Electric vehicle charging station planning method, device and program product

Abstract

The invention relates to an electric vehicle charging station planning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing a capacity planning model of a charging station, determining a plurality of capacity planning schemes of the charging station, constructing a charging station decision model of vehicles based on a traffic network in a target area, and determining a charging station of each vehicle by taking the shortest driving duration of the vehicle as a first target function; based on the value exchange scheme of the charging station, constructing a charging power optimization model of the vehicles, and determining the charging power of each vehicle by taking the maximum daily operation value of the charging station as a second objective function; calculating the value of the capacity planning scheme in the planning period based on the charging station and the charging power; and carrying out capacity planning on the charging station by taking the capacity planning scheme with the maximum value as a final capacity planning scheme of the charging station. By adopting the method, the utilization rate of resources can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of charging facility construction planning technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for planning electric vehicle charging stations. Background Technology

[0002] As the electric vehicle industry enters a new stage of large-scale popularization, the scientific planning and construction of charging infrastructure has become a key link in supporting the healthy development of the industry. However, the planning methods currently widely used in the layout and capacity configuration of charging stations still have significant shortcomings and are difficult to adapt to the grid operation needs under the future high proportion of electric vehicles connected to the grid.

[0003] In related technologies, most methods only consider the operational benefits of charging stations themselves, failing to effectively coordinate them with the overall development plan of the power distribution network. This relatively isolated planning approach may not only lead to concentrated load pressure on local power grids and increased voltage fluctuations, but also increase the cost of upgrading and transforming the power distribution system, thereby weakening the overall social benefits and failing to achieve optimal allocation of resources on a larger scale. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for planning electric vehicle charging stations that can effectively improve resource utilization, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for planning electric vehicle charging stations, including:

[0006] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0007] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0008] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0009] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0010] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0011] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0012] In one embodiment, the formula for calculating the first objective function is:

[0013] ;

[0014] Where t is the vehicle's travel time. For roads The overall impedance, For roads The passage time corresponding to zero traffic. As the vehicle passes the road The value is 1 when the vehicle does not pass through the road. In the case of , it is 0.

[0015] In one embodiment, the formula for calculating the second objective function is:

[0016] ;

[0017] in, The service value of charging stations To reduce the value of subsidies for peak-valley load differences, To reduce network loss equivalent value.

[0018] In one embodiment, the constraints of the charging station decision model include vehicle path continuity constraints, vehicle maximum waiting time constraints, vehicle charging pile selection mutual exclusion constraints, and vehicle charging pile quantity constraints.

[0019] In one embodiment, the constraints of the charging power optimization model include power flow constraints of the distribution network, node voltage safety constraints of the distribution network, branch current safety constraints of the distribution network, charging power constraints of the vehicle, power balance constraints of the charging station, and capacity constraints of the distribution transformer of the charging station.

[0020] In one embodiment, the capacity planning scheme includes the number of fast charging piles configured for each charging station, the number of slow charging piles configured for each charging station, the type of power distribution transformer for each charging station, and the type of power line for each charging station.

[0021] In one embodiment, the value calculation formula for each capacity scheme within the planning period is as follows:

[0022] ;

[0023] Among them, F UL Let Y represent the value of a charging station within its planning period, and C represent the planning period in years. op As a typical example of Kusaka's operational value, C inv This represents the initial investment cost.

[0024] Secondly, this application also provides an electric vehicle charging station planning device, comprising:

[0025] The module is used to build a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations.

[0026] The determination module is used to construct a vehicle charging station decision model based on the traffic network and capacity planning scheme in the target area, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0027] The determining module is also used to construct a vehicle charging power optimization model based on the value exchange scheme and capacity planning scheme of the charging station, and to determine the charging power of each vehicle with the maximum daily operating value of the charging station as the second objective function.

[0028] The calculation module is used to calculate the value of each capacity planning scheme within the planning period based on the charging station of each vehicle and the charging power of each vehicle.

[0029] The determining module is also used to select the capacity planning scheme with the highest value as the final capacity planning scheme for the charging station.

[0030] The planning module is used to perform capacity planning for the charging station based on the final capacity planning scheme.

