Resiliency of Power Distribution Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 666

Veröffentlichungsjahr: 2023

Beschreibung

RESILIENCY OF POWER DISTRIBUTION SYSTEMS A revolutionary book covering the relevant concepts for resiliency-focused advancements of the distribution power grid Most resiliency and security guidelines for the power industry are focused on power transmission systems. As renewable energy and energy storage increasingly replace fossil-fuel-based power generation over the coming years, geospatially neighboring distributed energy resources will supply a majority of consumers and provide clean power through long transmission lines. These electric power distribution systems--the final stage in the delivery of electric power--carry electricity from the transmission system to individual consumers. New distributed devices will be essential to the grid to manage this variable power generation and enhance reliability and resilience while keeping electricity affordable as the world seeks solutions to climate change and threats from extreme events. In Resiliency of Power Distribution Systems, readers are provided with the tools to understand and enhance resiliency of distribution systems--and thereby, the entire power grid. In a shift from the present design and operation of the power system, the book is focused on improving the grid's ability to predict, adapt, and respond to all hazards and threats. This, then, acts as a guide to ensure that any incident can be mitigated and responded to promptly and adequately. It also highlights the most advanced and applicable methodologies and architecture frameworks that evaluate degradation, advance proactive action, and transform system behavior to maintain normal operation, under extreme operating conditions. Resiliency of Power Distribution Systems readers will also find: * Chapter organization that facilitates quick review of distribution fundamental and easy-but-thorough understanding of the importance of resiliency * Real-world case studies where resilient power systems could have prevented massive financial and energy losses * Frameworks to help mitigate cyber-physical attacks, strategize response on multiple timescales, and optimize operational efficiencies and priorities for the power grid Resiliency of Power Distribution Systems is a valuable reference for power system professionals including electrical engineers, utility operators, distribution system planners and engineers, and manufacturers, as well as members of the research community, energy market experts and policy makers, and graduate students on electrical engineering courses.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

About the Editors

List of Contributors

Foreword

Part I: Foundation

1 Concepts of Resiliency

1.1 Introduction

1.2 Resilience of Complex Systems

1.3 Related Terms and Definitions for Power System

1.4 Need for Grid Resiliency

1.5 Resiliency of Power Distribution Systems

1.6 Taxonomy of Resiliency

1.7 Tools for Enabling Resiliency

1.8 Summary

References

Note

2 Measuring Resiliency Using Integrated Decision‐Making Approach

2.1 Introduction

2.2 Feature to Measure Resiliency of Power Distribution System

2.3 Integrated Decision‐Making Approach

2.4 Algorithm to Enable Resilient Power Distribution System

2.5 Case Study

2.6 Conclusion

References

3 Resilience Indices Using Markov Modeling and Monte Carlo Simulation

3.1 Introduction

3.2 Cyber‐Physical Interdependencies in Power Distribution Systems

3.3 Resilience of Power Distribution Systems

3.4 Mathematical Model for Resilience Analysis

3.5 Simulation Results

3.6 Conclusions

References

4 Measuring and Enabling Resiliency for Microgrid Systems Against Cyber‐attacks

4.1 Introduction

4.2 Testbed Description for Validating Resilience Tools

4.3 Test System for Validating Cyber‐Physical Resiliency

4.4 Dependencies Between Cyber and Physical Systems

4.5 Cyber‐Attack Implementations

4.6 Cyber‐Physical Resiliency Metrics and Tools – CyPhyR and CP‐SAM

4.7 Case Studies for Cyber‐Physical Resiliency Analysis

4.8 Summary

References

5 Resilience Indicators for Electric Power Distribution Systems

5.1 Introduction

5.2 Motivations for Resilience Indicators

5.3 Decision Analysis Methodologies for Resilience Indicators

5.4 An Application to Electric Power Distribution Systems

5.5 Future Work

5.6 Conclusion

Acknowledgments

References

6 Quantitative Model and Metrics for Distribution System Resiliency

6.1 Power Grids Performance in Recent Natural Disasters

6.2 Resilience Modeling Framework

6.3 Quantitative Resilience Metrics for Electric Power Distribution Grids

References

7 Frameworks for Analyzing Resilience

7.1 Metrics

7.2 Risk Analysis Modeling

7.3 Power System Monte Carlo Analysis

7.4 Summary

References

Part II: Enabling Resiliency

8 Resiliency‐Driven Distribution Network Automation and Restoration

8.1 Optimal Placement of Remote‐Controlled Switches for Restoration Capability Enhancement

8.2 Resiliency‐Driven Distribution System Restoration Using Microgrids

8.3 Service Restoration Using DGs in a Secondary Network

8.4 Summary

Acknowledgment

R

eferences

9 Improving the Electricity Network Resilience by Optimizing the Power Grid

9.1 Introduction

9.2 Microgrid Evaluation Tool

9.3 Overall Grid Modeling Tool

9.4 Conclusions

References

10 Robust Cyber Infrastructure for Cyber Attack Enabling Resilient Distribution System

10.1 Introduction

10.2 Cyber Security Analysis of Distribution System

10.3 Cyber Attack Scenarios for Distribution System

10.4 Designing Cyber Attack Resilient Distribution System

10.5 Mitigation Methods Against Cyber Attacks

10.6 Summary

References

11 A Hierarchical Control Architecture for Resilient Operation of Distribution Grids

11.1 Resilient Control Theory

11.2 A Hierarchical Control Strategy

11.3 Resilient Operation Using the Hierarchical Architecture

11.4 Conclusions

References

Biographies

Note

Part III: Real‐World Case Studies

12 A Resilience Framework Against Wildfire

12.1 Introduction

12.2 The Hazard of Wildfires

12.3 Modeling and Quantifying the Resilience of Distribution Networks to Wildfires

12.4 Case Study Application

12.5 Summary

Nomenclature

Parameters

Variables

References

13 Super Microgrid in Inner Mongolia

13.1 Definition and Significance of the Super Microgrid

13.2 Applying Load Control Technology to the Super Microgrid

13.3 Research on Load–Frequency Control Methods for the Super Microgrid

13.4 Implementation of the Load–Frequency Control Method for the Super Microgrid

13.5 Operation of the Super Microgrid

13.6 Summary

References

14 Technology and Policy Requirements to Deliver Resiliency to Power System Networks

14.1 Introduction

14.2 A Broad Perspective on the Need to Apply Technology

14.3 Use of Microgrids to Improve Resiliency Response

14.4 Use of Drones to Perform Advanced Damage Assessment

14.5 Case Study: Lessons Learned and Forgotten. The North American Hurricane Experience

14.6 Bringing it All Together – Policy and Practice

14.7 Conclusions

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Key differences between resiliency and reliability, security, and...

