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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
A comprehensive resource that covers all the key areas of smart grid communication infrastructures Smart grid is a transformational upgrade to the traditional power grid that adds communication capabilities, intelligence and modern control. Smart Grid Communication Infrastructures is a comprehensive guide that addresses communication infrastructures, related applications and other issues related to the smart grid. The text shows how smart grid departs from the traditional power grid technology. Fundamentally, smart grid has advanced communication infrastructures to achieve two-way information exchange between service providers and customers. Grid operations in smart grid have proven to be more efficient and more secure because of the communication infrastructures and modern control. Smart Grid Communication Infrastructures examines and summarizes the recent advances in smart grid communications, big data analytics and network security. The authors - noted experts in the field - review the technologies, applications and issues in smart grid communication infrastructure. This important resource: * Offers a comprehensive review of all areas of smart grid communication infrastructures * Includes an ICT framework for smart grid * Contains a review of self-sustaining wireless neighborhood that are network designed * Presents design and analysis of a wireless monitoring network for transmission lines in smart grid Written for graduate students, professors, researchers, scientists, practitioners and engineers, Smart Grid Communication Infrastructures is the comprehensive resource that explores all aspects of the topic.
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Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Chapter 1: Background of the Smart Grid
1.1 Motivations and Objectives of the Smart Grid
1.2 Smart Grid Communications Architecture
1.3 Applications and Requirements
1.4 The Rest of the Book
Chapter 2: Smart Grid Communication Infrastructures
2.1 An ICT Framework for the Smart Grid
2.2 Entities in the ICT Framework
2.3 Communication Networks and Technologies
2.4 Data Communication Requirements
2.5 Summary
Chapter 3: Self‐Sustaining Wireless Neighborhood‐Area Network Design
3.1 Overview of the Proposed NAN
3.2 Preliminaries
3.3 Problem Formulations and Solutions in the NAN Design
3.4 Numerical Results
3.5 Case Study
3.6 Summary
Chapter 4: Reliable Energy‐Efficient Uplink Transmission Power Control Scheme in NAN
4.1 Background and Related Work
4.2 System Model
4.3 Preliminaries
4.4 Hierarchical Uplink Transmission Power Control Scheme
4.5 Analysis of the Proposed Schemes
4.6 Numerical Results
4.7 Summary
Chapter 5: Design and Analysis of a Wireless Monitoring Network for Transmission Lines in the Smart Grid
5.1 Background and Related Work
5.2 Network Model
5.3 Problem Formulation
5.4 Proposed Power Allocation Schemes
5.5 Distributed Power Allocation Schemes
5.6 Numerical Results and A Case Study
5.7 Summary
Chapter 6: A Real‐Time Information‐Based Demand‐Side Management System
6.1 Background and Related Work
6.2 System Model
6.3 Centralized DR Approaches
6.4 Game Theoretical Approaches
6.5 Precision and Truthfulness of the Proposed DR System
6.6 Numerical and Simulation Results
6.7 Summary
Chapter 7: Intelligent Charging for Electric Vehicles—Scheduling in Battery Exchanges Stations
7.1 Background and Related Work
7.2 System Model
7.3 Load Scheduling Schemes for BESs
7.4 Simulation Analysis and Results
7.5 Summary
Chapter 8: Big Data Analytics and Cloud Computing in the Smart Grid
8.1 Background and Motivation
8.2 Pricing and Energy Forecasts in Demand Response
8.3 Attack Detection
8.4 Cloud Computing in the Smart Grid
8.5 Summary
Chapter 9: A Secure Data Learning Scheme for Big Data Applications in the Smart Grid
9.1 Background and Related Work
9.2 Preliminaries
9.3 Secure Data Learning Scheme
9.4 Smart Metering Data Set Analysis—A Case Study
9.5 Conclusion and Future Work
Chapter 10: Security Challenges in the Smart Grid Communication Infrastructure
10.1 General Security Challenges
10.2 Logical Security Architecture
10.3 Network Security Requirements
10.4 Classification of Attacks
10.5 Existing Security Solutions
10.6 Standardization and Regulation
10.7 Summary
Chapter 11: Security Schemes for AMI Private Networks
11.1 Preliminaries
11.2 Initial Authentication
11.3 Proposed Security Protocol in Uplink Transmissions
11.4 Proposed Security Protocol in Downlink Transmissions
11.5 Domain Secrets Update
11.6 Summary
Chapter 12: Security Schemes for Smart Grid Communications over Public Networks
12.1 Overview of the Proposed Security Schemes
12.2 Proposed ID‐Based Scheme
12.3 Single Proxy Signing Rights Delegation
12.4 Group Proxy Signing Rights Delegation
12.5 Security Analysis of the Proposed Schemes
12.6 Performance Analysis of the Proposed Schemes
12.7 Conclusion
Chapter 13: Open Issues and Possible Future Research Directions
13.1 Efficient and Secure Cloud Services and Big Data Analytics
13.2 Quality‐of‐Service Framework
13.3 Optimal Network Design
13.4 Better Involvement of Green Energy
13.5 Need for Secure Communication Network Infrastructure
13.6 Electrical Vehicles
Reference
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Enabling wireless technologies in HANs.
