Rural charging and distribution facility capacity demand prediction method considering new energy vehicle ownership

Abstract

The invention discloses a rural charging and distribution facility capacity demand prediction method considering new energy vehicle inventory, and belongs to the technical field of charging load prediction. Comprising the following steps: predicting the inventory of regional new energy vehicles; analyzing the number of regional rural electric vehicles according to the regional new energy vehicle ownership; according to the regional new energy vehicle ownership and the regional rural electric vehicle number, generating a regional typical scene; and predicting regional charging supporting facility demands based on the regional typical scene, and predicting the number of demands of newly-added charging piles and the charging load demand. According to the method, a variable-parameter year-by-year recursion prediction method is adopted, and the regional rural new energy automobile inventory can be accurately predicted without depending on historical inventory data.

Description

Technical Field

[0001] The present invention relates to the technical field of charging load prediction, and in particular to a method for predicting capacity demand of rural charging and distribution facilities taking into account the number of new energy vehicles in use. Background Art

[0002] The promotion of new energy vehicles to rural areas is an important measure of the rural revitalization strategy. On the one hand, rural population is relatively scattered, the spatial and temporal layout of charging infrastructure is misplaced, and the utilization efficiency is low; on the other hand, there is currently a lack of relevant research on the demand for rural charging infrastructure construction, and there is no method for predicting the capacity demand of rural charging and distribution facilities.

[0003] Many existing studies have provided many ideas and methods for the prediction of new energy vehicle ownership and construction demand, but they are not suitable for the prediction of the construction demand of rural new energy vehicle supporting charging infrastructure and distribution network, which lack various historical data. On the one hand, there is a lack of historical data on the ownership of rural new energy vehicles, and the ownership prediction method that relies on historical data cannot be directly applied; on the other hand, there are few cases of rural charging infrastructure projects, and such projects have the characteristics of large total volume but strong repetitiveness of individual projects.

[0004] Therefore, a method for predicting the capacity demand of rural charging and distribution facilities that takes into account the number of new energy vehicles is needed to predict the current capacity demand of rural charging and distribution facilities. Summary of the invention

[0005] The purpose of the present invention is to propose a method for predicting the capacity demand of rural charging and distribution facilities taking into account the number of new energy vehicles, comprising the following steps:

[0006] Predict the number of new energy vehicles in the region;

[0007] Analyze the number of electric vehicles in rural areas of a region based on the number of new energy vehicles in the region;

[0008] Generate regional typical scenarios based on the number of new energy vehicles in the region and the number of electric vehicles in rural areas;

[0009] Based on typical regional scenarios, the demand for regional charging facilities is predicted, and the number of new charging piles and charging load demand are predicted.

[0010] Furthermore, the forecast of regional new energy vehicle ownership includes:

[0011] Collect data on the number of rural households, natural villages, townships, counties, and the number of cars per 100 households in the region in the past five years;

[0012] Refine the number of rural household cars in the region based on the collected data, and repeat this step based on the data of the previous five years to form the annual data of the number of rural household cars in the region;

[0013] According to the trend of household car ownership in the past five years, the average annual increase in the number of household cars in the region was analyzed;

[0014] The replacement ratio of rural car-owning households is set based on the average annual increase in the number of household cars in the region;

[0015] Set the model's innovation coefficient and imitation coefficient year by year to predict the regional new energy vehicle ownership.

[0016] Furthermore, according to the regional new energy vehicle ownership, the number of electric vehicles in rural areas of the region is analyzed, including:

[0017] Based on the calculated regional new energy vehicle ownership and the number of rural households in the region, the regional rural new energy vehicle ownership per 100 households is calculated;

[0018] Based on the number of new energy vehicles owned by 100 rural households in the region, the average number of electric vehicles owned per village and the average number of electric vehicles owned per township in the region were calculated respectively.