Table 1.2 Summary of taxonomy of resilient power systems.

Chapter 2

Table 2.1 DG and load data.

Table 2.2 Unique network configurations with corresponding similar PNs and ...

Table 2.3 Network metrics for feasible networks (FNs).

Table 2.4 Pairwise comparative weights.

Table 2.5 Operational contingency scenario results.

Table 2.6 PCWL corresponding to four PNs.

Table 2.7 PoA and PF for each PN.

Table 2.8 Computation of ACPD for FN1.

Table 2.9 Choquet integral values with varying interaction degree.

Table 2.10 Resiliency metric ranking table for critical load restoration.

Chapter 3

Table 3.1 Nomenclature.

Table 3.2 Buses in physical subsystem.

Table 3.3 Power lines in physical subsystem.

Table 3.4 Characteristics of distributed generation units.

Table 3.5 Topology of cyber subsystem.

Table 3.6 Parameters for resilience analysis.

Table 3.7 Probability distribution over weather conditions.

Table 3.8 Component failures in extreme weather condition.

Table 3.9 Power output mismatch due to cyber subsystem failures.

Table 3.10 Restoration schedule (in time steps) for the power distribution ...

Table 3.11 Resilience evaluation.

Chapter 4

Table 4.1 Factors affecting microgrid resiliency and solution approach.

Chapter 5

Table 5.1 Major resilience components.

Table 5.2 Major level 1 and level 2 components constituting the RMI

a)

.

Table 5.3 Resilience attributes for the electric power distribution system

a

...

Chapter 8

Table 8.1 Data of generation and load in MGs.

Table 8.2 Restoration trees.

Table 8.3 Load groups.

Table 8.4 The detailed information for DGs.

Table 8.5 Node voltages in the SN.

Chapter 9

Table 9.1 Summary of building characteristics.

Table 9.2 Impact of the system parameters on the autonomy factor.

Table 9.3 Results of single failure tests.

Table 9.4 Distributions of DG, ESS, and RES installed.

Chapter 11

Table 11.1 Picked up loads, VR, capacitor bank position during the self‐hea...

Chapter 12

Table 12.1 Comparison of typical power system outages and extreme events.

Table 12.2 Comparison of resilience and reliability.

Table 12.3 Location and capacity of distribution system components.

Table 12.4 Parameters of microturbines.

Table 12.5 Parameters of energy storage systems.

Table 12.6 Revenues and costs for cases I, II, and III...

Chapter 13

Table 13.1 Power sources and loads of the super microgrid.

Table 13.2 Field test results of DC‐side voltage and current.

Table 13.3 Active power–voltage regulation capacity of the electrolytic alu...

List of Illustrations

Chapter 1

Figure 1.1 Interdependent complex network infrastructures in the smart grid....

Figure 1.2 Summary of power systems vulnerabilities.

Figure 1.3 Number of customers impacted by the Texas Freeze of 2021. The num...

Figure 1.4 Resiliency taxonomy.

Figure 1.5 Operational and planning resiliency.

Figure 1.6 Development and adoption cycle of new technologies in power syste...

Figure 1.7 Resiliency pyramids.

Figure 1.8 Resiliency Y‐chart.

Figure 1.9 Example of data‐driven resiliency, using smart vegetation trackin...

Chapter 2

Figure 2.1 Algorithm of resiliency quantification using CI.

Figure 2.2 Flowchart for finding FN and their resiliency computation.

Figure 2.3 Test system: two proximal CERTS microgrids.

Figure 2.4 Modifications made to standard IEEE 123 node distribution system....

Figure 2.5 Voltage profiles of all nodes over a 24 hour load‐profile.

Figure 2.6 Diurnal variation of number of available paths for load restorati...

Figure 2.7 Different risk regions in New York metropolitan area.

Chapter 3

Figure 3.1 Cyber‐physical power distribution system.

Figure 3.2 Close‐loop control of power distribution systems.

Figure 3.3 Disruptions due to cyber‐physical interdependencies. (a) RTUs los...

Figure 3.4 Evolution of operation performance following an extreme weather e...

Figure 3.5 Evolution of normalized operation performance.

Figure 3.6 Framework for resilience analysis.

Figure 3.7 Markov chain for simulating the extreme weather event.

Figure 3.8 One‐line diagram of a distribution feeder.

Figure 3.9 Flowchart for a cyber‐physical restoration.

Figure 3.10 Flowchart for the evaluation of operation performance.

Figure 3.11 Power demand evolution.

Figure 3.12 Markov chain for the extreme weather event.

Figure 3.13 Effects of adverse weather on the component survival probability...

Figure 3.14 Second‐order cone relaxation error.

Figure 3.15 Voltage magnitude deviation.

Figure 3.16 Operating condition after the extreme weather event. (a) Actual ...

Figure 3.17 Evolution of the normalized operation performance.

Figure 3.18 Convergence of Monte Carlo simulation.

Chapter 4

Figure 4.1 Cyber‐physical testbed for validating resiliency tools.

Figure 4.2 Modified CERTS microgrid system.

Figure 4.3 Multiple microgrid system.

Figure 4.4 Communication model for test system.

Figure 4.5 Cyber‐physical resilience impact and analysis.

Figure 4.6 Algorithm for (a) planning phase and (b) operation phase.

Figure 4.7 Overview of physical resiliency quantification.

Figure 4.8 Hasse diagram of POSET for test system.

Figure 4.9 Cyber physical modeling for microgrid security assessment to enab...

Figure 4.10 CP‐SAM computation.

Figure 4.11 CAIP for nodes.

Figure 4.12 CAIP for switches.

Figure 4.13 CIS vs. time.

Figure 4.14 Fault tree for a power system node failure.

Figure 4.15 CP‐SAM for various cases.

Chapter 5

Figure 5.1 Development of system resilience index.

Figure 5.2 Resilience components contributing to preparedness.

Figure 5.3 Resilience components contributing to responses capabilities.

Figure 5.4 Resilience components contributing to recovery mechanisms.

Chapter 6

Figure 6.1 Power lines damaged by Hurricane Maria in Puerto Rico.

Figure 6.2 A coastal area near High Island, Texas, after Hurricane Ike, with...

Figure 6.3 High Island, Texas after Hurricane Ike, showing little damage to ...

Figure 6.4 A typical area inland in Monmouth County, New Jersey where more t...

