Electric vehicle charging load prediction method based on time sequence production simulation

Abstract

The invention relates to a time sequence production simulation-based electric vehicle charging load prediction method. The method comprises the following steps of S1, selecting a sample field needing to be predicted; s2, as shown in Figure 2, predicting the electric vehicle demand scale of the sample plot by using a ten-thousand-person ownership method and a policy analysis method; s3, as shown in figure 3, dividing all the charging facilities according to categories by adopting a vehicle-pile ratio method, and respectively predicting the demand scale of each type of charging facilities; s4, according to the demand scale of each type of charging facilities obtained in the S3, analyzing load characteristics of each type of charging facilities one by one, and based on a time sequence production simulation method, respectively carrying out load characteristic clustering analysis on each type of charging facilities and drawing a load characteristic curve; and S5, according to the load characteristic curve obtained in the S4, a classification superposition method is adopted to obtain charging load coincidence rates of various types of charging facilities in different scenes, and based on accumulation of the charging load coincidence rates, the multi-type electric vehicle charging loads are predicted.

Description

Technical Field

[0001] The present disclosure relates to the field of distribution network planning and construction, and in particular to a method for predicting electric vehicle charging load based on time series production simulation. Background Art

[0002] The distribution network is an important part of the power system. It is directly facing power users and is an important infrastructure for improving people's livelihood and ensuring economic and social development. It not only undertakes power supply services for power users, but also affects the economic benefits of enterprises. The large-scale promotion of electric vehicles will bring positive effects in four aspects: energy conservation and emission reduction, clean energy, fuel substitution and green environmental protection.

[0004] According to data from the China Association of Automobile Manufacturers (CAAM), China's new energy vehicle production will reach 9.587 million units and sales will reach 9.495 million units in 2023, up 35.8% and 37.9% year-on-year respectively. Among them, pure electric vehicles (BEV) sales reached 6.685 million units, up 24.6% year-on-year, occupying a dominant position in the market, accounting for nearly 70% of sales, and the market share of plug-in hybrid vehicles (PHEV) is also increasing year by year. By the end of 2023, the number of new energy vehicles in China will reach 20.41 million, of which 15.52 million will be pure electric vehicles, accounting for 76.04%.

[0005] As the number of electric vehicles continues to increase, charging facilities will continue to improve, and large-scale electric vehicles will be connected to the power grid for charging, which will change the load composition and characteristics of the distribution network. Traditional conventional power load forecasting methods mainly rely on structured data such as historical load data, population size, output value and energy consumption. Although these factors are important, they cannot fully cover all factors that affect power load. When facing modern smart grids and complex and changeable power systems, they often lack sufficient adaptive capabilities and have some limitations. It is difficult to cope with the rapidly changing power market and consumption patterns. It is necessary to make corresponding adjustments to traditional conventional power load forecasting methods to adapt to the scenario of electric vehicles being widely connected to the distribution network.

[0006] It is very necessary to study a method for predicting the charging load of multiple types of urban electric vehicles based on time-series production simulation and provide guidance for this method. Summary of the invention

[0007] In view of the above technical problems in the related art, the present disclosure proposes a method for predicting electric vehicle charging load based on time-series production simulation, which can overcome the above shortcomings of the prior art.

