metadata: authors: 0: author: Farnham,David
1: author: Eisenberg,Ross Marc
2: author: Bonnafous,Luc Marius Jacques
count: Europe,World
docna: 0: docna: Assessing Power System Disruptions and
            Associated Economic Impacts from Increasing Extreme Heat
            Events in Southeast Europe Using an Idealized Design Methodology
docty: Policy Research Working Paper
theme: Urban Development,Mitigation,Disaster Risk Reduction,Environment and Natural Resource Management,Finance for Development,Finance,Urban Planning,Climate change,Urban and Rural Development,Adaptation,Disaster Risk Finance,Disaster Risk Management
abstracts: cdata!: As the world endeavors to decarbonize
            and shift toward sustainable energy sources, power systems
            will become increasingly dependent on weather conditions.
            This dependence creates the challenge of managing
            fluctuations in both power supply and demand (particularly
            for cooling), which can jeopardize system reliability,
            particularly during extreme weather events. To what extent
            will the increases in peak cooling demands manifest more
            frequent power system disruptions, posing risks to human
            health and economic activity? This paper focuses on urban
            centers in Southeast Europe and utilizes state-of-the-art
            climate simulations to estimate changes in the magnitude of
            extreme heat events. It also estimates the frequency of
            potential associated power system disruptions and their
            ensuing impacts on economic activity by assuming an
            idealized design methodology. The analysis reveals that
            between 2021 and 2070, urban centers in Southeast Europe may
            be at risk of an estimated four to nine power system
            disruptions per decade due to increasing trends in extreme
            heat events. These disruptions have the potential to incur
            annual economic costs of up to tens of millions of dollars
            in some cities. The projected disruptions highlight the
            challenges of adapting power systems to climate change, even
            with idealized regular redesign and maintenance efforts. To
            mitigate power system fragility during heat waves, the paper
            recommends implementing measures such as securing reserve
            power capacity, promoting urban cooling through greening
            initiatives, adopting demand-side management with smart-grid
            infrastructure, and increasing the deployment of solar
            power, which typically has high generation potential during
            heat waves.
display_title: Assessing Power System Disruptions
            and Associated Economic Impacts from Increasing Extreme Heat
            Events in Southeast Europe Using an Idealized Design Methodology
listing_relative_url: /research/2024/07/34367337/assessing-power-system-disruptions-associated-economic-impacts-increasing-extreme-heat-events-southeast-europe-using-idealized-design-methodology
topicv3: Climate Change m1326829 567,Power Systems m1327093 508,Extreme Temperature m1503647 3183,Heat Wave m1503649 3185,Climate Resilience m1504411 4184
publishtoextweb_dt: 2024-07-22T00:00:00Z
dois: 10.1596/1813-9450-10858
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: NA
1: author_key: 1795379
2: author_key: 1816480
model_extractions: Policy Research Working Paper 10858 Assessing Power System Disruptions and Associated Economic Impacts from Increasing Extreme Heat Events in Southeast Europe Using an Idealized Design Methodology _David Farnham Ross Eisenberg Luc Bonnafous_ Urban, Disaster Risk Management, Resilience and Land Global Practice July 2024
Policy Research Working Paper 10858

# **Abstract**

As the world endeavors to decarbonize and shift toward sustainable energy sources, power systems will become increasingly dependent on weather conditions. This dependence creates the challenge of managing fluctuations in both power supply and demand (particularly for cooling), which can jeopardize system reliability, particularly during extreme weather events. To what extent will the increases in peak cooling demands manifest more frequent power system disruptions, posing risks to human health and economic activity? This paper focuses on urban centers in Southeast Europe and utilizes state-of-the-art climate simulations to estimate changes in the magnitude of extreme heat events. It also estimates the frequency of potential associated power system disruptions and their ensuing impacts on economic activity by assuming an idealized design methodology. The analysis reveals that between 2021 and 2070, urban centers in Southeast Europe may be at risk of an estimated four to nine power system disruptions per decade due to increasing trends in extreme heat events. These disruptions have the potential to incur annual economic costs of up to tens of millions of dollars in some cities. The projected disruptions highlight the challenges of adapting power systems to climate change, even with idealized regular redesign and maintenance efforts. To mitigate power system fragility during heat waves, the paper recommends implementing measures such as securing reserve power capacity, promoting urban cooling through greening initiatives, adopting demand-side management with smart-grid infrastructure, and increasing the deployment of solar power, which typically has high generation potential during heat waves.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at david.f@climate.ai, reisenberg1@worldbank.org, and lbonnafous@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Assessing Power System Disruptions and Associated Economic Impacts from Increasing Extreme Heat Events in Southeast Europe Using an Idealized Design Methodology**

**David Farnham, Ross Eisenberg, Luc Bonnafous**

Keywords: Power system disruptions, Extreme heat events, Cooling power demands JEL Codes: R10, P48
# Table of Contents

