Smart Energy Management: A Computational Approach

Contents
Preface
1. Introduction
1.1 SMART Systems
1.1.1 Sense, Meaningfully
1.1.2 Analyze
1.1.3 Respond, Timely
1.2 Computational Techniques in Energy Management
1.3 Smart Electric Grid
1.3.1 The Grid of the Last 100 Years
1.3.2 Balancing Generation and Consumption
1.3.3 Peak Demand versus Aggregate Demand
1.3.4 Conventional Grid versus Smart Grid
1.4 Smart Buildings
1.4.1 Thermal Comfort in Buildings
1.4.2 Solar Energy in Buildings
1.4.3 Smart Techniques for Handling Power Deficit
1.5 About this Monograph
1.5.1 Why this Monograph?
1.5.2 Topics Covered by this Monograph
1.5.3 What this Monograph is not about?
1.5.4 Who Should Read this Monograph?
2. Smart Electric Grid: Applications and Data Analysis
2.1 Introduction
2.2 Sensing in the Grid, Meaningfully
2.2.1 Phasor Measurement Units (PMUs)
2.2.1.1 Optimal Provisioning of PMUs
2.3 Analyze and Respond, Timely
2.4 Smart Grid Applications: QoS Requirements and Background
2.4.1 QoS Requirements of Grid Applications
2.4.2 Background Information about Electrical Power Network/Grid
2.4.2.1 Maintaining Balance between Power Generation and Consumption
2.4.2.2 Electric Power Grid: A Distributed Network
2.4.2.3 Power Flow in a Grid
2.4.2.4 Grid Stability
2.5 Data Dissemination and Grid Applications
2.5.1 CEUT: Existing Approach of Data Dissemination
2.5.1.1 The Need for Timeliness
2.5.2 DEFT: Data Dissemination with Timeliness Guarantees
2.5.2.1 Modeling PMU Data as Continuous Threshold Queries
2.5.2.2 Prioritizing Data Dissemination and Processing
2.5.2.3 Combining Centralized and Decentralized Approaches
2.6 A Case Study on Data Dissemination for Bus Angle Monitoring (BAM) Application
2.6.1 CEUT: Centralized Execution with Unfiltered Data Forwarding Technique
2.6.2 Performance of BAM with CEUT
2.6.2.1 Data Generation
2.6.2.2 Estimating Latencies at LPDCs and SPDCs for CEUT
2.6.2.3 PMU Data Size for Simulated Eastern and Western Region Grids and Validation of Estimated Processing Latencies for PDCs
