Urban public charging facility planning method, system and device based on maximum power

Abstract

The present invention belongs to the field of urban planning, and specifically relates to a method, system and equipment for planning urban public charging facilities based on maximum power, aiming to solve the problem that the existing charging facility planning method is single, resulting in inaccurate charging demand prediction, unreasonable spatial layout of charging facilities, and difficulty in meeting diversified and large-scale charging needs. The present invention includes: obtaining charging behavior characteristic data of electric vehicle users; calculating the ownership of various types of electric vehicles in the planning year of the target city; predicting the power demand of public charging piles with different powers in the planning year; dividing the power demand of public charging piles for different grids in the planning area; determining the power demand data of different types of charging piles in each grid, and calculating the construction scale of each type of charging pile in each grid in combination with the standard pile power. The present invention can better predict the charging demand of electric vehicles, match the supply and demand of charging facilities in different spaces and land uses with the actual needs of user groups, and reduce the waste of charging facility resources.

Description

Background Art

[0002] With the improvement of global environmental awareness and the adjustment of energy structure, electric vehicles (EV) have been rapidly promoted around the world due to their zero emission and low noise characteristics. However, the construction of charging infrastructure faces many challenges, especially in the absence of unified planning at the city level. The spatial layout of charging facilities often shows the phenomenon of "hot and cold unevenness", which directly affects the quality and efficiency of charging services.

[0003] In recent years, research on charging facility planning has focused on planning and site selection, and there are few practical application cases. Common methods for charging facility site selection research include: spatial analysis method, multi-index comprehensive decision-making method, and mathematical programming method. Among them, research based on mathematical programming method is the most widely used, followed by multi-index comprehensive decision-making method, and spatial analysis method is the least. In the article entitled "A Review and Prospect of Research on Electric Vehicle Charging Facility Planning" published in the Journal of Electric Power Science and Technology, the research on the spatiotemporal distribution of charging demand is introduced from the two aspects of point demand and flow demand; at the same time, the planning objects are classified according to the use scenarios and construction scale of charging facilities, and the factors that need to be considered in planning are sorted out and analyzed. In the article entitled "Research on Demand Forecasting and Planning Layout of Urban Electric Vehicle Charging Facilities" published in the Power System Protection and Control journal, by analyzing the development trend and layout concept of electric vehicle charging facilities, the planning model, technical process, demand forecasting and planning layout methods of urban charging facilities are proposed, and applied to the planning and design of charging facilities in the central urban area of ​​Quanjiao County, Anhui Province.

[0004] In terms of charging facility planning and application cases, most studies focus on the development planning of charging facilities, and there are fewer studies on the spatial layout planning of charging facilities. In the article entitled Research on the Planning and Layout of Charging Facilities for Pure Electric Taxi in Shenzhen, the journal is Comprehensive Transportation. The article analyzes the operating characteristics and charging rules of pure electric taxis in Shenzhen from the three aspects of time, space and land use; based on the characteristics and rules, it proposes strategies and methods for the spatial planning of taxi charging facilities.

[0005] In summary, the current research on charging facility planning focuses on layout allocation algorithms, lacks a systematic charging facility planning method, and the relevant calculation scale is small, which makes it difficult to apply to the planning of large-scale urban charging facilities. In practical applications, there are still problems such as a single planning method, lack of systematic considerations, and limited scope of application. Especially in the process of large-scale promotion and application, how to achieve the effective layout of charging facilities to meet the rapidly growing demand for electric vehicle charging is still an important issue that needs to be solved.

[0006] Based on this, the present invention proposes a method, system and device for planning urban public charging facilities based on maximum power. Summary of the invention

[0007] In order to solve the above-mentioned problems in the prior art, namely, the charging facility planning method in the prior art is single, lacks systematic considerations and has a limited scope of application, resulting in unreasonable spatial layout of charging facilities and difficulty in meeting diverse and large-scale charging needs, the present invention provides a city public charging facility planning method, system and device based on maximum power.

[0008] In a first aspect of the present invention, a method for planning urban public charging facilities based on maximum power is proposed, the method comprising:

[0009] Obtain charging behavior characteristic data of electric vehicle users based on the historical data set of electric vehicles in the target city;

[0010] Predict the total number of electric vehicles in the target city in the planning year; classify electric vehicles according to their usage characteristics and predict the number of various types of electric vehicles in the target city in the planning year;

[0011] Based on the charging behavior characteristics of electric vehicle users and the number of electric vehicles of various types, the total power demand of public charging piles for various types of electric vehicles in the planning year is calculated. Combined with the annual change in the proportion of public charging piles of different powers in the current year and the planning year, the power demand forecast results of public charging piles of different powers in the planning year are obtained;

[0012] Divide the planned area into grids, obtain the historical charging data of public charging piles of different powers in each grid, calculate the hourly maximum power consumption in the historical charging data of each grid, and use it as the historical maximum charging demand power of the corresponding grid, and calculate the weight value of the power allocation of each grid. Combined with the power demand forecast results of public charging piles of different powers in the planning year, divide the power demand of public charging piles in different grids of the planned area;

[0013] Classify charging piles according to power and determine standard piles for charging piles of different power types; obtain the charging ratio of charging piles of different power types in the historical charging data of each grid and the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and calculate the construction scale of each type of charging pile in each grid based on the power of the standard piles.

[0014] In some preferred embodiments, the upper and lower limits of the total number of electric vehicles in the target city in the planning year are obtained, and the upper and lower limits of the number of each type of electric vehicles, the upper and lower limits of the total power demand of public charging piles for each type of electric vehicles in the planning year, and the upper and lower limits of the power demand forecast results of public charging piles with different powers in the planning year are calculated. Finally, the upper and lower limits of the demand for public charging piles with different powers in each grid in the area to be planned are calculated.