|**LIST OF FIGURES .................................................................................................................................................................... 3**|
|---|
|**LIST OF TABLES ...................................................................................................................................................................... 4**|
|**INTRODUCTION ....................................................................................................................................................................... 5**|
|URBANCENTERS OFSOUTHEASTEUROPE........................................................................................................................................... 6|
|**METHODOLOGY ...................................................................................................................................................................... 7**|
|DATA................................................................................................................................................................................................................ 7|
|QUANTILEDELTAMAPPING POST-PROCESSING FORCMIP6SIMULATIONS............................................................................. 8|
|ESTIMATING COOLING DEMANDS............................................................................................................................................................ 9|
|SIMULATING THE ELECTRICITY SYSTEM CAPACITY DESIGN PROCESS......................................................................................... 9|
|SIMULATING SYSTEM RELIABILITY BY SIMULATING THE NUMBER OF DEMAND EVENTS THAT ECLIPSE THE DESIGN|
|CAPACITY OVER THE SYSTEM LIFETIME.............................................................................................................................................. 11|
|ESTIMATING THE COST OF DISRUPTIONS............................................................................................................................................. 12|
|**RESULTS ................................................................................................................................................................................... 14**|
|PROJECTIONS OF UNDER-SUPPLY DISRUPTION FREQUENCY.......................................................................................................... 14|
|PROJECTIONS OF ECONOMIC COSTS FROM UNDER-SUPPLY DISRUPTIONS................................................................................. 16|
|DEPENDENCE ON DESIGN PARAMETERS.............................................................................................................................................. 20|
|_Dependence on design record length ........................................................................................................................................... 20_|
|_Dependence on design year (2021 to 2090) ............................................................................................................................... 21_|
|**MITIGATING THE COSTS OF OUTAGES AND NAVIGATING THE UNCERTAINTY OF**<br>**PROJECTIONS ........................................................................................................................................................................ 21**|
|**CONCLUSIONS ....................................................................................................................................................................... 23**|
|**REFERENCES ......................................................................................................................................................................... 25**|
|**SUPPLEMENTARY MATERIAL ...................................................................................................................................... 28**|
|QUANTILEDELTAMAPPING DETAILS................................................................................................................................................. 28|
|THE IMPACT OF A15%RESERVE MARGIN.......................................................................................................................................... 29|
|THE IMPACT OF CONSIDERING BOTH HEATING A COOLING DEMAND......................................................................................... 29|
|THE IMPACT OF DIFFERING DESIGN METHODOLOGIES.................................................................................................................... 31|
|DEPENDENCE ON DESIGN METHODOLOGY RESULTS....................................................................................................................... 32|
|IDEALIZED DESIGN PROCESS AND SOURCES OF UNCERTAINTY.................................................................................................... 33|
|**APPENDIX 1: CHANGES IN THE EXTREME PEAK HEATING AND COOLING DEMANDS OVER THE**<br>**FIRST HALF OF THE 21ST CENTURY FOR GLOBAL CITIES ........................................................................... 36**|

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# **List of Figures**

- FIGURE 1: (A) GLOBAL MAP WITH THE SOUTHEAST EUROPE REGION HIGHLIGHTED WITH A GREEN RECTANGLE; (B) THE URBAN CENTERS INCLUDED IN THE SOUTHEAST EUROPE REGIONAL ANALYSIS. ............................................................................................................................................................................................ 7

- FIGURE 2: OVERVIEW OF DESIGN AND EVALUATION PROCESS. ............................................................................... 12

- FIGURE 3: THE MEDIAN NUMBER OF POWER SYSTEM DISRUPTIONS DURING THE PERIOD OF 2021 TO 2070 DUE TO COOLING DEMANDS EXCEEDING SUPPLY ARE ESTIMATED TO BE BETWEEN ABOUT 4 TO 9 EVENTS PER DECADE FOR THE MAJOR URBAN CENTERS OF SOUTHEAST EUROPE. ............................................................................................................................................................................................... 14

- FIGURE 4: THERE IS SUBSTANTIAL UNCERTAINTY IN THE ESTIMATES OF MEAN NUMBER OF POWER SYSTEM DISRUPTIONS DURING THE PERIOD OF 2021 TO 2070 DUE TO COOLING DEMANDS EXCEEDING SUPPLY DUE TO A) CLIMATE MODEL UNCERTAINTY (BOXPLOT SPREAD) AND B) SOCIOECONOMIC PATHWAYS (THREE COLUMNS OF PANELS). THE HORIZONTAL LINES IN THE MIDDLE OF THE FILLED BOXES REPRESENT THE MEDIAN, THE FILLED BOXES SPAN THE 25TH TO 75TH PERCENTILES, AND THE WHISKERS EXTEND TO THE 5TH AND 95TH PERCENTILES. THE SPREAD SHOWN VIA THE BOXPLOTS REPRESENTS THE INTER-MODEL SPREAD; I.E., EACH POINT THAT DETERMINES THE BOXPLOTS IS A SINGLE MODEL ESTIMATE FOR THE MEAN NUMBER OF UNDER-SUPPLY EVENTS PER DECADE OVER THE PERIOD OF 2021 TO 2070. ................................................................................................................................................ 15

- FIGURE 5: THE MEAN NUMBER OF POWER SYSTEM DISRUPTIONS GENERALLY INCREASES AS THE DESIGN RECORD LENGTH INCREASES FOR THE HIGH EMISSIONS SCENARIO. THE EMPIRICAL DESIGN METHOD (HISTORICAL 90TH%) GENERALLY RESULTS IN MORE POWER SYSTEM DISRUPTIONS THAN EITHER OF THE STATISTICAL GEV-FIT DESIGN METHODS FOR THE HIGH EMISSIONS SCENARIO. ............................................................................................................................................................... 20

- FIGURE 6: THE MEAN NUMBER OF POWER SYSTEM DISRUPTIONS GENERALLY INCREASES AS YOU MOVE FURTHER INTO THE 21ST CENTURY FOR THE HIGH EMISSION SCENARIO, DECREASES AS YOU MOVE FURTHER INTO THE 21ST CENTURY FOR THE LOW EMISSION SCENARIO, AND MODESTLY DECREASES AS YOU MOVE FURTHER INTO THE 21ST CENTURY FOR THE MIDDLE OF THE ROAD EMISSIONS SCENARIO. ............................................................................................................................. 21

- FIGURE A1 1: ESTIMATES (USING THE MEAN OF ALL CLIMATE MODELS) OF THE PERCENT CHANGE (BETWEEN THE PERIODS 1990-2019 AND 2030-2059) IN THE 1-IN-10-YEAR PEAK COOLING (TOP) AND HEATING (BOTTOM) DAILY DEMANDS FOR THE GLOBAL SET OF LARGE URBAN CENTERS ACROSS EACH THREE OF THE FUTURE SOCIOECONOMIC PATHWAYS, NAMELY “LOW EMISSIONS” (SSP126; LEFT), “MIDDLE OF THE ROAD EMISSIONS” (SSP245; CENTER), AND “HIGH EMISSIONS” (SSP585; RIGHT). WE TRUNCATE THE INCREASES TO 100% FOR VISUALIZATION PURPOSES. ................................................................................................................................................... 38

