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Smart Grid as an Application Development Platform [Hardback]

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  • Formāts: Hardback, 220 pages
  • Izdošanas datums: 31-Aug-2017
  • Izdevniecība: Artech House Publishers
  • ISBN-10: 1630811092
  • ISBN-13: 9781630811099
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  • Cena: 136,64 €
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Smart Grid as an Application Development PlatformPalielināt
  • Formāts: Hardback, 220 pages
  • Izdošanas datums: 31-Aug-2017
  • Izdevniecība: Artech House Publishers
  • ISBN-10: 1630811092
  • ISBN-13: 9781630811099
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781630811099.html
Keywords:
George Koutitas and Stan McClellan take the reader through the evolution of the power grid, from its classical role as a utility or service provider to its new role as an application development platform. The book explores how new technologies are currently creating a cleaner and more sustainable ecosystem for new business models to prosper, and how the grid intelligence and energy production migrate to the edge of the network.The new smart grid is becoming a distributed system that supports decentralized services through modern trends and system architectures. The authors demonstrate the orchestration of these technologies as they transform the utility sector toward a more human-centric grid, and the active role consumers play in providing critical data for the industry. Readers will gain insight into their role in the utility sector, as well as problems, solutions and risks related to the development of the smart grid model.
Preface xiii
1 Smart Grid Business Model 1(6)
1.1 Introduction
1(1)
1.2 Vision
1(1)
1.3 Problem
2(1)
1.4 Solution
2(1)
1.5 Growth Strategy
3(1)
1.6 Business Model
4(1)
1.7 Risks
4(1)
References
5(2)
2 The Power Grid at a Glance 7(42)
2.1 Introduction
7(1)
2.2 Useful Data
7(10)
2.2.1 Power and Energy
7(1)
2.2.2 Capacity, Generation, Consumption, and Demand
8(1)
2.2.3 Alternating Current, Direct Current, Active Power, and Reactive Power
9(5)
2.2.4 Example from Smart Meter Data
14(3)
2.3 Grid Architecture
17(9)
2.3.1 Organization, Players, and Regions
17(3)
2.3.2 Production
20(4)
2.3.3 Transmission
24(1)
2.3.4 Distribution
25(1)
2.4 Drawbacks of Current Network Design
26(8)
2.4.1 Waste of Resources and Pollution
26(2)
2.4.2 Adaptation to Time-Variable Production and Consumption
28(2)
2.4.3 Passive Nature of the End Consumer
30(1)
2.4.4 Business Models
31(1)
2.4.5 Security/Outages
32(2)
2.5 Energy Markets
34(5)
2.5.1 Wholesale Market
34(4)
2.5.2 Retail Market
38(1)
2.5.3 Analyzing the Bill
39(1)
2.6 Understanding the Consumer
39(5)
2.6.1 Appliances Footprint
39(3)
2.6.2 Electricity Usage Analysis
42(1)
2.6.3 Archetypes of Consumers
43(1)
2.7 Lessons Learned from the Telecommunications Industry
44(2)
References
46(3)
3 Smart Grid Elements 49(56)
3.1 Introduction
49(1)
3.2 The System of Systems
49(11)
3.2.1 Evolution of the Grid
49(3)
3.2.2 Architecture and Standards
52(3)
3.2.3 Interoperability and Protocols
55(5)
3.3 Business of Businesses
60(12)
3.3.1 Utility of the Future
60(4)
3.3.2 New Business Models and Players
64(3)
3.3.3 Business-to-Consumer Providers
67(1)
3.3.4 Utility Customer Beyond 2020
67(2)
3.3.5 The Social Smart Grid
69(2)
3.3.6 Start-Up Ecosystem
71(1)
3.4 The ICT Layer
72(9)
3.4.1 Smart Metering
72(3)
3.4.2 Networking
75(2)
3.4.3 Advanced Metering Infrastructure
77(3)
3.4.4 Meter Data Management Systems
80(1)
3.4.5 Example of In-Home Smart Metering
80(1)
3.5 Evolution of Prosumers
81(10)
3.5.1 The Path to Off-Grid
81(2)
3.5.2 Connected Homes
83(5)
3.5.3 Standards
88(3)
3.6 Microgrids
91(2)
3.6.1 Architecture
91(1)
3.6.2 Types of Microgrids
92(1)
3.7 Virtual Power Plants
93(2)
3.7.1 Architecture
93(1)
3.7.2 Emerging Trends
94(1)
3.8 Electric Vehicles
95(3)
3.8.1 Electric Vehicle Types and Charging Technologies
95(1)
3.8.2 Effect on Consumption Patterns
96(2)
3.8.3 V2G Concept
98(1)
3.9 Smart Grid Pricing
98(4)
3.9.1 Pricing Models
98(1)
3.9.2 Net Metering
99(1)
3.9.3 Renewable Energy Credits and Peak Load Credits
100(2)
References
102(3)
4 The Cloud Environment of Application Providers 105(38)
4.1 Introduction
105(1)
4.2 Overview of Services
105(3)
4.3 Introduction to Cloud Computing
108(7)
4.3.1 Web Services and APIs
108(3)
4.3.2 Reserving Resources in the Cloud
111(2)
4.3.3 Example of Web Services for Home Automation
113(2)
4.4 Product Development in the Cloud
115(5)
4.4.1 Defining the Pricing Model of SaaS Service
115(1)
