Smart Cyber-Physical Power Systems, Volume 2 E-Book

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

Seitenzahl: 1009

Veröffentlichungsjahr: 2025

Beschreibung

A practical roadmap to the application of artificial intelligence and machine learning to power systems

In an era where digital technologies are revolutionizing every aspect of power systems, Smart Cyber-Physical Power Systems, Volume 2: Solutions from Emerging Technologies shifts focus to cutting-edge solutions for overcoming the challenges faced by cyber-physical power systems (CPSs). By leveraging emerging technologies, this volume explores how innovations like artificial intelligence, machine learning, blockchain, quantum computing, digital twins, and data analytics are reshaping the energy sector.

This volume delves into the application of AI and machine learning in power system optimization, protection, and forecasting. It also highlights the transformative role of blockchain in secure energy trading and digital twins in simulating real-time power system operations. Advanced big data techniques are presented for enhancing system planning, situational awareness, and stability, while quantum computing offers groundbreaking approaches to solving complex energy problems.

For professionals and researchers eager to harness cutting-edge technologies within smart power systems, Volume 2 proves indispensable. Filled with numerous illustrations, case studies, and technical insights, it offers forward-thinking solutions that foster a more efficient, secure, and resilient future for global energy systems, heralding a new era of innovation and transformation in cyber-physical power networks.

Welcome to the exploration of Smart Cyber-Physical Power Systems (CPPSs), where challenges are met with innovative solutions, and the future of energy is shaped by the paradigms of 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 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies

Acknowledgments

1 Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation

1.1 Introduction

1.2 Cyber-attacks on the State Variables of the Power System

1.3 Information Theory

1.4 Gray Level Transformation

1.5 Linear Transformation

1.6 Logarithmic Transformations

1.7 Power-Law Transformations

1.8 Simulation Results

1.9 Conclusion

References

2 Artificial Intelligence and Machine Learning Applications in Modern Power Systems*

2.1 The Need for AI/ML in Modern Power Systems

2.2 AL/ML Algorithms in Power System Applications

2.3 AI/ML-Based Applications in the Electricity Grid

2.4 Future of AI/ML in Power Systems

References

Note

3 Physics-Informed Deep Reinforcement Learning-Based Control in Power Systems

3.1 Introduction

3.2 Overview of RL/DRL

3.3 Grid Control Perspectives

3.4 Importance of Physics-Informed DRL in Grid Control and Different Methods

3.5 Grid Control Applications of Physics-Informed DRL

3.6 Discussion and Research Directions

3.7 Conclusions

References

Note

4 Digital Twin Approach Toward Modern Power Systems

4.1 Digital Twin Concept

4.2 Digital Twin: The Convergence of Recent Technologies

4.3 Cyber-Physical System and Digital Twin

4.4 Novelties and Suggestions of Digital Twin to Smart Grid Subsystems

4.5 Conclusions

References

5 Application of AI and Machine Learning Algorithms in Power System State Estimation

5.1 Introduction

5.2 Motivation and Theoretical Background

5.3 DNN Architecture for DSSE and TI

5.4 SMD Measurement Selection for DSSE and TI

5.5 Smart Meter Data Consideration

5.6 Implementation of DNN-Based TI and DSSE

5.7 Conclusion

Acknowledgment

Appendix

References

Note

6 ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings

6.1 Introduction

6.2 Problem Formulation

6.3 Application of AI in Energy Management of Smart Homes

6.4 Simulation and Results

6.5 Conclusion

References

7 Protection Challenges and Solutions in Power Grids by AI/Machine Learning

7.1 Introduction

7.2 Zonal Setting-Less Modular Protection Using ML

7.3 Traveling Wave Protection of DC Microgrids Using ML

7.4 Conclusion

References

8 Deep and Reinforcement Learning for Active Distribution Network Protection

8.1 Introduction and Motivation

8.2 Problem Statement

8.3 Proposed Methodology for Fault Detection and Classification

8.4 Case Study and Implementation

8.5 Results and Discussion

8.6 Hardware in-the-Loop Testing

8.7 Conclusion

Acknowledgments

References

9 Handling and Application of Big Data in Modern Power Systems for Planning, Operation, and Control Processes

9.1 Introduction

9.2 Intelligent Modeling and Its Applications

9.3 Case Study

9.4 Conclusions

Acknowledgment

References

10 Handling and Application of Big Data in Modern Power Systems for Situational Awareness and Operation

10.1 Introduction

10.2 Challenges for Using Big Data Techniques in Smart Grids

10.3 Solutions Using Big Data Techniques for Smart Grid Situational Awareness

10.4 Applications of Big Data Techniques for Smart Grid Operation

10.5 Numerical Results

10.6 Concluding

References

11 Data-Driven Methods in Modern Power System Stability and Security

11.1 Introduction

11.2 Data-Driven Wide-Area Damping Control

11.3 Data-Driven Wide-Area Voltage Control

11.4 Data-Driven Inertia Estimation for Frequency Control

11.5 A Data-Driven Polynomial Chaos Expansion Method for Available Transfer Capability Assessment

11.6 Using PCE to Assess the Ramping Support Capability of a Microgrid

References

12 Application of Quantum Computing for Power Systems

12.1 Quantum Computing in Renewable Energy Systems

12.2 Quantum Approximate Optimization Algorithm for Renewable Energy Systems

12.3 Typical Applications of Quantum Computing

Acknowledgment

References

13 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 1 Principles and Concepts

13.1 Introduction

13.2 Principles and Concepts of Building Hourly Energy Consumption Forecasting

13.3 Conclusion

References

14 High-Resolution Building-Level Load Forecasting Employing Convolutional Neural Networks (CNNs) and Cloud Computing Techniques: Part 2 Simulation and Experimental Results