[0008] To achieve the above technical objectives, the technical solution of the present disclosure is implemented as follows: The present disclosure provides a method for predicting electric vehicle charging load based on time series production simulation, which comprises the following steps: S1: Select the sample area to be predicted; S2: Figure 2 As shown, the 10,000-person ownership method and policy analysis method are used to predict the scale of electric vehicle demand in the sample areas; S2.1: Based on the population of the sample area in the past N years, derive the change law of the number of recognition in the sample area and predict the population change trend of the sample area in the next M years; N>=1, M>=1; S2.2: Combined with the standard specification documents of the sample area, predict the total number of various types of vehicles in the sample area in the next M years; S2.3: Analyze the new energy development plan of the sample area and predict the proportion and number of electric vehicles in the total number of various types of vehicles in the next M years; S2.4: The population change trend obtained from S2.1, the total number of various types of vehicles obtained from S2.2, and the proportion and number of electric vehicles obtained from S2.3 are matched one by one on an annual basis, and the demand for electric vehicles per 10,000 people in the sample area is finally analyzed - that is, the demand scale of electric vehicles in the sample area is predicted; S3: Figure 3 As shown, the vehicle-to-pile ratio method is used to classify all charging facilities by category, and the demand scale of each type of charging facilities is predicted separately; S4: According to the demand scale of each type of charging facilities obtained in S3, the load characteristics of each type of charging facilities are analyzed one by one, and based on the time series production simulation method, load characteristic cluster analysis is performed on each type of charging facilities and load characteristic curves are drawn; S5: Based on the load characteristic curve obtained in S4, the classification superposition method is used to obtain the charging load simultaneity rate of each type of charging facilities in different scenarios, and based on the accumulation of each charging load simultaneity rate, it is possible to predict the charging load of multiple types of electric vehicles.

[0009] Preferably, in S2, The value range of N is 1--10; The value range of M is 1--10.

[0010] Preferably, in S2, All types of vehicles include buses, logistics vehicles, sanitation vehicles, official vehicles, taxis, and private cars; Standard and specification documents include "Urban Road Traffic Planning and Design Specifications", "Urban Environmental Sanitation Implementation Planning Standards", and "Urban Road Traffic Planning and Design Specifications"; The new energy development plan includes the new energy vehicle industry development plan and the ecological environment protection plan.

[0011] Preferably, S3 specifically includes the following steps: S3.1: The operational charging characteristics of various types of electric vehicles are obtained through investigation. The operational charging characteristics include characteristic information of electric vehicles, including charging and swapping mode selection, vehicle operation mode, average daily mileage, energy consumption per unit mileage, power battery capacity, charging time requirements and parking stations; S3.2: Analyze the utilization efficiency of various types of charging facilities, combine the current application of charging facilities, and consider the usage demand and the general acceptance capacity of vehicle charging power, and according to the new energy development plan of the sample area, obtain the configuration principle of the vehicle-to-charging pile ratio of various types of electric vehicles in the sample area; S3.3: Combined with the electric vehicle demand scale predicted by S2 and the car-to-pile ratio configuration principle obtained by S3.2, the demand scale of each type of charging facility is calculated respectively - that is, the demand scale of each type of charging facility.

[0012] Preferably, in S4, various types of charging facilities include bus charging stations, special vehicle charging stations, public charging stations, and private charging piles; Bus charging stations are used to charge electric buses; Special vehicle charging stations are used to charge special vehicles of enterprises and institutions, including logistics vehicles and sanitation vehicles; Public charging stations are used for charging electric vehicles including taxis and private cars, and for temporary charging of other types of electric vehicles; Private charging piles are used to charge private cars.

[0013] Preferably, the load characteristics of the bus charging station include: fixed operating hours, fixed line length, fixed charging location, etc. The operating hours are generally daytime, and there are operating peaks during commuting hours; affected by the low electricity prices at night, buses are generally charged in the charging area of ​​the bus station during the night between 22:00 and 5:00. The charging time for each vehicle is different, depending on the battery capacity of the vehicle and the daily mileage. Generally, each charging takes about 3 hours, and some vehicles will be appropriately recharged during the day.

[0014] Preferably, the load characteristics of the dedicated vehicle charging station include: mainly for charging special vehicles such as logistics and sanitation vehicles, the station location is relatively fixed, mainly located inside enterprises and institutions or special parking lots. In order to meet work needs, special vehicles such as logistics and sanitation vehicles generally go out to work during the day, and return to the unit for charging during idle time. The charging time is related to their working hours. Affected by the nighttime electricity price policy, they are generally charged during the nighttime low period, that is, they mainly start charging after 23:00, and charge intensively from 5:00 to 9:00, and some vehicles are charged during the day.

