111,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley - IEEE
- Sprache: Englisch
POWER GRID RESILIENCE AGAINST NATURAL DISASTERS
How to protect our power grids in the face of extreme weather events
The field of structural and operational resilience of power systems, particularly against natural disasters, is of obvious importance in light of climate change and the accompanying increase in hurricanes, wildfires, tornados, frigid temperatures, and more. Addressing these vulnerabilities in service is a matter of increasing diligence for the electric power industry, and as such, targeted studies and advanced technologies are being developed to help address these issues generally—whether they be from the threat of cyber-attacks or of natural disasters.
Power Grid Resilience against Natural Disasters provides, for the first time, a comprehensive and systematic introduction to resilience-enhancing planning and operation strategies of power grids against extreme events. It addresses, in detail, the three necessary steps to ensure power grid success: the preparedness prior to natural disasters, the response as natural disasters unfold, and the recovery after the event. Crucially, the authors put forward state-of-the-art methods towards improving today’s practices in managing these three arenas.
Power Grid Resilience against Natural Disasters readers will also find:
- Data, tables, and illustrations to supplement and clarify the points put forward in each chapter
- Case studies on realistic power systems and industry standards and practices related to the topics covered
- Potential to be a supplementary text in advanced level power engineering courses
Power Grid Resilience against Natural Disasters will be of interest to specialists and engineers, as well as planners and operators from industry. It can also be a useful resource for senior undergraduate students, postgraduate students, researchers, and research libraries. More, it will appeal to all readers with a strong background in power system analysis, operation and control, optimization methods, the Markov decision process, and probability and statistics.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 507
Veröffentlichungsjahr: 2022
Ähnliche
Table of Contents
Cover
Title Page
Copyright
Dedication
About the Authors
Preface
Motivation for This Book
Organization of This Book
Prerequisites and Usage of This Book
Summary
Acknowledgments
Part I: Introduction
1 Introduction
1.1 Power Grid and Natural Disasters
1.2 Power Grid Resilience
1.3 Resilience Enhancement Against Disasters
1.4 Coordination and Co-Optimization
1.5 Focus of This Book
1.6 Summary
References
Part II: Preparedness Prior to a Natural Disaster
2 Preventive Maintenance to Enhance Grid Reliability
2.1 Component- and System-Level Deterioration Model
2.2 Preventive Maintenance in Consideration of Disasters
2.3 Solution Algorithms
2.4 Case Studies
2.5 Summary and Conclusions
Nomenclature
References
3 Preallocating Emergency Resources to Enhance Grid Survivability
3.1 Emergency Resources of Grids against Disasters
3.2 Mobile Emergency Generators and Grid Survivability
3.3 Preallocation Optimization of Mobile Emergency Generators
3.4 Solution Algorithms
3.5 Case Studies
3.6 Summary and Conclusions
Nomenclature
References
4 Grid Automation Enabling Prompt Restoration
4.1 Smart Grid and Automation Systems
4.2 Distribution System Automation and Restoration
4.3 Prompt Restoration with Remote-Controlled Switches
4.4 Remote-Controlled Switch Allocation Models
4.5 Solution Method
4.6 Case Studies
4.7 Impacts of Remote-Controlled Switch Malfunction
4.8 Consideration of Distributed Generations
4.9 Summary and Conclusions
Nomenclature of RCS-Restoration Models
Nomenclature of RCS Allocation Models
References
Part III: Response as a Natural Disaster Unfolds
5 Security Region-Based Operational Point Analysis for Resilience Enhancement
5.1 Resilience-Oriented Operational Strategies
5.2 Security Region during an Unfolding Disaster
5.3 Operational Point Analysis Resilience Enhancement
5.4 Case Studies
5.5 Summary and Conclusions
Nomenclature
References
6 Proactive Resilience Enhancement Strategy for Transmission Systems
6.1 Proactive Strategy Against Extreme Weather Events
6.2 System States Caused by Unfolding Disasters
6.3 Sequentially Proactive Operation Strategy
6.4 Summary and Conclusions
Nomenclature
References
7 Markov Decision Process-Based Resilience Enhancement for Distribution Systems
7.1 Real-Time Response Against Unfolding Disasters
7.2 Disasters' Influences on Distribution Systems
7.3 Markov Decision Processes-Based Optimization Model
7.4 Solution Algorithms – Approximate Dynamic Programming
7.5 Case Studies
7.6 Summary and Conclusions
Nomenclature
References
Part IV: Recovery After a Natural Disaster
8 Microgrids with Flexible Boundaries for Service Restoration
8.1 Using Microgrids in Service Restoration
8.2 Dynamically Formed Microgrids
8.3 Mathematical Formulation of Radiality Constraints
8.4 Adaptive Microgrid Formation for Service Restoration
8.5 Case Studies
8.6 Summary and Conclusions
Appendix 8.A
Nomenclature of Spanning Tree Constraints
Nomenclature of MG Formation Model
References
9 Microgrids with Mobile Power Sources for Service Restoration
9.1 Grid Survivability and Recovery with Mobile Power Sources
9.2 Routing and Scheduling Mobile Power Sources in Microgrids
9.3 Mobile Power Sources and Supporting Facilities
9.4 A Two-Stage Dispatch Framework
9.5 Solution Method
9.6 Case Studies
9.7 Summary and Conclusions
Nomenclature
References
10 Co-Optimization of Grid Flexibilities in Recovery Logistics
10.1 Post-Disaster Recovery Logistics of Grids
10.2 Flexibility Resources in Grid Recovery Logistics
10.3 Co-Optimization of Flexibility Resources
10.4 Solution Method
10.5 Case Studies
10.6 Summary and Conclusions
Appendix 10.A
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Outages in the United States for 1984–2006.
Chapter 2
Table 2.1 Harsh weather.
Table 2.2 Costs (
$) of Transformers.
Table 2.3 Unrepaired Probabilities over All Time Intervals.
Chapter 3
Table 3.1 Available MEGs' capacities.
Table 3.2 Demonstration of the proposed MEG dispatch approach.
Table 3.3 Statistics of capacity utilization rates in simulations.
Table 3.4 Statistics of load restoration and MEG travel time in simulations....
Table 3.5 Comparing the computation time of the preallocation problem (minut...
Chapter 4
Table 4.1 Number of PCRSs for each single-fault outage scenario.
Table 4.2 CIC parameters' impact on the optimal RCS allocation (IEEE 33-Node...
Table 4.3 RCS switching time's impact on the optimal RCS allocation (IEEE 33...
Table 4.4 Failure rates' impact on the optimal RCS allocation (IEEE 33-Node ...