14.1 Introduction

14.2 Case Study and Result of Building Hourly Energy Consumption Forecasting

14.3 Building Occupancy Measurement

14.4 Conclusion

14.A Appendix

15 PV Energy Forecasting Applying Machine Learning Methods Targeting Energy Trading Systems

15.1 Introduction

15.2 PV Energy Forecasting

15.3 Conclusion

References

16 An Intelligent Reinforcement-Learning-Based Load Shedding to Prevent Voltage Instability

16.1 Introduction

16.2 Stability Control Methods

16.3 Characteristics of Optimal Stability Controller

16.4 Utilizing Reinforcement Learning for Enhancing Voltage Stability

16.5 Taxonomy of RL

16.6 Proposed Algorithm

16.7 Reinforcement Learning Algorithm Components

16.8 Algorithm Implementation Process

16.9 Simulations and Results

16.10 Scenario I

16.11 Scenario II

16.12 Scenario III

16.13 Conclusion

References

17 Deep Learning Techniques for Solving Optimal Power Flow Problems

17.1 Introduction

17.2 Sensitivity-Informed Learning for OPF

17.3 Deep Learning for Stochastic OPF

17.4 Conclusions

References

18 Research on Intelligent Prediction of Spatial–Temporal Dynamic Frequency Response and Performance Evaluation

18.1 Introduction

18.2 Modeling Process and Evaluation Method

18.3 Case Study

18.4 Conclusion

References

Note

19 Emerging Technologies and Future Trends in Cyber-Physical Power Systems: Toward a New Era of Innovations

19.1 I

ntroduction

19.2 Paradigm Shifts in Power Transmission and Management

19.3 Innovations in Electric Mobility and Sustainable Transportation

19.4 Digital Transformation and Technological Convergence in Cyber-Physical Power Systems

19.5 Cyber-Physical Systems Enhancing Societal Well-Being

19.6 Toward a Decentralized and Automated Future

19.7 Overcoming Challenges with Advanced Technologies

19.8 Revolutionizing Modern Power Systems with Real-Time Simulators

19.9 Emerging Trends Shaping the Future Energy Landscape

19.10 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Results for false data injection attack (Case 108 measurement—Meth...

Table 1.2 Results for false data injection attack (Case 108 measurement–Meth...

Table 1.3 Results for false data injection attack (Case 108 measurement—Meth...

Chapter 5

Table 5.1 Statistical test results for Pecan Street data.

Table 5.2 Hyperparameters for DNN-based DSSE with three SMDs for System S1....

Table 5.3 Comparing the performance of DNN-based DSSE with LSE for System S1...

Table 5.4 Switch configurations for different topologies.

Table 5.5 Hyperparameters for DNN-based TI and DSSE with six SMDs for System...

Table 5.6 Comparing the performance of DNN-based DSSE with LSE for System S2...

Table 5.7 Comparison of DSSE and TI performance for integrated and non-integ...

Table 5.8 DNN-based DSSE performance with one SMD at feeder-head and 0.05% n...

Chapter 6

Table 6.1 Price, temperature, and must-run load data.

Chapter 7

Table 7.1 List of circuit configurations.

Table 7.2 IBRs' specifications.

Table 7.3 Circuit topology estimation accuracy at different LAMP units.

Table 7.4 Zone classification accuracy at different LAMP units in Configurat...

Table 7.5 Zone classification accuracy at different LAMP units in Configurat...

Table 7.6 Zone classification accuracy at different LAMP units in Configurat...

Table 7.7 Zone classification accuracy at different LAMP units in Configurat...

Table 7.8 Fault location estimation errors in the mesh DC microgrid system....

Chapter 8

Table 8.1 Fault impedance calculations.

Table 8.2 RES data.

Table 8.3 DRL DQN model hyperparameters.

Table 8.4 Forward faults classification models accuracy.

Table 8.5 Forward faults detection models accuracy.

Table 8.6 Reverse faults classification models accuracy.

Table 8.7 Reverse faults detection models accuracy.

Table 8.8 Fault detection models tripping time.

Table 8.9 Forward faults sample to sample fault detection models accuracy.

Table 8.10 Faults classification models overall accuracy.

Table 8.11 Faults detection models overall accuracy.

Chapter 9

Table 9.1 Data-driven building model inputs and outputs per meter.

Table 9.2 DNN architecture per transformer or .

Chapter 10

Table 10.1 Scenario 1: Proportions of absolute values of ISFs (or ISF estimat...

Table 10.2 Scenario 1: Comparison results of computing time on one core.

Table 10.3 Scenario 1 (9241-bus): Results of estimated spectral radius

k

and...

Table 10.4 Scenario 1 (9241-bus): Results of iteration numbers on multiple c...

Table 10.5 Scenario 1 (9241-bus): Comparison robustness results on

a

3433–666

...

Table 10.6 Scenario 7 (300-bus): Comparison results of transmission line con...

Table 10.7 Scenario 7 (300-bus): Comparison results of constraint number and...

Table 10.8 Scenario 8 (9241-bus): Comparison results of transmission line co...

Table 10.9 Scenario 8 (9241-bus): Comparison results of constraint number an...

Table 10.10 Scenario 9 (9241-bus): Vulnerability results of PA

M

-Lasso-based ...

Chapter 11

Table 11.1 Damping ratios of an electromechanical mode (Mode 2) under differ...

Table 11.2 Parameters of the VES with VIC.

Table 11.3 A comparison between the actual and estimated values of virtual i...

Table 11.4 A comparison between the actual and estimated values of SGs' iner...

Table 11.5 A comparison between the actual and estimated values of virtual i...

Table 11.6 A comparison between the actual and estimated values of SGs' iner...

Table 11.7 Comparison of the estimated statistics of the overall TTC by the ...

Table 11.8 The estimated TRM and resulting ATC (MW) for different confidence...