[0015] In some preferred embodiments, the electric vehicle user charging behavior characteristic data at least includes charging frequency, charging time, fast-slow charging ratio, charging facility selection preference, average daily mileage of electric vehicles, energy consumption per kilometer of electric vehicles, electric vehicle charging efficiency, peak hour coefficient, redundancy coefficient and charging ratio of public charging facilities.

[0016] In some preferred embodiments, the total power demand of the public charging pile is G , which is calculated as:

[0017] ;

[0018] in, g i For the i Total power demand of public charging piles for electric vehicles; V i For the i The number of electric vehicles in use; l i For the i Average daily mileage of electric vehicles; e i For the i Energy consumption per kilometer of electric vehicles; q i For the i The proportion of electric vehicles using public charging stations; β is the peak hour factor, which is obtained based on the highest hourly charging amount and the full-day charging amount data; γ is the redundancy coefficient, which is calculated based on the total charging capacity of electric vehicles in the city, the total installed capacity of public charging piles, and the change in charging pile utilization rate.

[0019] In some preferred implementations, the historical maximum charging demand power is preprocessed and used to calculate the weight value of power allocation of each grid after preprocessing. The preprocessing method is:

[0020] ;

[0021] in, Q i For the i The pre-processed value of the historical maximum charging demand power of each grid; N For the i The number of adjacent meshes of a mesh. Adjacent meshes are defined as follows: if two meshes have common edges or vertices, then the two meshes are adjacent; Q in For the i The historical maximum charging demand power of the nth adjacent grid of the grid.

[0022] In some preferred implementations, the weight value of each grid power allocation is calculated as follows:

[0023] ;

[0024] in, W i For the i The weight value of each grid; Q i For the i The preprocessed value of the historical maximum charging demand power of each grid; M is the total number of grids divided in the planned area; Q m It is the preprocessed value of the historical maximum charging demand power of the mth grid.

[0025] In some preferred embodiments, based on the number of public parking spaces required for the construction scale of each type of charging piles in the grid and the number of currently registered public parking spaces in each block, it is evaluated whether the existing public parking resources in each block meet the needs of charging facility construction.

[0026] Another aspect of the present invention provides a system for planning urban public charging facilities based on maximum power, and a method for planning urban public charging facilities based on maximum power, the system comprising:

[0027] A charging behavior characteristic data acquisition module configured to acquire charging behavior characteristic data of electric vehicle users based on a historical data set about electric vehicles in a target city;

[0028] The ownership calculation module is configured to predict the total number of electric vehicles in the target city in the planned year; classify electric vehicles according to their usage characteristics, and predict the ownership of various types of electric vehicles in the target city in the planned year;

[0029] The power demand prediction module is configured to calculate the total power demand of public charging piles for various types of electric vehicles in the planning year based on the charging behavior characteristic data of electric vehicle users and the number of various types of electric vehicles in stock, and combine the annual change in the proportion of public charging piles of different powers in the current year and the planning year to obtain the power demand prediction results of public charging piles of different powers in the planning year;

[0030] The public charging pile power demand division module is configured to divide the planned area into grids, obtain the historical charging data of public charging piles of different powers in each grid, calculate the hourly maximum power consumption in the historical charging data of each grid, and use it as the historical maximum charging demand power of the corresponding grid, and calculate the weight value of the power allocation of each grid. Combined with the power demand forecast results of public charging piles of different powers in the planning year, the power demand of public charging piles in different grids of the planned area is divided;

[0031] The construction scale generation module is configured to classify charging piles according to power and determine the standard piles of charging piles of different power types; obtain the charging ratio of charging piles of different power in the historical charging data of each grid, the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and calculate the construction scale of each type of charging pile in each grid based on the power of the standard pile.

[0032] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0033] at least one processor; and

[0034] a memory communicatively connected to at least one of the processors; wherein,

[0035] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement a method for planning urban public charging facilities based on maximum power.

[0036] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement a method for planning urban public charging facilities based on maximum power.

[0037] Beneficial effects of the present invention:

[0038] Accurate demand forecasting: On the one hand, the present invention predicts the charging power demand of electric vehicles based on the historical maximum charging power of electric vehicles, rather than the traditional method of roughly predicting the charging demand of electric vehicles based on the ratio of vehicles to charging piles, charging time, charging energy consumption, etc. On the other hand, the present invention takes into account the spillover effect and spatial correlation of the charging behavior of electric vehicle users, processes the historical maximum charging power of each grid, and avoids the erroneous conclusion that areas with no charging demand in the past will also have no charging demand in the future. In this way, the charging demand can be accurately predicted at both the macro and micro levels, avoiding over-construction or insufficient facilities, and ensuring the effective use of charging facilities.

[0039] Accurate spatial layout of charging facilities: In the spatial dimension, the present invention grids the area to be planned and determines the weight value based on the historical charging data in each grid, thereby guiding the reasonable layout of charging piles of different powers; in the temporal dimension, the present invention refines the forecast period and calculates the proportional annual demand for public charging piles of different powers. This method achieves accurate prediction of future charging demand at a smaller granularity in both the spatial and temporal dimensions, which can better connect with national land space planning at different levels, improve the flexibility of charging facility layout, and ensure efficient allocation of charging resources.

[0040] Enhanced adaptability: On the one hand, the present invention takes into account the uncertainty of the number of electric vehicles in stock, and by setting upper and lower limits of the number of electric vehicles in stock, it provides a flexible solution for charging facility planning under different scenarios. On the other hand, the present invention aims to predict the power demand of future charging facilities rather than the number of charging facilities, and can dynamically adjust the scale of charging facility demand according to the development of charging technology, thereby enhancing the adaptability and robustness of the planning scheme.

[0041] Improve user experience: By optimizing the spatial layout of charging facilities, reducing charging waiting time and improving charging efficiency, the user's charging experience is ultimately improved, which helps promote the healthy development of the electric vehicle market.

