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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Presents an up-to-date overview of resilient communication networks for smart electric power grids
Smart electric power grids require reliable communication networks to maintain efficiency, security, and stability. The interconnected nature of these systems creates unique challenges, including cascading failures, natural disasters, and network congestion. Despite the importance of building communication networks to connect the next generation of smart power grids, existing literature is lacking in both depth and relevance. Communication Networks in Smart Power Grids bridges this gap, offering a robust examination of cutting-edge technologies and techniques for ensuring uninterrupted data transmission.
In this authoritative volume, author Boyang Zhou provides a detailed exploration of smart grid communication channels, focusing on Quality of Service (QoS) requirements and the resilience necessary to counter data loss, network failures, and delays. Addressing a wide range of key topics, from Supervisory Control and Data Acquisition (SCADA) systems to high payload packet loss mitigation, the author presents practical strategies and solutions for fortifying data transport layers. Throughout the book, Zhou introduces cutting-edge research techniques to address communication link failures, link flooding attacks (LFAs), cascading grid failures, and other critical issues.
Offering innovative approaches to building the next generation of smart grid communication networks, this essential resource:
- Provides a comprehensive examination of the design and implementation of highly resilient communication networks in smart electric power grids
- Focuses on Quality of Service (QoS) and reliability, ensuring efficient data transmission and reduced packet loss
- Presents real-world strategies for mitigating network congestion, natural disasters, and high payload packet losses
- Features practical insights from a leading expert in smart grid communications, industrial internet security, and network resilience
- Explores data forwarding reliability, transmission control protocols, and routing reliability assurance tailored for smart grids.
Combining insights from communication networks, power grid operations, and advanced network security techniques, Communication Networks in Smart Power Grids is a must-read for advanced researchers and professionals in communication networks, network security, and smart grid systems. It is also an excellent textbook for courses on smart grid technology, network resilience, and industrial IoT in electrical engineering, computer science, and industrial technology programs.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Author
Preface
Acknowledgments
Acronyms
About the Companion Website
1 Introduction
1.1 Purposes and History of Networks
1.2 Networks in Power Grids
1.3 Evolving Networks for Smart Grids
1.4 Network Protocols in Power Grids
1.5 LFAs
1.6 Navigating the Chapters Ahead
Questions
Bibliography
2 Network Availability Vision and Technical Landscape
2.1 What Is Network Availability?
2.2 Availability Threats and Demands
2.3 Technical Landscape
2.4 Summary
Questions
Bibliography
3 Classical High-Available Networking Solutions
3.1 High-Availability Standards
3.2 Redundancy Provision at End Nodes
3.3 Redundancy Provision in Networks
3.4 Limitations of Classical Approaches
3.5 Summary
Questions
Bibliography
4 Challenges and Solutions for High-Available Networks
4.1 Non-disjoint Path Aggregation
4.2 Efficient and Robust FRR
4.3 Robust Data Transport Protocol
4.4 Redundant Application Data Codes
4.5 Safeguarding Against LFAs
4.6 Summary
Questions
Bibliography
5 Empowering Network Robustness with Non-disjoint Path Aggregation
5.1 Problem Formulation
5.2 Non-disjoint Path Aggregation
5.3 Performance Evaluation
5.4 Summary
Questions
Bibliography
6 Navigating Network Resilience Using Traffic-Aware Fast Reroute
6.1 Route Model and Problem
6.2 Fast Proactive Reroute Mechanism
6.3 Performance Evaluation
6.4 Summary
Questions
Bibliography
7 Elevating Reliability: Disruption-Resilient Transport Protocol (DRTP) Revolution
7.1 Communication Model and Routes
7.2 Protocol Design and Its Issue
7.3 Computation of Arrival Timeouts
7.4 Performance Evaluation
7.5 Summary
Questions
Bibliography
8 Implementing DRTP: Orchestrating State Transition Machines
8.1 Overall Design
8.2 State Transition Machines
8.3 Mechanism Correctness Verification
8.4 Exploring the Robustness of the Mechanism
8.5 Performance Evaluation
8.6 Summary
Questions
Bibliography
9 Enhancing DRTP Reliability: Cultivating Robust Routes
9.1 Problem on Generating MPSG Less Fragile
9.2 Overall Design
9.3 HeuMPSG Algorithms
9.4 Computational Complexity
9.5 Performance Evaluation
9.6 Summary
Questions
Bibliography
10 Crafting Application Data: Unleashing Adaptive RaptorQ Coding
10.1 Problem Modeling and Analysis
10.2 Adaptive RaptorQ Encoder and Decoder
10.3 Performance Evaluation
10.4 Summary
Questions
Bibliography
11 Mastering Link-Flooding Attack: Route Diversification
11.1 Defending Two-Stage Attacks
11.2 Defense Mechanism
11.3 Performance Evaluation
11.4 Summary
Questions
Bibliography
12 Practice Guidelines for High-Available Networks
12.1 Development Environments
12.2 Example for Testing DRTP
12.3 Solution Guidelines
12.4 Summary
Questions
Bibliography
13 Conclusive Remarks and Future Prospects
13.1 Concluding Remarks
13.2 Future Prospects
Bibliography
Copyright Page
Bibliography
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Comparison of redundancy protocols in IEC 62439.
Table 3.2 Comparison of IP routing approaches for power grid communications....
Chapter 5
Table 5.1 Mathematical symbols.
Table 5.2 Simulation parameters.
Table 5.3 Count of pairs generated by MPSGA, MADSWIP, and ODPG.
Chapter 6
Table 6.1 Messages extended by TA-FRR.
Chapter 7
Table 7.1 Conditions of success and failure situations.
Chapter 8
Table 8.1 Shared states among STMs in RSDD.
Table 8.2 Basic predicate actions.
Chapter 10
Table 10.1 Mathematical notations.
Table 10.2 AdaRQ control format.
Table 10.3 AdaRQ data format.
Table 10.4 Comparison in packet losses between ARED and LTE.
Chapter 11
Table 11.1 Mathematical notations.
Table 11.2 Experiment setup parameters.
Chapter 12
Table 12.1 Node information for the IEEE 14-bus system.
Table 12.2 Branch data for the IEEE 14-bus standard power system.
Table 12.3 Generator data for the IEEE 14-bus system.
Table 12.4 Generator costs for IEEE 14-bus system.
List of Illustrations
Chapter 1
Figure 1.1 Overview of power grids.
Figure 1.2 IoT communication networks in power grids.
Figure 1.3 SDN network structure in power grids.
Figure 1.4 OF network architecture.
Figure 1.5 P4-based SDN network architecture.
Figure 1.6 NDN architecture.
Figure 1.7 A typical communication example in NDN for the transmission grid....
Figure 1.8 IEC 61850 protocols and interactions.
Figure 1.9 IED abstraction of IEC 61850 protocols.