[0031] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0032] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0033] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0034] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0035] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0036] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0037] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0038] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0039] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0040] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0041] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0042] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0043] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0044] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0045] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0046] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0047] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0048] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0049] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0050] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0051] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0052] The aforementioned electric vehicle charging station planning method, apparatus, computer equipment, computer-readable storage medium, and computer program product first construct a capacity planning model for the charging station and determine multiple capacity planning schemes. Based on the traffic network and capacity planning schemes within the target area, a vehicle charging station decision model is constructed, using the shortest vehicle travel time as the first objective function to determine the charging station for each vehicle. Based on the charging station's value exchange scheme and capacity planning scheme, a vehicle charging power optimization model is constructed, using the maximum daily operating value of the charging station as the second objective function to determine the charging power for each vehicle. Based on each vehicle's charging station and charging power, the value of each capacity planning scheme within the planning period is calculated. The capacity planning scheme with the highest value is selected as the final capacity planning scheme for the charging station. Based on the final capacity planning scheme, capacity planning for the charging station is performed. By comprehensively considering the coordination relationship between the distribution network and the charging station, and simultaneously considering the impact of orderly charging guidance on the planning at the spatiotemporal scale, the scientific and rational nature of the planning is enhanced, and the utilization rate of power resources is improved. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a diagram illustrating the application environment of an electric vehicle charging station planning method in one embodiment.

[0055] Figure 2 This is a flowchart illustrating an electric vehicle charging station planning method in one embodiment;

[0056] Figure 3 This is a structural block diagram of an electric vehicle charging station planning device in one embodiment;

[0057] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0060] The electric vehicle charging station planning method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0061] In one exemplary embodiment, such as Figure 2 As shown, a method for planning electric vehicle charging stations is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 212. Wherein:

[0062] Step 202: Construct a capacity planning model for the charging station and determine multiple capacity planning schemes for the charging station.

[0063] For example, a capacity planning model for a charging station is constructed, with the objective function being to maximize the value within the planning period. The value within the planning period includes the initial investment cost and the operating value. The specific formula is shown in formula (1), and multiple capacity planning schemes for the charging station are determined.

[0064]

[0065] Among them, F UL Let Y represent the value of a charging station within its planning period, and C represent the planning period in years.op As a typical example of Kusaka's operational value, C inv This represents the initial investment cost.

[0066] Among them, C inv The formula for calculating the initial investment cost is shown in formula (2).

[0067]

[0068] in, , and For the first The number of slow charging piles, the number of fast charging piles, and the type of power distribution transformer configured for each candidate charging station. For the first Line selection; and The value of a single slow-charging pile and a fast-charging pile; For the first The value of a single distribution transformer configured for each candidate charging station; For the first Cost per unit length of line For the first Line length, For the discount rate, This represents the total number of charging stations.

[0069] In one embodiment, a genetic algorithm is used to solve the capacity planning model, where each chromosome is treated as a capacity planning scheme.

[0070] Step 204: Based on the traffic network and capacity planning scheme within the target area, construct a vehicle charging station decision model, using the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0071] Optionally, the traffic network within the target area can be abstracted as a directed graph H(M, Z), where Z is the set of roads pointing from the starting point to the destination. Indicates from node To the node The road, The nodes in the system can be the starting point, the ending point, an intersection, or a charging station.

[0072] Once a vehicle arrives at a charging station, it begins charging at maximum power. If the charging power exceeds the limit, the vehicle is reassigned to another charging station. When reassigning a vehicle to another charging station, road congestion and route considerations are taken into account to select the charging station with the shortest travel time. Therefore, based on the traffic network and capacity planning scheme in the target area, a vehicle charging station decision model is constructed, with the shortest travel time of the vehicle as the first objective function to determine the charging station for each vehicle.

[0073] Step 206: Based on the value exchange scheme and capacity planning scheme of the charging station, construct a vehicle charging power optimization model, and determine the charging power of each vehicle by taking the maximization of the daily operating value of the charging station as the second objective function.

[0074] For example, based on the value exchange scheme and capacity planning scheme of the charging station, a vehicle charging power optimization model is constructed, with the maximization of the daily operating value of the charging station as the second objective function, to determine the charging power of each vehicle.

[0075] Step 208: Calculate the value of each capacity planning scheme within the planning period based on the charging station and charging power of each vehicle.

[0076] Optionally, the Dijkstra algorithm is used to solve the charging station decision model to determine the charging station for each vehicle. The Cplx solver is used to solve the charging power optimization model to determine the charging power for each vehicle and calculate the value of each capacity planning scheme within the planning period.

[0077] Step 210: Select the capacity planning scheme with the highest value as the final capacity planning scheme for the charging station.

