Charging station planning method, device, equipment and medium

Abstract

The invention discloses a charging station planning method, apparatus and device, and a medium. The charging station planning method comprises the steps of obtaining planning related information of a to-be-planned area; according to the planning related information, grid division is carried out on the to-be-planned area, and the electric vehicle predicted inventory under each grid area is determined; optimization variable information is determined according to the predicted inventory and planning related information of each electric vehicle, and the optimization variable information comprises a site selection matrix, a construction scale matrix, a vehicle side charging behavior matrix and grid side charging behavior information of alternative charging stations; according to the optimization variable information and each grid region, constructing a charging station optimization planning model; and solving the charging station optimization planning model, and determining optimal charging station planning information of the to-be-planned area. And through multi-dimensional charging behavior tracking of a vehicle side and a grid side, constructing a charging station optimization planning model so as to determine optimal charging station planning information. And the timeliness and rationality of charging station planning are improved.

Description

Technical Field

[0001] The present invention relates to the field of charging infrastructure planning, and in particular to a charging station planning method, device, equipment and medium. Background Art

[0002] Against the backdrop of increasingly serious global environmental pollution and increasingly prominent resource shortages, electric vehicles are experiencing rapid development due to their green and low-carbon economic advantages. With the accelerated promotion and popularization of electric vehicles, the necessity of scientifically planning and building public charging infrastructure is becoming increasingly apparent. The research on the planning and layout of charging facilities can be summarized as a two-way selection problem of charging facilities and demand points. That is, while considering various factors affecting site selection, the research scope has gradually expanded to multi-objective planning such as cost, land, transportation, electricity, and user demand.

[0003] The commonly used charging facility layout planning methods can be mainly divided into three categories: spatial analysis method (GSA), multi-criteria comprehensive decision-making method (MCDM) and mathematical programming method (MP). Among them, the spatial analysis method is to layout charging facilities according to the spatial distribution form of characteristic index values, and to plan the layout of charging facilities by using the spatial analysis method.

[0004] However, the shortcoming of this spatial analysis method is that it cannot collaboratively consider the impact of multiple factors on the planning scheme. The multi-index comprehensive decision-making rule can support site planning and site selection that considers multiple characteristic indicators at the same time, but this method only stops at the problem of being unable to intuitively quantify the benefits of the planning scheme. Existing research on charging facility planning based on mathematical programming methods mostly analyzes from objective dimensions such as cost, facility scale and energy consumption, and lacks analysis that considers the uncertain behavior of vehicles when charging. Summary of the invention

[0005] The present invention provides a charging station planning method, device, equipment and medium to achieve reasonable planning of charging facilities.

[0006] According to a first aspect of the present invention, a charging station planning method is provided, comprising:

[0007] Obtain planning-related information for the area to be planned;

[0008] Divide the area to be planned into grids according to the planning-related information, and determine the predicted number of electric vehicles in each grid area;

[0009] Determine optimization variable information according to the predicted number of electric vehicles and the planning related information, wherein the optimization variable information includes a site selection matrix of candidate charging stations, a construction scale matrix, a vehicle-side charging behavior matrix, and grid-side charging behavior information;

[0010] Constructing a charging station optimization planning model according to the optimization variable information and each of the grid areas;

[0011] The charging station optimization planning model is solved to determine the optimal charging station planning information for the area to be planned.

[0012] According to a second aspect of the present invention, there is provided a charging station planning device, comprising:

[0013] An information acquisition module is used to obtain planning-related information of the area to be planned;

[0014] A vehicle ownership prediction module, used to divide the area to be planned into grids according to the planning related information, and determine the predicted vehicle ownership in each grid area;

[0015] An information determination module, for determining optimization variable information according to the predicted number of electric vehicles and the planning related information, wherein the optimization variable information includes a site selection matrix of candidate charging stations, a construction scale matrix, a vehicle-side charging behavior matrix, and grid-side charging behavior information;

[0016] A model building module, used to build a charging station optimization planning model according to the optimization variable information and each of the grid areas;

[0017] The result determination module is used to solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned.

[0018] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the charging station planning method described in any embodiment of the present invention.

[0022] According to 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 enable a processor to implement the charging station planning method described in any embodiment of the present invention when executed.

[0023] According to a fifth aspect of the present invention, an embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the charging station planning method of any embodiment of the present invention is implemented.

[0024] The technical solution of the embodiment of the present invention is to obtain planning-related information of the area to be planned; divide the area to be planned into grids according to the planning-related information, and determine the predicted number of electric vehicles in each grid area; determine the optimization variable information according to the predicted number of electric vehicles and planning-related information, and the optimization variable information includes the site selection matrix, construction scale matrix, vehicle-side charging behavior matrix and grid-side charging behavior information of the alternative charging stations; construct a charging station optimization planning model according to the optimization variable information and each grid area; solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned. Through multi-dimensional charging behavior tracking on the vehicle side and the grid side, a charging station optimization planning model is constructed to determine the optimal charging station planning information. The timeliness and rationality of charging station planning are improved.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flowchart of a charging station planning method provided according to Embodiment 1 of the present invention;

[0028] Figure 2 is a flow chart of a charging station planning method provided according to Embodiment 2 of the present invention;

[0029] Figure 3 is a structural schematic diagram of a charging station planning device provided according to Embodiment 3 of the present invention;

[0030] Figure 4 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1 A flowchart of a charging station planning method is provided for the first embodiment of the present invention. This embodiment is applicable to the setting planning of charging stations. The method can be executed by a charging station planning device. The charging station planning device can be implemented in the form of hardware and / or software. The charging station planning device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0035] S110: Obtain planning-related information for the area to be planned.

