Transmission Line Failures Due to High-Impact, Low-Probability Meteorological Conditions

Abstract

This study examines the impact of extreme weather events on electrical transmission lines, with a particular focus on high-impact, low-probability (HILP) meteorological conditions. Investigating how these conditions affect transmission lines and the potential effects of power outages is crucial for the reliability and continuity of electrical grids. The study conducts a comprehensive review of the literature on the effects of extreme weather events on electrical grids. Specifically, it categorizes and analyzes faults occurring on transmission lines caused by high-impact, low-probability meteorological conditions such as storms, hurricanes, and ice storms. Identifying and classifying these faults is a fundamental step in enhancing the reliability of power systems. Another focus of the study is examining various strategies to prevent power outages, including probabilistic modeling and resilience enhancement technologies. Solutions such as the development of advanced warning systems, design modifications to enhance the physical resilience of transmission lines, and emergency response plans have the potential to increase the reliability of electrical grids. In conclusion, the findings of this study contribute significantly to understanding the impact of HILP meteorological conditions on electrical transmission lines and identifying measures to enhance the reliability of electrical grids. The results of this study can provide valuable guidance to planners, engineers, and decision-makers in the energy sector.

1. Introduction

Extreme weather conditions encompass unpredictable severe weather events. In recent years, extreme weather events have been occurring due to global warming [1]. The National Oceanic and Atmospheric Administration (NOAA) of the United States record the historical records of environmental conditions such as temperature and precipitation. According to the NOAA, extreme weather events are defined as atmospheric occurrences that happen in at least 5% and at most 10% of cases [2]. Studies conducted by the United Nations Environment Programme Law and Environment Assistance Platform (UNEP-LEAP) indicate that the threat posed by extreme weather conditions is expected to increase in the future [1]. According to the Extreme Weather Events and Climate Change Attribution Committee of the United States Department of Agriculture (USDA), extreme weather events are defined as weather occurrences that can cause destructive effects on communities and natural ecosystems due to unusually severe weather or climate conditions. Extreme weather atmospheric events are typically short-lived. Extreme weather events include freezing events, severe rainfall, and hurricanes. The prolonged duration of extreme weather events affects the climate factor. Accumulations on climate facilitate the longer duration of extreme weather events [3,4]. According to the National Center for Biotechnology Information (NCBI), extreme weather refers to unusually severe weather events that occur rarely in a specific location and time. Such extreme weather events may include non-seasonal severe thunderstorms, snow and ice storms, hurricanes, and strong winds. Extreme weather events are not consistent from year to year. They may have minimal impact over many years but can cause significant losses with a single major atmospheric event [5]. Thus, extreme weather conditions pose a significant threat worldwide, causing millions of dollars in damage every year. One of the most notable consequences of these conditions is the increase in power outages. Power outages are a problem that significantly affects the functioning of modern society and adversely impacts industries reliant on energy [6].

This study distinguishes between transmission line failures and power outages to ensure analytical clarity. Transmission line failures refer to structural damage within physical grid assets. These assets include transmission towers, conductors, insulators, and substations. This type of failure is defined as a metric reflecting the impact of damage caused by meteorological effects. In contrast, power outages refer to the loss of energy supply continuity at the end-user level. This outcome represents a service-level metric resulting from physical failures. Many routine outages originate within the distribution grid. However, this review specifically focuses on High-Impact, Low-Probability (HILP) events. In these cases, the primary failure mechanism occurs at the transmission level. Such failures often lead to cascading effects and widespread power outages. Therefore, the presented case studies highlight events originating from transmission infrastructure vulnerabilities.

In August 2005, Hurricane Katrina left the city of New Orleans entirely without power. Following the hurricane, Entergy New Orleans, the electric utility company, reported spending USD 260–USD 325 million to repair damaged energy systems. Additionally, due to a USD 147 million loss in customers, Entergy New Orleans filed for bankruptcy in September 2005 [7].

In 2008, a severe ice storm in China resulted in power outages estimated to cost the country’s economy more than USD 2.2 billion. Due to the snow and ice storms experienced in 2008, China began reassessing its electrical grid infrastructure [8].

The Office of the President of the United States disseminated a report concerning power disruptions resulting from meteorological phenomena occurring between 2012 and 2013. According to the findings outlined in the ‘Economic Benefits of Augmenting Grid Resilience to Weather Induced Disruptions’ report, which was released in 2013, the United States incurred an average annual cost ranging between USD 18 billion and USD 33 billion attributable to power interruptions stemming from severe weather occurrences [9].

Hurricane Sandy, in October 2012, was a tropical storm that spread from the Caribbean to the United States and Canada. Hurricane Sandy resulted in power outages for more than 9.3 million customers in the United States and Canada. Eight nuclear power plants were temporarily shut down due to the hurricane. Heavy damage occurred to energy transmission lines and transformer substations. This damage is estimated to have cost the country’s economy approximately USD 3.5 billion [10].

In 2013, particularly in northern Europe, heavy rainfall caused severe flooding in Germany, the Czech Republic, Austria, Switzerland, and Hungary. Among these countries, Germany suffered the most damage due to flooding in the Danube and Elbe rivers. The cost of losses caused by the floods in the Czech Republic, Austria, Switzerland, and Hungary was approximately EUR 17 billion, while in Germany, it amounted to EUR 12 billion. Transformer substations were submerged during the disaster. The recurrence of long-term power outages seen in the 2002 flood in Europe and after the 2013 flood shows that extreme weather events are not adequately investigated and addressed [11].

The observations resulting from these findings indicate an increasing trend in power outages caused by extreme weather events [9]. According to research conducted by the U.S. Department of Energy on power quality in the transmission system, significant investments are made to ensure high power quality in the transmission grid, which contributes to minimizing power outages [12,13]. Investment of billions of dollars is required for the restoration of the power system following extreme weather events in the electricity grid [14,15,16,17].

Extreme weather events, when effectively mitigated on the electricity grid, significantly reduce the potential costs in the energy economy. This study focuses on the impact of extreme weather events on power systems, particularly addressing rare meteorological conditions with high impact on transmission lines. Failures occurring in the lines are classified, emphasized, and illustrated with examples. In this context, methods to prevent power outages are discussed and examined. The previous literature has largely focused on qualitative descriptions of weather-related outages [12,14]. In contrast, this study establishes a quantitative framework by integrating fragility curves and resilience metrics. It explicitly bridges the critical gap between historical HILP data and probabilistic risk assessment models. Consequently, the proposed analysis offers a novel perspective for enhancing future grid reliability strategies.

2. Materials and Methods

By scrutinizing the academic and technical literature spanning the last two decades, this study analyzes the economic losses, operational consequences, and failure mechanisms associated with the increasing frequency of High-Impact, Low-Probability (HILP) events Figure 1.

This systematic review adheres to rigorous bibliometric standards to establish the reproducibility and validity of the analyzed dataset. A comprehensive literature search was executed across four primary academic databases: IEEE Xplore, ScienceDirect, Web of Science, and Google Scholar. The data collection process targeted peer-reviewed publications and technical reports released between January 2005 and December 2023. The search strategy employed a combination of keywords related to power system reliability, meteorological hazards, and resilience metrics. The specific Boolean search strings utilized were:

• (“Transmission line failure” OR “Power outage” OR “Grid blackout”) AND (“Extreme weather” OR “HILP event” OR “Hurricane” OR “Ice storm” OR “Typhoon”)
• (“Power system resilience” OR “Fragility curve”) AND (“Wind load” OR “Icing”)

The literature search conducted for High-Impact, Low-Probability (HILP) events identified over 300 potential sources. A rigorous screening process was implemented to mitigate selection bias. This process ensured the topical relevance of the analyzed dataset. Consequently, studies were filtered based on the strict inclusion and exclusion criteria defined below:

• Relevance: The scope was strictly limited to studies focusing on high-voltage transmission systems or major grid-level disturbances. Papers solely addressing low-voltage distribution faults without cascading transmission impacts were excluded.
• Impact Threshold: A specific impact magnitude criterion was applied to the case studies. The primary objective is to focus on ‘High-Impact’ events. Therefore, only incidents affecting more than 100,000 customers were included in the study. Alternatively, events causing economic losses exceeding USD 10 million were also selected.
• Data Quality: Sources lacking verifiable quantitative data regarding the date, location, or impact magnitude were excluded from the analysis.

