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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Full-scope economic perspectives on planning, operations management, and maintenance of climate resilience building measures in power infrastructure
The Economics of Climate Resilience in Power Infrastructure sheds light on the engineering economics of climate adaptation in electric power infrastructure by covering the relevant decision-making processes involved in managing risk and resilience in these systems. The book offers a system-level perspective along with detailed modeling of the most pressing resilience issues, while also providing detailed numerical examples on small test systems throughout the text to help readers see the outcomes of models.
The book starts with an introduction to risk management and the techno-economic considerations for resilience building measures in power systems. Next, economic concepts and mechanisms for managing climate risk in power systems are introduced. Afterward, an economic model for resilience investment in these systems against climate shocks is presented. The authors then discuss an economic asset management model for long-term resilience building in critical infrastructure assets. Subsequently, an economic model for operations management during disasters is proposed, followed by a model for post-disaster restoration.
Written by a pair of distinguished thought leaders, the book explores other topics such as:
- Microgrid applications for decentralization, along with an economic model for resilience-oriented microgrid operations
- A deep defense framework for climate risk management in power systems, along with other factors influencing their operational and financial resilience
- Essential climate risk financing mechanisms and techno-economic factors in managing risk and resilience in the face of wildfires, heat waves, and hurricanes
- Steps for utility and infrastructure owners to recover from climate shocks and natural disasters, for the benefit of shareholders, ratepayers, and policymakers
The Economics of Climate Resilience in Power Infrastructure is an essential resource on the subject for industry practitioners, R&D engineers, infrastructure planners, and graduate students seeking to incorporate the economics of resilience with engineering solutions to streamline the success of climate adaptation measures in the power and energy industry.
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Seitenzahl: 412
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Authors
Foreword
Preface
Acknowledgments
1 Introduction
1.1 The Art of Climate Resilience
1.2 Climate Risk in Power Infrastructure
1.3 Resilience Metrics in Power Systems
1.4 Economic Impacts of Climate Shocks and Natural Disasters
1.5 Overview
References
2 The Economics of Financial Resilience Against Natural Disasters
2.1 Principles of Risk Management
2.2 Climate Risk Financing Mechanisms
2.3 A Financial Resilience Building Framework Using CAT Bonds
2.4 Wildfire Simulation Modeling
2.5 Case Studies
2.6 Summary
References
3 The Economics of Resilience-Centered Asset Management
3.1 Principles of Resilience-Centered Asset Management
3.2 Economic Variables for Resilience-Centered Asset Management
3.3 Economic Asset Management Model
3.4 Case Study
3.5 Summary
References
4 The Economics of Capacity Planning for Climate Shocks
4.1 Principles of Resilience Investment
4.2 Climate Risk Assessment Variables
4.3 An Economic Model for Resilience Investment
4.4 A Heatwave Case Study
4.5 Summary
References
5 The Economics of Resilience Planning in Power Infrastructure Expansion
5.1 Background
5.2 Economic Variables for Expansion Planning
5.3 An Economic Model for Resilience Planning in Infrastructure Expansion
5.4 Case Studies
5.5 Summary
References
6 The Economics of Operational Risk Management in the Face of Natural Disasters
6.1 Principles of Operations Management Against Wildfires
6.2 Variables for Quantifying Risk and Vulnerability Against Wildfires
6.3 A Model for Quantifying Risk and Vulnerability Against Wildfires
6.4 A Case Study for Wildfires
6.5 Summary
References
7 The Economics of Pre-Disaster Resource Mobilization
7.1 Principles of Resource Mobilization
7.2 Economic Variables in Resource Mobilization
7.3 An Economic Model for Pre-Event Resource Mobilization
7.4 A Case Study for Hurricanes
7.5 Summary
References
8 The Economics of Contingency Planning Under Extreme Events
8.1 Background
8.2 An Economic Model for Contingency Planning
8.3 Case Studies
8.4 Summary
References
9 The Economics of Decentralization Through Microgrids
9.1 Principles of Microgrids
9.2 Economic Variables for Decentralized Power Systems
9.3 An Economic Model for Resilience-Oriented Microgrid Operations
9.4 Case Study
9.5 Summary
References
10 The Economics of Post-Disaster Restoration
10.1 Principles of Power System Restoration
10.2 Economic Variables in System Restoration
10.3 A Generic Economic Model for Post-Disaster Restoration
10.4 Case Study
10.5 Summary
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Select risk metrics for measurement of damage due to climate-relat...
Chapter 2
Table 2.1 Derived premiums for wildfire ignition.
Chapter 3
Table 3.1 Dissolved key gas concentration limits (μl/l(ppm)).
Table 3.2 Parameter values for numerical analysis.
Table 3.3 The expected life-cycle costs for cases 1–4.
Table 3.4 The expected life-cycle costs for cases 5 and 6.
Chapter 4
Table 4.1 Parameters of fitted probability distribution functions.
