Electric Vehicle Charging Logistics in Spain: An In-Depth Analysis

Highlights

What are the main findings?

• The paradox of fast charging: while fast charging reduces charging times, its high energy intensity poses additional challenges to the grid and may require targeted investments to strengthen certain parts of the electricity infrastructure.
• Seasonal impact on charging: the number of electric vehicles that can be charged simultaneously is significantly affected by the time of year, due to seasonal variations in electricity demand and renewable energy generation.

What are the implications of the main findings?

• Challenges of fragmented renewable capacity: although wind and solar power generation are steadily increasing, their variability and lack of direct synchronization with EV charging schedules increases the need for intermediate storage or advanced grid management.
• Synchronization with nighttime demand: the Spanish grid’s ability to support electric vehicle (EV) charging benefits from the significant drop in energy demand at night, creating an optimal window for EV charging without compromising grid stability.

Abstract

Spain’s National Integrated Energy and Climate Plan (PNIEC) addresses the policies and measures needed to contribute to the European target of a 23% reduction in greenhouse gas emissions by 2030 compared to 1990 levels. To this end, the decarbonization of the transport sector is very important in order to increase electric mobility. Electric mobility depends on the conditions of the electrical infrastructure. This research focuses on the electrical distribution network in terms of its current capacity for recharging electric vehicles, which are estimated to account for 20.7% of vehicles, which is about 4 million vehicles. This, therefore, illustrates the need to legislate to improve the electrical infrastructure for recharging electric vehicles, with the aim of deploying electric vehicles on a larger scale and, ultimately, allowing society to benefit from the advantages of this technology.

1. Introduction

Global warming and air pollution are pressing problems caused by increasing greenhouse gases in the atmosphere and constitute major environmental challenges, especially with regard to CO₂ emissions and sustainability. The Intergovernmental Panel on Climate Change (IPCC) confirms that human activity has caused significant changes in the climate, affecting the atmosphere, hydrosphere, lithosphere, cryosphere, and biosphere [1,2,3]. Although the EU has reduced its GHG emissions, transport has increased its impact, accounting for 23% of emissions in 2015 [4,5,6]. Spain was committed to reducing its emissions by 20% by 2020 and the EU is seeking a reduction of 80–95% by 2050 [7]. In 2015, transport accounted for 41.6% of energy consumption in Spain [8], and, globally, road transport generated 20% of CO₂ emissions in 2014. Several countries have adopted strategies to decarbonize transport. France plans to ban the sale of GHG-emitting vehicles, and the United Kingdom will eliminate the sale of new gasoline and diesel cars and vans by 2040 [9]. The European Commission is promoting sustainable mobility by promoting electric vehicles, key to decarbonizing transport and improving air quality. These vehicles reduce dependence on fossil fuels, integrate renewable energy, and reduce noise pollution [10,11]. The harmful environmental consequences associated with the use of fossil fuels have catalyzed a global paradigm shift toward more sustainable transport alternatives. The environmental impact of fossil fuels has driven the transition to sustainable transport. Advances in batteries and manufacturing have made electric vehicles more accessible. By the end of 2022, there were more than 26 million of these vehicles, although they still represent only 2% of the global total [12]. In the context of Spain, electric vehicles represent an even smaller market share, approximately 0.8%, which translates to around 325,000 units in circulation [13]. Although these figures reflect the growing interest in electric mobility, they are not sufficient to promote significant improvements to urban air quality and environmental health in general. One of the challenges is to reduce the environmental impact of manufacturing lithium-ion batteries for electric vehicles (BEVs) [14] and also to consider replacing them in the future due to their degradation caused by successive charging cycles [15], with technologies to replace BEVs including wireless electric propulsion and move-and-charge [16]. Therefore, the motivation for customer adoption of electric vehicles currently depends mainly on the fast and full charging of BEVs [17,18].

To achieve significant environmental benefits, studies indicate that electric vehicles must reach between 26% and 40% of market penetration [19]. Fossil-fuel-based transport currently accounts for 95% of total energy consumption in the global transport sector [20,21]. However, advances in batteries have improved the efficiency and range of electric vehicles, helping to reduce pollution and improve urban air quality [22,23,24,25]. In 2020, electric vehicles accounted for only 0.001% of the global vehicle fleet. That year, the EU sold 75,331 units, with Spain contributing 1405. The IEA projects that, by 2050, they will account for 80% of global sales. Sales are expected to grow from 1.1 million in 2020 to 11 million in 2025 and 30 million in 2030, with China leading the market [26].