Figure 6.5 Portable transformer deployed in Galveston Island after Hurricane...

Figure 6.6 A large number of trucks deployed to restore electric power after...

Figure 6.7 A substation with its communications microwave antenna.

Figure 6.8 Model of an electric power grid based on its three domains.

Figure 6.9 Two recently installed poles of a new 66 kV emergency line built ...

Figure 6.10 Interactions among electric power, water distribution, and econo...

Figure 6.11 Conceptualization of resilience based on the resilience triangle...

Figure 6.12 Percentage of customers without power in four Texas counties aft...

Figure 6.13 Quality curve similar to that representing power grid performanc...

Figure 6.14 Fragility curve.

Chapter 7

Figure 7.1 System performance curve as a function of time.

Figure 7.2 Example of actual system performance

P

and the target level of sy...

Figure 7.3 Performance of a section of the Japanese grid during and after th...

Figure 7.4 Tohoku earthquake performance including metrics.

Figure 7.5 Tohoku earthquake electrical system performance

P

with example ta...

Figure 7.6 Example of a vulnerability function, showing percent damage to th...

Figure 7.7 Live‐tank circuit breakers.

Figure 7.8 Example fragility functions showing probability of damage state m...

Figure 7.9 Risk analysis modeling.

Figure 7.10 Alternative risk analysis modeling framework.

Figure 7.11 Example risk analysis framework as a discrete Bayesian network....

Figure 7.12 Circuit breaker example discrete probability network.

Figure 7.13 Estimated history of large (magnitude ∼8) and very large (magnit...

Figure 7.14 Estimated bedrock Peak Ground Acceleration (PGA) for the Oregon ...

Figure 7.15 Estimated bedrock Peak Ground Acceleration (PGA) for the Oregon ...

Figure 7.16 Fragility functions showing differential probabilities.

Figure 7.17 Sample 4 bus power system. There are two geographic regions: coa...

Figure 7.18 Flowchart for Monte Carlo analysis of power systems in conjuncti...

Chapter 8

Figure 8.1 One‐line diagram of the modified 32‐node test system.

Figure 8.2 A simplified MG model.

Figure 8.3 Topology after restoration.

Figure 8.4 Frequency variations of MG 3.

Figure 8.5 The restoration strategy for Pullman‐WSU system.

Figure 8.6 (a) System frequency and (b) generator voltages.

Figure 8.7 The typical topology of a SN distribution system.

Figure 8.8 Topology of modified IEEE 342‐node test system.

Figure 8.9 Generation schedule of DGs.

Figure 8.10 Voltage profile in the SN.

Figure 8.11 Inrush current by energizing transformers on feeder 1.

Figure 8.12 Synchronization of DG4 with the network: (a) frequency and (b) p...

Chapter 9

Figure 9.1 Microgrid planning system overview.

Figure 9.2 Main components of the MGE tool: the building controllers (EMS)....

Figure 9.3 Consumption profile examples: residential house (left) and small ...

Figure 9.4 Left: Selected overall grid topology. Right: Loads in the adapted...

Figure 9.5 Node 3, Microgrid net consumption and normal simulation.

Figure 9.6 Node 3, Microgrid net consumption and outage simulation (six hour...

Figure 9.7 Grid configuration.

Figure 9.8 Plots for: [Case 1: Top left: The energy generation and its balan...

Figure 9.9 Top panel: ESS operation state. Bottom panel: Resilience metric d...

Chapter 10

Figure 10.1 The cyber‐physical system structure of a typical distribution au...

Figure 10.2 Overview of ICT network diagram and security threats for distrib...

Figure 10.3 Man‐in‐the‐middle attack in distribution automation system. (a) ...

Figure 10.4 Protection coordination between OCR and recloser.

Figure 10.5 A structure of centralized distribution automation system.

Figure 10.6 Detecting the fault location using fault indicator information....

Figure 10.7 Fault detection and clearance by recloser.

Figure 10.8 Fuzzy‐based restoration for distribution automation system.

Figure 10.9 Multi‐agent‐based distribution automation system.

Figure 10.10 Authentication process for the communication between agents.

Figure 10.11 An operation of centralized and distributed (decentralized) dis...

Figure 10.12 Fault occurring in a decentralized distribution automation syst...

Figure 10.13 Fault isolation and initial restoration process using multi‐age...

Figure 10.14 Restoration process using multi‐agents.

Figure 10.15 Tie breaker reallocation due to load balancing. (a) Before. (b)...

Figure 10.16 Multi‐agent‐based protection coordination.

Figure 10.17 Multi‐agent‐based protection coordination and FDIR process.

Figure 10.18 Protection coordination update for the newly changed system.

Figure 10.19 Mitigation method for faulted agent in the multi‐agent‐based DA...

Figure 10.20 Bus voltages with normal system operation.

Figure 10.21 System condition estimation method for control command (Switch ...

Figure 10.22 System condition estimation method for control command (Switch ...

Figure 10.23 Mitigation method for the man‐in‐the‐middle attack.

Figure 10.24 Mitigation method for configuration change attack. (a) Before t...

Figure 10.25 Proposed mitigation method against DoS attack during FDIR proce...

Chapter 11

Figure 11.1 Feasible and nonfeasible transitions of a resilient and a nonres...

Figure 11.2 A hierarchical resilient control architecture with the top layer...

Figure 11.3 Modified IEEE 34 node test feeder.

Figure 11.4 Typical home data (left) and global horizontal irradiance (right...

Figure 11.5 (a) Total active power contribution of DGs; (b) total reactive p...

Figure 11.6 (a) voltage at Node 22; (b) voltage at Node 27; (c) voltage at N...

Chapter 12

Figure 12.1 Number of forest fires in Greece for the years 2000–2021.

Figure 12.2 Burned forest area in Greece for the years 2000–2021.

Figure 12.3 Damages to the distribution system in Peloponnese due to the wil...

Figure 12.4 A framework of the proposed approach.

Figure 12.5 Single‐line diagram of the simulated distribution system and wil...

Figure 12.6 Mean value of active demand and solar radiation.

Figure 12.7 Mean value of wind speed at

L

WT

and wind direction.

Figure 12.8 Ambient temperature and power exchange price.

Figure 12.9 Distance between fire and lines 1–2 in the reduced number of sce...

Figure 12.10 Conductor 1–2 heat gain rate from the fire in the reduced numbe...

Figure 12.11 Expected energy exchange with the upstream system for Cases I a...

Figure 12.12 Expected discharging power and SOC of ESS at bus 19 for Case I....

Figure 12.13 Expected discharging power and SOC of ESS at bus 19 for Case II...