2.6.3 CEUT-direct: A Centralized Approach to Handle Loss of Data in the Network
2.6.4 BAM using DEFT (Distributed Execution with Filtered Data Forwarding Technique)
2.6.5 Creating the Data Model
2.6.5.1 Latency for BAM from PMU to SPDC
2.6.5.2 Estimated Processing Latency for BAM with DEFT on the Indian Electric Grid
2.6.6 Handling Concurrent Applications
2.6.6.1 DEFT with Priority Cognizance of Applications
2.6.6.2 Latency from PMU to SPDC
2.6.7 Making All Data Available at SPDC
2.6.7.1 Latency from PMU to SPDC for Applications with All the Data Dissemination Techniques
2.6.7.2 Latency from PMU to SPDC during Loss of Filtered Data Packets for BAM from LPDC to SPDC with DEFT+CEUT
2.7 Summary and Takeaways
3. Energy Management Systems for Modern Buildings
3.1 Commercial Buildings
3.2 Residential Buildings
3.3 Sensing Facets of a Building Meaningfully
3.3.1 Terms Related to Sensing within a Building
3.3.2 Hard Sensors
3.3.3 Fusion of Hard Sensors for Occupancy Sensing
3.4 Smart Sensor Suite for Buildings: A Case Study
3.4.1 Hardware Architecture Design
3.4.1.1 Design of the Controller Node
3.4.1.2 Design of the Sensor Node
3.4.2 Communication Protocol
3.4.3 Network Technology (WiFi)
3.5 Analysis – Soft-Sensing and Energy Management in Buildings
3.5.1 Soft Sensors: Non-Intrusive Monitoring (NIM)
3.5.1.1 Primary Soft Sensors
3.5.1.2 Secondary Soft Sensors
3.5.2 Achieving Desired Observability of Various Facets of a Building
3.5.2.1 Using Knowledge of the Structure of a Building
3.5.2.2 Using the Semantics of Composition of Facets
3.5.2.3 Exploiting the Facet-Sensor Relationship
3.5.3 Sensor Placement
3.5.3.1 Using Only Hard Sensors
3.5.3.2 Using Both Hard and Soft Sensors
3.5.4 A Case Study on Observability of Facets
3.5.4.1 The Smart Classroom Complex (SCC)
3.5.5 Observing Facets of Interest
3.5.5.1 Techniques used in Observing Facets of SCC
3.6 Respond (Timely) using Hybrid Sensing: a Case Study
3.7 Summary and Takeaways
4. A Systematic Approach to Thermal Comfort
4.1 Introduction
4.1.1 Thermal Conditioning for Individual Comfort
4.1.1.1 What is Thermal Comfort?
4.1.2 Thermal Conditioning for Server "Comfort"
4.1.3 Thermal Conditioning Resources
4.1.3.1 Air-Conditioners (AC)
4.1.3.2 De-Humidification and Humidification
4.2 Challenges in Providing Thermal Comfort in a Building
4.2.1 Reducing Consumption by Preventing Wastage
4.2.2 Reducing Peak Demand
4.2.3 Improving Thermal Comfort
4.3 A Holistic Approach to Climate Control
4.3.1 Factors Inuencing Thermal Comfort
4.3.2 Stages Involved in Providing Thermal Comfort
4.3.3 Sensing Undesirable Phenomena in Spaces
4.3.4 Analyzing Possible Pro-active and Reactive Interventions
4.4 Thermal Modeling of Building
4.4.1 First Principles of Thermodynamics (FPT) and Electrical Analogy
4.4.2 Data-Driven Approach
4.4.3 Hybrid Modeling
4.4.4 Practical Limitations
4.5 Adaptive Hybrid Modeling Approach
4.5.1 Notations
4.5.2 System Model
4.5.3 Thermal Modeling of a Building Space
4.5.3.1 Modeling Building Space Cooled by an AC
4.5.3.2 Estimation of Characteristic Constants
4.5.4 E ect of Changes in Ambient Temperature Ta on Energy Consumption
4.5.5 E ect of Changes in Set Temperature Ts on Energy Consumption
4.5.6 Energy Consumed in Maintaining Thermal Comfort
4.5.6.1 Energy Consumption within a Period [0; tb]
4.5.6.2 Predicting Energy Savings
4.5.7 Responding with Data Driven Decisions: A Case Study
4.5.7.1 Pre-Cooling
4.5.8 To Cool or Not To Cool during Unoccupied-Period
4.5.8.1 Early Shut O
4.6 Thermal Comfort Under Peak Demand Constraints
4.6.1 Candidate Scheduling Policies and their Limitations
4.6.2 Thermal Characteristics of Heating, Ventilation and Air-Conditioning (HVAC) Systems
4.6.2.1 Cooling Down (AC Switched ON)
4.6.2.2 Warming Up (AC Switched OFF)
4.6.3 Analysis of Feasibility | Maintaining Thermal Comfort with TCBM Scheduling
4.6.3.1 TCBM Scheduling
4.6.3.2 TCBM Feasibility for Cooling
4.6.3.3 TCBM Feasibility for Heating
4.6.4 Analysis of Energy Consumption
4.6.4.1 A Case Study on Energy Savings
4.7 Adaptive Demand-Response (D-R) Control
4.7.1 Adapting Energy Consumption with TOD Charges
4.7.2 Handling Varying Ambient Temperature and Occupancy
4.7.2.1 Scheme to adapt to the changes in ambient temperature
4.7.3 TCBM as Anytime Algorithm to Handle Varying Peak Limit
4.7.3.1 Imprecise Computation and Inferior Thermal Comfort
4.7.4 Adaptive Demand-Response Policy