[0036] In this embodiment, the area to be planned can be understood as a region for the construction of charging stations. Planning related information can be understood as information that is helpful for planning decisions, and can be information related to planning costs and personnel and vehicles in the region.

[0037] Specifically, the processor may obtain planning-related information of the area to be planned that is preset by relevant personnel.

[0038] S120. Divide the planned area into grids according to the planning-related information, and determine the predicted number of electric vehicles in each grid area.

[0039] In this embodiment, the grid area can be understood as each grid result after the area to be planned is divided into a finer grid form. The predicted number of electric vehicles can be understood as the number of electric vehicles predicted in each grid area for the next year or multiple years.

[0040] Specifically, the processor can determine the overall predicted ownership of the area to be planned based on data related to the predicted electric vehicle ownership in the planning-related information, such as car sales, ownership and population data in the area to be planned, and then divide the area to be planned into grids using road-related data in the planning-related information, and split the overall predicted ownership according to the population distribution in each grid area to determine the predicted electric vehicle ownership in each grid area.

[0041] S130. Determine optimization variable information based on the predicted number of electric vehicles and planning-related information. The optimization variable information includes a site selection matrix of candidate charging stations, a construction scale matrix, a vehicle-side charging behavior matrix, and grid-side charging behavior information.

[0042] In this embodiment, the optimization variable information can be understood as the variables that need to be determined by the model. The alternative charging station can be understood as the pre-selected charging station. The site selection matrix can be understood as the charging station site in matrix form. The construction scale matrix can be understood as the construction scale of the alternative charging station in matrix form, such as the number of charging piles in each alternative charging station. The vehicle-side charging behavior matrix can be understood as the charging behavior determined from each vehicle end, such as the charging time. The grid-side charging behavior matrix information can be understood as the charging behavior of all vehicles in the grid area determined in units of each grid area, such as the number of charging vehicles in each grid.

[0043] Specifically, the processor can determine the site selection matrix based on the site location data of the alternative charging stations in the planning-related information, determine the construction scale matrix through the site area and surrounding conditions in the planning-related information, determine the vehicle-side charging behavior matrix through the vehicle charging behavior of existing electric vehicles in the planning-related information, and determine the grid-side charging behavior information through the charging behavior in each grid area in the planning-related information.

[0044] S140: Construct a charging station optimization planning model based on the optimization variable information and each grid area.

[0045] In this embodiment, the charging optimization planning model can be understood as an optimization model set for the area to be planned, for example, it can be a multivariate nonlinear optimization model.

[0046] Specifically, the processor can set optimization goals for each grid area based on the optimization variable information. For example, according to the cost of charging station construction, operation and maintenance, and charging behavior, the total cost of the area to be planned is determined, and the minimum total cost is used as the optimization goal. By optimizing the variable information and constructing constraints according to the charging pile constraints, vehicle side constraints, and grid side constraints, the charging station optimization planning model is obtained.

[0047] S150: Solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned.

[0048] In this embodiment, the optimal charging station planning information can be understood as the optimal charging station planning situation, such as the final charging station determined from the candidate charging stations, and the planned number of charging piles for each final charging station.

[0049] Specifically, the processor may solve the charging station optimization planning model according to a pre-selected solver and termination condition to obtain optimal charging station planning information for the area to be planned.

[0050] For example, the termination condition may be set to a tolerance Gap between the upper and lower bounds = 5%, and the charging station optimization planning model is solved using the GUROBI solver to obtain the optimal charging station location and scale plan, where Gap = (current optimal solution - known optimal solution) / known optimal solution * 100%.

[0051] The technical solution of the embodiment of the present invention is to obtain planning-related information of the area to be planned; divide the area to be planned into grids according to the planning-related information, and determine the predicted number of electric vehicles in each grid area; determine the optimization variable information according to the predicted number of electric vehicles and planning-related information, and the optimization variable information includes the site selection matrix, construction scale matrix, vehicle-side charging behavior matrix and grid-side charging behavior information of the alternative charging stations; construct a charging station optimization planning model according to the optimization variable information and each grid area; solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned. Through multi-dimensional charging behavior tracking on the vehicle side and the grid side, a charging station optimization planning model is constructed to determine the optimal charging station planning information. The timeliness and rationality of charging station planning are improved.

[0052] Embodiment 2

[0053] Figure 2 This is a flow chart of a charging station planning method provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above embodiment. Figure 2 As shown, the method includes:

[0054] S201: Obtain planning-related information for the area to be planned.

[0055] S202. When the planning-related information includes sales volume forecast values ​​and penetration rates, determine the target year forecast inventory of the area to be planned based on the sales volume forecast values ​​and penetration rates.

[0056] In this embodiment, the sales volume forecast value can be understood as the predicted sales volume of electric vehicles in the area to be planned in the next year or several years. The penetration rate can be understood as the proportion of electric vehicle sales in a specific time period to the total vehicle sales. The target year forecast stock can be understood as the predicted stock of electric vehicles in the area to be planned in the next year or several years.

[0057] Specifically, when the planning-related information includes sales volume forecast values ​​and penetration rates, the processor can determine the target year forecast inventory of the area to be planned based on the sales volume forecast values ​​and penetration rates.