The global growth of the electric vehicle (EV) market has driven the need to improve electricity demand forecasts to prevent mismatches between production and consumption while optimizing the environmental impact. In Spain, data from Red Eléctrica Española (REE) show a decline in electricity consumption since 2008 due to the economic crisis, followed by a slight recovery between 2014 and 2018 and further declines in 2019 and 2020. These fluctuations highlight the importance of adaptive energy strategies in response to economic and technological factors. Between 2004 and 2023, the maximum instantaneous power output in the Spanish grid ranged between 37,724 and 44,876 MW, peaking in 2007. The analysis of installed electricity capacity up to 2022 shows a 5.1% increase, driven by renewable energy growth: 1443 MW in wind and 4658 MW in photovoltaics, while coal-fired plants have reduced their capacity by 6566 MW since 2016. Regarding electricity generation, wind power production increased from 11,720 GWh in 2003 to 61,194 GWh in 2022. That year marked a peak in renewable energy production on the mainland, covering 23.3% of the total electricity demand, driven by a 23.9% increase in hydroelectric generation and a 68.5% rise in the solar photovoltaic output. This trend aligns with the EU’s goal of achieving 45% renewable energy by 2030. Therefore, it is essential that electricity providers increase electricity production from renewable sources to ensure that electric vehicles do not contribute to greenhouse gas emissions [4,27].

The impact of electric vehicle charging will be conditioned by regions with a high dependence on renewables and by those with a high population density and energy demand [18,28]:

1. Regions with a high dependence on renewables:

• Galicia, Castile and León, Aragón, Extremadura: high wind and hydroelectric production. In these areas, the generation capacity may be sufficient during times of high renewable production, but problems arise in low-wind or drought conditions. The need for storage and reinforcement in transport networks is key and can be solved with storage systems (batteries, hydraulic pumping) and improved interconnection with high demand areas.
• Andalusia, Castile–La Mancha: high solar photovoltaic production. During the day, simultaneous charging can be managed well, but, at night or on cloudy days, there may be supply problems if there is no adequate storage. Greater interconnection with other regions would be needed.

2. Regions with high population density and energy demands:

• Madrid, Catalonia, Valencian Community, and Basque Country: high population density and energy consumption. These areas depend more on the national grid and on energy imports from other communities. A simultaneous peak load can saturate the distribution networks and require backup generation. This can be solved by reinforcing the electricity grid, through decentralization with self-consumption, or using demand–response systems (intelligent consumption management).

As for the EV charging system in Spain, there are currently only policies for implementing charging points in car parks and homes. There is no organized strategy for improving the electrical infrastructures necessary to fully implement EVs and replace combustion vehicles.

This paper aims to analyze the real maximum capacity of the Spanish electricity grid for simultaneous charging of electric vehicles [20]. It is necessary to have a reference value of the real needs of large-scale infrastructures (electrical substations, high voltage power grids, power plants), due to the long time it takes to implement them. Section 2 describes the current and historical electricity demand in Spain. Section 3 explains the data sources and the techniques used to simulate electric vehicle charging scenarios, taking as a reference the energy that can be made available with the current electricity grid. Section 4 presents the analytical results, and Section 5 summarizes the conclusions.

2. Materials and Methods

Our research describes a method to obtain and analyze data related to the Spanish electricity grid. The main objective is to determine the maximum number of electric vehicles that can be charged in Spain, considering the existing capacity of the electricity distribution network. To do so, the available electric power is calculated as the difference between the peak power for the years examined and the demand power, which is measured every 10 min for a typical day of each month. Three different charging scenarios for electric vehicles were created to determine the maximum number of electric vehicles that can be recharged (as shown in Figure 1).

Data for this study were obtained mainly from the Spanish Electricity System Report for 2022 [29] and supplemented with extensive datasets from REE [28]. The data collection and analysis processes are related to the Spanish electrical grid over a 15-year period, from 2007 to 2022. Herein, we must take into account the following key points:

• The data were gathered over a span of 15 years and sampled every 10 min. This high-frequency data collection provides a comprehensive look at the grid’s performance and requirements.
• The data were used to calculate both an annual average and a monthly average demand. This allows for the identification of patterns and trends in electrical consumption over different time periods.
• Demand and accessible power calculations:

  −

  Electrical power demand: for each month, the amount of electrical power needed by the grid’s users was evaluated. This shows how much electricity was demanded.

  −

  Accessible power: the difference between the instantaneous power demand and the maximum power demand was calculated. This result indicates the amount of power that is available on the grid and could be used for additional purposes.

• Application to battery electric vehicles (BEV): this study focused on the power requirements for recharging electric vehicles (BEVs). By understanding the available power, it was possible to calculate the number of BEVs that could be recharged without exceeding the capacity of the grid.
• Charging modes: the number of BEVs that could be recharged was calculated considering the various charging modes, such as slow, accelerated, and fast. These modes likely have different power needs and charging times, a fact which could affect the grid’s capacity to support them.

This research appears to be a significant development in comprehending the capability of the electrical grid to sustain the increasing number of EVs, particularly in the preparation of infrastructure and grid improvements to meet future requirements. In addition, this examines three different types of EV recharging, which are discussed in more detail in [19]. These include slow, fast, and accelerated charging. Electrical parameters that are considered include available power, the average monthly power demand, the peak demand power in the past, and the daily available power.