The dataset was subjected to a deduplication process prior to analysis. Then, the defined filters were applied. Thus, a final dataset was established for analysis. This set comprises over 100 major outage events and key technical papers. The complete inventory of analyzed events is provided in the Table 1.

Data Standardization and Analysis Method

A rigorous data standardization protocol was applied to the trend analyses presented in the subsequent sections. This protocol ensured statistical consistency across the dataset. A rigorous data standardization protocol was applied to ensure analytical consistency. The study strictly distinguishes between ‘affected population’ and ‘customer accounts’ (meter points). For North American and European events, direct utility data regarding disconnected meters were utilized. In contrast, reports from Asian regions typically quantify impact by population. These figures were converted into estimated customer accounts using a household coefficient of 3.5. This conversion enables a unified comparison of grid-level impacts across all cases in Table 1. Furthermore, the dataset was screened for duplicate records. This issue is common in large-scale events traversing multiple states. To resolve this, utility-level outage data were aggregated. This method prevented data redundancy, unlike cumulative news reports. Consequently, the temporal trends presented in this study are derived entirely from this verified and deduplicated dataset. The complete inventory of events is provided in Table 1. The statistical analysis and visualization of the dataset were performed using Python version 3.13 (Python Software Foundation, Wilmington, DE, USA), Microsoft Excel for Microsoft 365 (Microsoft Corporation, Redmond, WA, USA), and SPSS Statistics version 26 (IBM, Armonk, NY, USA) software.

When examining failures on transmission lines under high-impact low-probability meteorological conditions, the following factors are considered in determining the scope of research: period, geographic area, types of events, frequency and effects of failures, relevant data and sources, characteristics of transmission lines.

The period in the research determines the scope of the study by helping to understand the effects of factors such as current technology, infrastructure, and climate change. In the geographic area factor, it is determined which geographical regions will be examined. The differences in climate conditions and infrastructure in different regions reveal the results of your research. Additionally, the types of events inspected in the research scope are clearly stated. For example, the focus is on different meteorological events such as storms, heavy rainfall, snowstorms, hurricanes, and ice storms. The frequency and effects of failures provide information on which types of failures will be examined in the research. Emphasizing rare but impactful failures forms an important aspect of your research. Moreover, determining which data sources and documents you can access is important in defining the research scope. Accordingly, a comprehensive analysis was conducted in this study utilizing academic literature, technical reports, and industrial data, as well as official sources such as state-level event maps and regional resilience studies [18,19].

Table 1. Analysis of Power Outages Due to Extreme Weather Conditions.

No	Region	Location	Date	Hazard-Based Event Type	Grid Level *	Affected Customers	Estimated Affected Customer Accounts *	Ref.
1	North America	USA (Gulf Coast)	August 2005	Hurricane (Katrina)	Trans. and Dist.	2.7 million	>USD 125 Billion	[9,20]
2	Asia	China (Central/East)	January 2008	Winter Storm/Ice Storm	Transmission	>200 million **	>USD 2.2 Billion	[8,21,22]
3	Europe	Finland	July 2010	Windstorm/Severe Storm	Dist.	481 thousand	Not Specified	[23]
4	North America	USA (East Coast)	August 2011	Hurricane (Irene)	Trans. and Dist.	6.5 million	Not Specified	[21,24]
5	Finland	West/South Regions	December 2011	Winter Storm (Dagmar)	Dist. and Trans.	570 thousand	Not Specified	[23]
6	India (North/East)	Northern, Eastern, and NE Grids	July 2012	Extreme Heat	Transmission	630 million	Not Specified	[25]
7	North America	USA, Canada, Caribbean	October 2012	Hurricane (Sandy)	Trans. and Dist.	8.5 million	>USD 3.5 Billion	[10,25]
8	Europe	Norway (Trondheim)	January 2013	Windstorm (Hilde)	Dist.	83 thousand	51 million NOK	[26]
9	Europe	Norway (Central)	December 2013	Windstorm (Ivar)	Dist.	110 thousand	93 million NOK	[26]
10	North America	USA (Massachusetts)	February 2013	Blizzard (Nemo)	Trans. and Dist.	700 thousand	Not Specified	[26]
11	Europe	Northern Ireland	March 2013	Ice/Snowstorm	Transmission	200 thousand	Not Specified	[27]
12	Europe	Poland	April 2013	Heavy Snow	Dist.	140 thousand	Not Specified	[27]
13	Europe	Czech Republic, Austria, Switzerland, and Hungary	June 2013	Heavy Rainfall/Flood	Dist.	Not Specified	>EUR 29 Billion	[11]
14	North America	Canada (Toronto)	July 2013	Severe Storm/Flood	Dist.	300 thousand	>USD 106 Million	[3,28]
15	Europe	Finland	October 2013	Windstorm (Eino)	Transmission	250 thousand	Not Specified	[29]
16	Europe	United Kingdom	December 2013	Winter Storms	Dist.	750 thousand	Not Specified	[30]
17	Asia	The Philippines (Luzon)	July 2014	Super Typhoon	Transmission	13 million **	Not Specified	[31,32,33]
18	Europe	Finland/Baltic Region	October 2015	Windstorm/Severe Storm (Valio)	Dist.	170 thousand	Not Specified	[23]
19	Europe	Norway (Southwest)	January 2015	Windstorm (Nina)	Dist.	250 thousand	Not Specified	[26]
20	Europe	Norway (South)	January 2016	Windstorm (Tor)	Dist.	150 thousand	Not Specified	[26]
21	Oceania	Australia (South)	September 2016	Severe Storm/Tornado	Transmission	850 thousand	Not Specified	[34]
22	North America	Canada (New Brunswick)	January 2017	Ice Storm	Dist.	133 thousand	Not Specified	[35]
23	North America	USA (Texas, Louisiana)	August 2017	Hurricane (Harvey)	Trans. and Dist.	2 million	>USD 125 Billion	[21,36]
24	Caribbean	Puerto Rico	September 2017	Hurricane (Maria)	Transmission	1.5 million (Grid Collapse)	>USD 90 Billion	[21,37,38]
25	Asia	China (Guangdong)	September 2018	Typhoon (Mangkhut)	Trans. and Dist.	4.6 million	Not Specified	[39]
26	North America	USA (Southeast)	October 2018	Hurricane (Michael)	Trans. and Dist.	1.7 million	~USD 25 Million	[40,41]
27	North America	USA (South)	October 2020	Hurricane (Zeta)	Dist.	2.2 million	Not Specified	[42]
28	North America	USA (Texas)	February 2021	Winter Storm (Uri)	Gen. and Trans.	4.5 million	>USD 130 Billion	[43,44,45]
29	Europe	Germany, Belgium	July 2021	Heavy Rainfall/Flood	Dist. (Substations)	>200,000	>EUR 40 Billion	[6,46]
30	Europe	United Kingdom	November 2021	Windstorm with snow (Arwen)	Dist.	1 million	Not Specified	[47]
31	Africa	South Africa	April 2022	Heavy Rainfall/Flood	Dist.	>150 thousand	>USD 1 Billion	[48]
32	Asia	China (South)	July 2022	Typhoon (Chaba)	Trans. and Dist.	590 thousand	Not Specified	[39,49]
33	North America	USA (Florida)	September 2022	Hurricane (Ian)	Trans. and Dist.	2.6 million	Not Specified	[50,51]
34	North America	USA and Canada	December 2022	Winter Storm (Elliott)	Trans. and Dist.	6 million	USD 5.4 Billion	[52]
35	South America	Brazil (Southeast)	February 2023	Heavy Rainfall/Flood	Dist.	>400 thousand	Not Specified	[48]
36	Asia	Uzbekistan	June 2023	Sand/Dust Storm	Transmission	30 thousand	Not Specified	[53]

* Grid Level: “Transmission” indicates failures in HV lines/towers; “Dist.” indicates MV/LV failures; “Gen.” indicates generation loss. ** Estimated values derived from affected population data to ensure unit consistency. The reported population figures (200 million for China, 630 million for India and 13 million for the Philippines) were divided by an average household size of 3.5, resulting in approximately 57 million and 3.7 million customer accounts, respectively.