Figure 1.10 The two-stage stealthy crossfire attack model.
Chapter 2
Figure 2.1 Primary threats to network availability across protocol layers.
Figure 2.2 Landscape of innovative network architecture, solutions, and issu...
Chapter 3
Figure 3.1 PRP illustration.
Figure 3.2 HSR illustration.
Figure 3.3 CRP illustration.
Figure 3.4 BRP illustration.
Figure 3.5 RRP illustration.
Figure 3.6 MRP illustration.
Figure 3.7 DRP illustration.
Figure 3.8 Limitations of IEC 62439 and research trends.
Chapter 4
Figure 4.1 Percentage of pairs with no path and 1 path vs. max delay.
Chapter 5
Figure 5.1 Model of packet aggregating points.
Figure 5.2 OPAM architecture.
Figure 5.3 Workflows of OPAM's algorithms.
Figure 5.4 Simulation steps for OPAM in NS-3.
Figure 5.5 Non-disjoint paths of MPSGA in IEEE 30 bus network.
Figure 5.6 Performance evaluation of MPSGA. (a) Probability Density Function...
Figure 5.7 Execution time for non-disjoint path algorithms.
Figure 5.8 QSDP of MPSGA to disjoint and raw algorithms.
Figure 5.9 Mean and max execution time for MPSGA in PEGASE and French.
Figure 5.10 Min execution time for MPSGA in PEGASE and French.
Figure 5.11 Performance evaluation of PAFM. (a) TCP throughput with and with...
Figure 5.12 Average potential changes per neuron at node 0 in CPN.
Figure 5.13 Performance of CPN with reordering buffer.
Figure 5.14 CDF of HRRT in CPN and OPAM. (a) No link loss and (b) 2% link lo...
Figure 5.15 CDF of the number of hops in CPN and OPAM. (a) no link loss and ...
Chapter 6
Figure 6.1 Example of to an RSG from 165th node to 100th node constructed fr...
Figure 6.2 The average number of RSPs per node in the PP in a transmission g...
Figure 6.3 Reroute based on an RSG.
Figure 6.4 TA-FRR architecture.
Figure 6.5 Overall design of TA-FRR.
Figure 6.6 Workflows of TA-FRR's GenRSGV and ComputeDSP algorithms.
Figure 6.7 Workflow of TA-FRR's ConstructRSG algorithm.
Figure 6.8 State transitions in the link state probing and advertisement.
Figure 6.9 State transitions in the link state updating and path selection....
Figure 6.10 Workflow of link state updates.
Figure 6.11 Workflow of distributed preferred path selection.
Figure 6.12 Example of rerouting over two components on the PP.
Figure 6.13 Workflow of data packet resequencing.
Figure 6.14 Performance of TA-FRR under different PP links disruption degree...
Figure 6.15 Performance of TA-FRR with different link check period parameter...
Figure 6.16 Performance of TA-FRR with different sending frequency at the pr...
Figure 6.17 Performance of TA-FRR for all pairs in the synthesized Internet ...
Figure 6.18 Algorithmic performance of TA-FRR for all pairs in the synthesiz...
Figure 6.19 Comparison in RFR for a network with 100 routers.
Figure 6.20 Comparison in the quality of routes for a network with 100 route...
Chapter 7
Figure 7.1 Communication pattern of IEEE C37.118.2.
Figure 7.2 An MPSG manually constructed from the IEEE 300 bus dataset.
Figure 7.3 The features of the generated MPSG under different topologies. (a...
Figure 7.4 The mean execution time of GenMPSG.
Figure 7.5 Packet format in DRTP. (a) Interest packet and (b) data packet.
Figure 7.6 Communication pattern of DRTP.
Figure 7.7 The normal packet delivery process of DRTP.
Figure 7.8 Retransmission control process of DRTP.
Figure 7.9 Distributed collaboration issue.
Figure 7.10 Condition #1 under the success situation (the message type, loss...
Figure 7.11 Condition #2 under the success situation.
Figure 7.12 Condition #3 under the failure situation.
Figure 7.13 Condition #4 under the failure situation.
Figure 7.14 Cyber–physical simulation environment.
Figure 7.15 Performance of DRTP in the typical MPSG under the full PP disrup...
Figure 7.16 Performance of DRTP in the typical MPSG under the complete MPSG ...
Figure 7.17 The average MSR of DRTP in the typical MPSG.
Figure 7.18 Performance of DRTP in general MPSG under the full PP disruption...
Figure 7.19 Performance of DRTP in the general MPSG under the complete MPSG ...
Figure 7.20 Comparative studies in the typical MPSG under the full PP disrup...
Figure 7.21 Performance comparison in the general MPSG under the full PP dis...
Figure 7.22 Comparative analysis in the reliability of transport protocols i...
Chapter 8
Figure 8.1 RSDD architecture based on NDN.
Figure 8.2 Interactions among STMs and entities in RSDD.
Figure 8.3 STM diagram of : INTEREST_PROCESSOR.
Figure 8.4 STM diagram of : CAPSULE_PROCESSOR.
Figure 8.5 STM diagram of : REPORT_PROCESSOR.
Figure 8.6 STM diagram of : WAIT_NEXT_CAP.
Figure 8.7 STM diagram of : WAIT_CUR_CAP.
Figure 8.8 STM diagram of : DO_WAIT_CAP.
Figure 8.9 STM diagram of : DO_RETRAN.
Figure 8.10 STM diagram of : REQUEST_PROCESSOR.
Figure 8.11 State transition sequence in RSDD.
Figure 8.12 NMNs of RSDD under the full PP disruptions across varying LLRs i...
Figure 8.13 NMNs of RSDD under the full MPSG disruptions across varying LLRs...
Figure 8.14 Performance of RSDD under different disruption cases across vary...
Figure 8.15 Comparative performance studies of transport protocols in the So...
Chapter 9
Figure 9.1 HeuMPSG design.
Figure 9.2 MPSG generation process.
Figure 9.3 Workflow of the GenRRV and ComRSP algorithms.
Figure 9.4 The performance of HeuMPSG compared to GenMPSG in the networks wi...
Figure 9.5 The resilience improvements of HeuMPSG to DRTP compared to GenMPS...
Figure 9.6 EEFR comparison in 300-bus system for 10 NFL and 50% LLR.
Figure 9.7 EEDT comparison in 300-bus system for 10 NFL and 50% LLR.
Chapter 10
Figure 10.1 Illustration of LTE terminal deployment in power distribution ne...
Figure 10.2
Packet Inter-Arrival Time
(
PIAT
) CDF of typical measurement and ...
Figure 10.3 Architecture of ARED.
Figure 10.4 Architecture of ARED.
Figure 10.5 Packet loss rates in up and downlinks under different noise inte...
Figure 10.6 Uplink delay under different packet loss rate.
Figure 10.7 Downlink delay under different packet loss rate.
