# Extreme Climate Events and Energy Market Vulnerability: A Systematic Global Review
## 4 Impacts and Adaptation Strategies to Natural Events: A Review of Global Studies
### North America
Among the conclusions of the analysis, it can be observed that, without mitigation, annual electricity production costs in 2050 could increase by 14% (approximately $51 billion) due to increased cooling demand, but, on the other hand, with mitigation measures that reduce emissions from the United States electricity sector by 36% below 2005 levels by 2050, the increase in costs to meet elevated demand will be comparable to that of the scenario that does not present any mitigation [34].  
On the other hand, a study analyzes the performance of the Florida electrical system against storms and hurricanes, focusing specifically on storm-induced damage to the electrical infrastructure and restoration times. To do this, the researchers develop damage assessment models for various components of the electrical system, in addition to evaluating the duration of interruptions through statistical and stochastic models. The study concludes that their models are very effective in predicting vulnerable areas and estimating hurricane-related outages, achieving overall network error rates between 3% and 9% when validated against Hurricanes Katrina and Wilma. In addition, the researchers found that the main cause of power interruptions are trees and the wind momentum produced by hurricanes [35]. The exploration of the impacts of long-term droughts on hydropower generation is carried over to a discussion of natural gas price fluctuation and how these factors shape the strategies applied to mitigate financial risk.  
In the context of regional energy systems, particular attention is given to the western part of the United States, where researchers examine the effects of climate change on the electrical system. This is performed using climate models and energy analysis to try to predict when temperatures will be very high, along with changes in precipitation and streamflow. As expected, the results indicate that climate change significantly influences the generation capacity of the electrical system, including hydroelectric generation. The study shows that average summertime generating capacity could decrease by 1.0-2.7 GW by mid-century (2040-2060) in the Western Electricity Coordinating Council (WECC) region. The changes indicated lead to higher operating costs and the need for additional investments in capacity to meet demand. Therefore, the study emphasizes the vulnerability of the electrical system in this area of the United States, in addition to recommending the implementation of adaptive strategies to mitigate these challenges [36].  
Attention is also focused on strengthening and maintaining electric power infrastructure to mitigate the effects of hurricanes, in an area of the United States where the damage that hurricanes cause to electric power systems and the importance of developing management strategies for the assets are highlighted [37]. To achieve this, the authors design a partially observable Markov decision process, modeling the survival function of the components of the electric system under hurricane-induced stress. The final results demonstrate that customized strengthening measures and strategic maintenance planning improve the resilience of electric power systems in the specified area studied, reducing ex pected lifecycle costs by up to 74% when permanent hardening strategies are implemented compared to corrective-only maintenance approaches.  
Building on this focus, a framework is proposed to efficiently assess the resilience of electric power systems affected by hurricanes, with emphasis on computational tools to quantify outages and restoration processes. The present study develops a probabilistic approach with the help of Bayesian networks to model the system response to hurricane-induced failures and models the physical and operational constraints of power flow [38]. The methodology implements the framework in a case study concerning Harris-County Texan resilience to Hurricane Ike in 2008. The proposal assesses the outages to customers within distributed blocks of 1 km\({}^{2}\) and performs a simulation of restoration on the basis of historical resource mobilization data. The study mined outage and restoration sequences and presented the predictive accuracy of the model as reasonably satisfactory with a mean error of 15.4% at the ZIP code level. The conclusions highlighted the framework for generating insights for reinforcing power system resilience.  
Moreover, a complementary approach involves integrating climate change uncertainties into power generation planning using stochastic optimization models. Discretized modeling of climate change scenarios focuses on investigating the uncertainties and variability of temperature, precipitation, and energy demand [39].  
From this perspective, strategies must be based on reducing costs while improving the resilience and flexibility of energy systems to the long-term impacts of climate change. Moreover, a stochastic optimization model is used that considers the uncertainties relevant to each climate change scenario. Two optimization strategies are considered: minimizing the total expected cost across all scenarios and minimizing maximum regret, so that, once decisions are made, the company does not find itself in a situation that renders it unviable. The model considers not only costs but, more importantly, the variability of climate impacts to illustrate the trade-off between costs and risk management considerations in planning decision-making. The findings highlight the benefits of using integrated planning frameworks when addressing discrete climate change scenarios [40].  