Table 2.2 Enabling wired technologies in HANs.
Table 2.3 PLC operating frequency bands.
Table 2.4 Data communication latency requirements.
Chapter 3
Table 3.1 Selected DoDs and their maximum cycles.
Table 3.2 The remaining parameters for the Erceg model.
Chapter 4
Table 4.1 Key notations and terminology.
Chapter 5
Table 5.1 Key sets and variables.
Chapter 6
Table 6.1 Key sets and variables.
Table 6.2 Residential settings for the case study.
Chapter 7
Table 7.1 List of notations and variables.
Chapter 8
Table 8.1 Useful data in the smart grid
Chapter 9
Table 9.1 A sample set of smart metering data.
Chapter 10
Table 10.1 Functional Requirements.
Table 10.2 Security requirements for data transmitted over private networks.
Table 10.3 Security requirements for data transmitted over the public networks.
Table 10.4 Selected standards for the Smart Grid.
Chapter 11
Table 11.1 Security services.
Table 11.2 Notations of the keys.
Chapter 12
Table 12.1 Computational complexity.
Table 12.2 Computational time for each operation.
Table 12.3 Computational time of each algorithm.
Chapter 13
Table 13.1 Latency requirements in smart grid communications.
List of Illustrations
Chapter 1
Figure 1.1 Dispatchable renewable resources.
Figure 1.2 NIST conceptual domain model for the smart grid.
Figure 1.3 High‐level illustration of the smart grid communication architecture.
Figure 1.4 Smoother power load achieved by DR.
Figure 1.5 High‐level illustration of AMI.
Figure 1.6 High‐level illustration of the monitoring system.
Chapter 2
Figure 2.1 An overview of the proposed ICT framework.
Figure 2.2 Examples of internal data collectors.
Figure 2.3 Cloud computing service and the power grid.
Figure 2.4 Example of power generators in the ICT framework.
Figure 2.5 High‐level illustration of the advanced metering infrastructure.
Figure 2.6 High‐level illustration of NANs.
Chapter 3
Figure 3.1 Overview of the proposed NAN structure.
Figure 3.2 Solar panel charging rate estimate.
Figure 3.3 Modeling of maximum cycles against depth of discharge.
Figure 3.4
‐function
.
Figure 3.5 A quasiconcave function.
Figure 3.6 Illustration of rings.
Figure 3.7 Illustration of “one on each” method.
Figure 3.8 Illustration of “outsider ring first” method.
Figure 3.9 Global uplink transmission power efficiency.
Figure 3.10 Optimal number of gateways.
Figure 3.11 Global power efficiency with respect to number of gateways.
Figure 3.12 Global transmission rate with respect to number of gateways.
Figure 3.13 Global power consumption with respect to number of gateways.
Figure 3.14 Total cost of a DAP.
Figure 3.15 Battery capacity of a DAP.
Figure 3.16 Solar panel size of a DAP.
Figure 3.17 Impact on charging thresholds of a DAP.
Figure 3.18 Remaining energy at
every day.
Figure 3.19
at
every day.
Chapter 4
Figure 4.1 Illustration of the studied NAN structure.
Figure 4.2 Illustration of utility function with/without penalty.
Figure 4.3 Illustration of
.
Figure 4.4 The estimate of
and
with respect to different
.
Figure 4.5 Reliability of NAN.
Figure 4.6 Total uplink transmission power usage comparison.
Figure 4.7 Convergence of the NE.
Figure 4.8 Convergence of the SE.
Chapter 5
Figure 5.1 A section between two towers with fiber‐optic connections.
Figure 5.2 Source of interference for link
Figure 5.3 Illustration of
.
Figure 5.4 Illustration of the modified utility function.
Figure 5.5 Simulation setting for transmission line.
Figure 5.6 Computational time of
and
.
Figure 5.7 Total transmission power by solving
and
.