2.1. Generation of Electric Power

This study looks at the electricity demand in Spain over a 15-year period from 2007 to 2022. Data on daily grid activity were used to calculate a monthly daily average for each year to show how the electric power distribution grid is used.

It has been empirically shown that electricity demand drops significantly during the night. This decrease is mainly due to the programmed operation of the power plants and planned blackouts during these times of low demand. Therefore, the idea of accessible electrical power can be understood as the remaining power capacity available during off-peak hours. This is based on the supposition that power plants keep running constantly at their highest historical demand levels throughout the 24 h cycle.

2.2. Accessible Electric Power

The idea of accessible electrical power is expressed as the remaining power capacity available during times of decreased demand, generally referred to as “valley” hours. This idea assumes that power plants will maintain continuous operation at their maximum historical output levels for the period from 2007 to 2022. It has been observed that electricity demand decreases at night, which is when power plants usually stop working during these low-demand periods.

Accessible electrical power can be quantified by considering the highest power consumption of the grid under operational conditions over the 15-year study period. Accessible electrical power is then defined as the difference between the historical maximum power demand and the actual power demand. This is mathematically expressed in Equation (1), as follows:

P_a = max(P_d) − P_d

(1)

The instantaneous value of power that the grid has available relative to the maximum value that it can handle is calculated using Equation (1). This equation is graphically represented in Figure 2, which shows the daily average of available electrical energy for January over the period 2007–2022. Calculations are made for both requested and available power for each month. The highest daily average value of maximum demand is observed in February, which serves as an empirical upper limit for the operational capacity of the electric power distribution system. This value can then be used as a reference point to calculate the minimum number of EVs that could be charged throughout the year. This is based on the assurance that recharging capabilities would be available in the following months, as the daily average demand for those months is evidently lower.

The peak power demand of 41,483 MW was achieved on 8 January 2018 between 1 p.m. and 2 p.m., as indicated by the equation max(P_d). The value of P_d is the power demand at any given time. To calculate accessible power, monthly averages were determined from daily power demand data collected at 10 min intervals for each year. This method of computation allows one to estimate the power available in the most extreme case.

This research does not rely on the maximum capacity of the electrical grid, but rather on the actual conditions of the electricity distribution. It considers that some parts of the power system may not be functioning at any given moment. For example, although Spain’s total generation capacity is 119,295 MW, this does not mean that the whole electrical network can operate at this level. There may be restrictions in the substation bus bars or in the low voltage network. This study uses empirical data related to generation, high-voltage distribution, transformation, and low-voltage distribution to conclude that the network can operate with a certain degree of certainty at a peak power demand of 41,483 MW for the years under consideration.

2.3. Considerations Prior to Data Analysis

The sale of BEVs in Spain has been on the rise, with an estimated 30,000 units sold each year. Data from 2015 to 2022 show a sharp increase in BEV registrations, as seen in

Table 1. New battery electric vehicle registrations per year in Spain from 2015 to 2022.

Month	2015	2016	2017	2018	2019	2020	2021	2022	Total
January	39	109	97	339	597	1619	498	1774	5072
February	33	104	218	371	862	1589	927	2348	6452
March	116	229	313	453	1300	739	2025	3087	8262
April	60	171	177	443	618	86	1430	1816	4801
May	59	196	214	333	894	368	1787	1995	5846
June	75	116	337	366	1171	774	2566	3140	8545
July	76	149	269	391	736	1497	1554	2080	6752
August	63	76	312	289	476	1200	1294	1400	5110
September	76	111	518	483	794	2036	2895	3240	10,153
October	301	116	843	544	810	1803	2358	2643	9418
November	212	398	302	678	790	1856	2763	3318	10,317
December	47	343	452	1380	977	4120	3493	3400	14,212
Total	1157	2118	4052	6070	10,025	17,687	23,590	30,241	94,940

. According to the most recent figures, nearly 95,000 BEVs have been registered with the Spanish Directorate General of Traffic [30].

Despite the prevalence of lithium-ion technology in most models, there is a wide range of battery capacities, ranging from 20 kWh for smaller utility vehicles to almost 100 kWh, or even more for long-range editions. To take into consideration the variability in battery capacities, a weighted average capacity was computed using Equation (2), which considers both sales volume S and battery capacity C. This leads to an average capacity of 56.7 kWh for further calculations.

$$\overline{\mathit{C}}={\sum }_{\mathit{i}}{\displaystyle \frac{{\mathit{S}}_{\mathit{i}}{\mathit{C}}_{\mathit{i}}}{{\sum }_{\mathit{i}}{\mathit{S}}_{\mathit{i}}}}$$

(2)

The registration of BEVs and PHEVs in Spain in 2022 is illustrated in Figure 3. A total of 47,559 PHEVs were registered in addition to BEVs, resulting in a total of 77,800 electric vehicles sold that year, representing just under 10% of the total number of vehicles registered.

