Smart Cyber-Physical Power Systems, Volume 1 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: IEEE Press Series on Power and Energy Systems
• Sprache: Englisch

Seitenzahl: 1435

Veröffentlichungsjahr: 2025

Beschreibung

Authoritative, highly comprehensive guide on how emerging technologies can address various challenges in different sectors of smart cyber-physical power systems

As the world shifts towards smarter and more resilient energy systems, cyber-physical power systems (CPSs) represent a critical step in modernizing the power infrastructure. Smart Cyber-Physical Power Systems, Volume 1: Fundamental Concepts, Challenges, and Solutions, offers an in-depth exploration of the fundamental concepts, structures, and major challenges that underlie these complex systems. It covers the essential theories and frameworks that drive the integration of digital technologies with physical power systems, including smart grids, microgrids, and the Internet of Energy.

This volume addresses a range of crucial topics, from global demand response strategies and microgrid architectures to smart energy management in cities and advanced distributed control strategies. Additionally, it highlights key challenges such as ensuring resiliency, protecting against cyberattacks, and maintaining reliability in the face of rapid technological advancements.

Experts from around the world contribute to this volume, sharing vital insights into the transformation of traditional power systems into adaptive, cyber-physical networks. Their focus on the growing importance of privacy, security, and data analytics makes this book a critical resource for anyone involved in power system research, offering essential tools to navigate and shape the future landscapes of energy systems.

Whether you’re a researcher, engineer, or industry professional, this volume provides the foundational knowledge needed to understand the evolving landscape of smart cyber-physical power systems and the significant challenges they face.

Join us on a journey through the landscape of Smart Cyber-Physical Power Systems (CPPSs), where cutting-edge solutions meet the challenges of today and forge the energy paradigms of tomorrow, driven by AI/ML, Big Data, Blockchain, IoT, Quantum Computing, Information Theory, Edge Computing, Metaverse, DevOps, and more.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Editors

List of Contributors

Foreword (John D. McDonald)

Foreword (Massoud Amin)

Key Areas Addressed

Advanced Solutions and Applications

Resilience and Policy Integration

Emerging Trends and Innovations

Economic and Equity Impacts

Lessons from Past Failures

Why This Series Matters

References

Preface for Volume 1: Smart Cyber‐Physical Power Systems: Fundamental Concepts, Challenges, and Solutions