Figure 5.8 Normalized transmission efficiency of each sensor.
Figure 5.9 SINR of each sensor.
Figure 5.10 Delay of each link.
Figure 5.11 Comparison of normalized transmission power.
Figure 5.12 Dynamic power allocation for
.
Figure 5.13 Corresponding
and
.
Chapter 6
Figure 6.1 Demand‐side power management system.
Figure 6.2 Power sale to the customers in United States.
Figure 6.3 Existing net capacity by energy source and producer type [108].
Figure 6.4 Daily consumption of customers.
Figure 6.5 Solution to
with
.
Figure 6.6 Solution to
with
.
Figure 6.7 Load schedules by
,
and
.
Figure 6.8 Load of different types of residential customers.
Figure 6.9 Load of different types of business customers.
Figure 6.10 Load of different types of industrial customers.
Figure 6.11 Load scheduling from different distributed approaches.
Figure 6.12
illustration.
Figure 6.13 Load achieved by
for type
residential customers.
Figure 6.14 Load achieved by
for type
business customers.
Figure 6.15 Load achieved by
for type
industrial customers.
Figure 6.16 Convergence of
and mixed
.
Figure 6.17 Load schedule without storage units.
Figure 6.18 Impact of storage units on PAR.
Figure 6.19 An estimate of different power suppliers.
Figure 6.20 Corresponding total cost estimates for future years.
Chapter 7
Figure 7.1 Demand‐side power management system with BESs.
Figure 7.2 Distribution of incoming customers.
Figure 7.3 An example of a power load without PHEVs in the power grid.
Figure 7.4 Smoothed load with BESs.
Figure 7.5 The impact of
on fully charged batteries.
Figure 7.6 Impact of port number on fully charged batteries.
Figure 7.7 Impact of
on fully charged batteries.
Figure 7.8 Impact of
on PAR.
Figure 7.9 The impact of port number on PAR.
Figure 7.10 Load schedules achieved by distributed scheme.
Figure 7.11 Estimate of battery storage by distributed scheme.
Chapter 8
Figure 8.1 The world's effective data capacity.
Figure 8.2 Data processing procedure.
Figure 8.3 Energy consumption and temperature.
Figure 8.4 Energy forecast.
Figure 8.5 A cloud computing architecture for the smart grid.
Chapter 9
Figure 9.1 Traditional centralized learning process for big data applications.
Figure 9.2 Proposed security scheme based on zero‐knowledge proof.
Figure 9.3 The ICT architecture of AMI in the smart grid.
Figure 9.4 Regularization results using learning entity 1.
Figure 9.5 Regularization results using learning entity 5.
Figure 9.6 Regularization results using learning entity 6.
Figure 9.7 Convergence on the values of cost functions
.
Chapter 11
Figure 11.1 Initial authentication process for DAP
.
Figure 11.2 Initial authentication process for a smart meter.
Figure 11.3 Detailed initial authentication process through one active neighbor.
Figure 11.4 Data aggregation process in an uplink transmission.
Figure 11.5 Multiflow data aggregation process.
Figure 11.6 Data recovery process in uplink transmission.
Figure 11.7 Data integrity check in uplink transmission.
Figure 11.8 Encryption of broadcast control message
.
Figure 11.9 Encryption of control message
for
.
Figure 11.10 Example of control message
to
.
Chapter 12
Figure 12.1 Signing rights delegation from
to
.
Figure 12.2 Signing rights delegation from
to a group of
s.
Guide
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Smart Grid Communication Infrastructures
Big Data, Cloud Computing, and Security
Feng Ye
University of Dayton Dayton, Ohio
Yi Qian
University of Nebraska-Lincoln Omaha, Nebraska
Rose Qingyang Hu
Utah State University Logan, Utah
Copyright
This edition first published 2018
© 2018 John Wiley & Sons Ltd
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Library of Congress Cataloging-in-Publication Data
Names: Ye, Feng, 1989- author. | Qian, Yi, 1962- author. | Hu, Rose Qingyang, author.
Title: Smart grid communication infrastructures : big data, cloud computing, and security / by Feng Ye, Yi Qian, and Rose Qingyang Hu.
Description: Hoboken, NJ : John Wiley & Sons, 2018. | Includes bibliographical references and index. |
Identifiers: LCCN 2018001007 (print) | LCCN 2018012065 (ebook) | ISBN 9781119240181 (pdf) | ISBN 9781119240167 (epub) | ISBN 9781119240150 (cloth)
Subjects: LCSH: Smart power grids-Communication systems. | Smart power grids-Security measures.