FIGURE A1 2: SAME AS FIGURE A1 1 BUT FOR THE PERIODS OF 1990-2019 AND 2070-2099. ............................. 38 FIGURE A1 3: THE REGIONAL BOUNDARIES OF THE CLIMATE REGIONS USED FOR GROUPING RESULTS. .... 39

- FIGURE A1 4: ESTIMATES (USING THE MEAN OF ALL CLIMATE MODELS) OF THE CHANGE (BETWEEN THE PERIODS 1990-2019 AND 2030-2059) IN THE 1-IN-10 YEAR EXTREME PEAK DAILY COOLING AND HEATING DEMANDS GROUPED BY CLIMATE REGION. THE PERCENT

3
INCREASE THE HORIZONTAL LINES IN THE MIDDLE OF THE FILLED BOXES REPRESENT THE MEDIAN, THE FILLED BOXES SPAN THE 25TH TO 75TH PERCENTILES, AND THE WHISKERS EXTEND TO THE 5TH AND 95TH PERCENTILES. THE SPREAD SHOWN VIA THE BOXPLOTS REPRESENTS THE SPREAD OF THE MEAN MODEL ESTIMATES FOR EACH OF THE URBAN CENTERS IN EACH REGION. NOTE THAT WE HAVE ALSO ADDED A REGION CALLED “CAR” TO INCLUDE THE CARIBBEAN CITIES OF PORT-AU-PRINCE (HAITI), SANTO DOMINGO (DOMINICAN REPUBLIC), AND HAVANA (CUBA). .................................................................................................... 41 FIGURE A1 5: SAME AS FIGURE A1 4 BUT FOR THE PERIODS OF 1990-2019 AND 2070-2099. ............................. 42

- FIGURE A1 6: GRAY POINTS SHOW THE MEAN MODEL ESTIMATES FOR THE 1-IN-10-YEAR PEAK HEATING AND COOLING DEMAND FOR EACH URBAN CENTER OVER THE PERIOD OF 19902019, PURPLE POINTS SHOW THE MEAN MODEL ESTIMATES FOR THE 1-IN-10-YEAR PEAK HEATING AND COOLING DEMAND FOR EACH URBAN CENTER OVER THE PERIOD OF 20302059, AND THE ARROWS CONNECT EACH URBAN CENTER AND ILLUSTRATE THE UPWARD TRENDS IN PEAK COOLING DEMANDS (Y-AXIS) AND THE DOWNWARD TRENDS IN PEAK HEATING DEMANDS (X-AXIS). .............................................................................................................................................. 42

FIGURE A1 7: SAME AS FIGURE A1 6 BUT FOR THE PERIODS OF 1990-2019 AND 2070-2099 .......................... 43

# **List of Tables**

TABLE 1: THE MEDIAN, 5TH, AND 95TH PERCENTILE ESTIMATES FOR THE NUMBER OF POWER SYSTEM DISRUPTIONS DURING THE PERIOD OF 2021 TO 2070 DUE TO COOLING DEMANDS EXCEEDING SUPPLY. ................................................................................................................................................................... 16

- TABLE 2: THE MEDIAN, 5TH, AND 95TH PERCENTILE ESTIMATES FOR THE PERCENT OF ANNUAL GDP LOST DUE TO UNDER-SUPPLY EVENTS DURING THE PERIOD OF 2021 TO 2070 DUE TO COOLING DEMANDS EXCEEDING SUPPLY USING THE HIGH ESTIMATE OF 0.35% PER DISRUPTION EVENT. .................................................................................................................................................................... 18

TABLE 3: SAME AS TABLE 2 BUT FOR 2015 GDP (IN 2015 US $MILLION) .................................................................. 18

- TABLE 4: THE MEDIAN, 5TH, AND 95TH PERCENTILE ESTIMATES FOR THE PERCENT OF ANNUAL GDP LOST DUE TO UNDER-SUPPLY EVENTS DURING THE PERIOD OF 2021 TO 2070 DUE TO COOLING DEMANDS EXCEEDING SUPPLY USING THE LOW ESTIMATE OF 0.06% PER DISRUPTION EVENT. .................................................................................................................................................................... 19

TABLE 5: SAME AS TABLE 4 BUT FOR 2015 GDP (IN 2015 US $MILLION) .................................................................. 19 TABLE A1 1: THE FULL NAMES OF THE CLIMATE REGIONS DEFINED IN FIGURE A1 3. ............................................ 40 4
# **Introduction**

As efforts to decarbonize progress, it is vital to ensure the reliability of power systems within the context of the challenges of phasing out fossil fuels, aging transmission and distribution infrastructure, and increasing weather-induced fluctuations on energy supply (i.e., wind, solar, and hydro-power) and demand (i.e., heating and cooling demand). Even without these increasing challenges, the economic costs of unreliable critical infrastructure are already costly, particularly in less developed countries. A 2019 World Bank report estimated that the cost of unreliable infrastructure in 137 low- and middle-income countries (a cohort of countries that represents about 80 percent of the GDP of all low- and middle-income countries) was nearly $300 billion a year (about 1.5% of GDP), with over half of that cost associated with electricity outages and the subsequent losses to sales and the costs of self-generated electricity (Rentschler et al. 2019 _)_ . A 2019 survey found that power outages in a typical month in Kosovo, North Macedonia, Albania, and Serbia were estimated to occur about 8, 5, 4, and 2 times each month, respectively (Dalloshi et al. n.d.).