Figure 12.14 Expected load shedding for cases I and II.

Figure 12.15 Bus 33 voltage in the reduced number of scenarios for case II....

Figure 12.16 Objective function value (social cost) and load shedding cost c...

Figure 12.17 Spatial expected load shedding for Case II.

Figure 12.18 Spatial expected load shedding for Case IV.

Chapter 13

Figure 13.1 Structure diagram of the super microgrid system.

Figure 13.2 Schematic diagram of the production process of electrolytic alum...

Figure 13.3 Equivalent circuit of the electrolytic aluminum load.

Figure 13.4 Structure of the saturation reactor.

Figure 13.5 Structure of the controller for the saturation reactor to partic...

Figure 13.6 Active power–voltage relationship of the electrolytic aluminum l...

Figure 13.7 Block diagram of the closed‐loop control excitation system to in...

Figure 13.8 Block diagram of the SFR equivalent model considering closed‐loo...

Figure 13.9 Curves of corresponding frequency response of different load dam...

Figure 13.10 The overall architecture of the wide‐area information‐based con...

Figure 13.11 Physical installation of the NCU unit in the excitation regulat...

Figure 13.12 Data flow of control instructions in the downlink channel of th...

Figure 13.13 The operation interface of the wide‐area information‐based supe...

Chapter 14

Figure 14.1 Observed outages to the bulk electric system, 1992–2012.

Figure 14.2 Architecture of the next‐generation microgrid.

Guide

Cover

Table of Contents

Title Page

Copyright

About the Editors

List of Contributors

Foreword

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Resiliency of Power Distribution Systems

Edited byAnurag K. Srivastava, Chen‐Ching Liu, and Sayonsom Chanda

This edition first published 2024

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Library of Congress Cataloging‐in‐Publication Data

Names: Srivastava, Anurag K., editor. | Liu, Chen‐Ching, editor. | Chanda,   Sayonsom, editor.

Title: Resiliency of power distribution systems / edited by Anurag K.   Srivastava, Chen‐Ching Liu, and Sayonsom Chanda.

Description: Hoboken, NJ : Wiley, 2024. | Includes bibliographical   references and index.

Identifiers: LCCN 2022044390 (print) | LCCN 2022044391 (ebook) | ISBN   9781119418672 (cloth) | ISBN 9781119418733 (adobe pdf) | ISBN   9781119418726 (epub)

Subjects: LCSH: Electric power distribution. | Electric power   systems–Reliability.

Classification: LCC TK3001 .R385 2024 (print) | LCC TK3001 (ebook) | DDC   621.319–dc23/eng/20221017

LC record available at https://lccn.loc.gov/2022044390

LC ebook record available at https://lccn.loc.gov/2022044391

Cover Design: Wiley

Cover Image: © Zhao jian kang/Shutterstock

About the Editors

Anurag K. Srivastava is a Raymond J. Lane Professor and Chairperson of the Computer Science and Electrical Engineering Department at the West Virginia University. He is also an adjunct professor at Washington State University and a senior scientist at Pacific Northwest National Lab. He received his PhD degree in electrical engineering from the Illinois Institute of Technology in 2005. His research interest includes data‐driven algorithms for power system operation and control including resiliency analysis. In past years, he has worked in a different capacity at the R'seau de transport d'électricit' in France; RWTH Aachen University in Germany; PEAK Reliability Coordinator, Idaho National Laboratory, PJM Interconnection, Schweitzer Engineering Lab (SEL), GE Grid Solutions, Massachusetts Institute of Technology and Mississippi State University in the USA; Indian Institute of Technology Kanpur in India; as well as at Asian Institute of Technology in Thailand. He is serving as co‐chair of the IEEE Power & Energy Society's (PES) tools for power grid resilience TF and a member of CIGRE C4.47/C2.25 Resilience WG. Dr. Srivastava is serving or served as an editor of the IEEE Transactions on Smart Grid, IEEE Transactions on Power Systems, IEEE Transactions on Industry Applications, and Elsevier Sustainable Computing. He is an IEEE Fellow and the author of more than 300 technical publications including a book on power system security and 3 patents.

Chen‐Ching Liu is an American Electric Power Professor and Director, Power and Energy Center, at Virginia Tech. During 1983–2017, he was on the faculty of the University of Washington, Iowa State University, University College Dublin (Ireland), and Washington State University. Dr. Liu is a leader in the areas of power system restoration, resiliency and microgrids in distribution systems, and cyber security of the power grid. Professor Liu received an IEEE Third Millennium Medal in 2000 and the Power and Energy Society Outstanding Power Engineering Educator Award in 2004. In 2013, Dr. Liu received a Doctor Honoris Causa from Polytechnic University of Bucharest, Romania. He chaired the IEEE Power and Energy Society Fellow Committee, Technical Committee on Power System Analysis, Computing and Economics, and Outstanding Power Engineering Educator Award Committee. Professor Liu is the US Representative on the CIGRE Study Committee D2, Information Systems, and Telecommunication. He was elected a Fellow of the IEEE, a Member of the Virginia Academy of Science, Engineering, and Medicine, and a Member of the US National Academy of Engineering.

Sayonsom Chanda is a senior researcher of Energy Systems Integration at the National Renewable Energy Laboratory in Golden, Colorado, USA. He is a tech evangelist for grid modernization, digital transformation in electric utilities, and citizen science. He is the founder of clean‐tech start‐up companies like Plexflo and Sync Energy. He has received his master's and PhD degree in Electrical Engineering from Washington State University. Earlier in his career, Sayonsom worked as a senior analyst at National Grid and a research engineer at Idaho National Laboratory. He has served as the VP of IEEE Young Professional Society. Dr. Chanda has three patents in cloud computing for the power grid.

List of Contributors

Anuradha M. Annaswamy

Department of Mechanical Engineering

Massachusetts Institute of Technology

Cambridge

MA

USA

Prabodh Bajpai

Department of Sustainable Energy Engineering

I.I.T.

Kanpur

U.P.