Classification: LCC TK3105 (ebook) | LCC TK3105 .Y44 2018 (print) | DDC 621.31-dc23
LC record available at https://lccn.loc.gov/2018001007
Cover Design: Wiley
Cover Image: © DuKai photographer/Getty Images
1Background of the Smart Grid
The world has been rapidly developing higher efficiency in many aspects. As one of these important aspects, power grids in many countries have been evolving from traditional power grids into smart grids in recent years. What is a smart grid? In this chapter, we will introduce the background of the smart grid, including its motivations, communication architecture, applications, and requirements.
1.1 Motivations and Objectives of the Smart Grid
The traditional power grid, or just “the grid,” is a network of transmission lines, substations, transformers, and more that delivers electricity from the power plant to consumers (e.g. your home, business, etc.). Our current power grid was built over a century ago. To move forward, we need a new kind of power grid that can automate and manage the increasing need for and complexity of delivering electricity. You may have heard of this new kind of power grid, which is called the smart grid. The smart grid is a revolutionary upgrade to the traditional power grid that adds communication capabilities, intelligence, and modern control [1–10]. The US Department of Energy (DoE), Office of Electricity Delivery & Energy Reliability has listed the benefits associated with the smart grid as follows [11]:
More efficient transmission of electricity.
Quicker restoration of electricity after power disturbances.
Reduced operations and management costs for utilities and ultimately lower power costs for consumers.
Reduced peak demand, which will also help to reduce electricity rates.
Increased integration of large‐scale renewable energy systems.
Better integration of customer‐owner power generation systems, including renewable energy systems.
Improved security.
We further summarize the benefits into three major motivations for the smart grid: 1) To better adapt renewable energy resources, 2) to increase the efficiency of grid operations and to reduce losses, and 3) to improve system reliability and security.
1.1.1 Better Renewable Energy Resource Adaption
In the current power grid, the majority of the power generation stations are based on fossil fuels (e.g. coal, natural gas, petroleum, etc.) [12]. While such power stations have supported civilization for over a century, we have come to realize the shortcomings and disadvantages of such power supplies. Since fossil fuel is limited resource, its ever‐decreasing supply will ultimately affect the power stations. Moreover, the emission of greenhouse gases from those power stations has gradually contributed to global climate change, especially global warming. Therefore, cleaner energy resources and renewable energy resources have been deployed to the grid in the past few decades. Some examples of existing power stations using cleaner energy resources are hydropower, geothermal, etc.
The smart grid will include both central and distributed generation sources with a mix of dispatchable and nondispatchable resources, as illustrated in Figure 1.1. Despite their obvious benefits, renewable sources other than hydropower provide only about 5% of the electricity supply for our grid. What's holding us back is not the amount of electricity we can generate from those renewable resources but the power grid itself. Due to the remote locations of most renewable resources, extra infrastructure is required to deliver electricity to consumers, who are mostly in urban areas. Moreover, the current power grid has difficulty accommodating renewable sources of power due to their unpredictability. The smart grid will have much better control systems to manage those renewable energy resources to supplement existing fossil‐fuel power stations. For example, the smart grid will give grid operators new tools to reduce power demand quickly when the level of wind or solar power dips, and it will have more energy storage capabilities to absorb excess wind and solar power when it isn't needed, then to release that energy when the level of wind and solar power dips.
Figure 1.1 Dispatchable renewable resources.
1.1.2 Grid Operation Efficiency Advancement
The smart grid is able to increase the efficiency of the grid operations while reducing losses because of its advanced monitoring and controlling systems. In the current power grid, the pricing of electricity is based on peak and off‐peak demand. The price of electricity during peak hours is higher than during off‐peak hours. For some consumers, the higher price may be a few times more than the lower one. The uneven pricing of electricity is due to the dynamic cost of power generation, especially in fossil‐fuel‐based power stations. It is costlier per unit to generate electricity for periods of higher demand. Furthermore, the transition process from high demand to low demand (or vice versa) cannot happen instantly, which results in a large amount of fuel waste in power stations and extra greenhouse gas emission. Even worse, the extra cost due to inefficient grid operation is distributed to consumers. With the advent of electric vehicles (EVs) and plug‐in hybrid electric vehicles (PHEVs), the electricity requirement of the power grid will increase immensely [13, 14]. Peak and off‐peak hours may have a more complicated and dynamic pattern in the future.
