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Smart Grid Technology: A Cloud Computing and Data Management Approach [Kõva köide]

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  • Formaat: Hardback, 270 pages, kõrgus x laius x paksus: 248x192x21 mm, kaal: 570 g, Worked examples or Exercises
  • Ilmumisaeg: 12-Jul-2018
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108475205
  • ISBN-13: 9781108475204
Teised raamatud teemal:
Smart Grid Technology: A Cloud Computing and Data Management ApproachSuurem pilt
  • Formaat: Hardback, 270 pages, kõrgus x laius x paksus: 248x192x21 mm, kaal: 570 g, Worked examples or Exercises
  • Ilmumisaeg: 12-Jul-2018
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108475205
  • ISBN-13: 9781108475204
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781108475204.html
Märksõnad:
This comprehensive text covers fundamental concepts of smart grid technologies, integrating the tools and techniques of cloud computing and data management for application in smart grids. Different cloud and data management approaches are explained, highlighting energy management, information management, and security in the smart grid. The concepts of plug-in hybrid electric vehicle and virtual energy storage are explained in separate chapters. The text covers recent trends in cloud computing and data analytics in the field of smart grid. A glossary of important technical terms is provided for the benefit of the readers.

Covers smart grid technologies, their concepts and underlying principles, from the perspective of integration with cloud computing and data management approaches. The text is useful to graduate students, researchers, and practitioners working in different fields spanning computer science, system science, and information technology.

Muu info

Discusses concepts of smart grid technologies, from the perspective of integration with cloud computing and data management approaches.
Figures
xv
Tables
xix
Foreword xxi
Preface xxv
Part I Introduction
1 Introduction to Smart Grid
3(15)
1.1 Smart Grid Framework and Communication Model
3(3)
1.2 Smart Grid Vision
6(2)
1.3 Requirements of a Smart Grid
8(3)
1.3.1 Energy management
8(1)
1.3.2 Need to support multiple devices
9(1)
1.3.3 Information management
9(1)
1.3.4 Layered architecture
10(1)
1.3.5 Security
10(1)
1.4 Components of the Smart Grid
11(3)
1.4.1 Bi-directional communication
11(1)
1.4.2 Smart meter
12(1)
1.4.3 Micro-grid
13(1)
1.4.4 Plug-in hybrid electric vehicles
14(1)
1.5 Smart Grid Interoperability
14(1)
1.6 Summary
15(3)
References
17(1)
2 Introduction to Cloud Computing
18(20)
2.1 Allied Computing Models
20(8)
2.1.1 Mainframes
20(3)
2.1.2 Client-server architecture
23(2)
2.1.3 Cluster computing
25(1)
2.1.4 Grid computing
26(1)
2.1.5 Service oriented architecture (SOA)
27(1)
2.1.6 Utility computing
28(1)
2.1.7 Pay-per-use model
28(1)
2.2 Virtualization
28(1)
2.3 Hypervisor
29(2)
2.4 Types of Services
31(2)
2.4.1 Infrastructure as a service
31(1)
2.4.2 Platform as a service
31(1)
2.4.3 Software as a service
32(1)
2.5 Types of Deployment
33(1)
2.5.1 Public cloud
33(1)
2.5.2 Private cloud
33(1)
2.5.3 Hybrid cloud
34(1)
2.5.4 Community cloud
34(1)
2.6 Advantages of Cloud Computing
34(1)
2.6.1 Elastic nature
34(1)
2.6.2 Shared architecture
35(1)
2.6.3 Metering architecture
35(1)
2.6.4 Supports existing internet services
35(1)
2.7 Architecture of Cloud Computing
35(1)
2.8 Summary
36(2)
References
37(1)
3 Introduction to Big Data Analytics
38(11)
3.1 Attributes of Big Data
39(2)
3.1.1 Volume of data
39(1)
3.1.2 Velocity of data
40(1)
3.1.3 Variety of data
41(1)
3.2 Overview of Big Data Analytics