4.4.2 Web App or Mobile App?
116(1)
4.4.3 Security and Privacy
117(1)
4.4.4 Steps for Accessing Open APIs with Product Innovators
118(1)
4.4.5 White Labeling
119(1)
4.5 Open Data and APIs
120(15)
4.5.1 Energy Information Administration
120(3)
4.5.2 Green Button
123(2)
4.5.3 Orange Button
125(1)
4.5.4 PVWatts API
126(2)
4.5.5 Microinverter APIs
128(1)
4.5.6 Smart Thermostat and Connected Home Device APIs
129(4)
4.5.7 Energy Usage Datasets
133(1)
4.5.8 MultiSpeak
134(1)
4.6 Open ADR
135(4)
4.6.1 Key Actors and Services
135(1)
4.6.2 Demand Response Event
136(1)
4.6.3 Communication Architecture
137(1)
4.6.4 Rush Hour Example
138(1)
4.7 Conclusions and Concerns
139(1)
References
140(3)
5 User-Centric Applications 143(36)
5.1 Introduction
143(1)
5.2 Data Processing Overview
143(1)
5.3 Energy Analytics
144(4)
5.3.1 Hourly and Daily Energy Analytics
144(2)
5.3.2 Bill Forecasting
146(2)
5.4 Load Disaggregation
148(11)
5.4.1 Hidden Information in Appliance Footprints
149(4)
5.4.2 Signal Processing on Smart Meter Data
153(1)
5.4.3 Event Detection by Extracting Power Pulses from Smart Meter Data
154(2)
5.4.4 Clustering
156(1)
5.4.5 Pulse to Appliance Association
157(1)
5.4.6 NIALM Results and Business Intelligence
158(1)
5.5 Direct Load Control
159(6)
5.5.1 Modeling User Comfort
161(2)
5.5.2 Command Flow for DLC Demand Response
163(1)
5.5.3 Fairness Issues Related to DR Commands
163(2)
5.5.4 Simplified DLC Pseudocode
165(1)
5.6 Load Scheduling
165(5)
5.6.1 Elastic Demand and Consumer Behavior
165(2)
5.6.2 Objective of LS: Example of the EV Charging Garage
167(2)
5.6.3 Types of LS Implementation
169(1)
5.6.4 Simplified LS Pseudocode
170(1)
5.7 Gamification Demand Response
170(5)
5.7.1 Participatory Games
170(3)
5.7.2 Rewards and Social Recognition
173(1)
5.7.3 Objectives of Gamification
174(1)
5.7.4 Simplified DR Gamification Pseudocode
175(1)
5.8 Example: A Day of Smart Living in 2017
175(3)
5.8.1 Energy Usage Analysis
176(1)
5.8.2 Active Utility Customer
177(1)
5.8.3 Home Automation
177(1)
References
178(1)
6 Transactive Energy Economy 179(30)
6.1 Introduction
179(1)
6.2 Energy in the Sharing Economy
179(4)
6.2.1 Evolution of Smart Cities: From Centralized to Distributed Architectures
179(2)
6.2.2 The Concept of Energy Giving
181(1)
6.2.3 Value Proposition and Business Impact
182(1)
6.3 The Transactive Grid
183(9)
6.3.1 Foundations of Transactive Energy
183(2)
6.3.2 Examples at the Retail and Distribution Levels
185(2)
6.3.3 Modes of Operation and New Entities
187(1)
6.3.4 Analysis of Transactions
188(2)
6.3.5 End-User Transactive Energy Implementation
190(2)
6.4 Cryptocurrencies: Their Role in the Energy Sector
192(7)
6.4.1 The Blockchain
192(3)
6.4.2 Bitcoin
195(2)
6.4.3 Smart Contracts and Ethereum
197(1)
6.4.4 The Concept of an Energy Coin
198(1)
6.5 Evolution of Collaborative Prosumers
199(7)
6.5.1 System Model
199(2)
6.5.2 Coalition Games and the Shapley Value
201(2)
6.5.3 Various Pricing Schemes
203(3)
6.6 Implementation Challenges
206(1)
6.7 Conclusion
207(1)
References
207(2)
7 Summary and Conclusions 209(2)
About the Authors 211(2)
Index 213
George Koutitas is the CEO and co-founder of Gridmates and assistant professor at Texas State University. He received his B.Sc. in physics from Aristotle University of Thessaloniki Greece, his M.Sc. degree in mobile and satellite communications from the University of Surrey, UK and his Ph.D. in electrical engineering under EPSRC scholarship from the Center of Communications Systems Research, UK.Stan McClellan is the Director of the Ingram School of Engineering at Texas State University where he is a professor of electrical engineering. Stan received his Ph.D., M.S., and B.S. in electrical engineering from Texas A&M University.