Forecasted datasets of electric vehicle consumption on the electricity grid of Spain

The information included in this study were calculated on the basis of data provided by the Spanish electricity grid, for thirteen years between 2007 and 2019. This data includes: the average consumption demand on the Spanish electricity grid at national level, and its availability. Subsequently, the report looks at the number of electric vehicles that could be supported in the years 2020–2023, depending on the consumption demand and availably of the electricity grid for those future years. The data presented in the article refers to the research study: ‘Electric vehicles in Spain: An overview of charging systems’[1] and ‘Analysis of charging stations for electric vehicles in Spain’ [2].


Specifications
Renewable Energy, Sustainability and the Environment Energy Engineering and Power Technology Fuel Technology

Specific subject area
In Spain, the transport sector was responsible for 41.6% of total energy consumption in 2015 [3] and road transport is currently the second largest source of CO 2 emissions in the European Union (EU) [4] . With the evolution of battery storage capacity, the efficiency and autonomy of electric vehicles have accelerated their introduction worldwide and in Spain. In this sense, it is important to know the availability of the Spanish Electricity grid to determine the introduction of future electric vehicles that could support simultaneous charging. Table  How data were  acquired In Spain, the demand for electricity grid consumption is available in a database. This database was used to forecast the average demand for electricity consumption and its availability, as well as a forecast regarding the numbers of electric vehicles that could be charged in future years, based on current capacity.

Data format
Direct URL to data: Mendeley Data, http://data.mendeley.com/datasets/658jkcht9g/1 Parameters for data collection The primary data was extracted from the Spanish electricity grid database. The database includes detailed information on daily consumption in Megawatts at national level.

Description of data collection
The primary data in the database register for the last thirteen years (2007-2019) are organized by time, day, month and year. This information is used to forecast the demand and availability of electricity consumption, as well as to establish the number of electric vehicles that could be simultaneously recharged in the future.

Data source location
Country: Spain

Value of the Data
• This dataset can be used to evaluate the effectiveness of policies implemented to promote the use of electric vehicles in Spain, potentially useful to other researchers. • The data presented in this paper can save time for researchers who need to use this information as a starting point for forecasting the availability of renewable energy in Spain on an hourly basis over the twelve months of the years, 2020,2021, 2022, 2023. • The data provided in this paper can be used to complement studies on the introduction and integration of different renewable energy sources with Spanish electricity [ 5 , 6 ]. • The benchmarks can be used to the analysis in the mass introduction of electric vehicles in Spain. • The information provided in the article is useful for research into the forecast/trend of consumption behaviour in the Spanish electricity network.

Data
The electrical capacity in kWh of eighteen models of electric vehicles are shown in the Table 1 . Each of these models ranges between 15.2 and 95 kWh, and is on sale in Spain with prices below 85 thousand euros, being the German car BMW i3s (42.2 kWh) a vehicle with average electric capacity according to the mentioned criterion.
The Spanish electricity grid provides raw data of samples with frequencies of 10 min, openly accessible to the public, on the demand for electricity consumption at the national level [7] . Each of these values is averaged with all the values of the month for a specific time and year and this is what we observe in the data set stored on the Mendeley data website ( https://data. mendeley.com ) in the Excel spreadsheet file: Resume_Raw_Data.xlsx. Subsequently, the average of each hour of the day per month between 2007 and 2019 is described in Table 2 .  To obtain the values of the average monthly electrical availability during 2007-2019 in megawatts, we the following four steps: 1) The average for a specific time in a month between 2007 and 2019 from the file Re-sume_Raw_Data.xlsx is located the highest consumption value. 2) This highest consumption value is subtracted with all the values of step1 (average of the month between 2007 and 2019 for a specific time in a given month) and we obtain the availability every 10 min (the sampling frequency of the Spanish electricity network) 3) The lowest value of electricity availability in each hour is averaged over each of the twelve months between 2007 and 2019 for the same time, the result of each value is shown in Table 3 . The Table 4 determines the number of new EVs that can be simultaneously recharged in 60-min. intervals with a connection power of 50 kW, to this purpose, the values in Table 3 are divided by 50 kW, because it has an approximate value reference the BMW i3s (42.2 kWh) that appears in Table 1 , to determine the amount of EV that would support the Spanish electric grid.

Experimental design, materials, and methods
Is applied linear forecasting technique between the years 2007 to 2019 in the file Re-sume_Raw_Data.xlsx, The mathematical equation for developing the linear forecast is a + bx, where x and y are the sample means Average(electric-demand) and Average(year). In the file Resume_Raw_Data.xlsx is visualized the process to obtain the linear forecast.
On the other hand, the maximum value per month, located in every column of the Table 5 ,  Table 6 , Table 7 , Table 8 minus the demand value recorded within each time band per day rep- resents electricity availability in Megawatts for the years from 2020 to 2023 shown each of them in Table 9 , Table 10 , Table 11 , Table 12 .
The values in the Table 13 , 14 , 15 , 16 were obtained by dividing every data of the Tables 9 , 10 , 11 , 12 per 50 kW. Each result determines the number of new EVs that could be recharged using a fast charge in 60-min. intervals with a connection power of 50 kW for the forecasted Table 17 Margin of error between forecast and actual value of the consumption demand of the Spanish electricity grid.  The summary of Tables 13 , 14 , 15 , 16 shows the forecast for 2020-2023 in graphs per month for easy reading in the Fig. 1 .
The first two months of the year 2020 recorded values close to the linear forecast, with an error of less than 4% in January (a maximum of 3.8% in the 23 to 24 h), and a maximum error of 4.5% in February (in the 18 to 19 h interval). However, in March 2020, the error rose to 10% due to the decrease in national electricity demand in Spain as a result of the COVID-19, especially in the 7-11 a.m. time slot. The maximum error peaks are highlighted with green color in Table 17 .

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.