The solution provided in the smart grid is demand response, which can be applied to balance the supply and demand of electricity. Electricity usage during peak periods can be reduced or shifted in response to time‐based rates and other forms of financial incentives. As a result, the power load is smoother in the smart grid, without sudden transitions. Thus the efficiency of the fossil‐fuel power stations can be increased dramatically. Moreover, losses caused by energy theft, system failure, and other factors can be reduced in the smart grid because of the accurate and reliable automated monitoring systems [15, 16]. At first glance, such features enabled by the smart grid involve sacrifice on the part of consumers. Fortunately, consumers have opportunities to earn savings on their electricity bills in return. In addition to the pricing difference between peak and off‐peak hours, the smart grid will allow utilities to launch more sophisticated programs as incentives. For example, some utilities may offer credits if consumers will allow a central office to control cycling their air conditioners on and off during times of peak power demand.
Consumers will use the grid in different ways. More consumers will become “prosumers”‐both consumers and producers of energy [17]. Power will flow both ways, and other ancillary services may also be provided by these new prosumers. Some utilities even purchase back electricity generated by consumers. In theory, some consumers may receive checks from utilities, which could only happen in the smart grid. On top of potential savings, the smart grid offers consumers active control of their energy bills, allowing them to opt in and out of the demand response program; thus customer experience could be enhanced from just one‐way communication.
1.1.3 Grid Reliability and Security Improvement
The current power grid has been improved quite a lot decade by decade in terms of reliability and security; however, blackouts still occur once or twice each year. The notorious blackout on Aug. 14, 2003 in parts of America and Canada affected 45 million people in the United States and 10 million people in Canada. The blackout was triggered by a relatively insignificant but overheated power line. In the smart grid, many intelligent sensors and actuators are deployed to monitor and control the grid's transmission system in real time [18–21]. New technologies in the smart grid, such as phasor measurement units (PMU), sample voltage and current many times per second, as opposed to once every few seconds in the current power grid. On systems equipped with smart grid communications technologies, system failure and hazardous situations can be reported to control centers promptly for fast reaction. As a result, distribution outages will be reduced in the smart grid. It would have been easier to detect the types of oscillations that led to the 2003 blackout in the smart grid. Moreover, advanced and comprehensive cybersecurity is provided in the smart grid communication infrastructures. Therefore, system reliability and security in the smart grid are greatly improved compared to the traditional power grid. However, the system will be even more complex.
With all of the new entities and energy resources, managing and optimizing the system will become increasingly challenging. Based on the aforementioned motivations, research and practical deployment in the smart grid need to achieve the objectives shown in the following list:
The smart grid needs to be adaptive to changing situations and able to self‐heal when some system failures occur.
Customers are actively involved in the smart grid, based on dynamic pricing and other incentive programs.
The smart grid needs to increase the efficiency of grid operations while reducing losses.
The smart grid needs to be able to handle the integration of a large variety of generation options.
In order to achieve those objectives, it is necessary to develop and deploy communication infrastructures and advanced monitoring and control systems with cutting‐edge technologies in the smart grid.
1.2 Smart Grid Communications Architecture
Its special publication NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 3.0 [22], the National Institute of Standards and Technology (NIST) has defined that the smart grid is a complex cyber‐physical system that must support 1) devices and systems developed independently by many different solution providers; 2) different utilities; 3) millions of industrial, business, and residential customers; and 4) different regulatory environments.
1.2.1 Conceptual Domain Model
A conceptual domain model was published in the NIST special publication to support planning, requirements development, documentation, and organization of the increasingly diverse collection of interconnected networks and equipment that will compose the smart grid, as illustrated in Figure 1.2. The NIST divides the smart grid into seven domains. Their roles and services in the smart grid conceptual model are described as follows:
Figure 1.2 NIST conceptual domain model for the smart grid.
Customers
: the end users of electricity. May also generate, store, and manage the use of energy. Traditionally, three customer types are discussed, each with its own domain: residential, commercial, and industrial.
Markets
: the operators of and participants in electricity markets.
Service providers
: The organizations providing services to customers and to utilities.
Operations
: the managers of the movement of electricity.
Generation
: the generators of electricity. May also store energy for later distribution. This domain includes traditional sources (traditionally referred to as generation) and distributed energy resources (DER). At a logical level, “generation” includes coal, nuclear, and large‐scale hydro generation systems that are usually attached to transmission. Generation is associated with customer and distribution‐domain‐provided generation and storage and with service‐provider‐aggregated energy resources.
Transmission
: the carriers of bulk electricity over long distances. May also store and generate electricity.
Distribution
: the distributors of electricity to and from customers. May also store and generate electricity.