Table 4.2 Reduced scenarios for the baseline.
Chapter 5
Table 5.1 Generating unit data of six-bus system.
Table 5.2 Transmission line data of six-bus system.
Table 5.3 Yearly peak load forecast of six-bus system.
Table 5.4 Load blocks in the first year.
Table 5.5 Candidate unit installation year of six-bus system.
Table 5.6 Candidate unit installation year of six-bus system using TC.
Table 5.7 Candidate line installation year of six-bus system.
Table 5.8 Candidate line installation year of six-bus system using TC.
Table 5.9 Candidate unit data of IEEE 118-bus system.
Table 5.10 Candidate transmission line data of IEEE 118-bus system.
Table 5.11 Switchable line data of IEEE 118-bus system.
Table 5.12 Candidate unit installation year for IEEE 118-bus system in case ...
Table 5.13 Candidate line installation year for IEEE 118-bus system in case ...
Table 5.14 Candidate unit installation year for IEEE 118-bus system in case ...
Table 5.15 Candidate unit installation year for IEEE 118-bus system in case ...
Chapter 6
Table 6.1 Assessed values for vulnerability of lines.
Table 6.2 Assessed values for risk of lines.
Table 6.3 Assessed values for vulnerability of nodes.
Table 6.4 Assessed values for the impact of nodes.
Chapter 7
Table 7.1 Probability of damage and scale parameter of time to repair for ge...
Table 7.2 Probability of damage, time to repair parameter, and the derived r...
Table 7.3 Probability of damage, time to repair scale parameter and the deri...
Chapter 8
Table 8.1 Characteristics of generating units.
Table 8.2 Characteristics of transmission lines.
Table 8.3 Hourly load demand.
Table 8.4 UC schedule of six-bus system in case 1.
Table 8.5 Line schedule of six-bus system in case 1 using TC.
Table 8.6 UC schedule of six-bus system in case 3 without TC.
Table 8.7 Line schedule of six-bus system in case 3 using TC before outage o...
Table 8.8 UC schedule of IEEE 118-bus system in case 1 using TC.
Table 8.9 Line schedule in case 1 using TC.
Chapter 9
Table 9.1 Aggregated generation of non-dispatchable units (MW).
Table 9.2 Hourly fixed load (MW).
Table 9.3 Adjustable load.
Table 9.4 Dispatchable units.
Table 9.5 Distributed energy storages.
Table 9.6 Hourly market price ($/MWh).
Table 9.7 Super-microgrid formation in a 24-hour horizon.
Table 9.8 Spinning reserve and power deficiency of microgrids in four hours ...
Chapter 10
Table 10.1 Damaged buses and time to repairs.
Table 10.2 Damaged transmission lines and time to repairs.
Table 10.3 Damaged generation units and time to repairs.
Table 10.4 Optimal repair schedule for buses in Scenarios I–III.
Table 10.5 Optimal repair schedule for transmission lines in Scenarios I–III...
Table 10.6 Economic indices for Scenarios I–III (costs × 10
3
).
Table 10.7 Optimal repair schedule for buses in Scenario IV.
Table 10.8 Optimal repair schedule for transmission lines in Scenario IV.
Table 10.9 Economic indices for different cases in Scenario IV (costs × 10
3
)...
List of Illustrations
Chapter 1
Figure 1.1 The 2023 update to the planetary boundaries.
Figure 1.2 Sustainable economic development framework.
Figure 1.3 1980–2024 year-to-year cost of billion-dollar climate-related dis...
Chapter 2
Figure 2.1 Three lines of defense framework for wildfire risk management in ...
Figure 2.2 Grid resilience against climate shocks.
Figure 2.3 CAT bond components and cash flows.
Figure 2.4 Geographic map for the study area.
Figure 2.5 Geographic visualization of the IEEE 30-bus test system.
Figure 2.6 Spatial distribution of ignition points originating from power li...
Figure 2.7 Corresponding burned area for each scenario.
Figure 2.8 Comparison of failed lines needing reconstruction.
Figure 2.9 Comparison of total loss for each grid-ignited scenario.
Figure 2.10 Composition of CAT bond yield components.
Chapter 3
Figure 3.1 Transition diagram for the first element of the state space.
Figure 3.2 Structure of optimal policy on sample path.
Figure 3.3 Comparison of expected cost-to-go and simulated model.
Chapter 4
Figure 4.1 Wind roses of April–September (left) versus the entire year (righ...
Figure 4.2 Return period plot (years) for maximum temperature exceedance (°F...
Figure 4.3 Expected load interruption under various hazard scenarios and cas...
Figure 4.4 Projected diurnal load interruption in various scenarios and case...
Chapter 5
Figure 5.1 Proposed expansion planning using TC.
Figure 5.2 Six-bus system.
Figure 5.3 Investment cost in Case 3.
Figure 5.4 Operating cost in Case 3.