Table 4.5 RL-oriented optimal RCS allocation (IEEE 33-Node Test System).
Table 4.6 Comparing different RCS allocation models (IEEE 33-Node Test Syste...
Table 4.7 CIC parameters' impact on the optimal RCS allocation (IEEE 123-Nod...
Table 4.8 Optimal RL-oriented RCS allocation result (IEEE 123-node test syst...
Table 4.9 Computational efficiency comparisons.
Chapter 5
Table 5.1 Line data.
Table 5.2 Component failure scenarios.
Table 5.3 Component failure scenarios.
Chapter 6
Table 6.1 Probabilities of component failure.
Table 6.2 Parameters of generators.
Table 6.3 Different failure Scenarios.
Table 6.4 Different maximum failure component scenarios.
Chapter 7
Table 7.1 Four Post-decision states.
Table 7.2 Post-decision states
,
,
, and
.
Table 7.3 The first case of state-based strategy.
Table 7.4 The second case of state-based strategy.
Table 7.5 State-based strategy for IEEE 123-bus system.
Chapter 8
Table 8.1 Comparing different types of spanning tree constraints.
Table 8.2 Number of constraints and variables in different MG formation mode...
Table 8.3 Comparing different MG formation models
Table 8.4 Computation time and the number of infeasible cases (IEEE 33-node ...
Table 8.5 Summary statistics for restored loads (IEEE 33-node test system)....
Table 8.6 Summary statistics for DG capacity utilization rate (IEEE 33-node ...
Table 8.7 Computation time and the number of explored nodes in the B&C searc...
Table 8.8 Computation time and the number of infeasible cases (IEEE 123-node...
Table 8.9 Summary statistics for restored loads (IEEE 123-node test system)....
Table 8.10 Summary statistics for DG capacity utilization rate (IEEE 123-nod...
Table 8.11 Computation time and the number of explored nodes in the B&C sear...
Chapter 9
Table 9.1 MPS pre-positioning results and DS topologies: w/ and w/o proactiv...
Table 9.2 Simulation statistics for survived loads: w/ and w/o proactive rec...
Table 9.3 Time sequence of repairing damaged branches.
Table 9.4 Connecting nodes of MPSs in each time period: proposed method and ...
Table 9.5 Dynamic network reconfiguration of the DS.
Table 9.6 MPS pre-positioning results and DS topologies: w/ and w/o proactiv...
Table 9.7 Simulation statistics for survived loads: w/ and w/o proactive rec...
Table 9.8 Dynamic network reconfiguration of the DS.
Table 9.9 Connecting nodes of MPSs in each time period: proposed method and ...
Chapter 10
Table 10.1 Preassignment of a minimal set of repair tasks (Case I).
Table 10.2 Routing and scheduling of RCs (Case I).
Table 10.3 Routing and scheduling of MPSs (Case I).
Table 10.4 Dynamic network reconfiguration of the DS (Case I).
Table 10.5 Routing and scheduling of RCs (Case II).
Table 10.6 Routing and scheduling of MPSs (Case II).
Table 10.7 Dynamic network reconfiguration of the DS (Case II).
Table 10.8 Computation time of the co-optimization model (Cases I & II).
List of Illustrations
Chapter 2
Figure 2.1 (a) Transitions between different states without maintenance acti...
Figure 2.2 (a) Load curve. (b) Load losses with different offline transforme...
Figure 2.3 (a) Errors of expected costs based on the proposed model and Mont...
Figure 2.4 Sample paths of states with regard to the costs of two scenarios....
Figure 2.5 (a) Sample path of states with external conditions. (b) Sample pa...
Figure 2.6 Distribution of inconsistent strategies on
,
,
, and
.
Figure 2.7 Distribution of inconsistent maintenance strategies on (a)
, (b)...
Figure 2.8 (a) Expected costs for proposed maintenance activities and given ...
Chapter 3
Figure 3.1 Timing of preallocation and real-time allocation.
Figure 3.2 Conceptual resilience curves associated with an event.
Figure 3.3 Relationships among task modules and data sets.
Figure 3.4 Geographic information for RNs, staging locations, DSs, and candi...
Figure 3.5 DSs' topologies (DS1: a modified IEEE 34-node DS [38]; DS2: a mod...
Chapter 4
Figure 4.1 A schematic curve of supplied customers associated with a permane...
Figure 4.2 A sample four-node system (
for
).
Figure 4.3 IEEE 33-node test system's topology.
Figure 4.4 Pareto-optimal solutions of the SAIDI-oriented RCS allocation mod...
Figure 4.5 IEEE 123-node test system' topology.
Figure 4.6 Pareto-optimal solutions of the SAIDI-oriented RCS allocation mod...
Chapter 5
Figure 5.1 A simplified system under an unfolding event.
Figure 5.2
Security region
(
SR
) over sequential time periods.
Figure 5.3 Sequential security regions illustrated by three two-dimension fi...
Figure 5.4 Feasible dispatch region in
with ramping rates
p.u. (a) and
Figure 5.5 Power flows under different failure scenarios with ramping rate a...
Figure 5.6 Power Flow with ramping rate 0.35 p.u. in
t
1
(a) and
t
2
(b), resp...
Figure 5.7 Feasible dispatch regions in
with different line capacities
(...
Chapter 6
Figure 6.1 Generic fragility curve.
Figure 6.2 Two components on the trajectory of a typhoon.
Figure 6.3 (a) Failure rates at different time intervals. (b) Markov states ...
Figure 6.4 A scenario with three decision epochs.
Figure 6.5 Probabilities of states.
Figure 6.6 IEEE 30-bus system topology.
Figure 6.7 The process of mapping a state to a strategy.
Figure 6.8 Optimal strategies for (a) scenario 1, (b) scenario 2, (c) scenar...
Figure 6.9 (a) Mapping states to strategies over all decision epochs. (b) Ma...
Figure 6.10 Differences of LoL with M1 and M2.
Figure 6.11 Differences of LoL with M1 and M2.
Figure 6.12 Outputs of the critical generator on the trajectory.
Figure 6.13 Objective values with different numbers of available generators ...
Chapter 7
Figure 7.1 Markov decision processes.
Figure 7.2 An example of a distribution system under an unfolding event.
Figure 7.3 Markov state-based decision at (a)
, (b)
, and (c)
.
Figure 7.4 Decision processes with post-decision states.
Figure 7.5 Topology of IEEE 33-bus system.
Figure 7.6 Iterations for estimated values of post-decision states.
Figure 7.7 Iterations for estimated values of post-decision states with diff...
