Infrastructure Barriers to the Electrification of Vehicle Fleets in Russian Cities

Abstract

Switching to electric vehicles (EVs) could help reduce air pollution in cities. This is especially important for cities in Russia that have grown quickly because of industry, like those in Siberia, where environmental problems are particularly acute. However, several factors continue to hinder the rapid expansion of EVs on the market, such as an additional strain on the energy infrastructure, which threatens to cause power outages. This study proposes a model for estimating the electricity consumption by EVs in the largest Russian cities, taking into account the technical characteristics of the EV fleet and climatic conditions. The calculations indicate that if 15% of the current car fleet are replaced by EVs, electricity consumption in the 16 largest cities in Russia would increase by 2.2 TWh per year in total. The estimated additional demand in particular cities varies between 33 mln and 769 mln kWh per year, depending on the number of vehicles and the local climate. Furthermore, we conducted an intra-day simulation of electricity consumption from EVs in a conditional Russian city with a population of over one million people. Three scenarios for the power grid load have been developed: (A) the maximum scenario, in which all EVs have a battery level of 0%; (B) the medium scenario, where EVs’ state of charge is distributed between 0% and 100%, and (C) the minimum scenario, involving charging scheduling that allows only EVs with a battery level of 20% or less to charge. The findings show that replacing just 15% of the car fleet with electric vehicles will trigger an increase in current daily household urban consumption of 28.4% in scenario (C), 75.6% in scenario (B) and 141.8% in scenario (A). Consequently, even in Russia’s largest cities, the further proliferation of EVs requires large-scale investments in power infrastructure. An additional 1 mln kWh used by EVs per day may require $160.7 mln investments in energy facilities and urban distribution networks. These findings highlight the necessity of a more thorough cost–benefit analysis of widespread electric vehicle adoption in densely populated urban areas.

1. Introduction

Transition to electric-powered vehicles (EVs) is perceived as one of the promising options for achieving global climate goals and solving localized environmental protection problems of large cities [1,2]. According to the IPCC 6th Assessment Report, electric vehicles have the greatest potential to decarbonize the land transport [3]. The Paris Declaration on Electro-Mobility and Climate Change and Call to Action adopted in 2015 suggests that limiting the global temperature increase to below 2 °C requires at least 20% of all road vehicles to be electric by 2030 [4]. Furthermore, the Net Zero Emissions scenario of the International Energy Agency (IEA), which corresponds to limiting the global temperature rise to 1.5 °C by 2050, requires plug-in hybrids (PHEVs), battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs) to account for 100% of sales by 2035 [1,5,6].

Inland transport is the second largest greenhouse gas emitter after the energy industry, causing 20.7% of global CO₂ emissions from fossil fuels [7]. In particular, private cars and vans account for more than 25% of global oil consumption and around 10% of global energy-related CO₂ emissions in 2022 [8]. Furthermore, internal combustion engine vehicles (ICEVs) are usually associated with the environmental problems, such as air pollution and smog, which are particularly important for developing countries with rapidly growing economies [9,10,11]. One of the most significant negative effects of using ICEVs is traffic-related air pollution, which is linked to a range of heart and lung diseases and even neurological disorders [12,13,14,15]. The environmental benefits of switching from internal combustion engine vehicles to electric engines are evident: emissions from transportation are moved far away from urban centers or disappear altogether. Thus, the electrification of transport could also induce strong health benefits to city dwellers. According to the IEA, using EVs instead of ICEVs could help avoid 1.8–2.5 Gt of CO₂-equivalent emissions worldwide in 2035 [1]. In Russia, almost half of the urban population suffers from high levels of air pollution [16]. This is especially evident in industrial cities in the Ural regions, Siberia, and the Far East, as well as in large, highly urbanized centers like Moscow, Yekaterinburg, Krasnoyarsk, Omsk, and Chelyabinsk [17,18,19]. The negative consequences are evident in increased mortality rates from lung cancer in polluted cities [20]. About a quarter of all harmful emissions into the atmosphere in Russia come from automobile exhaust [11]. This highlights the potential for improving air quality in urban areas through the transition to electric transportation.

As a result, the need to meet national and global climate targets, growing environmental concerns and ongoing competition for low-carbon technologies are pushing nations to create incentives for the electrification of transport [21,22]. The level of EV adoption in different countries is highly dependent on the composition of government incentives for consumers to purchase an electric car [23,24,25,26]. European countries show the fastest pace of transition: the share of electric vehicle sales is highest in Norway (95%), Sweden (60%), the Netherlands (30%), Germany, France and the UK (25%) [1,5,27]. China occupies a special position as both the largest producer, accounting for 65% of global EV sales in 2023, and the largest market with 60% of global new electric car registrations [28,29,30]. Together, China (60%), Europe (25%) and the United States (10%) account for 95% of all electric vehicles sold worldwide [1]. Developing countries including oil-producers are also declaring their intention to increase the share of electric vehicles [31,32,33,34,35].

The EV market in Russia is growing rapidly in recent years, but it is still barely noticeable, like in most developing countries [9,31,33,36]. At the beginning of 2025, there were 59,600 EVs in Russia, representing about 0.12% of the total Russia’s car fleet [37]. About 6500 charging stations have been installed in order to charge these EVs. Moscow, where the charging infrastructure is most advanced (12.7% of the total number of charging stations), accounts for almost one in four EVs [38]. The regions of Moscow Oblast’ (7.5% of all EVs and 11.7% of all charging stations), Krasnodar Krai (6% of all EVs and 6% of all charging stations) and Saint Petersburg (5% of all EVs and 6% of all charging stations) follow, and these regions also have the highest number of conventional vehicles in Russia. The Siberian and Far Eastern regions of Russia account for 13.5% and 12.5% of all EVs, respectively [38]. Of these, the Sakhalin Oblast, with 2.6% of all charging stations in Russia, the Krasnoyarsk Krai, with 2.5%, and the Novosibirsk Oblast, with 2.2%, are the best provided with charging infrastructure. Moreover, about 1/3 of all stations in Russia support fast charging.

In 2024, over 17,800 EVs were purchased in Russia, accounting for 1.1% of all new car sales [37]. This represents a 1.3-fold increase compared to 2023 and a 6-fold increase from 2022. One of the key reasons for this rapid growth is that car manufacturers from countries that have imposed sanctions against Russia have left the market. Domestic and Chinese manufacturers, including electric and hybrid vehicle makers, have met the increased demand for new cars. Also, as charging stations have been built in major Russian cities, electric vehicle ownership has become more appealing.