It is necessary to comprehend the recharging requirements of the most popular BEVs in Spain. This was achieved by examining the sales data of the top-selling models, as depicted in Figure 3 (right), and the battery capacities, which are presented in Figure 4.

2.4. Charging Scenarios and Method of Analysis

The analysis incorporates three different charging scenarios, as defined in

Table 2. Performance range of charging scenarios. SCS refers to slow charge scenario, ACS refers to accelerated charge scenario, and FCS refers to fast charge scenario.

Power Levels and Connection Types for Different EV Charging Scenarios
Power Range of Recharging Scenarios	Power Range of Recharging Facility	Connection Type
SCS	3–3.7 kW	AC single phase
ACS	7–43 kW	AC three phase
FCS	30–250 kW	DC

. The feasibility of these scenarios is evaluated within the context of the Spanish electricity system, utilizing empirical data on electricity demand.

The methodology for analyzing each recharging scenario comprised the following steps:

• Identification of accessible electrical power, captured at hourly intervals, which can be allocated for BEV recharging throughout diurnal and nocturnal cycles.
• Accessible power was divided by the time interval needed to recharge the BEVs, such a time interval determined from the supplied power and the battery capacity. This is expressed in Equation (3), where P is the power in watts (W), C is the battery capacity in kilowatt hours (kWh), and Δt is the time interval in hours (h) for recharging.

$$\mathbf{P}={\displaystyle \frac{\mathbf{C}}{\mathrm{∆}\mathbf{t}}}$$

(3)

For the case under examination, a uniform battery capacity C of 56.7 kWh was assumed for all recharging scenarios considered. Additional analytical considerations were as follows:

1. Upon dividing the time interval by the hours required for a complete (100%) recharge of a BEV, the least favorable data point for the availability of electricity within the period was selected. This ensured a constant level of accessible power for each interval under worst-case conditions.

2. Using the accessible power of the worst case, the maximum number of BEVs that could be fully recharged (100%) was calculated within the studied period.

3. Results

As mentioned above, data were collected from the Spanish electrical grid over a 15-year period from 2007 to 2022. The sampling was carried out at 10 min intervals, allowing the calculation of an annual average monthly demand and an aggregate average monthly demand for the entire period. For each month, the electrical power demanded, and the accessible power was calculated. Accessible power was determined by subtracting the instantaneous power demand from the maximum power demand, resulting in the net available power that the grid could allocate to BEV recharging. The number of BEVs that could be recharged was then calculated considering the specific charging mode (slow, accelerated, or fast).

3.1. Slow-Charging Scenario

In the present point, the emphasis is placed on slow-recharging scenarios, characterized by a power range of 3 to 3.7 kW and a voltage of 230 V in single-phase alternating current. Specifications for current requirements in the charging connector were stipulated in accordance with the Spanish Low Voltage Technical Instruction number 25, commonly referred to as ITC-BT-25 [31].

In the context of domestic applications, slow recharging is the recommended modality. Using Equation (4), the calculated waiting times for complete recharge—up to 100% of the battery capacity—range between 15 and 19 h for the BEVs considered.

$$\mathrm{∆}t={\displaystyle \frac{C}{P}}={\displaystyle \frac{56.7 \mathrm{kWh}}{3.0 \mathrm{kW}}}\approx 19 \mathrm{h},\mathrm{∆}t={\displaystyle \frac{C}{P}}={\displaystyle \frac{56.7 \mathrm{kWh}}{3.7 \mathrm{kW}}}\approx 15 \mathrm{h}$$

(4)

The slow-charging mode is considered suitable for use in metropolitan areas with large residential areas. It can be used intermittently at different times of the day rather than charging the BEV to its full battery capacity in one session. This approach is beneficial for shorter trips, as the vehicle can be connected to individual charging points.

This slow-charging paradigm allows vehicles to be charged at any time during the day, with no predetermined time frame. The vehicles will be connected to dedicated charging stations and will not exceed the power allocated to the grid for a particular property. This approach assumes that the total energy available from the grid will be used throughout the day, without specific hourly divisions.

The availability of power is subject to changes in demand, which can be affected by daylight hours, the use of heating and air conditioning systems, and other factors [32]. Using the data in Figure 2, calculations were performed to estimate the number of EVs that could be recharged daily via slow charging throughout the year. The most conservative estimate for accessible power is 269 GW, which is associated with the month of January, while the most optimistic figure is 369 GW, which is related to the month of May. Figure 5 shows the potential number of rechargeable vehicles for each day of the month in relation to the available energy for that day. By using slow-charging protocols and dividing the accessible energy by the evaluated connection power, we can determine the number of vehicles that could be fully recharged daily for each month of the year. January has the lowest favorable index with the ability to recharge around 4.7 million EVs, while May has the highest favorable index with the potential to recharge up to 6.5 million EVs.