[0078] For example, the value of each capacity planning scheme within the planning period is calculated, and the capacity planning scheme with the highest value is taken as the final capacity planning scheme for the charging station.

[0079] Step 212: Based on the final capacity planning scheme, perform capacity planning for the charging station.

[0080] Optionally, capacity planning can be carried out on the relevant configurations of the charging station based on the final determined capacity planning scheme.

[0081] The above-mentioned electric vehicle charging station planning method constructs a capacity planning model for charging stations and determines multiple capacity planning schemes. Based on the traffic network and capacity planning schemes within the target area, a vehicle charging station decision model is constructed, using the shortest vehicle travel time as the first objective function to determine the charging station for each vehicle. Based on the charging station's value exchange scheme and capacity planning scheme, a vehicle charging power optimization model is constructed, using the maximum daily operating value of the charging station as the second objective function to determine the charging power for each vehicle. Based on each vehicle's charging station and charging power, the value of each capacity planning scheme within the planning period is calculated. The capacity planning scheme with the highest value is taken as the final capacity planning scheme for the charging station. Based on the final capacity planning scheme, capacity planning is performed for the charging station. By comprehensively considering the coordination relationship between the distribution network and charging stations, and simultaneously considering the impact of orderly charging guidance on planning at the spatiotemporal scale, the scientificity and rationality of the planning are enhanced, and the utilization rate of power resources is improved.

[0082] In an exemplary embodiment, the calculation formula of the first objective function of the charging station decision model is shown in Formula (3).

[0083]

[0084] Where t is the driving duration of the vehicle, is the comprehensive impedance of the road and is the passing duration corresponding to the zero-flow situation of the road . is 1 when the vehicle passes through the road and 0 when the vehicle does not pass through the road .

[0085] The time-flow road impedance model is used to quantify the relationship between the passing time and the road flow under different traffic conditions. The specific comprehensive impedance model of the road is shown in Formula (4).

[0086]

[0087] Where is the section impedance model; is the node impedance model. is related to and the road traffic state.

[0088] In one of the embodiments, according to the relevant urban traffic condition division criteria, the road saturation S is used as an evaluation index to divide the road condition levels, which can be specifically divided into: unobstructed (0 < S ≤ 0.6), slow-moving (0.6 < S ≤ 0.8), crowded (0.8 < S ≤ 1.0), and severely congested (1.0 < S ≤ 2.0).

[0089] Among them, the specific section impedance model is shown in Formula (5).

[0090]

[0091] Where the saturation , where is the section traffic flow, is the passing capacity, , are the impedance model influence factors.

[0092] The specific node impedance model is shown in Formula (6).

[0093]

[0094] Where is the signal cycle; For green credit ratio; This refers to the vehicle arrival rate on the road segment.

[0095] The saturation degree S is the only dynamic variable in the road resistance model described above; the other parameters are inherent properties of the road. Finally, based on different road condition levels, the segment impedance and node impedance are integrated to obtain a comprehensive road resistance model.

[0096] The comprehensive road resistance model is shown in formula (7).

[0097]

[0098] Among them, the definition As at the node The set of starting nodes of the road that ends there, defined As a slave node The set of ending nodes of the starting road.

[0099] To model path selection decisions, a set of binary variables is defined. ,and =1 means road Being selected, that is The calculation formula is shown in formula (8).

[0100]

[0101] "start" indicates the EV's starting point; "dest" indicates the EV's destination. The constraint allows only one path to be chosen from the node. marching to Formula (8) ensures the continuity of the selected path.

[0102] In the above embodiments, by minimizing travel time, the model can rationally guide electric vehicles to choose the optimal route, avoiding traffic congestion. This helps optimize the flow distribution of the entire transportation network, reduce traffic congestion, and improve road efficiency.

[0103] In an exemplary embodiment, the calculation formula for the second objective function of the charging power optimization model is shown in formula (9).

[0104]

[0105] in, The daily operational value of the charging station. The service value of charging stations To reduce the value of subsidies for peak-valley load differences, To reduce network loss equivalent value.

[0106] Among them, the service value of charging stations The calculation formula is shown in formula (10), which reduces the value of the load peak-valley difference subsidy. The calculation formula is shown in formula (11), which reduces the equivalent value of network loss. The calculation formula is shown in formula (12).