Figure 7.8 Iterations for estimated values of post-decision states with diff...
Figure 7.9 Topology of IEEE 123-bus system.
Figure 7.10 System topologies (a) in the third period and (b) in the sixth p...
Chapter 8
Figure 8.1 The search region (i.e. included flexibilities) of the MG formati...
Figure 8.2 (a) A spanning tree. (b) A spanning forest. (Demonstrated on the ...
Figure 8.3 Illustrating the
tightness
concept.
Figure 8.4 (a) An example system with three nodes. (b) The
set of spanning t
...
Figure 8.5 (a) The
original
example system. (b) The
transformed
system attai...
Figure 8.6 A small example to illustrate
infeasibility
of the MG formation m...
Figure 8.7 An illustrative case of MG formation on the IEEE 33-node test sys...
Figure 8.8 (a) Histograms of the extra restored loads of the proposed MG for...
Figure 8.9 An illustrative case of MG formation on the IEEE 123-node test sy...
Figure 8.10 (a) Histograms of the extra restored loads of the proposed MG fo...
Chapter 9
Figure 9.1 A demonstrative case illustrating resilience-enhancing effects of...
Figure 9.2 Conceptual resilience curves associated with an outage event.
Figure 9.3 The modified IEEE 33-node test system.
Figure 9.4 Relative optimality gap
in each iteration.
Figure 9.5 Restored loads in each time period for different cases.
Figure 9.6 System load, SoC of EV fleet 1, SoC of MESS 1, and real power out...
Figure 9.7 The modified IEEE 123-node test system.
Figure 9.8 Restored loads in each time period for different cases.
Chapter 10
Figure 10.1 Relationships among MPS dispatch, RC dispatch, and DS restoratio...
Figure 10.2 An illustration for the construction of
. (a) Graph
, the orig...
Figure 10.3 IEEE 33-node test system split into multiple PIs.
Figure 10.4 (a) A spanning tree. (b) A spanning forest.
Figure 10.5 (a) An example of three-node network. (b) Possible spanning tree...
Figure 10.6 Real power outputs of MPSs in each time period (Case I).
Figure 10.7 Restored loads over time for different restoration strategies (C...
Figure 10.8 DS service restoration process co-optimized with both RC dispatc...
Figure 10.9 IEEE 123-node test system and its restoration state at
.
Figure 10.10 Real-power outputs of MPSs in each time period (Case II).
Figure 10.11 Restored loads over time for different restoration strategies (...
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Authors
Preface
Acknowledgments
Begin Reading
Index
End User License Agreement
Pages
iii
iv
v
xv
xvi
xvii
xviii
xix
xx
xxi
xxiii
xxiv
1
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
35
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
Power Grid Resilience Against Natural Disasters
Preparedness, Response, and Recovery
Shunbo Lei
Chong Wang
Yunhe Hou
This edition first published 2023© 2023 John Wiley & Sons Ltd
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of Shunbo Lei, Chong Wang, and Yunhe Hou to be identified as the authors of this work has been asserted in accordance with law.
Registered Office(s)John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USAJohn Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats.
Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
Limit of Liability/Disclaimer of Warranty
While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.
Library of Congress Cataloging-in-Publication Data Applied for:
Hardback ISBN: 9781119801474
Cover Design: WileyCover Image: © MNBB Studio/Shutterstock
To
Jinhui, Qiuying, and Shunxia
– Shunbo Lei
Xingxing, Xueheng, Yan, and Hui
– Chong Wang
Lijuan and Jesse
– Yunhe Hou
About the Authors
Shunbo Lei received the BE degree in Electrical Engineering and Automation from Huazhong University of Science and Technology, Wuhan, China, in 2013, and the PhD degree in Electrical and Electronic Engineering from The University of Hong Kong, Hong Kong SAR, China, in 2017. He was a visiting scholar at Argonne National Laboratory, Lemont, IL, USA, from 2015 to 2017, a postdoctoral researcher with The University of Hong Kong from 2017 to 2019, and a research fellow with the University of Michigan, Ann Arbor, MI, USA, from 2019 to 2021. He is currently an assistant professor with the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China. He is the Secretary of the IEEE PES Loads Subcommittee, and the Chair of the IEEE PES Task Force on Flexible Grid-interactive Efficient Buildings to Enhance Electric Service Resilience. He is also an associate editor of the IEEE Transactions on Smart Grid, and a Young Editorial Board Member of the Protection and Control of Modern Power Systems. He is the awardee of the 2019–2021 IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award, the 2022 IEEE PES General Meeting Best Conference Papers Award, and the Young Resilience Fellowship by the Netherlands' 4TU Centre for Resilience Engineering. His research interests include power systems, resilience, grid-interactive efficient buildings, optimization, and learning.
Chong Wang received the BE and MS degrees in Electrical Engineering from Hohai University, Nanjing, China, in 2009 and 2012, respectively, and the PhD degree in Electrical Engineering from The University of Hong Kong, Hong Kong SAR, China, in 2016. He was a postdoctoral researcher at The University of Hong Kong in 2016 and was a postdoctoral researcher at Iowa State University, Ames, IA, USA, from 2017 to 2018. He is currently a professor with the College of Energy and Electrical Engineering, Hohai University. His research interests include power system resilience, renewable integration, and integrated energy system modeling and operation.
Yunhe Hou received the BE and PhD degrees in Electrical Engineering from Huazhong University of Science and Technology, Wuhan, China, in 1999 and 2005, respectively. He was a postdoctoral research fellow at Tsinghua University, Beijing, China, from 2005 to 2007, and a postdoctoral researcher at Iowa State University, Ames, IA, USA, and the University College Dublin, Dublin, Ireland, from 2008 to 2009. He was also a visiting scientist at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA, in 2010. He has been a guest professor with Huazhong University of Science and Technology from 2017 and an Academic Adviser of the China Electric Power Research Institute from 2019. He joined the faculty of The University of Hong Kong, Hong Kong SAR, China, in 2009, where he is currently an associate professor with the Department of Electrical and Electronic Engineering. Dr. Hou is the chair of IEEE PES Task Force on “Power System Restoration with Renewable Energy Sources” under the PSDP/PSSC-Power System Stability Controls Subcommittee. Dr. Hou was an associate editor of the IEEE Transactions on Smart Grid from 2016 to 2021. Dr. Hou is currently an associate editor of the IEEE Transactions Power Systems and the Journal of Modern Power Systems and Clean Energy.