Extreme demand for cooling is among the many reasons why power systems can fail to maintain reliable supply. High temperatures heat the land surface and infrastructure, trigger the usage of air conditioning where available, and cause some of the highest electricity demand spikes that systems experience (Franco et al. 2007, Waite et al. 2017). As such, periods of elevated electricity demand driven by hot temperatures are critical times for electrical grids and can lead to soaring energy prices and, most critically, power outages or rolling blackouts (Hatvani-Kovacs et al. 2016). Examples include the blackouts triggered in California in July 2018 when spikes in electricity demand during a heat wave could not be met by the power system (Khan 2018). The July heat wave of 1995 was an even more dire example, where power failures were partially to blame in the deaths of over 800 people in the United States (Changnon et al. 1996). Furthermore, anthropogenic warming is expected to exacerbate these power system reliability issues (Allen et al. 2016, Li et al. 2012, Franco et al. 2007, Miller et al. 2008) via rising temperatures, and the anticipated increased adoption of air conditioning will lead to more energy systems being stressed during hot times (Waite et al. 2017).

In addition to climate change’s effects of exacerbating the magnitude of extreme heat, the trend of increasing urbanization can also increase the magnitude of extreme cooling demand events for two 5
reasons. First is the well-known phenomenon of urban heat islands, whereby urban environments become substantially hotter than the neighboring lands due to the increased absorption of energy (in the form of solar radiation) and reduced cooling through evapotranspiration. Second, higher population density centralizes the demand for cooling during extreme heat events in locations where air conditioning is available, thereby eliminating the natural process of smoothing demand spikes via more spatially distributed demand nodes that are unlikely to all peak in cooling demands simultaneously.

Having established that the costs of power system blackouts are considerable, and that anthropogenic warming is increasing the magnitude and frequency of heat waves, the goal of this paper is to use the latest generation of climate projections to estimate the propensity of urban power systems to eclipse their design capacity as a result of extreme cooling demands into the 21st century in major urban centers globally, with a special focus on the urban centers of Southeast Europe (Figure 1). Secondarily, this paper provides estimates of the economic costs associated with these urban power system disruptions. Lastly, this paper discusses the implications of increasing extreme heat events on critical power (and energy) system infrastructure and what adaptive responses can help ensure system reliability into the coming decades.

# **Urban Centers of Southeast Europe**

Power system disruptions can be particularly salient in urban areas because of the high population density, the potential for cascading failures due to interconnected and interdependent systems, and the concentration of economic and industrial activities in and around urban centers. Higher population densities mean that more humans are impacted by power system disruptions. The interconnected and interdependent nature of many critical systems in urban settings, such as transportation and water systems, mean that power system interruptions can spark cascading failures and disruptions to multiple systems. Lastly, due to the high concentration of businesses and industries that rely on a stable power supply to operate in and around urban centers, urban power system disruptions can result in substantial lost revenue, reduced productivity, and increased costs for businesses.

6
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is acquired from the ERA-5 reanalysis dataset (Hersbach 2018). We derive estimates for the maximum and minimum temperatures at the daily level by taking the maximum and minimum of the hourly values for each day.

- **Future temperature projections from climate model simulation data:** Maximum and minimum hourly temperatures over the previous 24 hours are acquired from Earth system model simulations from the Coupled Model Intercomparison Project, phase 6 (CMIP6). We consider three shared socioeconomic pathways, namely SSP1 (Sustainability, taking the green road), SSP2 (middle of the road), and SSP5 (fossil-fueled development). SSP2 currently appears to be the most likely scenario from an emissions perspective (Pielke Jr et al. 2022). And while SSP1 and SSP5 currently appear to be unlikely, understanding climate projections under these relatively extreme scenarios provides us with a view of the full range of possibilities. Moving forward, we refer to these scenarios as “low emissions” (SSP1), “middle of the road emissions” (SSP2), and “high emissions” (SSP5). We consider projections from the following global climate models: AWI-CM-1-1-MR, EC-Earth3-VegLR, FGOALS-g3, GFDL-ESM4, INM-CM4-8, INM-CM5-0, IPSL-CM6A-LR, KACE-10-G, MIROC6, MPI-ESM1-2-HR, MPI-ESM1-2-LR, MRI-ESM2-0, and NESM3 since each of these models has less than 2% missing daily values, over the full simulation period of 1979 to 2100, for each of the three socioeconomic scenarios considered here.

- **Economic data:** Gross domestic product (GDP) estimates, based on purchasing power parity, at a resolution of 30 seconds in both latitude and longitude (i.e., a spatial resolution of just under 1 km in the region of Southeast Europe) from Kummu et al. (2018). We use estimates from the year 2015 since these are the most recent available estimates.

# **Quantile Delta Mapping Post-Processing for CMIP6 Simulations**

We post-process the Earth system model simulations using a quantile mapping method, which corrects systematic distributional biases in climate model output data by removing historical biases relative to observations. Specifically, we apply the Quantile Delta Mapping (QDM) method (Cannon et al. 2015) in order to preserve the trends in the climate model simulations in such a way as to ensure that the climate sensitivity (i.e., the temperature response to greenhouse gas forcing) of the model is not affected by bias correction. QDM adjusts the distribution of simulated climate data to more closely match the distribution of observed (or reanalysis) climate data, while 8
preserving the magnitude of the trends in each of the quantiles. Quantile Delta Mapping is described in detail, including presentation of the underlying equations, in the Supplementary Material.

**Estimating Cooling Demands** We defined maximum inferred cooling demand during day (CD( )) using the daily time-series of maximum temperature (i.e., the hottest hour of the day) as defined in eq. 1. tt tt CD max max C, 0)                                         eq. 1 Where max is the maximum temperature for day . We note that the results shown in this paper (tt) = (TT (tt) −18 ° are not sensitive to modest adjustments of the threshold at which we presume power demand for TT (tt) tt cooling begins to occur (i.e., 18 °C in this case).