Figure 5.5 Total planning cost in Case 3.
Chapter 6
Figure 6.1 Trend of wildfires caused by the grid over time.
Figure 6.2 Frequency and impact comparison for wildfire causes.
Figure 6.3 The multi-layered data foundation for the framework.
Figure 6.4 Wildfire simulation output mapped with the network topology.
Figure 6.5 Geographic mapping of the IEEE 30-bus test system in the selected...
Figure 6.6 Visualization of average vulnerabilities and risk of lines.
Figure 6.7 Visualization of average vulnerabilities and impact of nodes.
Chapter 7
Figure 7.1 Expected cost breakdown for three scenarios.
Figure 7.2 Optimal resource level for three scenarios.
Chapter 8
Figure 8.1 SCUC using TC.
Figure 8.2 Flowchart of SCUC using TC.
Figure 8.3 Six-bus system.
Figure 8.4 Total operating cost comparison of integrated and decomposed mode...
Figure 8.5 Execution time comparison of integrated and decomposed models.
Chapter 9
Figure 9.1 Proposed spinning reserve-based model for integrated microgrids i...
Figure 9.2 Load curtailment of integrated microgrids, before and after super...
Chapter 10
Figure 10.1 Interruption vs. operations duration for different cases in Scen...
Figure 10.2 Optimal restoration cost breakdown for different cases in Scenar...
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Authors
Foreword
Preface
Acknowledgments
Begin Reading
Index
End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor-in-Chief
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Desineni Subbaram Naidu
Tony Q. S. Quek
Behzad Razavi
Thomas Robertazzi
Diomidis Spinellis
The Economics of Climate Resilience in Power Infrastructure
Ali Arabnya
Quanta Technology, USA
University of Denver, USA
Amin Khodaei
University of Denver, USA
Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc.All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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To our families
About the Authors
Ali Arabnya, PhD, is the Director of Infrastructure Finance & Climate Risk with Quanta Technology based in Washington, DC. He is also a research professor of electrical and computer engineering with the University of Denver. Previously, he was a consultant climate economist with the World Bank. Prior to that, he was a Data & Analytics Manager with Protiviti in New York City. He has published over 40 peer-reviewed technical papers and is the co-author of the book The Economics of Microgrids. He is a Senior Member of the IEEE Power & Energy Society and holds a PhD in Industrial Engineering from the University of Houston.
Amin Khodaei, PhD, is a Professor of Electrical and Computer Engineering at the University of Denver. His research is focused on the climate crisis, the grid of the future, and grid-enabling technologies including artificial intelligence and quantum computing. He has published over 220 peer-reviewed technical articles on various aspects of electric grid modernization. He is a Senior Member of the IEEE and holds a PhD in Electrical Engineering from the Illinois Institute of Technology.
Foreword
Electrical power and energy systems are critical to power the prosperity of our society. They form the backbone of modern economy and are central to every aspect of our lives. Present-day electrical systems are confronted with unprecedented challenges, such as weather-related impacts and physical and cybersecurity threats. They are also enablers of the decarbonization of the environment through deployment of renewable generation, electrification of transportation and heating, and other sources of carbon emissions.
Power system resilience is critical to achieving society’s goals by adapting to and mitigating climate change and is a cornerstone of societal stability and prosperity. Adaptation means designing and operating resilient energy infrastructure that can withstand and quickly recover from the shocks of climate change, while mitigation includes reducing the greenhouse gas emissions that are driving these extreme events.
This book addresses adaptation challenges to ensure that our power systems remain resilient, reliable, and sustainable in the face of climate change and helps with achieving resilience goals. The authors provide a comprehensive overview and analysis of the engineering economics of climate adaptation in electric power infrastructure. Through a series of developed models, case studies, and real-world applications, the authors present strategies to enhance the resilience of our power systems, focusing on the critical role of economic decision-making in managing climate risks. From understanding the economic implications of climate shocks to developing new financial instruments, asset management frameworks, and operational strategies, this book lays out a blueprint for building more resilient and adaptive power systems.
Each chapter provides both theoretical insights and practical solutions through comprehensive framework that can guide engineers, policymakers, and utilities in the critical task of future-proofing our power systems. Whether it is through advanced optimization techniques, innovative insurance models, or existing and new technologies, the solutions presented here emphasize the importance of a holistic, interdisciplinary approach to resilience.
The importance of resilience in power systems cannot be overstated. As we continue to witness the increasing frequency and intensity of climate-related disruptions, the ability of our energy infrastructure to quickly recover—and to adapt to new risks—will determine the well-being of communities, economies, and societies worldwide. This book provides invaluable tools and strategies for meeting this challenge.
In an age where the need for sustainability is an immediate necessity, this book stands as a guide to ensuring that our electrical power systems are resilient, adaptable, affordable, and prepared for the opportunities and challenges ahead.
