Future Urban Energy System for Buildings: The Pathway Towards Flexibility, Resilience and Optimization

Description

This book investigates three main characteristics of future urban energy system for buildings, including flexibility, resilience and optimization. It explores the energy flexibility by considering renewable energy integration with buildings, sector coupling, and energy trading in the local energy market. Energy resilience is addressed from aspects of future climate change, pandemic crisis, and operational uncertainties. Approaches for system design, dynamic pricing and advanced control are discussed for the optimization of urban energy system. Knowledge from this book contributes to the effective means in future urban energy paradigm to closely integrate multiple energy systems (i.e., distribution, mobility, production and storage) with different energy carriers (i.e., heat, electricity) in an optimal manner for energy use. It would facilitate the envision of next-generation urban energy systems, towards sustainability, resilience and prosperity. This book targets at a broad readership with specific experience and knowledge in energy system, transport, built environment and urban planning. As such, it will appeal to researchers, graduate students, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.   Preface Contents 1 The Importance of Urban Energy System for Buildings Abstract 1.1 Introduction 1.1.1 Background 1.1.2 The Importance of Urban Energy System for Buildings 1.2 Aim and Objectives 1.3 Motivations and Novelties 1.3.1 Motivations 1.3.2 Novelties 1.4 Structure and Contents 1.5 Conclusion References 2 Integration of Urban Energy Systems with Renewable Envelope Solutions at Building Cluster Level Abstract 2.1 Introduction 2.1.1 Building Cluster and Its Influencing Factors 2.1.1.1 Definition of Building Cluster 2.1.1.2 Why Building Cluster? 2.1.1.3 Spatio-Temporal Dimension of Building Cluster 2.1.1.4 Influencing Factors 2.1.1.5 RES Envelope Solutions 2.1.2 Solar Energy Potential 2.1.2.1 Density of Buildings 2.1.2.2 Energy Demand 2.1.2.3 Integrated Cluster-Scale Energy Systems 2.2 Energy Hub 2.2.1 General Concept 2.2.2 Modelling and Optimization 2.3 Discussion 2.4 Future Work 2.5 Summary References 3 Urban Solar Mobility: From Solar to Buildings, Vehicles, and Storage Abstract 3.1 Introduction 3.1.1 Background 3.1.1.1 Market Trends of PVs 3.1.1.2 Market Trends of Electric Vehicles 3.1.1.3 Market Trends of Storages 3.1.1.4 Building Prosumers Role 3.1.2 Defining the Concept of Solar Mobility 3.1.3 Values, Problems, and Challenges to the Solar Mobility 3.1.4 Aim and Contributions of This Chapter 3.2 Overview of the Existing Studies on Solar Mobility 3.2.1 PV and EV Interaction via the Public Grid 3.2.2 PV and EV Interaction via the Buildings 3.2.3 PV and EV Interaction via the Energy Sharing Network Considering Buildings and Energy Storage 3.3 Modeling of Sub-systems 3.3.1 Building Side Modeling 3.3.1.1 Solar Resource Mapping 3.3.1.2 PV Design Optimization 3.3.1.3 Electric and Thermal Energy Demand 3.3.1.4 Electric and Thermal Energy Storage 3.3.2 EV Side Modeling 3.3.2.1 EV Demand Modeling 3.3.2.2 Design/Plan of EV Charging Stations 3.3.3 Grid Modeling 3.3.3.1 Overall Power Grid Architecture 3.3.3.2 Local Microgrid Structure 3.3.3.3 Energy Sharing Networks 3.3.4 Advanced Controls 3.3.4.1 Individual Controls 3.3.4.2 Coordinated Controls 3.4 Simulation Platforms and Performance Metrics 3.4.1 Potential Modeling Platform for S2BVS 3.4.1.1 Modeling Platforms for the Demand/Supply of Buildings 3.4.1.2 Modeling Platform for