Preface
Motivation for This Book
Resilience indicates the capabilities of dealing with the unexpected and is one of the most important merits of a variety of systems. Climate change, which results in more frequent extreme weather events, and threats from malicious attacks, especially highlight the urgency of establishing a resilient power and energy system, as the resilience of many other critical infrastructures depends on it. Generally, decision-makers have to consider various challenges including those associated with the climate change, infrastructure interdependencies, and information uncertainties, in establishing social, economic, and environmental resilience.
Power grid resilience, specifically, characterizes its ability to prepare for and adapt to changing conditions, and withstand and recover rapidly from disruptions. Effective functioning and resilience of power grids are critical for the economic stability, etc. Recent years have witnessed more frequent and more severe power outages due to cyber-attacks (e.g. by hackers) and physical attacks (e.g. by extreme weather events). Those large-scale outages have resulted in significant economic loss and greatly increased the life risk of sensitive groups of people, thus highlighting the importance of improving grid resilience. In power grids, we must understand the size and scope of key parameters required to facilitate the establishment of resilience.
First, emergency preparedness is essential for resilient power grids anticipating a natural disaster, e.g. a hurricane. It greatly determines the ability of the grid to withstand and reduce the magnitude and duration of disruptive events. Effective disaster preparedness measures include preventive maintenance of critical system components, installation or upgradation of grid automation systems, and prepositioning or predispatching of emergency resources for future response. Other new preparedness strategies of engineering application values are being proposed and evaluated, too. Many existing studies are particularly devoted to optimizing disaster planning based on approximate dynamic programming, scenario decomposition-based stochastic programming, etc. While there have already been some disaster preparedness strategies adopted by grid operators for years, there are also some recently developed strategies that have been or are being transitioned from academia to industry. In the future, industry needs of new disaster preparedness strategies and more efficient algorithms will increase, especially for fully utilizing and coordinating different flexibility resources.
Second, natural disaster-induced damages to the power grid sequentially unfold themselves during such a catastrophic event, resulting in disturbances to system states that require operators to implement appropriate and real-time response. Effective and efficient mitigation response should take into full consideration the sequential and uncertain characteristics of power grid component damages. Currently, there are some approaches using optimization methods such as robust programming and scenario-based stochastic programming to deal with the above challenges. Some researchers have also been establishing Markov decision process-based approaches that map the stochastic disturbances with corresponding optimal mitigation strategies in consideration of complex system dynamics. Still, there are currently inadequate studies on efficient decision-making approaches for sequential responses against uncertain damages over time. In future years, there will be increasing industry needs of methods for real-time state-based operation and control against natural disaster-induced disturbances. Techniques and technologies of engineering application values will also start transitioning from academia to industry in the near future.
Third, postdisaster recovery of power grids, including the restoration of electric service and repairing of grid infrastructures, needs to be improved. Different from common single fault-induced outages, natural disaster-induced blackouts are typically wide-range and long-duration, requiring novel and efficient recovery strategies especially for distribution grids. There have been many studies on resilience-enhancing service restoration and infrastructure recovery of power grids utilizing and coordinating distributed renewables, automation systems, and interdependent infrastructures. Microgrid technologies specifically provide unique opportunities of leveraging grid flexibility resources to achieve prompt recovery of power grids. However, many critical issues, e.g. the management of flexible boundaries of microgrids, are not fully addressed. In future years, there will be increasingly more studies to fulfill industry needs of decision-making tools for the postdisaster operation of power grids, and the dispatch of repair crews and available mobile power sources. Holistic and integrated optimization approaches for disaster recovery logistic of power grids are also needed.
Therefore, we perceive the necessity of disseminating fundamental theories and methodologies that are useful in improving power grid resilience against natural disasters.
Organization of This Book
This book covers three important topics related to the planning and operational resilience of power grids against natural disasters, including (i) preparedness before a natural disaster, which increases both component- and system-level reliability, extends grid flexibility, and enhances system readiness for prompt and effective dispatch of emergency resources; (ii) response as a natural disaster unfolds, which analyzes and implements real-time state-based sequential strategies for mitigating the impact of transmission or distribution grid damages caused by the natural disaster; and (iii) postdisaster recovery, which establishes a systematic methodology for electric service restoration and infrastructure recovery exploiting microgrid technologies and utilizing a comprehensive set of flexibility resources.
To help readers have a better understanding of what we have done, we would like to provide a short review of the 10 chapters as follows:
Chapter 1 provides an overview on several critical issues of power grid resilience against natural disasters. The definition of power grid resilience is discussed. The benefits and challenges of improving grid resilience are elaborated on. Different stages involved in power grid resilience enhancement, i.e. before, during, and after a natural disaster, are also discussed. The importance of coordinating and co-optimizing measures of different stages and flexibility resources of different kinds is highlighted.
Chapter 2 explores a dynamic model for condition-based maintenance strategies considering harsh external conditions. A Markov process is used to describe the physical characteristics of component deterioration, and the effects of harsh external conditions are represented as probabilistic models. A cost-to-go model, including the maintenance cost and the system reliability cost, is constructed to optimize the maintenance strategies to enhance power grid resilience.
Chapter 3 uses truck-mounted mobile emergency generators (MEGs) as an example of emergency resources and proposes scheduling MEGs as distributed generations in distribution systems to recover critical loads by temporarily forming microgrids. A two-stage dispatch approach consisting of preallocation and real-time allocation is presented. Particularly, preallocation is conducted via a two-stage scenario-based stochastic optimization problem, in which the first-stage preallocation decisions are assessed by a number of second-stage real-time allocation subproblems associated with the considered scenarios.
Chapter 4 presents a new approach to grid automation planning that allocates remote-controlled switches (RCSs) to improve the restoration performance and optimize resilience and reliability benefits with an appropriate cost. Particularly, the optimal number and locations of to-be-upgraded switches can be determined by the approach for different objectives: (i) To maximize the reduction in customer interruption cost; (ii) To maximize the reduction in system average interruption duration index; or (iii) To maximize loads that can be recovered by the grid automation system with upgraded RCSs.
Chapter 5 studies the sequential steady-state security region (SSSR). SSSR-based resilience enhancement of power transmission systems, considering uncertain time-varying topology changes due to the extreme weather event, is investigated. In consideration of uncertain time-varying topology changes with SSSR, the resilience enhancement problem is constructed as a bilevel optimization model, which can be utilized by grid operators to find an optimal strategy against the most threatening weather-related scenario.
Chapter 6 proposes a proactive operation strategy to enhance power transmission system resilience during an unfolding extreme event. A Markov process model is used to represent the uncertain sequential transition of system states driven by the evolution of the extreme event. Transition probabilities depend on failure rates caused by the extreme event. For each state, a recursive value function with a current cost and a future cost is modeled in consideration of intertemporal constraints and operation constraints.