Acknowledgments

1 Overview of Smart Cyber‐Physical Power Systems: Fundamentals, Challenges, and Solutions

1.1 Introduction

1.2 Structural Overview and Roadmap of the Book

1.3 General Concepts of the Cyber‐Physical Systems

1.4 Cyber‐Physical Energy and Power Systems (CPEPSS)

1.5 From Conventional Distribution Networks to Smart Grids

1.6 Smart Grid Ecosystem (From Smart Buildings to Smart Grid)

1.7 Cybersecurity in Modern Power Systems

1.8 Conclusions

References

2 Global Demand Response Status: Potentials, Barriers, and Solutions

2.1 Background

2.2 Global Status of DR Programs

2.3 AI and ML Applications in DR

2.4 Case Study

2.5 Discussion

References

3 Smart Power/Energy Management and Optimization in Microgrids

3.1 Introduction

3.2 Materials and Methods

3.3 Simulation and Results

3.4 Discussion

3.5 Conclusions

References

4 Smart City Energy Infrastructure as a Cyber‐Physical System of Systems: Planning, Operation, and Control Processes

4.1 Introduction

4.2 Cyber‐Physical System of Systems

4.3 Cyber‐Physical System of System Application Domains

4.4 Smart City Cyber‐Physical System of Systems

4.5 Smart City Energy Cyber‐Physical System of Systems

4.6 Planning, Operation, and Control Process in Smart City Energy Cyber‐Physical System of Systems

4.7 Emergence in Smart City Energy Cyber‐Physical System of Systems

4.8 Conclusions

References

5 Metaverse Local Energy Market in Smart City: A Descriptive Model and Strategic Development Analysis

5.1 Introduction

5.2 Background

5.3 Concepts

5.4 Case Study: Local Energy Market in Metaverse

5.5 Discussions and Conclusions

References

6 Cooperative and Distributed Control Strategies of Microgrids

6.1 Introduction

6.2 Fault‐Tolerant Secondary Control Schemes in Islanded AC Microgrids

6.3 Finite‐Time Fault‐Tolerant Voltage Control

6.4 Case Studies

6.5 Concluding Remarks

References

7 Interconnected Microgrid Systems: Architecture, Hierarchical Control, and Implementation

7.1 Introduction

7.2 Architecture

7.3 Hierarchical Control of Interconnected MGs

7.4 The Multi‐Agent System

7.5 The Implementation on a Real‐Time Cyber‐Physical Testbed

7.6 Conclusions

References

8 Internet of Energy, and Internet of Microgrids (IoE, IoM)*

8.1 Introduction

8.2 Interfacing of the IoT Node for Self‐Healing Strategies

8.3 Performance Assessment Results

8.4 Concluding Remarks

References

Note

9 Voltage Regulation and Reactive Power Optimization for Integration of Distributed Energy Resources into Smart Grids

9.1 Introduction

9.2 Traditional Volt/Var Control

9.3 Network Model

9.4 Chance‐Constrained Volt/Var Control

9.5 Solution Algorithm

9.6 Results

9.7 Approximate Load Models for Advanced VVC Functions

9.8 Binomial Approximation Method

9.9 Linear Regression Method

9.10 Results

9.11 Conservation Voltage Reduction

9.12 Conclusions

References

10 The Role of Data Analysis in Hosting Capacities of Distribution Power Systems for Electric Vehicles

Nomenclature

10.1 EVs' Power Demand Forecast Methods

10.2 Review of EVs' Energy Management Strategies

10.3 Uncertainties Regarding EVs and Their Impact on the Power Networks

10.4 Data Analyses Application in Technical Issues of EVs

10.5 Concluding Remarks

References

11 Energy Efficiency in Smart Buildings Through IoT Sensor Integration

11.1 Introduction

11.2 Building Automation Solution Landscape

11.3 BEMOSS

TM

FEATURES

11.4 Targeted Buildings and Loads

11.5 BEMOSS

TM

Architecture

11.6 BEMOSS

TM

Auxiliary Functions

11.7 Multiple‐protocol Interoperability

11.8 Test Results

11.9 BEMOSS

TM

Platform for Campus Applications

11.10 Conclusion

11.11 Exploring Other Capabilities of the BEMOSS™ Platform

References

12 Optimal Dispatch of Smart Energy System Based on Cyber–Physical–Social Integration

12.1 Introduction

12.2 CPSS Model

12.3 The Cooperative Operation in V2G

12.4 Framework of a Charging Station with Battery Swapping Mode

12.5 Conclusion

References

13 Power Distribution Systems Self‐Healing

13.1 Introduction

13.2 Historical Notes

13.3 Self‐Healing Concept

13.4 Mathematical Formulation

13.5 Case Studies

13.6 Concluding Remarks

References

14 Resiliency, Reliability, and Security of Cyber‐Physical Power System

Abbreviations

14.1 Introduction and Motivation

14.2 Conceptual and Definitional Studies

14.3 Application of Machine Learning in Power Systems

14.4 Case Study

14.5 Conclusion

Acknowledgments

References

15 Cyberattacks on Power Systems

15.1 Introduction

15.2 Cyber Kill Chain

15.3 Review of Major Cyberattacks

15.4 Taxonomy of Cyberattacks on Power Grids

15.5 Impact of Cyberattacks on Power Grids

15.6 Study Case and Simulation Results

15.7 Conclusion

Acknowledgement

List of Acronyms

References

16 Vulnerabilities of Machine Learning Algorithms to Adversarial Attacks for Cyber‐Physical Power Systems

16.1 Introduction

16.2 Vulnerabilities of ML Algorithms to Adversarial Attacks

16.3 Theoretical Foundations and Applications of Adversarial Attacks

16.4 Attack Models Under Different Scenarios Including Full, Limited, and No Knowledge About the Target Model

16.5 Real‐Life Practical Adversarial Example Generation and Implementation in CPPS

16.6 Protection Strategies Against Adversarial Attacks

16.7 Conclusion and Recommendation

References

17 Synchrophasor Data Anomaly Detection for Wide‐Area Monitoring and Control in Cyber‐Power Systems

17.1 Introduction

17.2 Synchrophasor‐Based Wide‐Area Monitoring and Control

17.3 Synchrophasor Data Flow, Anomalies, and Impacts

17.4 Synchrophasor Anomalies Detection and Classification (SyADC)

17.5 Quality‐Aware Synchrophasor‐Based Monitoring and Control Applications

17.6 Summary

Acknowledgements

References

18 Application of State Observers and Filters in Protection and Cyber‐Security of Power Grids

18.1 Introduction

18.2 State–Space Model of Systems

18.3 Properties of State–Space Models

18.4 State Observers and Filters

18.5 Application of Observers and Filters in Improving the Authenticity and Accuracy of Measured Data

18.6 Case Study 1: Attack Detection and Identification for Automatic Generation Control Systems

18.7 Case Study 2: Developing Wide‐Band Current Transformers for Traveling‐wave‐based Protection

18.8 Case Study 3: Fault Diagnosis in Transformers Using LPV Observers

18.9 Conclusion

References

19 Anomaly Detection and Mitigation in Cyber‐Physical Power Systems Based on Hybrid Deep Learning and Attack Graphs

Abbreviations

19.1 Power Grid Cyber Resilience

19.2 Operational Technologies and Secure Communication Protocols

19.3 Cyber‐Physical System Co‐Simulation and Cyber Ranges

19.4 Network Security Controls

19.5 Hybrid Deep Learning for Anomaly Detection in Power System OT Networks

19.6 Hybrid Deep Learning Model for Anomaly Detection

19.7 Attack Graph for Situational Awareness

19.8 Cyber Attack Case Studies

19.9 Conclusions

Acknowledgments

References

20 Attack Detection and Countermeasures at Edge Devices

20.1 Introduction

20.2 Attack Surfaces for Edge Devices

20.3 Security Issues and Common Attacks in Edge Devices

20.4 Attack Detection Techniques and Countermeasures

20.5 Conclusions and Future Research Directions

Acknowledgments

References

21 Privacy‐Preserving Outage Detection in Modern Distribution Grids: Challenges and Opportunities

21.1 Introduction

21.2 Preliminaries

21.3 Privacy‐Aware Line Outage Detection with Boosted Performance

21.4 Validation on Extensive Outage Scenarios with Real‐World Data

21.5 Conclusions

References

Note

22 Transactive Energy Management and Distribution System Reform Using Market Concepts

Nomenclature

22.1 Introduction

22.2 Proposed TEM Market Platform

22.3 Demonstrative Case Studies

22.4 Conclusion Remarks and Prospects for the Future

References

Appendix 22.A Line Segment Data of the 34-bus Test System (ohms)

23 Transactive Energy Systems in Decentralized Autonomous Renewable Energy Communities

23.1 Introduction

23.2 RECs as DAOs

23.3 Toward the Tokenization of the Governance

23.4 Conclusions

References

24 Transactive Coordination Paradigm for Efficient Charging Management of Plug‐in Electric Vehicles in Future Distribution Networks

24.1 Introduction

24.2 Transportation Electrification

24.3 Demand‐Side Management Approaches

24.4 Examples of TE Model Worldwide Projects

24.5 TE Paradigm in Charging Management of EVs

24.6 Conclusions and Future Works

References

25 Optimal Peer‐to‐Peer Energy Trading Using Machine Learning: Architecture, Strategies, and Algorithms

25.1 Introduction

25.2 P2P Energy Trading Architecture

25.3 ML Operation in P2P Energy Trading

25.4 Simulation

25.5 Conclusion

References

26 Optimal Peer‐to‐Peer Power Sharing in DC Islanded Microgrids

26.1 Introduction

26.2 Modeling of Islanded DC Microgrid System

26.3 Optimal Power Flow Problem Formulation of DC Islanded Microgrid System

26.4 Results and Discussion

26.5 Conclusion and Future Work

Acknowledgment

Bibliography

27 Blockchain‐Based Energy Trading Employing Hyperledger and Anomaly Detection Algorithms

27.1 Introduction

27.2 Literature Review

27.3 Anomaly Detection

27.4 Blockchain‐Based Anomaly Detection Case Study

27.5 Conclusion

References

28 Optimal Coordination of VSC‐Interfaced Subsystems to Safeguard the Frequency Performance of Cyber‐Physical Power Systems

28.1 Motivation and Scope of the Chapter

28.2 The HVDC–HVAC Cyber‐Physical Test Power System

28.3 Statement of the Optimization of FFC for PEI

28.4 Solution by Mean–Variance Optimization

28.5 Simulation Analysis

28.6 Concluding Reflections

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Assimilation of smart human with smart grid.

Table 1.2 Attacks on SCADA systems [54, 63].

Table 1.3 Top 10 targeted industries ranked by attack volume, 2019 vs. 2018...

Chapter 2

Table 2.1 Monetary benefit achieved by RTP and TOU pricing schemes.