Figure 10.8 Average channel utilization ratio under different noise interfer...
Chapter 11
Figure 11.1 Defense process using the proxy creation.
Figure 11.2 Defense process using the traffic diverging.
Figure 11.3 Defense process using the traffic suppressing.
Figure 11.4 Components collaboration of PRDD in defense.
Figure 11.5 Workflows of the LCLA algorithm.
Figure 11.6 Workflow for identifying attack intention.
Figure 11.7 Workflow for the GenProxies algorithm.
Figure 11.8 Workflow for the GenTrafficSteers algorithm.
Figure 11.9 Experiment workflow of PRDD in NS-3.
Figure 11.10 The number of bots assigned to PRs in the topology of 4000 node...
Figure 11.11 Effectiveness for detecting victim PRs under LFA. (a) Congestio...
Figure 11.12 Effectiveness for defending LFA in NS-3. (a) TCP Cwnd size at t...
Figure 11.13 Effectiveness for defending LFA in Mininet. (a) TCP bandwidth a...
Figure 11.14 Performance in DPG. (a) execution time of , (b) the average nu...
Figure 11.15 Comparative studies on disappeared PRs and steered PRs ratios....
Figure 11.16 Comparative studies on positive ratios.
Chapter 12
Figure 12.1 Development environments used for solution verification.
Figure 12.2 Outlook of our testbed.
Figure 12.3 Communication network simulation.
Figure 12.4 Electromagnetically shielded chamber for wireless networks.
Figure 12.5 2G emulation environment with power meters.
Figure 12.6 4G emulation environment with power meters.
Figure 12.7 Analysis of 2G wireless resources.
Figure 12.8 WSN emulation environment (the left side is TelosB nodes, while ...
Figure 12.9 IEEE 14-bus power systems (This figure is copied from: https://i...
Figure 12.10 500 kV substation connection to power grids in RTDS.
Figure 12.11 500 kV substation implementation in RTDS.
Figure 12.12 10 kV power distribution grid implementation in RTDS.
Figure 12.13 Operating status of power grids in RTDS.
Figure 12.14 Periodic electric variations at bus #1 in a power transmission ...
Figure 12.15 Comparative analysis in protocol reliability impacts on SE unde...
Figure 12.16 Comparative analysis in protocol reliability impacts on SE unde...
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Author
Preface
Acknowledgments
Acronyms
About the Companion Website
Begin Reading
Copyright Page
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
Moeness Amin
Jón Atli Benediktsson
Adam Drobot
James Duncan
Ekram Hossain
Brian Johnson
Hai Li
James Lyke
Joydeep Mitra
Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Patrick Chik Yue
Communication Networks in Smart Power Grids
Boyang Zhou
Zhejiang LabHangzhou, China
Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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To my parents and teachers, whose love shaped me, and to my wife, children, friends, and all who supported me.
About the Author
Boyang Zhou is a researcher/research expert at the Research Center for High Efficiency Computing Systems at Zhejiang Lab, and an adjunct research fellow at the School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study, UCAS. Prior to this, he served as a senior engineer at State Grid Corporation of China and as a postdoctoral researcher at Zhejiang University. He earned his PhD in computer science from Zhejiang University. He has authored more than 40 papers and holds 30 patents. He has received several prestigious awards, including the first prize for Technological Progress from the China Automation Society, the first prize for Scientific and Technological Progress in Zhejiang Province, and the third prize for Technological Progress in the China Electric Power Technology Progress Awards. His research focuses on computer networks, smart grid communications, data center networks, and industrial Internet security.
Preface
Over the past three decades, communication networks have undergone a transformative digitalization process, evolving electric power grids into smart grids capable of addressing diverse global demands. Modern network architectures now seamlessly integrate optical and wireless communications, utilizing redundancy designs, such as diverse routing, to ensure reliability and optimize grid operations across various regions, including China, the USA, Europe, and Japan. The critical focus on network availability, characterized by low packet loss and low latency, has intensified due to aging grid infrastructure and the escalating demand for energy. High-profile power outages, such as the 2023 Northeast event in the USA and Canada, Hurricane Harvey in 2017, and the Texas winter storm in 2021, underscore the urgency of these challenges. The relentless pursuit of innovative network development is reshaping grid architecture through next-generation solutions that emphasize centralized control flexibility and data caching capabilities, all aimed at establishing high-availability networks for enhanced power grid stability.
This book, Communication Networks in Smart Power Grids, serves as a comprehensive guide that delves into the complexities and demands of smart grid communication networks. It is structured to provide readers with a broad and in-depth understanding of both classical and cutting-edge solutions essential for enhancing network availability in this dynamic field. Key areas covered in this book include:
Knowledge about networks
: Developments in smart grid communications and advancements aimed at improving network availability are thoroughly explored. The technical landscape is examined, focusing on strategies and solutions for achieving high network availability in modern power grid operations, alongside the role of sustainable practices in enhancing grid resilience and efficiency.
Classical approaches
: A comprehensive presentation and analysis of classical approaches that have historically ensured the resilience and reliability of power grid communications is offered. The focus is on the standardized efforts underpinning the IEC 62439 protocols, which incorporate redundancy concepts and are widely adopted by manufacturers such as Siemens and Texas Instruments. These protocols are deployed at both end nodes and within the network, utilizing 12 distinct approaches, including packet aggregation and fast rerouting, primarily designed for wired networks, particularly in ring topologies or doubly independent routes. The principles, interactions, and applications of these protocols are thoroughly analyzed, alongside their limitations in addressing the evolving demands of modern power grids.
Challenges and landscape
: Despite the foundation established by IEC 62439, advancements in network architectures have identified five critical demands and research directions needed to tackle ongoing challenges: reliability of data delivery, path and data transport, application data completeness, and cybersecurity threats, particularly link flooding attacks (LFAs). Incidents such as the 2021 LUMA Energy attack in Puerto Rico highlight the urgent need for robust defenses against these threats. These challenges can significantly impact power grid stability by degrading monitoring accuracy and compromising control integrity, thereby increasing the risk of power outages. Addressing these issues requires technologies that span every layer of the TCP/IP stack. While the physical and link layers depend on equipment providers, the flexibility of the network, data transport, and application layers is vital for enhancing overall network availability.
Cutting-edge solutions
: Innovative solutions that supplement and enhance classical approaches to significantly improve network availability and power grid stability are presented. Key solutions include:
The OpenFlow-based Non-Disjoint Path Aggregation Mechanism (OPAM) facilitates reliable data delivery through enhanced redundant route exploitation and zero-time fault recovery for large-scale power networks.
The Traffic-Aware Fast Reroute (TA-FRR) solution improves path reliability by utilizing topology redundancy via disjoint subpaths, leading to reduced rerouting time.