[0058] For example, starting with the forecast of annual automobile sales and the penetration rate of electric vehicles, we can obtain the forecast of annual electric vehicle sales, and add them together to obtain the target year's electric vehicle ownership, as shown in the following formula.

[0059]

[0060] In the formula, and are the target year predicted holdings in the i-th year and the current year respectively; and k j are the automobile sales volume and the corresponding electric vehicle sales proportion in the jth year; is the number of electric vehicles retired in year j.

[0061] S203. When the population forecast value and the number of cars per capita are included in the planning related information, the forecast number of cars in the target year is determined based on the population forecast value and the number of cars per capita.

[0062] In this embodiment, the population forecast value can be understood as the predicted population in the next year or many years. The population in the planned area over the years and its annual growth rate, as well as the number of cars per capita, can be used as the basis to estimate the annual growth rate of the population in the target year based on the population development of the area, and obtain the population forecast value in the target year of the planned area. The number of cars per capita can be understood as the number of cars owned by each person in the next year or many years.

[0063] Specifically, when the population forecast and per capita car ownership are included in the planning related information, the target year forecast is determined based on the population forecast and per capita car ownership. In summary, the maturity, data credibility, and objective rationality of the two forecasting methods are considered, and the forecast results of the number of electric vehicles that meet the actual scenarios of the region are selected in combination with the planning regional policy objectives.

[0064] For example, the processor may estimate the per capita car ownership in the target year of the region by referring to the prediction results of the future per capita car ownership in the automobile industry documents and combining the automobile development situation in the region, as shown in the following formula:

[0065]

[0066] In the formula, The predicted stock for the target year; P 0 is the population in the current year; r p is the annual growth rate of population in the future; r ei is the number of cars per capita in year i.

[0067] S204, dividing the planned area into grids according to the road population information in the planning related information, and determining the grid areas and grid levels.

[0068] In this embodiment, the road population information can be understood as information related to the road conditions and population distribution in the area to be planned, such as a trunk road network or municipal units, a heat map of urban population distribution, etc.

[0069] Specifically, the processor can divide the planning area into grids according to the road information in the road population information in the planning-related information (such as the trunk road network or municipal unit information), determine the grid areas, and estimate the distribution of the number of electric vehicles in the grid based on the urban population distribution heat map. Based on the predicted total number of predicted ownership in the target year, and taking into account factors such as the permanent population distribution in each grid area and the thermal distribution of the population during peak hours, the processor can hierarchically estimate the number of electric vehicles in each grid area to obtain a grid level.

[0070] S205. Determine the predicted number of electric vehicles in each grid area according to each grid level and the predicted number of electric vehicles in the target year.

[0071] In this embodiment, the predicted number of electric vehicles can be understood as the predicted number of electric vehicles at each grid area level.

[0072] Specifically, the processor can set the proportion of each grid level according to each grid level, and determine the predicted electric vehicle ownership in each grid area through comprehensive calculation of the proportion of each grid area and the predicted ownership in the target year.

[0073] For example, considering factors such as the permanent population distribution of each grid and the thermal distribution of the population during peak hours, the processor can divide the grid levels into A, B, C, and D levels, and determine that the proportions of the four levels corresponding to the number of vehicles owned can be 1:3:5:7. The predicted number of electric vehicles corresponding to each grid area can be determined by multiplying the proportion by the predicted number of vehicles owned in the target year.

[0074] Through such a setting, in terms of the prediction of the number of electric vehicles in the grid, it is considered that the factors that determine the increase in the number of electric vehicles in the actual scenario are complex and numerous, such as social and economic development, policies, etc. Moreover, the scale of electric vehicles is currently in a stage of rapid popularization. It is not feasible to predict the growth trend of the number of electric vehicles from a time series perspective based on historical data alone. Therefore, the present invention comprehensively considers the feasibility of the method and the convenience of data collection, and proposes a method for predicting the number of electric vehicles that comprehensively considers automobile sales data and per capita automobile ownership. On the basis of using automobile sales data and per capita automobile ownership data to predict the number of electric vehicles, the maturity, data credibility, objective rationality and other factors of the two prediction methods are considered, and the policy objectives of the planning area are combined to select the prediction results of the number of electric vehicles that meet the actual scenarios of the region. In order to accurately and maximally restore the differences in the distribution of electric vehicles between grids in the real county-level city scenario and verify the effectiveness of the method proposed in the present invention, it is necessary to estimate the distribution of the number of electric vehicles in the grid. In order to truly reflect the differences in the number of electric vehicles in different grids in the city, this method, on the basis of determining the total number of electric vehicles predicted in the planning target year, considers factors such as the permanent population distribution of each grid and the thermal distribution of the population during peak hours, and divides the estimated value of the electric vehicle ownership of each grid into several levels, and estimates the number of electric vehicles in each grid accordingly.

[0075] S206: Determine a site selection matrix for the candidate charging stations according to the site coordinates of the candidate charging stations in the planning-related information.

[0076] Charging station sites are usually located in shopping malls, office building parking lots or dedicated land. There are usually alternative sites before planning. Charging facility planning is usually based on a certain optimization goal, and the optimal site is selected from the alternative sites. Therefore, the optimization variables of the optimization model are usually the site and construction scale of the alternative charging station.

[0077] In this embodiment, the station site coordinates can be understood as the longitude and latitude coordinate addresses of each candidate charging station. The site selection matrix can be understood as a matrix including the station site locations of all candidate charging stations.

[0078] Specifically, the processor may determine a site selection matrix of the candidate charging stations according to the site coordinates of the candidate charging stations in the planning related information.