Table 2.2 Optimization results without energy storage.

Table 2.3 Optimization results with energy storage.

Chapter 3

Table 3.1 Buildings with rooftop PVs and load consumption in 2016.

Table 3.2 Battery energy storage for the community.

Table 3.3 Total predicted energy generation (kWh) for each month.

Table 3.4 Accuracies of forecasting models.

Table 3.5 System economics.

Table 3.6 Cost summary.

Chapter 4

Table 4.1 Maier characteristics for a CPSoS.

Table 4.2 The qualifying Maier's criteria for smart city energy CPSoS.

Chapter 6

Table 6.1 Test microgrid parameters.

Chapter 9

Table 9.1 Control set points for the slow classical VCDs.

Table 9.2 Coefficients of the pre‐defined rule for DER reactive power dispa...

Table 9.3 Robustness performance of the TS‐VVC.

Chapter 10

Table 10.1 Typical EV characteristics.

Table 10.2 The main EV types in the German market and their battery capacit...

Table 10.3 Normal probability distribution parameters for EV characteristic...

Table 10.4 Commuting probability for different reasons.

Chapter 11

Table 11.1 Building automation solution landscape.

Table 11.2 Communication technologies.

Table 11.3 Data exchange protocol.

Table 11.4 List of supported hardware.

Table 11.5 Comparison of compressor consumption before and after installing...

Table 11.6 Comparison of energy usage before/after HVAC control.

Table 11.7 An average energy savings of 35% was achieved through dimming co...

Table 11.8 Comparison of consumption before/after lighting control.

Table 11.9 Scheduled dimming level from 6:30 am to 9:00 pm.

Table 11.10 Energy savings by increasing set point by 5 °F in one suite.

Chapter 12

Table 12.1 Types, number, and parking periods of different EVs.

Table 12.2 Parameters of the CPSS.

Table 12.3 Comparison of optimization performance based on CPS and CPSS mod...

Table 12.4 Cost components of all EV aggregators.

Chapter 13

Table 13.1 Modified 123 nodes switches' information.

Table 13.2 Modified 123 nodes customers' information.

Table 13.3 Test cases conditions.

Table 13.4 Self‐healing benefits summary.

Chapter 14

Table 14.1 Categorization of resilience‐enhancing actions.

Table 14.2 Some of the studies on the applications of learning‐based method...

Table 14.3 Performance evaluation parameters for the algorithm naive Bayes....

Table 14.4 Performance evaluation parameters for the algorithm logistic regr...

Table 14.5 Performance evaluation parameters for the algorithm SVMs.

Table 14.6 Performance evaluation parameters for the algorithm K‐nearest nei...

Table 14.7 Performance evaluation parameters for the algorithm decision tree...

Table 14.8 Performance evaluation parameters for the algorithm artificial n...

Chapter 15

Table 15.1 Summary of cyberattacks targeting ICSs.

Table 15.2 Ukraine 2015 cyber kill chain.

Table 15.3 Ukraine 2016 cyber kill chain.

Table 15.4 Comparison of Ukraine 2015 and 2016 cyberattacks.

Table 15.5 Comparison of malware capabilities.

Table 15.6 History of malware involved in major ICS cyber‐related incidents...

Table 15.7 Summary of known cyberattacks on power grids and their impact.

Table 15.8 Cascading failure sequence.

Chapter 16

Table 16.1 Adversarial attack types against ML models.

Table 16.2 Adversarial attack types on target model.

Table 16.3 Protection strategies against adversarial attack categories.

Chapter 17

Table 17.1 Test results of anomaly detection for buses 6 and 8.

Table 17.2 Processing time for different window sizes.

Table 17.3 Performance of SyADC on the IEEE 68‐bus system.

Table 17.4 Comparative analysis of SyADC.

Table 17.5 Anomaly detection performance.

Table 17.6 Oscillation monitoring.

Chapter 18

Table 18.1 The parameters of continuous and discrete state–space models in ...

Table 18.2 CT parameters.

Table 18.3 Variation of FSR indices across different fault resistances.

Table 18.4 Variation of FSR indices across different fault types.

Table 18.5 Variation of FSR indices across different fault inception‐angles...

Table 18.6 Variation of FSR indices across different fault locations.

Table 18.7 The maximum value of the RF observed during the transformer ener...

Table 18.8 The maximum value of the RF during internal faults.

Table 18.9 Maximum value of the RF recorded during energization of the faul...

Chapter 19

Table 19.1 Cyber‐physical system models for power systems research.

Table 19.2 Summary of network security control applications.

Table 19.3 Cyber attack scenarios.

Table 19.4 Performance comparison of anomaly detection methods.

Chapter 20

Table 20.1 Common malware found in IoT edge devices.

Chapter 21

Table 21.1 Summarize of detection statistics.

Table 21.2 Performance comparison on various systems. and .

Table 21.3 Performance comparison at different noise levels in the IEEE 8‐bu...

Chapter 22

Table 22.1 Energy bidding of all participants of TEM market mechanism.

Table 22.2 Nominal demand summary of the 34‐bus test system (kW).

Chapter 25

Table 25.1 Total profit comparison.

Chapter 26

Table 26.1 OPF‐1 for modified 14 bus DC microgrid system.

Table 26.2 OPF‐2 for modified 14 bus DC microgrid system.

Table 26.3 OPF‐1 with unbalanced SOCs.

Table 26.4 OPF‐2 with unbalanced SOCs.

Chapter 27

Table 27.1 Pros and cons of predictive confidence level approach.

Table 27.2 Definition of true/false/positive/negative.

Table 27.3 Anomaly injection start time and end time.

Table 27.4 Forecast model RMSE summary.

Table 27.5 Negawatt‐hours anomaly detection performance facing scaling anom...

Table 27.6 Negawatt‐hours anomaly detection performance facing simple ramp a...

Table 27.7 Negawatt‐hours anomaly detection performance facing two‐way ramp ...

Table 27.8 Negawatt‐hours anomaly detection performance facing random anomal...

Table 27.9 Negawatt‐hours anomaly detection overall performance.

Table 27.10 PV energy anomaly detection performance facing scaling anomaly ...

Table 27.11 PV energy anomaly detection performance facing simple ramp anoma...

Table 27.12 PV energy anomaly detection performance facing two‐way ramp anom...

Table 27.13 PV energy anomaly detection performance facing random anomaly in...

Table 27.14 PV energy anomaly detection overall performance.

List of Illustrations

Chapter 1

Figure 1.1 An overview of a smart CPPS.