December 05, 2024
Dr. Damir Novosel
President of Quanta Technology
Preface
Climate change is one of the grand challenges of the 21st century. It poses an increasing risk to our critical infrastructure and requires significant investment for upgrades, mitigation, and adaptation. Power grid infrastructure in particular is expected to be highly stressed by these events, combined with an ever-increasing power demand, reduced operational capacity, and increased probability of system failures resulting in significant power outages. The economic and financial losses caused by climate-related events such as wildfires, heatwaves, and hurricanes can reach to a level that threatens the financial health and solvency of the utilities and local governments. In addition, they can significantly hinder the ability of utilities and infrastructure owners to recover, which can lead to dire consequences for shareholders, ratepayers, and policymakers. That requires developing a wide range of innovative engineering economic solutions to achieve the financial, operational, and physical resilience expected from a 21st-century electric power infrastructure. The economics of resilience should be incorporated in technological solutions to streamline the success of climate adaptation measures in the power and energy industry. However, despite significant engineering research and development efforts on climate adaptation in power infrastructure, there is a lack of concerted effort to provide an engineering economic perspective on this issue. This book intends to provide a full-scope economic perspective on planning, implementation, operations, and maintenance of resilience enhancement measures in power infrastructure in the face of climate shocks and natural disasters. Our goal is to equip readers, including industry practitioners, policymakers, investors, and academic researchers, with the knowledge required to analyze power infrastructure resilience from an engineering economic lens.
Acknowledgments
Several chapters of this book were written based on valuable contributions made by the following co-authors:
Mohammad Shahidehpour, PhD
Suresh Khator, PhD
Zhu Han, PhD
Saeed Nematshahi
Eylem Tekin, PhD
Matthew P. Thompson, PhD
Yu Wei, PhD
Erin Belval, PhD
Rozhin Eskandarpour, PhD
Kevin Ding, PhD
Valentine A. Emesih
Behrouz Sohrabi
1Introduction
1.1 The Art of Climate Resilience
Climate change is one of the most complex issues of our time. Addressing climate change as one of the grand challenges of the 21st century involves a two-pronged approach, which includes: (i) climate adaptation, i.e., adjusting to actual or expected future climate scenarios; and (ii) climate mitigation, i.e., reducing and stabilizing the flow of heat-trapping greenhouse gas emissions in the atmosphere [1]. William D. Nordhaus, a co-recipient of the 2018 Nobel Memorial Prize in Economic Sciences, describes climate change as the ultimate challenge for economics [2], calling for collaborative efforts by all sectors of society, including government, business, and civil society.
Energy systems, including the electric power infrastructure, are at the epicenter of this critical challenge, necessitating multidisciplinary collaboration among research communities to understand, assess, predict, and proactively respond to this climate phenomenon. This collaboration may be viewed from at least two critical lenses. The first lens focuses on climate change’s impacts on energy systems and their interdependent critical infrastructure systems coupled with other techno-socioeconomic systems. The second lens is centered around the impacts of energy systems on climate change and its cascading effects on the different areas of planetary boundaries within which humanity can continue to develop and thrive for generations to come [1]. The concept of planetary boundaries identifies nine processes that are critical for maintaining the stability and resilience of the Earth system as a whole. As shown in Figure 1.1, six out of nine planetary boundaries have already been breached, while simultaneous pressure within all boundaries is increasing. Transgression of these boundaries identified in Earth system models indicates that the anthropogenic impacts on the Earth system must be considered in a systemic context. According to these models, if the planetary boundaries are respected, then global environmental functions and life-support systems on Earth would be allowed to remain in their interglacial state—similar to those experienced over the past ∼10,000 years—rather than transitioning into an uncertain state without prior analog in human history [3].
Figure 1.1 The 2023 update to the planetary boundaries.
Source: Stockholm Resilience Centre, based on analysis in [3].
Both climate mitigation and climate adaptation efforts involve making decisions under deep uncertainty over time in dynamic interaction with the Earth system. Deep uncertainty exists when parties to a decision do not know or cannot develop consensus over (i) a system model that relates actions to outcomes; (ii) uncertainty of inputs to the model; and (iii) outcomes to be considered among various possible outcomes and their relative importance [4]. In other words, decision-making under deep uncertainty can involve situations where “we don’t know what we don’t know” about a system.