Table 1. Analysis of Power Outages Due to Extreme Weather Conditions.

No	Region	Location	Date	Hazard-Based Event Type	Grid Level *	Affected Customers	Estimated Affected Customer Accounts *	Ref.
1	North America	USA (Gulf Coast)	August 2005	Hurricane (Katrina)	Trans. and Dist.	2.7 million	>USD 125 Billion	[9,20]
2	Asia	China (Central/East)	January 2008	Winter Storm/Ice Storm	Transmission	>200 million **	>USD 2.2 Billion	[8,21,22]
3	Europe	Finland	July 2010	Windstorm/Severe Storm	Dist.	481 thousand	Not Specified	[23]
4	North America	USA (East Coast)	August 2011	Hurricane (Irene)	Trans. and Dist.	6.5 million	Not Specified	[21,24]
5	Finland	West/South Regions	December 2011	Winter Storm (Dagmar)	Dist. and Trans.	570 thousand	Not Specified	[23]
6	India (North/East)	Northern, Eastern, and NE Grids	July 2012	Extreme Heat	Transmission	630 million	Not Specified	[25]
7	North America	USA, Canada, Caribbean	October 2012	Hurricane (Sandy)	Trans. and Dist.	8.5 million	>USD 3.5 Billion	[10,25]
8	Europe	Norway (Trondheim)	January 2013	Windstorm (Hilde)	Dist.	83 thousand	51 million NOK	[26]
9	Europe	Norway (Central)	December 2013	Windstorm (Ivar)	Dist.	110 thousand	93 million NOK	[26]
10	North America	USA (Massachusetts)	February 2013	Blizzard (Nemo)	Trans. and Dist.	700 thousand	Not Specified	[26]
11	Europe	Northern Ireland	March 2013	Ice/Snowstorm	Transmission	200 thousand	Not Specified	[27]
12	Europe	Poland	April 2013	Heavy Snow	Dist.	140 thousand	Not Specified	[27]
13	Europe	Czech Republic, Austria, Switzerland, and Hungary	June 2013	Heavy Rainfall/Flood	Dist.	Not Specified	>EUR 29 Billion	[11]
14	North America	Canada (Toronto)	July 2013	Severe Storm/Flood	Dist.	300 thousand	>USD 106 Million	[3,28]
15	Europe	Finland	October 2013	Windstorm (Eino)	Transmission	250 thousand	Not Specified	[29]
16	Europe	United Kingdom	December 2013	Winter Storms	Dist.	750 thousand	Not Specified	[30]
17	Asia	The Philippines (Luzon)	July 2014	Super Typhoon	Transmission	13 million **	Not Specified	[31,32,33]
18	Europe	Finland/Baltic Region	October 2015	Windstorm/Severe Storm (Valio)	Dist.	170 thousand	Not Specified	[23]
19	Europe	Norway (Southwest)	January 2015	Windstorm (Nina)	Dist.	250 thousand	Not Specified	[26]
20	Europe	Norway (South)	January 2016	Windstorm (Tor)	Dist.	150 thousand	Not Specified	[26]
21	Oceania	Australia (South)	September 2016	Severe Storm/Tornado	Transmission	850 thousand	Not Specified	[34]
22	North America	Canada (New Brunswick)	January 2017	Ice Storm	Dist.	133 thousand	Not Specified	[35]
23	North America	USA (Texas, Louisiana)	August 2017	Hurricane (Harvey)	Trans. and Dist.	2 million	>USD 125 Billion	[21,36]
24	Caribbean	Puerto Rico	September 2017	Hurricane (Maria)	Transmission	1.5 million (Grid Collapse)	>USD 90 Billion	[21,37,38]
25	Asia	China (Guangdong)	September 2018	Typhoon (Mangkhut)	Trans. and Dist.	4.6 million	Not Specified	[39]
26	North America	USA (Southeast)	October 2018	Hurricane (Michael)	Trans. and Dist.	1.7 million	~USD 25 Million	[40,41]
27	North America	USA (South)	October 2020	Hurricane (Zeta)	Dist.	2.2 million	Not Specified	[42]
28	North America	USA (Texas)	February 2021	Winter Storm (Uri)	Gen. and Trans.	4.5 million	>USD 130 Billion	[43,44,45]
29	Europe	Germany, Belgium	July 2021	Heavy Rainfall/Flood	Dist. (Substations)	>200,000	>EUR 40 Billion	[6,46]
30	Europe	United Kingdom	November 2021	Windstorm with snow (Arwen)	Dist.	1 million	Not Specified	[47]
31	Africa	South Africa	April 2022	Heavy Rainfall/Flood	Dist.	>150 thousand	>USD 1 Billion	[48]
32	Asia	China (South)	July 2022	Typhoon (Chaba)	Trans. and Dist.	590 thousand	Not Specified	[39,49]
33	North America	USA (Florida)	September 2022	Hurricane (Ian)	Trans. and Dist.	2.6 million	Not Specified	[50,51]
34	North America	USA and Canada	December 2022	Winter Storm (Elliott)	Trans. and Dist.	6 million	USD 5.4 Billion	[52]
35	South America	Brazil (Southeast)	February 2023	Heavy Rainfall/Flood	Dist.	>400 thousand	Not Specified	[48]
36	Asia	Uzbekistan	June 2023	Sand/Dust Storm	Transmission	30 thousand	Not Specified	[53]

* Grid Level: “Transmission” indicates failures in HV lines/towers; “Dist.” indicates MV/LV failures; “Gen.” indicates generation loss. ** Estimated values derived from affected population data to ensure unit consistency. The reported population figures (200 million for China, 630 million for India and 13 million for the Philippines) were divided by an average household size of 3.5, resulting in approximately 57 million and 3.7 million customer accounts, respectively.

Power outages have a significant impact on the energy security and economic stability of modern societies, both in technical and economic dimensions. From a technical perspective, these outages often stem from various factors such as damage to transmission lines, transformer failures, and natural disasters causing interruptions. The durations, frequencies, and effects of outages can vary greatly across different geographical regions. For instance, rural areas may typically experience longer outage durations compared to urban centers, leading to disruptions in essential services such as agricultural production, healthcare services, and communication.

From an economic standpoint, power outages can incur significant costs. Factors such as job loss, production loss, commercial damages, and repair expenses determine the economic impacts of outages. Particularly in the industrial and commercial sectors, power outages can adversely affect production continuity and commercial activities, leading to widespread economic losses.

On the other hand, climate change may introduce new challenges to power systems. Factors such as rising temperatures, more intense and frequent extreme weather events, and changes in rainfall patterns can affect the resilience and performance of power systems. For example, high temperatures can increase electricity demand, while extreme weather events can lead to damage to transmission lines and an increase in failures. This situation may necessitate the development of new strategies for future planning and infrastructure investments in power systems.