[0042] Economy and practicality: By introducing the peak hour coefficient and redundancy coefficient, it ensures that charging facilities can meet demand during peak periods while avoiding unnecessary waste of resources, thereby improving the economic and social benefits of charging facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0044] Figure 1 It is a flow chart of a method for planning urban public charging facilities based on maximum power of the present invention;

[0045] Figure 2 It is the spatial distribution of the maximum hourly power consumption (i.e., the maximum hourly charging capacity) of the fast charging pile in a method for planning urban public charging facilities based on maximum power of the present invention;

[0046] Figure 3 It is the spatial distribution of the maximum hourly power consumption (i.e., the maximum hourly charging capacity) of the slow charging pile in a method for planning urban public charging facilities based on maximum power of the present invention;

[0047] Figure 4 This is a schematic diagram of the spatial layout scale planning results of slow charging piles in the first planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0048] Figure 5 This is a schematic diagram of the spatial layout scale planning result of slow charging piles in the second planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0049] Figure 6This is a schematic diagram of the spatial layout scale planning results of slow charging piles in the third planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0050] Figure 7 This is a schematic diagram of the spatial layout scale planning results of ordinary fast charging piles in the first planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0051] Figure 8 This is a schematic diagram of the spatial layout scale planning results of ordinary fast charging piles in the second planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0052] Fig. 9 This is a schematic diagram of the spatial layout scale planning results of ordinary fast charging piles in the third planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0053] Fig.10 This is a schematic diagram of the spatial layout scale planning results of high-power fast charging piles in the first planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0054] Fig.11 This is a schematic diagram of the spatial layout scale planning results of high-power fast charging piles in the second planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0055] Fig.12 This is a schematic diagram of the spatial layout scale planning results of high-power fast charging piles in the third planning year in a method for planning urban public charging facilities based on maximum power of the present invention; wherein the left figure is a schematic diagram corresponding to the lower limit value, and the right figure is a schematic diagram corresponding to the upper limit value;

[0056] Fig.13 It is a grid statistical curve diagram of different numbers of slow charging piles in different planning years in a method for planning urban public charging facilities based on maximum power of the present invention;

[0057] Fig.14 It is a grid statistical curve diagram of the number of different common fast charging piles in different planning years in a method for planning urban public charging facilities based on maximum power of the present invention;

[0058] Fig.15 It is a grid statistical curve diagram of the number of high-power fast charging piles in different planning years in a method for planning urban public charging facilities based on maximum power of the present invention;

[0059] Fig.16 A schematic diagram of the structure of a computer system of a server for implementing the method, system, and device embodiments of the present application. DETAILED DESCRIPTION

[0060] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0061] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0062] like Figure 1-Figure 15 As shown, the present invention provides a method for planning urban public charging facilities based on maximum power, the method comprising:

[0063] Obtain charging behavior characteristic data of electric vehicle users based on the historical data set of electric vehicles in the target city;

[0064] Predict the total number of electric vehicles in the target city in the planning year; classify electric vehicles according to their usage characteristics and predict the number of various types of electric vehicles in the target city in the planning year;

[0065] Based on the charging behavior characteristics of electric vehicle users and the number of electric vehicles of various types, the total power demand of public charging piles for various types of electric vehicles in the planning year is calculated. Combined with the annual change in the proportion of public charging piles of different powers in the current year and the planning year, the power demand forecast results of public charging piles of different powers in the planning year are obtained;

[0066] Divide the planned area into grids, obtain the historical charging data of public charging piles of different powers in each grid, calculate the hourly maximum power consumption in the historical charging data of each grid, and use it as the historical maximum charging demand power of the corresponding grid, and calculate the weight value of the power allocation of each grid. Combined with the power demand forecast results of public charging piles of different powers in the planning year, divide the power demand of public charging piles in different grids of the planned area;

[0067] Classify charging piles according to power and determine standard piles for charging piles of different power types; obtain the charging ratio of charging piles of different power types in the historical charging data of each grid and the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and calculate the construction scale of each type of charging pile in each grid based on the power of the standard piles.

[0068] In order to more clearly explain the urban public charging facility planning method based on maximum power of the present invention, the following is combined with Figure 1 The embodiments of the present invention are described in detail, and each step is described in detail as follows:

[0069] Obtain charging behavior characteristic data of electric vehicle users based on the historical data set of electric vehicles in the target city;

[0070] In this embodiment, the historical data on electric vehicles includes analyzing the problems existing in the city's electric vehicle charging facilities by acquiring the charging history data of urban electric vehicle users and the spatial distribution data of charging facilities, combining land use data, population distribution data, employment distribution data, parking data, traffic operation data, questionnaire survey data, etc., and analyzing the charging behavior characteristic data of electric vehicle users, including charging frequency, charging time, fast and slow charging ratio, charging facility selection preference, average daily mileage of electric vehicles, energy consumption per kilometer of electric vehicles, charging efficiency of electric vehicles, peak hour coefficient, redundancy coefficient, charging ratio of public charging facilities, etc.

[0071] Predict the total number of electric vehicles in the target city in the planning year; classify electric vehicles according to their usage characteristics and predict the number of various types of electric vehicles in the target city in the planning year;

[0072] Specifically, this embodiment can use the trend extrapolation method and other forecasting methods to forecast the total number of electric vehicles in the city planning year under conservative, neutral, and radical scenarios through the analysis of higher-level planning, policy requirements, and comparison of cities of the same type, and use these forecasting methods as the lower limit, recommended value, and upper limit of the forecast. Then, electric vehicles are classified according to their usage characteristics, and can be divided into taxis, online ride-hailing vehicles, private cars, logistics delivery vehicles, tourist buses, municipal vehicles, official vehicles, etc., and the number of vehicles of each type in the planning year is predicted in combination with the policy requirements and planning requirements of different types of vehicles.

[0073] Based on the charging behavior characteristic data of electric vehicle users and the number of various types of electric vehicles in use, the total power demand of public charging piles for various types of electric vehicles in the planning year is calculated. Combined with the annual change in the proportion of public charging piles of different powers between the current year and the planning year, the power demand forecast results of public charging piles of different powers in the planning year are obtained.