[0107]

[0108]

[0109]

[0110] in, For the first One charging station Periodic charging load; For the first Each charging station is connected to a transformer. Total load during the period; For disordered charging branch The square of the current; For orderly charging regulation of the branch circuit The square of the current; To optimize the time interval; branch road resistance; For the first One charging station The service value of charging stations during certain time periods; Subsidize the value of individual units to reduce the peak-valley load difference; To reduce network losses and equivalent benefit per unit value; To optimize the total time period; N represents the total number of nodes and branches in the distribution network.

[0111] In the above embodiments, the objective function is to maximize operational value, directly focusing on the economic value of the charging station and ensuring that every decision during planning and operation maximizes economic returns. This includes optimizing the utilization efficiency of charging piles, rationally configuring charging station equipment, and optimizing charging service fees, thereby improving overall operating profits.

[0112] In one exemplary embodiment, the constraints of the charging station decision model include vehicle path continuity constraints, vehicle maximum waiting time constraints, vehicle charging pile selection mutual exclusion constraints, and vehicle charging pile quantity constraints.

[0113] In actual implementation, it is necessary to ensure that the vehicle path is continuous during the driving process. Therefore, the path continuity constraint of the vehicle is as shown in formula (8).

[0114] Each vehicle has a maximum waiting time. If the waiting time at the charging station exceeds this value, the vehicle will leave. Therefore, the maximum waiting time constraint for a vehicle is shown in formula (13).

[0115]

[0116] in, For the first Vehicles in Waiting time for a specific period; For the first Maximum waiting time for a vehicle.

[0117] make and for electric vehicles The time period is in The decision variables for choosing between fast charging piles and slow charging piles at a charging station are as follows: electric vehicles cannot choose both fast charging piles and slow charging piles at the same time. The mutual exclusion constraint for choosing charging piles for vehicles is shown in formula (14).

[0118]

[0119] Since the number of charging piles at each charging station is limited, the number of electric vehicles charging at the same charging station at the same time cannot exceed the number of charging piles. This must meet the constraint of the number of charging piles for the vehicle, as shown in formulas (15) and (16).

[0120]

[0121]

[0122] In the above embodiments, by setting power flow constraints, branch current constraints, and voltage constraints for the distribution network, it can be ensured that the operation of the charging station will not have a negative impact on the distribution network, such as voltage fluctuations or overloads. This helps to ensure the stable operation of the power grid and reduce the risk of power grid failures.

[0123] In an exemplary embodiment, the constraints of the charging power optimization model include power flow constraints of the distribution network, node voltage safety constraints of the distribution network, branch current safety constraints of the distribution network, charging power constraints of the vehicle, power balance constraints of the charging station, and capacity constraints of the distribution transformer of the charging station.

[0124] In practical implementation, in a radial power grid, each node of the distribution network uses... express, Represents the root node. (Using...) Represents a node The parent node, Represents a node The set of child nodes. Nodes with its parent node The branch roads between them are also from The general DistFlow branch power flow model is used to describe the power flow constraints of the power grid, as shown in formula (17).

[0125]

[0126] in, and They represent the nodes respectively Flow to Node Active power and reactive power; and Representing the injection nodes Active power and reactive power; and Representing branches Resistance and reactance; and Representing branch roads The square of the current and the square of the voltage.

[0127] To ensure the safety of the distribution network, the node voltage safety constraints and branch current safety constraints of the distribution network are as shown in formula (18).

[0128]

[0129] in, and Node voltages The minimum and maximum values, and branch road The minimum and maximum values ​​of the current.

[0130] The charging power constraint of the vehicle is shown in formula (19).

[0131]

[0132] in, and These are the maximum slow charging power and the maximum fast charging power of the i-th vehicle, respectively.

[0133] The power balance constraint of the charging station is shown in formula (20).

[0134]

[0135] in, For exceeding the limit indication, if Time period Vehicles in If a charging station exceeds the limit, then Conversely, it is .

[0136] To ensure the safety of the transformer, the load cannot exceed the boundary. The capacity constraint of the distribution transformer of the charging station is shown in formula (21).

[0137]

[0138] in, For the first The maximum capacity of the distribution transformer connected to each charging station; For the first The power distribution transformer connected to each charging station Normal load during the period.

[0139] In the above embodiments, by setting these constraints, the charging power optimization operation model can operate more scientifically and rationally, ensuring its feasibility and effectiveness in practical applications, while also ensuring the stable operation of charging stations, improving user experience, and optimizing resource allocation.

[0140] In one exemplary embodiment, the capacity planning scheme includes the number of fast charging piles configured at each charging station, the number of slow charging piles configured at each charging station, the type of power distribution transformer at each charging station, and the type of power line at each charging station.