3. Case Studies About Power Outages

Ensuring an uninterrupted supply of electricity is crucial for meeting the basic needs of individuals. However, in recent years, numerous breakdowns have occurred in electrical systems due to extreme weather events. The rapid expansion of energy grids and increased interconnection of networks has made the operation of electrical grids more challenging. The growing number of electrical grids becoming more susceptible to extreme weather conditions has increased the risk of power outages in interconnected networks. In this study, 33 major power outage events occurring globally over the last twenty years are examined. These cases were compiled from technical reports (NOAA, NERC, ENTSO-E), academic literature, and utility post-event analysis reports, and are presented in Table 1 and Figure 2.

Illustrates the cumulative impact of extreme weather events presented in Figure 3. The graph summarizes the number of affected customers on a country basis. Upon analysis, the highest impact is observed in the USA. This data correlates directly with the frequency of events in the region. The Philippines and Puerto Rico represent other high-risk regions. A single typhoon in the Philippines affected 13 million people. Similarly, to maintain unit consistency, this value was also converted from population data. The reported population figure of 13 million for the Philippines was divided by an average household size of 3.5, yielding approximately 3.7 million customer accounts. This case demonstrates the sudden and devastating impact of HILP events on the grid.

The multifaceted impacts of extreme weather events on global power systems from 2005 to 2023 are shown in

Table 2. Overview of Significant Economic Losses and Societal Impacts Caused by Extreme Weather Events.

Region	Country/Area	Event Type	Date	Grid Level	Affected (Approx.)	Estimated Economic Loss *	Ref.
North America	USA (Various States)	Hurricane, Snowstorm	2005–2023	Trans. & Dist.	>10 Million	>USD 300 Billion	[9,20,43,54]
Asia	China (South)	Typhoon, Ice Storm	2008, 2022	Transmission	>57 Million	>USD 10 Billion	[8,39]
Europe	Germany, Belgium	Heavy Rainfall/Flood	July 2021	Dist. (Substations)	>200,000	>EUR 40 Billion	[46,55]
Oceania	Australia (South)	Severe Storm	September 2016	Transmission	850,000	Not Specified	[34]
South America	Brazil (Southeast)	Heavy Rainfall/Flood	February 2023	Dist.	>400,000	Not Specified	[48]
Africa	South Africa	Heavy Rainfall/Flood	April 2022	Dist.	>150,000	>USD 1 Billion	[48]
Caribbean	Puerto Rico	Hurricane (Maria)	September 2017	Transmission	1.5 Million (100% Grid)	>USD 90 Billion	[37,38]

This table includes additional examples compiled from the literature and reports such as the World Bank Climate Risk Country Profiles to broaden the geographic scope and illustrate different HILP threats [48]. * Economic loss values represent the estimated total financial impact (including direct infrastructure damage and indirect business interruption costs) reported at the time of the event (nominal values). Figures have not been adjusted for inflation to maintain consistency with the original citation sources.

. The presented data indicate that these events result in substantial economic and societal consequences extending beyond mere technical failures. Specifically, costs exceeding USD 300 billion in the USA and reaching levels of EUR 40 billion in Europe demonstrate the magnitude of the associated financial risk.

Beyond economic losses, power outages adversely affect millions of individuals; the fact that over 200 million people were impacted in China underscores the critical societal importance of energy supply security. To ensure unit consistency, this value is an estimate derived from the affected population data. The reported population figure of 200 million for China was divided by an average household size of 3.5, resulting in approximately 57 million customer accounts. As observed in Figure 4, the increasing frequency of extreme weather conditions in recent years points to an escalation in climate change-induced threats. The susceptibility of even countries with advanced infrastructure to these events highlights the vulnerability of existing grids, rendering traditional reliability approaches insufficient. Consequently, the development of resilience-oriented strategies is imperative for the future of power systems.

Classification shown in Figure 4 is derived from a comprehensive analysis of over 100 major power outage events documented in the literature. The data indicates that hurricanes and typhoons constitute the predominant cause (approximately 40%), followed by severe snow and ice storms (approximately 35%). Heavy rainfall and flooding account for approximately 15%, while the remaining 10% comprises other phenomena such as sandstorms and extreme temperatures.

Annual Count of Reported Major Global Power Outage Events data, compiled from literature and news archives, illustrates the frequency of events impacting more than 100,000 subscribers. A generally increasing trend is observed throughout the analyzed period in Figure 5.

The United States’ energy infrastructure comprises over 7300 power plants, approximately 260,000 km of transmission lines, and around 145 million distribution customers. In countries with such significant power capabilities, power outages can lead to serious damage [56].

The statistical trends illustrated in Figure 5 are derived from the comprehensive dataset detailed in Table 1. The specific High-Impact Low-Probability (HILP) case studies that constitute this dataset are listed in Table 1 and are analyzed in detail below to provide context for the observed aggregate trends.

In January 2008, a winter storm hit the provinces of Hubei, Henan, Shandong, Jiangsu, Anhui, and the Shanghai region in China. This winter storm severely impacted China’s energy system, leading to widespread power outages across the country. Approximately 200 million customers in China were left without electricity due to this ice storm [21,22].

Finland is a country frequently exposed to power outages due to harsh weather conditions. In July 2010, severe rain and windstorms caused power outages in many regions of the country. Approximately 481,000 customers were left without electricity due to trees falling on power transmission lines during severe winds. One year later, in December 2011, Cyclone Dagmar, a snowstorm, caused prolonged power outages in Finland. Around %18 of all customers in Finland were affected by these outages [23].

In August 2011, Hurricane Irene hit the eastern coasts of the United States, causing power loss for approximately 6.5 million customers. This hurricane brought heavy rain and winds. The heavy rain and winds from Hurricane Irene also affected east coast of the USA, leaving approximately 600,000 customers without electricity [21,24].

In October 2012, Hurricane Sandy significantly impacted Jamaica, Cuba, the Bahamas, eastern Canada, and the eastern United States. This high-impact meteorological event resulted in power outages for approximately 8.5 million customers across North America. The hurricane caused extensive damage to energy transmission lines and submerged critical transformer substations due to storm surges. The economic cost of the damage to the energy infrastructure was estimated at approximately USD 3.5 billion [10,25].

Distinct from the storm-related outages in North America, another massive grid failure occurred in India during the same year. In July 2012, two consecutive massive grid collapses affected the Northern, Eastern, and North-Eastern electricity grids of India. Unlike the direct physical damage caused by Sandy, this event was a cascading failure triggered by load encroachment and weak inter-regional corridors. This blackout remains the largest in history, leaving approximately 630 million people (nearly half of the country’s population) without electricity. This incident highlights the vulnerability of large, interconnected transmission systems to cascading failures even in the absence of a direct storm impact [25].

During the winter months of 2013, prolonged power outages occurred in energy transmission systems worldwide due to snowstorms. In the United States, heavy snowfall on 8–9 February 2013 caused power transmission lines to break. This outage affected 700,000 customers in the United States. Due to the interruptions in power transmission lines, a nuclear power plant in Plymouth, Massachusetts, automatically shut down. Canada was also affected by this outage, with 21,000 customers reported to be without electricity on 9 February [26].

In Norway, power outages caused by four storms named “Hilde,” “Ivar,” “Tor,” and “Nina” between 2013 and 2016 were analyzed Formun Üstü.