41(3)
3.3 Benefits of Big Data Analytics
44(1)
3.4 Big Data Analytics for Smart Grid
45(1)
3.5 Big Data Analytics Tools
46(1)
3.6 Summary
47(2)
References
47(2)
4 Fundamental Mathematical Prerequisites
49(12)
4.1 Linear Programming
49(1)
4.2 Integer Linear Programming
50(1)
4.3 Mixed Integer Linear Programming
51(1)
4.4 Non-Linear Programming
51(1)
4.5 Quadratic Function
52(1)
4.6 Different Distributions
53(2)
4.6.1 Normal distribution
53(1)
4.6.2 Poisson distribution
54(1)
4.6.3 Gaussian distribution
54(1)
4.7 Dimension Reduction Methods
55(1)
4.7.1 Principal component regression (PCR) method
55(1)
4.7.2 Reduced rank regression (RRR) method
55(1)
4.8 Approximation Algorithms
56(1)
4.9 Summary
57(4)
References
57(4)
Part II Cloud Computing Applications for Smart Grid
5 Demand Response
61(20)
5.1 Fundamentals of Demand Response and Challenges
61(1)
5.2 Different Demand Response Mechanisms
62(4)
5.2.1 Economic demand response
63(2)
5.2.2 Emergency demand response
65(1)
5.2.3 Ancillary demand response
66(1)
5.3 Problems with Existing Approaches without Cloud
66(1)
5.4 Cloud-Based Demand Response in Smart Grid
67(10)
5.4.1 Demand response in smart grid energy management
68(6)
5.4.2 Demand response in data centers for the smart grid
74(3)
5.5 Future Trends and Issues
77(1)
5.6 Summary
78(3)
References
79(2)
6 Geographical Load-Balancing
81(15)
6.1 Need for Load-Balancing in Smart Grid
81(1)
6.2 Challenges
82(1)
6.3 Problems with Existing Load-Balancing Approaches without Cloud
83(1)
6.3.1 Coalition formation
83(1)
6.3.2 Flexible demand forecasting
83(1)
6.3.3 Centralized load controller
84(1)
6.4 Cloud-Based Load-Balancing
84(8)
6.4.1 Price-based energy load-balancing
85(1)
6.4.2 Load-balancing at the smart grid data centers
86(3)
6.4.3 Renewable energy-aware load-balancing
89(1)
6.4.4 Load-balancing at data center networks
90(2)
6.5 Future Trends and Issues
92(1)
6.6 Summary
93(3)
References
94(2)
7 Dynamic Pricing
96(10)
7.1 Deployment of Dynamic Pricing in Smart Grids
96(1)
7.1.1 Determination of actual time-slot
96(1)
7.1.2 Need for adequate infrastructure
97(1)
7.2 Existing Dynamic Pricing Policies without Cloud
97(3)
7.2.1 Day-ahead pricing policy
97(1)
7.2.2 Demand-based pricing policy
98(1)
7.2.3 Supply-based pricing policy
99(1)
7.2.4 Supply-demand-based pricing policy
99(1)
7.3 Problems with Existing Approaches without Cloud
100(1)
7.3.1 Local knowledge of supply-demand information
100(1)
7.3.2 Unfair pricing tariffs for customers
100(1)
7.4 Cloud-Based Dynamic Pricing Policies
100(3)
7.5 Future Trends and Issues
103(1)
7.6 Summary
104(2)
References
104(2)
8 Virtual Power Plant
106(12)
8.1 Concept of Virtual Power Plant
106(3)
8.1.1 Commercial VPP
108(1)
8.1.2 Technical VPP
108(1)
8.2 Advantages of Virtual Power Plant
109(2)
8.2.1 Acts as internet of energy
109(1)
8.2.2 Energy efficiency
110(1)
8.2.3 Online optimization platform
110(1)
8.2.4 Systems security
111(1)
8.3 Virtual Power Plant Control Strategy
111(1)
8.4 Virtual Power Plant: Different Methodologies
111(4)
8.4.1 Integration of electric vehicles
112(1)
8.4.2 Implementation of energy storage devices
113(2)
8.5 Future Trends and Issues
115(1)
8.6 Summary
116(2)
References
116(2)
9 Advanced Metering Infrastructure
118(29)
9.1 Requirements
118(1)
9.2 Different Approaches of AMI
119(23)
9.2.1 Data collection for AMI
119(7)
9.2.2 Classification of AMI data
126(1)
9.2.3 Security for AMI
127(12)
9.2.4 Electricity theft detection in AMI
139(3)
9.3 Future Trends and Issues
142(2)
9.4 Summary
144(3)
References
145(2)
10 Cloud-Based Security and Privacy
147(22)
10.1 Security in Data Communication
147(6)
10.1.1 Overview of PKI
150(3)