[0015] Preferably, the load characteristics of public charging stations include: mainly charging taxis and private cars, and temporarily charging other vehicles. Among them, taxi operations are generally divided into two operating modes: one shift a day and two shifts a day. Taxis for one shift a day use newer models with larger battery capacity. They use fast or slow charging during the low-peak electricity price period at night, that is, after 23:00. Taxis for two shifts a day use newer models with larger battery capacity. The charging time is mainly between 5:00 and 7:00 (low-peak electricity price) and 14:00 and 17:00 (flat electricity price). Older models of taxis have smaller battery capacity and still need to be properly charged during the rest time around 23:00 and 2:00 at night (low-peak electricity price). Some private cars without private charging piles choose to charge at public charging stations. On weekdays, they generally choose to charge at 19:00 after get off work. Charging is done at 22:00. On non-working days, the vehicles are mainly used for leisure and entertainment. They are mostly charged before departure in the morning and after returning in the evening. The general charging time is from 10:00 to 22:00. Fast charging is mainly used. A small number of vehicles will choose to use public charging stations near their homes and choose slow charging after 23:00 during the night when electricity prices are low. Other social vehicles charge at public charging stations mostly for temporary charging, and the time is not fixed.

[0016] Preferably, the load characteristics of private charging piles include: the average daily mileage of private passenger cars on weekdays is about 30km, and the average daily mileage on non-working days is about 50km. According to the battery capacity and mileage, they are generally charged once a week in summer and three times in two weeks in winter. Charging is mainly done after get off work. Considering the impact of time-of-use electricity prices, charging is mostly done during the low-peak electricity price period at night. The starting charging time mainly starts to rise rapidly at 22:00. For some private cars that choose to charge at public charging stations, their behavioral characteristics are detailed in the description of public charging station characteristics.

[0017] Preferably, S5 specifically includes the following steps: S5.1: Conduct surveys and statistics to obtain load data for each type of charging facility; S5.2: Determine a number of scenarios to be analyzed and list all scenarios into a scenario set; S5.4: Select a scene from the scene set in order as the current scene; S5.5: Combined with the load characteristic curves of various types of charging facilities drawn in S4, the classification superposition method is adopted to superimpose the loads of various types of charging facilities at the same time in the current scenario to obtain the load superposition value, and the contract capacity of various types of charging facilities is accumulated to obtain the load capacity value. The corresponding charging load simultaneous rate is calculated according to the following formula; Charging load simultaneous rate = load capacity value / load capacity value; S5.6: If the current scene is the last scene in the scene set, continue to S5.7; otherwise, return to S5.4; S5.7: Multiply the rated capacity of each type of charging facility by the charging load synchronization rate and add them together. The cumulative result is the predicted electric vehicle charging load, which reflects the total charging load of each type of electric vehicle.

[0018] Compared with the prior art, the beneficial effects of the present disclosure are as follows: The present disclosure is the first to predict the scale of electric vehicle demand by predicting population growth, analyzing local electric vehicle development policies, and using the 10,000-person ownership method and policy analysis method. Next, based on the operating characteristics and charging characteristics of various types of vehicles, combined with the development policies of local charging piles, the vehicle-to-pile ratio method is used to classify the scale of charging facility demand. Then, based on the time series production simulation method, a characteristic clustering analysis is carried out on multiple types of charging facilities to obtain the load characteristics of the charging facilities. Finally, the classification superposition method is used for the loads of multiple types of charging facilities at the same time to obtain the charging load simultaneity rate of multiple types of charging facilities and predict the charging loads of multiple types of electric vehicles. The present disclosure provides a method for predicting the charging load of multiple types of electric vehicles in a city, which solves the problem that traditional load forecasting methods are not applicable to predicting the charging load of multiple types of electric vehicles in a city, so as to achieve the purpose of guiding the prediction of the charging load of multiple types of electric vehicles in a city. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 It is a flow chart of the method for predicting charging load of multiple types of electric vehicles in a city described in the present disclosure.