The Disruption-Resilient Transport Protocol (DRTP) ensures data transport reliability through hop-by-hop multipath retransmissions with less fragile route constructions, thereby maintaining grid state estimation accuracy and grid observability even under significant network disruptions. This solution incorporates a concrete implementation using state transition machines and enhancements for route reliability.
The Adaptive RaptorQ Encoder and Decoder (ARED) enhances reliability in wireless cellular networks by employing adaptive parameter tuning based on channel quality feedback.
The Persistent Route Diversification Defense Mechanism (PRDD) is designed to protect against LFAs, effectively mitigating congestion states.
Practice guidelines
: Practical guidelines are provided to assist readers in developing and implementing both classical and cutting-edge solutions in real-world power grid environments. The environment is used to test and assess the impact of a protocol on the power grid's monitoring and control. The content covers features, implementation practices, and unresolved concerns, equipping readers to construct high-availability networks for modern power grids.
Future prospects
: Insights into future prospects for operators, researchers, and educators are offered, encouraging strategic shifts to navigate the challenges posed by an increasingly complex energy landscape.
By synthesizing theoretical knowledge with practical applications, this book aims to equip professionals and students alike with the tools needed to navigate the multifaceted challenges of smart grid communications. Each chapter includes pedagogical questions, with answers available online, fostering active engagement and deeper understanding.
We invite you to explore the knowledge, challenges, and solutions in smart grid communications, where availability is not merely a goal but a vital aspect of sustainability, adaptability, and innovation in this dynamic field.
October 2024
Boyang Zhou Zhejiang Lab Hangzhou, China
Acknowledgments
This book marks the culmination of a thrilling exploration into the domain of pioneering highly-available communication networks in smart electric power grids. Throughout this captivating journey, I express my sincere gratitude for the invaluable support received from various sources.
I extend special thanks to the National Natural Science Foundation of China (62102375) and the Key Research and Development Program of Zhejiang Province of China (2020C01021) for their generous support, laying a foundation for this research endeavor. I am also grateful for the support from Zhejiang Lab, my alma mater Zhejiang University, and the State Grid Corporation of China, where I gained significant experiences that greatly contributed to the creation of this book.
I am deeply appreciative of the researchers, developers, and operators who dedicate themselves to advancing smart grid communication technologies. Your unwavering commitment has been the driving force behind the exploration of smart power grid communication networks.
A special acknowledgment goes to the collaborative efforts of industry, academia, and research communities. The synergy of these domains has played a pivotal role in fostering innovation, enhancing the resilience of communication networks, and ensuring industrial reliability in smart grids.
Heartfelt thanks to supervisors, mentors, and colleagues whose guidance and insights have shaped the trajectory of this research. Your wisdom and encouragement have been invaluable, guiding this endeavor through challenges and triumphs.
To friends and family, your unwavering support has been a constant source of inspiration, fueling the passion for unraveling the mysteries of resilient communication networks.
This book stands as a testament to the collaborative spirit that defines the pursuit of knowledge. May it serve as a valuable resource for those delving into the intricate heart of network dynamics, offering insights and inspiration for the ongoing exploration of smart grid communication systems.
Boyang Zhou
Acronyms
AC
Alternating Current
ACSI
Abstract Communication Service Interface
ADCT
AdaRQ Dynamic Coding Tunnels
ADSS
All-Dielectric Self-Supporting Cable
AFR
Active Fault Recovery
APS
Automatic Protection Switching
ARED
Adaptive RaptorQ Encoder and Decoder
ARFCN
Absolute Radio Frequency Channel Number
ARQ
Automatic Repeat reQuest
AS
Autonomous System
ASIC
Application-Specific Integrated Circuit
BCCH
Broadcast Control Channel
BEC
Binary Erasure Channel
BFS
Breadth-First Search
BGP
Border Gateway Protocol
BMv2
Behavioral Model v2
BPDU
Bridge Protocol Data Unit
BRP
Beacon Redundancy Protocol
CDF
Cumulative Probability Distribution Function
CDT
Capsule Delivery Time
CIAT
Capsule Inter-Arrival Time
CPN
Cognitive Packet Network
CPRF
Conditional Probability of Retransmission Failure Cases
CR
Confidence Ratio
CRC
Cyclic Redundancy Check
CRP
Cross-Network Redundancy Protocol
CS
Content Store
CSMA
Carrier Sense Multiple Access
DANH
Doubly Attached Node with HSR protocol
DC
Direct Current
DDoS
Distributed Denial-of-Service
DNP3
Distributed Network Protocol
DPDK
Data Plane Development Kit
DPE
Defense Policy Enforcement
DPG
Defense Policy Generation
DRP
Distributed Redundancy Protocol
DRTP
Disruption-Resilient Transport Protocol
DSP
Disjoint Subpath
DTU
Distribution Terminal Unit
EEDT
End-to-End Payload Packet Delivery Time
EEFR
End-to-End Failure Rate in Packet Delivery
EELR
End-to-End Packet Loss Rate
EPON
Ethernet Passive Optical Network
EWSSA
Electric Wireless Security Situational Awareness System
FCS
Frame Check Sequence
FEC
Forward Error Correction
FI
Fragility Index
FIB
Forwarding Information Base
FLP
Fault Link Percentage
FPGA
Field Programmable Gate Array
FPR
False Positive Rate
FRR
Fast Reroute
FTU
Feeder Terminal Unit
GOOSE
Generic Object-Oriented Substation Event
GPON
Gigabit Passive Optical Network
GPS
Global Positioning System
GSM
Global System for Mobile Communications
HARQ
Hybrid Automatic Repeat-reQuest
HRRT
Half Round-Trip Time
HSR
High-availability Seamless Redundancy
ICMP
Internet Control Message Protocol
ID
Identification
IED
Intelligent Electronic Device
IP
Internet Protocol
IT
Information Technology
ITP
Internet Transport Protocol
IoT
Internet-of-things
KSP
K Shortest Paths
LCLA
Link Congestion Level Analysis
LFA
Link-Flooding Attack
LLDP
Link Layer Discovery Protocol
LLR
Link Loss Rate in Packets
LOF
Local Outlier Factor
LQI
Link Quality Indicator
LRT
Link Recovery Time
LSA
Link State Advertisement
LSDB
Link State Database
LSR
Label Switch Router
LTE
Long-Term Evolution
LoRa
Long-Range Radio
MADSWIP
Maximally Disjoint Shortest & Widest Paths
MAR
Messages in DRTP Arrived at the Router Per Second
MMS
Manufacturing Message Specification
MPLS
Multiprotocol Label Switching
MPPS
Million Packets Per Second
MPSG
Multipath Subgraph
MPSGA
Multipath Subgraph Generation Algorithm
MPTCP