Table 11.9 Comparison of the estimated statistics of the overall TTC by the ...

Table 11.10 The estimated TRM and resulting ATC (MW) for confidence level at...

Table 11.11 Pre/postsmoothing RSC of the test MC in selected time slots.

Chapter 13

Table 13.1 Building characteristics.

Table 13.2 Building zone characteristics.

Table 13.3 Statistics of ambient temperature and humidity.

Table 13.4 HVAC setpoint schedule on weekdays and weekends.

Table 13.5 Building occupancy level schedule on weekdays and weekends.

Table 13.6 Result trained in three shallow NARX neural networks.

Table 13.7 Pros and cons of three tuning approaches.

Table 13.8 Activation function description.

Table 13.9 Comparison of common scalers.

Table 13.10 Tuning summary of RNN hyperparameters.

Table 13.11 Tuning summary of CNN hyperparameters.

Chapter 14

Table 14.1 Study cases definition.

Table 14.2 Cloud GPU server details.

Table 14.3 Dataset separation.

Table 14.4 Model performance of CNN, GRU, and LSTM.

Table 14.5 Final selected model parameter for CNN type 5.

Table 14.6 Selected model train/validation/test performance.

Table 14.7 Class schedule of rooms 6051 and 6053.

Table 14.8 Room 6051 occupancy research dataset separation.

Table 14.9 Room 6053 occupancy research dataset separation.

Table 14.10 Result trained in three NARX neural networks for room 6051.

Table 14.11 Result trained in three NARX neural networks for room 6053.

Table 14.A.1 Result of study case 0 with selected input parameters [“Ambient...

Table 14.A.2 Result of study case 1 with selected input parameters [“Weekday...

Table 14.A.3 Result of study case 2 with selected input parameters [“Weekday...

Table 14.A.4 Result of study case 3 with selected input parameters [“Weekday...

Table 14.A.5 Result of study case 4 with selected input parameters [“Weekday...

Table 14.A.6 Result of study case 5 with selected input parameters [“Weekday...

Table 14.A.7 Result of study case 6 with selected input parameters [“Weekday...

Chapter 15

Table 15.1 Weather cloud type classification.

Table 15.2 Solar irradiance forecast dataset separation.

Table 15.3 Solar irradiance forecast accuracy using the Levenberg-Marquardt ...

Table 15.4 Solar irradiance forecast accuracy using Bayesian regularization ...

Table 15.5 Solar irradiance forecast accuracy using the scaled conjugate gra...

Table 15.6 PV module parameters.

Table 15.7 Site Location Information.

Table 15.8 Next-hour PV energy forecast accuracy.

Chapter 16

Table 16.1 Summary of RL algorithms used in recent research work.

Chapter 17

Table 17.1 Average test MSE (in 10−

6

p.u.) and training time (in sec) after 1000...

Table 17.2 Average test MSE () and training time (in sec) for predicting MA...

Chapter 18

Table 18.1 List of input features.

Table 18.2 List of input features (proposed model).

Table 18.3 Detailed settings.

Table 18.4 The errors of different models in South Carolina 500-bus system....

Table 18.5 Correlation of different accuracy metrics (

f

).

Table 18.6 Original metrics in the South Carolina 500-bus system.

Table 18.7 Normalized metrics in the South Carolina 500-bus system.

Table 18.8 Comparison of different weighting methods.

Table 18.9 Geometric average weights.

Table 18.10 SMAPEs of different weighting methods.

Table 18.11 The score of different models.

Table 18.12 Final weights in this scenario.

Table 18.13 The score of different models (only accuracy metrics).

List of Illustrations

Chapter 1

Figure 1.1 False Data Injection Attack (FDIA) on the state variables by an a...

Figure 1.2 Map 11 areas of NYISO into IEEE 14-Bus System (New York Control A...

Figure 1.3 Map load profile of Bus 2 at IEEE test system to WEST zone in NYS...

Figure 1.4 Measurement variation histogram—Before cyber-attack. (a) January ...

Figure 1.5 Measurement variation histogram, December 2019—After cyber-attack...

Figure 1.6 Histogram of measurement variations, December 2019—After attack o...

Figure 1.7 November (normal) vs. December absolute distance (after cyber att...

Figure 1.8 November (normal) vs. December absolute distance (after cyber att...

Figure 1.9 November (normal) vs. December absolute distance (after cyber att...

Figure 1.10 November (normal) vs. December absolute distance (after cyber at...

Figure 1.11 November (normal) vs. December absolute distance (after cyber at...

Figure 1.12 November (normal) vs. December absolute distance (after cyber at...

Figure 1.13 Absolute distance index comparison for November (No FDIA) and De...

Figure 1.14 Entropy on November (no FDIA) vs. December (with FDIA), Method-1...

Figure 1.15 Mutual information on November (no FDIA) and December (with FDIA...

Figure 1.16 Mutual information on November (no FDIA) and December (with FDIA...

Figure 1.17 November (normal) vs. December relative entropy (after cyber att...

Figure 1.18 November (normal) vs. December relative entropy (after cyber att...

Figure 1.19 November (normal) vs. December relative entropy (after cyber att...

Figure 1.20 November (normal) vs. December relative entropy (after cyber att...

Figure 1.21 November (normal) vs. December relative entropy (after cyber att...

Figure 1.22 November (normal) vs. December relative entropy (after cyber att...

Figure 1.23 Comparing relative entropy index on November (no FDIA) and Decem...

Figure 1.24 Detection rate employing different gamma—Case 1.

Figure 1.25 Detection rate employing different gamma—Case 2.

Figure 1.26 Detection rate employing different gamma—Case 7.

Figure 1.27 Detection rate employing different gamma—Case 10.

Figure 1.28 Detection rate employing different C—Case 1.

Figure 1.29 Detection rate employing different C—Case 2.