Chapter 7 proposes a state-based decision-making model to enhance distribution grid resilience throughout an unfolding event. The time-varying system topologies are modeled as Markov states, and the probabilities between different Markov states depend on the component failures caused by the unfolding event. A Markov decision process-based model is proposed to make state-based actions, i.e. system reconfiguration, at each decision time. An approximate dynamic programming method is employed to optimize the proposed model.
Chapter 8 presents a new method for formulating radiality constraints to fully include topological and related flexibilities in reconfiguration-related microgrid and distribution grid optimization problems, whose feasibility and optimality thus are extended and enhanced, respectively. The method's theoretical validity is certified by graph–theoretic analyses. As it involves integer variables, this chapter further analyzes the tightness and compactness issues. The new radiality constraints are particularly used for postdisaster microgrid formation that is involved in many resilience-enhancing service restoration and infrastructure recovery problems.
Chapter 9 presents a two-stage framework for resilient routing and scheduling of mobile power sources (MPSs). In the first stage, i.e. prior to the natural disaster, MPSs are pre-positioned in the distribution grid to enable prompt prerestoration of electric service. The first-stage decisions are obtained by a two-stage robust optimization model. In the second stage, i.e. after the disaster, MPSs are dynamically dispatched in the distribution grid to cooperate with conventional service recovery efforts, in order to improve system recovery. A novel mixed-integer programming model is formulated.
Chapter 10 proposes a resilient approach for disaster recovery logistics co-optimizing distribution grid restoration with the dispatch of MPSs and repair crews (RCs). A novel co-optimization model is constructed to coordinately route MPSs and RCs in the transportation network, schedule them in the distribution grid, and reconfigure the distribution grid for forming microgrids. Resilient recovery strategies therefore are obtained to improve service restoration, especially by dynamically forming microgrids that are energized by MPSs and topologized by network reconfiguration of the distribution grid and repairing actions of RCs.
Prerequisites and Usage of This Book
The prerequisite knowledge to read this book is an undergraduate level of understanding of power grid analysis and operation, basic knowledge of optimization methods including the Markov decision process, graph theory, and probability theory.
This book may be of interest to senior undergraduate students, postgraduate students, researchers from academia (in the fields of engineering, operations research, etc.), and specialists, engineers, planners, and operators from industry. We also hope that this book will be useful as a reference for advanced courses related to power and energy, industrial engineering, and operations research.
Summary
To summarize, this book provides a comprehensive and systematic introduction to resilience-enhancing planning and operation strategies of power grids against natural disasters. It covers three closely related important problems, i.e. preparedness prior to an extreme event, response as the event unfolds, and postevent recovery. State-of-the-art methods are introduced and illustrated in detail with examples for the readers to learn how to use them to address realistic problems and improve today's practices.
Shunbo Lei
Shenzhen, China
Chong Wang
Nanjing, China
Yunhe Hou
Hong Kong SAR, China
August 2022
Acknowledgments
This book summarizes some of our research on power grid resilience in recent years. Many people contributed to this book in various ways. We would like to thank Prof. Jianhui Wang from Southern Methodist University, Prof. Ping Ju and Prof. Feng Wu from Hohai University, Prof. Chen Chen from Xi′an Jiaotong University, Prof. Zhaoyu Wang from Iowa State University, Dr. Hui Zhou from the State Grid Zhejiang Electric Power Company, Prof. Tao Liu and Dr. Yue Song from the University of Hong Kong, Dr. Yupeng Li from Hong Kong Baptist University, for their contributions to some research presented in this book.
We also would like to thank Prof. Felix F. Wu from the University of California, Berkeley, Prof. David J. Hill from the University of New South Wales, Sydney, Prof. Ian A. Hiskens and Prof. Johanna L. Mathieu from the University of Michigan-Ann Arbor, Prof. Kai Strunz from the Technical University of Berlin, Prof. Mingbo Liu from South China University of Technology, Dr. Feng Qiu from Argonne National Laboratory, Prof. Feng Liu and Prof. Wei Wei from Tsinghua University, Dr. Miao Miao from the Department of Development and Planning, State Grid Qinghai Electric Power Company, Prof. Wei Sun from the University of Central Florida, Prof. Liang Liang from Harbin Institute of Technology (Shenzhen), and Dr. Chaoyi Peng from China Southern Power Grid Company, for valuable discussions and suggestions when conducting some of these research.
We thank Ms. Chenxi Hu and Mr. Qinfei Long for contributing some materials to Chapter 1 of this book. We also thank Dr. Wenqian Yin, Dr. Yujia Lauren Li and Ms. Yixuan Chen from the University of Hong Kong, Ms. Mingze Xu, Mr. Haoran Liu, Mr. Cheng Ma, Mr. Fengqi Lyu, Mr. Weimin Wu, Mr. Aichao Zhang, and Mr. Haochen Song from the Chinese University of Hong Kong, Shenzhen, for pointing out typos and checking the whole book.
In addition, we acknowledge the innovative research and practice contributed by others in this increasingly important field, and appreciate the staff at Wiley and IEEE Press for their assistance and help in preparing this book.
This book is supported in part by the National Natural Science Foundation of China (NSFC) under Grant 52177118 and Grant 51907050, in part by the Joint Research Fund in Smart Grid under cooperative agreement between the NSFC and State Grid Corporation of China under Grant U1966601, in part by the Research Grants Council of Hong Kong under Grant GRF17207818, in part by the University Development Fund (No. 01002140) of the Chinese University of Hong Kong, Shenzhen, and in part by the Shenzhen Institute of Artificial Intelligence and Robotics for Society. The authors really appreciate their support.
Shunbo Lei
Chong Wang
Yunhe Hou
Part IIntroduction
1Introduction
1.1 Power Grid and Natural Disasters
The electric power grid is the largest and most complex machine in the world. It is employed to supply, transfer, and utilize electric power. Its history can be traced back to 1881, when the world's first power system was built at Godalming in England. In the twentieth century, electricity had gradually become one of the basic necessities in the modern society. The power grid has been performing effectively in satisfying the energy need and, meanwhile, has been causing adverse impacts on the natural environment. The associated carbon emissions also contribute to the climate change that has been causing more frequent natural disasters.
In Asia and the Pacific, the primary energy demand is estimated to have more than 2.4% increase each year by 2030, while typically the electricity demand has a higher increase, at about 3.4%. For the increased electricity demand, it is desired to have an efficient and reliable power supply. In recent years, the economy, society, and environment have also introduced new pressures or requirements on power grids, e.g. shifting from centralized to decentralized structures. Hence, long-term sustainability of power grids is of critical importance in developing the twenty-first century power grids, i.e. smart grids.