Multipath Transmission Control Protocol
MRDP
Mean Rate of Data Flows
MRP
Media Redundancy Protocol
MSR
Message Sent and Received by Each Router of the PP in Delivering a Capsule
MSTP
Multi-Service Transport Platform
MTD
Moving Target Defense
MTTFN
Mean Time to Failure of Network
MTTRN
Mean Time to Repair Network
MU
Merging Unit
NASPI
North American Synchrophasor Initiative
NDN
Named Data Network
NDPA
Non-Disjoint Path Aggregation
NF
Noise Figure
NIST
National Institute of Standards and Technology
NMN
Number of Messages Sent Per Node of PP
NTH
Non-Threatened Host
NTP
Network Time Protocol
ODPG
Optimal Disjoint Paths Generation
OF
OpenFlow
OGR
Observable Grid Ratio
OOR
Out-of-Order Rate at the Receiver Side
OPAM
OpenFlow-based Non-Disjoint Path Aggregation Mechanism
OPC-UA
Open Platform Communications Unified Architecture
OPGW
Optical Ground Wire
OSPF
Open Shortest Path First
OT
Operational Technology
OTN
Optical Transport Network
P4
Programming Protocol-Independent Packet Processors
PAFM
Packet Aggregation and Forwarding Module
PDC
Phasor Data Concentrator
Probability Density Function
PEGASE
Pan European Grid Advanced Simulation and State Estimation
PIAT
Packet Inter-Arrival Time
PIT
Pending Interest Table
PLC
Programmable Logic Controller
PMS
Production Management System
PMU
Phasor Measurement Unit
PON
Passive Optical Network
POR
Packet Out-of-Order Rate
PP
Primary Forwarding Path
PR
Persistent Route
PRDD
Persistent Route Diversification Defense
PRF
Probability of Retransmission Failure Cases
PROTAG
Proxy-based Moving Target Architecture
PRP
Parallel Redundancy Protocol
PRT
Packet Reordering Time at the Consumer
PTN
Packet Transport Network
QSDP
Qualified Source–Destination Pairs
QSRP
Quality of Service in Receiving a Packet
QoS
Quality-of-Service
QuadBox
Quadruple Port Device
R2T
Rapid and Reliable Transport Mechanism
RFR
Reroute Failure Rate
RIP
Routing Information Protocol
RNN
Recurrent Neural Network
RQ
RaptorQ
RRP
Ring-based Redundancy Protocol
RRV
Resilient Route Vector
RSDD
Resilient Sensor Data Dissemination
RSG
Resilient Subgraph
RSGA
RSG Generation Algorithm
RSGV
RSG vector
RSP
Redundant Sub-Path
RSSI
Received Signal Strength Indicator
RSTP
Rapid Spanning Tree Protocol
RTDS
Real-Time Digital Simulator
RTT
Round-Trip Time
RTU
Remote Terminal Unit
RedBox
Redundant Box
SC
South Carolina
SCADA
Supervisory Control and Data Acquisition
SCL
System Configuration Language
SCSM
Specific Communication Service Mapping
SCTP
Stream Control Transmission Protocol
SDATP
Adaptive Transmission Protocol based on Software-Defined Networking
SDH
Synchronous Digital Hierarchy
SDN
Software-Defined Networking
SDR
Software-Defined Radio
SE
State Estimation
SFH
States of Forwarding History
SGCC
State Grid Corporation of China
SONET
Synchronous Optical Networking
SPT
Shortest Path Tree
SRAM
Static Random-Access Memory
SRE
Satisfaction Ratio of EEDT
SROP
Recent Out-of-order Packets
SRRP
Success Rate of Receiving a Packet
STD
Standard Deviation
STM
State Transition Machine
STP
Spanning Tree Protocol
SV
Sampled Values
TA-FRR
Traffic-Aware Fast Reroute
TCP
Transmission Control Protocol
TH
Threatened Host
TLV
Type–Length–Value
TTL
Time-To-Live
TTU
Transformer Terminal Unit
UDP
User Datagram Protocol
VANET
Vehicle Ad Hoc Network
WAMS
Wide-Area Monitoring System
WSN
Wireless Sensor Network
WiMAX
Worldwide Interoperability for Microwave Access
iPRP
Improved Parallel Redundancy Protocol
p.u.
power unit
About the Companion Website
This book is accompanied by a companion website:
www.wiley.com/go/SmartPowerGrids1e
The website includes:
Answers to the Questions in the Chapters
1Introduction
Powering the journey of electric energy from generation plants to end-users involves a complex web of transmission, distribution, and consumption, all accomplished by the power grids [Kirtley, 2009]. In the evolving landscape of this electrical infrastructure, smart power grids [Fang et al., 2012] represent a significant advancement over traditional power systems. They integrate modern communication networks with traditional electrical grid components, enabling a more responsive, efficient, and reliable electricity supply. As Figure 1.1 depict, the architecture of a smart power grid is composed of two categories of systems: primary systems and secondary systems, each playing a crucial role in the grid's functions.
Primary systems. Primary systems pave the foundation of the power grid [Kirtley, 2009]. These components are responsible for the direct transmission and transformation of electrical energy. They operate at high-voltage levels and include essential elements such as transformers, circuit breakers, transmission lines, and generators. Primary devices are designed to handle substantial amounts of power and are critical for maintaining the flow of electricity from generation points to end users. Their robust design ensures that the grid can transmit electricity efficiently over long distances, while also protecting against faults and ensuring system stability.
The primary systems mainly involve high-voltage fundamental devices, responsible for generating, transmitting, and distributing electricity. The electricity is usually in Alternating Current (AC), while in hybrid with Direct Current (DC) for modern and future power grids to reduce power losses. These devices generally include power transformers, power inverters, power lines, power transducers, circuit breakers, and reactive compensation devices, which is introduced below.
The power transformers facilitate the efficient transfer of electricity across different voltage levels. During operation, transformers can step up the voltage for long-distance travel, reducing transmission losses, and step the voltage down to safer, usable levels, as electricity approaches distribution networks and consumers.
Figure 1.1 Overview of power grids.
The power inverters can convert between DC and AC, which is useful for ultra-high-voltage (1000 kV) and extra-high-voltage (330–750 kV) power grids, and interconnections with renewable energy sources like solar panels.
The power transducer measures electrical quantities such as voltage, current, and frequency, providing real-time data that allows control systems to assess the grid's health and performance. Power lines ensure the physical transfer of electricity between substations and end users, forming a vast network over long distances.
The circuit breakers can instantly disconnect a section of the grid in the event of a fault, preventing potential damage to equipment and ensuring the safety of operators.
Reactive compensation devices, such as capacitor banks and synchronous condensers, maintain the grid's voltage stability by managing reactive power flows.
Together, these primary systems form the physical layer of the smart grid, interacting with advanced control and communication systems to ensure safe, reliable, and efficient electricity delivery.