Figure 1.30 Detection rate employing different C—Case 7.

Figure 1.31 Detection rate employing different C—Case 10.

Figure 1.32 False positive rate employing different and (normal case). (...

Chapter 2

Figure 2.1 Power systems and machine learning landscape.

Figure 2.2 Small-scale solar is changing hourly utility electricity demand i...

Figure 2.3 Root cause analyses overall architecture (see Source: Jain et al....

Figure 2.4 Feature importance through Shapley values: [top] a normal segment...

Figure 2.5 Primary drivers for price spikes in CAISO and ISONE (see Source: ...

Figure 2.6 System states as identified by the K-means for ISO-NE. [left] Hig...

Figure 2.7 Flowchart of detecting and classifying anomalies in PMU data usin...

Figure 2.8 PCA biplots showcasing the identified events utilizing various PM...

Figure 2.9 Comparison of model accuracy across various encoding methods.

Figure 2.10 Boxplots depicting the duration of outages, categorized accordin...

Chapter 4

Figure 4.1 Digital twin concept.

Figure 4.2 Applications of DT.

Figure 4.3 DT systematic structure.

Figure 4.4 A simultaneous view at CPS and DT technologies.

Chapter 5

Figure 5.1 Basic DNN architecture for DNN-based DSSE with dropout

Figure 5.2 IEEE 34-node system with three DG units added (System S1).

Figure 5.3 SCC matrix for (a) voltage phase angles (b) voltage phase magnitu...

Figure 5.4 Clustering results for SMD placement for DNN-based DSSE for Syste...

Figure 5.5 System S2 with SMD locations

Figure 5.6 Metering infrastructure at a house connected to a distribution tr...

Figure 5.7 An overview of the historical smart meter data preprocessing stra...

Figure 5.8 (a) Availability of measurements from houses for day d, and (b) A...

Figure 5.9 Obtaining the load profile for a house, by considering the averag...

Figure 5.10 (a) Smart meter measurements are missing for the first 400 hours...

Figure 5.11 (a) Smart meter measurements for a house are missing for the ent...

Figure 5.12 (a) Hourly load profile for a house is characterized by bad data...

Figure 5.13 (a) Average transformer loading is used to compute the hourly lo...

Figure 5.14 (a) Hourly load profile for a house, and (b) Skewed histogram of...

Figure 5.15 (a) The consistent low values for the first 500 hours of the low...

Figure 5.16 (a) Hourly load profile for a house for three months of Summer, ...

Figure 5.17 (a) A six-hour rolling average technique has been used to smooth...

Figure 5.18 Modified DNN architecture for unbalanced distribution networks...

Figure 5.19 Implementation of the proposed DNN-based TI and DSSE

Figure 5.20 (a) Voltage magnitude MAPE and (b) voltage phase angle MAE of ph...

Figure 5.21 State estimates for Phase C voltage angle along with true values...

Figure 5.22 Comparative study of DNN-based DSSE with and without fine-tuning...

Figure 5.23 System S3 with one SMD at feeder-head.

Figure 5.A.1 Sample distribution system.

Chapter 6

Figure 6.1 Overall structure of the ANN.

Figure 6.2 ANN-based scenario generation framework.

Figure 6.3 ANN-based scenario generation algorithm.

Figure 6.4 Real historical solar radiation data and the output of the traine...

Figure 6.5 Real historical solar radiation data and the output of the traine...

Figure 6.6 Generated scenarios for solar radiation.

Figure 6.7 The day-ahead and real-time prices in case 1.

Figure 6.8 The day-ahead and real-time prices in case 2.

Figure 6.9 The scheduled charging and discharging profile of the battery in ...

Figure 6.10 The scheduled charging and discharging profile of the EV in the ...

Figure 6.11 Scheduled amount and different scenarios for PV output in case 1...

Figure 6.12 The purchased power in the real-time stage for different scenari...

Figure 6.13 The sold power in the real-time stage for different scenarios in...

Figure 6.14 Scheduled amount and different scenarios for PV output in case 2...

Figure 6.15 The purchased power in the real-time stage for different scenari...

Figure 6.16 The sold power in the real-time stage for different scenarios in...

Chapter 7

Figure 7.1 (a) LAMP in a DS; (b) LAMP protection zones

Figure 7.2 LAMP architecture

Figure 7.3 Modified IEEE 123 node system that shows the LAMPs' locations and...

Figure 7.4 LAMPs' locations and the boundary of their Zone 1 in Configuratio...

Figure 7.5 LAMPs' locations and the boundary of their Zone 1 in Configuratio...

Figure 7.6 LAMPs' locations and the boundary of their Zone 1 in Configuratio...

Figure 7.7 Fault type classification results for LAMP R1

Figure 7.8 MRA block diagram

Figure 7.9 Sample Parseval energy curve for faults at different locations on...

Figure 7.10 Gaussian process regression model training procedure

Figure 7.11 Mesh DC microgrid system

Figure 7.12 Level-1 Parseval energy curve pattern comparison between (a) the...

Figure 7.13 Level 2 Parseval energy curve pattern comparison between (a) the...

Chapter 8

Figure 8.1 CNN-GRU architecture.

Figure 8.2 Overview of reinforcement learning.

Figure 8.3 Novel DRL model architecture.

Figure 8.4 Modified CIGRE MV network.

Figure 8.5 Grid mode tripping timing.

Figure 8.6 Overview of communication with Raspberry Pi.

Chapter 9

Figure 9.1 Framework for implementing data-driven models for power system ap...

Figure 9.2 Circuit model using real data from Avista.

Figure 9.3 Reduced-order circuit model of the CEF2 SEE microgrid.

Figure 9.4 Architecture of TESP.

Figure 9.5 GridLAB-D, weather, load shedding, and virtual battery agents in ...