[0079] Exemplarily, the site is represented by longitude and latitude coordinates, as follows:

[0080] stationLoc i =[xStation i yStation i ]

[0081] Where: stationLoc i is the location of the i-th candidate charging station; xStation i is the longitude coordinate of the i-th candidate charging station; yStation i is the latitude coordinate of the i-th charging station. In the absence of alternative station sites, xStation i and yStation i The value of can be sampled from the average distribution. It is also possible to first optimize the large grid where the charging station is located according to the coordinates of the larger grid, and then divide the large grid into smaller grids, and formulate a more refined charging station layout plan from the small grids.

[0082] S207. Determine the construction scale matrix of each candidate charging station based on the site area and surrounding power grid conditions in the planning-related information.

[0083] In this embodiment, the site area can be understood as the pre-set floor area of ​​each candidate charging station. The surrounding power grid conditions can be understood as the location of the power grid around each candidate charging station, and the surrounding power grid conditions directly determine the construction scale of each candidate charging station.

[0084] Specifically, the processor can determine the construction scale matrix of each alternative charging station based on the site area and surrounding power grid conditions in the planning-related information.

[0085] Exemplarily, the construction scale matrix of the candidate charging stations can be determined by the following formula:

[0086]

[0087] Where: Charging_P_Space_NUM is the construction scale matrix of the alternative charging stations; Charging_P_Space_NUM numStation is the construction scale of the numStationth charging station.

[0088] S208. Determine grid-side charging behavior information according to each candidate charging station and each grid area.

[0089] Specifically, the processor may comprehensively determine the grid-side charging behavior information according to the number of charging vehicles at each alternative charging station and the driving distance between each grid area and the alternative charging station.

[0090] Further, based on the above embodiment, the step of determining the grid-side charging behavior information according to each candidate charging station and each grid area can be refined as follows:

[0091] According to the equivalent coordinates of each grid area and the coordinates of each station site, the driving distance from each grid area to each alternative charging station is determined; according to each driving distance, the grid-side driving distance matrix is ​​determined; according to the number of charging vehicles in each grid area in the whole time period in the planning related information, the grid-side charging behavior matrix is ​​determined; the grid-side driving distance matrix and the grid-side charging behavior are used as the grid-side charging behavior information.

[0092] In this embodiment, the equivalent coordinates can be understood as representing each grid area in the form of a central coordinate. The driving distance can be understood as the distance from the grid to different alternative charging stations. The grid-side driving distance matrix can be understood as representing the driving distance in the form of a matrix. The full time period can be understood as the overall time range, such as all the moments of a day, or all the moments of a year, etc.

[0093] Specifically, the processor can determine the driving distance from each grid area to each alternative charging station based on the equivalent coordinates of each grid area and the coordinates of each station site. The processor can determine the grid-side driving distance matrix based on each driving distance; the processor can determine the grid-side charging behavior matrix based on the number of charging vehicles in each grid area in all time periods in the planning-related information; the processor can use the grid-side driving distance matrix and the grid-side charging behavior as grid-side charging behavior information.

[0094] For example, the grid-side driving distance matrix can be expressed as:

[0095]

[0096] Where: dist numBlock_numStation It is the driving distance from the numBlockth grid to the numStationth alternative charging station.

[0097] The grid-side charging behavior matrix can be expressed as:

[0098] Set the grid-side charging behavior matrix ChargeCar_Block_Station (three-dimensional matrix) as follows:

[0099] Where: ChargeCar_Block_Station (i,j,t) It represents the number of vehicles belonging to the jth grid that are charging at the i-th charging station at time t.

[0100] S209: Determine a vehicle-side charging behavior matrix according to the charging status of each vehicle in the planning-related information at all times.

[0101] In this embodiment, the charging status can be understood as the charging time of each vehicle, etc.

[0102] Specifically, the processor can determine the vehicle-side charging behavior matrix based on the charging status of each vehicle in the planning-related information at all times.

[0103] Exemplarily, the vehicle-side charging behavior matrix Charge_Mat (0-1 matrix) can be expressed by the following formula:

[0104] In order to determine the charging status of each vehicle at all times, the full-time charging status matrix is ​​set.

[0105]

[0106] Where: Charge_Mat i,j is the charging status of the i-th vehicle at the j-th moment, 0 means not charged and 1 means charged.

[0107] S210. Using the site selection matrix, construction scale matrix, grid-side charging behavior matrix and vehicle charging behavior matrix as optimization variable information.

[0108] S211. Determine the overall cost based on the optimization variable information and planning related information.

[0109] In this embodiment, the overall cost can be understood as the sum of all cost factors involved in building a charging station in the planned area.

[0110] Specifically, the planning of charging stations should not only consider the maximization of the interests of charging station operators, but also consider meeting the charging needs of electric vehicle users, charging costs, and access conditions to the distribution network. As for grid access, since the situation is diverse and does not directly affect the site selection of charging facilities, the model does not consider distribution network access for the time being. Instead, a distribution network access plan will be formulated after the planning of charging stations is completed to verify the grid capacity. The processor can determine the overall cost based on the construction and charging costs in the planning-related information combined with the optimization variable information.