From a climate mitigation perspective, transitioning to net-zero emissions (NZE) energy systems by the middle of this century is at the center of climate action in the power and energy industry. Achieving the NZE milestone by 2050 requires a whole-of-society (WoS) approach to ensure that a set of policies; technologies; and societal, political, and economic solutions are developed and work in sync to achieve realistic decarbonization targets under economic and time constraints. There is a wide range of transition pathways that can be adopted to achieve decarbonization targets. The adopted pathway, however, will directly impact the decarbonization timeline and economic variables for stakeholders in the process. Reaching these targets without considering the short-, medium-, and long-term economic costs and benefits of reaching and maintaining these targets can result in serious impediments to the sustainability of NZE energy systems. Therefore, sustainability of the decarbonization pathways can play a critical role in streamlining their success. The National Environmental Policy Act (NEPA) of 1969 in the United States [5] defines sustainability as “to create and maintain conditions under which humans and nature can exist in productive harmony, that permit fulfilling the social, economic, and other requirements of present and future generations.” The United Nations’ Sustainable Development Goals (SDGs) provide a framework for peace and shared prosperity for people and planet Earth, now and into the future. At its heart are 17 SDGs with SDG 7 focusing on affordable and clean energy and SDG 13 addressing climate action involving mitigation and adaptation efforts [6]. To achieve this shared goal of sustainability, we need to build a global economy that meets the needs of our society without violating the planetary boundaries of the environment, as illustrated in Figure 1.2.
From a climate adaptation perspective, climate resilience enhancement measures are at the core of addressing this challenge for the power and energy industry. Climate change poses an increasing risk to our critical infrastructure and requires significant investment for upgrades, mitigation, and adaptation. Power infrastructure, in particular, is expected to be highly stressed by extreme events. Physical, operational, and financial losses caused by climate-related events such as wildfires, heatwaves, floods, and hurricanes can reach hundreds of billions of dollars, threatening the financial health and solvency of the electric utilities and local governments. In addition, these losses can significantly hinder the ability of utilities and infrastructure owners to recover from these events—which can lead to dire consequences for shareholders, ratepayers, and policymakers. This requires developing a wide range of innovative engineering solutions, financial mechanisms, and policy incentives to achieve the financial, operational, and physical resilience levels expected from a 21st-century power infrastructure.
Figure 1.2 Sustainable economic development framework.
It is further worth mentioning that while climate mitigation strategies (e.g., emission reduction) require global cooperation and local action, climate adaptation strategies (e.g., resilience enhancement measures) primarily require local solutions that form our first line of defense against climate extremes. A wide range of technological, economic, political, and community-based solutions must work together to achieve the desired level of resilience in the face of an ever-changing uncertainty due to climate shocks and natural disasters. This process is more of an art than a science!
1.2 Climate Risk in Power Infrastructure
Climate change poses an increasing risk to critical interdependent infrastructure systems, requiring significant investment to mitigate the risk in electric power infrastructure and the communities that are served. There are several studies in the literature indicating that climate change has increased the frequency and intensity of disasters in recent years [4–10]. The Fifth Assessment Report [11] by the Intergovernmental Panel on Climate Change (IPCC) concludes that “It is virtually certain that there will be more frequent hot and fewer cold temperature extremes over most land areas on daily and seasonal time scales, as global mean surface temperature increases.” As a result, power systems are expected to be highly stressed by climate shocks due to ever-increasing power demand, reduced operational capacity during extreme climate events, and increased probability of system failures that can result in significant power outages [12]. As the risk landscape for climate shocks becomes more complex, it exposes the natural and built environments, including power infrastructure, to an unprecedented level of vulnerability.
Over half a century after the publication of one of the earliest studies on efficient response to climate-related events [13]—motivated by Hurricane Carla that slammed into the Gulf Coast and moved onward into the United States and Canada—the issue of efficient response to climate shocks and natural disasters still seems to remain in its immature stage [14]. Climate shocks can result in significant economic, social, and physical disruptions, and cause considerable inconvenience for residents living in disaster areas due to loss of electricity, water, and communication [15]. The power infrastructure, one of the most critical lifeline systems and of utmost importance to our daily lives, is increasingly exposed to extreme weather events. The electric power grid transfers the electricity generated by large-scale power plants to various industrial, commercial, and residential customers via transmission and distribution networks; hence, it is dispersed over a vast geographical area which increases its exposure to extreme events. For instance, more than 2.8 million customers experienced a power outage in Houston, Texas area after Hurricane Ike hit the United States in 2008, which lasted from a few days to several weeks. Damages to the coastal and inland areas were estimated at $29.5 billion [15].
The structure of the decision-making process before, during, and after climate shocks is an integral part of a disaster risk reduction strategy. Decentralized versus centralized decision-making is an age-old question. Each of these decision structures can outperform its alternative depending on the decision context and the decision maker’s goals and performance metrics for the decisions made. When responsiveness through immediacy (i.e., taking the right action as quickly as possible in response to threats and opportunities) is the primary goal, decentralized decision-making can outperform its centralized alternative. However, when other decision metrics, such as reliability through compliance, efficiency through syndication, or perennity through detachment, are the primary goals in decision-making, centralized decision-making can be more advantageous than its alternative [16].