4.8 Learnings from an Academic Building
4.8.1 Auditorium
4.8.1.1 Thermal Properties of the Space
4.8.1.2 Interventions and their Benefits
4.8.2 Small Classroom
4.8.2.1 Thermal Properties of the Space
4.8.2.2 Interventions and their Benefits
4.8.3 Big Classroom
4.8.3.1 Thermal Properties of the Space
4.8.3.2 Interventions and their Benefits
4.8.4 Lab Space
4.8.4.1 Thermal Properties of the Space
4.8.4.2 Interventions and their Benefits
4.9 Summary and Takeaways
5. Customized Thermal Comfort
5.1 Individual Thermal Comfort
5.1.1 Predicting Thermal Comfort
5.1.1.1 Predicted Mean Vote (PMV)
5.1.1.2 Predicted Percentage Dissatisfied (PPD)
5.1.1.3 Acceptable Temperature and Design Criteria
5.1.2 Predicted Personal Vote (PPV) Model
5.1.2.1 Measuring Constituent Parameters of Thermal Comfort
5.1.2.2 A Case Study on Personalised Thermal Comfort
5.2 Occupancy Based Customization
5.2.1 Occupancy Sensing
5.2.1.1 Fusion of Sensors for Occupancy Sensing
5.2.1.2 Exploiting Wi-Fi System for Occupancy Sensing
5.2.1.3 Occupancy-Based HVAC Control
5.2.1.4 Schedule Driven HVAC Control
5.3 Chiller Sequencing – Customization for Varying Loads
5.3.1 Chiller Control Techniques
5.3.2 Data-Driven Techniques in Chiller Sequencing
5.3.2.1 Time-Constrained Data-Driven Chiller Sequencing (TCDD-CS)
5.3.2.2 Practical Considerations
5.3.2.3 Data-Driven COP Prediction
5.3.2.4 Determination of Optimum Sequencing of Chillers
5.4 Adaptive Thermal Comfort
5.5 Summary and Takeaways
6. Solar Energy in Buildings
6.1 Exploiting Solar Energy: Potential and Approaches
6.1.1 Assessing the Rooftop Solar Potential – Case Study of Mumbai
6.1.2 Building Integrated Photovoltaics (BIPV)
6.1.2.1 BIPV Optimization
6.1.2.2 Partial Shading and MPPT
6.2 Mitigation of the Effect of Partial Shading
6.2.1 Output Control using MPPT
6.2.2 Beyond MPPT Control
6.2.3 Dynamic Array Reconfiguration (DAR) and Current Injection (CI)
6.2.4 CI-based DAR (CI-DAR)
6.2.4.1 PV Array Size and Cost-Eectiveness of CI-DAR
6.2.4.2 Current Injection (CI) along with Reconfiguration
6.2.4.3 CI-DAR Strategy
6.2.4.4 Mitigation of Non-uniform Partial Shading and CI-DAR Algorithm
6.2.5 Experimental Validation in a Prototype System
6.2.5.1 The Prototype System
6.2.5.2 The Experiment
6.2.5.3 Results
6.3 Summary and Takeaways
7. Making the Best of Available Energy
7.1 Managing Building Loads According to Available Power in the Grid
7.1.1 Dealing with Blackouts: Existing Approach
7.1.2 GFB: A Smarter Solution to Prevent Blackouts
7.1.2.1 A Comprehensive Framework for GFB-based Electricity Scheduling
7.1.2.2 Consumers' Participation
7.1.2.3 Brownout Algorithm
7.2 NILM: Non-Intrusive Load Monitoring
7.2.1 The Goal of NILM
7.2.1.1 Supporting Utility-Consumer Interaction
7.2.1.2 Identification and Segregation of Loads
7.2.2 NILM Techniques
7.2.2.1 Edge Detection Methods
7.2.2.2 Model-Driven Methods
7.3 Modeling Residential Electrical Loads
7.3.1 Modeling Individual Loads
7.3.2 Basic Model Types
7.3.2.1 On-O Model
7.3.2.2 On-O Growth/Decay Model
7.3.2.3 Stable Min-Max Model
7.3.2.4 Random Range
7.3.3 Compound Model Types
7.3.3.1 Cyclic Model
7.3.3.2 Composite Model
7.4 Summary and Takeaways
Appendix A Electrical Energy: Some Basic Concepts
A.1 Power Consumption and Loads in AC Circuit
Appendix B Short Introduction to Power System Stability
B.1 Rotor Dynamics and Swing Equation
B.2 Power Flow and Power Angle Equation
Appendix C Thermodynamic Principles and RC-Modeling of Buildings
C.1 Basic Concepts of Thermodynamics
C.2 Principles of Thermodynamics and RC Modeling of Building Space
C.2.1 Thermal Parameters and its Electrical Analogs
C.2.1.1 Conduction
C.2.1.2 Energy Storage
C.2.1.3 Convection
C.2.1.4 Radiation
C.2.1.5 Ventilation
C.2.2 Equivalent RC Modeling
Appendix D Excerpts from IEC Standard 7730 for Calculation of PMV
D.1 Calculating PMV
D.2 Clothing Insulation Level
Appendix E More on Grid Applications and Data Dissemination
E.1 Introduction to MCGG and SE
E.1.1 Coherent Group of Generators and Islanding
E.1.2 SE of Power System
E.2 PSSE: Power System State Estimation
E.2.1 PSSE with DEFT
E.2.2 Comparison of PSSE with DEFT versus CEUT
E.2.2.1 Latency for PSSE from PMU to SPDC
E.2.2.2 Estimated Processing Latency for PSSE on the Indian Electric Grid
E.3 MCGG: Monitoring Coherent Groups of Generators
E.3.1 MCGG with DEFT
E.3.2 Comparison of MCGG: DEFT versus CEUT
Bibliography
Index
About the Authors