[0074] Among them, the total power demand of public charging pilesG , which is calculated as:

[0075] ;

[0076] in, g i For the i Total power demand of public charging piles for electric vehicles; V i For the i The number of electric vehicles in use; l i For the i Average daily mileage of electric vehicles; e i For the i Energy consumption per kilometer of electric vehicles; q i For the i The proportion of electric vehicles using public charging stations; β is the peak hour factor, which is obtained based on the highest hourly charging amount and the full-day charging amount data; γ is the redundancy coefficient, which is calculated based on the total charging capacity of electric vehicles in the city, the total installed capacity of public charging piles, and the change in charging pile utilization rate.

[0077] The peak hour coefficient is calculated based on the ratio of peak hour charging volume to total daily charging volume. The redundancy coefficient is calculated based on the actual peak hour maximum power demand of public charging piles, the total installed capacity of public charging piles, and the utilization rate change trend of charging piles.

[0078] Through investigation, the present invention found that the main models using public charging piles in relevant cities include 6 categories: ① cruising taxis; ② online car-hailing (including social online car-hailing); ③ urban logistics delivery vehicles (light trucks); ④ highway, tourism, social large and medium-sized buses; ⑤ other vehicles such as official and corporate vehicles; ⑥ private passenger cars. Therefore, the present invention mainly predicts the planned ownership of these 6 types of electric vehicles. The current year is 2024, and the planned years are 2025, 2030, and 2035.

[0079] According to the yearbooks and related data of relevant cities, the current stock of 6 types of motor vehicles in relevant cities is about 5.914 million. Combined with the motor vehicle lottery indicators in relevant cities, it is predicted that the stock in 2025, 2030 and 2035 will be 6.494 million, 7.081 million and 7.678 million respectively. For details, please refer to Table 1:

[0080] Table 1:

[0081]

[0082] The present invention calculates the upper limit and lower limit of the holding amount respectively, calculates the total power demand of the public charging piles based on the upper limit and the lower limit, obtains the upper limit and lower limit of the power demand forecast results of the public charging piles with different powers in the planning year, and finally calculates the upper limit and lower limit of the public charging pile demand with different powers of each grid in the planned area;

[0083] The upper limit value of the ownership is the planned target value of electric vehicles in the planning year, and the lower limit value of the ownership is the predicted electric vehicle ownership at the same growth rate in the target city through the preset electric vehicle development target and growth rate of the city.

[0084] Specifically, according to the policy requirements of relevant cities, by 2025, the cumulative number of electric vehicles will strive to reach 2 million. Taking into account the actual situation, in this invention, the goal of 2 million electric vehicles in 2025 is set as the upper limit of the forecast value. Combined with the electric vehicle development goals and growth rates of other cities of the same level, the number of new energy motor vehicles in the relevant cities at the same growth rate is predicted and used as the lower limit of the forecast value. Further, based on the new energy motor vehicle proportion targets of different models formulated in the relevant policy documents issued by the relevant cities, the penetration rate of new energy motor vehicles by model is predicted, as shown in Table 2, and the number of electric vehicles in the planning year is predicted, as shown in Table 3;

[0085] Table 2:

[0086]

[0087] Table 3:

[0088]

[0089] In this method, according to calculation, the current peak hour coefficient is 0.062, and the peak hour coefficient in 2025 is the same as the current situation. Considering that the implementation of intelligent and orderly charging will help reduce the peak hour coefficient, the peak hour coefficient in 2030 is 0.059, the peak hour coefficient in 2035 is 0.054, and the redundancy coefficient is 9.7. The prediction results of other parameters and the total power demand of public charging piles are shown in Table 4.

[0090] Table 4:

[0091]

[0092] Among them, the unit of average daily mileage of a bicycle is: kilometers / day (km / d); the unit of energy consumption per 100 kilometers of a bicycle is: kilowatt-hour / vehicle-100 kilometers (kWh / 100km).

[0093] Taking cruising taxis as an example, the specific calculation is as follows:

[0094] Average daily mileage of a bicycle = 200km / d

[0095] Energy consumption of a bicycle per 100 kilometers = 12kWh / 100km

[0096] Public charging pile ratio = 85%

[0097] Calculate the average daily charging demand for a single vehicle:

[0098] Average daily charging demand = 200km × 12kWh / 100km = 24kWh / d

[0099] Calculate the charging demand borne by public charging piles:

[0100] Public charging pile charging demand = 24kWh / d × 85% = 20.4kWh / d

[0101] Calculate peak hour charging power demand:

[0102] Using the 2025 peak hour factor of 0.062

[0103] Power demand = 20.4 kWh / d × 0.062 × 9.7 ≈ 12.3 kW

[0104] For the forecast value in 2025, considering that the number of electric cruising taxis is 64,000, then:

[0105] Total power demand = 64,000 vehicles × 12.3kW ≈ 787,000 kW

[0106] According to the data given in the table, the power demand forecast for cruising taxis is 787,000 kW (the upper and lower limits are the same).

[0107] Based on the charging behavior characteristics of electric vehicle users and the number of electric vehicles of various types, the total power demand of public charging piles for various types of electric vehicles in the planning year is calculated. Combined with the annual change in the proportion of public charging piles of different powers in the current year and the planning year, the power demand forecast results of public charging piles of different powers in the planning year are obtained;

[0108] Specifically, charging piles can be divided into slow charging piles and fast charging piles according to the size of the charging power. Slow charging piles occupy a small area, have flexible locations, and have low construction and operating costs. Users can choose to charge during the low electricity hours at night when electricity prices are relatively cheap, and the cost of use is low. However, slow charging piles have a slow charging speed and a long charging time, which makes it difficult to meet users' emergency charging needs. Fast charging piles have a short charging time and can meet users' emergency charging needs, but the construction and operating costs are high, and the requirements for power pipeline facilities are high. In addition, charging a large amount of electricity into the power battery in a short period of time will cause the battery to overheat, affecting its service life.