The growth of EVs in Russia is seen as an important part of the country’s efforts to reduce carbon emissions [39,40,41,42]. If 25% of ICEVs are replaced by electric vehicles, greenhouse gas emissions savings in Russia could potentially amount to 25–28 million tons of CO₂, or 11–12% of Russia’s CO₂ transport emissions, depending on the structure of electricity generation sources [43]. The Concept for the Development of the Production and Use of Electric Vehicles in the Russian Federation up to 2030 (Russia’s EV Concept) aims to reduce the anthropogenic pressure on ecosystems and increase the competitiveness of the Russian automotive industry by stimulating the production of electric vehicles and localization of their components [44]. The plan is to produce 217,000 EVs and install 44,000 slow charging stations and 29,000 fast charging stations by 2030. Another measure to support domestic production and stimulate consumption is the state subsidy for purchasing on credit Russian-built electric cars until 2026 [45]. The government reimburses the bank 35% of the cost of an EV after the buyer signs for the loan agreement. The subsidy is limited to 925,000 Russian rubles ($10,000).

Despite the rapid growth in sales of electric cars in Russia, demand for them is still limited. A survey of 3000 car owners in Russia found that only 32.4% of them are ready to buy an electric car [46]. There are several reasons for this. One is that Russia has the same barriers to electric vehicle adoption as other countries. These include a lack of EV infrastructure and government support, as well as customer concerns about the performance of EVs compared to ICEVs [47,48]. The costs of electricity and vehicle maintenance can also be a barrier to the growth of electric vehicle sales. However, this is not a major concern for Russia, as the cost of electricity remains relatively low by global standards. Depending on the region of residence, the price per kilowatt-hour for households ranges from around $0.025 to $0.15 [49]. However, as power grids become more outdated, tariffs are gradually increasing. Maintaining BEVs also seems to be cheaper than maintaining ICEVs, as there is no need to regularly change oil and filters [50,51]. In addition to these common barriers, Russia has specific challenges for electrification of transport. Among them are the remoteness of Russian settlements from each other, which requires a longer range for an electric car, the harsh climatic conditions, reflected in hot summers and cold winters, and the abundance of fossil fuels [42,52].

As a result, the leaders in the EV adoption in Russia are the largest cities with a population of over 1 million, whose residents are more inclined to try out new technologies, have access to the rapidly developing charging infrastructure and have a lower average daily mileage compared to residents of more remote areas. The most notable example is Moscow, which aims to have 320,000 EVs by 2030, representing 7% of the overall fleet, according to city’s transportation strategy [53]. Therefore, a crucial aspect in developing a state policy to enhance support for electric transportation is considering the potential limitations of EV adoption in the largest cities of Russia. In this paper, we address one of the major infrastructure challenges related to the proliferation of electric vehicles: the sufficiency of the electric infrastructure in cities. Although Russia is a major global producer and exporter of energy and electricity [54,55,56], the continued rapid growth of the EV fleet could lead to an additional sharp increase in the load on existing electricity grids. The total capacity of power plants across all Russian energy systems (269.1 gigawatt) is significantly higher than the peak power consumption (168.3 gigawatt), leaving ample reserves. At the same time, the amount of electricity generated and consumed remains at a similar level, reaching 1.18 trillion kilowatt-hour (kWh) and 1.174 trillion kWh in 2024, respectively [57]. There is a risk of electricity shortages in a number of regions, particularly in southern Russia, Siberia, and the Far East, due to the rapid increase in electricity consumption and the limited power grid infrastructure [58]. This issue is particularly significant in major metropolitan areas, where the population, urban electricity consumption, and number of electric vehicles are experiencing rapid growth.

Currently, there is a lack of literature that analyzes the potential change in urban electricity consumption if electric vehicles in Russian cities become more widespread. In this paper, we aim to address this gap by achieving the following objectives:

• To estimate the additional household electricity consumption resulting from several scenarios of electric vehicle proliferation in the 16 largest Russian cities, considering the technical specifications of the EV fleet and the heterogenous climate conditions of the study areas.
• To develop scenarios for the intra-daily load on cities’ power grids based on different patterns of electric vehicle charging.
• To estimate the amount of investment needed to develop the electric infrastructure based on scenarios of increasing numbers of electric vehicles and their charging patterns throughout the day.

The obtained estimates contribute to a better understanding of the potential challenges associated with the rapid growth of EVs in Russia and can be useful for decision makers to develop effective strategies for changing urban infrastructure to accommodate the electric transport proliferation.

The remainder of this study is organized as follows. Section 2.1 provides a literature review on the global urban electrical infrastructure challenges arising from the proliferation of electric vehicles. Section 2.2 examines the current body of academic literature on the perspectives and constraints of EV adoption in Russia, with a specific focus on its impact on the power grid. Section 3 presents the electricity consumption model for Russian cities and describes the data sources. Section 4 presents the main results of the study. Section 5 discusses the novelty of this study in relation to the existing literature, as well as the limitations of the conducted research. The main findings and their potential implications are summarized in Section 6.

2. Literature Review

2.1. The Impact of the Rapid EV Adoption on the Power Grid

The rapid increase in the number of electric vehicles demands a reliable and sufficient supply of electricity to charge them [59], which raises several major concerns about the load on electricity grids. The first one is the lack of sufficient electricity to charge EVs in settlements with electricity shortages [60,61]. The power of the most commonly used household appliances is 1–3 kilowatts (kW), whereas electric vehicles require 7–11 kW [62]. This significantly increases the household electricity demand, particularly in large cities.

Another issue relates to the potential impact of emission reduction from the EV adoption, which depends on the proportion of renewable energy sources. [63]. The intermittent nature of renewable energy sources, such as wind and solar power, can adversely impact the stability and quality of electrical power grids [64,65,66]. Furthermore, solar and wind power have lower capacity factors and operating lives compared to thermal power plants [67]. As a result, government incentives to promote the use of electric vehicles as a way to reduce greenhouse gas emissions have led to the need for significant investment in energy infrastructure.

Finally, the impact of peak loads on electricity delivery systems could be deleterious. Consumers prefer to charge their cars during peak evening grid loads [62]. The additional electricity demand during peak summer hours can be 7–14% for the average household with in-home EV charging [68]. Without proper regulation, this charging behavior may result in power grid overload in densely populated urban areas and increase the risk of possible blackouts. This issue is particularly important for countries such as China and the United States, where extreme weather events frequently lead to power outages [59]. To safeguard critical urban infrastructure and mitigate the impact on the power grid, local authorities are implementing warning systems for temporary reductions in consumption [69] or use various demand response measures [70]. In the United States, states such as California and Texas, which have been leaders in the adoption of EVs, have had to request voluntary energy savings from the public during hours of maximum power grid demand, which has occurred due to record heat waves [71,72].

These challenges have prompted a surge in academic interest within the field of EV charging scheduling and its impact on the power grid [73,74]. Pasha et al. [75] conducted a comprehensive review of 165 studies on EV scheduling and found that energy produced during low-demand periods, such as nighttime, may be less expensive but has higher emissions. In the southwestern regions of the US, energy produced during the late hours of the summer has been observed to result in a 65% increase in CO₂ emissions compared to the peak hours [76,77].