India

Sandford Bessler

Digital Safety & Security Department

AIT Austrian Institute of Technology GMBH

Vienna

Austria

Ted Brekken

School of Electrical Engineering and Computer Science

Oregon State University

Corvallis

OR

USA

KokKeong Chai

School of Electronic Engineering and Computer Sciences

Queen Mary University of London

London

UK

Sayonsom Chanda

National Renewable Energy Laboratory Golden

CO

USA

Yue Chen

School of Electronic Engineering and Computer Sciences

Queen Mary University of London

London

UK

Adam Hahn

Washington State University

Pullman

WA

USA

John (JD) Hammerly

The Glarus Group

Spokane

WA

USA

Nikos Hatziargyriou

Electric Energy Systems Laboratory

School of Electrical and Computer Engineering

National Technical University of Athens

Athens

Greece

Junho Hong

University of Michigan‐Dearborn

Department of Electrical and Computer Engineering

Dearborn

MI

USA

Oliver Jung

Digital Safety & Security Department

AIT Austrian Institute of Technology GMBH

Vienna

Austria

Hyung‐Seung Kim

Myongji University

Department of Electrical Engineering

Yongin

South Korea

Alexis Kwasinski

Department of Electrical and Computer Engineering

University of Pittsburgh

Pittsburgh

PA

USA

EngTseng Lau

School of Electronic Engineering and Computer Sciences

Queen Mary University of London

London

UK

Seung‐Jae Lee

Myongji University

Department of Electrical Engineering

Yongin

South Korea

Zhiyi Li

The College of Electrical Engineering

Zhejiang University

Hangzhou

Zhejiang

China

Siyang Liao

School of Electrical Engineering and Automation

Wuhan University

Wuhan

China

Chen‐Ching Liu

The Bradley Department of Electrical and Computer Engineering

Virginia Polytechnic Institute and State University

Blacksburg

VA

USA

Ahmad R. Malekpour

Department of Mechanical Engineering

Massachusetts Institute of Technology

Cambridge

MA

USA

Pierluigi Mancarella

Department of Electrical and Electronic Engineering

The University of Melbourne

Parkville

Melbourne

Australia

Mathaios Panteli

Department of Electrical and Computer Engineering

University of Cyprus

Nicosia

Cyprus

Frédéric Petit

European Commission

Ispra

Lombardy

Italy

Julia Phillips

The Perduco Group

Beavercreek

OH

USA

Jalpa Shah

Sensata Technologies

Eaton Corporation Inc.

Eden Prairie

MN

USA

Mohammad Shahidehpour

The Robert W. Galvin Center for Electricity Innovation

Illinois Institute of Technology

Chicago

IL

USA

Anurag K. Srivastava

Lane Department of Computer Science and Electrical Engineering

West Virginia University

Morgantown

WV

USA

Gerald Stokes

Stony Brook University

Long Island

NY

USA

Yuanzhang Sun

School of Electrical Engineering and Automation

Wuhan University

Wuhan

China

Dimitris Trakas

Electric Energy Systems Laboratory

School of Electrical and Computer Engineering

National Technical University of Athens

Athens

Greece

Mani Vadari

Modern Grid Solutions

Redmond

WA

USA

Venkatesh Venkataramanan

National Renewable Energy Laboratory

Golden

CO

USA

Ying Wang

School of Electrical Engineering

Beijing Jiaotong University

Beijing

China

Jian Xu

School of Electrical Engineering and Automation

Wuhan University

Wuhan

China

Yin Xu

School of Electrical Engineering

Beijing Jiaotong University

Beijing

China

Foreword

Electrification has been recognized by the National Academy of Engineering as the engineering achievement having the greatest impact on the quality of life in the twentieth century. The centralized system designs and associated organizational structures that have provided reliable power delivery for the last century, however, face increasing challenges in meeting the key characteristics of power delivery that modern society demands: resilience, reliability, security, affordability, flexibility, and sustainability. The US Department of Energy Grid Modernization Initiative has identified emerging trends that drive the need for transformational changes in the grid. These trends posing challenges to the existing grid infrastructure include the following: a changing mix of types and characteristics of electric generation (in particular, distributed and clean energy); growing demand for a more resilient and reliable grid (especially due to weather impact, cyber, and physical attacks); growing supply‐ and demand‐side opportunities for customers to participate in electricity markets; the emergence of interconnected electricity information and control systems; and aging electricity infrastructure.

These emerging trends have significantly affected electric power distribution systems, as an increasing level of penetration of distributed energy resources (DERs, such as demand response, energy storage, and microgrids) changes the traditional one‐directional power flow paradigm into a bi‐directional one, fundamentally altering how protection systems should be designed and operate. The increased variability of both electricity demand and supply introduced from DERs requires advanced sensing, communications, and control technologies – also known as smart grid technologies – to improve situational awareness and manage a balanced supply‐and‐demand operation in real time. Furthermore, exogenous events such as extreme weather and physical/cyber attacks have presented increasing threats to resilient and secure grid operations. According to the National Oceanic and Atmospheric Administration, the United States has sustained 219 weather and climate disasters since 1980 with the total cost of these events exceeding US$1.5 trillion. Further, a 2017 industry survey of utility professionals identified cyber and physical security as one of the five most important issues facing electric utilities, with the other four being DER policy, rate design reform, aging grid infrastructure, and the threat to reliability from integrating variable renewables and DERs.

The 2017 report entitled Enhancing the Resilience of the Nation's Electricity System, published by the National Academies of Sciences, Engineering, and Medicine, documents its national‐level study findings on the future resilience and reliability of the nation's electric power transmission and distribution system. The report assesses various human and natural events that can cause outages with a range of consequences and concludes that the risks of physical and cyber attacks pose a serious and growing threat. The study recommendations include the following: conducting a coordinated assessment of the numerous resilience metrics being proposed for transmission and distribution systems and seeking to operationalize the metrics within the utility setting; supporting research, development, and demonstration of infrastructure and cyber monitoring and control systems; exploring the extent to which DERs can be used to prevent wide‐area outages and improve restoration and overall resilience; and improving the cybersecurity and cyber resilience of the grid.

This timely book delves into critical topics of grid modernization and its associated challenges and technological solutions and examines some of the recommendations in the National Academies report, with a primary focus on the resiliency of distribution systems in the context of the new paradigm of smart grid technology and DERs. It also addresses improving the security of power system communications, control, and protection systems. Beginning with chapters on concept, framework, and metrics of resiliency, the book moves into measuring and visualizing resiliency; enabling, improving, and optimizing electricity network and cyber resiliency; and resilient operations employing control systems and distributed assets. It concludes by presenting some examples of practical implementation. Thus, this book addresses distribution system resiliency from concept to research and development, and through implementation and operation.

I am honored to write the foreword for this book, as it spans the interests of many US Department of Energy programs I have been involved in. These programs range from DER integration to smart grids, and to my current program focus areas of microgrid and resilient distribution system. This book encompasses what has been developed, what is being developed, and areas in need of further development. It also identifies exemplary development and implementation. I fully expect this volume to become a valuable resource for research, planning, implementation, and education on the key topics affecting the resiliency of distribution systems, a subject of national importance.