A power grid usually covers a wide geographical region, and many of its components in the system are exposed to the external environment, which makes the power grid vulnerable to natural disasters, e.g. wind storms, ice storms, thunderstorms, earthquakes, wildfires, hurricanes, and flooding [1–3]. In recent years, more frequent natural disasters have resulted in severe power outages that are large-scale and long-duration. For example after the Hurricane Sandy struck the East Coast of the United States in 2012, approximately 8.35 million customers were reported without power [4]. Some studies have indicated that the climate change leads to the increase in disastrous events. The global temperature rise has been considered as one of the important underlying causes of disastrous events with higher intensity and frequency [4, 5].
Disastrous event-related power outages have introduced tremendous economic losses and significant life risks, highlighting the importance of enhancing power grid resilience [6], which generally refers to the ability to withstand and rapidly recover from disruptive events [7, 8]. A natural disaster can inflict widespread and severe damages to the power grid, leaving numerous customers without power for days, sometimes even for over a week. One of the critical requirements on resilient power grids is that the system can effectively prepare for, response to and recover from natural disasters, as most social activities greatly depend on the reliable power supply [9].
Aside from external natural disasters (e.g. weather-related events), the cyber systems, which enable system operators to efficiently monitor and control the power grid, make the system vulnerable to cyber intrusions. Conventional strategies, e.g. common preventive and emergency measures, due to their little consideration of weather-related and cybersecurity-related events, fail to be resilient preparedness, response, or recovery strategies. Considering potential weather-related events attacking the physical system and cybersecurity-related events attaching the cyber system, resilient power grids have to be constructed.
In this chapter, the definition and importance of power grid resilience will be introduced. Then, challenges brought by different kinds of events that may jeopardize resilient power grid operation will be discussed, and the corresponding resilience enhancement strategies will also be discussed.
1.2 Power Grid Resilience
1.2.1 Definitions
As mentioned above, sustainable power grids have to balance economic growth and social progress, meanwhile, preserving the natural environment [10]. With more frequent severe power outages caused by natural disasters, power grid resilience is receiving much attention. Resilience can be generally understood as the ability of power grids to avoid or reduce failures and to recover quickly after failure occurrence [11]. Currently, there is not a unified definition of power grid resilience. Several definitions given by different organizations are shown as follows:
In
[12]
, the U.S. National Academies of Sciences, Engineering, and Medicine define resilience as “the ability to prepare and plan for, absorb, recover from and more successfully adapt to adverse events.”
In
[13]
, the Cabinet Office of the United Kingdom refers to resilience as “the ability of assets, networks and systems to anticipate, absorb, adapt to and/or rapidly recover from a disruptive event.”
In
[14]
, the U.S. President's National Infrastructure Advisory Council specifies resilience as “the ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event.”
In
[15]
, the IEEE Power and Energy Society Industry Technical Support Task Force prescribes resilience as “the ability to withstand and reduce the magnitude and/or duration of disruptive events, which includes the capability to anticipate, absorb, adapt to, and/or rapidly recover from such an event.”
In
[8]
, the U.S. Electric Power Research Institute states that “grid resilience includes hardening, advanced capabilities, and recovery/reconstitution.”
Although the resilience definitions given by different organizations are different, the key understanding that decision-makers should plan for, ride through, and recover from each potential disastrous event is consistent. According to the goals of enhancing power grid resilience, a sequence of resilience merits, including robustness, resourcefulness, rapid recovery, and adaptability, have been highlighted [16, 17]:
Robustness prior to an event
. This necessitates that the grid is capable of remaining to stand and operate in the face of extreme events. For instance, hardening a grid's critical structure, from a mid- or short-term perspective, can be performed to guarantee a strong system prior to an event. In addition, to ensure the robustness, investments, and maintenance scheduling of critical electric devices, from a long- or mid-term perspective, can be entailed prior to weather-related and cybersecurity-related disastrous events.
Resourcefulness during an event
. This requires skillful abilities to manage the power grid when a disruptive event unfolds. Effective and real-time strategies are expected to be implemented to mitigate the negative impacts. For instance, determining what should be conducted to mitigate the damages is a critical issue during a disastrous event. Furthermore, adequate resources for communications among different decision-makers are important in implementing established mitigation strategies.
Rapid recovery after an event
. This demands the ability to recover the power grid back to a normal state quickly after adverse events. For example, detailed recovery plans under the conditions of various blackouts should be established in time and adequate resources for implementing the recovery strategies should be guaranteed.
Adaptability to future events
. This denotes the ability to absorb new lessons from past events and generalize to new situations. Instead of case-by-case methods, more flexible measures and strategies are necessary to be fitted into various situations as well as to improve the power grid's capability of dealing with extreme events.
1.2.2 Importance and Benefits
As power grids are critical infrastructures for social and economic development [12], a power outage might cause severe consequences. The U.S. National Research Council [12] and the U.K. House of Lords [18] have emphasized the importance of resilient power and energy infrastructures. The North American Electric Reliability Corporation [14, 19, 20] and the U.S. Electric Power Research Institute [21] have further recognized the functionalities of power grid resilience. In general, enhancing power grid resilience can improve economic, social, and environmental sustainability.
Note that a reliable power grid is not necessarily resilient. Specifically, power grid reliability guarantees its operation under normal-state conditions, or in high-probability, low-impact events. On the other hand, a resilient power grid is capable of performing well in low-probability, high-impact events such as natural disasters [12]. Many resilience-oriented power grid planning and operation strategies have been proposed and studied in the literature. Nevertheless, many problems, including distribution grid automation to enhance the restoration capability and the utilization of mobile generation resources which involve the consideration of road networks, have not been addressed.
In the following, the importance and benefits of power grid resilience are briefly discussed from three perspectives.
1.2.2.1 Dealing with Weather-Related Disastrous Events
As mentioned above, electric power grids have the characteristic of wide geographical coverage, making grid components exposed to extreme weather events such as tornadoes, typhoons, windstorms, hurricanes, and blizzards. These disastrous weather events are major causes of power outages. In addition, the aging nature of electric devices also makes power grids more susceptible to extreme weather events. For instance, about 679 power outages were caused by weather events from 2003 to 2012 in the United States, and each event affected at least 50 000 customers [8].
Table 1.1 shows the number of blackouts between 1984 and 2006 in the United States [22]. As indicated, around 44% of the outage events were weather-related. Based on the analysis of the U.S. President's Executive Office, weather-related outages lead to about $25 billion in economic losses annually. In addition, weather-related outages have an increasing trend [8].