[0109] At present, the ratio of fast and slow charging of public charging piles in relevant cities is about 1:1.8, but in terms of power, the ratio of fast and slow charging power is about 5.4:1. According to relevant urban planning, the ratio of fast and slow charging is not less than 1:2. Among the public charging piles in relevant cities, slow charging piles are mainly 7kW, accounting for more than 86%, followed by 3.5kW, accounting for about 12%; the power of fast charging piles is mostly 60kW, accounting for more than 31%, followed by 30kW, accounting for about 17%, and then 15kW, accounting for about 7%. Overall, charging piles below 120kW account for about 89%, charging piles between 120-480kW account for about 11%, and charging piles above 480kW account for less than 1%.

[0110] Since charging piles with different powers meet different needs of electric vehicle users, the present invention predicts slow charging piles and fast charging piles respectively, and divides fast charging piles into two categories based on 120kW for prediction: ordinary fast charging piles, whose power is less than 120kW; high-power fast charging piles, whose power is greater than or equal to 120kW.

[0111] According to the survey, 90% of users choose fast charging piles, and 72% of users choose charging piles of 120kW and above. Therefore, the present invention assumes that this ratio will be achieved in 2035, that is, the installed power of fast charging piles (including ordinary fast charging piles and super charging piles) accounts for 90% of the total installed power of public charging piles, and the installed power of super charging piles accounts for 72% of the total installed power of public charging piles. During this period, the installed power ratio of each type of charging pile increases or decreases at a uniform rate year by year. The predicted results of the installed power ratio of public charging piles of different powers in the planning year are shown in Table 5.

[0112] Table 5:

[0113]

[0114] Here are the specific steps:

[0115] Determine the current year and the planning year:

[0116] Current year: 2024.

[0117] Planning year: 2035.

[0118] Determine the target ratio for the planning year:

[0119] The installed power ratio of fast charging piles (including fast charging piles <120kW and fast charging piles ≥120kW) is 90%.

[0120] Among them, the installed power of fast charging piles of 120kW and above accounts for 72%.

[0121] Determine the proportion of the current year:

[0122] According to the survey, the power of fast charging piles accounts for about 84.4%, and the power of slow charging piles accounts for about 15.6%.

[0123] For fast charging piles, charging piles below 120kW account for about 50.7%, and charging piles above 120kW (including 120kW) account for about 33.7%.

[0124] Calculate the proportion of fast charging piles and slow charging piles each year:

[0125] The power share of fast charging piles (including fast charging piles of less than 120kW and fast charging piles of ≥120kW) will increase from the current 84.4% to 90% in the planned year, with a difference of 5.6%.

[0126] From the current year, the power share of fast charging piles in the nth year (the current year is the 0th year) = 84.4% + n × 5.6% / (2035-2024).

[0127] The power share of slow charging piles decreased accordingly from 15.6% to 10%, a difference of -5.6%.

[0128] The power share of slow charging piles in the nth year (the current year is the 0th year) = 15.6%-n×5.6% / (2035-2024).

[0129] The power share of ≥120kW fast charging piles will increase from the current 33.7% to 72%, a difference of 38.3%.

[0130] The power share of ≥120kW fast charging piles in the nth year (the current year is the 0th year) = 33.7% + n × 38.3% / (2035-2024).

[0131] Calculate the power share of each type of charging pile year by year:

[0132] Starting from 2024, the power share of each type of charging pile will be calculated year by year according to the above method until the target value for 2035 is reached.

[0133] The total power demand forecast results of public charging piles in the planning year calculated by the maximum power method and the installed power ratio forecast results of public charging piles with different powers can be used to calculate the power demand forecast results of public charging piles with different powers in the planning year, as shown in Table 6.

[0134] Table 6:

[0135]

[0136] The area to be planned is divided into grids, and the historical charging data of public charging piles of different powers in each grid are obtained. The data is used to calculate the hourly maximum power consumption in the historical charging data of each grid, which is used as the historical maximum charging demand power of the corresponding grid. The weight value of the power allocation of each grid is calculated, and the power demand of public charging piles of different powers in the planning year is combined to divide the power demand of public charging piles for different grids in the planning area.

[0137] In order to overcome the problem of zero historical charging data of some grids and the overflow effect of charging behavior, the historical maximum charging demand power of each grid is processed as follows:

[0138] ;

[0139] in, Q i For the i The processed value of the historical maximum charging demand power of each grid; N For the i The number of adjacent meshes of a mesh. Adjacent meshes are defined as follows: if two meshes have common edges or vertices, then the two meshes are adjacent; Q in For the i The historical maximum charging demand power of the nth adjacent grid of the grid.

[0140] Calculate the weight value of each grid power allocation, the calculation method is as follows:

[0141] ;

[0142] in, W i For the i The weight value of each grid; Q i For the i The processed value of the historical maximum charging demand power of the grid; M is the total number of grids divided into the planned area; Q m It is the preprocessed value of the historical maximum charging demand power of the mth grid.

[0143] Specifically:

[0144] See also Figure 2 and Figure 3 First, the area to be planned is divided into grids (usually 250-meter or 500-meter square grids). This embodiment takes a 500-meter square grid as an example. Specifically:

[0145] The central urban areas of relevant cities are divided into grids of 500 m × 500 m to form a series of small area grids.

[0146] Collect and analyze historical charging data within each grid, including but not limited to historical charging records of different types of charging piles (such as fast charging piles and slow charging piles).

[0147] A day is divided into 24 time periods, each of which is 1 hour, from 0:00 to 23:00.

[0148] For each 500-meter grid, the power consumption (or charging amount) of the fast charging pile and the slow charging pile in each time period is counted respectively, and the maximum value among them is selected as the maximum unit hourly power consumption of the fast charging pile and the maximum unit hourly power consumption of the slow charging pile in the grid, also known as the maximum power demand of the fast charging pile and the maximum power demand of the slow charging pile.