The severe storm named Hilde, which occurred on 16–17 January 2013, affected the region between Trondheim and Bodo in Norway with brief hurricane-force winds. Approximately 400 people participated in the short-term recovery process of the energy power systems. About 83,000 customers experienced interruptions due to the extreme weather event. It is estimated that the total cost of the damage caused by this storm to Norway amounted to 51 million NOK. The severe storm named Ivar, which occurred on 12 December 2013, struck the regions of Trondelag County and More og Romsdal County in central Norway. As a result of this storm, approximately 110,000 customers were affected. About 630 individuals participated in the short-term recovery process of the energy power systems. It is estimated that the total cost of the damage caused by this storm to Norway amounted to 93 million NOK [27].

On 22 March 2013, Northern Ireland experienced heavy snowfall, causing ice accumulation on the power transmission lines, resulting in line failures and leaving 200,000 customers without electricity [28].

Additionally, in Poland on 1 April 2013, heavy snowfall led to more than 140,000 customers being left without electricity. Power outages occurred due to ice loading on the transmission lines and tree branches falling onto the power lines [28].

On 9 July 2013, Toronto, Canada, experienced an ice storm because of extreme weather conditions. An ice storm occurs when raindrops freeze due to temperatures dropping below freezing. If the thickness of the ice formed by freezing raindrops exceeds 6 mm, it is classified as an ice storm. During this disaster, the Manby transformer station was submerged, leading the Leaside transformer station, which supplies power to downtown Toronto, to become overloaded and necessitate load shedding. Consequently, electricity was provided to the downtown area on a rotating basis. Approximately 719,000 customers of Toronto Hydro were affected by this extreme weather event, with about 70,000 customers experiencing prolonged power outages and around 300,000 customers affected by short-term power outages. The damage caused by the ice storm resulted in losses exceeding USD 106 million for the city of Toronto [26,57].

In October 2013, Cyclone Eino caused prolonged power outages in Finland. This storm led to the disruption of 110 kV Fingrid electricity transmission lines in Finland, leaving approximately 250,000 customers without electricity. Both the energy transmission and distribution systems suffered significant damage. Elenia, the energy company in Finland, incurred the highest losses with approximately 92,000 customer outages [29].

In December 2013, the United Kingdom experienced extreme weather conditions. According to the Met Office, the winter of 2013–2014 was the wettest in England since records began in 1776. Heavy snowfall and storms led to severe flooding in February 2014. Extreme weather events caused prolonged power outages across the UK. The Energy Networks Association (ENA) stated on 8 January 2014, regarding the power outages experienced, attributing them to the most intense extreme weather in 250 years, resulting in significant damage to energy systems. ENA indicated that approximately 750,000 customers were affected by this damage [30].

In July 2014, one of the largest Super Typhoons, named Rammasun, hit the city of Luzon in the Philippines. This typhoon affected 13 million customers, causing widespread power outages in coastal areas of the Philippines. The severe winds and heavy rainfall caused critical damage to energy transmission towers and substations in the Philippines, leaving millions of people without electricity [31,32,33].

According to a report by the Australian Energy Market Operator (AEMO) in 2016, extreme weather events cause damage to electricity systems, hindering the continuous operation of the energy system. In September 2016, a severe storm system with tornadoes in South Australia caused a 50-h power outage, affecting 850,000 customers [34].

Many regions of North America suffer significant damage to both infrastructure and the economy due to the impact of winter storms. On 24 January 2017, severe ice storms hit New Brunswick, Canada. The ice storm caused power outages throughout the province, leaving approximately 133,000 customers without electricity for two weeks [35].

In August 2017, Hurricane Harvey struck Texas and Louisiana, causing severe rain and winds that damaged the energy system and left approximately 2 million customers without electricity [21,36].

In September 2017, Hurricane Maria caused severe rain and winds in Puerto Rico, Dominica, and the Caribbean islands. This storm left approximately 7.6 million customers without electricity [21,37,38].

The Mangkhut typhoon in September 2018 caused significant damage to the distribution lines in Guangdong Province, China, with 69,000 distribution transformers damaged, resulting in approximately 4.6 million customers being left without electricity [39].

In October 2018, Hurricane Michael occurred in Alabama, Florida, Georgia, North Carolina, South Carolina, and Virginia states in the United States. According to the report issued by the National Oceanic and Atmospheric Administration (NOAA), this hurricane is estimated to have caused approximately USD 25 million in damage. Additionally, about 1.7 million customers were affected by the power outages caused by this hurricane [40,41].

In October 2020, Hurricane Zeta, characterized by heavy rainfall and strong winds, caused significant damage in the states of Louisiana, Mississippi, Alabama, Florida, Georgia, and North Carolina in the United States. It was reported that 2.2 million customers were left without electricity due to this hurricane [42].

In February 2021, an unprecedented nine-day period of extreme winter weather occurred in the southeastern region of Texas. This extreme weather event brought sleet, snowfall, and freezing conditions to the region, with the Texas National Weather Service recording peak snowfall on February 15. The formation of a low-pressure system in the area rapidly lowered temperatures to single-digit figures. Across all regions of Texas, consecutive days of record-low temperatures were observed, accompanied by record-breaking snowfall. This event led to widespread damage across Texas, particularly in the southern regions, causing disruptions to the electricity grid, bursting water pipes, and limited road and air travel. Additionally, over 4.5 million households and businesses were left without electricity for several days. The Electric Reliability Council of Texas (ERCOT), the independent system operator for the state’s power grid, had anticipated a maximum outage of 14 gigawatts (GW) according to extreme winter planning scenarios. However, the Uri winter storm resulted in outages totaling 30 GW in Texas. This mismatch between supply and demand led to the stringent distribution of energy loads among millions of customers, exacerbating the strain on the electricity system [43,44,45].

In November 2021, the United Kingdom experienced the Arwen Snowstorm, with wind speeds reaching up to 98 miles per hour in some areas. The storm left more than one million customers without electricity in the UK. According to the UK’s Energy Regulator Office (OFGEM), approximately 82% of customers were re-energized within 24 h. However, around 40,000 customers remained without power for more than three days. Additionally, OFGEM reported that approximately 4000 customers were without electricity for more than a week. The Arwen Snowstorm in November 2021 caused significant damage to distribution networks across the United Kingdom. The strong winds during the Arwen Storm resulted in over 9700 electrical faults [47].

In July 2022, the Chaba typhoon hit southern China, leaving approximately 590,000 customers without electricity [39,49].

In September 2022, the severe rains caused by Hurricane Ian in Florida submerged areas from the west to the east coast, leaving approximately 2 million customers without electricity. [50,51].

On 21 December 2022, a severe snowstorm affected a large part of the United States and Canada. Record low temperatures were recorded due to heavy snowfall and freezing events. This snowstorm caused around USD 5.4 billion in damage in the United States and affected over 6 million customers [52].

On 19 June 2023, a sand and dust storm occurred in the Bukhara and Navoiy Regions of Uzbekistan. Large-scale power outages occurred in some areas due to windy conditions carrying sand and dust. Approximately 30,000 customers were affected by this power outage, causing significant damage to the country’s economy. Therefore, it is necessary to build a flexible power system for extreme weather events with HILP risks like sandstorms [53].

An Integrated Framework for Evaluating the Direct and Indirect Economic Consequences of Power System Failures

The economic impact of power grid outages must be analyzed through the lens of direct and indirect cost components. While direct costs encompass the restoration of transmission and distribution infrastructure as well as operational response expenses, indirect costs entail broader macroeconomic consequences, such as industrial production losses, disruption of commercial continuity, supply chain damages, and interruptions in critical public services (e.g., healthcare and digital infrastructure). For instance, the estimated cost of the 2021 Texas winter storm, ranging from USD 130 to USD 150 billion [43,58], and the economic collapse and demographic displacement following Hurricane Maria in Puerto Rico [38,59], underscore the magnitude of these indirect costs.