[0021] Figure 2 It is a flow chart for predicting the scale of electric vehicle demand as described in the present disclosure.

[0022] Figure 3 It is a flow chart for predicting the scale of demand for charging facilities as described in the present disclosure. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure.

[0024] like Figure 1-3 As shown, in order to facilitate the understanding of the above technical solutions of the present disclosure, the above technical solutions of the present disclosure are described in detail below through specific usage methods.

[0025] The present disclosure provides a method for predicting electric vehicle charging load based on time series production simulation, which comprises the following steps: S1: Select the sample area to be predicted; S2: Figure 2 As shown, the 10,000-person ownership method and policy analysis method are used to predict the scale of electric vehicle demand in the sample areas; S2.1: Based on the population of the sample area in the past N years, derive the change law of the number of recognition in the sample area and predict the population change trend of the sample area in the next M years; N>=1, M>=1; S2.2: Combined with the standard specification documents of the sample area, predict the total number of various types of vehicles in the sample area in the next M years; S2.3: Analyze the new energy development plan of the sample area and predict the proportion and number of electric vehicles in the total number of various types of vehicles in the next M years; S2.4: The population change trend obtained from S2.1, the total number of various types of vehicles obtained from S2.2, and the proportion and number of electric vehicles obtained from S2.3 are matched one by one on an annual basis, and the demand for electric vehicles per 10,000 people in the sample area is finally analyzed - that is, the demand scale of electric vehicles in the sample area is predicted; S3: Figure 3 As shown, the vehicle-to-pile ratio method is used to classify all charging facilities by category, and the demand scale of each type of charging facilities is predicted separately; S4: According to the demand scale of each type of charging facilities obtained in S3, the load characteristics of each type of charging facilities are analyzed one by one, and based on the time series production simulation method, load characteristic cluster analysis is performed on each type of charging facilities and load characteristic curves are drawn; S5: Based on the load characteristic curve obtained in S4, the classification superposition method is used to obtain the charging load simultaneity rate of each type of charging facilities in different scenarios, and based on the accumulation of each charging load simultaneity rate, it is possible to predict the charging load of multiple types of electric vehicles.

[0026] In one embodiment, in S2, The value range of N is 1--10; The value range of M is 1--10.

[0027] In one embodiment, in S2, All types of vehicles include buses, logistics vehicles, sanitation vehicles, official vehicles, taxis, and private cars; Standard and specification documents include "Urban Road Traffic Planning and Design Specifications", "Urban Environmental Sanitation Implementation Planning Standards", and "Urban Road Traffic Planning and Design Specifications"; The new energy development plan includes the new energy vehicle industry development plan and the ecological environment protection plan.

[0028] In a certain embodiment, S3 specifically includes the following steps: S3.1: The operational charging characteristics of various types of electric vehicles are obtained through investigation. The operational charging characteristics include characteristic information of electric vehicles, including charging and swapping mode selection, vehicle operation mode, average daily mileage, energy consumption per unit mileage, power battery capacity, charging time requirements and parking stations; S3.2: Analyze the utilization efficiency of various types of charging facilities, combine the current application of charging facilities, and consider the usage demand and the general acceptance capacity of vehicle charging power, and according to the new energy development plan of the sample area, obtain the configuration principle of the vehicle-to-charging pile ratio of various types of electric vehicles in the sample area; S3.3: Combined with the electric vehicle demand scale predicted by S2 and the car-to-pile ratio configuration principle obtained by S3.2, the demand scale of each type of charging facility is calculated respectively - that is, the demand scale of each type of charging facility.

[0029] In one embodiment, in S4, various types of charging facilities include bus charging stations, dedicated vehicle charging stations, public charging stations, and private charging piles; Bus charging stations are used to charge electric buses; Special vehicle charging stations are used to charge special vehicles of enterprises and institutions, including logistics vehicles and sanitation vehicles; Public charging stations are used for charging electric vehicles including taxis and private cars, and for temporary charging of other types of electric vehicles; Private charging piles are used to charge private cars.