[0149] The maximum power demand of the fast charging pile and slow charging pile of each grid is preprocessed, and the maximum power demand values ​​of the adjacent grids (including the grid) of each grid are averaged as the preprocessed value of the maximum power demand of the grid.

[0150] Calculate the ratio of the preprocessed value of the maximum power demand of each type of charging pile (fast charging pile and slow charging pile) in each grid to the sum of all grids, and use the ratio as the weight of the grid.

[0151] According to the weight of each grid, the total power demand of fast charging piles and slow charging piles of all grids is allocated respectively. For example, for fast charging piles, the power that should be allocated to the grid is determined according to the weight of each grid in the total power demand of fast charging piles of all grids; for slow charging piles, the power that should be allocated to the grid is determined according to the weight of each grid in the total power demand of slow charging piles of all grids.

[0152] The weight definition in the present invention means that the "maximum hourly power consumption (or maximum charging power demand)" in each 500-meter grid is used as an indicator to indicate the maximum value of the hourly charging demand intensity of the grid. Specifically, it means that in every hour of the day, the charging power of all charging piles of the same type in each grid (that is, the power consumed by all charging piles of the same type in use within that hour) is counted, and then the maximum value is taken, which represents the "maximum hourly power consumption (or maximum charging power demand)" of the grid.

[0153] Weight refers to the value used to measure the importance of a factor in this model. In this case, the proportion of the processed value based on "maximum hourly power consumption (or maximum charging power demand)" in all grids is used as the weight, which means that it will affect the subsequent power demand allocation decision. Specifically, if a grid has a high maximum hourly power consumption, then the allocation weight of this grid is high, otherwise the allocation weight is low.

[0154] In simple terms, the weight definition step is:

[0155] For each grid, count the charging amount of each type of charging pile in each time period of the day (for example, 0 o'clock to 1 o'clock, 1 o'clock to 2 o'clock, and so on), and find out the time period and charging amount with the largest charging amount.

[0156] The maximum charge value is preprocessed, and its proportion in all grids is calculated after preprocessing, which is used as the weight value of the grid.

[0157] When performing the spatial allocation of total power demand, grids with higher weights are allocated more power because they represent higher charging demand.

[0158] Doing so can ensure that areas with greater charging demand have access to more charging resources, thereby optimizing the overall layout of charging facilities.

[0159] Taking the central urban area of ​​the relevant city as an example, according to the historical charging volume, the charging volume of electric vehicles in the central urban area accounts for 55% of the total charging volume of electric vehicles in the whole city. Assuming that this proportion will not change significantly in the future, according to the above steps, the charging power requirements of different types of charging piles in each grid in the central urban area of ​​the relevant city can be calculated.

[0160] The calculated charging power demand is difficult to connect with the national land space planning system. In order to better connect with the national land space planning system, the charging power demand can be further converted into the charging pile scale demand.

[0161] Classify charging piles according to power and determine standard piles for charging piles of different power types; obtain the charging ratio of charging piles of different power in the historical charging data of each grid and the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and obtain the construction scale of each type of charging pile in each grid based on the power of the standard piles.

[0162] Determine the standard charging pile. Based on the current status of urban charging facilities, classify charging piles according to power and determine the standard charging piles. For example, they can be divided into three categories: ≤10kW, 10kW-120kW, and >120kW. Among them, charging piles ≤10kW use 7kW as the standard pile, charging piles <120kW use 60kW as the standard pile, and charging piles ≥120kW use 120kW as the standard pile.

[0163] The spatial calculation of the scale of different types of charging facilities is carried out, and the allocation results are as follows: Figure 4-Figure 12 As shown in the figure, different colors are used to represent the number range of slow charging piles. The number of charging piles from light to dark is:

[0164] 0 charging piles, 1-5 charging piles, 6-10 charging piles, 11-20 charging piles, ≥21 charging piles.

[0165] The left picture shows the lower limit of charging piles, and the right picture shows the upper limit of charging piles. By comparing the two pictures, we can observe the distribution and quantity range of charging piles in different regions. The darker the color, the more slow charging piles there are.

[0166] Specifically, Figure 4 This is a schematic diagram of the spatial layout scale planning results of slow charging piles with a planning year of 2025; Figure 5 Schematic diagram of the spatial layout scale planning results of slow charging piles with the planning year of 2030; Figure 6 This is a schematic diagram of the spatial layout scale planning results of slow charging piles in the planning year of 2035; Figure 7 This is a schematic diagram of the spatial layout scale planning results of ordinary fast charging piles with the planning year of 2025; Figure 8 This is a schematic diagram of the spatial layout scale planning results of ordinary fast charging piles with the planning year of 2030; Fig. 9 This is a schematic diagram of the spatial layout scale planning results of ordinary fast charging piles with a planning year of 2035; Fig.10 This is a schematic diagram of the spatial layout scale planning results of high-power fast charging piles with a planning year of 2025; Fig.11 This is a schematic diagram of the spatial layout scale planning results of high-power fast charging piles with a planning year of 2030; Fig.12 This is a schematic diagram of the spatial layout scale planning results of high-power fast charging piles with the planning year of 2035.

[0167] like Figure 13-Figure 15 As shown, the 500-meter grid statistical curves of the number of slow charging piles, ordinary fast charging piles, and high-power fast charging piles in the planning years are displayed. It can be seen from the figure that, overall, whether it is the number of slow charging piles, ordinary fast charging piles, or high-power fast charging piles, the number of 500-meter grids decreases with the increase of the number of charging piles in the grid.

[0168] Obtain the scale of public charging piles with different powers for each grid in the current year and the target planning year, and calculate the annual change and construction scale of public charging piles with different powers.

[0169] According to the applicable scenarios of charging piles with different power, the construction scale of various types of charging piles in each block in the grid is further calculated.

[0170] Parking resource assessment from the perspective of charging facility construction. By comparing the number of public parking spaces required for future charging facility construction with the number of public parking spaces currently registered in each block, it is assessed whether the current public parking resources in each block meet the needs of charging facility construction.