To complement this main text of the paper, we also present an analysis of the projected changes in extreme cooling and heating demands events over the 21st century for all urban centers of the globe with populations over 1 million (Appendix 1). The associated dataset will allow practitioners to quickly identify which urban centers are likely to be most or least in need of investments to bolster their power systems in order to keep pace with climate changes.

# **Simulating the Electricity System Capacity Design Process**

We simulate the design process that a system designer would undertake to determine the appropriate size (or capacity) of a power system’s generation fleet during the summer months in order to achieve the ultimate goal of understanding the propensity of a system to become unreliable in the face of rising extreme temperature events. We assume that the largest loads on the electricity system during the summer occur during events of extreme temperature, and that other loads are relatively constant in nature. We focus on hourly extreme events and associated loads, though this same analysis can be carried out for longer duration events.

We simulate the capacity design of a power system by estimating the peak daily demand that an electricity system should be designed to support, assuming that we are concerned with designing 9
a summertime system to withstand the burden of a 1-in-10 year<sup>1</sup> extreme event. The method entails taking the event size from the historical record with a 1-in-10 year return period to be the design event. Since this paper is focused on the impact of climate change on the reliability of the power grid, we do not explicitly consider changing population dynamics, major changes in electricity demand patterns, the effect of energy efficiency investments in buildings that make cooling more efficient and increases in air conditioning system penetration (linked to an increase in income and demand for comfort). Neglecting population changes should not have an impact on the results of this analysis because changes in population would be reflected on both the demand and supply side and thus would not impact the difference between demand and supply, which is the most critical quantity. Increasing efficiency in buildings to lower the power demand for cooling and increases in the penetration of air conditioning will have opposing impacts on this analysis, though the magnitude of their impacts is yet unknown and will likely be heterogenous across regions and urban centers. As such, we hold these values fixed for the time being but note that a follow-on analysis should investigate the extent to which these impacts themselves can be estimated.

We repeat the system sizing simulation for each climate model (to estimate the impact of climate model uncertainty in our projections and subsequent design and system performance simulations), for each shared socioeconomic pathway (to estimate the dependence of our simulations on the future emissions scenario), and for each urban center separately. We have also included two alternative methods for estimating the system capacity to illustrate the uncertainty (or lack thereof) that is inherited from the design methodology (see Supplementary Material).

Lastly, we investigate the impact of varying two design parameters to understand the extent to which power system disruptions due to under-supply can be expected to accelerate into the future and the extent to which issues of potential under-design are related to the historical record length used in the capacity analysis. For all simulations, the system redesign is conducted every ten years.

- used in the capacity analysis. For all simulations, the system redesign is conducted every ten years. • **Design year (** d **)** : defined by the year in which the updated design and system updates are assumed to have occurred.

> 1This is a common design standard in power system capacity adequacy planning (https://www.esig.energy/five-principles-of-resource-adequacy-for-modern-powersystems/).

10
- **Record length (** r **)** : defined by the length of the record used to estimate the design event.

**Simulating System Reliability by Simulating the Number of Demand Events That Eclipse the Design Capacity over the System Lifetime**

Now that we have simulated the design process to establish the capacity of the system, the next step is to evaluate the performance of the hypothetical system to support the projected demands over the system lifetime (assumed to be 10 years in this case) via eq. 2. Dec 31st of d +10 #of cooling-demand-related-outages Jan 1st of d �C (d )� eq. 2 where d is the design year and d (d = ΣΣ) is the design capacity associated with design year tt= (tt) > d d .

The full process is illustrated in Figure 2. We also illustrate the impacts of a 15% safety margin applied to this process in the supplemental section (Figure S1). Not surprisingly, the implementation of a 15% safety margin reduces the number of estimated disruption events considerably (by about an order of magnitude for many urban centers.) We also illustrate the impacts of considering both peak heating and cooling demand simultaneously (assuming complete electrification of heating; Figure S2). Notably, under this assumed scenario, peak annual demand is dominated by heating for the next many decades still and the total numbers of disruptions are reduced by about 2x to 4x, depending on the urban center of interest.

There are several caveats associated with this analysis. The first is that this paper considers undersupply at the bulk system level (i.e., when the installed generation is not sufficient to meet demand) and does not explicitly consider outages due to stress on specific channels along the transmission and distribution systems during times of peak demand. Second, this framework does not explicitly estimate the duration nor size of an outage event (i.e., how long does the disruption last and what fraction of the city is impacted). Instead, this framework simply allows us to estimate the frequency of events when undersupply during heat waves is likely to stress the system to the point of failure. Lastly, the reality is that power systems are consistently undergoing slow gradual changes, rather than the stepwise system updates that we model here. We have simplified this into distinct, regular redesigns to isolate the effects of fluctuating capacity needs (required to meet peak demands during heat waves) over the course of decades. This approach aligns with the reality that power system capacity evaluations do occur at specific, discrete times.

11
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operator, the level of maintenance of the power system, etc. As such, it is difficult to derive a precise estimate for the impact of power disruptions on an economy tailored to specific locations.

Because of this, we use some relatively recent power outages that have been well documented and studied to derive a range of possible impacts on the economy of an urban center due to an undersupply event. Specifically, we use the February 2021 blackout in Texas and the 2019 rotating blackouts in South Africa. While these two events represent fundamentally different types of events, one where rotating blackouts were necessary throughout the year for a total of about 530 hours and one where a discrete event caused disruptions for many days, they both represent events that were brought on by insufficient generating capacity. We use economic damage estimates from these two events to provide a range of cost estimates.

In the case of Texas, the estimated cost of the power system disruption caused by insufficient supply was estimated at around $130 billion (Busby et al. 2021), out of an estimated GDP for Texas for that year of about $2.05 trillion (Economic Analysis 2022). In other words, the event cost about 6% of annual GDP. “Given the fact that the February 2021 event had associated power outages that spanned 17 days, we compute the average loss per day in GDP, which is about 0.35% per day.” In the case of South Africa, the rotating blackouts throughout the year lasted a total duration of about 530 hours (or about 22 days) (Trace 2020) and cost between $3.5 billion and $7 billion (Trace 2020), out of an estimated annual GDP of about $400 billion.<sup>2</sup> Taking a central cost estimate of about $5.25 billion, the cost of the rotating blackout were about 0.06% per day.

