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Smart Energy Management: A Computational Approach [Kõva köide]

(Indian Inst Of Technology Bombay, India & Sai Univ, Chennai, India), (Bhabha Atomic Research Centre Mumbai, India), (Univ Of Massachusetts, Amherst, Usa)
  • Formaat: Hardback, 312 pages
  • Ilmumisaeg: 11-Feb-2022
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9811252289
  • ISBN-13: 9789811252280
Teised raamatud teemal:
Smart Energy Management: A Computational ApproachSuurem pilt
  • Formaat: Hardback, 312 pages
  • Ilmumisaeg: 11-Feb-2022
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9811252289
  • ISBN-13: 9789811252280
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811252280.html
Märksõnad:
"The focus of this book is smart energy management with the recurring theme being the use of computational and data-driven methods that use requirements/measurement/monitoring data to drive actuation/control, optimization, and resource management. The computational perspective is applied to manage energy, with an emphasis on smart buildings and the smart electric grids. The book also presents computational thinking and techniques such as inferencing and learning for energy management. To this end, this book is designed to help understand the recent research trends in energy management, focusing specifically on the efforts to increase energy efficiency of buildings, campuses, and cities"--

The authors present a computational perspective on smart energy management, with an emphasis on smart buildings and smart electric grids and efforts to increase the energy efficiency of buildings, campuses, and cities through computational and data-driven methods. They describe the fundamentals of energy management systems and smart energy systems, energy management issues in the smart electric grid, energy management systems for modern buildings, an approach to thermal comfort and its customization for individual preferences and to prevent waste, solar energy, and grid-following brownouts and measuring energy consumption. Annotation ©2022 Ringgold, Inc., Portland, OR (protoview.com)

This monograph on smart energy management, using computational techniques, provides an overview of recent advances in the area of energy management in providing thermal comfort. Practitioners will find the book useful for understanding how technological advances can be put to practice, and learn from the case studies, which bring out practical challenges in doing so. The book will also be informative to researchers from other domains, such as, social and behavioral scientists, who will gain an understanding of how technology can be used to incentivize users to change their energy usage behavior in buildings.

Preface vii
1 Introduction
1(20)
1.1 SMART Systems
3(4)
1.1.1 Sense, Meaningfully
4(1)
1.1.2 Analyze
5(1)
1.1.3 Respond, Timely
6(1)
1.2 Computational Techniques in Energy Management
7(1)
1.3 Smart Electric Grid
8(5)
1.3.1 The Grid of the Last 100 Years
8(1)
1.3.2 Balancing Generation and Consumption
9(1)
1.3.3 Peak Demand versus Aggregate Demand
10(2)
1.3.4 Conventional Grid versus Smart Grid
12(1)
1.4 Smart Buildings
13(3)
1.4.1 Thermal Comfort in Buildings
14(1)
1.4.2 Solar Energy in Buildings
15(1)
1.4.3 Smart Techniques for Handling Power Deficit
15(1)
1.5 About this Monograph
16(5)
1.5.1 Why this Monograph?
16(1)
1.5.2 Topics Covered by this Monograph
17(2)
1.5.3 What this Monograph is not about?
19(1)