[0030] In a certain embodiment, the load characteristics of the bus charging station include: fixed operating hours, fixed line length, fixed charging location, etc. The operating hours are generally during the day, and there is an operating peak during commuting hours; affected by the low electricity prices at night, buses are generally charged in the charging area of ​​the bus station during the night between 22:00 and 5:00. The charging time for each vehicle is different, depending on the battery capacity of the vehicle and the daily mileage. Generally, each charging takes about 3 hours, and some vehicles will be appropriately recharged during the day.

[0031] In one embodiment, the load characteristics of a dedicated vehicle charging station include: mainly charging for special vehicles such as logistics and sanitation vehicles, the station location is relatively fixed, and is mainly located inside enterprises or institutions or in dedicated parking lots. To meet work needs, special vehicles such as logistics and sanitation vehicles generally go out to work during the day, and return to the unit for charging during idle time. The charging time is related to their working hours. Affected by the nighttime electricity price policy, they are generally charged during the nighttime low period, that is, charging mainly starts after 23:00, and concentrated charging is carried out from 5:00 to 9:00, and some vehicles are supplemented with electricity during the day.

[0032] In one embodiment, the load characteristics of a public charging station include: mainly charging taxis and private cars, and temporarily charging other vehicles. Among them, taxi operations are generally divided into two operating modes: one shift a day and two shifts a day. Taxis for one shift a day use newer models with larger battery capacity. They use fast or slow charging during the low-peak electricity price period at night, that is, after 23:00. Taxis for two shifts a day use newer models with larger battery capacity. The charging time is mainly between 5:00 and 7:00 (low-peak electricity price) and 14:00 and 17:00 (flat electricity price). Older models of taxis have smaller battery capacity and still need to be properly charged during the rest time around 23:00 and 2:00 at night (low-peak electricity price). Some private cars without private charging piles choose to charge at public charging stations. On weekdays, they generally choose to charge at 19:00 after get off work. Charging is done at 22:00. On non-working days, the vehicles are mainly used for leisure and entertainment. They are mostly charged before departure in the morning and after returning in the evening. The general charging time is from 10:00 to 22:00. Fast charging is mainly used. A small number of vehicles will choose to use public charging stations near their homes and choose slow charging after 23:00 during the night when electricity prices are low. Other social vehicles charge at public charging stations mostly for temporary charging, and the time is not fixed.

[0033] In one embodiment, the load characteristics of a private charging pile include: the average daily mileage of a private passenger car on weekdays is about 30 km, and the average daily mileage on non-working days is about 50 km. According to the battery capacity and mileage, it is generally charged once a week in summer and three times in two weeks in winter. Charging is mainly done after get off work. Considering the impact of time-of-use electricity prices, charging is mostly done during the low-peak electricity price period at night. The starting charging time mainly starts to rise rapidly at 22:00. For some private cars that choose to charge at public charging stations, their behavioral characteristics are detailed in the description of the characteristics of public charging stations.

[0034] In one embodiment, S5 specifically includes the following steps: S5.1: Conduct surveys and statistics to obtain load data for each type of charging facility; S5.2: Determine a number of scenarios to be analyzed and list all scenarios into a scenario set; S5.4: Select a scene from the scene set in order as the current scene; S5.5: Combined with the load characteristic curves of various types of charging facilities drawn in S4, the classification superposition method is adopted to superimpose the loads of various types of charging facilities at the same time in the current scenario to obtain the load superposition value, and the contract capacity of various types of charging facilities is accumulated to obtain the load capacity value. The corresponding charging load simultaneous rate is calculated according to the following formula; Charging load simultaneous rate = load capacity value / load capacity value; S5.6: If the current scene is the last scene in the scene set, continue to S5.7; otherwise, return to S5.4; S5.7: Multiply the rated capacity of each type of charging facility by the charging load synchronization rate and add them together. The cumulative result is the predicted electric vehicle charging load, which reflects the total charging load of each type of electric vehicle.