It is important to note that this situation assumes the best possible use of available energy, which is the highest number of vehicles that can be completely recharged in one day given the current electrical system.

In the context of slow charging, where the aim is not necessarily to achieve a full charge of a vehicle in a single session, it becomes crucial to determine the maximum number of vehicles that could be simultaneously charged given the network capacity. This consideration gains importance because energy availability exhibits significant fluctuations during a day, with variations that often exceed those observed on a month-by-month basis.

According to Figure 6, although energy availability fluctuates throughout the year, the hourly distribution of power follows a consistent pattern across all seasons. Specifically, nighttime hours (from 00:00 to 09:00) represent the most favorable period for charging due to the highest energy availability and their lower costs. Conversely, business hours generally constitute the least favorable period for charging, as energy availability is lower and costs are higher. This diurnal pattern is well aligned with the characteristics of slow charging, as the most advantageous hours for recharging coincide with periods when vehicles are typically stationed at their residential charging points. Using the mean accessible power available on the grid for all months, the findings shown in Figure 6 indicate that the current electrical infrastructure could accommodate a minimum of 2.6 million EVs during peak demand hours of the day and up to 5.2 million EVs during off-peak intervals, generally between 1 a.m. and 7 a.m.

The slow-charging system is a practical solution for implementation, as it eliminates the need for intricate electrical setups. The Spanish electrical system is capable of completely recharging up to 4.7 million EVs during peak demand times, which are usually the least desirable times of the year. In addition, it can accommodate the simultaneous connection of approximately 2.6 million vehicles to the grid. This strong capacity provides a realistic and immediate approach to incorporate EVs into the larger transportation network.

3.2. Accelerated-Charging Scenario

This scenario covers a wide range of charging setups, beginning with residential wallbox-type units, with a power capacity of 7.4 kW and running on a single-phase voltage of 230 V at 32 A, and extending to commercial charging stations that can reach power levels of up to 22 kW when using three-phase electrical lines.

This scenario has a lower threshold of 7.4 kW, which is approximately twice as powerful as the configurations seen in the slow-charging mode. However, the usage profile remains the same, which means that the results obtained before are still valid. Compared to other charging modes, the upper limit of this mode can reach 22 kW, which is often seen at public charging stations. This amount of power is enough to fully charge an EV in one session. In urban environments, particularly in places such as supermarkets, shopping centers, and recreational centers, this mode of recharge is optimally located. Given that these establishments primarily experience high footfall during daytime hours, this recharging modality is more congruent with daytime charging scenarios. Within the scope of the proposed methodology, the availability of energy for recharge is assessed for all diurnal periods.

Considering a mean battery capacity of 56.7 kWh, it is estimated that it takes approximately 3 h to fully charge an EV when using the high-power range to charge. This calculation is based on Equation (5). It is important to note that the actual charging time may be longer than the theoretical minimum due to factors such as battery cell heating and non-linear charging currents. These variables make it difficult to accurately determine the charging time, so a rounded-up approach is used in the time estimates.

$$\mathrm{∆}t={\displaystyle \frac{C}{P}}={\displaystyle \frac{56.7 \mathrm{kWh}}{7.4 \mathrm{kW}}}\approx 8 \mathrm{h},\mathrm{∆}t={\displaystyle \frac{C}{P}}={\displaystyle \frac{56.7 \mathrm{kWh}}{22 \mathrm{kW}}}\approx 3 \mathrm{h}$$

(5)

The quantity of EVs that can be recharged in a 3 h window is determined by the average energy available in that time frame. This ensures a steady supply of energy over the 3 h period, which is the estimated duration required to completely charge a battery in a 24 h cycle. The values presented in Figure 7 were derived from the mean electrical energy amounts that were grouped in three-hour intervals for each day. These values were obtained from graphs that illustrate the average electrical availability for each month.

The estimated number of EVs that can be charged in a given period of time is depicted in Figure 8. This estimate is based on current network capacity and assumes that there are enough charging stations to meet these numbers.

Most charging stations are in public areas, meaning that the most common time to charge is during the day, from 9 a.m. to 9 p.m. The least amount of energy is available between noon and 3 p.m. However, the highest levels of energy are available during the night, from midnight to 9 a.m., and this remains consistent throughout the year.

Note that changes in the energy available in the network for this type of charging are relatively small on a monthly basis. However, a slight decrease can be seen in the months of January and February, as well as from June to September, which are periods of low and high temperatures, respectively.

The bars in Figure 9 depict the daily EV charging capacity, considering both the 3 h daytime slots and the designated daytime charging periods. It is evident that time and power constraints, stemming from the availability of energy for recharging—which depends on instantaneous consumption—lead to a slight reduction in the total number of EVs that can be charged per day when all time slots are utilized. This reduction is particularly noticeable when compared to the estimates for the slow-charging scenario. However, as this charging mode is predominantly applied during the daytime, it substantially limits the total number of vehicles that can be recharged.