1.5.4 Who Should Read this Monograph?
19(2)
2 Smart Electric Grid: Applications and Data Analysis
21(38)
2.1 Introduction
21(3)
2.2 Sensing in the Grid, Meaningfully
24(1)
2.2.1 Phasor Measurement Units (PMUs)
24(1)
2.3 Analyze and Respond, Timely
25(1)
2.4 Smart Grid Applications: QoS Requirements and Background
26(6)
2.4.1 QoS Requirements of Grid Applications
26(2)
2.4.2 Background Information about Electrical Power Network/Grid
28(4)
2.5 Data Dissemination and Grid Applications
32(5)
2.5.1 CEUT: Existing Approach of Data Dissemination
32(2)
2.5.2 DEFT: Data Dissemination with Timeliness Guarantees
34(3)
2.6 A Case Study on Data Dissemination for Bus Angle Monitoring (BAM) Application
37(20)
2.6.1 CEUT: Centralized Execution with Unfiltered Data Forwarding Technique
38(1)
2.6.2 Performance of BAM with CEUT
38(5)
2.6.3 CEUT-direct: A Centralized Approach to Handle Loss of Data in the Network
43(1)
2.6.4 BAM using DEFT (Distributed Execution with Filtered Data Forwarding Technique)
43(4)
2.6.5 Creating the Data Model
47(5)
2.6.6 Handling Concurrent Applications
52(3)
2.6.7 Making All Data Available at SPDC
55(2)
2.7 Summary and Takeaways
57(2)
3 Energy Management Systems for Modern Buildings
59(40)
3.1 Commercial Buildings
60(3)
3.2 Residential Buildings
63(3)
3.3 Sensing Facets of a Building Meaningfully
66(6)
3.3.1 Terms Related to Sensing within a Building
69(1)
3.3.2 Hard Sensors
69(1)
3.3.3 Fusion of Hard Sensors for Occupancy Sensing
70(2)
3.4 Smart Sensor Suite for Buildings: A Case Study
72(6)
3.4.1 Hardware Architecture Design
72(3)
3.4.2 Communication Protocol
75(2)
3.4.3 Network Technology (WiFi)
77(1)
3.5 Analysis --- Soft-Sensing and Energy Management in Buildings
78(17)
3.5.1 Soft Sensors: Non-Intrusive Monitoring (NIM)
78(3)
3.5.2 Achieving Desired Observability of Various Facets of a Building
81(6)
3.5.3 Sensor Placement
87(2)
3.5.4 A Case Study on Observability of Facets
89(1)
3.5.5 Observing Facets of Interest
90(5)
3.6 Respond (Timely) using Hybrid Sensing: a Case Study
95(1)
3.7 Summary and Takeaways
96(3)
4 A Systematic Approach to Thermal Comfort
99(76)
4.1 Introduction
99(11)
4.1.1 Thermal Conditioning for Individual Comfort
100(1)
4.1.2 Thermal Conditioning for Server "Comfort"
101(1)
4.1.3 Thermal Conditioning Resources
102(8)
4.2 Challenges in Providing Thermal Comfort in a Building
110(5)
4.2.1 Reducing Consumption by Preventing Wastage
110(1)
4.2.2 Reducing Peak Demand
111(1)
4.2.3 Improving Thermal Comfort
112(3)
4.3 A Holistic Approach to Climate Control
115(5)
4.3.1 Factors Influencing Thermal Comfort
116(1)
4.3.2 Stages Involved in Providing Thermal Comfort
117(1)
4.3.3 Sensing Undesirable Phenomena in Spaces
118(1)
4.3.4 Analyzing Possible Pro-active and Reactive Interventions
119(1)
4.4 Thermal Modeling of Building
120(4)
4.4.1 First Principles of Thermodynamics (FPT) and Electrical Analogy
121(2)
4.4.2 Data-Driven Approach
123(1)
4.4.3 Hybrid Modeling
123(1)
4.4.4 Practical Limitations
123(1)
4.5 Adaptive Hybrid Modeling Approach
124(14)
4.5.1 Notations
126(1)
4.5.2 System Model
126(1)
4.5.3 Thermal Modeling of a Building Space
127(5)
4.5.4 Effect of Changes in Ambient Temperature Ta on Energy Consumption
132(1)
4.5.5 Effect of Changes in Set Temperature Ts on Energy Consumption
133(1)
4.5.6 Energy Consumed in Maintaining Thermal Comfort
134(2)
4.5.7 Responding with Data Driven Decisions: A Case Study
136(1)
4.5.8 To Cool or Not To Cool during Unoccupied-Period
136(2)
4.6 Thermal Comfort Under Peak Demand Constraints
138(15)