Bailey et al. [62] show that financial incentives have a significant impact on EV charging scheduling. Moreover, it has been posited that consumer price sensitivity for EV charging exceeds that of conventional household electricity consumption. In contrast to the efficacy of economic incentives, moral suasion to conserve electricity during peak hours has been found to be less effective, with a smaller and less persistent effect that tends to revert to the baseline over time [62,78].

Regardless of the success of electricity conservation measures, a substantial investment will be necessary for the further deployment of electric vehicles. Larson et al. [79] assert that to achieve a ‘Net-Zero America by 2050’ scenario would require $2.5 trillion in additional capital investment in the 2020s, surpassing the business-as-usual scenario. Achieving a 6.3 billion ton reduction in cumulative greenhouse gas emissions by the 2023–2032 period would necessitate more than doubling the historical rate of transmission expansion in the US [80]. To prepare for the projected increase in EV adoption, California alone might need to invest up to $50 billion by 2035 in distribution grids [81].

A lack of investment in electric infrastructure has the potential to lead to an increase in power outages and a decrease in demand for EVs [82]. Qiu et al. [59] showed that a one-per-month increase in power outages per district corresponds to a 0.99% decrease in the number of new EVs in the city. Furthermore, it is claimed that a twofold increase in blackouts within a year can lead to a decade-long decline in EV adoption, resulting in a reduction of up to $31.3 million per year in the emission reduction benefits of electric vehicle deployment.

It can be concluded that countries that actively encourage the transition to electric vehicles have already experienced the impact of increased demand for electricity on their infrastructure. Even for states with an energy surplus, local power outages and peak loads remain a significant challenge. Mitigating these problems requires both investments in infrastructure as well as EV charging scheduling policies.

2.2. A Literature Overview on the EV Adoption in Russia

In Russia, the rapid increase in the number of EV sales in recent years has led to a corresponding increase in academic research on the future prospects of EV adoption. A significant proportion of this research focuses on analyzing the current state of the market, predicting its future growth, and proposing government policies that could facilitate the EV transition [43,83,84,85].

Milyakin and Skubachevskaya [26] consider three scenarios for the further spread of electric vehicles, according to which their share of sales in Russia will be 20%, 30%, or 50% by 2045, depending on the state transportation and industrial policy. A predominant share of electric vehicle sales over the share of ICEV sales after 2045 would achieve total fleet emissions of 126.8 million tons of CO₂-equivalent by 2050, which is 28.5% less than in 2021 [86].

There are a number of obstacles to the implementation of EV adoption scenarios. The most commonly discussed challenges include severe weather conditions, a lack of charging infrastructure, and the limited availability of green power generation solutions [52,87]. It is also noteworthy that owning an electric car is not currently profitable for the majority of people in Russia. A comparison of the electric Evolute i-Pro with a similar Russian car with an internal combustion engine (Lada Vesta Sport) has shown that the EV is only competitive in terms of the total cost of ownership if there are significant government subsidies [88]. Furthermore, if free parking was removed from the list of incentives, owning an electric vehicle would become more expensive than owning an ICEV [88].

Consequently, the future growth of EVs in Russia requires long-term government support measures. Semikashev et al. [42] estimated that rapid development of the electric vehicle market in Russia requires investments of 250–350 billion rubles by 2030, 75–80% of which should be allocated to subsidize the purchase of new imported and domestic EVs. At the same time, people with high incomes are currently showing the greatest interest in electric vehicles in Russia. They see an electric car as a second vehicle in the family, as well as an opportunity to demonstrate that they can afford a green lifestyle [87]. This could potentially reduce the effectiveness of government incentives for purchasing new electric vehicles in Russia.

An additional measure to support the acceleration of electric vehicle adoption could be to localize EV production in Russia [89]. Rostovski [41] points out that, under the current conditions of sanctions, the production of electric vehicles could be more profitable than that of cars with internal combustion engines, but it will require advanced development in battery production. Kolpakov and Galinger [90] estimate that a complete transition of passenger car production in Russia to EVs would enable the replacement of 3.4% of the existing ICEV fleet annually. However, the cumulative economic impact of the transition to electrification of transport, metals, and electrical equipment industries could be negative if significant reliance on battery imports of batteries remains [90].

The literature on the impact of EVs on the power grid in Russia is limited, but it suggests a significant increase in demand if the transition to EVs were to accelerate. Kapustin and Grushevenko [91] show that the additional growth in electricity consumption from EVs could reach 11–20% on a global scale by 2040, while in Russia demand will grow to 1.6 million tons of oil equivalent energy (mtoe) as a result of changes in the structure of the car fleet [92]. Veselov et al. [93] estimated the additional consumption from EVs in Russia by 2050 in the range from 52 billion kWh with the share of electric passenger cars at 30% to 136 billion kWh with the share equal to 70%. The aggregate increase in electricity consumption within the transport sector, encompassing trucks, buses, and passenger vehicles, is projected to range from 168 to 460 billion kWh by 2050. Notably, commercial vehicles are expected to account for over one-third of this increase, despite their relatively modest proportion within the total fleet. The intra-day load difference, depending on charging schedules, is estimated to be between 33 million and 68 million kilowatts if 70% of all vehicles in the fleet are electric.

Thus, all of the above-mentioned studies used a scenario-based approaches to model the number of EVs and their energy needs based on country-level Russian statistics. A rare exception is a feeder-level study that modeled the load of 200 electric vehicles on a section of the electric distribution grid [94]. Using Monte Carlo and bootstrap methods the authors revealed a strong linkage between the probability of voltage drops at load nodes and the number of electric vehicles. It was found that an additional 20 EVs could increase this probability from 30% to 93% [94].

Among the limitations of the current literature, it should be noted that regional differences within Russia, the varying climatic conditions, as well as the structure of the electric vehicle fleet, were not considered. At the same time, the impact of peak loads on the energy infrastructure may be more pronounced at the level of particular settlements that have a deficit in their energy balance. This paper is the first attempt to address this gap by conducting an assessment of the impact of EV proliferation on household electricity consumption in 16 major Russian administrative centers with the largest car fleet. The next section introduces an urban electricity consumption model, which for the first time takes into account ambient temperature, vehicle fleet structure and size and technical specifications of EVs.

3. Methods and Data

Electric cars are particularly well-suited for inner-city travel [95], as their range is primarily constrained by battery capacity. Long-distance travel necessitates access to charging infrastructure, which can be challenging to obtain. The charging time of an electric vehicle can vary depending on factors such as the nominal battery capacity, grid output power, battery input power, state of charge, temperature and other parameters. The charging cycle time for an electric vehicle ranging from 15 to 45 min [96], which is significantly longer than the typical pump time at a gas station. Recent research indicates that frequent fast charging may lead to battery degradation [97,98,99,100]. Therefore, we also consider the slow-charge option. The unique characteristics associated with EV ownership have led to a tendency for EV purchases to occur predominantly in urban areas and metropolitan regions, as opposed to rural areas [101].

