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

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