Quantitative results for daytime charging range from 1.4 million EVs recharged daily during the least favorable months to 2.3 million recharged during the months characterized by diminished energy demand.

3.3. Fast-Charging Scenario

In the fast-charging scenario, Equation (6) was used. For a battery with an average capacity of 56.7 kWh, a power of 120 kW was assumed. The time required to charge the battery of the electric vehicle to 100% is, therefore, reduced to 30 min:

$$\mathrm{∆}\mathbf{t}={\displaystyle \frac{\mathbf{C}}{\mathbf{P}}}={\displaystyle \frac{\mathbf{56.7} \mathbf{k}\mathbf{W}\mathbf{h}}{\mathbf{120} \mathbf{k}\mathbf{W}}}\approx \mathbf{0.5} \mathbf{h}$$

(6)

The main advantage of fast charging is that the number of vehicles recharged increases compared to accelerated charging, since shorter intervals for a full charge allow for a better use of the available electrical energy, approaching the slow-charging model, which is considered a situation of maximum utilization. As a disadvantage, recharging during the day should be regulated with some management system because, depending on the month, the hourly interval of energy availability for charging changes. It can be seen that the Spanish electric system is capable of providing a full recharge for 1,631,000 EVs in a year, a value calculated and discussed in the Section 4, which could be a great incentive for the adoption of EVs.

The electrical availability, divided into blocks of 30 min periods for each day and month of the year, is shown in Figure 10. There, the highest availability of 20.415 MW occurs in April from 4:00 to 4:30 a.m., ensuring the charging of 170,125 EVs within the period, and the lowest of 6.1380 MW occurs in January from 20:30 to 21:00 a.m., equivalent to 51,150 EVs charged. Comparable trends are observed in the following months, with April and May typically having the highest availability and January, February, and July the lowest.

Although the data in Figure 11 express the energy and number of vehicles that can be charged with the available power in each period, the usage patterns of the fast-charging mode must be considered, as they significantly affect the number of vehicles that can be effectively charged. Like the accelerated-charging mode, the main bulk of the vehicles is concentrated around daytime hours, which we consider from 8 a.m. to 10 p.m. Within these hours, the energy available in the grid undergoes significant fluctuations, not only throughout the day, but also during the different seasons of the year. As a result, the data are classified according to three different time bands with distinct behaviors, comprising the night hours, from 10 p.m. to 8 a.m., morning hours from 8 a.m. to 3 p.m., and afternoon hours from 3 p.m. to 10 p.m.

The highest energy availability is concentrated during the night, with the maximum corresponding to the whole day. The minimum values in this period correspond to the hours before nightfall, with the lowest value between 10 p.m. and 10:30 p.m. in January. In the morning period, the minimums are located at the end of the period, with only 7109 MW of surplus between 1 p.m. and 1:30 p.m. in July. The maximum surpluses, on the other hand, are in the early morning hours, the most favorable case being between 8 a.m. and 8:30 a.m. in May with 15,333 MW. The afternoon period is characterized by maximum availability in the central hours of the interval. The period from 6:30 p.m. to 7 p.m. reaches 14,968 MW in April. The extremes show the lowest values reaching the minimum during January from 8:30 p.m. to 9 p.m. with just over 6000 MW.

The total number of EVs that can be fast charged within these 30 min intervals, segmented by month, is shown in Figure 12. According to these data, January is the least favorable month for EV charging, as it can only support 1,631,000 vehicles during the day due to higher overall energy consumption and lower charging capacity on the grid. In contrast, April and May are the most favorable months, with the capacity to charge around 2,670,000 EVs during daytime hours.

The computation of daytime hours represents a very important reduction with respect to the total number of vehicles that can be charged. However, it is necessary to consider that the pattern of use in the fast-charging mode is like that of gas stations, and, therefore, a corresponding hourly restriction should be applied.

In addition to this, the accelerated recharging approach enables a more efficient utilization of the available electrical energy, since its full charge time is approximately one-sixth of that required by the accelerated recharging method. However, this efficiency requires the integration of a dynamic energy management system capable of adapting to the fluctuations in energy availability that are inherent to different months and specific hourly intervals. In the context of Spain’s electrical infrastructure, it is configured to fully recharge an annual minimum of 1,631,000 EVs during daytime, thus establishing a strategic framework for the broader assimilation of EVs into the transportation matrix. However, fast recharging is not without its challenges, especially its high costs and the complexities associated with integrating the necessary power levels into the existing electrical distribution network. As a result, specialized charging stations, also known as “electric stations”, are identified as the most apt venues for fast recharging. Other feasible locations include vehicle rental agencies and commercial fleet centers.

4. Discussion

In this study, an assessment of vehicle recharging potential was made based on the available fleet, focusing on scenarios of minimal energy availability. This approach entailed the assumption of peak demand scenarios with respect to the electrical grid, characterized by continuous full-capacity recharging over hourly periods.