[0019] Furthermore, based on the number of new energy vehicles in the region and the number of electric vehicles in rural areas, the typical scenarios of the region are generated, including:

[0020] According to the average number of electric vehicles in villages and towns in the region, typical village and typical town scenes are set according to the average number of electric vehicles in villages and towns in the region;

[0021] According to the determined typical village scenario, each electric vehicle in the village is equipped with a household slow charging pile. According to the power demand of the household slow charging pile, the number of new charging loads and the number of new charging piles reported in the typical village are further calculated to form a typical scenario;

[0022] Based on the determined typical township scenarios, the number of newly built DC fast charging piles in typical townships is calculated according to the car-to-pile ratio, and the total new load of centralized public charging piles in typical townships is calculated to form typical scenario requirements.

[0023] Furthermore, the range of the vehicle-to-pile ratio is 1-100.

[0024] Furthermore, the calculation formula for the number of new charging piles required and the charging load demand at the village level is as follows:

[0025]

[0026] P SUM,1 =η1·(N SUM,1 ·P N,1 )

[0027] The calculation formula for the number of new charging piles required and charging load demand at the township level is as follows:

[0028]

[0029] P SUM,2 =η2·(N SUM,2 ·P N,2 )

[0030] Among them, N SUM,1 、N SUM,2 Refers to the number of new charging piles required in all villages and towns in the region; P SUM,1 , P SUM,2 They represent the new charging load demand of all villages and towns in the region; n1, n2, and n3 represent the total number of villages, towns, and townships, respectively. They represent the number of electric vehicles in typical villages and towns respectively; f1 and f2 represent the charging pile rates of typical villages and towns in the region respectively; η1 and η2 represent the simultaneous charging rates of charging piles in villages and towns respectively.

[0031] The beneficial effects of the present invention are:

[0032] 1. The present invention adopts a prediction method of variable parameters recursively year by year, which does not rely on historical rural electric vehicle ownership data and can more accurately predict the number of new energy vehicles in regional rural areas.

[0033] 2. The present invention adopts a typical to general calculation approach, combines rural new energy vehicle ownership data, analyzes the demand for charging and distribution facilities in typical villages and towns, and further promotes typical scenarios to the entire region, which can calculate the demand for charging and distribution facilities in the entire region. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flow chart of a method for predicting the capacity demand of rural charging and distribution facilities taking into account the number of new energy vehicles in the present invention.

[0035] Figure 2 This is a statistical chart of rural household car ownership in the five northern cities of Hebei Province. DETAILED DESCRIPTION

[0036] The present invention proposes a method for predicting the capacity demand of rural charging and distribution facilities taking into account the number of new energy vehicles. The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0037] Figure 1 This is a flow chart of the method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles in the present invention, which is as follows:

[0038] 1. Predict the number of new energy vehicles in a region

[0039] 1.1. Obtaining basic information about rural areas

[0040] Combined with the regional Rural Statistical Yearbook, the number of rural households, villages, towns and the number of household cars per 100 households in the region in the past 10 years were obtained, and the number of rural electric vehicles in the starting year of the forecast was also obtained.

[0041] 1.2. Basic situation of rural areas in the treatment area

[0042] First, based on the basic data obtained above, the annual rural household car ownership data of the region is further calculated from the household car ownership per 100 households and the number of rural households; secondly, the linear regression method is used to fit the average annual increase in rural household cars in the region; finally, the sum of the average annual increase and 10% of the future annual ownership (the part for the renewal of existing household cars) is used as the maximum potential reserve.

[0043] 1.3. Prediction of stock

[0044] The general form of the conventional Bass diffusion model is as follows (1).

[0045]

[0046] Where p, q, and m are the innovation coefficient, imitation coefficient, and maximum potential, respectively, which are constants in the conventional Bass diffusion model. t It refers to the sales volume at time t.

[0047] The inventory prediction module constructs an improved variable parameter Bass recursive diffusion model. t ,q t 、m t After setting year by year, the inventory is recursively predicted year by year with the year as the step length. Therefore, formula (1) can be restored to its original form as shown in the following formulas (2) and (3).

[0048]

[0049] f(t+1)=(p+q×F(t))×(1-F(t)) (3)

[0050] In the above formula, f(t) refers to the purchase probability at time t, and F(t) refers to the cumulative purchase ratio at time t.