The infrastructure of major Russian cities is undergoing rapid development, creating opportunities for the proliferation of electric vehicles. In recent years, numerous new charging stations have emerged, indicating a significant increase in available infrastructure. Conversely, small and medium-sized cities have limited opportunities for the development of such infrastructure. Consequently, our study focuses on assessing the electricity consumption of EVs in 16 largest cities in Russia with populations exceeding one million. These cities also function as the administrative centers of the respective 16 Russian regions.

The annual electricity consumption of EVs in urban areas is influenced by numerous pivotal factors. We propose an electricity consumption model for macro-level analysis, considering the EV fleet structure and size in 16 Russian cities, the technical specifications of the specific EV models, average monthly mileage, and seasonal variations in air temperature. The model also accounts for electricity losses during battery charging.

Our analysis encompasses 39 primary models of BEVs and PHEVs available in the Russian market, distinguishing them based on their prevalence in various Russian cities and their technical characteristics. The most prevalent electric and plug-in electric vehicles in Russia are outlined in

Table 1. Characteristics of popular BEVs & PHEVs in Russia.

Make & Model	Manufacturer	Market	Battery Nominal Capacity, kWh	Full Range, km	Market Share in Russia, %
BEVs
Nissan Leaf	Nissan Motor Co., Ltd., Yokosuka, Japan	Japan	40.00	322	37.57
Zeekr 001	Zeekr Intelligent Technology Holding Limited (owned by Geely Automobile Holdings Limited), Ningbo, China	China	100.00	732	10.63
Zeekr X	Zeekr Intelligent Technology Holding Limited, Chengdu, China	China	66.00	560	3.98
Tesla Model 3	Tesla, Inc., Shanghai, China	EU	50.00	354	2.73
Zeekr 007	Zeekr Intelligent Technology Holding Limited, Ningbo, China	China	75.60	688	2.44
PHEVs
Toyota Prius	Toyota Motor Corporation, Toyota, Japan	Russia	1.30	8	14.49
Toyota Aqua	Toyota Motor Corporation, Toyota, Japan	Japan	5.00	39	3.84
Li L7	Li Auto Inc., Changzhou, China	China	40.90	210	2.13
Li L9	Li Auto Inc., Changzhou, China	China	44.50	215	1.76
Tank 500	Great Wall Motor Company Limited, Tianjin, China	Russia	1.75	8	1.33

Source: Russia’s largest open database of car sale advertisements [102].

.

The scarcity and patchiness of data concerning the total car fleet in Russian cities as well as the share of specific models, poses a significant challenge in conducting reliable analyses. To address this gap in data, an estimation approach is employed that utilizes data on the car fleet in corresponding Russian regions, as reported by the Unified Interagency Information and Statistical System [103] and Russia’s largest open database of car sale advertisements [102]. We hypothesize that the concentration of cars in a city is highly correlated with the number of auto sales offers. Therefore, we estimate the city’s vehicle fleet N by multiplying the regional car fleet with the proportion of offers to sell a car in the regional administrative center compared to the total number of such advertisements in the whole region:

$$N=R\times {\displaystyle \frac{C_{sales}}{R_{sales}}},$$

(1)

where $N$ and $R$ are the city’s and regional car fleet (units), respectively, while the $R_{sales}$ and $C_{sales}$ represent the number of car sale advertisements (units) in the region and its administrative center, respectively. The estimation results are shown in Figure 1.

It is evident that the estimate is considerably elevated in comparison to the calculation of the number of cars in the region as a proportion of the city’s population. This methodological approach is commonly employed by analytical agencies operating within the Russian automotive market, and it typically yields a result that corresponds to 300–350 cars for every 1000 city residents [104].

To calculate the number of electric vehicles of a particular model, we estimate the proportion of advertisements for a specific EV model among all car advertisements in a given city. Hence the number of BEVs or PHEVs of a particular model i, ${\gamma }_i$ (units) is calculated as follows:

$${\gamma }_i=s_i\times N,$$

(2)

where $s_i$ is the share of the i-th BEV or PHEV model in a city’s fleet (%) and $N$ is the city’s fleet size including all types of passenger cars (units).

To calculate the monthly electricity demand, it is necessary to obtain data regarding mileage. According to [105], the average annual mileage in 2022 was approximately 18.7 thousand kilometers. To estimate the variation in mileage across months, we utilize data on the monthly average gasoline consumption by cars from the Federal State Statistics Service of Russia [103], under the assumption that there is a high correlation between these two variables. Following the [106] it is assumed that in a large city with a population of over one million people, the patterns of EV usage are similar to those of ICEV driving. Equation (3) provides an estimate of the monthly mileage:

$$u_j={\beta }_j\times M,$$

(3)

where $u$_j is estimation for mileage in month j (kilometers, km), $M$ is the annual mileage (km), ${\beta }_j$ is the share of the gasoline consumption during month j in total annual consumption (

Table A1. Estimates of average monthly mileage based on gasoline consumption.

Month	Average Monthly Gasoline Consumption, Thousand Tons	Share in Annual Gasoline Consumption ${\mathit{\beta }}_{\mathit{j}}$	Average Monthly Mileage, km
January	2810.45	0.081	1513.45
February	2657.37	0.077	1431.01
March	2908.46	0.084	1566.23
April	2532.98	0.073	1364.03
May	2645.39	0.076	1424.56
June	2973.85	0.086	1601.44
July	3259.42	0.094	1755.22
August	3278.27	0.094	1765.37
September	2922.44	0.084	1573.76
October	2831.59	0.082	1524.83
November	2770.81	0.080	1492.10
December	3134.60	0.090	1688.00
Year	34,725.64	1.000	18,700.00

Source: authors’ calculations based on the data from the Federal State Statistics Service of Russia [103].

). Each EV model has certain nominal battery capacity $c_i$ (kWh) and full range on one charge $r_i$ (km). The product of $c_i$ and ${\gamma }_i$ (Equation (2)) denotes the electricity consumption (kWh) required to charge all cars of model i. Hence, total amount of electricity (kWh) needed to charge all cars of the model i (units) to drive the average mileage $u_j$ (km) during the month j is calculated as follows:

$$c_i{\times \gamma }_i\times {\displaystyle \frac{u_j}{r_i}}.$$

(4)

In addition to Equation (3), we propose several adjusting coefficients for accounting weather conditions, electricity losses and differences between BEVs and PHEVs. First of all, extremely low and high temperatures can have a negative impact on the condition of the battery and, consequently, on the driving range of the electric vehicle [107,108,109]. In addition, prolonged exposure to extreme temperatures can negatively affect the battery lifespan which reduces the benefits of owning an EV. The reduction in the range of an electric vehicle and the degradation of the battery lead to the need for more frequent EV charging, which in turn increases electricity consumption in cities. The laboratory tests of several EV models, including the Ford Focus, Jetta TDI, Honda Insight, Sonata HEV, Toyota Prius, Chevrolet Volt, Nissan Leaf have shown that energy consumption is strongly dependent on the use of the air conditioning and car heating systems at high (above 35 °C) and low (below −6.67 °C) ambient air temperatures, respectively [110]. The primary power consumption was observed during the winter season, when temperatures dipped below −6.67 °C.