Disasters in the US were generated based on NOAA NCEI data [1]. This graph, shown in Figure 6, reveals the increasing trend in the frequency of disasters with a large economic impact in the US. According to data from the U.S. National Oceanic and Atmospheric Administration (NOAA), a statistically significant increase in the frequency of weather and climate events exceeding one billion dollars was observed during the 2013–2023 period. Alongside the effects of climate change, socio-economic factors such as urbanization in vulnerable regions and aging infrastructure have contributed to this scenario, which incurred a cumulative cost exceeding USD 1 trillion over the specified period [20,54]. Considering these data, the strategic importance of resilience-oriented investments in energy systems for mitigating such recurring economic losses becomes increasingly evident [9,60].

4. The Impact of Climate Change on Power System Failures

One of the most significant reasons for the increasing interruptions in electrical grids is the growing impact of global climate change. Research indicates a substantial increase in extreme weather events since 2005. These events have inflicted significant damage on the country’s economy, particularly on energy and related sectors, while also impeding the population’s ability to carry out essential activities. Extreme weather events resulting from climate change have profoundly destructive effects. Therefore, strong winds, severe snowfall, hail, blizzards, and freezing events significantly damage transmission lines, leading to substantial power outages.

The impacts of increasing extreme weather events due to global warming on energy systems are progressively intensifying. Although the likelihood of extreme weather events occurring may be low, their effects can be extremely significant. Yale Climate Connections’ report on the top 10 global weather and climate change events of 2021 emphasized the increasing intensity and frequency of extreme weather events. For example, an extreme weather event occurring between 12 and 18 July 2021, resulted in record-breaking floods in Germany, Luxembourg, Belgium, and neighboring countries. These floods caused significant damage to energy transmission lines and transformer stations. This extreme weather event resulted in USD 43 billion worth of damage in Europe [46].

The extreme weather event in Germany during these dates saw one month’s worth of rainfall occurring within 48 h. According to the report published by the World Weather Information Service (WWIS) in 2021, the occurrence of such extreme rainfall events in contemporary times is attributed to the consequences of global warming [55].

According to a study conducted by Loktionov and colleagues, the effects of climate change caused by global warming have already begun to manifest in the energy sector. As a result, many power outages occurred in energy companies in Russia. Data from power outages in Russia in 2018 indicated that the disruption rate in the energy system due to extreme weather events ranged from 20% to 90% [61].

Energy Systems Analyst and Planner Evan Mills conducted an analysis at the Lawrence Berkeley National Laboratory using data obtained from the U.S. Department of Energy (DOE). According to this analysis, an increase in power outages due to extreme weather conditions was observed from 1992 to 2010 [62].

Extreme weather conditions have a significant impact on transmission lines. However, since extreme weather events are not continuously occurring, they should be treated as low-probability events. Research communities and grid operators focused on improving energy systems have realized, through studies conducted by Yanling Lin and colleagues, that traditional reliability studies may not be sufficient to withstand all extreme weather events [63]. Therefore, various methods should be developed to mitigate the impacts of intense atmospheric events that may occur in the energy transmission system. Since the effects of extreme weather events can be destructive, it is essential to examine the damage that may occur in energy systems and take various precautionary measures in advance to ensure system security.

The devastation caused by extreme weather conditions poses a clear threat to power transmission systems. Power outages resulting from extreme weather events disrupt vital services such as medical care, radio, and television broadcasts. According to research on global climate change, power loss during extreme weather events is intensifying. This is due to increasingly severe hurricanes. The impact of severe hurricanes on the energy system becomes even more terrifying with the changing climate Formun Üstü [64].

According to the International Energy Agency’s (IEA) report published in 2020, only 16% of the member countries of the energy agency have planned efforts to increase the climate resilience of their electricity grids [65]. The scarcity of efforts directed towards grid resilience results in significant outage impacts. For instance, in a study conducted by Hossein Shahinzadeh and colleagues, heavy rainfall caused by monsoon currents in the Indian Ocean led to severe flooding in 24 provinces of Iran in July 2022. These floods resulted in substantial financial losses in the infrastructure of the electricity distribution networks. As this extreme weather persisted, the level of damage increased across various provinces of Iran. From the study by Hossein Shahinzadeh and colleagues, we can infer the importance of evaluating the resilience of power grids to mitigate the consequences of such extreme weather events and examining how system operators respond to these events [66].

According to research by Kenward and Raja, extreme weather conditions have a significant impact on electric power systems, causing low-probability faults. This research indicates that the transmission lines comprising power systems have been exposed to extreme weather conditions for years. Severe storms in the United States are considered a major cause of power outages. For example, based on Kenward and Raja’s research on extreme weather events between 2003 and 2012, it was found that 679 power outages occurred, affecting at least 50,000 households [67]. Additionally, it has been determined that 80–90% of the power outages are caused by faults in the energy distribution systems [68].

The extreme weather conditions resulting from global climate change lead to the collapse of numerous critical energy system infrastructures worldwide. Research conducted by H. F. Diaz and colleagues reveals that the island of Puerto Rico in the Caribbean Atlantic is prone to severe tropical storms and hurricanes. The power outages resulting from these hurricanes significantly affect the livelihoods of the island’s inhabitants [69]. Therefore, technological advancements used in energy systems have a beneficial impact on power systems. Preventing interruptions in energy systems should be considered to alleviate social suffering [59].

Greece is highly vulnerable to climate hazards due to its mountainous mainland and indented coastline. Additionally, since about 88% of Greece’s energy systems consist of overhead lines, it is one of the countries in Europe with the lowest underground network. The electric grid infrastructure of Greece is frequently exposed to extreme meteorological conditions, leading to significant disruptions affecting a substantial number of customers. According to data provided by the Hellenic Electricity Distribution Network Operator (HEDNO S.A.) in 2021, approximately 18.54% of Medium Voltage (MV) faults stem from extreme weather events Formun Üstü [70].

A summary of the findings in this section leads to three key conclusions. First, historical weather data is no longer a reliable predictor due to the non-stationary nature of climate change. Second, traditional deterministic criteria, such as N-1 security, are insufficient for mitigating High-Impact, Low-Probability (HILP) events. Finally, a fundamental methodological shift is necessary. Grid planning must transition to probabilistic risk assessment models that explicitly incorporate future climate projections.

5. Approaches to Increasing Power System Resilience

To enhance electricity supply security, strategies addressing extreme weather conditions should be realistically and effectively planned. To mitigate the impacts of climate change, electrical power systems need to be more resilient, efficient, and flexible. According to the findings of a study by Bie and colleagues, the flexibility of the power system is measured by its ability to withstand High Impact, Low Probability (HILP) events. To withstand HILP events, which have high impact but low probability, it is necessary to anticipate, withstand, absorb, respond to, and adapt to the faults occurring in transmission lines [70].

Olga Kondrateva and colleagues categorized the causes of power outages in their research conducted in the Russian region. According to the accident data obtained in these studies, it was concluded that the regions where power system outages occurred most frequently were various unified power systems in Russia. Another result of these studies indicates that the voltage levels most affected in electrical grids are those at 110 kV or lower. Additionally, the equipment most damaged in energy systems is overhead transmission lines, hence areas supplied by these lines are where power outages are most prevalent [71].

Extreme weather conditions have an impact on the electricity transmission network. Therefore, to understand the effects of extreme weather conditions on transmission lines, it is necessary to first measure the disruption trends caused by weather conditions on these lines. According to the World Energy Outlook (WEO) report published in 2022, governments need to anticipate the risks arising from extreme weather events for the expansion and modernization of energy systems. Anticipating risks in advance will not only reduce the adverse climate effects on energy systems but also enable a rapid recovery from these effects [72]. Reducing interruptions in energy power systems and improving transmission lines are crucial to justify investments in the electrical grid [73,74,75].