Each of the seven domains is a high‐level grouping of physical entities that rely on or participate in similar types of services. In general, roles in the same domain have similar objectives. However, communications within the same domain may have different characteristics and may have to meet different requirements to achieve interoperability. The roles in a particular domain interact with roles in other domains to achieve interoperability.
1.2.2 Two‐Way Communications Network
The interoperability that can be achieved in the NIST conceptual model for the smart grid is achieved by the secure communication flows that interconnect all seven domains. Generally speaking, it is a two‐way communications network between utilities and customers. A high‐level illustration of the smart grid communications network is shown in Figure 1.3. As it shows, the communications network in the smart grid consists of different types of networks and communication technologies that can be categorized into home‐area networks (HAN), neighborhood‐area networks (NAN), and wide‐area networks (WAN).
Home‐Area Networks in the Smart Grid
A HAN enables secure communication flows within a household. A gateway (usually a smart meter and a data aggregate unit) bridges a HAN to a NAN. Readers may have heard of the smart home that can be achieved with intelligent and remote control. Note that a HAN in the smart grid communications network serves a different purpose than a smart home network. A smart grid HAN is part of the utilities' infrastructure that may or may not have Internet access. Even customers in the household may not have direct access to the network. The network that enables a smart home is owned by the customers and usually has Internet access; for example, a home Wi‐Fi system. We are not ruling out the possibility that a smart grid HAN may merge with a smart home network in the future, especially when the Internet of Things and public Internet access will gain the support of utilities.
Neighborhood‐Area Networks
A NAN is also referred to as the
last‐mile
network to the customer side. It enables the secure flow of communication between households and the utilities' backbone network. Some existing NANs are composed of smart meters only, while others may utilize dedicated data aggregate units for relay. In either case, a smart meter is the gateway of a HAN that monitors and controls electricity consumption within that household. Through NANs and HANs, the smart grid achieves direct communications between customers and utilities. The communications are not only for metering and billing, but they also carry critical information for demand response, which actively controls the power load of some customers.
Wide‐Area Networks
Utilities have their backbone network infrastructure that connects all major components, such as substations, power stations, and operation centers. This backbone network infrastructure is upgraded and reused as the WAN in the smart grid. The current power grid has applied power line carriers (PLC) for data communications for many years, especially in remote areas. Although the technology theoretically achieves wide area coverage, other communication technologies, such as fiber optics and cellular networks, are deployed to the smart grid to improve the WAN.
Figure 1.3 High‐level illustration of the smart grid communication architecture.
The three types of networks are named after their coverage. Nonetheless, their roles and services are vastly different; thus the communication technologies applied to each of the three types of network are based on their specific requirements. For example, a WAN is required to be high‐speed, reliable, and secure while covering long‐distance communications. Utilities have already deployed high‐speed backhaul networks (also known as communication core networks) alongside most of their power transmission lines. The backhaul networks consist of optical fiber and Ethernet. PLCs [23, 24] are also applied in some areas to provide wide area communications. Most of PLC‐based WANs are deployed for monitoring purposes instead of large data communications [23, 24]. Cellular communication technologies are also deployed as WANs for similar purposes. NANs and HANs are last‐mile connections to customers [25, 26]. They are required to be low profile, low cost, reliable, and secure while providing enough bandwidth to meet latency requirements. In NANs and HANs, wireless communication technologies are preferred due to their flexibility and low‐cost deployment. Wireless technologies for longer distance transmission, such as GPRS, WiMAX, and LTE, are promoted for communications between NANs and concentrators that connect to the backhaul network. Several wireless technologies for local‐area networks, including Wi‐Fi, Zigbee, and Bluetooth, are promoted for HANs and intracommuncations in NANs.
1.3 Applications and Requirements
In order to achieve the objectives of the smart grid, several important applications must be added to or upgraded in the current power grid. The most important applications and requirements include demand response, advanced metering infrastructure, wide‐area situational awareness and wide‐area monitoring systems, and communication networks and cybersecurity.
1.3.1 Demand Response
Demand response (DR) has been mentioned in earlier sections. It is the key component applied to the smart grid that can smooth the power load of the grid [27–30]. A smoother load may not necessarily reduce power consumption. It aims to decrease the gap between peak and off‐peak grid loads and ease the transition process between high and low power demand, as shown in Figure 1.4. Maintaining a relatively steady power load would increase operational efficiency of renewable resources, as utilities worry only about a total demand that is given in advance. Fewer transition processes with less fluctuation would greatly reduce fossil‐fuel waste and greenhouse gas emission from those types of power stations.