Dan T. Ton

Program Manager

Office of Electricity Delivery and Energy Reliability

US Department of Energy

Part IFoundation

1Concepts of Resiliency

Sayonsom Chanda1, Anurag K. Srivastava2 and Chen‐Ching Liu3

1National Renewable Energy Laboratory, Golden, CO, USA

2Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

3The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

1.1 Introduction

Resilience is an evolving concept for the power grid. There are many related system concepts – such as reliability, security, and system hardening – which further makes it complex to clearly and uniquely define resilience. This chapter attempts to lay a clear foundation about the various definitions and interpretations of power systems resiliency and a structured taxonomy. All modern critical infrastructure are greatly impacted by poor power quality issues and frequent discontinuity of service. Higher‐reliability and better‐quality electricity service is indispensable to sustain any strong and progressive modern economy and minimize financial losses. However, electric power infrastructure is encountering unique challenges:

Climatological Challenges. Global climate change is attributed to number of weather‐related significant power outages

1

at the US Atlantic coast. The number of significant power outages increased from average of 22.3 between 1990 and 2000 to 76.4 between 2001 and 2015 – which corresponds to an alarming 342% increase. In 2018, power delivery infrastructure in large regions in the states of Texas, Florida, Louisiana, and the territories of Puerto Rico and Virgin Islands were completely devastated by three successive Category 4 and above hurricanes in a span of two months.

In other countries, devastating tsunamis (Japan, India, and Indonesia), and earthquakes (Nepal and Mexico) caused extensive infrastructure damage and power outages.

Power Resources. Push for sustainability and the aforementioned ecological concerns led to transition to clean and renewable energy. These

distributed energy resource

s (

DER

s) are interfaced to the power grid using power electronics causing harmonics. Also, distribution systems have to deal with intermittency. Reduced system inertia is also an associated result.

Aging Infrastructure. The power grid infrastructure was installed and has been operational (except for occasional repairs) for several decades. Several studies have shown that aging infrastructure, like transformers, are more prone to failures. Transmission and distribution system poles which may have been installed many decades ago, may not conform to modern ASCE standards for withstanding extreme wind speeds.

Changing Demographics. By 2030, more than 60% of the 9 billion human population will live in urban regions. Urban regions have higher per capita power consumption and will likely to further stress system.

Cybersecurity. Earlier adoption of digital devices and associated cyber infrastructure typically were not designed with security in mind, but security features was later added (as “patches”) to them as an afterthought following many cybersecurity breaches. Smart grid substations with remote monitoring, controllability, and automation are vulnerable to cyber attacks.

These challenges must be addressed in order to meet the surge in demand for higher‐reliability, better‐quality electricity service.

1.2 Resilience of Complex Systems

It is important to approach this topic by breaking it down to its constituent components. First, a clarification of the definition of complex systems will be presented; then, a brief discourse on its origins and its importance will be discussed, before introducing the concept of resilience, as it applies to such systems.

1.2.1 What are Complex Networks and Complex Systems?

We are all exposed to multiple complex networks every day. Power grids, airplane networks, interstate and road networks, railway freight networks, supply chain networks, and the Internet are complex networks.

Definition 1.1A complex network is a graph comprising many nodes (which can independently act as source or sink or a modifier or a temporary buffer for storing or processing a logically consistent form of matter and/or information) and edges (through which matter and/or information is transported from one node to another) [1].

Examples. The Internet complex network is made of millions of individual computers acting as nodes, with streams of data flowing via wireless or optical fiber communication channels, acting as edges (for the flow of information). In case of the power grid, load and generation buses are the nodes, while transmission or distribution lines are edges (for the flow of electrons). A detailed summary of industrial complex networks and a formal mathematical way of studying such networks has been presented in [2–4].

Definition 1.2A system is any machine with a large number of interdependent moving parts which might comprise even more granular moving parts, some of which can function autonomously, but constrained such that all of the moving parts must coordinate to solve a singular problem or perform a unique service.

Examples. The human body is composed of multiple systems, such as the cardiac system or the digestive system. A simple example in the physical world is an elevator, which is a system. The original power grid with only generators, transmission lines, transformers, and loads can be considered as a complex network, working as a system.

Definition 1.3A complex system comprises a large number of interdependent complex networks and systems, each of which can function independently, but can be controlled and coordinated to increase the efficiency of each of its constituent networks and systems, and the outcome of such coordination is a distinctly higher societal or evolutionary value [5].

Examples. The human body can be considered as the most complex system in the known universe. An airplane is a complex system, comprising of an interacting traffic communication system, with an internal communication complex network that connect with the mechanical engine, gyroscopes, radars, and auxiliary power generators within the aircraft. The evolving smart grid is also a complex network, which is the focus of this text.

1.2.2 Why Is Understanding Resilience of Complex Systems Important?

Complex systems often emerge and are not designed.

Unlike a carefully designed bridge, the complex power system topology grew without consideration for its cascading outages or cyber‐attacks, and thus, the smart grid developing upon the legacy power system inherits the complex system flaws that emerge out of ad hoc growth. Since complex systems can contain structural flaws which aid in rapid propagation of disruptive factors. Once individual systems and networks are integrated into the complex system, the effectiveness of each component (or the reason for their existence) can be curtailed by the lack of defense mechanism of the complex system as a whole. That is why understanding the resilience of complex systems is extremely important.

A complex system, characterized by interdependent complex networks and systems, can be susceptible to the following, unlike a single wire connecting a battery to a light bulb:

Butterfly Effect. Agents for damage, destruction, or disturbance can target a weakly guarded aspect of our lives to cause a widespread damage in another domain.

Amplification Effect. Flaws or vulnerabilities in one complex network can go unnoticed for long periods of time because other interdependent complex networks often compensate shortcomings of another network. This leads to accumulation and amplification of factors that introduced the vulnerabilities, leading to stronger threats to the overall functioning of the complex networks.

Domino Effect. Adversities can propagate from one domain of modern society to another across large geographical boundaries.

The abovementioned adverse implications of being reliant on interconnected complex networks can be conveniently remembered using the acronym “BAD” – from the first letters of each of the effects. It is well known that simplicity of networks lead to robustness, but complex systems, can lead to extremely efficient, high‐functional societies.

However, the proliferating presence and negative influence of BAD actors in modern society is neither viable nor practical reason to retract from interconnected, interdependent, or inter‐operating systems. Instead, it is upon us to deliver a carefully designed, engineering response – by leveraging latest tools and technologies to deploy materials and resources to empower existing infrastructure to minimize adversity and its consequences by responding to any known or unknown threat with resilience.