These estimates are simplistic but provide a crude range (spanning nearly an order of magnitude) with which to approximate the economic damage associated with blackouts or rotating blackouts due to insufficient generating capacity. We therefore use a low estimate of 0.06% of annual GDP per disruption and a high estimate of 0.35% of annual GDP per disruption.

> 2 https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=ZA

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Simulated power system outages from extreme cooling demands per decade (assuming 20-year design record and evaluated 2021-2070) <!-- Start of picture text --> (0) 99@999999999999909000998999999999 9989998888999999998890899898999S99900¢<br>(0 pepeeesesesssessqs00— poeeesess POSS S999 SSSSSS5599905 >= =e<br>5.0 - - -- -- --- - - - -- -- --------- -- -- --<br>2.5 -- -- -- --- - - - -- -- -- --- -- -- --<br>0.0<br>&Rd Ra s4 Ra Rd se ge AS_ & & oe<br>oe eOo xJ &> 3RaRO sxX ce)oO *< &oe «s) a3s<br>ae<br>Urban center<br><!-- End of picture text -->
Simulated power system outages from extreme cooling demands per decade (assuming 20-year design record and evaluated 2021-2070) <!-- Start of picture text --> low emissions middle of the road emissions high emissions<br>40 -- - - rr rr rr rrr rr rrr rrr rr rr rr rrr re reer:<br>30_ - em rsennnnnrnr] FUNTUNTOTEL IN ee rr eeicre gecei --|-cree| -<br>Seceledadsdadel Ceewowe e neryes ee ee ee ee ee Se ee ee ee eee eee ol<br>OF 2? SF 0 3% GL SP ® SF _O GLP SP SF 0 GP LP @<br>SPODD PSG RED PPP PFS FF RD PO PP KE GPS SSSSF RED PO PPS<br>PS FKLwF DIOF osDP yO PRGAV PONSSPP PF PIO DF yO PKA SPOOF BI DM yO PKS<br>we 2 weos Vi POEM MS F xos v<br>Urban center<br><!-- End of picture text -->
||historical|90th %-ile<br>middle of the road||
|---|---|---|---|
|Urban center|low emissions’|emissions’|high emissions’|
|Belgrade|5 (3-10)|6 (3-13)|12 (8-25)|
|Craiova|5 (2-7)|6 (8-11)|10 (7-18)|
|Plovdiv|4 (2-9)|5 (2-8)|12 (7-24)|
|Pristina|6 (2-10)|9 (3-13)|14 (7-19)|
|Sarajevo|5 (2-13)|6 (4-13)|17 (5-30)|
|Skopje|5 (2-12)|9 (3-14)|13 (8-22)|
|Sofia|5 (2-10)|6 (3-11)|9 (6-21)|
|Thessaloniki|4 (2-8)|4 (3-13)|11 (7-21)|
|Timisoara|6 (2-11)|6 (3-13)|11 (4-32)|
|Tirana|6 (1-13)|8 (4-19)|19 (10-31)|
|Zagreb|6 (2-14)|8 (3-12)|16 (7-29)|
|TMmedian (5th perce|ntile, 95th percentile)|||
in these estimates depending on which urban center is specifically of interest due to differences between regions in our simulated frequency of urban power system disruptions and because of the disparate sizes of the economies of urban centers. As a reminder, our estimates for GDP are based on 2015 GDP figures and no assumptions regarding economic growth are made for the economies modeled here. Because the estimated frequency of urban power system disruptions is a primary driver of these cost estimates, there are again substantial uncertainties in our estimates that result from the variation in climate model simulations and the assumed socioeconomic pathway (Table 3 and Table 5). In fact, in some cases, these uncertainties result in our 90 percent confidence interval spanning about half an order of magnitude, as is the case for Tirana.

Despite the large ranges of the estimated cost of disruptions due to extreme cooling demands, increases in the extreme cooling demands are unlikely to be a primary driver of lost economic activity in many parts of Southeast Europe given the fragile state of the electricity system that currently exists. For example, rolling blackouts have recently been introduced in Kosovo (including in the capital of Pristina) due to lack of supply, which has been blamed on lack of investment.<sup>3</sup> In the near-term, the power supply of many countries in Southeast Europe may be more threatened by aging coal infrastructure (resulting from a lack of investments in maintenance and rehabilitation) and drought due to large dependence on coal and hydropower generation. For example, as of 2020, it was estimated that Kosovo depended on coal for over 94% of electricity generation, while it was estimated that Albania, Montenegro, Bosnia and Herzegovina, Serbia, and North Macedonia relied on hydropower for over 99%, over 42%, over 27%, over 25%, and over 23% of electricity production, respectively.<sup>4</sup>

> 3 https://www.bloomberg.com/news/articles/2022-08-26/europe-energy-crisis-kosovolearns-to-live-with-rolling-power-blackoutsagain?utm_source=website&utm_medium=share&utm_campaign=copy

> 4 https://www.iea.org/countries/kosovo, https://www.iea.org/countries/albania, https://www.iea.org/countries/montenegro, https://www.iea.org/countries/bosnia-andherzegovina, https://www.iea.org/countries/serbia, https://www.iea.org/countries/north-macedonia