Secondary systems. Based on the primary systems, the integration of secondary systems into the grid architecture transforms traditional electrical systems into the communication networks. The networks involve wide range of distributed power substations and central servers in large-scale geographical areas. Each substation encompasses various kinds of terminal devices and data concentrators. Meanwhile, the control servers are represented by the Supervisory Control and Data Acquisition (SCADA) systems and Phasor Data Concentrator (PDC) [Thomas, 2020]. Together, these devices of the substations, equipped with billions of sensors, meticulously measure electric parameters such as voltage magnitudes, active power, reactive power, and phase angles [Huang et al., 2012]. Meanwhile, the role of actuators comes into play as they execute commands, regulating switch gears and transformers. The interactions between the control servers and the substations enable real-time monitoring, data collection, fault detection, and automated control processes, which are vital for maintaining grid reliability and performance. The communication interaction between these devices not only enhances the functionality of the grid but also lays the foundation for future innovations in energy management and sustainability.
After evolving for more than three decades, communication networks have become an integral part of smart electric power grids [Saleem et al., 2019; NASPI, 2019]. Within this networked environment, communication network reliability takes center stage. The system must exhibit resilience, capable of navigating through multiple link faults – whether triggered by natural disasters, network glitches, or extensive cascading power line failures [Bie et al., 2017]. Additionally, frame or bit errors during transmission, stemming from signal degradation, noise, or optical impairments, pose challenges. These issues manifest as severe packet losses and an increased Link Loss Rate in Packets (LLR) [IEC, 2014]. At the transport layer, such losses can amplify the End-to-End Failure Rate in Packet Delivery (EEFR) for sensor data dissemination protocols. Ensuring robust communication networks is paramount in this dynamic landscape.
1.1 Purposes and History of Networks
1.1.1 Purposes
Over the decades, communication networks [Yan et al., 2013] have played an essential role in connecting billions of automating terminals, such as sensors and actuators, for supporting information and signal exchange during power monitoring and dispatch. Building these two kinds of Internet-of-things (IoT) networks is always the continuous goals of existing power grid operators across the world, requesting endless efforts. These goals can be summarized below:
Supporting comprehensive services
: This network not only encompasses the traditional functions of grid production and enterprise management but also paves the way for future comprehensive energy services and customer-centric service systems. Through shared data and resources, it integrates internal operations with external services, connecting the entire energy business chain – from cloud to network to terminal. It enables data to be collected once and applied universally, transforming business systems from vertical to horizontal structures. In doing so, it supports the development of an interconnected energy ecosystem.
Providing ubiquitous connectivity
: The network ensures seamless, real-time interconnection between people, machines, and devices, anytime and anywhere. It integrates traditional grid equipment, information systems, and internal staff, along with participants in source–grid–load coordination, upstream and downstream power enterprises, and external service providers. At the terminal layer, it connects all devices; at the network layer, it offers pervasive communication capabilities; and at the platform layer, it manages and controls data and devices comprehensively. The integration of the ubiquitous network with the next-generation power system has become a vital component of modern energy enterprises.
This network, evolving alongside the power grid, forms the foundation of the information and communication infrastructure. With this network, all elements can be comprehensively interconnected. The full interconnection across all aspects of the power system enables the real-time collection of data from power production, operation, and control. With the widespread adoption of intelligent terminals and mobile applications, this network supports the next-generation power system and delivers diverse energy services to a range of terminal devices. The benefit of this network focuses on bringing a standardized data and service system that spans the entire energy business chain. This ensures real-time consistency, open data sharing, and unified management across all business scopes, data types, and time dimensions, enabling seamless integration and service delivery across the energy sector. In this way, the network fosters optimized architectures, improved operational efficiency, smarter decision-making, and increased value across the power sector.
1.1.2 History
Building such networks involve evolving roadmap that initiated the prosperity of digitalization in the 1990s and accelerated in the early 21st century. Before this period, the old analog communication, popular from 1970s to 1980s, was challenged by the development of SCADA systems that allow to monitor and control grid assets remotely, such as substations and transmission lines, reducing the need for manual intervention. This old communication technology impeded the increased automation need for real-time data collection and control. Later, this technology was replaced by more advanced communication technologies that can be categorized into the two classes below:
By 1990s, the fiber-optic communications [Tornatore et al.,
2020
], such as the
Synchronous Digital Hierarchy
(
SDH
), the
Optical Transport Network
(
OTN
), the
Gigabit Passive Optical Network
(
GPON
), and the
Ethernet Passive Optical Network
(
EPON
), provided and is providing faster, more reliable data transmission, enhancing the ability of utilities to manage grid operations [NASPI,
2019
]. In this period, the digital protective relays began replacing traditional electromechanical relays. These digital devices enabled faster, more reliable responses to faults in the grid and introduced programmability, which allowed for more sophisticated grid protection schemes. Additionally, the development of
Phasor Measurement Units
(
PMUs
) and their integration into
Wide-Area Monitoring System
(
WAMSes
) marked a significant leap in grid stability and situational awareness [Thomas,
2020
; Xie et al.,
2006
]. PMUs offered real-time monitoring of the grid's electrical parameters, improving reliability.
From 2000s to now, wireless communications were and are being increasingly integrated into grid operations, enabling more comprehensive control and monitoring. This kind of technologies began with
Worldwide Interoperability for Microwave Access
(
WiMAX
) and cellular networks, such as 2G to 5G networks, which mark the full-scale deployment of digital devices throughout the grid. This brought about the growth of smart power meters and data concentrators. Since 2010s, wireless sensor networks, with low power usage, have facilitated more granular control over grid assets.
Development of the two kinds of networks above has now formed a hybrid optical–wireless network architecture which has been widely adopted across the world. Examples include:
the nation-wide ubiquitous power IoT networks built by the State Grid Corporation of China, featured with the 2G to 5G networks [Huawei Technologies Co., Ltd.,
2025
; Yuan et al.,
2014
],
the networks built by the South California Edison Corporation of United States, employing WiMAX, 4G
Long Term Evolution
(
LTE
), and radio-based systems [Vyas and Combs,
2011
],
the networks of European power grids, widely using LTE or narrow-band IoT networks for connecting huge amount of smart meters [Sequans,
2023
; Giordano et al.,
2013
],
the networks employed by Tokyo Electric Power Company of Japan, used 4G, 5G, and WiFi mesh networks [Weissberger,
2022
], and
the networks built by India, utilizing WiMAX, WiFi, and ZigBee for extending smart meter connections [Kumar et al.,
2022
].
The hybrid architecture is an essential part of modern smart grids, offering a balance between high-performance, high-capacity fiber-optic networks, and the flexibility and coverage of wireless technologies. These communication technologies can enhance grid management, integrate renewables, and respond to the growing complexity of modern power networks.