Powerline/Power Grid 3.4.1.3 Modeling Platform for Advanced Controls 3.4.2 Metrics as Optimization Objectives of S2BVS Models 3.5 Future Directions 3.6 Summary References 4 Data Centers as Prosumers in Urban Energy Systems Abstract 4.1 Introduction 4.2 Data Center Overviews 4.2.1 Physical Organization 4.2.2 Environmental Requirements 4.2.3 Heat Dissipation Rates of Components 4.3 Cooling Systems in Data Centers 4.3.1 Air-Cooled Systems 4.3.2 Water-Cooled Systems 4.3.3 Two-Phase Cooled Systems 4.3.4 Comparison of Different Cooling Systems 4.4 Data Centers as Consumers—integration with Renewable energy Generations 4.4.1 Different Ways of Integration 4.4.1.1 Data Centers with Generation of Renewable Energy 4.4.1.2 Data Centers with Renewable Energy Provided by a Third Party 4.4.2 Advanced Controls to Maximize the Use of Renewable Energy 4.4.2.1 Principals of Controls for Maximizing the Renewable Energy Usage 4.4.2.2 Examples of Advanced Controls for Maximizing the Renewable Energy Usage 4.4.2.3 Integration of Energy Storage in Data Centers 4.5 Data Centers as Producers—Waste Heat Recovery 4.5.1 Locations for Waste Heat Recovery 4.5.2 Waste Heat Reuse for District Heating Networks 4.5.2.1 Different Thermodynamic Cycles in Heat Pumps for Upgrading Waste Heat 4.5.2.2 Different Prototypes of Integration Systems Connection at Data Center Side for Waste Heat Recovery Connection at the District Heating Network Side for Injecting Heat Architecture of the Overall System Connection 4.6 Data Center Projects with Renewable Energy Integrated or Waste Heat Reused 4.7 Economic, Energy, and Environmental Analysis for Data Centers as Prosumers 4.7.1 Economic Analysis for Data Centers as Prosumers 4.7.1.1 Economic Analysis for Data Centers as Energy Consumer 4.7.1.2 Economic Analysis for Data Centers as Energy Producer 4.7.2 Energy and Environmental Analysis for Data Centers as Prosumers 4.7.2.1 Energy and Environmental Analysis for Data Centers as Energy Consumer 4.7.2.2 Energy and Environmental Analysis for Data Centers as Energy Producer 4.8 Challenges and Future Work Discussion 4.9 Summary References 5 Characteristics of Urban Energy System in Positive Energy Districts Abstract 5.1 Introduction 5.2 Data Source and Research Methods 5.2.1 Data Source 5.2.2 Research Methods 5.2.2.1 Development of Database 5.2.2.2 Text Extraction and Mining Method for Keywords Abstraction 5.2.2.3 Data Visualization 5.3 Results 5.3.1 Characteristics of Existing PED Projects 5.3.1.1 Initiation Year 5.3.1.2 Location of Identified 60 PED-Related Projects 5.3.1.3 Status of the Identified Projects 5.3.1.4 Project Area (Spatial Scale) 5.3.1.5 Finance Models Used in PED Projects 5.3.1.6 Type of Buildings Involved 5.3.1.7 Major Energy Technologies 5.3.1.8 Challenges Under Different Implementation Stage 5.3.1.9 Most Commonly Used Words and Sentiment Analysis 5.3.2 Interactive Dashboard 5.4 Discussion 5.5 Future Work 5.6 Summary References 6 Economic Interactions Between Autonomous Photovoltaic Owners in a Local Energy Market Abstract 6.1 Introduction 6.1.1 Background and Literature Review 6.1.2 Novelty and Contribution 6.2 Materials and Methods 6.2.1 Agent-Based Model 6.2.2 Ownership Structures and Business Models 6.2.3 Case Study 6.3 Results 6.3.1 Self-Sufficiency of the Households 6.3.2 Exploitation of the Common Renewable Resources: Sheer Cumulative Consumption Versus Self-Sufficiency 6.3.3 LEC Gratis 6.3.4 LCOE of LEC 6.3.5 LEP N% 6.4 Discussion 6.4.1 Social and Cultural Differences Amongst Households Have a Huge Impact on Self-Sufficiency 6.4.2 High Cumulative Energy Demand is More Effective