Figure 1.2 The stages of the changes in the hype gartner chart

Figure 1.3 Hype cycle for emerging technologies, 2018

Figure 1.4 Hype cycle for emerging technologies, 2023

Figure 1.5 The classification of different concepts of the CPPs as the arran...

Figure 1.6 The smart grid architecture model (SGAM) and CEN‐CENELEC‐ETSI sma...

Figure 1.7 A four‐layer representation of CPPSs (IEC standard)

Figure 1.8 Open systems interconnection model (OSI).

Figure 1.9 Two main concepts: The maximum capacity is normally restricted to...

Figure 1.10 The simplified MG control flow diagram is based on the IEEE Std....

Figure 1.11 The basic control structures are (a) centralized control, (b) de...

Figure 1.12 Block diagrams of the hierarchical control of an AC MG. (a) Prim...

Figure 1.13 Circuit/block‐diagram representation of grid‐connected power con...

Figure 1.14 Basic control structure in (a) a three‐phase grid‐forming and (b...

Figure 1.15 A block‐diagram representation of the VPP.

Figure 1.16 The configuration of vehicle‐to‐grid technology

Figure 1.17 The Internet of Energy.

Figure 1.18 The elements of the industry 4.0.

Figure 1.19 (a) WACS communication network for sample smart MG (distances ar...

Figure 1.20 The big framework for wide‐area control and EI of MGs, including...

Figure 1.21 The smart grid ecosystem.

Figure 1.22 Smart city building blocks.

Figure 1.23 Optical‐based traffic signal preemption system for emergency and...

Figure 1.24 Intelligent traffic management in smart cities.

Figure 1.25 Smart lamppost with camera and sensor.

Figure 1.26 Smart garbage bin concept.

Figure 1.27 Smart grid elements.

Figure 1.28 Traditional power systems vs. modern power systems.

Figure 1.29 Power‐information flow overview.

Figure 1.30 Big data emergence in moden power systems.

Figure 1.31 Big data emergence in moden power systems (a) WACS communication...

Figure 1.32 In cyber‐physical co‐multi‐MGs, the red dotted lines express the...

Figure 1.33 Power system layers and their vulnerabilities.

Figure 1.34 Attack classification, (a) passive attack and (b) active attack....

Figure 1.35 General classification of threats in a typical system.

Figure 1.36 Different attacks in power systems.

Figure 1.37 Most frequently targeted industries in 2018 [54, 63].

Chapter 2

Figure 2.1 Barriers for utilizing untapped DR potential.

Figure 2.2 The multiple fields of AI [2].

Figure 2.3 New England hourly demand during winter and summer [25].

Figure 2.4 Optimized RTP prices during winter and summer days.

Figure 2.5 Optimized TOU prices during winter and summer.

Figure 2.6 Reinforcement learning flowchart.

Chapter 3

Figure 3.1 Selected community.

Figure 3.2 Photovoltaic panels on building rooftop.

Figure 3.3 Load profile for each month.

Figure 3.4 Load box‐and‐whisker chart.

Figure 3.5 System schematic diagram.

Figure 3.6 Monthly electric production.

Figure 3.7 GBR model performance. (a) Solar power (Wh) forecasting model lin...

Figure 3.8 Forecasted PV generation and load consumption. (a) PV generation ...

Chapter 4

Figure 4.1 Key differences of CPSs and CPSoS.

Figure 4.2 CPSoS classification.

Figure 4.3 Constituent CPSs in critical infrastructure CPSoS.

Figure 4.4 Constituent CPSs of smart city CPSoS.

Figure 4.5 Functional CPSs in the smart city will be driven by four systems....

Figure 4.6 General model of EHs.

Figure 4.7 Existing real facilities in cities that can be considered as a MI...

Figure 4.8 Smart city energy CPSoS.

Figure 4.9 MIEHs and MAEH cooperation in planning and operation process of s...

Chapter 5

Figure 5.1 Business process model and notation (BPMN) model of local energy ...

Figure 5.2 BPMN model of an alternative way for LEM value chain in the metav...

Chapter 6

Figure 6.1 Schematic diagram of the test microgrid.

Figure 6.2 Communication graph topology among DER units.

Figure 6.3 Performance of the proposed secondary fault‐tolerant control mech...

Figure 6.4 Comparison of the proposed control mechanism with methods in

[19,

...

Figure 6.5 Control efforts of the proposed control mechanism and the control...

Chapter 7

Figure 7.1 An interconnected microgrid system.

Figure 7.2 Architecture of the interconnected microgrid systems with network...

Figure 7.3 Distributed MG control level.

Figure 7.4 Distributed interconnected MG control level.

Figure 7.5 The test case of interconnected microgrid system.

Figure 7.6 Real‐time cyber‐physical test bed.

Figure 7.7 Communication network.

Figure 7.8 Agent design in a Docker container.

Figure 7.10 Reactive power injection from each DG and bus in the interconnec...

Figure 7.9 Real power injection from each DG and bus in the interconnected M...

Figure 7.11 The frequency profiles of each DG and bus in interconnected MGs....

Figure 7.12 The voltage profiles of each DG and bus in interconnected MGs. (...

Chapter 8

Figure 8.1 Diagram of FA based on SGAM.

Figure 8.2 Diagram of ADMS based on SGAM.

Figure 8.3 Diagram of AMMS based on SGAM.

Figure 8.4 Harmonized IoT node device in the distribution system.

Figure 8.5 Publishing/subscribing message system over MQTT protocol.

Figure 8.6 Connection of IoT node devices into multi‐tier computational mode...

Figure 8.7 Abstract modeling of the IoT node device in UML.

Figure 8.8 Hardware‐in‐the‐loop setup for validation experiments.

Figure 8.9 Status information about the RabbitMQ®broker.

Figure 8.10 Monitoring capture of the SV payload from EDSIM to the IoT node ...

Figure 8.11 Harmonized IoT node device in the distribution system.

Figure 8.12 Message flow for automatic FLISR with SCADA detection and switch...

Figure 8.13 Wireshark

®

captures analysis in the publishing/subscribing m...

Figure 8.14 Monitoring capture of the GOOSE payload from IoT node device to ...

Figure 8.15 Wireshark captures analysis in publishing/subscribing message sy...

Figure 8.16 Screenshot from the supervisory with the logging script of an AD...

Chapter 9

Figure 9.1 The scenario enforcement algorithm.

Figure 9.2 95 bus UK Generic Distribution System.

Figure 9.3 Accuracy of the BAM and LRM for the ZIP loads.

Figure 9.4 Accuracy of the BAM and LRM for the exponential loads.

Figure 9.5 Percentage normalized error in the network voltage magnitudes.

Chapter 10

Figure 10.1 Summary of the models used in the EVs' load forecasting.