To calculate the average number of days with ambient air temperature within a specified range, we derived temperature data from 2018 to 2022, provided by the Federal Service for Hydrometeorology and Environmental Monitoring of Russia [111] (see Appendix B for details). The average daily temperature in the 16 studied Russian cities does not exceed 35 °C, although at certain hours the temperature might be higher than this threshold. Furthermore, we use open data from the technology company Geotab [112], which demonstrates the impact of temperature and speed on the full range of an EV with a 65-kWh battery. This analysis is based on a dataset comprising 350,000 trips, including 180,000 h of driving 500 electric sedans. The Geotab data clearly indicates that the effect of temperature on range is similar across different EV makes and models, with the low temperature having a greater impact than the high temperature [113]. By combining an estimate of the average speed of cars in Russian cities of 30 km/h [114,115] with Geotab data on the effect of temperature on the EV range, we obtained a driving range reduction coefficient δ (Figure 2).

Another adjusting coefficient is needed to account for the specific characteristics of electricity usage by PHEVs. A study based on fuel consumption and engine spacing data, found that PHEV electric mileage could be 25–65% lower than assumed on fuel economy labels placed on car windows that provide information on how much fuel or electricity it takes to drive 100 miles [116,117]. This suggests that PHEV owners, on average, may use 45% less electricity than the specifications of their vehicles would indicate. Thus, the coefficient p_i mitigates the necessity of charging for hybrid vehicles and reduces electric consumption in areas where drivers are more likely to utilize a gasoline engine rather than an electric one.

Finally, it is posited that a portion of energy is lost due to chemical reactions that release thermal energy as a by-product during EV charging [118]. The length of the charging cable and the input power also have a direct impact on energy losses [119]. According to the U.S. Environmental Protection Agency (US EPA), approximately 10% of the electricity is dissipated as heat when the EV is charging [120]. This loss is represented by the constant l in the model. Hence, the electricity consumption model for a specific city is as follows:

$$EC={\sum }_{i=1}^k{\sum }_{j=1}^m(c_i{\times \gamma }_i\times {\displaystyle \frac{u_j}{r_i}}\times {\displaystyle \frac{l{\times p}_i}{{\delta }_j\left(t\right)}}),$$

(5)

where

• $EC$ is an additional electricity consumption in a city from all BEV and PHEV models i∈ (1…k) for all months j∈ (1…m), kWh;
• $c_i$ is nominal battery capacity of the electric vehicle i, kWh;
• ${\gamma }_i$ is total number of the electric vehicle model i, units;
• $u_j$ is average monthly mileage for the month j, km;
• $r_i$ is full range of the electric vehicle model i, km;
• ${\delta }_j\left(t\right)$ is full range reducing coefficient, depending on average temperature t in the month j;
• $p_i$ is charging cycle frequency reduction coefficient, equal to 1 for all BEV models and 0.55 for PHEVs;
• $l$ is a constant equal to 1.1, assuming that each EV requires 10% more electricity to fully charge due to unavoidable losses.

4. Results

4.1. Electricity Production and Consumption in Russia

Russia is an energy surplus country. Over the period 2005–2022, the negative energy balance was observed only in 2013–2014 (Figure 3). This is due to the stagnation of the largest energy consumers–the metallurgical industry–and primarily to the decline in global demand for Russian aluminum [121,122]. While industry accounts for approximately 50% of Russia’s total electricity consumption, there has been a concurrent increase in household demand. Since the early 2000s, per capita electricity consumption by the Russian population has increased by 29% to 1118 kWh in 2020, due to an increase in living space and the number of household appliances [123]. At the same time, electricity generation volumes have been growing after a slight decrease during the early stages of the pandemic [55], and production reached 1170 terawatt-hours (TWh) in 2022 [124].

However, the significance of regional heterogeneity in terms of electricity supply and infrastructure obsolescence is paramount. At the regional level, heterogeneity in electricity supply across different parts of Russia’s vast territory is evident. Notwithstanding the substantial capacity reserve of the Russian energy system in general, a quarter of energy systems in specific regions of Russia encounter energy shortages that are offset by other territories [58]. Among the regions with 16 major cities under study, only five have energy surplus: Rostov Oblast (28 billion kWh), Voronezh Oblast (16.7 billion kWh), Krasnoyarsk Krai (14 billion kWh), Sverdlovsk Oblast (9 billion kWh), and Perm Krai with 5 billion kWh (Figure 4). The remaining regions experienced electricity deficit with the most significant figures observed in Krasnodar Krai (17 billion kWh), Chelyabinsk Oblast (11.8 billion kWh), and Nizhny Novgorod Oblast (9 billion kWh). From 2005 to 2022, electricity consumption exhibited growth that exceeded generation in nine out of sixteen regions (Figure 5).

The augmented growth of summer electricity consumption maximums, coupled with an escalation in the daily unevenness of consumption, engenders heightened risks of mass power outages in the Southern and Central regions of Russia [125]. To overcome the current limitations, the total investment in the industry by 2042 is expected to be 40 trillion rubles ($514 billion) for generating capacity and 2.55 trillion rubles ($33 billion) for the construction and renovation of electrical grids [57].

4.2. Additional Electricity Demand from Growing EV Fleet

The baseline scenario assumes that electric vehicles will account for 5% of the total number of passenger cars in the 16 most populous cities in Russia. Consequently, the combined BEV and PHEV vehicle fleet in cities with a population exceeding 1 million will amount to 908,000 units. According to Equation (5), in order to meet the increased demand, an additional 2.2 TWh per year would be needed. In this case, the projected median annual growth rate for urban electricity consumption in these 16 cities is 6.75%.

Figure 6 presents the additional electricity demand for 16 cities based on a baseline scenario, per 10,000 inhabitants. The scenario inherently incorporates a scale effect, implying that the augmentation of electricity consumption due to the proliferation of electric vehicles is contingent on the magnitude of the city’s automobile fleet. Consequently, Moscow, which possesses the most substantial fleet, emerges as the leader in absolute terms. The number of cold days per year exerts a modest influence on the growth of electricity consumption.