Table 3. Sum of EVs that can be recharged in different charging scenarios (×1000).

Month	Slow Charge	Accelerated Charge	Fast Charge
Jan	4748	1395	1631
Feb	4909	1496	1771
Mar	5632	1828	2173
Apr	6492	2282	2669
May	6509	2322	2656
Jun	5779	1947	2232
Jul	4902	1464	1734
Aug	5542	1784	2092
Sep	5792	1895	2239
Oct	6255	2171	2541
Nov	569	1878	2139
Dec	5588	1806	2074
Minimum	4902	1395	1631

provides a comprehensive overview of the potential for EVs to be charged under different scenarios over various months. The scenarios considered include slow-charge, accelerated-charge, and fast-charge modes, each of which reflects different charging speeds and, as a result, different capacities for the number of vehicles that can be charged. In January, the number of vehicles that can be charged is 4,748,000 under the slow-charge scenario, such an estimate falling significantly to 1,395,000 when the accelerated-charge mode is used during daytime; when the fast-charge option is employed, similar values are obtained, with a total of 1,631,000 EVs. This pattern of higher capacity with faster-charging modes is consistent throughout the year. February shows a higher capacity, with 4,909,000 vehicles chargeable under the slow-charge scenario, with lower figures for the accelerated- and fast-charge scenarios. The slow-charge scenario has the least capacity in July, with 4,902,000 vehicles able to be charged. The accelerated-charge scenario has the least capacity in January, with an estimated value of 4,748,000 vehicles able to be charged. The fast-charge scenario has the least capacity in January as well, with 1,631,000 vehicles able to be charged. The highest capacity for each scenario is seen in different months. April and May correspond to the peak months for the slow-charge scenario, with approximately 65,000 vehicles, the accelerated-charge scenario, with more than 2,280,000 EVs charged, and the fast-charge scenario, exhibiting similar numbers. These variations likely reflect the changing grid demands and available power throughout the year. Overall, these data demonstrate the significant impact of charging speed on the capacity of the grid to handle EV charging, with faster charging methods allowing for a greater number of vehicles to be charged, especially during months of lower overall power demand.

The number of electric vehicles that can be introduced in mainland Spain is shown in

Table 4. EVs that could be replaced in peninsular Spain categorized by autonomous communities and recharging scenario.

Autonomous Community	Conventional Vehicles 2021	Slow Charge	Accelerated Charge	Fast Charge
Andalusia	4,282,637	887,876	260,865	304,997
Aragon	628,766	128,196	37,665	44,037
Asturias	527,307	109,204	32,085	37,513
Cantabria	317,172	66,472	1953	22,834
Castile and Leon	1,348,505	280,132	82,305	96,229
Castile–La Mancha	1,133,982	232,652	68,355	79,919
Catalonia	3,524,212	731,192	21,483	251,174
Valencia	2,655,140	550,768	16,182	189,196
Estremadura	619,287	128,196	37,665	44,037
Galicia	1,593,438	332,36	9765	11,417
Madrid	3,973,768	821,404	241,335	282,163
Murcia	806,097	166,18	48,825	57,085
Navarre	340,073	7122	20,925	24,465
Basque Country	1,020,550	213,66	62,775	73,395
La Rioja	153,361	33,236	9765	11,417
TOTAL EV	22,924,295	4,752,748	1,395,395	1,632,631
% Charge scenario	100	207	61	71

. It is ordered by autonomous community and categorized according to different charging scenarios: slow charging, accelerated charging, and fast charging. It is compared with the number of gasoline and diesel vehicles in Spain. The data juxtapose the number of conventional vehicles in each community with the number of vehicles that could be viable for the transition to electric vehicles under each charging scenario.

In Andalusia, for example, out of 4,282,637 conventional vehicles, 887,876 could be replaced by EVs under the slow-charge scenario, 260,865 under the accelerated-charge scenario, and 304,997 under the fast-charge scenario. This pattern of increasing replacement capacity with faster charging methods is observed in all communities. Catalonia, with 3,524,212 conventional vehicles, has the potential to replace 731,192 vehicles under the slow-charge scenario, reduced to 214,830 and 251,174 vehicles under the accelerated- and fast-charge scenarios, respectively. Similarly, Madrid, with a conventional vehicle count of 3,973,768, could transition 821,404 vehicles to EVs under the slow-charge scenario, allowing up to 241,335 and 282,163 under the accelerated- and fast-charge scenarios, respectively.

The total count of EVs that could potentially replace conventional vehicles in all autonomous communities amounts to 4,752,748 for the slow-charge scenario, 1,395,395 for the accelerated-charge scenario, and 1,632,631 for the fast-charge scenario. This represents 20.7%, 6.1%, and 7.1% of the total count of conventional vehicles, respectively. These figures highlight the significant impact of charging speed on the feasibility of transitioning to EVs, with faster charging methods enabling a higher replacement rate. The evidence presented in the data emphasizes the necessity of creating and utilizing effective charging systems to encourage the extensive use of EVs in Spain.