Figure 10.2 HEMS with EVs

Figure 10.3 Schematic of a PLEMS

Figure 10.4 PDF of daily required charging energy

Figure 10.5 The arrival rate of EVs

Figure 10.6 Upper and lower limit of distance traveled by EVs

Figure 10.7 Distance traveled in a year in the USA

Figure 10.8 Factors that affect the driving pattern

Figure 10.9 Effect of season on EV electricity consumption

Figure 10.10 The effect of temperature on EV electricity consumption

Figure 10.11 The effect of ambient temperature and vehicle speed on rolling ...

Figure 10.12 The lower and upper bounds of EV arrival time

Figure 10.13 Arrival rate of EVs counted every 15 minutes (the gray area ind...

Figure 10.14 The effect of battery degradation on the daily cost

Figure 10.15 Effect of charge and discharge depth on the number of cycles...

Figure 10.16 Number of EVs sold in the UK by the model

Figure 10.17 Change in the grid voltage under uncontrolled charging

Figure 10.18 Main categories of ML algorithms and widespread examples of the...

Figure 10.19 Supervised ML model development procedure

Figure 10.20 Significance of each feature in estimating the EV departure tim...

Figure 10.21 EV entry and exit times to and from the charging point

Figure 10.22 Linear relationship between stay duration and energy consumptio...

Figure 10.23 Input‐output linkage by an ML model

Figure 10.24 Parameters affecting EV energy demand in different levels

Figure 10.25 EV owner's classification based on the time of charge

Figure 10.26 The schematic of a RL algorithm

Figure 10.27 The general structure of Q‐learning algorithm

Figure 10.28 Different categories of heuristic optimization algorithms

Figure 10.29 (a) Ants facing a barrier in their path, (b) they explore avail...

Chapter 11

Figure 11.1 The number of commercial buildings and floorspace, 1979–2018 [1]...

Figure 11.2 Percentage of commercial buildings and floorspace by principal b...

Figure 11.3 Share of number of buildings and floorspace by year constructed,...

Figure 11.4 Illustration of typical building size category [1].

Figure 11.5 Total commercial buildings and floorspace by square footage cate...

Figure 11.6 Four end uses are common among more than three‐fourths of commer...

Figure 11.7 IoT‐enabled smart building devices.

Figure 11.8 BEMOSS™ applications.

Figure 11.9 Discover new devices by BEMOSS™ platform.

Figure 11.10 Discover new devices by BEMOSS™ platform (pending).

Figure 11.11 Discover new devices by BEMOSS™ platform (approved).

Figure 11.12 Alarm and notification.

Figure 11.13 Authenticate using BEMOSS™ OAuth 2.

Figure 11.14 Energy use by type of US commercial building, total: 6787 trill...

Figure 11.15 Average floorspace by principal building activity (2018) (sourc...

Figure 11.16 Major fuel energy intensities by principal building activity (2...

Figure 11.17 Electricity and natural gas consumption by principal building a...

Figure 11.18 Major fuel energy intensities by principal building activities ...

Figure 11.19 Consumption in US commercial buildings by end uses, 2018 (sourc...

Figure 11.20 Building sector floorspace and air‐conditioning consumption (20...

Figure 11.21 BEMOSS™ system architecture for small buildings with a few load...

Figure 11.22 BEMOSS™ system architecture for larger buildings.

Figure 11.23 BEMOSS™ architecture.

Figure 11.24 Building information.

Figure 11.25 Manage users.

Figure 11.26 Manage gateways.

Figure 11.27 Password manager.

Figure 11.28 Multiple‐protocol interoperability.

Figure 11.29 Supporting multiple IoT devices by BEMOSS™.

Figure 11.30 VT‐ARI lab.

Figure 11.31 BEMOSSTM UI home page.

Figure 11.32 Lighting control.

Figure 11.33 Thermostat control.

Figure 11.34 Plug load control.

Figure 11.35 Power meter data (brightness is 100%).

Figure 11.36 Power meter data (brightness is 25%).

Figure 11.37 CO

2

sensor data.

Figure 11.38 Occupancy estimation using a CO

2

sensor.

Figure 11.39 Illuminant sensor.

Figure 11.40 Distributed energy resources.

Figure 11.41 Historical data (rooftop solar power).

Figure 11.42 Historical data (rooftop solar power and voltage).

Figure 11.43 Historical data (rooftop solar current and temperature).

Figure 11.44 Historical data (temperature and humidity).

Figure 11.45 Historical data (pressure and CO

2

).

Figure 11.46 Historical data (noise and occupancy).

Figure 11.47 Set schedule.

Figure 11.48 BEMOSS™ measured energy saving across deployments.

Figure 11.49 BEMOSS™ deployment in four buildings.

Figure 11.50 Deployment of wireless devices in VT classroom building.

Figure 11.51 Indoor/outdoor environmental monitoring.

Figure 11.52 Energy savings—after increasing set points...

Figure 11.53 (a) Temperature profile before BEMOSS™ demand reduction and (b)...

Figure 11.54 (a) Office working area with skylight and ceiling lights, (b) s...

Figure 11.55 Blacksburg retail office building.

Figure 11.56 (a) June 6, 2016: day‐time cool set...

Figure 11.57 Rooftop solar installed at VT‐ARI.

Figure 11.58 Solar PV system monitoring and control.

Figure 11.59 Battery energy storage system installed in the building.

Figure 11.60 Battery energy storage system monitoring and control.

Figure 11.61 Transition from smart building to smart campus.

Chapter 12

Figure 12.1 Framework of a CPSS with WT–PV–ES CG.

Figure 12.2 The optimal dispatch based on CPS and CPSS.

Figure 12.3 The real‐time electricity prices of EV aggregator charging and d...

Figure 12.4 The coordinated framework of multiple EV aggregators participati...

Figure 12.5 The impacts of cooperation: (a) power load, (b) power deviation ...

Figure 12.6 The charging station with battery swapping mode.

Figure 12.7 Planning area.

Figure 12.8 Annual comprehensive cost.

Figure 12.9 Each cost of planning.

Figure 12.10 Planning result.

Figure 12.11 Locations of BCCSs.

Chapter 13

Figure 13.1 Outage resiliency curve.

Figure 13.2 Conceptual restoration process: (a) over‐current protection sche...

Figure 13.3 Medium‐length transmission line nominal‐

π

model.

Figure 13.4 Modified 123 nodes distribution system schematic.

Figure 13.5 Case I switching status over time.

Figure 13.6 Case I network configuration after restoration.

Figure 13.7 Case I outage restoration breakdown.

Figure 13.8 Case I restoration benefit over time.