[0035] Working principle: Figure 1-3 As shown, the present disclosure analyzes the population size change trend of the sample area in the past N years (N can be 1-10 years) based on the 10,000-person ownership method and the policy analysis method, and then predicts the development needs of the electric vehicle demand scale in the sample area in the next M years (M can be 1-10 years) in combination with the policy. Next, the car-to-pile ratio method is used to predict the demand scale of various types of charging facilities in the sample area in the next M years, and then based on the time series production simulation method, the load characteristics of the charging facilities are obtained. Finally, the classification superposition method is used to obtain the charging load synchronization rate of the charging facilities, and the electric vehicle charging load of the sample area is predicted. The electric vehicle charging load reflects the sum of the charging loads of multiple types of electric vehicles.

[0036] In summary, through the above-mentioned unique technical solution, the present disclosure takes the lead in predicting the scale of electric vehicle demand by predicting population growth, analyzing local electric vehicle development policies, and adopting the 10,000-person ownership method and policy analysis method. Next, based on the operating characteristics and charging characteristics of various types of vehicles, combined with the development policy of local charging piles, the vehicle-to-pile ratio method is used to classify the scale of charging facility demand. Then, based on the time series production simulation method, a characteristic clustering analysis is carried out on multiple types of charging facilities to obtain the load characteristics of the charging facilities. Finally, the classification superposition method is used for the loads of multiple types of charging facilities at the same time to obtain the charging load simultaneity rate of multiple types of charging facilities and predict the charging load of multiple types of electric vehicles. The present disclosure provides a method for predicting the charging load of multiple types of electric vehicles in a city, which solves the problem that traditional load forecasting methods are not applicable to predicting the charging load of multiple types of electric vehicles in a city, so as to achieve the purpose of guiding the prediction of the charging load of multiple types of electric vehicles in a city.

[0037] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for predicting electric vehicle charging load based on time series production simulation, characterized in that: The following steps are involved: S1: Select the sample area to be predicted; S2: Use the 10,000-person ownership method and policy analysis method to predict the scale of electric vehicle demand in the sample area; S2.1: Based on the population of the sample area in the past N years, derive the change law of the number of recognition in the sample area and predict the population change trend of the sample area in the next M years; N>=1, M>=1; S2.2: Combined with the standard specification documents of the sample area, predict the total number of various types of vehicles in the sample area in the next M years; S2.3: Analyze the new energy development plan of the sample area and predict the proportion and number of electric vehicles in the total number of various types of vehicles in the next M years; S2.4: The population change trend obtained from S2.1, the total number of various types of vehicles obtained from S2.2, and the proportion and number of electric vehicles obtained from S2.3 are matched one by one on an annual basis, and the demand for electric vehicles per 10,000 people in the sample area is finally analyzed - that is, the demand scale of electric vehicles in the sample area is predicted; S3: Use the vehicle-to-pile ratio method to classify all charging facilities by category and predict the demand scale of each type of charging facility; S4: According to the demand scale of each type of charging facilities obtained in S3, the load characteristics of each type of charging facilities are analyzed one by one, and based on the time series production simulation method, load characteristic cluster analysis is performed on each type of charging facilities and load characteristic curves are drawn; S5: Based on the load characteristic curve obtained in S4, the classification superposition method is used to obtain the charging load simultaneity rate of each type of charging facilities in different scenarios, and based on the accumulation of each charging load simultaneity rate, it is possible to predict the charging load of multiple types of electric vehicles.

2. The electric vehicle charging load prediction method according to claim 1, characterized in that: In S2, The value range of N is 1--10; The value range of M is 1--10.

3. The electric vehicle charging load prediction method according to claim 1, characterized in that: In S2, The various types of vehicles include buses, logistics vehicles, sanitation vehicles, official vehicles, taxis, and private cars; The standard specification documents include "Urban Road Traffic Planning and Design Specifications", "Urban Environmental Sanitation Implementation Planning Standards", and "Urban Road Traffic Planning and Design Specifications"; The new energy development plan includes the new energy vehicle industry development plan and the ecological environment protection plan.