Figure 1.1 Interdependent complex network infrastructures in the smart grid.

The origin of the word resilience dates back to the early seventeenth century in southeastern Europe, where the present participle form of a Latin word resilire was being used to describe the ability of an object to rebound to its original state after having endured damaging forces. Five centuries later, the meaning of the word has not changed, though it has gained new definitions and contexts across many social and scientific domains, ranging from ecology to economics and engineering.

Definition 1.4Study of resilience is the scientific approach of optimizing the robustness with the control and structural complexity of the system, such that BAD effects are minimized.

Figure 1.1 shows a normal interdependence of multiple complex networks – the power system, the communication, and the road transport system – that comprise the distribution system. In case a physical event (e.g. an earthquake) affects the region, it will impact all the three complex networks to various degrees. However, if each of the complex networks can be coordinated effectively, the overall functioning of the distribution system can be maximized despite the damages. That is the goal of resilient distribution systems.

In the next section, a review of closely related terms that partially overlap with the concept of resiliency (and help achieve resilience) is discussed.

1.3 Related Terms and Definitions for Power System

The most common terminologies associated with power system operations has been put forth by North American Reliability Corporation (NERC) [6]. In this section, we summarize the common terminologies, as a staging ground to highlight the uniqueness of the notion of resiliency.

1.3.1 Protection

Since a vast majority of the power system infrastructure, such as transmission and distribution lines, is exposed to the elements of nature and weather phenomena, faults such as line‐line, line‐ground, and over‐current due to short circuits caused by insulation breakdowns are everyday occurrences [7].

Power system protective devices – such as electromechanical or digital relays – primarily protect the healthy parts of the power system from the currents induced by faults through the isolation of faulted parts from the rest of the electrical network.

Power system protection devices (over/under/rate‐of‐change‐of current, frequency, voltage relays; differential relays, distance protection relays, and machine‐specific relays) are the most fundamental component of ensuring high reliability and for preventing systemic damage from small, localized events.

1.3.2 Vulnerability

Vulnerabilities of any infrastructure complex network are the most probable points of failures at both component level and systems level. Vulnerabilities exist due to physical limitations of protection systems (e.g. strength of metal casing in case of component‐level vulnerabilities or melting point of a fuse wire) or due to implementation (e.g. vulnerabilities due to weak tightening of screws connecting conductors to bushings or software bugs in the program used to control the systems operations). The summary of power system vulnerabilities is depicted in Figure 1.2.

Figure 1.2 Summary of power systems vulnerabilities.

In power systems, some researchers [8] have studied impacts of vulnerabilities that cause voltage instability and devised strategies that minimize costs of impact of other interdictions that disrupt normal power system operations [9, 10]. Implementation of strategic power infrastructure defense (SPID) design methodology for future power systems to respond faster to power system vulnerabilities was discussed by [11]. There also exists different metrics – such as Anticipate–Withstand–Recover (AWR) metrics – for quantifying the vulnerabilities of power system components [12, 13].

1.3.3 Threat

Events which increases the probability of failure of the power system as a whole or an individual component is considered as a threat. Threats can either expose and exploit existing known or unknown power system vulnerabilities, or create new ones. Like reliability, threat in power systems has been treated as a probabilistic measure by many researchers [14, 15]. Threat has also been studied based on how soon it is detected (temporal) [16, 17] or by the size of the power system it impacts (spatial) [18] or by the means of the attack – such as terror‐based [19, 20] or cyber [21].

In spatial studies, a threat is considered high‐impact if a large area or large number of customers are affected by the event and low‐impact if it is local in scope. Power system events such as loss of large generators, or natural phenomena such as hurricanes and earthquakes can be used categorized as power system threats in such analyses.

In temporal studies of threat – if both the threat and the vulnerability are known long before the event takes place, and a power system protection is typically equipped to mitigate the event by an automated sequence of actions (such as breaker trip or recloser operations) and it is considered a low‐impact threat. Else if, either the threat or the vulnerability can be known sufficiently long before the event takes place, and a power system protection can be configured to handle the threatening event (by installing special protection schemes [SPSs], it is considered a medium‐impact threat. However, if both the threat and vulnerability become only apparent immediately before, during, or after the events through its consequences or an investigation, such threats are considered high‐impact threat. Distribution‐system faults or cyber‐attack, quantified by measures such as Common Vulnerabilities and Exposures (CVEs) and Common Vulnerability Scoring System (CVSS) [22], can be grouped into one of the three categories in temporal studies of threat.

Other power system threats include irrational malicious human behavior, such as acts of terrorism, inadequate installation of power protection devices in distribution and transmission systems, and aging equipment, which have higher probability of failure and higher mean‐time to repair (MTTR).

1.3.4 Reliability

According to NERC [6], power system reliability is the degree of performance of the elements of the bulk electric system that results in electricity being delivered to customers adequately, and within accepted power quality standards. Power system reliability is typically measured by using probabilistic indices such as LOLP (loss of load probability), and ENS (Energy Not Served). Utilities annually report to regulatory authorities metrics such as system average interruption frequency index (SAIFI), SAIDI (system average interruption duration index), and momentary average interruption frequency index (MAIFI) (among many others [23, 24]), in order to showcase their conformance of the NERC definition of reliability.

1.3.5 Security

Power system security refers to the degree of risk in the grid's ability to survive imminent contingencies without interruption to customer service [25]. Power system security assessment has been a standard industry practice for nearly three decades, ever since the mainstream use of state estimation. Many researchers [26] have proposed on‐line screening filters for both static and dynamic security analysis, which is now been being used by several transmission and distribution system companies. Power system security assessment also uses optimal power flow schemes to recommend optimal preventive and restorative strategies – such that small signal and transient stability of the power system is not impacted. The advent of synchrophasors, high‐resolution power system data, and security calculations for the power system are also being implemented in near real‐time [27].

There is a strong conceptual interdependence of security in power systems and its reliability and stability [28, 29]. This is because it is not possible for a power system to be secure without being small‐signal stable and nor can it be reliable without being secure. However, it is important to note that security is a time‐varying attribute of the power system, while reliability is measured over longer periods of time.