Figure 13.9 Case II switching status over time.

Figure 13.10 Case II network configuration after restoration.

Figure 13.11 Case II outage restoration breakdown.

Figure 13.12 Case II restoration benefit over time.

Chapter 14

Figure 14.1 Threats facing power systems.

Figure 14.2 Performance of the power system after a HILF event.

Figure 14.3 Modified distribution feeder of RBTS bus 4

Chapter 15

Figure 15.1 Cyber kill chain.

Figure 15.2 Cyberattack on the power grid in Ukraine in 2015.

Figure 15.3 Cyberattack on the power grid in Ukraine in 2016.

Figure 15.4 Taxonomy of cyberattacks on power systems and ICS.

Figure 15.5 Layout of communication architecture in a digital substation.

Figure 15.6 Simulation results showing impact of a cyber‐induced cascading f...

Figure 15.7 Single‐line diagram of IEEE‐39 bus system after cyberattack.

Chapter 16

Figure 16.1 Cyber‐physical power system.

Figure 16.2 Power system ML pipeline.

Figure 16.3 Adversarial attacks on input sensors.

Figure 16.4 Adversarial attack against ML model.

Figure 16.5 Adversarial attack categories.

Figure 16.6 Adversarial attack against traffic sign.

Chapter 17

Figure 17.1 Applications of phasor measurement unit in wide‐area monitoring ...

Figure 17.2 PMU‐PDC data flow architecture.

Figure 17.3 Outliers and missing measurements in real‐life industrial PMU da...

Figure 17.4 SyADC tool for PMU anomaly detection and classification.

Figure 17.5 Classification of anomalies in PMU.

Figure 17.6 Working of the SyADC tool.

Figure 17.7 Correlation distribution between two PMUs.

Figure 17.8 The IEEE 14 bus network with PMUs on buses and

Figure 17.9 PMU measurements for different events at buses 6 and 10. All eve...

Figure 17.10 IEEE 68 bus system.

Figure 17.11 The AUC‐ROC for 10% anomaly rate in the data.

Figure 17.12 (a) Field PMU measurements, (b) filtered PMU measurements witho...

Figure 17.13 (a) Distribution of mean absolute errors without and (b) with b...

Figure 17.14 Three bus system with fault at bus .

Figure 17.15 Magnitude of real power in line 2 measured from bus 2.

Chapter 18

Figure 18.1 Block diagrams of the state–space model for systems (a) without ...

Figure 18.2 The block diagram of the state–space model of systems in the dis...

Figure 18.3 Classification of state estimation.

Figure 18.4 The block diagram of the Luenberger observer.

Figure 18.5 The block diagram of KFs.

Figure 18.6 The linearized model of LFC system in area “,” featuring a sing...

Figure 18.7 Single‐line diagram of the three‐area test system.

Figure 18.8 Schemes for detecting and identifying attacks in the AGC system....

Figure 18.9 Main UIO's RF, (a) without noise and (b) with noise.

Figure 18.10 RFs of scenario 1, (a) main UIO, (b) UIO A, (c) UIO B, and (d) ...

Figure 18.11 RFs of scenario 2, (a) main UIO, (b) UIO A, (c) UIO B, and (d) ...

Figure 18.12 RFs of scenario 3, (a) main UIO, (b) UIO A, (c) UIO B, and (d) ...

Figure 18.13 Current transformer: (a) physical circuit, (b) HF model of CTs ...

Figure 18.14 CIGRE 20 kV benchmark European distribution test grid

Figure 18.15 CIGRE North American HV transmission network benchmark

Figure 18.16 Primary, actual secondary, and compensated secondary currents f...

Figure 18.17 Spectral analysis of the first TW within primary and compensate...

Figure 18.18 Primary, actual secondary, and compensated secondary currents f...

Figure 18.19 Spectral analysis of the first TW within primary and compensate...

Figure 18.20 Transformer differential scheme after implementing the proposed...

Figure 18.21 (a) Equivalent circuit of a transformer and (b) equivalent circ...

Figure 18.22 Single‐line diagram of the test system.

Figure 18.23 (a) The dual‐slope characteristic used for the differential rel...

Figure 18.24 (a) Estimated and actual primary currents and (b) the RF of the...

Figure 18.25 (a) The estimated and actual primary currents, and (b) the RF o...

Figure 18.26 (a) The estimated and actual primary currents and (b) the RF of...

Figure 18.27 (a) The estimated and actual primary currents and (b) the RF du...

Figure 18.28 (a) The estimated and actual primary currents and (b) the RF as...

Figure 18.29 (a) The estimated and actual primary currents and (b) the RF co...

Chapter 19

Figure 19.1 Mapping of OSI layers, cyber attacks, and mitigation techniques....

Figure 19.2 Summary of secure protocol research and classification.

Figure 19.3 Comparison of communication network simulators for CPS modeling....

Figure 19.4 Comparison of real hardware, virtual machines, and container‐bas...

Figure 19.5 CPS architecture in CRoF at TU Delft.

Figure 19.6 CPS and cyber range architecture of CRoF at TU Delft.

Figure 19.7 Blue and red team tools for power grid IT/OT systems in CRoF at ...

Figure 19.8 SDN architecture for power grid OT networks.

Figure 19.9 CyResGrid attack graph generation processes from (a) GC‐LSTM tra...

Figure 19.10 Attack graph representation for normal and anomalous traffic: (...

Figure 19.11 Cyber‐physical experimental architecture to analyze the impact ...

Figure 19.12 Attack graph results for cyber attacks on digital substation un...

Figure 19.13 Cyber‐attack location identification and visualization using at...

Chapter 20

Figure 20.1 Predicted increase in edge devices from 2020 to 2030 in enterpri...

Figure 20.2 Number of IoT cyber attacks worldwide from 2018 to 2022

Figure 20.3 Basic components of a zero trust architecture.

Chapter 21

Figure 21.1 An overview of the privacy‐aware line outage detection problem i...

Figure 21.2 The decentralized randomizing scheme (21.6) to protect privacy o...

Figure 21.3 Outages are reported when the calculated statistic surpasses the...

Figure 21.4 The comparison of trade‐off functions of distinguishing unit‐var...

Figure 21.5 The logarithm of various detection statistics in IEEE 123‐bus sy...

Figure 21.6 The logarithm of detection statistics with different variance sc...

Figure 21.7 The average detection delay (a) and the false alarm rate (b) in ...

Figure 21.8 Increased ADD (unit) and FAR (%) under different ratios of data ...

Chapter 22

Figure 22.1 Three‐stage TEM market settlement procedure [24].

Figure 22.2 Modified 34‐bus test system.