Figure 9.6 Modeling a BESS, PV, or responsive building load as virtual batte...

Figure 9.7 Data-driven model of a building transformer's load or .

Figure 9.8 Training and validation losses for CCRS building and at 480 V...

Figure 9.9 Actual and predicted and per phase for 480 V loads of CCRS.

Figure 9.10 Actual and predicted and per phase for 208 V loads of HSB.

Figure 9.11 Market response with data-driven buildings in two August weekday...

Chapter 10

Figure 10.1 Implementation of the proposed PA

M

-Lasso framework.

Figure 10.2 Scenario 1: Comparison results of RE values on

a

221–223

, i...

Figure 10.3 Scenario 2: Comparison results of RE values on

a

221–223

, i...

Figure 10.4 Scenario 3: Comparison results of RE-

η

curves on

a

221–223

...

Figure 10.5 Scenario 3: Tracking results of RE values of the A

M

-Lasso on

a

22

...

Figure 10.6 Scenario 4: RES uncertainty impact on the necessary condition. 2...

Figure 10.7 Scenario 4: Comparison results of RE-(aSPD/aAPL) curves on

a

221–

...

Figure 10.8 Scenario 4: Comparison results of the probabilities of adverse s...

Figure 10.9 Scenario 4: Tracking results of RE values of the A

M

-Lasso on

a

22

...

Figure 10.10 Scenario 5: Topology change for illustration. Each orange point...

Figure 10.11 Scenario 5: Comparison tracking results of RE values of on

a

221

...

Figure 10.12 Scenario 5: Comparison results of RE-

N

curves on

a

221–223

Figure 10.13 Scenario 5: Tracking trajectory of the robust concomitant scale...

Figure 10.14 Scenario 5: Comparison results of a snapshot of all ISF estimat...

Figure 10.15 Scenario 5: Comparison results of worst-ever biased dominant IS...

Figure 10.16 Scenario 1 (9241-bus): Comparison results of computing time

T

C

...

Figure 10.17 Scenario 7 (300-bus): Comparison tracking results of LMP deviat...

Figure 10.18 Scenario 7 (300-bus): Comparison tracking results of the larges...

Figure 10.19 Scenario 7 (300-bus): Relationship between constraint reduction...

Figure 10.20 Scenario 8 (9241-bus): Comparison tracking results of LMP devia...

Figure 10.21 Scenario 8 (9241-bus): Comparison tracking results of largest F...

Figure 10.22 Scenario 9 (9241-bus): Vulnerability results of PA

M

-Lasso-based...

Figure 10.23 Scenario 9 (9241-bus): Vulnerability results of PA

M

-Lasso-based...

Chapter 11

Figure 11.1 The network reduced to generator and VES buses.

Figure 11.2 Objectives of the WADC strategy.

Figure 11.3 Flowchart of the data-driven WADC strategy.

Figure 11.4 The network topology of the IEEE 68-bus system with VES.

Figure 11.5 Trajectories of G1's rotor speed before and after adding the sma...

Figure 11.6 The actual and estimated values of the dynamic components. (a) V...

Figure 11.7 Comparison of eigenvalues with and without WADC. (a) Eigenvalues...

Figure 11.8 An illustration of the hierarchical voltage control scheme.

Figure 11.9 An illustration for the WAVC.

Figure 11.10 The purely data-driven WAVC.

Figure 11.11 The estimation results for matrix of the IEEE 39-bus system....

Figure 11.12 The voltage profile at bus 4 with three voltage-controlled buse...

Figure 11.13 The evolution of system inertia.

Figure 11.14 Virtual inertia control for a VES.

Figure 11.15 A typical structure of a PLL.

Figure 11.16 Flowchart of the proposed estimation strategy.

Figure 11.17 The network topology of the IEEE 68-bus system with three VESs ...

Figure 11.18 Comparisons of the actual values (in blue) and the estimated va...

Figure 11.19 A comparison of the actual values (in blue) and the estimated v...

Figure 11.20 A comparison of the actual values (in blue) and the estimated v...

Figure 11.21 A comparison of the frequency response of G12 for different vir...

Figure 11.22 Limits to total transfer capability.

Figure 11.23 ATC-related definitions in deterministic and probabilistic fram...

Figure 11.24 The PDF and CDF of the PTTC calculated by the MCS, DDSPCE, and ...

Figure 11.25 The PDF and CDF of the PTTC calculated by the MCS and the DDSPC...

Figure 11.26 Diagram of the test MG [103].

Figure 11.27 Comparison between the CDF of RSC obtained from MCS and DDSPCE ...

Figure 11.28 Distributions of pre/postsmoothing RSC in selected time slots. ...

Figure 11.29 Sobol's index for each random input and the dominant random var...

Chapter 12

Figure 12.1 A single qubit represented by Bloch sphere.

Figure 12.2 The system schematic of a -level QAOA.

Chapter 13

Figure 13.1 Peer-to-peer blockchain-based negawatt-hour trading with an anom...

Figure 13.2 Proposed deep-learning-based building hourly energy consumption ...

Figure 13.3 Energy Plus data preparation flow.

Figure 13.4 Negawatt-hour calculation from building hourly energy consumptio...

Figure 13.5 3-D model of DOE small office building.

Figure 13.6 Building zone plan view.

Figure 13.7 Spring day weather in Denver.

Figure 13.8 Summer day weather in Denver.

Figure 13.9 Autumn day weather in Denver.

Figure 13.10 Winter day weather in Denver.

Figure 13.11 Ambient temperature in Denver in one year.

Figure 13.12 Relative humidity in Denver in one year.

Figure 13.13 Ambient temperature, HVAC setpoint, and occupancy in one typica...

Figure 13.14 HVAC setpoint vs. occupancy for a summer week.

Figure 13.15 Building electricity energy consumption for a summer week.