4. The electric vehicle charging load prediction method according to claim 1, characterized in that: The S3 specifically includes the following steps: S3.1: The operational charging characteristics of various types of electric vehicles are obtained through investigation. The operational charging characteristics include characteristic information of the electric vehicles, including charging and replacement mode selection, vehicle operation mode, average daily mileage, energy consumption per unit mileage, power battery capacity, charging time requirements and parking stations; S3.2: Analyze the utilization efficiency of various types of charging facilities, combine the current application of charging facilities, and consider the usage demand and the general acceptance capacity of vehicle charging power, and according to the new energy development plan of the sample area, obtain the configuration principle of the vehicle-to-charging pile ratio of various types of electric vehicles in the sample area; S3.3: Combined with the electric vehicle demand scale predicted by S2 and the vehicle-to-pile ratio configuration principle obtained by S3.2, the demand scale of each type of charging facility is calculated respectively - that is, the demand scale of each type of charging facility.

5. The electric vehicle charging load prediction method according to claim 1, characterized in that: In S4, the various types of charging facilities include bus charging stations, special vehicle charging stations, public charging stations, and private charging piles; The bus charging station is used for charging electric buses; The dedicated vehicle charging station is used to charge the dedicated vehicles of enterprises and institutions, including logistics vehicles and sanitation vehicles; The public charging station is used for charging electric vehicles including taxis and private cars, and is also used for temporary charging of other types of electric vehicles; The private charging pile is used for charging private cars.

6. The electric vehicle charging load prediction method according to claim 5, characterized in that: The load characteristics of the bus charging station include: fixed operating hours, fixed line length, fixed charging location, daytime operating hours, and peak operating hours during commuting hours; affected by the low nighttime electricity prices, buses are charged in the charging area of ​​the bus station during the night from 22:00 to 5:00, with each charging taking about 3 hours, and some vehicles will be appropriately recharged during the day.

7. The electric vehicle charging load prediction method according to claim 5, characterized in that: The load characteristics of the dedicated vehicle charging station include: charging for dedicated vehicles, the station location is relatively fixed, and is located inside an enterprise or institution or in a dedicated parking lot.

8. The electric vehicle charging load prediction method according to claim 5, characterized in that: The load characteristics of the public charging station include: charging for taxis and private cars, and temporarily charging other vehicles.

9. The electric vehicle charging load prediction method according to claim 5, characterized in that: The load characteristics of the private charging pile include: the average daily mileage of private passenger cars on weekdays is 30 km, and the average daily mileage on non-working days is 50 km. It is charged once a week in summer and three times in two weeks in winter. It is charged after get off work and at night when electricity prices are low. The starting charging time mainly starts to rise rapidly at 22:

00.

10. The electric vehicle charging load prediction method according to claim 1, characterized in that: The S5, specifically The following steps are involved: S5.1: Conduct surveys and statistics to obtain load data for each type of charging facility; S5.2: Determine a number of scenarios to be analyzed and list all scenarios into a scenario set; S5.4: Selecting a scene from the scene set in order as the current scene; S5.5: Combined with the load characteristic curves of various types of charging facilities drawn in S4, the classification superposition method is adopted to superimpose the loads of various types of charging facilities at the same time in the current scenario to obtain the load superposition value, and the contract capacity of various types of charging facilities is accumulated to obtain the load capacity value, and the corresponding charging load simultaneous rate is calculated according to the following formula; Charging load simultaneous rate = load capacity value / load capacity value; S5.6: If the current scene is the last scene in the scene set, continue to S5.7; otherwise, return to S5.4; S5.7: Multiply the rated capacity of each type of charging facility by the charging load simultaneity rate and add them together. The accumulated result is the predicted charging load of the electric vehicle, which reflects the total charging load of each type of electric vehicle.

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