In studies on power system outages during extreme weather conditions, various approaches have been employed to predict fault rates and outage durations in transmission grids. These approaches involve the use of outage duration prediction models and statistical methods. Machine learning models have also been applied extensively for predicting power outage incidents in energy transmission grids. These machine learning models are developed based on historical outage data obtained from energy transmission companies, correlated with weather events. To improve the accuracy of machine learning models, it is essential to maintain outage data records for periods spanning 30 years or more in the energy transmission system Formun Üstü [76,77,78,79,80,81].

In a study conducted by Hans Hersbach, the sensitivity of the electrical transmission system was determined based on atmospheric data. Electrical outages resulting from storms that affected the power grids were evaluated using records spanning over 40 years. Hersbach developed a machine learning method to predict electrical outages caused by extreme weather events. In this study, the severity of electrical outages was classified, and machine learning techniques were employed to create outage prediction models. These algorithms, generated through machine learning, were derived from 16 years of outage data in the Eversource Energy region covering the state of Connecticut. The electrical outage data obtained through the ERA5 model generated by machine learning is utilized by the European Centre for Medium-Range Weather Forecasts [82,83].

Before an extreme weather event occurs, ensuring an uninterrupted power supply to critical loads should take precedence over passively managing the electrical grid. According to a Science for Policy report prepared by the Joint Research Centre (JRC), the science and knowledge service of the European Commission, various plans can be adopted before, during, and after an extreme weather event. These plans should be developed as actions, processes, policies, and procedures. To enhance the resilience of the electrical grid against extreme weather events, the grid infrastructure needs to be restructured to withstand multiple threats. Additionally, response times to threats occurring within the electrical grid should be improved [84].

According to the study conducted by Mathaios Panteli, the analysis of faults in the transmission system is carried out to reduce the impact of extreme weather events on the power system for an uninterrupted electricity supply. In electrical transmission and distribution systems, factors such as high temperatures, severe winds, lightning strikes, heavy snowfall, and ice accumulation can cause system faults. Faults arising from extreme weather events can be classified as temporary or permanent. In the case of electrical outages, transmission lines can be quickly re-energized with reclosing relays, classifying the existing fault as temporary. However, if extreme weather conditions cause serious damage to the transmission system equipment, the repair of the line may take several days, categorizing the fault as permanent. To enhance the resilience of energy systems against extreme weather events, faults are typically classified as short-term and long-term, and various defense plans are implemented accordingly [85,86].

In the study conducted by Ying Chen and colleagues, the methods identified for power outages during extreme winter conditions will facilitate the identification of weaknesses in the electrical grid. Conducting studies focused on modeling ice accumulation on transmission lines and assessing the probability of faults will help reduce line failures caused by extreme cold weather events [87].

According to National Grid’s “Winter Outlook 2013/14” study, measures against extreme weather events are divided into three categories. The first involves short-term measures taken before extreme weather events. The second category consists of corrective activities taken during extreme weather events, spanning days or weeks. The third category encompasses preventive and corrective measures implemented after extreme weather events. Figure 7 presents a comprehensive three-stage framework that systematically categorizes these resilience enhancement strategies into preventive, absorptive, and restorative phases.

Meteorological forecasts are considered by examining weather events to prepare transmission lines for approaching weather events. Measures that can be taken during extreme weather events include production system planning, energy distribution, coordination with interconnected networks, and energy storage. During the coldest periods of 2013 and 2014, National Grid PLC, the energy company in the UK, planned a short-term production reserve by reducing the electricity demand of 56.3 GW to approximately 2.7 GW. Additionally, the electricity transmission network was operated by system operators according to the “N-3” criterion for precautionary purposes instead of the normal “N-2” safety condition under regular weather conditions. After extreme weather events, it is crucial for the grid to recover rapidly. To achieve swift recovery of the transmission system, appropriate communication systems, emergency strategies, and repair of damaged facilities are necessary [88].

Analysis of faults occurring during extreme weather conditions reveals significant operational challenges in maintaining and repairing energized equipment. Consequently, reports published by the Executive Office of the President and National Grid in 2021 emphasize the necessity of long-term proactive measures to enhance transmission system resilience against the escalating impacts of climate change. These strategic interventions primarily encompass the implementation of comprehensive risk assessment frameworks, the integration of high-precision weather forecasting, and the optimization of emergency response plans, alongside rigorous vegetation management in transmission corridors. From an infrastructural perspective, resilience measures include the undergrounding of critical lines, retrofitting overhead components with more robust materials, reinforcing transmission towers, and relocating substations away from flood-prone zones. Furthermore, operational flexibility must be bolstered through the expansion of interconnection networks, the adoption of adaptive load rerouting strategies, and the deployment of advanced monitoring tools to ensure real-time situational awareness. During extreme weather events, short-term intervention in the transmission system has different effects on the electricity grid. The key aspect of short-term intervention is to increase emergency awareness. To achieve this, system operators must accurately allocate the assets and resources of the existing transmission network. By employing appropriate allocation methods, the effects of extreme weather events can be mitigated. Additionally, structural measures can be implemented in the electricity transmission grid to enhance its resistance to severe weather conditions [60,89].

This analysis identifies a clear gap between historical design standards and current meteorological realities. The rise in extreme weather events proves that standard safety protocols are no longer sufficient. For instance, the common ‘N-1’ rule assumes that only a single component will fail. Similarly, fixed operational limits cannot handle the widespread damage caused by severe storms. Therefore, a fundamental strategic change is required. Grid planning must move away from relying solely on past performance records. Instead, it must adopt future-oriented prediction models based on probability. These models better account for the uncertainty and scale of modern climate risks.

6. Fragility and Resilience in Power Systems

6.1. Stochastic Characterization of Infrastructure Fragility

Analyzing the impact of extreme weather on power systems requires a precise approach. This is particularly true for high wind speeds. Therefore, it is essential to shift from qualitative descriptions to probabilistic modeling. Fragility Curves represent the best method for this.

To strictly quantify the failure probability of transmission line components under extreme wind loading, fragility curves are employed based on statistical failure models. Unlike deterministic N-1 criteria, fragility functions provide a probabilistic correlation between environmental stress and component failure. Structural fragility is typically modeled using the Log-Normal Cumulative Distribution Function (CDF), expressed in Equation (1) below:

$$P_f\left(V\right)=\Phi ({\displaystyle \frac{ln\left(V\right)-\mu }{\sigma }})$$

(1)

where $P_f\left(V\right)$ denotes the probability of failure at a specific wind speed V (m/s), Φ(·) is the standard normal cumulative distribution function, µ is the logarithmic median capacity of the transmission tower or conductor, and σ is the logarithmic standard deviation describing the uncertainty in structural resistance. It is emphasized that for transmission towers, the parameter V generally corresponds to the square of the wind speed (V²) or the combined mechanical load of ice and wind. This probabilistic framework allows system operators to measure risk exposure before physical failure occurs. Risk exposure is found using Equation (2) [86,90].

$$Risk=Probability\times Impact$$

(2)

Figure 8 illustrates this relationship visually. It presents a representative fragility curve for a transmission tower. The graph demonstrates that as hazard intensity (e.g., wind speed) increases, the probability of structural failure significantly rises.

Figure 8 visualizes this relationship. These curves map meteorological intensity to the probability of infrastructure failure. Operators can calculate Expected Load Loss (ELL) using this data. This approach enables a proactive risk management strategy. Furthermore, it outperforms static N-1 contingency criteria [91].