1.2 Networks in Power Grids
Today, these communication networks in smart power grids are ubiquitously and comprehensively integrated with power grid systems on a large scale, forming large-scale and area-crossing IoT networks IEEE 2013. By incorporating new communication technologies, these networks achieve mutual penetration and deep integration with the next-generation power system. They enable real-time, online connectivity between people, machines, and devices at every stage of energy transmission, distribution, and consumption. This integration supports and streamlines operations across grid production, enterprise management, and customer services, forming the backbone of a more efficient, economical, and secure power grids at national and regional levels.
With those networks, a number of data concentrators and terminal devices, involving lots of sensors and actuators, interact with SCADA systems and Production Management System (PMSes) [Thomas, 2020]. Wherein, the terminal device directly controls the underlying electrical equipment for providing power flows for end consumers and the primary systems. This process is complicated, but we can understand their building purposes and functions, which is helpful to see why we need these networks to be highly available.
1.2.1 Network Structures
Figure 1.2 specifies the overall structure of IoT communication networks of power grids, where the network is tightly coupled with physical power lines for supporting interactions of electronic devices and SCADA systems and PMSs, and its performance can be challenged by many adversarial factors.
1.2.1.1 Network Types
As depicted in the middle of Figure 1.2, the modern networking structure can be partitioned into backbone network and edge network. These networks are specified as follows.
Backbone network
: A resilient backbone network, woven with optical ground wires adhering to the IEC 60794-4-10 standard, creates a robust framework of redundancy through SDH/
Synchronous Optical Networking
(
SONET
)/OTN and EPON/GPON technologies. This interconnected and ringed architecture, detailed in references [IEC,
2014
], [Zhou et al.,
2019
], and [Zhou et al.,
2022
], caters to bandwidth demands ranging from 10 to 400 Gbps. Its strategic deployment is especially prominent in power transmission and distribution grids. Different types of backbone networks are deployed in scenarios where communication distances matter. The communication distances of SDH/SONET/OTN can extend to several hundred kilometers, making them suitable for power transmission grids. Meanwhile, EPON/GPON cover distances of up to 20 kilometers and 60 kilometers, respectively, making them preferred choices for power distribution networks.
Figure 1.2 IoT communication networks in power grids.
Edge network
: Expanding the connectivity horizon with wireless networks, spanning second to fifth generations of mobile networks (2G to 5G), WiMAX, and
Long Range Radio
(
LoRa
), ensures seamless connectivity in areas where optical fiber deployment is challenging. The communication distance of these networks can extend up to several kilometers, and they have found widespread integration by operators into power distribution and consumption grids.
1.2.1.2 Networking Strategies and Practices
The communication networks are tightly structured along the power lines deployment across amount of power substations in the gird. In the networks, the data traffic is aggregated in from edge networks to backbone networks layer by layer. These network links are based on encapsulated optical fibers such as the Optical Ground Wire (OPGW) [IEC, 2014] and the All-Dielectric Self-Supporting Cable (ADSS) [IEEE, 2019] being compliant to ITU-T G.652 [ITU-T, 2024].
The backbone network is usually structured in ring and mesh to enhance connectivity to be fault tolerant, where the network across different geographical areas must be tolerant to no less than two fault links. The network topology forms the backbone of a highly reliable and efficient system that supports critical functions such as real-time monitoring, control, and data management. This infrastructure is vital to the grid's overall functionality, ensuring that power transmission, distribution, and operational management are seamlessly integrated. The network is structured into several layers, each designed to fulfill specific roles within the grid, from core backbone transmission to local access networks, all of which are interconnected to maintain resilience and operational efficiency.
At the heart of the communication network is the transmission backbone, which forms the essential framework for data transport across provincial and local levels. The provincial transmission network, operating with dual-plane architectures (SW-A and SW-B), ensures redundancy and disaster recovery capabilities. The SW-A plane, utilizing MSTP/SDH technology, primarily supports production control, covering high-voltage substations (220 kV and above) and ensuring that critical control signals are prioritized. The SW-B plane, on the other hand, relies on OTN technology to carry management information services, with a larger capacity to handle the growing demands of administrative data across provincial and municipal sites. These two planes work in parallel, ensuring that the grid remains operational even in the event of a failure on one side, thus enhancing overall network reliability.
The local and county transmission networks mirror the provincial structure, maintaining dual-plane designs (DW-A and DW-B) for redundancy. The DW-A plane is also based on Multi-Service Transport Platform (MSTP)/SDH [Sibley, 2020], focusing on controlling local substations (35 kV and above), while the DW-B plane, utilizing Packet Transport Network (PTN) technology [Murakami and Koike, 2014], is dedicated to carrying management information. This layered approach, with dedicated transmission channels for both control and management data, not only ensures security by isolating critical operational signals but also maximizes the use of network resources. The network's ring topology, combined with point-to-point connections at the access layer, adds another level of resilience, ensuring that even localized failures do not disrupt the broader communication system.
Optimizing these transmission networks is crucial for future scalability and performance. Network administrators continuously improve the layered aggregation structures to ensure efficient traffic management and better load distribution. This optimization enhances the network's ability to expand and increases its capacity for handling new services as the power grid evolves. In addition, ensuring high node connectivity and implementing N-1 reliability practices – where the network can withstand the failure of any single component – are key to maintaining uninterrupted operations across the grid.
Alongside the transmission network, the comprehensive data network underpins much of the grid's communication infrastructure. This network adopts advanced technologies such as IPv4/IPv6 and Multiprotocol Label Switching (MPLS) [Rosen et al., 2001] VPN to enable flexible and secure data transmission. The backbone of the data network follows a dual-star topology, with two core nodes positioned at the provincial aggregation points. These core nodes are connected by dual routers and dual 10GE links, forming a high-reliability structure that ensures seamless communication between core and backbone layers. The backbone network connects upward to the national data communication network and downward to local access networks, creating a cohesive and hierarchical communication structure that supports various power grid operations.
The access network plays an equally important role in ensuring that all levels of the grid, from provincial to local substations, have secure and efficient connectivity. It is designed with aggregation and access layers that connect the grid's different operational units. Provincial and municipal companies link through dual 10GE connections to the backbone, ensuring redundancy and robust communication. At lower levels, county companies and local substations use dual GE links and specialized technologies like MSTP [Sibley, 2020] and PTN [Murakami and Koike, 2014] to establish secure communication pathways, depending on their specific operational needs. This hierarchical structure allows for seamless integration of substations, control centers, and management systems into the broader network, ensuring that even the smallest nodes remain connected and operational.
At the terminal level, the communication access network is divided into 10 kV and 0.4 kV systems. The 10 kV network handles critical tasks such as distribution automation and power quality monitoring, serving as the primary link for control systems within the grid. The 0.4 kV network focuses on tasks like electricity information collection and load management, ensuring that even low-voltage areas of the grid remain integrated into the larger system. Both networks leverage fiber-optic technology, where possible, to ensure high-speed and reliable communication, with alternative technologies like wireless and carrier-based systems used in areas where fiber is impractical.