Than High Self-Sufficiency in Exploiting the Shared Renewable Resource 6.4.3 Different Selling Prices Generates Various Business Opportunities 6.5 Summary 6.5.1 Follow-Up Studies References 7 Electric Vehicle Smart Charging Characteristics on the Power Regulation Abilities Abstract 7.1 Introduction 7.2 Methodology 7.2.1 Step 1: Define Various Scenarios of EV Usage and Charging Limits 7.2.2 Step 2: Optimize EV Charging/discharging Rate Under Various Scenarios in Each day 7.2.3 Step 3: Evaluate the Degradation of EV Battery Under Various Scenarios 7.2.4 Step 4: Compare Performances Under Different Scenarios and Draw Conclusions 7.3 Simulation Configuration 7.3.1 Modeling of the Building Community Electricity Demand 7.3.2 Modeling of the Building Community Electricity Production 7.3.3 Modeling of the Electrical Vehicle 7.3.4 Configuration of the EV Charging and Usage Scenarios 7.4 Case Studies and Results 7.4.1 Building Community Power Demand and PV Power Production Results 7.4.2 Analysis of the Detailed Operation for a Typical week 7.5 Summary References 8 Three Fleet Smart Charging Categories of Electric Vehicles for the Grid Power Regulation Abstract 8.1 Introduction 8.2 Control Approaches of EV Fleets 8.2.1 Basic Idea of Different Control Approaches 8.2.2 Representative Control Algorithm for Each Approach 8.2.2.1 Control Algorithm for Individual Control 8.2.2.2 Control Algorithm for Bottom-Up Control 8.2.2.3 Control Algorithm for Top-Down Control 8.3 Buildings and System Modeling 8.3.1 Building Modeling 8.3.2 Renewable Energy System Modeling 8.3.3 EV Battery Modeling 8.4 Case Studies and Results Analysis 8.4.1 Building Electricity Demand, Renewable Generation, and Electricity Mismatch 8.4.2 Performances Comparative Investigation Under Objective of Minimizing Peak Power Exchanges with the Grid 8.4.3 Performances Comparative Investigation Under Objective of Maximizing PV Power Self-Consumption 8.4.4 Computational Performances Comparative Analysis 8.5 Summary References 9 Peer-to-Peer Energy Trading in a Local Community Under the Future Climate Change Scenario Abstract 9.1 Introduction 9.2 Methodology for Investigating the Future Climate Impacts 9.2.1 Prediction of the Future Climate Using the Morphine Method 9.2.1.1 Climate Models 9.2.1.2 Future Climate Scenarios 9.2.1.3 Morphed Method 9.2.2 Agent-Based Modeling of the P2P Energy Sharing Under Different Scenarios 9.2.3 Performance Indicators for Analysis 9.3 Buildings and System Modeling 9.3.1 Building Modeling 9.3.2 Renewable Energy System Modeling 9.4 Case Studies and Results Analysis 9.4.1 Comparison of the Present and Future Climates 9.4.2 Comparison P2P Energy Sharing Performances 9.4.2.1 Energy Performances 9.4.2.2 Economic Performances 9.5 Discussion of the Chapter Results 9.6 Summary References 10 Differential Evolution-based System for Net-zero Energy Buildings Under Climate Change Abstract 10.1 Introduction 10.2 Methodology 10.2.1 Overview 10.2.2 Prediction of Future Weather Using the Morphing Method 10.2.2.1 Generation of Typical Meteorological year (TMY) 10.2.2.2 Prediction of Future Monthly Weather Data Using the Identified GCMs 10.2.2.3 Morphing Method 10.2.3 Differential Evolution-Based NZEB System Design Using the Predicted Weather Data 10.2.3.1 Fitness Function of the Differential Evolution Optimizer 10.2.3.2 Search Constraints Based on User-Defined Performance Requirements 10.2.4 Validation Through Performance Comparisons Between the Proposed Method and Two Conventional Ones 10.3 Dynamic NZEB Platform 10.3.1 Building Modeling 10.3.2 Building Energy System Modeling 10.3.2.1 