6.2. Quantitative Metrics for System Resilience

While fragility focuses on the onset of component failure, resilience quantifies the system’s dynamic performance during and after the disturbance. The conceptual framework is widely visualized as the “Resilience Trapezoid” or “Multiphasic Performance Curve,” which delineates the system’s state through three distinct phases: (1) disturbance absorption and degradation, (2) degraded state operation, and (3) restorative recovery [90]. Mathematically, the resilience metric (R) is quantified by integrating the system’s performance function Q(t) over the event horizon (t₀, t_end). As suggested in basic durability studies, the normalized durability metric is calculated as shown in Equation (3):

$$R=({\displaystyle \frac{{\int }_{t_0}^{t_{end}}Q\left(t\right)dt}{{\int }_{t_0}^{t_{end}}{Q}_{nom}dt}})$$

(3)

In this equation, Q(t) denotes the time-dependent system performance. Common examples include the total load supplied or the percentage of active lines. The term Q_nom represents the nominal baseline performance in the absence of disturbances. Consequently, the denominator defines the target performance over the specified time window.

The process occurs in three continuous stages as shown in Figure 9. First, the system begins in the avoidance and preparation phase (Phase I). Here, preventive measures are fully active. Next, the event strikes, leading to the absorption phase (Phase II). During this phase, system performance degrades. Finally, the process concludes with the restoration phase (Phase III). In this stage, repair crews and automated systems restore the power supply.

Consequently, the loss of resilience corresponds to the area between the nominal and actual performance curves (the area of the trapezoid). Deepening this analysis requires incorporating restoration strategies, such as topology reconfiguration and black-start capabilities, into the integral function to minimize the “loss area” shown in Figure 9. It depicts the multiphasic response of a power system to an HILP event. The figure shows distinct phases of system behavior. First, there is initial failure and performance degradation. Second, a sustained outage phase occurs. Finally, the system undergoes restoration and recovery.

7. Policy and Market Dynamics in HILP Events

The rising probability of HILP events necessitates a fundamental paradigm shift in policy and planning frameworks [92]. Relying on deterministic historical data is no longer sufficient; instead, projection-based stochastic methods must be adopted. Consequently, policymakers need to intervene by mandating updated climate resilience criteria for infrastructure, such as revised wind and thermal limits, while developing financial instruments to incentivize grid modernization [93].

Technical resilience requires substantial infrastructure investment. Hardening transmission lines against HILP events is costly. Therefore, a robust economic framework is essential. Technical metrics, such as the resilience trapezoid, must align with financial incentives. Standard energy markets often fail to value low-probability risks. They focus primarily on short-term operational efficiency. Consequently, specific ‘resilience markets’ are necessary. These mechanisms should reward utility investments in grid hardening. For example, capacity payments can include specific resilience premiums. Furthermore, regulatory bodies must update investment criteria. The ‘Value of Lost Load’ (VLL) calculations should account for catastrophic HILP events. This adjustment justifies the high cost of undergrounding lines or upgrading towers. Ultimately, policy must bridge the gap between engineering solutions and economic viability.

Furthermore, the impact on electricity markets extends beyond physical infrastructure damage, creating significant volatility. HILP events often trigger sudden losses in generation capacity or transmission corridors, forcing Transmission System Operators (TSOs) to procure emergency reserves from the Balancing Market. This supply scarcity drives the Market Clearing Price (MCP) to ceiling levels. Therefore, future resilience strategies must address these financial shocks by effectively integrating market-based mechanisms, such as demand response [94,95,96,97,98]. In this context,

Table 3. Comparative Analysis of Technological Strategies to Increase Grid Resilience.

Technology/Strategy	Mechanism of Action	Cost Impact (CAPEX)	Resilience Contribution	Ref.
Undergrounding	Relocating overhead conductors to subterranean ducts.	Very High	High: Eliminates wind/ice risk, though repair times are longer.	[95]
Digital Twins	Creating a real-time virtual replica of the grid for simulation.	Medium	Medium/High: Enables predictive maintenance and fast scenario analysis.	[96]
Dynamic Line Rating (DLR)	Adjusting thermal limits based on real-time weather.	Low	Medium: Prevents thermal sagging/faults during high-load recovery.	[97]
Microgrids & Islanding	Localized generation operating independently during blackouts.	High	Very High: Ensures continuity for critical loads during collapse.	[98]
Hardening	Using steel-concrete composite poles and stronger cross-arms.	Medium	Medium: Increases the critical wind speed threshold (Vcrit).	[91]
AI-Based Forecasting	Using machine learning to predict outages before they occur.	Low	High: Optimizes crew positioning and pre-storm preparation.	[82]

presents a comprehensive comparative analysis of technological strategies to enhance grid resilience against extreme weather. It explicitly details the cost-benefit trade-offs associated with each approach. The analysis highlights the necessity of capital-intensive measures for infrastructure hardening. Furthermore, it distinguishes these physical investments from operational improvements, such as AI-driven forecasting and microgrid islanding capabilities.

8. Discussion

The frequency and intensity of extreme weather events are increasing because of global warming, impacting power systems. This study identifies, presents, and examines low-probability but high-impact weather events that may occur in electric power systems during extreme weather conditions. Previous literature largely focused on qualitative descriptions. In contrast, this study applies a numerical framework based on probability. This approach calculates the specific likelihood of equipment failure as weather severity increases. Traditional fixed safety rules often fail to capture the unpredictable nature of extreme events. Therefore, this method predicts grid vulnerability more accurately than standard static measures.

Extreme weather events occurring during winter months typically have longer durations and broader impact ranges. Overhead lines suffer the most severe damage in electric grids affected by extreme winter weather. Prolonged low temperatures and heavy snowfall can cause transmission line poles to collapse. Additionally, damage to transmission lines hinders power flow to other lines in the interconnected grid, leading to overloading and increased risk of power outages. Therefore, preventive measures need to be developed to minimize the risk of power outages during extreme weather conditions.

To minimize power outages during extreme weather events, electric grids should be planned to withstand such conditions. It is currently imperative to strengthen existing electric systems against extreme weather events by establishing the infrastructure of electric transmission and distribution systems and relocating lines to more remote areas. Enhancing existing energy infrastructure systems is crucial to preventing power system disruptions during extreme weather events. Increasing the proportion of underground cables in transmission and distribution lines is a prominent aspect of this improvement. However, the main challenge in such improvements is the higher cost of infrastructure compared to overhead lines. Nevertheless, it is known that the reliability of underground cabling systems is much higher than that of overhead lines in the face of potential damage during extreme weather events.

This study has certain limitations. First, the analysis relies on historical outage data. However, reporting standards vary across different regions. Second, the fragility curves use generalized structural models. These models may require calibration for specific local grids. Therefore, future research should aim to refine these probabilistic models. Specifically, real-time sensor data should be integrated. This approach will enhance site-specific accuracy.

9. Conclusions

Furthermore, the damage caused by extreme weather events may vary depending on the characteristics of the respective regions. Strategies and models developed for low-impact and high-probability weather events will reduce the duration of power outages. In this context, the following steps are crucial factors: predictive maintenance planning, proximity of teams to fault points and faster intervention.

Analyzing the significant physical degradation of transmission line equipment poses challenges. Therefore, reevaluation of transmission lines based on climate change scenarios, rather than past climate data, to establish parameters for mechanical strength calculations, considering the lifespan and construction cost of energy transmission and distribution lines, is seen as an appropriate solution.

The findings of this review offer practical applications for Transmission System Operators (TSOs) and policy makers. First, TSOs can apply the ‘Resilience Trapezoid’ framework. This tool benchmarks grid recovery performance against historical HILP events. Second, the vulnerability analysis establishes a data-driven foundation. It prioritizes high-cost investments like undergrounding critical corridors. Finally, the study advocates for probabilistic assessment methods. Traditional models rely on fixed parameters. In contrast, this proposed method captures the uncertainty of future weather patterns. It allows regulators to revise grid codes. Thus, a shift from static rules to risk-based resilience criteria becomes possible.