1.3.6 Restorability

Power system restoration of distribution systems has been an active area of research, since it was realized that high reliability of bulk power distribution systems did not directly translate into improved power delivery to end‐consumers at lower‐voltage levels. The first restoration algorithms estimated the “restorability” of radial networks using [30, 31]. Efficiency of power system operations can be studied on the ability of the network to restore the disrupted loads following the disruption based on the impact on power system stability [32], cost–benefit analysis [33, 34], or via speed of restoration [35, 36]. The objective of restorability studies is to minimize the ENS metric that is used for computation for SAIDI and SAIFI. In case of common power system outages (e.g. tree falling on a distribution line, car crash in a utility pole), restorability is not impacted by the ability of crew or personnel to mobilize and repair the damage. In completely islanded, mission‐critical systems such as hospital microgrids or shipboard systems, expert agents have been designed by several researchers to improve the restorability to outages [37–39].

Restorability, unlike reliability, takes into factors like time taken and path (i.e. redundancy) and other network topological parameters. Hence, several researchers have studied restorability of distribution systems as graph models, and developing spanning tree‐based algorithms to minimize the loss of load expectation (LOLE). Automation of restoration by means of installation of smart reclosers, automatic transfer switches, and remote‐controlled breakers have also increased restorability of power distribution systems in the recent years [40].

1.3.7 Storm Hardening

Storm hardening is physically changing the parts of the power grid infrastructure which are most vulnerable to exposure to the impact of a physical event (weather‐based or man‐based attack). The objective of storm hardening is to make power poles, lines, and other equipment less susceptible to extreme wind, flooding, or flying debris [41]. It involves upgrading the system to use cutting‐edge technology, upgrade insulation, strengthen or waterproof or wind proof cables, transformers, and repair ducts, install newer components that are rated to higher tolerance to sudden stress. Storm hardened parts of the power grid are then often linked to each other through advanced communication or automated systems to create pockets of extremely resilient distribution systems that can be useful in supplying critical loads or provide blackstart capabilities following a major physical event.

1.4 Need for Grid Resiliency

1.4.1 Managing Extreme Weather Events

Number of weather events including hurricanes, Nor'easters, and wildfire have been increasing over years. According to the NFPA 110 standard, critical loads like hospitals are required to maintain around 72–96 hours of emergency fuel supply for backup diesel generators, depending on the facility's capacity and scale. Any outage lasting more than four days would put hundreds of lives in danger, not to mention the cryogenic assets stored in the hospital premises. To cite one of the many blows to such contingency plans was Hurricane Zeta, the 26th hurricane of the year 2020. On 29 October 2020, Zeta caused power outages lasting more than a fortnight in parts of Alabama, Mississippi, and Georgia. At its peak, the superstorm left two million in the dark. Several more events like Texas Polar vortex in 2021 led to the largest forced power outage in US history. This further shows the important of resilience‐focused grid planning and design (Figure 1.3).

Figure 1.3 Number of customers impacted by the Texas Freeze of 2021. The numbers alongside the bar chart show the customers affected on the first day of the event.

1.4.2 Cyber and Other Events

In 2015, cyber‐attack on the power grid of Ukraine resulted in power outages for roughly 230,000 consumers for multiple hours. This was one of the first cyber event resulting into large impact on the power grid, even though most of the electric utilities reported cyber events happening very often.

COVID‐19 is another important event with impact on the power grid operation. The disease affected hundreds of millions of people in a short amount of time, with high mortality rate. The impact of the sudden onset of the pandemic meant novel resilience challenges for electric utilities worldwide.

Impact on Load Profiles. COVID‐19 upended traditional feeder‐level load profiles used by utilities to manage their operations. In the United States, residential electricity sales increased 6% after the lockdowns orders were issued, while commercial and industrial demand went down by 10% [42].

1.4.3 Role of Resiliency‐Focused Grid Planning

By running power system analytics with resiliency as one of its crucial optimization factors – utilities could have quickly identified parts of the network that can be designated to operate as microgrids. The resiliency‐driven power flow solutions can help the network operators identify strategic locations for the point‐of‐common‐coupling for the utility and the new microgrid (Figure 1.9).

The network studies conducted by resiliency‐enabling tools and software can concurrently identify costs and create workflows that expedite the necessary purchase orders for distributed generation with correct sizing, accessories, and integration equipment. It can also generate site acceptance criteria and testing protocols for engineers to evaluate before fully commissioning the microgrid. Researchers have claimed that there can be up to 74% time reduction in the feasibility planning stages of microgrid design [43]. This way utilities could have ensured extreme resilience for critical facilities in vulnerable regions by deploying functional microgrids faster. This can only be possible by automating several design steps of a typical project using resiliency‐enabling tools. Since these resiliency‐focused microgrids are tested via simulations from conception, they are derisked and compliant, using only equipment that meets IEEE 1547‐2018, IEEE P2030.7, Clean Air Act, and IEC 61727 standards. Thus, they require fewer field validations before meeting the acceptability criteria – and can be quickly deployed during the emergencies, such as the one posed by the COVID‐19 pandemic.

1.5 Resiliency of Power Distribution Systems

1.5.1 Existing Definitions and Interpretations

In US Department of Energy's 2015 Quadrennial Report [44], resilience of power distribution systems has been considered as one of the highest priorities during the elaborate and expensive process of modernizing the power grid. The report attributes the ability of the power system or its components to adapt to changing conditions and withstand and rapidly recover from disruptions as resilience.

Some commonly discussed definitions of resilience of power distribution systems are as follows:

Definition 1.5 According to North American Energy Resilience Model (NAERM) (2019), resilience is defined as the availability of potential solutions that enable systematic identification of threats to the power grid infrastructure, development of hardening options that reduce exposure to weather‐based threats, and situational awareness and sophisticated analytics to minimize the impact of threats as they evolve in real time [45].

Definition 1.6Resiliency is a power system attribute that will enable it to operate despite high‐impact, low‐frequency events [46].

Definition 1.7 Resiliency of a power distribution system is its ability to withstand impact of unfavorable events, recover rapidly from any damage incurred through the event, adapt the system to minimize the damages in successive events of similar strength, and prevent the system from further damages in future unfavorable events [47].

Definition 1.8Power system resilience is a quantitative way of understanding the system's boundary conditions and their changes during disturbances [48].

Definition 1.9 The resilience of a system presented with an unexpected set of disturbances is the system's ability to reduce the magnitude and duration of the disruption. A resilient system downgrades its functionality and alters its structure in an agile way [49].

Definition 1.10 The resilience of a system can be defined as a total amount of energy served to critical load during the preset time period and can be measured as a cumulative service time to critical load weighted by priority [50, 51].

There has been a body of literature developing steadily over the last five years, around the concept of resilience – in general, as well as specifically for power distribution systems [52, 53