Air-Conditioning System 10.3.2.2 Renewable System 10.3.2.3 Electrical Energy Storage System 10.4 Case Studies and Results Analysis 10.4.1 Future Weather Prediction and Validation 10.4.1.1 Selection of TMMs and GCMs for Future Weather Prediction 10.4.1.2 Validation of the Predicted Future Weather 10.4.2 Optimal System Sizing Results and Validation of the Proposed Method 10.4.2.1 System Sizing Results from the Three Design Methods 10.4.2.2 Method Validation by Performance Comparisons with the Two Conventional Designs 10.5 Summary Appendix 1 Appendix 2 References 11 A Preliminary Simulation Study About the Impact of COVID-19 Crisis on Energy Demand of a Building Mix at a District in Sweden Abstract 11.1 Introduction 11.2 Simulation Process and Definition of Occupancy Schedule Due to COVID-19 Outbreak 11.3 Description of the New District 11.3.1 Archetype Design 11.3.2 Climate Analysis 11.3.3 Boundary Conditions and Parameters Setup 11.3.3.1 Residential Buildings 11.3.3.2 Office Buildings 11.3.3.3 Retail Shops 11.3.3.4 School 11.3.3.5 Schedules 11.4 Results and Discussion 11.4.1 Detailed Simulation Results of Base Case (Level 1) 11.4.2 Uncertainty Analysis 11.4.2.1 Occupancy Profile Input 11.4.2.2 DHW Information Input 11.4.2.3 Input of Lighting and Equipment 11.4.2.4 Comparison to the Building Standards 11.4.3 Simulation Results of Different Confinement Levels 11.4.4 Overall Comparison and Discussion 11.5 Limitations and Future Work 11.6 Summary References 12 Uncertainty-Based Near-Zero Energy Buildings Life-Cycle Performance Analysis Abstract 12.1 Introduction 12.2 Methodology 12.2.1 Uncertainty-Based Life-Cycle Performance Analysis 12.2.1.1 Quantification of Uncertainties 12.2.1.2 nZEB System Sizing Considering the Quantified Uncertainties 12.2.1.3 Quantification of Degradation Rates Degradation Models Calculation of Degradation Rates 12.2.1.4 Life-Cycle Performance Analysis with Degradation Effects Considered 12.2.2 A Two-Stage Design Method to Improve nZEB Sizing 12.3 Case Studies 12.3.1 Configuration of the Case nZEB and Systems 12.3.2 Quantification of System Degradation Rates 12.4 Results and Discussions 12.4.1 Life-Cycle Performance Analysis Results 12.4.1.1 Energy Demand, Energy Supply, and Power Exchange 12.4.1.2 Thermal Comfort, Energy Balance, Operational Cost, and Grid Independence Indices 12.4.1.3 Imported/Exported Energy from/to the Power Grid 12.4.2 Results of Performance Improvements Using the Two-Stage Design Method 12.4.2.1 Life-Cycle Cost Results of the Two-Stage Design Method 12.4.2.2 Comparison of Performance Indices Before and After Performance Improvements 12.5 Summary References 13 Impact of the Demand Profile and the Normative Framework on a Residential Photovoltaic System Abstract 13.1 Introduction 13.1.1 The Long-Term Trend in Photovoltaic (PV) Technology Development 13.1.2 Possible Developments of PV Technology Outside the City 13.1.3 PV Design in the City: Cumulative KPIs 13.1.4 PV Design in the City: A Brief History of Self-Consumption 13.1.5 Novelty of This Chapter: What Happens When Hourly Input Data is not Available? Are Self-Consumption Optimization Techniques Still Valid? 