In the context of disaster risk management in power infrastructure, given the importance of responsiveness in the decision process, decentralized decision-making generally has more advantages over centralized decision processes [17]. Therefore, in the face of climate shocks and natural disasters, special attention should be given to a decentralized approach to disaster response and recovery resource allocation in power grids. The notion of decentralization in this context can be seen from at least two lenses: (i) decentralization of tasks and activities related to disaster response in power grids; and (ii) decentralization of organizational structure due to deregulation of the power industry, where generation, transmission, and distribution companies operate as independent entities. In both structures, however, coordination of tasks and activities with a central unit such as an Independent System Operator (ISO) or a command and control (C&C) center is required to improve the reliability and efficiency of these decisions.
Identification of climate-related hazard risk involves specifying hazards of concern based on stakeholders’ prioritized list of concerns. The hazard prioritization should be based on (i) the likelihood of the hazard occurrence; (ii) the likelihood of severe consequences should the hazard be realized; (iii) strategic priorities; and (iv) the availability of resources and budget constraints. Hazard risk identification also includes the development of hazard scenarios and associated uncertainties. Development of hazard scenarios includes detailing the specific hazard conditions. The hazard scenario should include the expected location, time, and duration of the event, as well as other conditions required to adequately characterize the hazard and its impacts on the power system. Once the hazard risks are identified, measuring the exposure of the power grid assets to the identified risks is an essential element of enhancing resilience and managing the disaster risk. This includes a specific assessment of the scope and value of physical and virtual assets and systems that might face a destructive event under various hazard risk scenarios identified in the previous step. In addition to exposure to assets, specific customers and loads under exposure to any given hazard scenario must be determined. Determining the level of disruption requires a specific assessment of the expected level of damage or stress in exposed assets under various hazard scenarios. This involves an assessment of the vulnerability, i.e., the expected consequences to exposed assets when a destructive event occurs. Specifying the disruption level should not only indicate which assets are damaged or degraded, but should also specify how severely the assets are impaired, what the consequences are, and what steps are required to recover the overall system functionality to its pre-disaster conditions [18]. For instance, expected physical damage to grid assets and loss of power due to a heatwave event might include burnout of a high-voltage transformer, experiencing load interruption due to loss of this transformer and reduction of transmission capacity in a line, requiring several hours to bring the system back to its pre-disaster conditions, and resulting in a significant cost in repair expenses and loss of revenue.
Once the three elements of disaster risk management (i.e., the probability of an identified hazard, power system exposure, and power system vulnerability) are identified, a set of system models is required to collect the data and estimate the expected consequences. Data collection from historical events that characterize the intensity and duration of the disruption to the power system and the community is required to assess the power grid’s resilience. Power utilities can maintain and improve the quality of data in their Outage Management System (OMS) to be used as a crucial input data set for a forward-looking resilience analysis process. System-level, data-driven predictive models capable of computing necessary power disruption estimates should be developed and regularly updated. These models should use risk data from previous steps (risk identification, risk exposure, and system vulnerability) as inputs to predict how the power system will be disrupted under various scenarios. For instance, expected physical damage due to a heatwave event can be used as input to a system model that relates them to energy not served (ENS) within the system over time. A set of system models may be required to capture various aspects of the system disruption, including power outage estimation models, restoration models, cost estimation models, and physical damage estimation models, among others. In addition, the interdependencies between models should be considered the system models as well [19]. For example, estimating the expected damage to the grid under various hurricane scenarios with different intensity, duration, and landfall trajectories requires a system model that uses three other system models, as follows:
where
ED
is the expected damage to the system due to a climate-related event
S
represents different scenarios
Pr
(
S
)
is a system model that gives the probability of a given scenario
EX
(
S
)
computes the exposure of the system under a given scenario
D
(
S
)
estimates the percentage of exposed assets that a loss will be materialized under a given scenario.
As another example, consider a restoration model that can determine the optimal restoration schedule of the power grid after a given natural disaster scenario. The schedule determined by such a model can inform systems models to assess system performance during the restoration and the required recovery time. The system models should be used to calculate the consequence estimates and resilience metrics defined in previous steps.
When uncertainty is involved in the analytical process, system models should be able to provide multiple probabilistic consequences and resilience metrics based on hazard scenarios, exposures, and vulnerability of the system obtained in previous steps. This will enable a risk-based decision-making process on resilience enhancement measures and prioritization of the mitigation options. Due to the probabilistic nature of this information and the uncertainty associated with the outcomes of these decisions, a specific risk metric based on both measures of central tendencies (e.g., mean) and dispersions (e.g., variance) can be used to describe the potential extent of these extreme events. When system models incorporate uncertainty in consequence estimates and resilience metrics, it is necessary to specify the statistical format of the metric, for example, the mean and variance of consequence estimates, the upper and lower bounds of consequence estimates, and the exceedance probability curve that describes the probability that certain loss levels will be exceeded. These risk metrics should be reflective of the stakeholders’ risk perspectives [18,19]. As an example, Table 1.1 describes four risk metrics that can statistically measure the potential damage from a climate-related scenario in power infrastructure.