[0171] In addition to planning for fast charging piles and slow charging piles, it is also necessary to plan for super charging piles (stations). Super charging piles refer to charging piles with a power of more than 480kW. A charging station with a super charging pile is called a super charging station.

[0172] Calculation of supercharging station scale. The functional positioning of supercharging stations is similar to that of gas stations, and they mainly serve mid-trip and emergency charging. Considering the shortage of supercharging piles and the alternative role of other charging piles, it can be foreseen that the actual number of vehicles served by supercharging stations configured according to the scale of gas stations will be much lower than the number of vehicles served by gas stations. Therefore, the layout of supercharging stations should focus on improving coverage rather than improving service capabilities. Based on the service level of supercharging stations, the number of vehicles served per unit time, and the current scale of gas stations in the planned area, the scale of supercharging stations in the planned area is preliminarily determined.

[0173] Supercharging station site selection plan. Supercharging stations are mainly deployed in public parking lots, existing gas stations, highway service areas, along important transportation corridors, logistics centers, transportation hubs, squares and green spaces, public buildings and other scenarios. Based on the urban planning land, a preliminary site selection plan for supercharging stations is proposed; in combination with the transportation network, the accessibility of the supercharging station site selection plan is evaluated; based on the accessibility evaluation results, the supercharging station site selection plan is optimized, with appropriate additions and deletions, so that fewer supercharging stations cover a larger area.

[0174] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0175] The second embodiment of the present invention proposes a city public charging facility planning system based on maximum power, based on a city public charging facility planning method based on maximum power, the system comprising:

[0176] A charging behavior characteristic data acquisition module configured to acquire charging behavior characteristic data of electric vehicle users based on a historical data set about electric vehicles in a target city;

[0177] The ownership calculation module is configured to predict the total number of electric vehicles in the target city in the planned year; classify electric vehicles according to their usage characteristics, and predict the ownership of various types of electric vehicles in the target city in the planned year;

[0178] The power demand prediction module is configured to calculate the total power demand of public charging piles for various types of electric vehicles in the planning year based on the charging behavior characteristic data of electric vehicle users and the number of various types of electric vehicles in stock, and combine the annual change in the proportion of public charging piles of different powers in the current year and the planning year to obtain the power demand prediction results of public charging piles of different powers in the planning year;

[0179] The public charging pile power demand division module is configured to divide the planned area into grids, obtain the historical charging data of public charging piles of different powers in each grid, calculate the hourly maximum power consumption in the historical charging data of each grid, and use it as the historical maximum charging demand power of the corresponding grid, and calculate the weight value of the power allocation of each grid. Combined with the power demand forecast results of public charging piles of different powers in the planning year, the power demand of public charging piles in different grids of the planned area is divided;

[0180] The construction scale generation module is configured to classify charging piles according to power and determine the standard piles of charging piles of different power types; obtain the charging ratio of charging piles of different power in the historical charging data of each grid, the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and calculate the construction scale of each type of charging pile in each grid based on the power of the standard pile.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0182] It should be noted that the above embodiment provides a system for planning urban public charging facilities based on maximum power, and only uses the division of the above functional modules as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations of the present invention.

[0183] An electronic device according to a third embodiment of the present invention includes:

[0184] at least one processor; and

[0185] a memory communicatively connected to at least one of the processors; wherein,

[0186] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned urban public charging facility planning method based on maximum power.

[0187] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned urban public charging facility planning method based on maximum power.

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the storage device and processing device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0189] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0190] Reference below Fig.16 , which shows a schematic diagram of the structure of a computer system of a server for implementing the method, system, and device embodiments of the present application. Fig.16 The server shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0191] like Fig.16As shown, the computer system includes a central processing unit (CPU) 1601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1602 or the program loaded from the storage part 1608 to the random access memory (RAM) 1603. In RAM 1603, various programs and data required for system operation are also stored. CPU 1601, ROM 1602 and RAM 1603 are connected to each other through bus 1604. Input / output (I / O) interface 1605 is also connected to bus 1604.

[0192] The following components are connected to the I / O interface 1605: an input section 1606 including a keyboard, a mouse, etc.; an output section 1607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1608 including a hard disk, etc.; and a communication section 1609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1609 performs communication processing via a network such as the Internet. A drive 1610 is also connected to the I / O interface 1605 as needed. A removable medium 1611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1610 as needed so that a computer program read therefrom is installed into the storage section 1608 as needed.

[0193] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1609, and / or installed from the removable medium 1611. When the computer program is executed by the central processing unit (CPU) 1601, the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, an apparatus or a device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, an apparatus or a device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0194] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0195] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0196] The terms "first", "second", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0197] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0198] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for planning urban public charging facilities based on maximum power, characterized in that: The method includes: Based on the historical data set of electric vehicles in the target city, the charging behavior characteristic data of electric vehicle users are obtained; the charging behavior characteristic data of electric vehicle users at least include charging frequency, charging time, fast and slow charging ratio, charging facility selection preference, average daily mileage of electric vehicles, energy consumption per kilometer of electric vehicles, charging efficiency of electric vehicles, peak hour coefficient, redundancy coefficient and charging ratio of public charging facilities; Predict the total number of electric vehicles in the target city in the planning year; classify electric vehicles according to their usage characteristics and predict the number of various types of electric vehicles in the target city in the planning year; Based on the charging behavior characteristics of electric vehicle users and the number of electric vehicles of various types, the total power demand of public charging piles for various types of electric vehicles in the planning year is calculated. Combined with the annual change in the proportion of public charging piles of different powers in the current year and the planning year, the power demand forecast results of public charging piles of different powers in the planning year are obtained; Total power demand of public charging piles G , which is calculated as: ； in, g i For the i Total power demand of public charging piles for electric vehicles; V i For the i The number of electric vehicles in use; l i For the i Average daily mileage of electric vehicles; e i For the i Energy consumption per kilometer of electric vehicles; q i For the i The proportion of electric vehicles using public charging stations; β is the peak hour coefficient, which is calculated based on the proportion of peak hour charging to the total day charging; γ is the redundancy coefficient, which is calculated based on the total charging capacity of electric vehicles in the city, the total installed capacity of public charging piles, and the change in charging pile utilization rate; Divide the planned area into grids, obtain the historical charging data of public charging piles of different powers in each grid, calculate the hourly maximum power consumption in the historical charging data of each grid, and use it as the historical maximum charging demand power of the corresponding grid, and calculate the weight value of the power allocation of each grid. Combined with the power demand forecast results of public charging piles of different powers in the planning year, divide the power demand of public charging piles in different grids of the planned area; The weight value is calculated as follows: The average value of the maximum charging demand power of each grid and its adjacent grids is taken as the pre-processed value of the maximum charging demand power of the grid; Calculate the ratio of the preprocessed value of the maximum charging demand power of each type of charging pile in each grid to the sum of all grids, and use the ratio as the weight value of the grid; Classify charging piles according to power and determine standard piles for charging piles of different power types; obtain the charging ratio of charging piles of different power types in the historical charging data of each grid and the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and calculate the construction scale of each type of charging pile in each grid based on the power of the standard piles.