[0111] Further, based on the above embodiment, the steps of determining the overall cost according to the optimization variable information and planning related information can be refined as follows:

[0112] Determine the construction cost of the charging station based on the first cost information in the site selection matrix, construction scale matrix and planning-related information; determine the operation and maintenance cost of the charging station based on the second cost information in the charging station construction cost and planning-related information; determine the electric vehicle charging cost based on the third cost information in the grid-side charging behavior information and planning-related information; determine the electric vehicle charging driving cost based on the fourth cost information in the grid-side charging behavior information and planning-related information; determine the overall cost based on the charging station construction cost, the charging station operation and maintenance cost, the electric vehicle charging cost and the electric vehicle charging driving cost.

[0113] In this embodiment, the first cost information can be understood as the cost information related to the construction of the charging station, for example, it may include the unit price of the charging pile, the construction cost, the site fee and the additional estimated distribution and transformation cost. The construction cost of the charging station can be understood as the total cost information related to the construction of the charging station. The second cost information can be understood as the cost related to operation and maintenance, for example, it may include the ratio between the construction cost and the operation and maintenance cost. The operation and maintenance cost of the charging station can be understood as the cost of freight for each year or multiple years. The third cost information can be understood as the driving cost of the electric vehicle. The charging cost of an electric vehicle can be understood as the cost required to charge an electric vehicle. The fourth cost information can be understood as the cost associated with the electric vehicle traveling to the charging station. The charging driving cost of an electric vehicle can be understood as the cost of the power loss generated by the electric vehicle during the charging journey.

[0114] Specifically, the processor can determine the construction cost of the charging station based on whether each candidate charging station in the site selection matrix is ​​selected, combined with the number of charging piles in the corresponding construction scale matrix and the first cost information in the planning-related information. The processor can determine the operation and maintenance cost of the charging station based on the proportion of operation and maintenance costs in the second cost information in the charging station construction cost and planning-related information. The annual driving cost of users on the charging journey mainly includes the cost of idle power loss generated by users on the charging journey. The processor can determine the charging cost of electric vehicles based on the third cost information in the grid-side charging behavior information and planning-related information; determine the charging driving cost of electric vehicles based on the fourth cost information in the grid-side charging behavior information and planning-related information; determine the overall cost based on the charging station construction cost, the charging station operation and maintenance cost, the electric vehicle charging cost and the electric vehicle charging driving cost.

[0115] For example, (1) the construction cost of the charging station C 1 It can be determined by the following formula:

[0116]

[0117] Where: selStation is the optimization variable, representing whether the alternative charging station is selected, which is a 0-1 variable, 0 represents unselected, and 1 represents selected; costStation is the construction cost matrix of the charging station (known); r 0 is the discount rate; z is the operating life; costStation i is the estimated construction cost of the i-th charging station, μ is the estimated distribution transformer cost, ν is the estimated charging pile unit price, and f add This is an additional venue fee (determined by the actual venue conditions).

[0118] selStation = [selStation 1selStation 2 L selStation numStation ]

[0119] costStation = [costStation 1 costStation 2 L costStation numStation ]

[0120] costStation i =μ*selStation_NUM+

[0121] ν*Charging_P_Space_NUM+f add

[0122] (2) Charging station operation and maintenance cost C 2 It can be determined by the following formula:

[0123] The operation and maintenance cost of the charging station can be calculated by multiplying the construction cost of the charging station by the fixed operation and maintenance cost ratio in the second cost information:

[0124] C 2 =selStation*costStation·β

[0125] Where: β is the annual operation and maintenance cost ratio.

[0126] (3) Annual charging cost of electric vehicles C 3 It can be determined by the following formula

[0127]

[0128] Where: Cost_EVCar_perKM is the driving cost per kilometer of electric vehicles, Time is the algorithm simulation duration, for example, the electric vehicle is in grid A, charging station 3, then dist is the driving distance between grid A and charging station 3.

[0129] (4) Electric vehicle driving cost C 4 It can be determined by the following formula:

[0130]

[0131] Where: selBlkToSta ij is an optimization variable, representing whether the electric vehicle in grid i goes to charging station j to charge. It is a 0-1 variable, 0 represents no, 1 represents yes; Cost_perKM is the driving cost per kilometer of the electric vehicle; dist ij is the distance from grid i to charging station j.

[0132] S212. Taking the minimization of the overall cost as the optimization goal, construct an objective function.

[0133] Specifically, the processor may take minimizing the overall cost as an optimization goal to obtain an objective function.

[0134] For example, using the above symbolic definition, the objective function can be determined by the following formula:

[0135] minC=C 1 +C 2 +C 3 +C 4

[0136] S213. Construct constraint conditions according to the optimization variable information and each grid area.

[0137] Specifically, the processor can construct constraints based on the optimization variable information and grid area, which may include basic constraints on charging piles (charging station planning parking space constraints and total parking space constraints), electric vehicle side charging behavior constraints (each electric vehicle needs to choose a charging station for charging within a certain period of time), and electric vehicle grid side charging behavior constraints (constraining the overall behavior of the number of vehicles in a certain grid charging at each charging station), etc.

[0138] Further, based on the above embodiment, the step of constructing constraint conditions according to the optimization variable information and each grid area can be refined as follows:

[0139] According to the optimization variable information, the basic constraints of the charging pile are determined; according to the vehicle side charging behavior matrix and the preset charging cycle, the vehicle side charging behavior constraints are determined; according to the grid side charging behavior information and the preset grid vehicle upper limit, the grid side charging behavior constraints are determined; the basic constraints of the charging pile, the vehicle side charging behavior constraints and the grid side charging behavior constraints are used as constraints.