A comparison of the additional electricity demand from electric vehicles and the current electricity consumption of the urban population shows that the highest growth rate is in Ufa, the administrative center of the Republic of Bashkortostan (17.1%), while the lowest growth rate is observed in Samara (5.8%). This is due to the fact that the current electricity consumption of the urban population in Ufa is the lowest among the largest cities in Russia, while the fleet of vehicles is relatively large and comparable to that of Novosibirsk. Conversely, Samara stands out for its high electricity consumption, which is balanced out by a relatively small car fleet. The estimates for all 16 cities under study are presented in

Table 2. Electricity consumption changes from EV adoption, million kWh per year.

City	Current Consumption		Additional Consumption If EV’s Share in Current City Fleet Reaches
	Excluding BEVs and PHEVs	BEVs and PHEVs	5%	15%	25%
Moscow	11,657.9	668.7	702.1	769.0	835.8
St. Petersburg	5101.2	164.0	172.2	188.6	205.0
Ekaterinburg	1990.0	161.4	169.5	185.6	201.7
Novosibirsk	1390.1	142.0	149.1	163.3	177.5
Ufa	1301.1	118.8	124.7	136.6	148.5
Nizhniy Novgorod	1854.7	118.1	124.0	135.8	147.7
Krasnodar	1213.8	71.9	75.5	82.7	89.9
Volgograd	976.2	67.7	71.1	77.9	84.7
Chelyabinsk	1110.9	65.1	68.3	74.8	81.3
Voronezh	1169.0	50.2	52.7	57.7	62.7
Rostov-on-Don	1166.4	49.5	52.0	57.0	61.9
Kazan	1075.8	48.5	50.9	55.8	60.6
Perm	1190.5	48.3	50.8	55.6	60.4
Krasnoyarsk	810.2	43.5	45.6	50.0	54.3
Samara	1233.1	41.8	43.9	48.1	52.2
Omsk	1530.6	28.7	30.2	33.0	35.9
Total consumption, TWh/year	34.8	1.9	2.0	2.2	2.4

Source: authors’ calculations.

.

To identify which input variables in Equation (5) have the greatest influence on electricity consumption growth, a model sensitivity analysis was conducted. The effects of a 10% increase in each of the following factors on electricity consumption were studied: the proportion of BEVs and PHEVs in the city’s fleet, average daily mileage, battery charging losses, battery capacity, and air temperature. The results suggest that the impact of temperature is the least significant, while the contributions of the other variables are approximately equal (

Table 3. Sensitivity analysis for a 10% positive parameter change.

Parameter	Base Value	Electricity Consumption Change, %
Share of BEVs and PHEVs in the city’s car fleet, %	5	10.00
Mileage, thousand km	18.7	9.17
Battery charging losses, %	10	8.14
Battery capacity, kWh	The value depends on the individual characteristics of the EV	9.04
Air temperature, °C	The value depends on the temperature in a particular month	−1.18

Source: authors’ calculations.

).

The additional electricity demand resulting from the increased number of electric vehicles, as shown in Table 2, may seem insignificant compared to the overall electricity generation mentioned in Section 4.1. However, it should be taken into account that the pattern of electricity consumption experiences significant variations throughout the day, with a notable increase in usage occurring in the evening hours [59]. While residential consumption represents a negligible proportion of total electricity generation, it is important to consider the capacity of the city’s step-down transformers, the maximum load capacity of the wiring infrastructure that most residential buildings are designed for, and the current load level from the use of electric stoves, heaters, and other household appliances.

The maximum allowable power for electrical appliances in most residential buildings in Russia, constructed after 1964, is 7 kW. While electric cars are not directly charged from an apartment, it is hypothetically possible that if all appliances in the apartment were to be used simultaneously, such as an electric stove (8.5 kW), washing machine (2.5 kW), kettle (2 kW), and electric car charging (3.6 kW), the fuses designed for 25 A would likely fail. The capacity and operational duration of appliances primarily determine the electrical load capacity on the mains. Therefore, the next subsection will focus on hourly fluctuations in electricity consumption.

4.3. Hourly Electricity Consumption Modeling

In this subsection, we examine the variation in hourly electricity consumption among urban households with and without electric vehicles. The hourly fluctuations in urban household electricity consumption are generally similar across the globe. The peak hours occur in the early morning and evening, which correspond to business cycles [126,127,128,129]. Thus, to develop a model for household electricity consumption without electric vehicles, we combined the shape of the electricity consumption curve from U.S. statistics [130] with data on urban electricity consumption in Russia [131]. According to these data, the median daily consumption per person in 16 Russian cities with populations over one million was 2.8 kWh. These values served as inputs for the obtained electricity consumption function for the average Russian household without EV (Figure A1).

To model the peak electricity consumption during a day, with the increasing energy demand from electric vehicles, several assumptions have been made:

• The simulation is carried out for a conditional large Russian city, with a median population size and median daily electricity consumption, calculated for a sample of 16 cities under study;
• In the median large Russian city, the temperature is between −6.67 and 35 °C for about 300 days. This means that the temperature is insignificant for modeling daily consumption;
• An Evolute i-Pro, with a 53-kWh battery, is considered an average electric vehicle;
• The EV owner utilizes a standard electricity grid with a voltage of 220 V and a current of 16 A. Upon returning home after work, the average EV owner activates the charging process using a standard charger with an input power of 3.6 kW. During this time, the load on the grid will be approximately 3.5 kW.

The relevant electricity consumption curve for charging a single EV is illustrated in Figure A2.

The median population size of the 16 Russian cities under study is 1,173,081, which, multiplied by the median daily household consumption of 2.8 kWh, results in a total daily household electricity consumption of 3.3 million kWh. On average, every second resident of these cities owns a car, resulting in a fleet of 586,540 vehicles. According to the baseline scenario, with 5% of all passenger cars being electric, the total number of EVs is expected to reach 29,327 units. It is difficult to accurately describe the randomness associated with the state of charge (SOC) for each electric vehicle in a city using traditional prediction methods. A common probabilistic method to model this uncertainty is a Monte Carlo simulation [132,133,134,135]. We employed the Monte Carlo method, using the Python programming language and the random 3.11.4 library [136]. Each electric vehicle was assigned a random state of charge level that remained at the end of the day, following a uniform distribution. This allowed for the individual characteristics of city residents’ transportation behavior to be taken into account. The algorithm has been iterated for one million runs, which is usually sufficient to achieve convergence [137]. As a result, three scenarios reflecting the maximum (A), medium (B), and minimum (C) load on the power grids are obtained (Figure 7).

The maximum consumption peak (Scenario A) can be observed when the entire city’s EV fleet is being charged, and the battery charge of each electric vehicle reaches 0%. In such a case, replacing 5% of the city’s total car fleet with EVs would lead to an increase in daily electricity consumption of 1.6 million kWh. The medium scenario B assumes that, irrespective of the battery level remaining at the end of the day, electric car owners recharge their vehicles to 100% for their subsequent trips, thereby increasing daily electricity consumption by 0.8 million kWh. Finally, the minimum scenario C reflects a situation in which owners of electric cars return home after work, starting at 5 p.m., and charge their vehicles only if the battery is at or below 20% capacity. This scenario would result to a daily increase of 0.3 million kWh in electricity consumption.