[0141] In actual implementation, based on the final capacity planning scheme, the number of fast charging piles, the number of slow charging piles, the model of the power distribution transformer, and the model of the power line for each charging station are determined.

[0142] In the above embodiments, the final capacity planning scheme of the charging station is determined through the coordinated planning of the power distribution network and the charging station, thereby improving the rationality of the charging station planning within a reasonable range.

[0143] To illustrate the electric vehicle charging station planning method in this application in detail, an embodiment is described below. For example, this application describes an electric vehicle charging station planning method in a specific scenario.

[0144] First, a capacity planning model for the charging station is constructed, with the objective function being to maximize the value within the planning period. The value within the planning period includes the initial investment cost and the operating value. The specific formula is shown in formula (1). Then, multiple capacity planning schemes for the charging station are determined.

[0145] The traffic network within the target area is abstracted as a directed graph H(M, Z), where Z is the set of roads pointing from the starting point to the destination. Indicates from node To the node The road, The nodes in the system can be the starting point, the ending point, an intersection, or a charging station.

[0146] Once a vehicle arrives at a charging station, it begins charging at maximum power. If the charging power exceeds the limit, the vehicle is reassigned to another charging station. When reassigning a vehicle to another charging station, road congestion and route considerations are taken into account to select the charging station with the shortest travel time. Therefore, based on the traffic network and capacity planning scheme in the target area, a vehicle charging station decision model is constructed, with the shortest travel time of the vehicle as the first objective function to determine the charging station for each vehicle.

[0147] Based on the value exchange scheme and capacity planning scheme of the charging station, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0148] The Dijkstra algorithm is used to solve the charging station decision model to determine the charging station for each vehicle. The Cplx solver is used to solve the charging power optimization model to determine the charging power for each vehicle and calculate the value of each capacity planning scheme within the planning period.

[0149] Calculate the value of each capacity planning scheme within the planning period, and select the scheme with the highest value as the final capacity planning scheme for the charging station. Based on the final capacity planning scheme, determine the number of fast charging piles, the number of slow charging piles, the type of power distribution transformer, and the type of power line for each charging station.

[0150] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0151] Based on the same inventive concept, this application also provides an electric vehicle charging station planning device for implementing the electric vehicle charging station planning method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the electric vehicle charging station planning device provided below can be found in the limitations of the electric vehicle charging station planning method described above, and will not be repeated here.

[0152] In one exemplary embodiment, such as Figure 3 As shown, an electric vehicle charging station planning device is provided, including: a construction module 301, a determination module 302, a calculation module 303, and a planning module 304, wherein:

[0153] The module is used to build a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations.

[0154] The determination module is used to construct a vehicle charging station decision model based on the traffic network and capacity planning scheme in the target area, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0155] The determining module is also used to construct a vehicle charging power optimization model based on the value exchange scheme and capacity planning scheme of the charging station, and to determine the charging power of each vehicle with the maximum daily operating value of the charging station as the second objective function.

[0156] The calculation module is used to calculate the value of each capacity planning scheme within the planning period based on the charging station of each vehicle and the charging power of each vehicle.

[0157] The determining module is also used to select the capacity planning scheme with the highest value as the final capacity planning scheme for the charging station.

[0158] The planning module is used to perform capacity planning for the charging station based on the final capacity planning scheme.

[0159] In one exemplary embodiment, the above-described computing module is further configured to:

[0160] The formula for calculating the first objective function is:

[0161] ;

[0162] Where t is the vehicle's travel time. For roads The overall impedance, For roads The passage time corresponding to zero traffic. As the vehicle passes the road The value is 1 when the vehicle does not pass through the road. In the case of , it is 0.

[0163] In one exemplary embodiment, the above-described computing module is further configured to:

[0164] The formula for calculating the second objective function is:

[0165] ;

[0166] in, The service value of charging stations To reduce the value of subsidies for peak-valley load differences, To reduce network loss equivalent value.

[0167] In one exemplary embodiment, the constraints of the charging station decision model include vehicle path continuity constraints, vehicle maximum waiting time constraints, vehicle charging pile selection mutual exclusion constraints, and vehicle charging pile quantity constraints.

[0168] In one exemplary embodiment, the constraints of the charging power optimization model include power flow constraints of the distribution network, node voltage safety constraints of the distribution network, branch current safety constraints of the distribution network, charging power constraints of the vehicle, power balance constraints of the charging station, and capacity constraints of the distribution transformer of the charging station.