2. A method for planning urban public charging facilities based on maximum power according to claim 1, characterized in that: The upper and lower limits of the total number of electric vehicles in the target city in the planning year are obtained, and the upper and lower limits of the number of each type of electric vehicles, the upper and lower limits of the total power demand of public charging piles for each type of electric vehicles in the planning year, and the upper and lower limits of the power demand forecast results of public charging piles with different powers in the planning year are calculated. Finally, the upper and lower limits of the demand for public charging piles with different powers in each grid in the area to be planned are calculated.

3. The method for planning urban public charging facilities based on maximum power according to claim 1, characterized in that: The historical maximum charging demand power is preprocessed and used to calculate the weight value of power allocation of each grid. The preprocessing method is as follows: ； in, Q i For the i The pre-processed value of the historical maximum charging demand power of each grid; N For the i The number of adjacent meshes of a mesh. Adjacent meshes are defined as follows: if two meshes have common edges or vertices, then the two meshes are adjacent; Q in For the i The historical maximum charging demand power of the nth adjacent grid of the grid.

4. The method for planning urban public charging facilities based on maximum power according to claim 3, characterized in that: The weight value of each grid power allocation is calculated as follows: ； in, W i For the i The weight value of each grid; Q i For the i The preprocessed value of the historical maximum charging demand power of each grid; M is the total number of grids divided in the planned area; Q m It is the preprocessed value of the historical maximum charging demand power of the mth grid.

5. The method for planning urban public charging facilities based on maximum power according to claim 1, characterized in that: Based on the number of public parking spaces required for the construction scale of various types of charging piles in the grid and the number of existing registered public parking spaces in each block, evaluate whether the existing public parking resources in each block can meet the needs of charging facility construction.

6. A system for planning urban public charging facilities based on maximum power, based on a method for planning urban public charging facilities based on maximum power as claimed in any one of claims 1 to 5, characterized in that: The system includes: A charging behavior characteristic data acquisition module is configured to acquire charging behavior characteristic data of electric vehicle users based on a historical data set of electric vehicles in a target city; the charging behavior characteristic data of electric vehicle users at least includes charging frequency, charging time, fast and slow charging ratio, charging facility selection preference, average daily mileage of electric vehicles, energy consumption per kilometer of electric vehicles, charging efficiency of electric vehicles, peak hour coefficient, redundancy coefficient and charging ratio of public charging facilities; The ownership calculation module is configured to predict the total number of electric vehicles in the target city in the planned year; classify electric vehicles according to their usage characteristics, and predict the ownership of various types of electric vehicles in the target city in the planned year; The power demand prediction module is configured to calculate the total power demand of public charging piles for various types of electric vehicles in the planning year based on the charging behavior characteristic data of electric vehicle users and the number of various types of electric vehicles in stock, and combine the annual change in the proportion of public charging piles of different powers in the current year and the planning year to obtain the power demand prediction results of public charging piles of different powers in the planning year; Total power demand of public charging piles G , which is calculated as: ； in, g i For the i Total power demand of public charging piles for electric vehicles; V i For the i The number of electric vehicles in use; l i For the i Average daily mileage of electric vehicles; e i For the i Energy consumption per kilometer of electric vehicles; q i For the i The proportion of electric vehicles using public charging stations; β is the peak hour coefficient, which is calculated based on the proportion of peak hour charging to the total day charging; γ is the redundancy coefficient, which is calculated based on the total charging capacity of electric vehicles in the city, the total installed capacity of public charging piles, and the change in charging pile utilization rate; The public charging pile power demand division module is configured to divide the planned area into grids, obtain the historical charging data of public charging piles of different powers in each grid, calculate the hourly maximum power consumption in the historical charging data of each grid, and use it as the historical maximum charging demand power of the corresponding grid, and calculate the weight value of the power allocation of each grid. Combined with the power demand forecast results of public charging piles of different powers in the planning year, the power demand of public charging piles in different grids of the planned area is divided; The weight value is calculated as follows: The average value of the maximum charging demand power of each grid and its adjacent grids is taken as the pre-processed value of the maximum charging demand power of the grid; Calculate the ratio of the preprocessed value of the maximum charging demand power of each type of charging pile in each grid to the sum of all grids, and use the ratio as the weight value of the grid; The construction scale generation module is configured to classify charging piles according to power and determine the standard piles of charging piles of different power types; obtain the charging ratio of charging piles of different power in the historical charging data of each grid, the ratio of different land use types in the planning of each grid, determine the power demand data of different types of charging piles in each grid, and calculate the construction scale of each type of charging pile in each grid based on the power of the standard pile.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement a method for planning urban public charging facilities based on maximum power as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the urban public charging facility planning method based on maximum power as described in any one of claims 1-5.

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