[0051] Combined with equation (4), the conventional Bass diffusion model can be further transformed into an improved variable parameter recursive Bass diffusion model as shown in equation (5).

[0052] N t =m t ·F(t) (4)

[0053]

[0054] In the above formula, N tRefers to the number of new energy vehicles in year t.

[0055] The maximum potential of electric vehicles in rural areas consists of two parts: the annual increase in household cars and the replacement of existing household cars. The increase in household cars is obtained by linear regression fitting; for the number of annual updates of existing household cars, according to the survey, it is assumed that 10% of car-owning households in rural areas will replace their vehicles every year. In summary, 10% of the annual rural household car ownership and the sum of the annual increase in household cars are substituted into the Bass diffusion model as the maximum potential for each year. The maximum potential can be calculated according to formula (6).

[0056] m t =H t-1 0.1+(H t -H t-1 ) (6)

[0057] In the above formula, H t With H t-1 They represent the number of rural household cars in year t and year t-1 respectively.

[0058] For the innovation coefficient, the analogy empirical estimation method is used to set the innovation coefficient p. The empirical range of the innovation coefficient p is 0.01-0.03. Here, it is considered that the innovation coefficient p will grow rapidly in a certain stage and then continue to increase, and as time goes by in the future, it can be inferred that the growth rate of the p value will be lower.

[0059] For the imitation coefficient, considering that rural residents will be more affected by the reference group, we also use the analog empirical estimation method to set the imitation coefficient, setting q to 0.33 in the first year. As the industrialization of electric vehicles gradually advances, the q value will continue to increase, and as time goes by, we can know that the growth rate of the q value will increase and eventually stabilize.

[0060] The innovation coefficient and imitation coefficient of this embodiment are selected as shown in Table 1.

[0061] Table 1

[0062]

[0063] 2. Analyze the situation of electric vehicles in rural areas

[0064] 2.1 Analysis of the number of electric vehicles per village

[0065] Based on the predicted number of electric vehicles and the number of natural rural villages in the region, the average number of electric vehicles per village in the region can be further calculated.

[0066] 2.2 Analysis of the average number of electric vehicles in towns and villages

[0067] Based on the predicted number of electric vehicles and the number of towns in the region, the average number of electric vehicles per town in the region can be further calculated.

[0068] 3. Generate typical regional scenarios and analyze their charging facilities needs

[0069] Analysis of the demand for charging facilities in typical villages and towns:

[0070] 3.1. Based on the calculated results of the average number of electric vehicles per village and per township in the region, typical village and typical township scenarios are set according to the average number of electric vehicles in the region’s villages and the average number of electric vehicles in the townships.

[0071] 3.2. According to the typical village scenario determined in step 3.1, one household slow charging pile is configured for each electric vehicle in the village. Each household slow charging pile is considered to have a power of 8kW. The number of new charging loads and the number of new charging piles reported in the typical village are further calculated to form a typical scenario.

[0072] 3.3. Based on the typical township scenario determined in step 3.1, calculate the number of new DC fast charging piles in typical townships at a ratio of 50:1 for cars to piles. At the same time, calculate the total additional load of centralized public charging piles in typical townships based on the power of 60kW for a single charging pile.

[0073] 3.4. Integrate the results of steps 3.2 and 3.3 to form a typical scenario demand table. The demand for charging facilities can be calculated according to formula (7) and formula (8).

[0074]

[0075] P = η·(N·P N ) (8)

[0076] In the above formula, N represents the demand for charging piles in typical scenarios. Table 2 shows the number of new energy vehicles in typical scenarios. F represents the charging rate, P represents the new charging load, η represents the simultaneous charging rate, P represents the charging rate, N Represents the single pile power.

[0077] 4. Calculate the overall demand for charging facilities in the region

[0078] The unit indicator forecasting method is used to calculate the demand for new charging facilities in the region based on typical scenarios. A project is divided into several relatively independent unitized modules. Each module can be a functional module, a subsystem, or any part of the project stage that can be divided and independent. Each divided module is relatively small in complexity and easy to calculate. Then, by accumulating all the divided modules, the overall demand for charging facilities can be obtained.