10.2 Security and Privacy Challenges and Opportunities
153(2)
10.3 Security and Privacy Approaches without Cloud
155(4)
10.4 Cloud-Based Security and Privacy Approaches
159(4)
10.4.1 Identity-based encryption
160(3)
10.5 Future Trends and Issues
163(1)
10.6 Summary
163(6)
References
164(5)
Part III Smart Grid Data Management and Applications
11 Smart Meter Data Management
169(20)
11.1 Smart Metering Architecture
169(1)
11.2 Challenges and Opportunities
170(2)
11.2.1 Requirement of scalable computing facility
170(1)
11.2.2 Presence of heterogeneous data
171(1)
11.2.3 Requirement of large storage devices
171(1)
11.2.4 Information integration from different levels
172(1)
11.2.5 Complex architecture in the presence of multiple parties
172(1)
11.3 Smart Meter Data Management
172(14)
11.3.1 Cluster-based management
172(4)
11.3.2 Data compression and pattern extraction
176(3)
11.3.3 Cloud computing for big data management
179(4)
11.3.4 Calibration with big data
183(1)
11.3.5 Fuzzy logic-based management
184(2)
11.4 Future Trends and Issues
186(1)
11.5 Summary
186(3)
References
187(2)
12 PHEVs: Internet of Vehicles
189(18)
12.1 Convergence of PHEVs and Internet of Vehicles
190(1)
12.2 Electric Vehicles Management
191(13)
12.2.1 Charging and discharging of PHEVs
191(8)
12.2.2 Energy management for data centers and PHEVs
199(2)
12.2.3 Providing on-board internet service facility
201(3)
12.3 Future Trends and Issues
204(1)
12.4 Summary
205(2)
References
206(1)
13 Smart Buildings
207(20)
13.1 Concept of Smart Building
207(1)
13.2 Challenges and Opportunities
208(1)
13.3 Different Approaches for Establishing Smart Buildings
209(13)
13.3.1 Automatic energy management systems
209(8)
13.3.2 Intelligent information management systems
217(5)
13.4 Future Trends and Issues
222(1)
13.5 Summary
223(4)
References
224(3)
Part IV Smart Grid Design and Deployment
14 Simulation Tools
227(4)
14.1 Simulation Tools
227(3)
14.1.1 Open DSS
227(1)
14.1.2 MATPOWER
228(1)
14.1.3 NS-2 and NS-3
228(1)
14.1.4 GridSim
229(1)
14.1.5 OMNeT++
229(1)
14.1.6 GridLAB-D
229(1)
14.1.7 SUMO
230(1)
14.2 Summary
230(1)
References
230(1)
15 Worldwide Initiatives
231(14)
15.1 Initiatives Taken by EU
232(5)
15.2 Initiatives Taken by US Department of Energy
237(1)
15.3 Smart Grid Initiatives in Other Countries
238(3)
15.3.1 Initiatives in China
239(1)
15.3.2 Initiatives in India
239(2)
15.4 Smart Grid Standards
241(1)
15.5 Summary
242(3)
References
243(2)
Index 245
Sudip Misra is an Associate Professor in the School of Information Technology at the Indian Institute of Technology, Kharagpur. He received his Master's degree from the University of New Brunswick, Canada and his Ph.D. degree from Carleton University, Canada. Dr Misra has contributed to a number of articles in national and international journals of repute. He has published books in the field of wireless ad hoc networks, wireless sensor networks, wireless mesh networks, communication networks and distributed systems, network reliability and fault tolerance, and information and coding theory. His current research interests include algorithm design for emerging communication networks. Samaresh Bera is working as a senior project officer in the Smart Wireless Applications and Networking (SWAN) Lab in the School of Information Technology at the Indian Institute of Technology Kharagpur. He has published a number of research articles in prestigious journals such as IEEE Transactions on Smart Grid, Parallel and Distributed Systems and IEEE Systems Journal. His current research interests include smart grid communications and networking, cloud computing, and wireless ad-hoc and sensor networks.