Figure 13.16 Sample architecture of shallow neural network time series nonli...

Figure 13.17 Hourly building energy consumption forecast shallow neural netw...

Figure 13.18 Levenberg–Marquardt validation performance.

Figure 13.19 Levenberg–Marquardt training state.

Figure 13.20 Bayesian regularization validation performance.

Figure 13.21 Bayesian regularization training state.

Figure 13.22 Scaled conjugate gradient validation performance.

Figure 13.23 Scaled conjugate gradient training state.

Figure 13.24 Recurrent neural network (RNN) structure.

Figure 13.25 Long short-term memory (LSTM) structure.

Figure 13.26 Gated recurrent unit (GRU) structure.

Figure 13.27 Conventional layer architecture.

Figure 13.28 Max pooling layer architecture.

Figure 13.29 Fully connected layer (FC layer) architecture.

Figure 13.30 Flowchart of the forward propagation process.

Figure 13.31 Flowchart of the backpropagation process.

Figure 13.32 LeNet-5 convolutional neural network representation.

Figure 13.33 1D convolutional neural network architecture.

Figure 13.34 Dropout neural network model (a) network before dropout, (b) ne...

Figure 13.35 Early stopping method.

Figure 13.36 MAPE vs. hyperparameters for random search. (a) Units in Layer ...

Chapter 14

Figure 14.1 Cloud GPU running status.

Figure 14.2 Cloud GPU architecture with parallel computing in three partitio...

Figure 14.3 Deep neural network (DNN) performance estimate flow chart.

Figure 14.4 Dataset (train, validation, test).

Figure 14.5 Batch size tuning comparison result (RNN).

Figure 14.6 Epochs tuning comparison result (RNN).

Figure 14.7 No. of hidden layers tuning comparison result (RNN).

Figure 14.8 Dropout rate in hidden layer one tuning comparison result (RNN)....

Figure 14.9 Dropout rate in hidden layer two tuning comparison result (RNN)....

Figure 14.10 Dropout rate in hidden layer three tuning comparison result (RN...

Figure 14.11 Neuron no. in the hidden layer one comparison result (RNN).

Figure 14.12 Neuron no. in the hidden layer two comparison result (RNN).

Figure 14.13 Neuron no. in the hidden layer three tuning comparison result (...

Figure 14.14 Learning rate tuning comparison result (RNN).

Figure 14.15 Initializer tuning comparison result (RNN).

Figure 14.16 The activation function tuning comparison result (RNN).

Figure 14.17 L1 regularization tuning comparison result (RNN).

Figure 14.18 L2 regularization tuning comparison result (RNN).

Figure 14.19 L1 + L2 regularization tuning comparison result (RNN).

Figure 14.20 Conv-Pool-Conv-Pool-Conv-Pool-FC-FC architecture CNN with three...

Figure 14.21 Batch size tuning comparison result (CNN).

Figure 14.22 Epochs tuning comparison result (CNN).

Figure 14.23 No. of convolutional layers tuning comparison result (CNN).

Figure 14.24 Convolutional layer one filter number tuning comparison result ...

Figure 14.25 Convolutional layer two filter number tuning comparison result ...

Figure 14.26 Convolutional layer three filter number tuning comparison resul...

Figure 14.27 Filter size in convolutional layer one tuning comparison result...

Figure 14.28 Filter size in convolutional layer one, two tuning comparison r...

Figure 14.29 Filter size in convolutional layer one, two, and three tuning c...

Figure 14.30 Pool size in pooling layer one tuning comparison result (CNN)....

Figure 14.31 Pool size in pooling layer one, two tuning comparison result (C...

Figure 14.32 Pool size in pooling layers one, two, and three tuning comparis...

Figure 14.33 Batch normalization tuning comparison result (CNN).

Figure 14.34 Hidden layer dropout rate one tuning comparison result (CNN).

Figure 14.35 Hidden layer dropout rate two tuning comparison result (CNN).

Figure 14.36 Hidden layer dropout rate three tuning comparison result (CNN)....

Figure 14.37 FC layer no. tuning comparison results (CNN).

Figure 14.38 First FC layer neuron no. one tuning comparison result (CNN).

Figure 14.39 Two FC layer neuron no. tuning comparison result (CNN).

Figure 14.40 Dropout rate in fully connected layer one tuning comparison res...

Figure 14.41 Dropout rate in fully connected layer two tuning comparison res...

Figure 14.42 Learning rate tuning comparison result (CNN).

Figure 14.43 Initializer tuning comparison result (CNN).

Figure 14.44 The activation function in the hidden layer tuning comparison r...

Figure 14.45 The activation function in the last layer tuning comparison res...

Figure 14.46 L1 regularization tuning comparison result (CNN).

Figure 14.47 L2 regularization tuning comparison result (CNN).

Figure 14.48 L1 + L2 regularization tuning comparison result (CNN).

Figure 14.49 Overall model performance of CNN, GRU, and LSTM.

Figure 14.50 MAPE performance case study summary.

Figure 14.51 Selected model learning curve.

Figure 14.52 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.53 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.54 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.55 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.56 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.57 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.58 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.59 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.60 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.61 Actual building hourly energy consumption vs. predicted buildin...

Figure 14.62 Location of rooms 6051 and 6053.

Figure 14.63 One Netatmo CO

2

sensor is installed in each room.

Figure 14.64 Three-month experimental data.

Figure 14.65 Data from one typical study week.

Figure 14.66 Hourly building occupant number forecast neural network model....

Figure 14.67 Levenberg-Marquardt validation performance for room 6051.

Figure 14.68 Levenberg-Marquardt training state for room 6051.

Figure 14.69 Levenberg-Marquardt validation performance for room 6053.

Figure 14.70 Levenberg-Marquardt training state for room 6053.