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||High en|d ofassumed damages||
|---|---|---|---|
|Urban center|low emissions’|middle ofthe road emissions’|high emissions’|
|Belgrade|0.18% (0.09%-0.34%)|0.21% (0.11%-0.44%)|0.43% (0.27%-0.89%)|
|Craiova|0.19% (0.09%-0.25%)|0.2% (0.11%-0.39%)|0.36% (0.24%-0.63%)|
|Plovdiv|0.13% (0.08%-0.31%)|0.18% (0.09%-0.29%)|0.42% (0.24%-0.82%)|
|Pristina|0.2% (0.07%-0.36%)|0.3% (0.12%-0.44%)|0.48% (0.24%-0.68%)|
|Sarajevo|0.16% (0.08%-0.46%)|0.2% (0.14%-0.47%)|0.59% (0.19%-1.03%)|
|Skopje|0.17% (0.07%-0.4%)|0.33% (0.11%-0.47%)|0.47% (0.3%-0.78%)|
|Sofia|0.17% (0.07%-0.36%)|0.22% (0.11%-0.37%)|0.32% (0.22%-0.72%)|
|Thessaloniki|0.15% (0.08%-0.27%)|0.15% (0.11%-0.47%)|0.4% (0.25%-0.72%)|
|Timisoara|0.22% (0.07%-0.39%)|0.21% (0.1%-0.45%)|0.38% (0.15%-1.11%)|
|Tirana|0.22% (0.05%-0.46%)|0.27% (0.13%-0.68%)|0.67% (0.36%-1.08%)|
|Zagreb|0.2% (0.08%-0.48%)|0.29% (0.12%-0.41%)|0.57% (0.23%-1.03%)|
|7median (5th percent|ile, 95th percentile)|||

|Urban center|High end ofassumed dam<br>low emissions’|ages (2015 GDP, PPP in US $mil <br>middle ofthe road emissions’|from 2015)<br>high emissions’|
|---|---|---|---|
|Belgrade|54(27-100)|63 (32-131)|129(79-264)|
|Craiova|10 (6-14)|11 (6-22)|20 (13-35)|
|Plovdiv|8 (5-19)|11 (5-18)|25 (14-50)|
|Pristina|6 (2-12)|10 (4-14)|15 (8-22)|
|Sarajevo|14 (7-42)|18 (12-42)|53 (17-92)|
|Skopje|22 (10-53)|43 (14-62)|61 (39-102)|
|Sofia|91 (38-198)|122 (62-202)|175 (119-394)|
|Thessaloniki|31 (16-58)|33 (24-99)|85 (54-153)|
|Timisoara|23 (7-42)|22 (11-48)|40 (16-117)|
|Tirana|33 (8-71)|42 (20-104)|104 (55-166)|
|Zagreb|69 (27-161)|100 (41-138)|194 (77-349)|
|7median (5th per|centile, 95th percentile)|||
||Low en<br>|d ofassumed damages<br>||
|---|---|---|---|
|Urban center|low emissions’|middle ofthe road emissions’|high emissions’|
|Belgrade|0.03% (0.02%-0.06%)|0.04% (0.02%-0.08%)|0.07% (0.05%-0.15%)|
|Craiova|0.03% (0.01%-0.04%)|0.03% (0.02%-0.07%)|0.06% (0.04%-0.11%)|
|Plovdiv|0.02% (0.01%-0.05%)|0.03% (0.01%-0.05%)|0.07% (0.04%-0.14%)|
|Pristina|0.03% (0.01%-0.06%)|0.05% (0.02%-0.08%)|0.08% (0.04%-0.12%)|
|Sarajevo|0.03% (0.01%-0.08%)|0.03% (0.02%-0.08%)|0.1% (0.03%-0.18%)|
|Skopje|0.03% (0.01%-0.07%)|0.06% (0.02%-0.08%)|0.08% (0.05%-0.13%)|
|Sofia|0.03% (0.01%-0.06%)|0.04% (0.02%-0.06%)|0.06% (0.04%-0.12%)|
|Thessaloniki|0.03% (0.01%-0.05%)|0.03% (0.02%-0.08%)|0.07% (0.04%-0.12%)|
|Timisoara|0.04% (0.01%-0.07%)|0.04% (0.02%-0.08%)|0.06% (0.03%-0.19%)|
|Tirana|0.04% (0.01%-0.08%)|0.05% (0.02%-0.12%)|0.12% (0.06%-0.19%)|
|Zagreb|0.03% (0.01%-0.08%)|0.05% (0.02%-0.07%)|0.1% (0.04%-0.18%)|
|1median (5th percent|ile, 95th percentile)|||

|Lo<br>|w end of assumed dam<br>|ages (2015 GDP, PPP inUS $mi<br>|lfrom2015)<br>|
|---|---|---|---|
|Urban center|low emissions’|middle ofthe road emissions’|high emissions’|
|Belgrade|9 (6-17)|11 (6-22)|22 (14-45)|
|Craiova|2 (1-2)|2 (1-4)|3 (2-6)|
|Plovdiv|1 (1-3)|2 (1-3)|4 (2-9)|
|Pristina|1 (0-2)|2 (1-2)|3 (1-4)|
|Sarajevo|2 (1-7)|3 (2-7)|9 (3-16)|
|Skopje|4 (2-9)|7(2-11)|11 (7-17)|
|Sofia|16 (7-34)|21 (11-35)|30 (20-68)|
|Thessaloniki|5 (3-10)|6 (4-17)|14 (9-26)|
|Timisoara|4 (1-7)|4 (2-8)|7 (3-20)|
|Tirana|6 (1-12)|7 (8-18)|18 (9-29)|
|Zagreb|12 (6-28)|17 (7-24)|33 (13-60)|
|7median (5th per|centile, 95th percentile)|||
<!-- Start of picture text --> Mean # of disrupted days per decade<br>(median of all climate models for each urban center separately)<br>low emissions middleof the road emissions high emissions<br>16 -------- eo ee or rr rr rrr rr rrr rrr rr rr rr rr rr rr rr ee ee ee ee eee eee ==:<br>(|) ea ad a aS,WEBBZZ = 7<br>-2e2ee-- Se =< wee eee eee See eee eee eee ee Sse e eee eee ee<br>8-- == S aos SEF———<br>© aS aS) RS) xs BS) © aS aS)<br>Record length for design (years)<br><!-- End of picture text -->
<!-- Start of picture text --> Mean # of disrupted days per decade<br>(median of all climate models for each urban center separately)<br>low emissions middle of the road emissions high emissions<br>20 - - - ------ 2 rn nnn nnn ee nr ee ere pecs i [\f..<br>BS<br>7 LIRSG<br>0 oo<br>ss FF KC KF KF SC KF KF KF KF S<br>Design year<br><!-- End of picture text -->
assessments often, increasing the extent to which generation and demand co-vary, and reducing the impact of urban heat island effects through infrastructural and land cover interventions, respectively.