13.2 Methodologies and Input Data 13.2.1 Methodologies 13.2.2 Simulation and Optimization Environment 13.2.3 Case Study Description 13.2.4 Input Data 13.3 Results and Discussion 13.3.1 Assuming Net Billing Incentive 13.3.2 Results in a Self-Consumption Regime 13.3.3 Self-Sufficiency Versus NPV 13.4 Summary References 14 Generating Hourly Electricity Demand Data for Large-Scale Single-Family Buildings by a Decomposition–Recombination Method Abstract 14.1 Introduction 14.2 Modeling Hourly Electricity Demand 14.2.1 The Importance of Acquiring Hourly Data in Buildings 14.2.2 Data Generation 14.2.2.1 GAN 14.2.2.2 Statistical Methods 14.2.2.3 Summary 14.3 Method 14.3.1 Time Series Decomposition and Recombination 14.3.2 Locally Weighted Regression 14.3.3 Inner Loop and Outer Loop for STL 14.3.3.1 Inner Loop 14.3.3.2 Outer Loop 14.3.4 Components 14.3.5 Workflow 14.4 Data 14.4.1 Public Data 14.4.2 Data for the Reference Building 14.5 Results 14.5.1 Remainder Component of the Public Data 14.5.1.1 Autoregressive Generation 14.5.1.2 Transformed Distribution 14.5.2 Seasonal Component of the Public Data 14.5.3 Trend Component 14.5.4 Recombination 14.6 Summary References 15 Design Optimization of Distributed Energy Storage Systems by Considering Photovoltaic Power Sharing Abstract 15.1 Introduction 15.2 Methodology 15.2.1 Basic Idea of Energy Sharing and Typical Design Scenarios 15.2.2 A Hierarchical Design of Distributed Batteries for a Solar Power Shared Building Community 15.2.2.1 Step 1: Evaluation of the Aggregated Electricity Demand and Supply of the Building Community 15.2.2.2 Step 2: Optimization of the Virtual ‘Shared’ Battery Capacity of the Building Community Using GA 15.2.2.3 Step 3: Optimization of Distributed Battery Capacity for Single Building Using NLP 15.2.2.4 Step 4: Performance Comparison and Analysis 15.2.3 Buildings and System Modelling 15.2.3.1 Electricity Demand Modelling 15.2.3.2 PV System Modelling 15.2.3.3 Electrical Battery Modelling 15.3 Case Studies and Results Analysis 15.3.1 Building Electricity Demand, Renewable Power Generation and Electricity Mismatch 15.3.2 Performance Comparison at Community(Cluster)-Level 15.3.3 Performance Comparison of a Single Building 15.4 Summary Appendix References 16 Geographic Information System-Assisted Optimal Design of Renewable-Powered Electric Vehicle Charging Stations in High-Density Cities Abstract 16.1 Introduction 16.2 Geographic Information System-Assisted Optimal Design of Renewable Powered Electric Vehicle Charging Stations 16.2.1 Building Geographical Locations and Roof Areas Obtained Using Geographic Information System Technique 16.2.2 Estimation of Renewable Generation Potentials Based on the Collected Geographic Information 16.2.3 Generation of a Feasible Design Alternative Pool and Reduction of the Alternatives by a Rule-Based Screen 16.2.3.1 Initialization of Charging Station Number 16.2.3.2 Generation of Possible Design Alternatives by Using an Integer Partition Algorithm 16.2.3.3 Rule-Based Filter of Impractical Alternatives 16.2.4 Performance Evaluation of the Feasible Design Alternatives 16.2.4.1 Search of the Maximum Coverage Area by Genetic Algorithm 16.2.4.2 Analysis of Life Cycle Cost 16.2.5 Search for the Optimal Design Alternative by Comparing the Obtained Performance 16.3 Application of the Proposed Design Method in Hong Kong 16.3.1 Geographic Information Collected by Geographic Information System 16.3.2 Renewable Energy Generation Evaluation Results 16.3.3 Initialization of Design Alternatives 16.3.4 Performance Evaluation and Search Results of the Optimal Design 16.4 Discussions 16.5 Summary References 17 Clustering Nearly Zero Energy Buildings for Improved Performance Abstract 17.1 Introduction 17.2 A Grouping Method of nZEBs for Performance Improvements 17.2.1 Illustration of Similarity and Diversity of Power Mismatch Curves 17.2.2 A Grouping Method of nZEBs for Performance Improvements 17.2.2.1 Clustering of Power Mismatch Curves to Identify the Representative Energy Characteristics 17.2.2.2 Exhaustive