Figure 14.71 Bayesian regularization validation performance for room 6051.

Figure 14.72 Bayesian regularization training state for room 6051.

Figure 14.73 Bayesian regularization validation performance for room 6053.

Figure 14.74 Bayesian regularization training state for room 6053.

Figure 14.75 Scaled conjugate gradient validation performance for room 6051....

Figure 14.76 Scaled conjugate gradient training state for room 6051.

Figure 14.77 Scaled conjugate gradient validation performance for room 6053....

Figure 14.78 Scaled conjugate gradient training state for room 6053.

Figure 14.79 The test result of room 6051: predicted occupant number vs. act...

Figure 14.80 Error histogram of the whole test period for room 6051.

Figure 14.81 Error histogram of the period when a course is in progress for ...

Figure 14.82 The test result of room 6053: predicted occupant number vs. act...

Figure 14.83 Error histogram of the whole test period for room 6053.

Figure 14.84 Error histogram of the period when a course is in progress for ...

Chapter 15

Figure 15.1 PV energy trading anomaly detection.

Figure 15.2 Ambient temperature in Denver.

Figure 15.3 Relative humidity in Denver.

Figure 15.4 Wind speed in Denver.

Figure 15.5 Pressure in Denver.

Figure 15.6 Diffused horizontal irradiance (DHI) in Denver.

Figure 15.7 Direct normal irradiance (DNI) in Denver.

Figure 15.8 Global horizontal irradiance (GHI) in Denver.

Figure 15.9 Solar zenith angle in Denver.

Figure 15.10 A typical winter day with a clear sky on January 2nd, 2012.

Figure 15.11 A typical spring day with a clear sky on April 1st, 2012.

Figure 15.12 A typical summer day with a clear sky on June 21st, 2012.

Figure 15.13 A typical autumn day with a clear sky on October 17th, 2012.

Figure 15.14 Summer day with cloudy sky vs. with clear sky.

Figure 15.15 Hourly solar irradiance forecast neural network model.

Figure 15.16 Levenberg-Marquardt validation performance of solar irradiance ...

Figure 15.17 Levenberg-Marquardt training state of the solar irradiance fore...

Figure 15.18 Bayesian regularization validation performance of solar irradia...

Figure 15.19 Bayesian regularization training state of the solar irradiance ...

Figure 15.20 Scaled conjugate gradient validation performance of solar irrad...

Figure 15.21 Scaled conjugate gradient training state of the solar irradianc...

Figure 15.22 Rooftop PV energy during one sunny day at Virginia Tech Arlingt...

Figure 15.23 Rooftop PV energy during one cloudy day at Virginia Tech Arling...

Figure 15.24 Actual GHI vs. predicted GHI in one week.

Figure 15.25 Actual DNI vs. predicted DNI in one week.

Figure 15.26 Actual DHI vs. predicted DHI in one week.

Figure 15.27 Actual GHI vs. predicted GHI in the test dataset.

Figure 15.28 Actual DNI vs. predicted DNI in the test dataset.

Figure 15.29 Actual DHI vs. predicted DHI in the test dataset.

Figure 15.30 PV modules deployed on the PERT at NREL, Golden, Colorado [7]....

Figure 15.31 PVLIB architecture

Figure 15.32 PVLIB input parameters.

Figure 15.33 Solar position.

Figure 15.34 Air mass.

Figure 15.35 Extra-terrestrial radiation.

Figure 15.36 The solar angle of incidence.

Figure 15.37 Plane of Array (POA) sky diffuse.

Figure 15.38 Plane of Array (POA) ground diffuse.

Figure 15.39 Plane of Array (POA) irradiance.

Figure 15.40 Cell temperature.

Figure 15.41 DC Module power.

Figure 15.42 Actual PV energy vs. predicted PV energy in one week.

Figure 15.43 Actual PV energy vs. predicted PV energy in the test period.

Chapter 16

Figure 16.1 Components of the database.

Figure 16.2 Flowchart of the proposed algorithm.

Figure 16.3 IEEE 39-bus test system.

Figure 16.4 Database of the presented algorithm on IEEE 39-bus test system....

Figure 16.5 Data collected from a scenario.

Figure 16.6 Rewards that collected from executed scenarios.

Figure 16.7 Performance of the presented RL algorithm in scenario I.

Figure 16.8 Converging of the RL algorithm in scenario I.

Figure 16.9 Performance of the presented RL algorithm in scenario II.

Figure 16.10 Converging of the RL algorithm in scenario II.

Figure 16.11 Comparison of voltage correction using RL algorithm and not usi...

Figure 16.12 Comparison of reward acquisition in using RL algorithm and not ...

Chapter 17

Figure 17.1 The (a) depicts the optimal generation dispatch for bus 5 as a...

Figure 17.2 A modified IEEE 37-bus feeder showing additional solar generator...

Figure 17.3 Training and testing errors achieved by P-DNN and SI-DNN over ep...

Figure 17.4 (a) Average training and testing errors for different training s...

Figure 17.5 Workflow for the training and testing (operation) phases of the ...

Figure 17.6 The IEEE 37-bus feeder used for the numerical tests. Node number...

Figure 17.7 Evaluation using training data. (a) Time-averaged losses during ...

Figure 17.8 Results for averaged formulation over

testing data

during the in...

Figure 17.9 Results for probabilistic formulations for the 12–4 pm window. V...

Chapter 18

Figure 18.1 Inertia center frequency and the frequencies of some generator n...

Figure 18.2 The structure of ASF.

Figure 18.3 The structure of LSTM block.

Figure 18.4 The modeling process of frequency prediction model.

Figure 18.5 The flow chart of proposed model.

Figure 18.6 Schematic diagram of input data sampling.

Figure 18.7 The structure of the proposed model.

Figure 18.8 The set of metrics.

Figure 18.9 The process of comprehensive evaluation.