Table 4. Daily electricity consumption in median million-plus Russian city under various scenarios of EV proliferation and charging.

Simulation Conditions	EVs’ Share in the City, %	EVs Being Charged, Thousands	EV Consumption While Charging During a Day, Million kWh	Share of EV Consumption in Current Household Consumption, %	Additional Investments to Meet the Needs from Annual EV Consumption, Billion Rubles
Scenario A: Maximum load on the city grid
All EVs have a charge at nearly 0%.	5	29.3	1.6	47.3	20.4
	15	87.9	4.7	141.8	59.8
	25	146.6	7.8	236.6	99.2
Scenario B: Medium load on the city grid
The charge ranges from 0 to 100%. The electric car is being charged from its current charge level to 100% regardless of the remaining charge. Some EVs may have a full charge and hence are not charged at all. Charging time is calculated upon the remaining charge level for each vehicle.	5	29.1	0.8	25.3	10.2
	15	87.5	2.5	75.6	31.8
	25	145.9	4.2	126.4	53.4
Scenario C: Minimum load on the city grid
The charge varies from 0 to 100%. Only cars with a charge ≤ 20% are allowed to charge. The electric car is charged from the current charge level (0–20%) to 100%.	5	6.3	0.3	9.6	3.8
	15	18.5	0.9	28.4	11.4
	25	30.6	1.5	47.0	19.1

Note: Voltage used: 220 V, 16 A. Nominal battery capacity: 53 kWh. The values in bold are the ones where the EV daily consumption exceeds the current daily consumption of the city’s population. Source: authors’ calculations.

combines the scenarios of EV share growth in current city fleets outlined in Table 2 with the scenarios for load on the electric grid, depending on the behavior of EV owners (Figure 7).

The findings indicate that a 25% adoption of EVs in the city’s vehicle fleet, operating under the minimum load conditions, could result in an electricity consumption surge of nearly 50% of the current level of 3.3 million kWh. Moreover, the medium (B) and maximum (A) scenarios demonstrate that, under certain circumstances, the additional demand from EVs might surpass the current daily electricity consumption in Russian cities. This is especially critical for a number of Russian cities that are already facing energy shortages. Krasnodar, for example, which ranks fourth in the number of electric vehicles in Russia, experiences peak loads every year. Its network is loaded at 80% of full capacity during normal operation and at 95% during peak hours in the summer [138]. This small reserve indicates that even a modest increase in the number of electric vehicles could significantly increase the risk of local power outages. Another example is Novosibirsk, where in 2023, during a period of low temperatures in winter, the actual load on the electricity grid exceeded the maximum capacity. One of the reasons for this was the increased demand due to the transition of private heating from coal to electricity [139].

Consequently, at the current moment the limitations of the power grid infrastructure in Russia’s major metropolitan areas significantly curtail the feasibility of expeditiously expanding the number of electric vehicles. In addition, it is essential for municipal authorities to carefully regulate the timing and amount of charging in order to prevent potential problems at urban power plants.

According to the development plan of the unified energy system of Russia for 2025–2030, it is estimated that electricity consumption will increase by approximately 176.2 billion kWh in 2030 compared to 2023 [140]. The total investment volume for construction and modernization of infrastructure and power generation facilities is projected to reach 6.1 trillion rubles ($79 billion). This suggests that the incremental investment necessary to accommodate a 1 million kWh increase in consumption would be approximately 34.8 million rubles. Consequently, an additional EV consumption of 1 million kWh per day or 365 million kWh per year would cost 12.7 billion rubles ($160.7 million) for an average Russian city. Estimates of required investments under different scenarios are presented in Table 4. Considering that the median budget expenditure of the 16 largest cities of Russia in 2025 amounts to 72 billion rubles ($910 million), additional costs for electrical infrastructure seem significant. Therefore, the state policy on the development of electric vehicles in Russia should take into account the unpreparedness of the electric infrastructure to handle a rapid increase in the number of EVs. The further development of charging infrastructure and the implementation of support measures for electric vehicle owners should be accompanied by significant investments in the renovation and expansion of urban electric infrastructure.

5. Discussion

Following the existing literature [26,41,42,87,90], this paper discusses various scenarios regarding the replacement of internal combustion engine vehicles with electric ones. Earlier studies have shown a potential dual nature of the effects of the widespread use of EVs in Russia [42,87,90]. The positive environmental benefits for residents of the largest cities are accompanied by the need for significant investment in charging infrastructure and energy systems, as well as difficulties in disposing of used batteries. This paper contributes to the existing body of knowledge by introducing an electricity consumption model for Russian cities that accounts for the technical characteristics of EVs spread in Russia, average mileage, charging losses, and climatic conditions. Although this model has been tested on data from Russian cities, it could also be useful for predicting energy consumption of EVs in other countries with different climate conditions.

The results obtained demonstrate that the current state of the power grid is not sufficient for the potential wide-scale adoption of electric vehicles. 11 out of 16 regions have an energy deficit that is compensated for by neighboring territories. Despite this, the overall level of energy production in Russia remains high and there is a significant reserve of capacity. As a result, distribution-level constraints are likely to become the main challenge for future development.

The findings suggest that, in certain scenarios related to car charging, even a moderate EV share of 15% in a city’s fleet could nearly double the current electricity consumption of urban households. Additionally, forecasts indicate a high probability of significant peak loads occurring between 8 p.m. and 11 a.m., creating risks of power outages. However, this paper is a pioneering effort to estimate the impact of EV adoption on urban electricity infrastructure in Russia, and it has several limitations that could be addressed in future research.