[0140] In this embodiment, the basic constraints of the charging pile can be understood as the constraints determined at each charging pile angle. The preset charging cycle can be understood as the average charging cycle of each electric vehicle, such as charging once every 72 hours. The vehicle-side charging behavior constraints can be understood as the constraints on the charging behavior of each vehicle at all times, such as the number of charging times. The grid-side charging behavior constraints can be understood as the constraints on the charging behavior of all electric vehicles in each grid area, with the grid side as the unit. The preset grid vehicle upper limit can be understood as the upper limit of the number of all electric vehicles in a grid area.

[0141] Specifically, the processor can determine the basic constraints of the charging pile based on the optimization variable information, which may include the constraints on the planned parking spaces of the charging station and the total number of parking spaces. The processor can determine the vehicle-side charging behavior constraints that each electric vehicle needs to select a charging station for charging within a certain period of time based on the vehicle-side charging behavior matrix and the preset charging cycle. The processor can determine the grid-side charging behavior constraints that constrain the overall behavior of the number of vehicles in a certain grid that charge at each charging station based on the grid-side charging behavior information and the preset grid vehicle upper limit. The processor can use the basic constraints of the charging pile, the vehicle-side charging behavior constraints, and the grid-side charging behavior constraints as constraints.

[0142] Exemplarily, the constraint condition can be determined by the following formula:

[0143] The basic constraints of charging piles include the constraints on the planned parking spaces of charging stations and the total number of parking spaces:

[0144] min(Charging_P_Space_NUM)≥0

[0145] sum(Charging_P_Space_NUM)≥Car_Num_Sum*x

[0146] max(Charge_Mat_q)≤sum(Charging_P_Space_NUM)

[0147] Among them, Charge_Mat_q is the number of cars being charged at each moment.

[0148]

[0149] The vehicle-side charging behavior constraints of electric vehicles are:

[0150] It means that each electric car needs to choose a charging station to charge within a certain period of time. Assume that the average charging cycle of electric vehicles is 72 hours.

[0151] Charge_Mat_p(i)==Time / 72

[0152] Charge_Mat_q(i)≤Charging_P_Space_NUM

[0153] Among them, Charge_Mat_p is the number of times each car is charged in the whole period.

[0154]

[0155] The grid-side charging behavior constraints of electric vehicles are:

[0156] It represents the overall behavior of the number of vehicles in a certain grid charging at each charging station, including the constraint on the number of vehicles charged at each moment, the upper limit constraint on the number of vehicles in the grid, and the total number of vehicles in each grid.

[0157] ChargeCar_Block_Station (j,k,i) ≥0

[0158]

[0159]

[0160]

[0161]

[0162] Where: ChargeCar_Block_Station (i,j,t) It represents the number of vehicles belonging to the jth grid that are charging at the i-th charging station at time t.

[0163] S214: Determine the charging station optimization planning model according to the objective function and constraint conditions.

[0164] S215: Solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned.

[0165] For example, according to the current level of industry status, the vehicle data in the simulation are as follows: battery capacity 80kWh, single charge 60kWh, power consumption 17kWh / km per 100 kilometers; the initial SOC obeys the normal distribution N(0.5, 0.2); considering the driver's mileage anxiety, the SOC threshold that generates charging demand is set to 0.2.

[0166] The upper and lower limits of the number of fast charging piles for each charging station are derived from the actual parking spaces and the upper limit of the power capacity and used as inputs; the charging power Pc is set to akW; the charging efficiency ηc is set to 0.95; the construction cost of the charging station is b yuan / unit of distribution transformer and c yuan / unit of DC fast charging pile (including site construction costs); the operation and maintenance cost of a single charging pile (2 guns) is estimated to be d yuan / year. Considering that the factors that determine the increase in the number of electric vehicles in actual scenarios are complex and numerous, such as social and economic development, policies, etc. Moreover, the scale of electric vehicles is currently in a stage of rapid popularization. It is not feasible to predict the growth trend of the number of electric vehicles from a time series perspective based on historical data alone. Therefore, the present invention comprehensively considers the feasibility of the method and the convenience of data collection, and proposes a method for predicting the number of electric vehicles that comprehensively considers automobile sales data and per capita automobile ownership. In addition, the charging infrastructure planning method proposed by the present invention that considers multi-dimensional charging behavior tracking constrains the charging behavior of electric vehicles from two dimensions: a single vehicle and the grid to which the vehicle belongs, establishes an identity tag corresponding to each vehicle, including the initial grid to which the vehicle belongs and the vehicle SOC value, etc., to solve the optimization problem of county and township charging infrastructure.

[0167] The technical solution of the embodiment of the present invention predicts the number of electric vehicles in the target year by using a method for predicting the number of electric vehicles based on comprehensive automobile sales data and per capita automobile ownership; divides the entire area to be planned into grids based on trunk road networks or municipal units, and estimates the distribution of the number of electric vehicles in the grid based on the urban population distribution heat map; constructs a multivariate nonlinear optimization model with the minimum social cost as the optimization target, constructs a matrix from the single vehicle side dimension to describe the vehicle-level charging behavior at all times, and constructs a three-dimensional matrix from the grid side dimension to describe the charging behavior of the vehicles belonging to the grid at all times, constructs constraints through charging piles, vehicle-side charging behavior and grid-side charging behavior, takes the above matrix as the optimization variable, and takes the optimal social cost as the optimization target. Combined with the constraints, a charging station optimization planning model is constructed, which is more in line with the regional characteristics of the area to be planned, and provides a basis for the accurate planning of subsequent charging stations. The solver is called to solve the optimization model to obtain the optimal charging station planning information for the area to be planned. The timeliness and rationality of charging station planning are improved.