Figure 18.10 The diagram of the South Carolina 500-bus system.

Figure 18.11 The results of different models in South Carolina 500-bus syste...

Chapter 19

Figure 19.1 Electrification—clean energy in the decarbonization transition o...

Figure 19.2 Dynamic wireless power transfer (WPT) for a receiver coil for ea...

Figure 19.3 Vehicle-to-grid (V2G) and grid-to-vehicle (G2V)

Figure 19.4 Industrial revolution.

Figure 19.5 Industry 4.0.

Figure 19.6 Energy Internet platform for transactive energy and demand respo...

Figure 19.7 Using blockchain technology to visualize renewable energy

Figure 19.8 The metaverse, a collective virtual shared space, virtual realit...

Figure 19.9 Remote training with virtual reality in future power systems.

Figure 19.10 Smart and connected community (S&CC) concept in future power sy...

Figure 19.11 Emerging technologies shaping future of smart cyber-physical po...

Figure 19.12 Hardware in the loop (HIL) test (

Figure 19.13 DevOps and MLOps life cycle.

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

About the Editors

List of Contributors

Foreword (John D. McDonald)

Foreword (Massoud Amin)

Preface for Volume 2: Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies

Acknowledgments

Begin Reading

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

Diomidis Spinellis

Smart Cyber-Physical Power Systems

Solutions from Emerging Technologies

Volume 2

Edited by

Ali Parizad

Virginia Tech

United States

Hamid Reza Baghaee

Tarbiat Modares University

Iran

Saifur Rahman

Virginia Tech

United States

Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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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.

Dr. Baghaee is also the winner of four national and international prizes, as the best dissertation award, from the Iran Scientific Organization of Smart Grids (ISOSG) in December 2017, the Iranian Energy Association (IEA) in February 2018, Amirkabir University of Technology in December 2018, and the IEEE Iran Section in May 2019 for his PhD dissertation. After pursuing his post-doctoral fellowship in AUT (October 2017–August 2019), in August 2019, he joined AUT as an Associate Research Professor in the Department of Electrical Engineering. He is the Project Coordinator of the AUT pilot microgrid project, one of the sub-projects of the Iran grand (National) Smart Grid Project. He has been a co-supervisor and consulting professor of more than 15 PhD and 20 MSc students since 2017. In 2022, he joined the Faculty of Electrical and Computer Engineering (ECE) at Tarbiat Modares University (TMU), Tehran, Iran, where he is now an Assistant Professor. In December 2023, has was selected as a distinguished researcher at TMU for the reputation and citations of his research among papers and patents. He also was a short-term scientist with CERN and ABB Switzerland. Besides, Dr. Baghaee is a member and Vice-Chairperson of the IEEE Iran Section Power Chapter (since 2022), a member and secretary-chair of the IEEE Iran Section Communication Committee (from 2020 to 2023), and a member of the IEEE, IEEE Smart Grid Community, IEEE Internet of Things Technical Community, IEEE Big Data Community, IEEE Smart Cities Community, and IEEE Sensors Council. Since August 2021, he has been elected as a member of the board and chairperson of the committee on publication and conferences at the ISOSG, the Vice-Chairperson and international representative of CIGRE Iran C6 working group on “Active distribution systems and distributed energy resources,” a member of the IEE Transmission and Distribution (TD) Committee, IEEE PES Transmission Sub-Committee and its working groups of Reliability impacts of Inverter-based Resources, Generation and Energy Storage Integration, Voltage Optimization, and Transmission Power System Switching, and also IEEE PES Subcommittee on Big Data Analytics for Power Systems, and IEEE PES Task Force on Application of Big Data Analytic on Transmission System Dynamic Security Assessment, IEEE PES Task Force on Resilient and Secure Large-Scale Energy Internet Systems (RSEI), and IEEE Task Force on Microgrid Design. He is also the reviewer of several IEEE, IET, and Elsevier journals, and Guest Editor of several special issues in IEEE, IET, and Elsevier, MDPI, and a scientific program committee member of several IEEE conferences. Since December 2020, he served as an Associate Editor and Energy Section Editor of the IET Journal of Engineering. He has also been selected as the best and outstanding reviewer of several journals, such as IEEE Transactions on Power Systems (Top 0.66 of reviewers, among more than 8000 reviewers in 2020), Elsevier Control Engineering Practice (in 2018, 2019, and 2020), Wiley International Transaction on Electrical Energy Systems in 2020, and the Pablon best and listed among top 1 of the reviewers in Engineering (in 2018) and both Engineering and Cross-Field (in 2019). He was selected as the Star Reviewer of the IEEE JESTPE and IEEE Power Electronics Society (PELS) in 2020, commemorated and presented during the IEEE ECCE 2021 conference in Vancouver, Canada. He has also been listed in 2020, 2021, and 2022 editions of the top 2% of scientists in the field of Energy, Electrical Engineering, and Enabling and Strategic Technologies according to the Science-Wide Citation Indicators (reported by Stanford University, USA), and mentioned among World's top 1% of Elite Scientists according to Web of Science (WoS) and Essential Science Indicators (ESI) ranking since 2020.

Prof. Saifur Rahman, Director, Virginia Tech Advanced Research Institute, Virginia, USA 2023 IEEE President and CEO

Professor Saifur Rahman is the founding director of the Advanced Research Institute at Virginia Tech, USA, where he is the Joseph R. Loring professor of electrical and computer engineering. He also directs the Center for Energy and the Global Environment at the University. He is a Life Fellow of the IEEE and an IEEE Millennium Medal winner. He was the 2023 IEEE President and CEO. He was the IEEE Power and Energy Society (PES) President in 2018 and 2019. He is the founding Editor-in-Chief of the IEEE Electrification Magazine and the IEEE Transactions on Sustainable Energy