[0079] On the basis of the unit index prediction method, the typical scenario calculation content is further adjusted. As for the number of charging piles in towns and villages, it should be further divided into urban integration areas represented by towns and rural integration areas represented by townships for prediction. Urban integration areas can be predicted according to the number of charging piles in typical towns and villages, and rural integration areas should be predicted according to half of the number of charging piles in typical towns. The specific prediction of new charging piles and charging loads at the village level is carried out according to formulas (14) and (15), and at the township level according to formulas (16) and (17).

[0080]

[0081] P SUM,1 =η1·(N SUM,1 ·P N,1 ) (15)

[0082]

[0083] P SUM,2 =η2·(N SUM,2 ·P N,2 ) (17)

[0084] In the above formula, N SUM,1 、N SUM,2 Refers to the forecast of new charging pile demand in all villages and towns in the region; P SUM,1 , P SUM,2 They represent the newly added charging load demand of all villages and towns in the region; n1, n2, and n3 represent the total number of villages, towns, and townships, respectively. They represent the number of electric vehicles in typical villages and towns respectively; f1 and f2 represent the charging pile rates of typical villages and towns in the region respectively; η1 and η2 represent the simultaneous charging rates of charging piles in villages and towns respectively.

[0085] This embodiment selects the rural areas of the five cities in northern Hebei as the target areas, takes 2021 as the base year and 2030 as the target year for calculation. The basic situation of the rural areas of the five cities in northern Hebei is shown in Table 2.

[0086] Table 2

[0087]

[0088]

[0089] 1. Forecast of the number of new energy vehicles in rural areas

[0090] According to estimates, there are 2.381 million household cars in rural areas of the five cities in northern Hebei by 2021. Similarly, analyzing the data on rural household car ownership in northern Hebei from 2015 to 2021, it can be seen that the number of rural household cars increases by about 180,000 each year. In addition, as of the end of 2021, new energy vehicles accounted for 1.55% of private cars in Hebei Province. Therefore, it is believed that the proportion of new energy vehicles in rural areas of the five cities in northern Hebei should be slightly lower than this, about 1%, or 23,800 vehicles. For the number of household cars updated each year, the update rate is assumed to be 10% based on the survey.

[0091] This example sets the innovation coefficient and imitation coefficient used in the improved Bass diffusion model to three groups: conservative, conventional, and radical, and predicts the number of new energy vehicles in rural areas of northern Hebei. The specific input parameters and the maximum market potential can be seen in Table 3 below.

[0092] Table 3

[0093]

[0094] According to the model and corresponding data of the present invention, it can be calculated that by 2030, under the conventional scenario, the number of rural household cars in the five cities in northern Hebei will reach 4 million, and the number of new energy vehicles will reach 384,700, accounting for 9.6% of the total number of household cars; under the radical and conservative scenarios, the number of new energy vehicles will be 443,000 and 293,700 respectively, accounting for 11.1% and 7.3% of the total number of household cars respectively. Figure 2 Under the normal scenario, the forecast results are consistent with the national data for the corresponding year.

[0095] According to the forecast results, it can be calculated that under the normal scenario, by 2030, the number of new energy vehicles per 100 households in the rural areas of the five cities in northern Hebei will be about 7.12.

[0096] 2. Typical scenario settings and charging and distribution demand analysis in northern Hebei

[0097] According to the rural conditions of the five cities in northern Hebei and the forecast results of the number of new energy vehicles per 100 households in 2030, the average number of new energy vehicles in each village and township in the five cities in northern Hebei can be calculated. Further, typical village and township scenarios can be extracted, and the charging and distribution power demand can be calculated according to the method of the present invention. Villages are equipped with charging piles per household according to a single pile power of 8kW, and townships are equipped with DC fast charging piles according to a single pile power of 60kW. The specific results are shown in Table 4 below.