[0169] In one exemplary embodiment, the capacity planning scheme includes the number of fast charging piles configured for each charging station, the number of slow charging piles configured for each charging station, the type of power distribution transformer for each charging station, and the type of power line for each charging station.

[0170] In one exemplary embodiment, the above-described calculation module is further configured to: calculate the value calculation formula for each capacity scheme within the planning period:

[0171] ;

[0172] Among them, F UL Let Y represent the value of a charging station within its planning period, and C represent the planning period in years. op As a typical example of Kusaka's operational value, C inv This represents the initial investment cost.

[0173] Each module in the aforementioned electric vehicle charging station planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0174] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output interfaces (I / O), a communication interface, a display unit, and input devices. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface, display unit, and input devices are also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores capacity planning data for charging stations. The I / O interfaces allow the processor to exchange information with external devices. The communication interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements an electric vehicle charging station planning method.

[0175] The display unit of this computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of this computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad set on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0176] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0177] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0178] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0179] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0180] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0181] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0182] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0183] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0184] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0185] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0186] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0187] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0188] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0189] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0190] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0191] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0192] Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations;

[0193] Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle.

[0194] Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle.

[0195] Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period.

[0196] The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station.

[0197] Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

[0198] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0199] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0200] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0201] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for planning electric vehicle charging stations, characterized in that, The method includes: Construct a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations; Based on the traffic network and capacity planning scheme within the target area, a vehicle charging station decision model is constructed, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle. Based on the value exchange scheme and capacity planning scheme of charging stations, a vehicle charging power optimization model is constructed. The second objective function is to maximize the daily operating value of the charging station to determine the charging power of each vehicle. Based on the charging station and charging power of each vehicle, calculate the value of each capacity planning scheme within the planning period. The capacity planning scheme with the highest value will be used as the final capacity planning scheme for the charging station. Based on the final capacity planning scheme, capacity planning is carried out for the charging stations.

2. The method according to claim 1, characterized in that, The formula for calculating the first objective function is: ； Where t is the vehicle's travel time. For roads The overall impedance, For roads The passage time corresponding to zero traffic. As the vehicle passes the road The value is 1 when the vehicle does not pass through the road. In the case of , it is 0.

3. The method according to claim 1, characterized in that, The formula for calculating the second objective function is as follows: ； in, The service value of charging stations To reduce the value of subsidies for peak-valley load differences, To reduce network loss equivalent value.

4. The method according to claim 1, characterized in that, The constraints of the charging station decision model include vehicle path continuity constraints, vehicle maximum waiting time constraints, vehicle charging pile selection mutual exclusion constraints, and vehicle charging pile quantity constraints.

5. The method according to claim 1, characterized in that, The constraints of the charging power optimization model include power flow constraints of the distribution network, node voltage safety constraints of the distribution network, branch current safety constraints of the distribution network, charging power constraints of vehicles, power balance constraints of charging stations, and capacity constraints of distribution transformers of charging stations.

6. The method according to claim 1, characterized in that, The capacity planning scheme includes the number of fast charging piles configured at each charging station, the number of slow charging piles configured at each charging station, the model of the power distribution transformer at each charging station, and the model of the power line at each charging station.

7. The method according to claim 1, characterized in that, The value calculation formula for each capacity option within the planning period is as follows: ； Among them, F UL Let Y represent the value of a charging station within its planning period, and C represent the planning period in years. op As a typical example of Kusaka's operational value, C inv This represents the initial investment cost.

8. An electric vehicle charging station planning device, characterized in that, The device includes: The module is used to build a capacity planning model for charging stations and determine multiple capacity planning schemes for charging stations. The determination module is used to construct a vehicle charging station decision model based on the traffic network and capacity planning scheme in the target area, with the shortest vehicle travel time as the first objective function, to determine the charging station for each vehicle. The determining module is also used to construct a vehicle charging power optimization model based on the value exchange scheme and capacity planning scheme of the charging station, and to determine the charging power of each vehicle with the maximum daily operating value of the charging station as the second objective function. The calculation module is used to calculate the value of each capacity planning scheme within the planning period based on the charging station of each vehicle and the charging power of each vehicle. The determining module is also used to select the capacity planning scheme with the highest value as the final capacity planning scheme for the charging station. The planning module is used to perform capacity planning for the charging station based on the final capacity planning scheme.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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