Search of the Optimal Grouping Way that Maximizes the Collaboration Benefits 17.3 Building and Systems Modeling 17.3.1 Building Modeling 17.3.1.1 Renewable Energy System Modeling and Battery Modeling 17.4 Case Studies and Results Analysis 17.4.1 Clustering of Power Mismatch Curves 17.4.2 Operational Cost Evaluation Results 17.4.2.1 Grouping Results for Minimizing the Operational Cost 17.4.2.2 Operational Costs Comparison with no Grouping 17.4.2.3 Operational Costs Comparison with Random Grouping 17.4.3 Peak Energy Exchange Evaluation Results 17.4.3.1 Grouping Results for Minimizing the Peak Energy Exchanges 17.4.3.2 Peak Energy Exchanges Comparison with No Grouping 17.4.3.3 Peak Energy Exchanges Comparison with Random Grouping 17.5 Summary Appendix: The Top-Down Control Method References 18 Dynamic Pricing for Improving Bi-Directional Interactions with Reduced Power Imbalance Abstract 18.1 Introduction 18.2 Main Challenge of Bi-Directional Interactions 18.3 A Genetic Algorithm-Based Dynamic Pricing for Improving Bi-Directional Supply–Demand Interactions with Reduced Power Imbalance 18.3.1 Grid Operator Action: Search of the Optimal Dynamic Prices by Genetic Algorithm 18.3.2 Demand Side Action: Demand Response of an ndividual Building at a Given Dynamic Price 18.3.3 Building Modeling 18.3.4 Case Studies and Results Analysis 18.3.5 Grid Power Imbalance Reduction as Energy Supply from Thermal Power Plants 18.3.5.1 Optimal Dynamic Prices and Grid Power Imbalance 18.3.5.2 Single Building’s Demand Response and Associated Cost Savings 18.3.5.3 Elasticity Impacts on the Grid Power Balance 18.3.6 Grid Power Imbalance Reduction as Energy Supply from Renewables 18.3.6.1 Optimal Dynamic Prices and Grid Power Balance 18.3.6.2 Single Building’s Demand Response and Associated Cost Savings 18.3.6.3 Elasticity Impacts on the Grid Power Balance 18.4 Summary References 19 Hierarchical Coordinated Demand Response Control for Building Cluster Abstract 19.1 Introduction 19.2 A Hierarchical Demand Response Control for Improved Building Group Performances 19.2.1 Basic Ideas of the Independent DR Control and Coordinated DR Control 19.2.2 A Hierarchical Coordinated DR Control for Improved Building Group Performances 19.3 Building and Systems Modeling 19.3.1 Building Modeling 19.3.2 HVAC System Modeling 19.3.2.1 Chiller Model 19.3.2.2 Cooling Tower Model 19.3.2.3 Air Handling Unit Model 19.3.2.4 Pump Model 19.3.3 PCM Storage Tank Model 19.4 Case Studies and Results Analysis 19.4.1 Building Energy Demand Profiles 19.4.2 Computational Efficiency Comparison 19.4.3 Single Building’s Performance Comparison 19.4.4 Building Group Performance Comparison 19.5 Summary References 20 Optimization of Near-Zero Energy Buildings Cluster with Top-Down Control Abstract 20.1 Introduction 20.2 Control for Performance Optimization at nZEB- Cluster-Level 20.2.1 Basic Idea of Individual nZEB Control and nZEB Cluster Control 20.2.2 Top-Down Control Method for Performance Optimization at nZEB-Cluster-Level 20.3 Building and Systems Modeling 20.3.2 Renewable Energy System Modeling and Battery Modeling 20.4 Case Studies and Results Analysis 20.4.1 Building Energy Demand and Renewable Energy Supply 20.4.2 Load Matching Evaluation Results 20.4.2.1 Individual nZEB Battery Charging, Battery Energy and Power Exchange Results 20.4.2.2 nZEB Cluster Load Matching and Operational Cost Results 20.4.3 Grid Interaction Evaluation Results 20.4.3.1 Individual nZEB Battery Charging, Battery Energy and Power Exchange Results 20.4.3.2 nZEB Cluster Grid Interaction and Operational Cost Results 20.5 Summary References