• This study focuses only on passenger BEVs and PHEVs while commercial EVs were not included in the analysis. Currently, data on the commercial electric vehicle fleet in Russia is not available. However, their impact on cities’ power grids may also be significant. Given the general skepticism about switching to electric vehicles among the majority of the population, cargo transportation, taxi, and public transport may become important drivers for the further development of electric transport in Russia. The financial capacity of potential buyers, including state-owned companies, may also have a significant impact on the adoption of electric vehicles in Russia.
• The model does not account for differences in the average daily mileage of vehicles. These differences may be due to various factors, such as individual driver patterns, distance to points of arrival, day of the week, season, and other variables that can vary depending on the specific study area. In addition, this study assumes that the charging behavior of EV owners in Russia is similar to those in other countries and follows business cycles. However, the charging behavior can be influenced by the availability of private parking spaces with charging options near a person’s home, the difference in access to public chargers in commercial and residential areas, etc. Depending on these factors, the distribution of peak loads in urban areas may vary significantly. Furthermore, this paper uses the uniform distribution for the Monte Carlo simulation of the EV’s state of charge level. A more accurate assessment of the SOC undoubtedly requires a detailed analysis of data on traffic patterns within each Russian city. Therefore, travel heterogeneity might affect the simulation outcomes, and this provides fertile ground for future research.
• The scenarios presented in this paper are based on a conditional large Russian city that has experienced rapid growth in the number of electric vehicles. This approach allowed us to clearly illustrate the challenges associated with increased pressure on the city’s energy infrastructure using some assumptions and simplifications due to a lack of publicly available data on the electrical grid of Russian cities. For instance, the grid stress was estimated only through electricity consumption growth without explicitly modeling voltage violations or thermal overloads. Even with a small increase in total consumption, there may be local problems in the bottlenecks of the electrical infrastructure. These issues can only be fully understood through a detailed analysis of the distribution network in a particular Russian city. The complexity and variability of the issue necessitate further investigation to more accurately take into account these factors, along with a quantitative validation against observed grid data.
• The spatial distribution of the EVs in certain areas of the city (for example, with most developed charging infrastructure) could potentially increase the impact on the electricity grid. Further modeling based on this effect would require detailed maps of power grids, transformers, and charging infrastructure in cities. This could be a promising direction for obtaining more accurate estimates for particular cities in Russia.
• Investment estimates in Table 4 are provided for a conditional large Russian city. Naturally, depending on the specific region, the current status of the power grid, and the volume of energy generation, the amount of investment required will vary significantly. The financial resources of cities are also uneven. Moscow, being the capital city, has a significant advantage over other municipalities. Additionally, the territories of Russia differ significantly in terms of energy generation methods, such as hydroelectric, nuclear, and coal-fired power plants. These differences also affect the costs of modernizing or constructing these generating facilities. The breakdown of investments based on the established grid reinforcement cost models, using detailed data on generation facilities and the state of infrastructure in Russian cities, should be the focus of future research.
• Another area for further research could be related to incorporating time into the model, taking into account the technology learning effects, improvements in charging efficiency, evolving battery capacity, changing consumer preferences and developing public charging infrastructure.

The described study limitations may reduce the applicability of the results for a particular city, as they do not account for the city layout, availability and current state of infrastructure in certain areas or traffic patterns. However, in general, the obtained findings show that, on average, the infrastructure of Russia’s largest cities is not yet prepared for the rapid increase in the number of electric vehicles. This requires the search for ways to reduce their impact on the electricity grid.

Despite the risks to the energy infrastructure, electric vehicles also have the potential to help alleviate some of the challenges associated with supplying electricity to the grid. The potential of vehicle-to-grid technology [141], which assumes that EVs act as energy storage devices during periods of low consumption, making up the shortfall during peak hours, has been extensively discussed in the literature. This is particularly relevant in the context of the ongoing transition to renewable energy sources, which are characterized by intermittent generation, necessitating the development of energy storage solutions. However, the utilization of EVs for power grid optimization can result in the premature degradation of their batteries [142,143]. Moreover, for Russia with its limited wind and solar power generation, this issue is not expected to be a significant concern in the medium term. Controlled charging holds greater promise in addressing grid congestion during periods of high demand for electric vehicle charging. Such systems may include increased charging rates during peak hours [144], as well as advanced technologies for centralized control and optimization of electric vehicle charging [145,146].

6. Conclusions

Climate policy goals and the competition for low-carbon technologies are driving the rapid adoption of electric vehicles worldwide, especially in Europe, the United States, and China. Russia also sees the introduction of electric vehicles as an essential part of its low-carbon development strategy and a way to alleviate the environmental pressure in major cities. However, this pursuit of technological advancement and achieving carbon neutrality is accompanied by substantial infrastructure challenges. These challenges are just starting to be faced by countries that are actively stimulating the electric vehicle market. Therefore, states that are not at the forefront of EV adoption have a unique opportunity to learn from the pioneering experiences. This paper examines the potential impact of the rapid increase in the number of electric vehicles in the 16 most populous Russian cities on urban electrical infrastructure. The main findings can be summarized as follows.

• A model for estimating the additional electricity consumption resulting from several scenarios of EV adoption in Russian cities is proposed. Replacing 15% of the current vehicle fleet with EVs would add to the household electricity consumption in 16 Russia’s largest cities of 33–769 million kWh per year, depending on the size and composition of the EV fleet, as well as city’s geographical location and climate.
• To analyze the peak loads on the power grid during the day three scenarios for EV charging have been developed. The maximum scenario assumes that all EVs in the city are charging at the same time, leading to an increase of 1.6 million kWh in daily electricity consumption due to the additional 5% of EVs in the overall car fleet. The minimum scenario represents state control over charging patterns, resulting in a daily increase of 0.3 million kWh in electricity consumption due to the additional 5% of EVs.
• The limitations of the current power grid infrastructure in Russia’s largest cities pose significant challenges to the feasibility of rapidly increasing the urban EV fleet. Even under minimal load conditions, a 25% share of electric vehicles in the urban fleet could lead to an increase in household electricity consumption by almost 50% compared to the current level.
• The estimated additional costs for infrastructure development are significant and will be a burden for most of Russia’s largest cities. An additional 1 million kwh of electricity used by EVs per day would require 12.7 billion rubles ($160.7 million) in additional infrastructure investments for an average city under study.

Altogether, the obtained results indicate that Russia’s major urban areas are currently poorly equipped to handle the significant increase in electric vehicle adoption. The replacement of a modest proportion of the existing car fleet with EVs would result in a substantial surge in household electricity consumption. Furthermore, the electricity consumption in the maximum grid load scenario (A) is five times higher than in the minimum scenario (C) emphasize. These findings indicate that the current plans for urban infrastructure development in Russia should be revised to include estimates of additional electricity consumption from EVs and consideration of controlled charging options at the local level.

A cost–benefit analysis of the decision to invest in electrification of transport should be conducted prior to state planning of the development of automotive transport in Russia. Currently, the strategic documents in Russia, such as the Low-Carbon Development Strategy [147] or the Transport Strategy [148], promote the growth of electric vehicles in the country, without considering the potential consequences. Our findings clearly indicate that significant investments in infrastructure are essential to implement a strategy for the active replacement of internal combustion engine vehicles with EVs in Russia. This is especially true in southern Russia, Yenisey Siberia, and the Far East, where electricity consumption is increasing and a shortage of energy capacity has been predicted.

Therefore, the importance of conducting preliminary research before launching the active promotion of electric transportation cannot be overstated. A more pragmatic approach involves identifying specific areas where electric vehicles can be introduced, such as public transportation, taxi, and cargo transportation. The potential economic impact of the further EV adoption should be carefully measured using multiple quantified benefit metrics, such as the development of domestic technologies for EV manufacturing, the exploration of lithium deposits, avoided emissions and urban health effects. The results of this cost–benefit analysis should be included in key strategic documents and considered when developing public policy related to electric transportation.