Table 1.1 Outages in the United States for 1984–2006.
Cause
Percentage of events
Mean size in MW
Mean size in customers
Earthquake
0.8
1408
3 75 900
Hurricane/tropical storm
4.2
1309
7 82 695
Lightning
11.3
270
70 944
Wind/rain
14.8
793
1 85 199
Ice storm
5
1152
3 43 448
Tornado
2.8
367
1 15 439
Other cold weather
5.5
542
1 50 255
Fire
5.2
431
1 11 244
Intentional attack
1.6
340
24 572
Supply shortage
5.3
341
1 38 957
Other external causes
4.8
710
2 46 071
Equipment failure
29.7
379
57 140
Operator error
10.1
489
1 05 322
Voltage reduction
7.7
153
2 12 900
Volunteer reduction
5.9
190
1 34 543
To sum up, outages induced by weather-related disastrous events happen more frequently all around the world, causing significant economic and safety damages to the human society. With the advancement of power grids, this type of extreme weather will incur greater risks to various security issues, making it an urgent task for each country to improve the power grid resilience against such disastrous events. As aforementioned, improving power grid resilience in terms of the handling of weather-related disastrous events can significantly enhance the economic, social, and environmental sustainability.
1.2.2.2 Facilitating the Integration of Renewable Energy Sources
Renewable energy sources (RESs), including but not limited to wind turbines and photovoltaic panels, have been integrated into power grids worldwide at an increasing rate. Their effects in relieving the energy crisis concern are promising. However, the variable and uncertain natures of RESs have introduced new challenges to power grid planning and operation and brought about new issues to power grid resilience. Conventionally, the power grid consisted of controllable generators and semipredictable electric power demands. Thus, power grid operators could just adjust generation sources to accommodate admissible deviations of power demands. Now with the growing RES integration, such an operation paradigm becomes ineffective. New operating strategies utilizing smart grid technologies, e.g. advanced optimization methods, are needed, so that power grid resilience regarding the integration and utilization of RESs can be improved.
To generate operation strategies with both robustness and economy, stochastic optimization and robust optimization have been applied to cope with the uncertainty of wind power. With probability distribution information of uncertain parameters, stochastic optimization is a mature methodology to provide decisions against uncertainties. To name a few, stochastic unit commitment and stochastic economic dispatch models were proposed in [23, 24]. Nevertheless, an accurate probability distribution of unknown parameters can be quite difficult to identify [25]. In this regard, robust optimization is a promising alternative receiving much attention in recent years, partially because it does not require accurate distribution information of uncertainties. It evaluates the worst-case performance of a decision, resulting in an optimization solution that is robust against any possible scenario in the uncertainty set.
However, to build resilient power grids with high penetration of RESs, many critical problems, e.g. restoration of transmission grids with large-scale RESs, have not yet been addressed.
In general, RESs can empower more flexible strategies for improving power grid resilience, but we must also consider the impact of RESs' characteristics on the power grid. Reaching a fair balance between RES integration and resilience improvement in power grid is critical for relevant research. In fact, only with resilient strategies that sufficiently mitigate the adverse impacts of RESs, the economic, social, and environmental benefits of RESs can be fully utilized.
1.2.2.3 Dealing with Cybersecurity-Related Events
The former two parts are mainly about security and resilience of the physical system of a power grid. Apart from that, security of the cyber system is also an important part of power grid resilience. The reliable operation of modern power grids is fundamentally supported by cyber systems. The devices that monitor and control power grids are typical information and communications technologies-based systems. Those systems face the threats of cyber-attacks, which can undermine the control systems and endanger the secure operation of power grids.
In recent years, cyber-attacks have resulted in many security problems. The U.S. National Security Agency has reported that there have been some cyber intrusions to critical infrastructures and has emphasized the importance and benefits of improving the resilience of cyber systems. The following shows several reported incidents of cyber intrusions into existing information and communications technologies-based systems [26]:
BlackEnergy
. It was first reported in 2007, with critical energy infrastructures being its targets. In 2014, several information and communications technologies-based systems were infected by BlackEnergy. With BlackEnergy, cyber-attackers can deliver some plug-in modules for audio recording, keylogging, and grabbing screenshots, etc.
HAVEX
. For the early version of HAVEX, it was distributed through spear-phishing attacks or spam e-mails. After several revisions, HAVEX has become a Trojan horse used to modify “legitimate” software in information and communications technologies-based systems including the supervisory control and data acquisition (SCADA) system, and
supervisory control and data acquisition
(
SCADA
) systems by adding additional instructions to codes.
Sandworm
. It is a Trojan horse, which is used to deliver malware on thumb drives.
As indicated by the aforementioned events, cyber-attacks cannot be neglected. With the development of cyber networks, cyber security of power grids faces new threats from attacks that are very stealthy and less expensive. Therefore, the cyber security in power grids is currently a hot topic in resilience research. For example in [27], a novel criterion for assessing the resilience of power supply to data centers was proposed to evaluate the system's capability of sustaining functionality during an outage. Enhancing the cyber system resilience of power grids not only mitigates the threats from cyber-attacks but also enables the cyber system to more effectively and more efficiently monitor and control power grids.
1.2.3 Challenges
Conventional strategies, e.g. common normal state-based preventive and emergency strategies in the planning and operation timescales, fail to consist of resilient measures against disastrous events, as they have little consideration of such low-probability, high-impact events either weather-related or cybersecurity-related. Since power grid resilience requires robustness, resourcefulness, rapid recovery, and adaptability at different stages, appropriate and sophisticated strategies of different stages should be implemented [28]. Furthermore, complicated characteristics of modern power grids, from the perspectives of the source, network, storage, and load, pose great challenges to the construction of resilient power grids.
In the following, some challenges to improving power grid resilience are briefly discussed:
Component reliability enhancement
. Electric power companies try to maximize their profits and maintain a good system technical behavior from the reliability perspective. As a weather-related disastrous event unfolds, the critical devices on the trajectory of the event are expected to have high reliability, which can reduce the probability of being in failure and therefore ensure system resilience
[29]
. In this regard, component reliability enhancement, e.g. maintenance scheduling in consideration of potential extreme events, is needed to guarantee good conditions of electric devices. Establishing a comprehensive maintenance plan considering potential natural disasters is not an easy task. The following challenges should be considered: First, weather-related events occur with uncertainties, and the influences of those events on the deterioration of each component are stochastic. Second, the dimension of enormous electric devices in power grids results in large-scale optimization problems. For short-term maintenance scheduling, many system operation constraints, including the
security, ramping rates of generating units, power balance, and spinning reserve capacity requirements, should also be considered. The relevant optimization models are usually computationally intractable with large-scale systems
[30]
. Third, considering a fast growth of renewable energy in power grids, the influences of uncertainties of renewables on maintenance scheduling considering weather-related extreme events are critical issues. To tackle those problems, proactive operation strategies to enhance system resilience considering the uncertain sequential transitions of states are needed.