[0098] Table 4

[0099]

[0100] 3. Analysis of overall charging and distribution demand in the five cities in northern Hebei

[0101] According to the method proposed in the present invention, combined with the charging piles and new loads in typical villages and towns, it can be estimated that the number of various charging piles and charging loads that need to be added by 2030 in the five cities in northern Hebei can be calculated. See Table 5 below for details.

[0102] Table 5

[0103]

[0104] In summary, the present invention adopts a typical to general calculation approach, combines rural new energy vehicle ownership data, analyzes the demand for charging and distribution facilities in typical villages and towns, and further promotes typical scenarios to the entire region, which can calculate the demand for charging and distribution facilities in the entire region.

Claims

1. A method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles, characterized in that: The following steps are involved: Predict the number of new energy vehicles in the region; Analyze the number of electric vehicles in rural areas of a region based on the number of new energy vehicles in the region; Generate regional typical scenarios based on the number of new energy vehicles in the region and the number of electric vehicles in rural areas; Based on typical regional scenarios, the demand for regional charging facilities is predicted, and the number of new charging piles and charging load demand are predicted.

2. The method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles according to claim 1 is characterized in that: The forecast of regional new energy vehicle ownership includes: Collect data on the number of rural households, natural villages, townships, counties, and the number of cars per 100 households in the region in the past five years; Refine the number of rural household cars in the region based on the collected data, and repeat this step based on the data of the previous five years to form the annual data of the number of rural household cars in the region; According to the trend of household car ownership in the past five years, the average annual increase in the number of household cars in the region was analyzed; The replacement ratio of rural car-owning households is set based on the average annual increase in the number of household cars in the region; Set the model's innovation coefficient and imitation coefficient year by year to predict the regional new energy vehicle ownership.

3. The method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles according to claim 1 is characterized in that: According to the regional new energy vehicle ownership analysis, the number of electric vehicles in rural areas includes: Based on the calculated regional new energy vehicle ownership and the number of rural households in the region, the regional rural new energy vehicle ownership per 100 households is calculated; Based on the number of new energy vehicles owned by 100 rural households in the region, the average number of electric vehicles owned per village and the average number of electric vehicles owned per township in the region were calculated respectively.

4. The method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles according to claim 3 is characterized in that: According to the number of new energy vehicles in the region and the number of electric vehicles in rural areas, the typical scenarios of the region are generated, including: According to the average number of electric vehicles in villages and towns in the region, typical village and typical town scenes are set according to the average number of electric vehicles in villages and towns in the region; According to the determined typical village scenario, each electric vehicle in the village is equipped with a household slow charging pile. According to the power demand of the household slow charging pile, the number of new charging loads and the number of new charging piles reported in the typical village are further calculated to form a typical scenario; Based on the determined typical township scenarios, the number of newly built DC fast charging piles in typical townships is calculated according to the car-to-pile ratio, and the total new load of centralized public charging piles in typical townships is calculated to form typical scenario requirements.

5. The method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles according to claim 4 is characterized in that: The vehicle-to-pile ratio ranges from 1 to 100.

6. The method for predicting the capacity demand of rural charging and distribution facilities considering the number of new energy vehicles according to claim 1 is characterized in that: The calculation formula for the number of new charging piles required and the charging load demand at the village level is as follows: P SUM,1 =η1·(N SUM,1 ·P N,1 ) The calculation formula for the number of new charging piles required and charging load demand at the township level is as follows: P SUM,2 =η2 (N SUM,2 ·P N,2 ) Among them, N SUM,1 、N SUM,2 Refers to the number of new charging piles required in all villages and towns in the region; P SUM,1 , P SUM,2 They represent the new charging load demand of all villages and towns in the region; n1, n2, and n3 represent the total number of villages, towns, and townships, respectively. They represent the number of electric vehicles in typical villages and towns respectively; f1 and f2 represent the charging pile rates of typical villages and towns in the region respectively; η1 and η2 represent the simultaneous charging rates of charging piles in villages and towns respectively.

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