An analysis of EV charging scenarios in Spain has revealed a limited capacity to substitute the current vehicle fleet, with a maximum of 20.7%. This is a major impediment to the widespread acceptance of EVs. To make this shift feasible, a thorough overhaul of the electricity production and distribution systems within the national power grid is necessary.

The challenges of managing EV recharge should not be overlooked. Due to the limited number of vehicles that can be charged simultaneously and the high expenses of establishing efficient systems for rapid recharging, a gradual strategy is more reasonable. This would involve slow recharging, which does not necessitate specialized charging stations, thus reducing the initial impediments to EV adoption.

The implementation of a mass EV strategy is hampered by the limited power available during the optimal hours for charging under the accelerated- and fast-charging scenarios which currently limits the substitution potential to only 6.1%. This is based on the current capacity of the peninsula’s electricity grid, in particular its average monthly peak output. To facilitate a smoother and more efficient transition to EVs, and to meet environmental and sustainability goals, grid capacity and charging infrastructure need to be improved, for example through EV charging car parks and wireless charging in large cities.

To have a reference of the characteristics of the Spanish electricity grid compared to other countries, it is compared with Norway and Germany in

Table 5. Comparison of Spain’s electrical grid situation with countries that have high rates of electric vehicle (EV) adoption, such as Norway and Germany.

Factor	Spain	Norway	Germany
Energy sources	A total of 50–60% renewables (mainly wind and solar). Relies on gas during peak demand.	A total of 98% renewables (mainly hydropower). Stable and abundant energy.	A total of 50% renewables (wind and solar). Still reliant on coal and gas during peaks.
Grid capacity	Expanding, but needs more storage and flexibility. Weaker in rural areas.	Very stable due to flexible hydropower generation.	Strong grid but faces challenges from renewable intermittency.
EV charging infrastructure	Growing, but challenges during nighttime peaks and in relation to rural coverage.	Highly developed, well-integrated with the grid.	High EV penetration, but congestion issues in some regions.
Peak demand management	Developing, with variable tariffs and increasing storage.	Easily managed due to hydropower flexibility.	Uses battery storage and market flexibility.
Energy imports	High dependence (imports gas and electricity during low renewable generation).	Low dependence (almost energy self-sufficient).	High dependence (imports gas and electricity when needed).

. The factors that are compared are the energy sources, the capacity of the grid, the recharging infrastructure, the demand peaks, and when it is necessary to import energy.

Future technologies can significantly improve the ability of the electric grid to handle peak loads from electric vehicles (EVs) through several key strategies:

• Advanced energy storage: battery and hydrogen energy storage can be considered to absorb excess electricity during times of low demand.
• Smart grids and demand management: smart grids will be able to dynamically adjust EV charging based on energy availability, incentivizing off-peak charging.
• Distributed charging and microgrids: integrating renewables with local storage systems to reduce reliance on the main grid.

5. Conclusions

The results of this research provide significant insights and highlight the implications for Spain’s transition to electric mobility, contributing to the promotion of further research in response to the identified challenges. The current capacity of Spain’s electricity grid is sufficient to support the start of the progressive deployment of electric vehicles, considering that the average daily use of these vehicles is 50 km. It is estimated that the current electrical infrastructure allows for the charging of 20.7% of the country’s vehicles, translating to about 4 million vehicles. This implies that the grid has the potential to accommodate between three and four million additional electric vehicles, discounting the current million available. Therefore, to ensure the efficiency and stability of the grid, it is necessary to implement advanced control methods and protocols, such as capacity control algorithms applied to the transmission network, allowing for optimal management of energy distribution. For example, the installation of superchargers in urban centers where the highest density of electric vehicles is concentrated would complement the current network of fast chargers, which are mainly located along highways.

Given the demographic reality of Spain, where 90% of the population live in urban buildings, there is a significant challenge with respect to ensuring adequate electric vehicle charging infrastructure in cities with a high density of vehicles and limited charging facilities. It is, therefore, essential to develop and promote policies that encourage the expansion of the existing charging infrastructure and to seek charging solutions during working hours and at night. In particular, the technical and economic viability of slow charging is highlighted as an optimal solution. Slow charging not only extends battery life, but also avoids unnecessary use of electrical capacity in short periods, a factor which is especially relevant in urban areas where daily energy consumption of electric vehicles is minimal due to short journeys from home to work or the supermarket.

The data indicate that improved grid capacity and charging infrastructure are needed to facilitate a more comprehensive and efficient transition to electric mobility. This would require policy interventions and investments to modernize the electricity grid and integrate renewable energy with an emphasis on nighttime synchronization to maximize its use. These advances would be in line with environmental objectives and the broader goal of sustainable mobility.