[0168] Embodiment 3

[0169] Figure 3 This is a schematic diagram of the structure of a charging station planning device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an information acquisition module 31, an inventory prediction module 32, an information determination module 33, a model construction module 34 and a result determination module 35.

[0170] An information acquisition module 31 is used to acquire planning related information of the area to be planned;

[0171] The vehicle ownership prediction module 32 is used to divide the area to be planned into grids according to the planning related information, and determine the predicted vehicle ownership in each grid area;

[0172] An information determination module 33 is used to determine the optimization variable information according to the predicted number of electric vehicles and the planning related information, wherein the optimization variable information includes the site selection matrix of the candidate charging stations, the construction scale matrix, the vehicle-side charging behavior matrix and the grid-side charging behavior information;

[0173] A model building module 34, used to build a charging station optimization planning model according to the optimization variable information and each of the grid areas;

[0174] The result determination module 35 is used to solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned.

[0175] The technical solution of the embodiment of the present invention is to obtain planning-related information of the area to be planned; divide the area to be planned into grids according to the planning-related information, and determine the predicted number of electric vehicles in each grid area; determine the optimization variable information according to the predicted number of electric vehicles and planning-related information, and the optimization variable information includes the site selection matrix, construction scale matrix, vehicle-side charging behavior matrix and grid-side charging behavior information of the alternative charging stations; construct a charging station optimization planning model according to the optimization variable information and each grid area; solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned. Through multi-dimensional charging behavior tracking on the vehicle side and the grid side, a charging station optimization planning model is constructed to determine the optimal charging station planning information. The timeliness and rationality of charging station planning are improved.

[0176] Furthermore, the vehicle inventory prediction module 32 is specifically used for:

[0177] When the planning related information includes a sales volume forecast value and a penetration rate, determining the target annual forecast holdings of the area to be planned according to the sales volume forecast value and the penetration rate;

[0178] When the population forecast value and the number of cars per capita in the planning related information are present, the target year forecast ownership is determined based on the population forecast value and the number of cars per capita;

[0179] Divide the area to be planned into grids according to the road population information in the planning related information, and determine the grid areas and grid levels;

[0180] The predicted number of electric vehicles in each grid area is determined based on each grid level and the predicted number of electric vehicles in the target year.

[0181] Furthermore, the information determination module 33 includes:

[0182] A first determining unit, configured to determine a site selection matrix of candidate charging stations according to the site coordinates of the candidate charging stations in the planning related information;

[0183] A second determining unit is used to determine the construction scale matrix of each of the candidate charging stations according to the site area and surrounding power grid conditions in the planning related information;

[0184] A third determining unit, configured to determine grid-side charging behavior information according to each of the candidate charging stations and each of the grid areas;

[0185] A fourth determining unit, configured to determine a vehicle-side charging behavior matrix according to the charging status of each vehicle in the planning-related information at all times;

[0186] The fifth determination unit is used to use the site selection matrix, the construction scale matrix, the grid-side charging behavior matrix and the vehicle charging behavior matrix as optimization variable information.

[0187] The third determining unit is specifically used for:

[0188] Determining the driving distance from each of the grid areas to each of the candidate charging stations according to the equivalent coordinates of each of the grid areas and the coordinates of each of the station sites;

[0189] Determining a grid-side driving distance matrix according to each of the driving distances;

[0190] Determine a grid-side charging behavior matrix according to the number of charging vehicles in each of the grid areas during the entire time period in the planning related information;

[0191] The grid-side driving distance matrix and the grid-side charging behavior are used as grid-side charging behavior information.

[0192] Furthermore, the module construction model 34 includes:

[0193] A cost determination unit, configured to determine the overall cost based on the optimization variable information and the planning related information;

[0194] A function determination unit, used for taking the minimum overall cost as an optimization goal and constructing an objective function;

[0195] A condition determination unit, used for constructing constraint conditions according to the optimization variable information and each of the grid regions;

[0196] The module determination unit is used to determine the charging station optimization planning model according to the objective function and the constraint conditions.

[0197] The cost determination unit is specifically used for:

[0198] Determining the construction cost of the charging station according to the site selection matrix, the construction scale matrix and the first cost information in the planning related information;

[0199] Determining the operation and maintenance cost of the charging station according to the construction cost of the charging station and the second cost information in the planning related information;

[0200] Determining the charging cost of the electric vehicle according to the grid-side charging behavior information and the third cost information in the planning related information;

[0201] Determine the charging driving cost of the electric vehicle according to the grid-side charging behavior information and the fourth cost information in the planning related information;

[0202] The overall cost is determined based on the construction cost of the charging station, the operation and maintenance cost of the charging station, the charging cost of the electric vehicle, and the charging and driving cost of the electric vehicle.

[0203] Wherein, the condition determination unit is specifically used for:

[0204] Determine the basic constraints of the charging pile according to the optimization variable information;

[0205] Determining vehicle-side charging behavior constraints according to the vehicle-side charging behavior matrix and a preset charging cycle;

[0206] Determining a grid-side charging behavior constraint according to the grid-side charging behavior information and a preset grid vehicle upper limit;

[0207] The charging pile basic constraint, the vehicle-side charging behavior constraint and the grid-side charging behavior constraint are used as constraint conditions.