There are various approaches for identifying the impact of events on the level of disruption. For instance, a model-based approach and analysis will allow the electric utility to plan and differentiate between two planning options. It will aim to provide a tool to inform the most efficient investment to mitigate a climate-related hazard. The value of impacted customers, outage duration, level of disruption, risk spend efficiency, and other metrics can be evaluated for several different investment prioritization strategies [18].
Table 1.1 Select risk metrics for measurement of damage due to climate-related events [19,20].
Risk metric
Description
Value-at-risk (VaR)
A measure of risk that involves determining the worst loss expected over a target horizon within a given confidence interval.
Conditional value-at-risk (CVaR)—also known as expected shortfall
A risk assessment measure that quantifies the amount of tail risk, which is derived by taking a weighted average of the “extreme” losses in the tail of the distribution of loss, beyond the VaR cutoff point.
Probable maximum loss (PML)
The maximum loss expected from a disaster event, expressed in dollars or as a percentage of the total value of the asset.
Maximum foreseeable loss (MFL)
The worst loss that is likely to occur because of a single disaster event.
1.3 Resilience Metrics in Power Systems
A resilient power grid that ensures an uninterrupted electricity supply to citizens and interdependent lifeline systems is the keystone of modern society. The power grid continues to evolve as the needs and demands of society continue to change, and grid operators are faced with an emerging risk landscape. The changing power demand, increased reliance on renewable and distributed energy resources, and growth in penetration of smart technologies introduce additional layers of complexities to grid operations. Furthermore, the increasing frequency of natural disasters [21], climate change [22], manmade disasters (e.g., 2014 physical attack on PG&E’s substation [23]), and cyber-attacks (e.g., malware BlackEnergy) pose a significant level of compounded risk to the uninterrupted operations of the modern power grid.
The complexity and interdependency of power infrastructure systems, either in urban or rural settings, can increase with time and are vulnerable to disasters. Developing mitigation strategies that outflank the process of risk transference of mega-disasters is the key to successfully managing disasters. In this context, resilience refers to the capacity to “bounce back” to a pre-disaster condition [24]. Based on the definition from [25], “local resiliency with regard to disasters means that a locale is able to withstand an extreme natural event without suffering devastating losses, damage, diminished productivity, or quality of life and without a large amount of assistance from outside the community.”
Commonly used reliability metrics—such as System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), Customer Average Interruption Duration Index (CAIDI), and Customer Average Interruption Frequency Index (CAIFI), among others—are highly effective in measuring the reliability of the system and ensuring grid preparedness for disruptions and expected outages that occur under relatively normal conditions. However, due to the changing climate risk landscape, there is a broad consensus across the board that these metrics are inadequate to effectively address many of the emerging risk exposures in the power grid [26]. This is due to the fact that commonly used reliability metrics typically do not include outage information when low-probability, high-consequence disruptive events occur. In this emerging risk landscape, reliability metrics, which are built on historical data, may not be appropriate for capturing the complex risk dynamics as emerging risks can significantly deviate from their historical precedents. Similarly, security measures do not apply to grid resilience. While power grid security is primarily concerned with preventing a disruptive event from occurring in the first place, grid resilience is primarily concerned with ensuring the continuity of the service by the power grid to the communities that rely upon them in the face of disruptive events. Even though it may not be feasible to guarantee the continuity of operations at nominal or pre-disruption levels, ensuring a reduced service level can minimize the hazardous impacts of these events on communities. Therefore, a new set of metrics is required to address the resilience requirements of the evolving power grid.
Enhancing and maintaining power grid resilience requires not only a well-thought-out strategic vision and a well-executed action plan but also an effective allocation of capital and resilience investment prioritization under budget constraints to ensure the desired return on investment and the achievement of resilience goals. Specifying the resilience goals is the first step in the resilience optimization framework, laying the foundation for all steps in the process. To set resilience goals, senior management in electric utilities should form a cross-functional team of experts from key stakeholders to determine the acceptable (or unacceptable) impact of a given undesired event under various circumstances. Discussion during this phase is required to decide whether the resilience goals should be set based on (i) previous historical events, or (ii) aspirational resilience goals set by senior management. It is important to quantify the resilience goals in terms of acceptable duration, amount of lost load, and recovery cost for each load per each specific outage event. That quantification should be as specific as possible, including detailed information on the geographical boundary (e.g., urban, rural), load type (residential, commercial, industrial, or critical loads such as hospitals, police stations), hazard type (e.g., hurricane, wildfire), hazard intensity (e.g., a category 4 hurricane), and the time period after an event strike (e.g., the first hour, the first week). Once the resilience goals are defined, determining the consequence categories that serve as the basis for resilience metrics is the second step in the process. The consequence categories should be reflective of the resilience goals. In some instances, the consequence estimation and resilience metrics may focus on the impacts directly realized by the power utility, such as power not delivered, loss of revenue, and cost of recovery, among others. However, in some instances, direct impacts are only part of the resilience assessment process. Power and energy systems provide energy not just for generation or distribution but for some larger community benefit (e.g., transportation, healthcare, manufacturing, economic gain). Resilience analyses that aim to include a broader community perspective may convert power disruption estimates into community consequence estimates (e.g., number of emergency service assets affected, business interruption costs, impact on gross regional product, etc.). All the consequence categories are measured for the defined system specifications and therefore may be measured across spatial (geographical) and temporal (duration) dimensions. Data availability may also affect the selection of consequence categories. Resilience analyses are not restricted to a single consequence category for developing resilience metrics. Instead, a scenario-based approach using multiple consequence categories can be beneficial for representing various stakeholder perspectives [18, 19].