System state acquisition
. System state acquisition is a prerequisite for power grid operators to perform resilient, proactive, and emergency strategies, before the extreme event, during its unfolding, and after its occurrence
[31]
. Facing extreme events, a system is under continuous and severe disturbances. In this regard, the dynamic states of the system should be identified. Measurements from
phasor measurement unit
s (
PMU
s) are rapidly updated and can be employed to perform dynamic state estimation
[32]
. Performing dynamic state estimation should consider the following challenges. First, multiple control areas, resulting from the power industry's deregulation, should be considered. These interdependent areas usually do not share all information of their own control areas. Second, appropriate approaches for estimating dynamic states are needed. Currently, some approaches can be used for estimating dynamic states with the assumption of Gaussian distributions of measurements' noises [
33
,
34
]. However, some measurements' noises are not satisfied with the assumption of Gaussian distributions. Third, a rapid computation speed for dynamic state estimation is required.
Multiple energy systems
. It is expected that power grids have more contributions to the sustainability and low-carbon development of energy sectors. In China, carbon neutrality is an essentially important target, and a series of policy measures have been implemented to reduce greenhouse gas emissions. By means of integrating different energy sources across different pathways, the multi-energy system provides a promising way to reduce carbon emissions. However, the multi-energy system has more complicated characteristics compared to traditional power grids, and in consequence needs sufficient and novel measures to guarantee high system resilience. In 2021, one severe power outage occurred in Texas, partly due to inappropriate operation of the multi-energy system in the face of extreme cold weather. In fact, different energy carriers of the multienergy system have different responses to disturbances, and this leads to difficulties in constructing coordinated strategies. Therefore, it is a challenge for multiple energy systems to construct systematic frameworks and techniques that enhance system resilience.
Renewable energy uncertainty
. With increasing concerns on possible energy shortage, worldwide efforts have been carried out to integrate enormous RESs into power grids
[35]
. Among different kinds of renewable energy, wind power attains the highest penetration in some countries, partially owing to the relatively mature wind turbine technologies. Although wind power is promising in easing the worries over the energy crisis, its variability and uncertainty have brought about great challenges to the reliable and economic operation of power grids. Other kinds of renewable energy also possess variability and uncertainty features, which also impose similar challenges and worsen relevant problems. Two critical issues need to be considered when a power grid is under the threat of a natural disaster, especially an extreme weather event. The first one is that the extreme weather event has a great impact on renewable generation, and the second one is that the grid in the face of the event may not be as strong as that under normal conditions. These two critical issues make conventional strategies improper to the resilient operation of high-renewable power grids against extreme weather events. In addition, RESs such as photovoltaic power and wind power are usually connected to the grid via power electronics devices with low inertia, which reduces the grid strength against disturbances. The abovementioned factors pose great challenges to resilient high-renewable grid operation in the face of natural disasters.
Cyber-physical systems
. Many research studies focus on power grid resilience from the perspective of physical systems. However, information and communications technologies have been playing important roles in physical system monitoring and control and have driven the conventional power system into the cyber-physical system. Many components in the cyber-physical system are directly exposed to external environment, and they are both vulnerable to natural disasters (including extreme weather events) from the perspectives of the security and resilience of information networks and physical networks. If a cyber-physical system is affected by an extreme weather event, unavailability of some parts of the cyber system might result in incomplete information, which in consequence can lead to failed state estimation and large control errors. Therefore, how to analyze and mitigate the impacts of disastrous events on cyber-physical systems is a critical challenge with regard to power grid resilience improvement.
Multi-area networks
. The development of power markets, multiple energy systems, and system expansion, etc. have been driving power grids into multi-area interconnected systems. Conventional centralized strategies usually cannot be directly employed for multi-area interconnected systems when considering specific jurisdictional mandates, extensive communication burdens, and information privacy, etc. Boundary restrictions between different energy systems complicate the interaction problems and coordinated strategies and result in challenges to the improvement of system resilience.
Note that other than the abovementioned issues, many other challenges need to be addressed in building resilient power grids. Relevant discussions are included in relevant parts, where appropriate, of this book.
1.3 Resilience Enhancement Against Disasters
A power grid resides in different stages when it is exposed to natural disasters including extreme weather events. It is necessary to define these stages to enable systematic enhancements of power grid resilience against these events. In October 2010, the U.S. President's National Infrastructure Advisory Council released a report presenting a resilience structure with four features, based on the sequence of “prior to an event,” “during an event,” “after an event,” and “postincident learning,” respectively [14, 16, 17]. Building effective resilience enhancement strategies requires an understanding of preventive, real-time and recovery strategies, and an awareness of how the related actions impact grid planning and operation [36].
Specifically, prior to an event, “robustness” requires the grid to stay standing or keep operating in the face of the event that can be catastrophic. Strengthening and hardening the system is one of the acceptable preventive strategies. In general, maintaining and investing in critical infrastructure elements can improve grid robustness so that the system can withstand those extreme events. During an event, “resourcefulness” requires that the grid has sufficient capabilities of managing the event as it unfolds. In this stage, it is necessary to identify available strategies and prioritize what can be implemented to mitigate the impact of damages caused by the event. After an event, it is desired that we can get the system back to its normal state as quickly as possible. To this end, determining emergency and restoration approaches is important, and scheduling appropriate resources and right people to right places is also critical [37, 38].
1.3.1 Preparedness Prior to Disasters
Prior to natural disasters, assessments and preventive strategies need to be implemented to improve the power grid's capability of dealing with the events, such as state assessments using historical data-based models [39–41], and resilience enhancement strategies based on distributed generation (DG) allocation [42], microgrid technologies [43, 44], and switch placement [45]. Overall, methods of enhancing power grid resilience can be divided into component level and system level. A brief discussion is provided in the following:
1.3.1.1 Component-Level Resilience Enhancement
Good conditions of electrical devices are important for power grid resilience enhancement. Usually, maintenance activities are employed to mitigate the deterioration of grid components. However, such activities often increase the total operating cost of grids. To achieve an appropriate trade-off between grid resilience and operating cost, system operators need to develop a series of combined long-, mid-, and short-term maintenance activities for various components in the power grid.