[0208] The charging station planning device provided in the embodiment of the present invention can execute the charging station planning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0209] Embodiment 4

[0210] Figure 4A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0211] like Figure 4 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0212] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0213] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the charging station planning method.

[0214] In some embodiments, the charging station planning method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the charging station planning method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the charging station planning method in any other appropriate manner (e.g., by means of firmware).

[0215] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0216] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0217] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0218] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0219] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0220] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0221] In one embodiment, the embodiment of the present invention further includes a computer program product, the computer program product includes a computer program, and when the computer program is executed by a processor, the charging station planning method of any embodiment of the present invention is implemented.

[0222] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can 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 the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0223] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0224] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging station planning method, characterized in that: include: Obtain planning-related information for the area to be planned; Divide the area to be planned into grids according to the planning-related information, and determine the predicted number of electric vehicles in each grid area; Determine optimization variable information according to the predicted number of electric vehicles and the planning related information, wherein the optimization variable information includes a site selection matrix of candidate charging stations, a construction scale matrix, a vehicle-side charging behavior matrix, and grid-side charging behavior information; Constructing a charging station optimization planning model according to the optimization variable information and each of the grid areas; The charging station optimization planning model is solved to determine the optimal charging station planning information for the area to be planned.

2. The method according to claim 1, characterized in that The step of dividing the area to be planned into grids according to the planning related information and determining the predicted number of electric vehicles in each grid area includes: When the planning related information includes a sales volume forecast value and a penetration rate, determining the target annual forecast holdings of the area to be planned according to the sales volume forecast value and the penetration rate; When the population forecast value and the number of cars per capita in the planning related information are present, the target year forecast ownership is determined based on the population forecast value and the number of cars per capita; Divide the area to be planned into grids according to the road population information in the planning related information, and determine the grid areas and grid levels; The predicted number of electric vehicles in each grid area is determined based on each grid level and the predicted number of electric vehicles in the target year.

3. The method according to claim 1, characterized in that Determining the optimization variable information according to the predicted number of electric vehicles and the planning-related information includes: Determining a site selection matrix for the candidate charging stations according to the site coordinates of the candidate charging stations in the planning related information; Determine the construction scale matrix of each candidate charging station according to the site area and surrounding power grid conditions in the planning related information; Determining grid-side charging behavior information according to each of the candidate charging stations and each of the grid areas; Determine a vehicle-side charging behavior matrix according to the charging status of each vehicle in all time periods in the planning related information; The site selection matrix, the construction scale matrix, the grid-side charging behavior matrix and the vehicle charging behavior matrix are used as optimization variable information.

4. The method according to claim 3, characterized in that The determining grid-side charging behavior information according to each of the candidate charging stations and each of the grid areas includes: Determining the driving distance from each of the grid areas to each of the candidate charging stations according to the equivalent coordinates of each of the grid areas and the coordinates of each of the station sites; Determining a grid-side driving distance matrix according to each of the driving distances; Determine a grid-side charging behavior matrix according to the number of charging vehicles in each of the grid areas during the entire time period in the planning related information; The grid-side driving distance matrix and the grid-side charging behavior are used as grid-side charging behavior information.

5. The method according to claim 1, characterized in that The constructing of a charging station optimization planning model according to the optimization variable information and each of the grid areas includes: Determining the overall cost according to the optimization variable information and the planning related information; Taking the minimum overall cost as the optimization goal, constructing an objective function; Constructing constraint conditions according to the optimization variable information and each of the grid regions; According to the objective function and the constraint conditions, a charging station optimization planning model is determined.

6. The method according to claim 5, characterized in that Determining the overall cost according to the optimization variable information and the planning related information includes: Determining the construction cost of the charging station according to the site selection matrix, the construction scale matrix and the first cost information in the planning related information; Determining the operation and maintenance cost of the charging station according to the construction cost of the charging station and the second cost information in the planning related information; Determining the charging cost of the electric vehicle according to the grid-side charging behavior information and the third cost information in the planning related information; Determine the charging driving cost of the electric vehicle according to the grid-side charging behavior information and the fourth cost information in the planning related information; The overall cost is determined based on the construction cost of the charging station, the operation and maintenance cost of the charging station, the charging cost of the electric vehicle, and the charging and driving cost of the electric vehicle.

7. The method according to claim 5, characterized in that The step of constructing constraint conditions according to the optimization variable information and each of the grid regions includes: Determine the basic constraints of the charging pile according to the optimization variable information; Determining vehicle-side charging behavior constraints according to the vehicle-side charging behavior matrix and a preset charging cycle; Determining a grid-side charging behavior constraint according to the grid-side charging behavior information and a preset grid vehicle upper limit; The charging pile basic constraint, the vehicle-side charging behavior constraint and the grid-side charging behavior constraint are used as constraint conditions.

8. A charging station planning device, characterized in that: include: An information acquisition module is used to obtain planning-related information of the area to be planned; A vehicle ownership prediction module, used to divide the area to be planned into grids according to the planning related information, and determine the predicted vehicle ownership in each grid area; An information determination module, for determining optimization variable information according to the predicted number of electric vehicles and the planning related information, wherein the optimization variable information includes a site selection matrix of candidate charging stations, a construction scale matrix, a vehicle-side charging behavior matrix, and grid-side charging behavior information; A model building module, used to build a charging station optimization planning model according to the optimization variable information and each of the grid areas; The result determination module is used to solve the charging station optimization planning model to determine the optimal charging station planning information for the area to be planned.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the charging station planning method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the charging station planning method according to any one of claims 1 to 7 when executed.

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