Figure 1.4 Smoother power load achieved by DR.
There are generally two ways to apply DR. One is direct load control from utilities, while the other is to actively involve customers by utilizing mechanisms and incentives. Both approaches have the goal of shifting some of the power demand from peak hours to off‐peak hours. Direct load control can be implemented if consumers such as large business, government buildings, and some factories are willing to participate. Residential customers, on the other hand, may not be willing to give away their own control. Dynamic pricing motivates those types of customers in the smart grid. Generally speaking, electricity pricing is higher during peak hours. Customers adaptively control their appliances so that they can lower their bills. The incentive mechanisms are more complex than just a load shift in the smart grid. For example, in the past, the hours of 11 a.m. and 6 p.m. were set as off‐peak hours in California with lower tariffs. However, the peak hours have shifted and extended over the years. Incentive mechanisms for DR in the smart grid need to accommodate these dynamic changes in a timely fashion.
1.3.2 Advanced Metering Infrastructure
Utilities and grid operators in the smart grid need to predict conditions in close to real time with sophisticated modeling and state estimation capabilities. Doing this will allow for more efficient dispatch and system balancing. This capability relies on advanced metering infrastructure (AMI) that carries near real‐time data in the smart grid. Defined by the US DoE, AMI is an integrated system of smart meters, communications networks, and data management systems that enables two‐way communication between utilities and customers, as shown in Figure 1.5. Customer systems include in‐home displays, home‐area networks, energy management systems, and other customer‐side‐of‐the‐meter equipment that enable smart grid functions in residential, commercial, and industrial facilities.
Figure 1.5 High‐level illustration of AMI.
AMI is an ideal application of machine‐to‐machine (M2M) communications [31, 32] that achieves two‐way communication between customers (through smart meters) and utilities [10, 33–36]. AMI equips each customer with a smart meter, whose basic function is to gather the energy consumption status and upload the information to the control center (also known as the power distributor or service provider). A smart meter is also capable of receiving control information (e.g. electricity pricing/tariff) from the control center. Such a two‐way information exchange is near real‐time. The information and control needed to implement residential demand response relies significantly on the development of AMI. AMI deployment in the smart grid will benefit both system operation and customer service.
1.3.3 Wide‐Area Situational Awareness and Wide‐Area Monitoring Systems
Wide‐area situational awareness (WASA) and wide area monitoring systems (WAMS) in the smart grid monitor the status of power‐system components over large geographic areas in near real time. The advanced monitoring system improves visibility and understanding of stresses in the power system and detects transient behavior that is not detected with traditional supervisory control and data acquisition (SCADA) systems.
As shown in Fig. 1.6, many types of intelligent electronic devices (IEDs) are deployed in the monitoring and control system, such as synchronized phasor measurement units (PMUs), phasor data concentrators (PDCs), circuit break monitors, solar flare detectors, etc. [37–42]. Management of power‐network components can be better achieved with the monitoring and control system. A system failure or a blackout can be ultimately anticipated, prevented, or quickly recovered from. While being considered a stand‐alone system, the monitoring system is a composite of SCADA, AMI, energy management systems (EMS), and other systems in the smart grid that provides near real‐time monitoring and control of the grid.
Figure 1.6 High‐level illustration of the monitoring system.
1.3.4 Communication Networks and Cybersecurity
The smart grid has a complicated and advanced communication infrastructure that may involve both private and public networks. In order to achieve interoperability between different domains, various types of communication technologies, including both wired and wireless technologies, are needed to support the infrastructure.
As mentioned earlier, the utility backbone network is mostly a private network deployed by utilities and grid operators, using fiber optics and Ethernet to meet the requirement of fast and reliable data delivery. Other parts of the communication infrastructure may not have fiber optics support, due to high cost of its development, implementation, and maintenance. For example, the “last mile” in AMI can be deployed with wireless technologies. A HAN may be supported by ZigBee, Wi‐Fi, and other local area wireless technologies. A NAN may be supported by WiMAX, multihop Wi‐Fi, etc. Communications in the smart grid monitoring system may be achieved by cellular networks or PLC. The exchange of information between the transmission and distribution systems will be automated and optimized by the development of standard data structures. Techniques from big data analytics and cloud computing will play a critical role in leveraging exponential growth in data.
