyses 37813.5.1.1 Scenario Settings 37913.5.1.2 Economic and Environmental Trade-Offs Under Different Scenario 37913.5.1.3 Power Generation Results Under Different Scenarios 38013.5.1.4 Power Trading Results Under Different Scenarios 38013.5.2 Key Findings 381References 38214 Emerging Hybrid Energy Storage Systems 387References 394Index 395