1.4 Economic Impacts of Climate Shocks and Natural Disasters
When a climate-related extreme event strikes, it affects not only physical assets including power infrastructure but also businesses and their ability to contribute to the local economy. These multidimensional, second-order effects are linked to both the physical characteristics of these extreme events (e.g., hurricane category, flood levels, heatwave intensity and duration) as well as the socioeconomic characteristics of the affected communities. The socioeconomic disparities of the affected communities shape the severity of shocks on local economies as well as the duration of subsequent recovery process. This is due to the fact that disaster consequences can remain long after they occur. A recent analysis of socioeconomic impacts of natural disasters by the author indicates that the impacts on the economic wellbeing of the affected communities can be far more than the cost of recovering the physical damages to the infrastructure. It also indicates that the recovery process not only is a function of the extent of physical damages due to disasters but also the economic structure and the socioeconomic resilience of the affected communities. The study shows that the value of lost income in the affected communities is far greater than the cost of damages to the infrastructure assets due to natural disasters [27].
The United States alone has sustained over 390 climate-related disasters from 1980 to mid-2024 where cost of damages due to each of these events was at least $1 billion (adjusted for inflation as of 2024). The total cost of damage due to these events reached over $2.755 trillion ($61.3 billion per year), as shown in Figure 1.3[28]. The U.S. Department of Energy estimates that the annual economic cost of power outages in the United States alone is over $150 billion (adjusted for inflation as of 2015) [29]. Among them, about 83% of reported major power outages between 2000 and 2021 in the United States are estimated to be due to weather-related extreme events. Moreover, the average number of weather-related power outages per year is estimated to have increased by about 80% since 2011. From 2000 to 2021, there were over 1500 weather-related power outages leading to an estimated seven hours of outage per year for an average home or business in the United States [30].
Despite the increasing traction of climate economics in the power and energy community, there is still a lack of concerted efforts to address this gap using a multidisciplinary, multisectoral approach. Without considering the economic viability, business imperatives, cost of natural capital, political realities, and most importantly the socioeconomic resilience level of the communities, any engineering solutions for climate mitigation and adaptation will remain just an interesting idea that may not prove to be sustainable in the long run.
Figure 1.3 1980–2024 year-to-year cost of billion-dollar climate-related disasters in the United States [28].
1.5 Overview
This book discusses the engineering economics of climate resilience in electric power infrastructure by covering the economic decision-making processes involved in the design, planning, operation, and restoration of the grid in the face of climate shocks and natural disasters. The remainder of the book is organized as follows:
Chapter 2
:
The Economics of Financial Resilience Against Natural Disasters
presents an analytical framework for a risk transfer mechanism for power infrastructure.
Chapter 3
:
The Economics of Resilience-centered Asset Management
presents an integrated maintenance strategy for critical components of power infrastructure incorporating the risk of climate shocks and long-term climatological patterns.
Chapter 4
:
The Economics of Capacity Planning for Climate Shocks
introduces a stress testing method to measure extreme temperature impacts on power systems’ transmission capacity.
Chapter 5
:
The Economics of Resilience Planning in Power Infrastructure Expansion
introduces a long-term capacity planning model considering future disruptions in the generation and transmission facilities due to climate shocks and natural disasters.
Chapter 6
:
The Economics of Operational Risk Management in the Face of Natural Disasters
presents a risk-rating framework for power infrastructure in the face of natural disasters.
Chapter 7
:
The Economics of Pre-disaster Resource Mobilization
introduces an analytical framework for decision-making under uncertainty for allocating restoration resources before an imminent natural disaster.
Chapter 8
:
The Economics of Contingency Planning Under Extreme Events
discusses an optimal approach to allocate energy generation resources when one or more of the generation units or transmission lines are temporarily disrupted or permanently damaged due to natural disasters.
Chapter 9
:
The Economics of Decentralization through Microgrids
proposes a framework for integrating microgrids into power infrastructure to improve resilience of power generation against natural disasters.
Chapter 10
:
The Economics of Post-disaster Restoration
introduces a generic model for optimally restoring the power infrastructure after a disaster strikes.
Each chapter of this book is designed to stand on its own and has its own nomenclature, bibliography, and acronyms.
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