Figure 22.3 Hourly energy price of the transmission system at bus # 800.

Figure 22.4 Loading percentage per hour.

Figure 22.5 Percentage of total transactions transmission versus distributio...

Figure 22.6 Hourly injected power of each considered supply entity.

Figure 22.7 Hourly social welfare after TEM market settlement.

Figure 22.8 Obtained DLPMs for the three‐phase set at each bus in the system...

Figure 22.9 The three‐phase voltage magnitudes at each bus in the system at ...

Figure 22.10 Hourly state‐of‐charge for the battery at bus # 810.

Figure 22.11 Hourly battery power injected at bus # 810.

Figure 22.12 The three‐phase voltage magnitudes at each bus in the system at...

Chapter 23

Figure 23.1 REC schematic representation.

Figure 23.2 DAO contribution to REC.

Figure 23.3 Conceptualization of a DAO model under a TE optic for REC using ...

Figure 23.4 Two‐token governance model.

Figure 23.5 Two‐token governance model: example of usages.

Figure 23.6 AMM functioning.

Chapter 24

Figure 24.1 Global EV stock, 2018–2022.

Figure 24.2 Smart energy management matrix.

Figure 24.3 Algorithm for determining EV's bid and offer price/quantity.

Figure 24.4 A sample of the response curve submitted by the EV owner.

Figure 24.5 Different market‐clearing models: (a) centralized, (b) decentral...

Figure 24.6 PVUR in all buses of test system with different power flow model...

Chapter 25

Figure 25.1 Centralized P2P energy trading market configuration with single ...

Figure 25.2 Decentralized P2P configuration with direct transactions between...

Figure 25.3 Hierarchical transactions in hybrid P2P energy trading.

Figure 25.4 Classification of network cost allocation methods.

Figure 25.5 Module of long short‐term memory forecasting technique.

Figure 25.6 Algorithm of hybrid P2P energy trading with multi‐aggregators.

Figure 25.7 Diagram of 141‐bus distribution system.

Figure 25.8 Comparison of trading amount.

Chapter 26

Figure 26.1 Nanogrid model with components.

Figure 26.2 Distributed generation distributed storage architecture.

Figure 26.3 Framework for loss estimation.

Figure 26.4 Loading vs. efficiency curve of a DC–DC converter.

Figure 26.5 Proposed algorithm for optimal power dispatch.

Figure 26.6 Modified 14 bus test system architecture as DC microgrid for rur...

Figure 26.7 PV generation and load multipliers.

Figure 26.8 Power scheduled in OPF‐1 for modified 14 bus DC microgrid system...

Figure 26.9 Power scheduled in OPF‐2 for modified 14 bus DC microgrid system...

Figure 26.10 Converter efficiency in OPF‐1 for 14 bus system.

Figure 26.11 Converter efficiency in OPF‐2 for 14 bus system.

Figure 26.12 Comparison of operating converters in 14 bus system.

Figure 26.13 Comparison of distribution losses of 14 bus system.

Figure 26.14 Comparison of conversion losses of 14 bus system.

Figure 26.15 Comparison of total losses of 14 bus system.

Chapter 27

Figure 27.1 Negawatt‐hour trading with responsive building hourly energy con...

Figure 27.2 Confidence interval for 95%.

Figure 27.3 Semi‐supervised anomaly detection architecture.

Figure 27.4 PV energy data before and after scaling the anomaly injection in...

Figure 27.5 PV energy data before and after scaling the anomaly injection in...

Figure 27.6 Negawatt‐hour data before and after scaling the anomaly injectio...

Figure 27.7 Negawatt‐hour data before and after scaling the anomaly injectio...

Figure 27.8 PV energy data before and after simple ramp anomaly injection in...

Figure 27.9 PV energy data before and after simple ramp anomaly injection in...

Figure 27.10 Negawatt‐hour data before and after simple ramp anomaly injecti...

Figure 27.11 Negawatt‐hour data before and after simple ramp anomaly injecti...

Figure 27.12 PV energy data before and after the two‐way ramp anomaly inject...

Figure 27.13 PV energy data before and after the two‐way ramp anomaly inject...

Figure 27.14 Negawatt‐hour data before and after the two‐way ramp anomaly in...

Figure 27.15 Negawatt‐hour data before and after the two‐way ramp anomaly in...

Figure 27.16 PV energy data before and after the random anomaly injection in...

Figure 27.17 PV energy data before and after the random anomaly injection in...

Figure 27.18 Negawatt‐hour data before and after the random anomaly injectio...

Figure 27.19 Negawatt‐hour data before and after the random anomaly injectio...

Figure 27.20 Negawatt‐hour trading between DRA and buildings(left) and Peer‐...

Figure 27.21 DRA negawatt‐hour trading hourly bidding process.

Figure 27.22 DRA negawatt‐hour trading transaction flow in Hyperledger Compo...

Figure 27.23 Peer‐to‐peer negawatt‐hour trading hourly bidding process.

Figure 27.24 Peer‐to‐peer negawatt‐hour trading transaction flow implemented...

Figure 27.25 Code sample of negawatt‐hours anomaly detection in Hyperledger ...

Figure 27.26 Error message showing the abnormal negawatt‐hours is detected s...

Figure 27.27 Peer‐to‐peer PV energy trading transaction flow implemented in ...

Figure 27.28 Code sample of PV energy seller's anomaly detection in Hyperled...

Figure 27.29 Error message showing the abnormal PV energy quantity is detect...

Chapter 28

Figure 28.1 Single‐line diagram of the proposed futuristic multi‐area and mu...

Figure 28.2 Structure of the proposed modification for P/Vdc controller atta...

Figure 28.3 Proposed frequency controller for PEM electrolysers.

Figure 28.4 Example visualization of the optimal tuning of FFC.

Figure 28.5 Solution procedure performed by MVMO.

Figure 28.6 Dynamic frequecny evolution of the synchronous areas, with and w...

Figure 28.7 Resulting convergence of the optimization's objective function....

Guide

Cover

Table of Contents

Series Page

Title Page

Copyright

Dedication

About the Editors

List of Contributors

Foreword (John D. McDonald)

Foreword (Massoud Amin)

Preface for Volume 1: Smart Cyber‐Physical Power Systems: Fundamental Concepts, Challenges, and Solutions

Acknowledgments

Begin Reading

Index

End User License Agreement

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Diomidis Spinellis

Smart Cyber‐Physical Power Systems

Fundamental Concepts, Challenges and Solutions

Volume 1

Edited byAli ParizadVirginia TechUnited States

Hamid Reza BaghaeeTarbiat Modares UniversityIran

Saifur RahmanVirginia TechUnited States

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Library of Congress Cataloging‐in‐Publication Data:

Names: Parizad, Ali, editor. | Baghaee, Hamid Reza, editor. | Rahman, Saifur, editor.