The backbone and access layers of the smart grid's communication network are supported by robust synchronization and management systems. The frequency synchronization network ensures that the entire grid operates on a unified time base, which is essential for coordinating grid operations. This network is anchored by a provincial clock synchronization zone, which is supported by high-precision cesium clocks and satellite systems such as Beidou or Global Positioning System (GPS) [Hofmann-Wellenhof et al., 2012]. These synchronization systems ensure that both production and management services within the grid operate in harmony, minimizing the risk of data misalignment or timing issues.
Furthermore, the communication management system serves as an integrated platform that oversees the entire communication infrastructure [Der Wielen, 2020]. It centralizes real-time monitoring, resource management, and operational controls, enabling the grid to function as a cohesive unit. This system is designed to integrate with key management tools, allowing for streamlined communication across various levels of grid management. As the grid expands, continuous improvements in standardization, intelligence, and usability within the communication management system ensure that operational efficiency keeps pace with the growing complexity of the grid's infrastructure.
The communication network also relies on a robust power supply system to maintain operation during outages or disruptions. At major substations and control centers, dual independent power systems are installed to provide redundancy, ensuring that reliability is maintained [IEC 62439, 2008]. These power systems are backed up by batteries capable of providing at least four hours of emergency power, guaranteeing that critical communication systems remain functional even during power disruptions.
In conclusion, the communication network topology of smart power grids is an intricate and highly resilient system. Through its layered structure, dual-plane redundancy, and advanced technologies, it supports the efficient and reliable operation of the grid. From backbone transmission networks to terminal access systems, every component of this network is designed with reliability, scalability, and security in mind, ensuring that the smart power grid can continue to meet the growing demands of modern power management.
1.2.2 Networked Systems
1.2.2.1 Terminal Devices
Terminal devices provide monitoring and control points. The terminal devices are the critical interface between the physical grid and its control and communication layers. Chief among them are the Remote Terminal Units (RTUs) [Thomas, 2020], which monitor electrical variables such as voltage, current, and phase angles, and report this data to centralized control systems. RTUs operate as the data acquisition hubs for substation automation, enabling real-time monitoring of power flows and system conditions. Specialized devices, such as Transformer Terminal Units (TTUs), Distribution Terminal Units (DTUs), and Feeder Terminal Units (FTUs) [Han et al., 2014], are deployed for specific components in the grid, enhancing the granularity of data collection. These terminal devices not only provide detailed performance data but also facilitate rapid control actions, such as opening or closing circuit breakers, to protect critical infrastructure in case of faults or overloads.
Complementing RTUs are smart power meters [Barai et al., 2015], which are key to the implementation of demand-side management in smart grids. These meters track energy consumption patterns at consumer endpoints in real-time and allow utilities to implement dynamic pricing models or grid load shedding during peak demand periods. For real-time system stability, PMUs measure electrical waves on an electricity grid using synchronized phasor measurements [IEEE, 2011]. These devices are crucial in monitoring grid stability, detecting grid anomalies, and enabling WAMSs to manage the dynamic behavior of the grid under various operating conditions [Lin et al., 2012; Xie et al., 2006].
Additionally, protection units and environmental monitors serve to enhance the reliability and safety of the grid [Thomas, 2020]. Protection units guard against system faults by automatically isolating problem areas, thus preventing the cascading failures that can lead to widespread outages. Environmental monitors, on the other hand, track ambient conditions like temperature, humidity, and other environmental factors that could impact grid performance. These terminal devices act as critical agents of the smart grid's real-time control systems, enabling a higher level of operational intelligence and automation in grid management.
1.2.2.2 Data Concentrators
Data concentrators enables wide-area and large-scale networked power grid control [IEEE, 2011; Thomas, 2020]. Data concentrators play a pivotal role in the communication networks of a smart power grid by gathering, processing, and forwarding large volumes of data from the terminal devices to the central control systems. These concentrators reduce the burden on communication channels by aggregating data from geographically distributed endpoints and delivering it in a consolidated form. Meter data concentrators collect energy usage data from numerous smart meters, compressing and transmitting this information to utilities for billing, load forecasting, and energy management [Barai et al., 2015]. The scale of data handled by these devices is significant, as millions of smart meters may be feeding information into a concentrator.
For real-time grid monitoring, Phasor Data Concentrators (PDCs) process synchronized phasor data from PMUs, merging inputs from across the grid into a unified dataset [IEEE, 2011; Thomas, 2020]. PDCs are integral to real-time situational awareness, enabling grid operators to detect instability patterns or faults across vast areas within milliseconds. This is critical for ensuring grid stability in high-demand scenarios or during extreme weather conditions.
Merging Units (MUs) combine multiple measurements from various sensors to ensure synchronized data flows from substations and other key assets in the grid [IEC, 2011d]. These units often use the IEC 61850 protocol [IEC, 2020], which standardizes communication within substations and across the broader network. Additionally, intelligent edge proxies enhance the smart grid's efficiency by performing pre-processing at the network edge, reducing latency and the volume of data transmitted to central servers [Feng et al., 2021]. These proxies also contribute to local decision-making capabilities, such as detecting anomalies or performing load-balancing operations before reporting back to central systems. The role of data concentrators is fundamental in transforming vast amounts of raw data into actionable intelligence for grid control, allowing for better forecasting, faster response times, and enhanced resilience.
1.2.2.3 SCADA
The SCADA system is the heart of modern grid operations and dispatches, which provide real-time monitoring, control, and automation of the power grid [Thomas, 2020]. SCADA systems form the backbone of the grid's control infrastructure, enabling operators to oversee and manage assets across vast geographical areas from centralized control centers. These systems gather data from RTUs, PMUs, and other terminal devices, offering a comprehensive view of the grid's operational status. SCADA systems are also responsible for executing control commands, such as remotely opening or closing breakers, adjusting transformer tap settings, or rerouting power flows to balance loads. Furthermore, advanced SCADA systems are integrated with predictive maintenance algorithms, enabling utilities to identify potential equipment failures before they occur and proactively manage grid health.
Alongside SCADA systems, production management systems optimize energy generation and distribution by aligning production with real-time demand. These systems coordinate the dispatch of power from various sources, including traditional generation (such as coal and natural gas) and renewable energy sources (such as wind and solar), to ensure grid reliability. In modern smart grids, production management systems must balance the intermittency of renewables with grid stability requirements. As grid operators shift toward decarbonized energy systems, these systems use sophisticated forecasting algorithms, demand response techniques, and energy storage solutions to stabilize energy supply. The coordination between SCADA and production management systems is crucial for maintaining not only operational efficiency but also the economic viability of the grid in a future where decentralized energy generation is increasingly common.
By integrating real-time data collection, control, and automation with predictive and adaptive algorithms, SCADA and production management systems form the nervous system of the smart grid, ensuring it can respond dynamically to changes in demand, generation capacity, or system health.