With all those types of communication technologies and data involved, cybersecurity and control of access to the communication networks are critical issues to the smart grid [4, 43, 44]. Cybersecurity needs to be designed into the new systems that support the smart grid without impacting operations. In addition to the protection of information from traditional cyberattacks, smart grid cybersecurity must expand it focus to address the combination of information technology, industrial control systems, communication systems, and their integration with physical equipment and resources to maintain the security of the grid and to protect the privacy of consumers.
1.4 The Rest of the Book
In the rest of this book, we will explore some of the major topics in smart grid communication infrastructures and provide our solutions and suggestions. Some of the highlights are as follows:
The overall communication infrastructures in the smart grid are studied.
A complete information and communication technologies framework for the smart grid is proposed.
The communication networks of the advanced metering infrastructure are studied.
A self‐sustaining neighborhood‐area network design is proposed.
An efficient power control scheme for the proposed network design is proposed.
Demand response is studied, based on the communication infrastructures.
Big data analytics and cloud computing are introduced into the smart grid communications to enhance grid operations and control.
Network security is studied for smart grid communications.
Security schemes are proposed for communications in the advanced metering infrastructure.
ID‐based security schemes are proposed for transmission over the Internet in the smart grid.
2Smart Grid Communication Infrastructures
In this chapter, an information and communication technologies (ICT) framework will be explored to support the smart grid. The focus will be on the communication networks and their roles and requirements in the smart grid communication infrastructures.
2.1 An ICT Framework for the Smart Grid
2.1.1 Roles and Benefits of an ICT Framework
We have seen that the smart grid will have greatly improved communication networks compared to the traditional power grid. Two major achievements of the communication networks in the smart grid are 1) frequent and timely two‐way communication capability between customers and utilities and 2) real‐time monitoring and control of the vast majority of the power grid. In the current power grid, network communications are one‐way only, with little information exchange. A better two‐way communication network is required to control those detachable renewable energy sources, along with energy storage units, on the smart grid. Moreover, existing monitoring and controlling systems in the current power grid cannot provide the means to prevent system failures or blackouts such as the one in 2003. Given the massive scale and complexity of the smart grid, it is better to develop a unified ICT framework for the smart grid.
An ICT framework gives a clear view of the entire communication network and its integration with the physical components in the smart grid. It helps utilities to realize the interoperability of domains in the smart grid. An ICT framework is a step further from simple motivations of the smart grid. It lays a practical path for researchers and developers of the smart grid to follow for implementing features. For example, demand response (DR) is a promising feature in the smart grid that would improve the efficiency of grid operations by smoothing the power load. However, decisions and actions may not be made accurately in real time, even in the smart grid. Therefore, forecasting plays an important role in the smart grid. On one hand, an energy forecast helps power generators to plan electricity generation ahead of time. Thus fuel waste due to sudden transitions between different loads can be reduced. On the other hand, a pricing forecast helps customers to schedule their electricity usage more economically 45], 46]. How to model and achieve pricing forecast and energy forecast is the practical problem to be addressed. An ICT framework would reveal the information flow between the domains of customers, utilities, and power stations.
An ICT framework would also provide required technologies and equipment to implement features such as DR in the smart grid. Several existing research works on DR rely on real‐time power consumption information from metering data and real‐time response from both power generators and customers. Some research works assume precise power requests from customers instead. However, all of the assumptions are hard to achieve in practice. Moreover, with renewable energy sources that are difficult to control and detachable microgrids 47–49], an optimal control of the power grid is extremely hard and expensive to implementwith just the information and computing resources from the utilities. An ICT framework would reveal such issues and allow grid planners to add extra tools, such as cloud computing and big data anlytics.
2.1.2 An Overview of the Proposed ICT Framework
Figure 2.1 shows an overview of the proposed ICT framework. The ultimate purpose of the proposed ICT framework is to develop systems that can improve the efficiency and reliability of the smart grid. To make the illustration clearer, our proposed ICT framework is described as developing DR with incentive mechanisms that are based on dynamic pricing. Entities, components, and their roles are intended to enable energy forecasts for power generators, and pricing/tariff forecasts (pricing forecast hereafter) for customers. It is intuitive to research and develop other systems, such as real‐time monitoring and controlling systems, within the proposed ICT framework. Three types of networks are applied in this framework:
Local‐area networks
, established for customers to enable communications within a household;
Private networks
, established by utilities and service providers; and
Internet
, provided by a third‐party Internet service provider (ISP).
The combination of the three types of networks enables two‐way communications between utilities and customers. The ICT framework is divided into four entities: internal data collectors (i.e. customers and grid monitoring sensors), a service provider, power generators, and external information sources.
Figure 2.1 An overview of the proposed ICT framework.