Title: Smart cyber‐physical power systems : challenges and solutions Volume 1/ edited by Ali Parizad, Hamid Reza Baghaee, Saifur Rahman.

Description: Hoboken, New Jersey : Wiley‐IEEE Press, [2025] | Includes index.

Identifiers: LCCN 2024048504 (print) | LCCN 2024048505 (ebook) | ISBN 9781394191499 (cloth) | ISBN 9781394191505 (adobe pdf) | ISBN 9781394191512 (epub)

Subjects: LCSH: Cooperating objects (Computer systems) | Electric power systems–Automation. | Artificial intelligence.

Classification: LCC TJ213 .S485 2025 (print) | LCC TJ213 (ebook) | DDC 006.2/2–dc23/eng/20241214

LC record available at https://lccn.loc.gov/2024048504

LC ebook record available at https://lccn.loc.gov/2024048505

Cover Design: Wiley

Cover Image: © metamorworks/Shutterstock

To my parents, whose unwavering support and guidance illuminate my journey at every step.

To my beloved wife, whose love, patience, and encouragement have been my greatest source of strength and inspiration.

To my professors and colleagues, from whom I have learned immensely, whose wisdom and mentorship have profoundly shaped my professional path.

And to those envisioning a future where sustainable living, smart cities, and the pioneering spirit of artificial intelligence converge to create a world where technology harmoniously enhances our environment and society, fostering an era of unparalleled freedom and possibilities.

– Ali Parizad

To my beloved family: my parents, whose unwavering support has been my foundation; my wife, who has stood by me at every step; my children, who bring joy to my life; and my entire family for their constant encouragement. I also dedicate this work to my esteemed professors for their valuable supports and to researchers in this field for their dedication to advancing knowledge. This two‐volume work, “Smart Cyber‐Physical Power Systems: Challenges and Solutions,” is a humble reflection of your support and inspiration.

– Hamid Reza Baghaee

I dedicate this book to my parents Mr. Serajur Rahman and Mrs. Sahara Rahman for their deep affection and love and for injecting deep moral values in me. These have laid the foundation on which my life's achievements stand.

– Professor Saifur Rahman

About the Editors

Ali Parizad, Postdoctoral Associate, Virginia Tech, Advanced Research Institute (ARI), Virginia, USA

Ali Parizad is a Postdoctoral Associate at Virginia Tech's Advanced Research Institute. His tenure at Virginia Tech involves leveraging machine learning (ML) to enhance energy efficiency within smart grids, under the mentorship of Professor Saifur Rahman, IEEE President 2023. Ali's academic foundation was laid at Southern Illinois University, where he obtained his PhD from the Electrical and Computer Engineering Department in 2021. His doctoral research, which was honored with the Dissertation Research Award for the 2020–2021 academic year, focused on pioneering solutions for modern power systems and smart grids. Specifically, he developed innovative software for Ameren Electric Company, aimed at optimizing distribution system planning with an emphasis on distributed energy resources (DERs) to boost the performance of electric distribution networks. His PhD dissertation emphasized the application of machine/deep learning algorithms for load forecasting, alongside exploring cyber‐security and false data detection methods within power systems.

Before embarking on his PhD, Ali joined MAPNA Electric and Control Engineering and Manufacturing Company, Iran's premier power company, as a Power Systems Analysis Engineer in 2010. His roles expanded to include Energy Management System and Supervisory Control and Data Acquisition (SCADA) engineer, as well as Commissioning Supervisor in substation and power plant projects in collaboration with ABB and SIEMENS companies. His innovative work in the realm of real‐time simulators culminated in the registration of a patent for a real‐time islanded simulator for industrial power plants.

Ali's research interests are extensive, covering the application of artificial intelligence, deep learning, big data, information theory techniques in modern power systems and smart grids, distributed generation, renewable energies, and the operation and control of power systems. He has also explored the potential applications of real‐time simulators in enhancing power system operations.

His contributions to the field are substantial, with three books, two book chapters, a patent, and numerous papers in reputable power systems journals to his name. Ali is a valued peer reviewer for several prestigious academic journals, including IEEE Transactions on Power Delivery, IEEE Transactions on Power Electronics, and IEEE Access, among others. His work not only contributes to the academic community but also to the advancement of practical solutions for power systems and smart grid challenges.

As a Senior Data Scientist in the Information and Data Analytics (IDA), Data Science & Machine Learning department at Shell Energy, Ali applied his profound expertise to develop and implement advanced data science solutions for energy demand forecasting and electric vehicle charging station analysis. This role underscored his commitment to leveraging data analytics and machine learning to solve complex challenges in the energy sector, marking his transition from academia to a leading role in industry innovation. Continuing on this path, he holds the position of Staff Power Systems Machine Learning Engineer at Thinklabs AI, where he is dedicated to furthering his impact by addressing critical power systems challenges through state-of-the-art AI technologies.

Hamid Reza Baghaee, Faculty of Electrical and Computer Engineering (ECE) at Tarbiat Modares University (TMU), Tehran, Iran

Hamid Reza Baghaee (SM' 2008, M' 2017) received his PhD in Electrical Engineering from Amirkabir University of Technology (AUT) (Center of Excellence in Power Engineering and the most prestigious university of Iran in electrical power engineering) in 2017. From 2007 to 2017, he was a teaching and research assistant in the Department of Electrical Engineering at AUT. He is the author of three books, three published book chapters, 85 ISI‐ranked journal papers (mostly published in IEEE, IET, and Elsevier journals), 70 conference papers, and the owner of one registered patent. Additionally, he has presented 20 workshops and 15 invited talks at national and international conferences and scientific events. His book entitled Microgrids and Methods of Analysis was selected as the best book of the year in the power and energy industry of Iran by the technical committee of the Iran Ministry of Energy (MOE) in November 2021 and the winner of the Distinguished Author of the International Books Award in the AUT in December 2021. He has many HOT and HIGHLY‐CITED papers in his journal and conference papers, based on SciVal and Web of Science (WoS) statistics. His special fields of interest are micro‐ and smart grids, cyber‐physical power systems, power system cyber security and cyber‐resiliency, application of artificial intelligence (AI) and machine learning (ML) and big data analytics in power systems, real‐time simulation of power systems, distributed generation, and renewable energy resources, FACTS, HVDC and custom power devices, power electronics applications in power systems, Power Electronics‐Dominated Grids (PEDGs), power quality, real‐time simulation of power systems, and power system operation, control, monitoring, and protection.