4.6.1 Candidate Scheduling Policies and their Limitations
140(2)
4.6.2 Thermal Characteristics of Heating, Ventilation and Air-Conditioning (HVAC) Systems
142(3)
4.6.3 Analysis of Feasibility --- Maintaining Thermal Comfort with TCBM Scheduling
145(3)
4.6.4 Analysis of Energy Consumption
148(5)
4.7 Adaptive Demand-Response (D-R) Control
153(6)
4.7.1 Adapting Energy Consumption with TOD Charges
153(1)
4.7.2 Handling Varying Ambient Temperature and Occupancy
154(1)
4.7.3 TCBM as Anytime Algorithm to Handle Varying Peak Limit
155(3)
4.7.4 Adaptive Demand-Response Policy
158(1)
4.8 Learnings from an Academic Building
159(14)
4.8.1 Auditorium
159(5)
4.8.2 Small Classroom
164(3)
4.8.3 Big Classroom
167(1)
4.8.4 Lab Space
168(5)
4.9 Summary and Takeaways
173(2)
5 Customized Thermal Comfort
175(24)
5.1 Individual Thermal Comfort
175(5)
5.1.1 Predicting Thermal Comfort
176(2)
5.1.2 Predicted Personal Vote (PPV) Model
178(2)
5.2 Occupancy Based Customization
180(9)
5.2.1 Occupancy Sensing
181(8)
5.3 Chiller Sequencing --- Customization for Varying Loads
189(6)
5.3.1 Chiller Control Techniques
190(2)
5.3.2 Data-Driven Techniques in Chiller Sequencing
192(3)
5.4 Adaptive Thermal Comfort
195(2)
5.5 Summary and Takeaways
197(2)
6 Solar Energy in Buildings
199(22)
6.1 Exploiting Solar Energy: Potential and Approaches
200(5)
6.1.1 Assessing the Rooftop Solar Potential --- Case Study of Mumbai
201(1)
6.1.2 Building Integrated Photovoltaics (BIPV)
201(4)
6.2 Mitigation of the Effect of Partial Shading
205(15)
6.2.1 Output Control using MPPT
205(3)
6.2.2 Beyond MPPT Control
208(1)
6.2.3 Dynamic Array Reconfiguration (DAR) and Current Injection (CI)
208(2)
6.2.4 Cl-based DAR (CI-DAR)
210(7)
6.2.5 Experimental Validation in a Prototype System
217(3)
6.3 Summary and Takeaways
220(1)
7 Making the Best of Available Energy
221(18)
7.1 Managing Building Loads According to Available Power in the Grid
221(6)
7.1.1 Dealing with Blackouts: Existing Approach
222(1)
7.1.2 GFB: A Smarter Solution to Prevent Blackouts
222(5)
7.2 NILM: Non-Intrusive Load Monitoring
227(2)
7.2.1 The Goal of NILM
227(2)
7.2.2 NILM Techniques
229(1)
7.3 Modeling Residential Electrical Loads
229(9)
7.3.1 Modeling Individual Loads
230(1)
7.3.2 Basic Model Types
231(4)
7.3.3 Compound Model Types
235(3)
7.4 Summary and Takeaways
238(1)
Appendix A Electrical Energy: Some Basic Concepts
239(8)
A.1 Power Consumption and Loads in AC Circuit
240(7)
Appendix B Short Introduction to Power System Stability
247(8)
B.1 Rotor Dynamics and Swing Equation
248(3)
B.2 Power Flow and Power Angle Equation
251(4)
Appendix C Thermodynamic Principles and RC-Modeling of Buildings
255(10)
C.1 Basic Concepts of Thermodynamics
255(2)
C.2 Principles of Thermodynamics and RC Modeling of Building Space
257(8)
C.2.1 Thermal Parameters and its Electrical Analogs
257(4)
C.2.2 Equivalent RC Modeling
261(4)
Appendix D Excerpts from IEC Standard 7730 for Calculation of PMV
265(4)
D.1 Calculating PMV
265(2)
D.2 Clothing Insulation Level
267(2)
Appendix E More on Grid Applications and Data Dissemination
269(12)
E.1 Introduction to MCGG and SE
269(1)
E.1.1 Coherent Group of Generators and Islanding
269(1)
E.1.2 SE of Power System
270(1)
E.2 PSSE: Power System State Estimation
270(5)
E.2.1 PSSE with DEFT
272(2)
E.2.2 Comparison of PSSE with DEFT versus CEUT
274(1)
E.3 MCGG: Monitoring Coherent Groups of Generators
275(6)
E.3.1 MCGG with DEFT
277(1)
E.3.2 Comparison of MCGG: DEFT versus CEUT
278(3)
Bibliography 281(12)
Index 293(4)
About the Authors 297