- a. _Using explicitly forward-looking methods for power system capacity design, which will ultimately entail securing greater reserve capacity in order to keep up with climate change and the increasing frequency and magnitude of extreme heat events._ One way of accomplishing this is to use forward-looking temperature projections rather than exclusively relying on the historical record. This should be undertaken with care, however, as many climate models have some deficiencies with respect to their ability to simulate extreme weather events (Ehret et al. 2012, Boberg & Christensen 2012).

- b. _Increasing the frequency with which capacity assessments are conducted_ will allow system analysts to update the system to withstand current climates and will limit the extent to which they must rely on extrapolations from climate models.

- c. _Increasing the resilience of the energy system to support increasing space cooling demand spikes during heat waves by increasing the potential power supply during heat waves._ One such approach is the increased deployment of solar power. This is because the fluctuations in solar power are generally well correlated with cooling demand at the hourly timescales (their daily curves are generally strongly correlated), multi-day timescales (extreme hot surface conditions during heat waves are associated with increased solar radiation at the surface resulting from clear skies), and seasonal timescales (both peak in summer) (Richardson & Harvey 2015, Solomon et al. 2016, Barnham et al. 2013, De Jong et al. 2013). This same solar radiation can be transformed into power via solar photovoltaics or other solar collection methods, and that power can be used to cool indoor spaces to give humans a place to stay cool during periods of high heat.

- d. _Reducing the impact of the urban heat island effect by increasing the local reflectivity of the urban landscape_ to absorb less solar radiation and thus lower surface and near-surface temperatures. This can be achieved through the application of lighter paint colors or other engineered reflective coatings on roofs and roads, as has been shown to reduce urban heat in many cases (Sen and Khazanovich, 2021).

- e. _Increasing the ability of the land surface to cool through evapotranspiration by increasing the density of trees and other vegetation_ within urban centers.

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- f. _Scaling up energy efficiency in buildings_ to achieve the same level of cooling with less energy.

- g. _Adopting more efficient cooling systems_ (e.g., inverter AC units).

- h. _Adopting demand-side response solutions_ (e.g., the power utility can remotely lower consumption of AC units to avoid an outage when conditions require).

While the most effective and efficient approach for implementing these design and infrastructural interventions will depend on the specific conditions of each urban environment, reducing demands during heat waves (e.g., reducing the urban heat island effect) is generally likely to be more economically efficient than simply attempting to extrapolate increases in extreme demands and building out more generation capacity. In other words, reducing the amount of power generation capacity that a system must maintain (e.g., through the implementation of energy efficiency measures) is generally preferable to increasing the size of the system to meet increased demands.

As was alluded to above, climate projections carry with them uncertainty, rooted in scenario uncertainty (i.e., to what extent will we collectively alter our greenhouse gas emissions into the coming decades), model uncertainty (the ability of global climate models to simulate the earth system’s response to greenhouse gas inputs), and natural internal variability. As such, it is not recommended that practitioners rely on exact projections 30 or 50 years into the future. Instead, power system capacity assessments should be updated at regular cadences (at a higher frequency than the ten years used here). As more observations are collected over time, the reality with respect to emission levels and how they manifest in near-surface extreme temperatures will become increasingly well understood and from a practical standpoint it is more important to attempt to keep pace with these changes rather than projecting them perfectly. Therefore, while this study is useful for understanding the approximate potential magnitude of disruptions, it should not be viewed as a precise blueprint of what to expect over the next several decades. We include a comprehensive discussion of the sources of uncertainty inherent in this study in the Supplementary Material.

# **Conclusions**

Designing, maintaining and operating power system infrastructure that can keep up with a changing climate is a significant challenge. This challenge is expected to increase in the coming 23
decades. Power system failures caused by insufficient power supply during extreme temperature events can be costly and can pose risks to human health and safety. In the Southeast Europe region, we have projected that the frequency and cost of power system failure events due to under-supply may be tens of millions of $US per year in some urban centers if we apply cost assumptions based on the 2021 February blackout in Texas.

Mitigating the economic damage from power outage events entails improved system planning and design (e.g., securing enough reserve capacity during months of the year when peak events are possible), improving the way in which power grids are operated during times of high demands to ensure that demands do not exceed supplies (e.g., controlled rotating blackouts), and/or decreasing the impact of the urban heat island effect by increasing the reflectivity and increasing the ability of the land surface to cool through evapotranspiration by increasing the density of trees and other vegetation within urban centers.

From an investment perspective, investing in types of energy generation that align with demand peaks is one strategic approach to mitigate the risks associated with power outages and disruptions. By investing in energy sources that have high availability during times of peak demand, such as solar during midday summer peak demands driven by cooling demand or wind power during winter peak demands driven by heating demand, investors can help to ensure a reliable and sustainable power supply. These types of energy sources are becoming increasingly costcompetitive and are substantially less carbon intensive than fossil fuel generation.

In conclusion, this analysis highlights the increasing challenges that power systems are facing with a changing climate, specifically the overburdening of grids during extreme temperature events, that can lead to power outages and disruptions to economic activity. The study illustrates the importance of timely investment in resilient and sustainable power systems that can help to mitigate these risks and ensure a reliable power supply by keeping up with the increasing demands stimulated by a warming climate.

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