In addition, operational efficiency in power restoration following hurricanes is also addressed, with an analysis based on data from 27 restoration events at 13 utility companies using a data envelopment analysis (DEA). Part of the analysis shows that during Hurricane Sandy, approximately 8.5 million customers experienced power outages, and more than 1 million were left without power for more than a week. In addition, the DEA results reveal consistent performance trends among utility companies. For example, some companies achieved higher efficiency levels due to resource allocation and restoration strategies; however, utility companies had problems due to inadequate resource mobilization. Knowing this, researchers emphasize the importance of systematic performance evaluations, in order to identify best practices and improve restoration processes [41].  
Additionally, the impact of geomagnetic storms on power transmission systems is analyzed, with a specific emphasis on how voltages are induced transmission lines under volatile Earth conductivity conditions. The study evaluates how 3D Earth impedance models improve the understanding and quantification of the effects of geomagnetic storms on power grids compared to 1D models. An example of this is what happened during the 1989 storm: 62 transmission lines exceeded 100 volts under 3D modeling, compared to only 16 lines using 1D models. 3D models provide a more accurate assessment of the impacts of geomagnetic storms on power systems [42].  
Furthermore, risk mitigation in power transmission systems, analyzed using a probabilistic model, was designed to reduce potential vulnerabilities. The model has the following components: a weighted deterministic hazard analysis model, component vulnerability assessments, system performance modeling, and life-cycle cost analysis to assess the cost-effectiveness in reducing risks. This methodology was applied to different electrical systems in regions of the United States, including Charleston, South Carolina, and New York City, New York. Probabilistically weighted hazard scenarios can better capture the cumulative impacts of seismic and hurricane events, and the vulnerability of the system could be identified, such as the fragility of transmission lines under strong winds and seismic activity [43].  
In a related study is the implementation by Nateghi [44], where the researchers present a resilience strategy in electric networks of areas that are specifically vulnerable to hurricanes; for this, the authors used advanced statistical machine learning techniques (artificial intelligence) to quantify the interactions between the system topology, the characteristics of the hazards, and the environmental conditions of the study site; Bayesian networks were also used to model the relationships between the predictors, which include different parameters such as customer density, tree pruning frequency, hurricane intensity, land cover, and soil moisture. The study concludes that factors such as high customer density and inadequate tree maintenance significantly increase the vulnerability of the system to hurricanes and similar climatic hazards.  
Similarly, in the same year, [PERSON] et al. [45] conducted research analyzing how socioeconomic vulnerability affected power restoration after Hurricane Irma in Florida. The data used were from 9 to 29 September 2017. At the peak of the hurricane, more than 36% of all accounts in Florida experienced power outages. In addition, associations are identified between prolonged power outages and specific socioeconomic factors, such as higher proportions of minority populations, residents with disabilities, and higher unemployment rates.  
Subsequently, years later, the research carried out by [PERSON] and [PERSON] [46] developed a two-level stochastic resilience model addressing interconnected failures between traffic and power systems. The study carried out in Galveston, Texas, USA, shows as a result that prioritizing the restoration of traffic signals reduces the average recovery time of energy services by up to 15%, in addition to emphasizing problems such as poorly connected nodes in the network.  
Additionally, climate-driven interactions between air temperature and river flows are examined with a focus on how these variables influence electricity market dynamics on the West Coast of the United States. The findings imply that temperature increases create additional electricity demand for summer cooling, while shifting hydroelectric availability to spring and winter [47]. In the Pacific Northwest (PNW), climate risks are mainly linked to variations in river flows, while in California extreme heat waves markedly increase electricity demand with consequent influences on system reliability.  
Moreover, scholars have started to focus on resilience to very large-scale weather events (VLSWEs), as exemplified by Winter Storm Uri in 2021, which exposed the vulnerabilities of electric systems during extreme events. For example, during Winter Storm Uri in Texas, nearly 4 million people experienced a loss of power and water for days, with estimates suggesting that 210 people died, and there was nearly $120 billion in economic impacts [48].