Electric vehicle charging pile planning method, system, equipment and medium

Abstract

The invention discloses an electric vehicle charging pile planning method, system and device and a medium, and the method comprises the steps: obtaining urban road data, and building a dynamic traffic network matrix; the influence of different building types on traffic is considered, the dynamic traffic network matrix is corrected, and driving parameters are calculated; through a path search and optimization algorithm and a multi-dimensional traffic demand analysis technology, the travel path of the electric vehicle is simulated, and the charging demand of the electric vehicle is predicted; and establishing an optimization model by taking charging economic benefit maximization as a target, and generating a charging station capacity configuration scheme. The method breaks through the limitation of traditional experience planning, and achieves the efficient configuration of charging resources through the coupling analysis of building features and traffic dynamics.

Description

Technical Field

[0001] This invention relates to the field of power system spatial load planning technology, and in particular to a method, system, equipment and medium for planning electric vehicle charging piles. Background Technology

[0002] With the global energy structure transformation and increasing environmental awareness, electric vehicles (EVs) have experienced rapid development due to their low-carbon and environmentally friendly characteristics. According to data from the International Energy Agency (IEA), the global EV fleet grew by approximately 70% between 2019 and 2021, and is projected to reach tens of millions by 2030. However, the large-scale adoption of EVs faces challenges such as insufficient and irrationally distributed charging infrastructure, which has become one of the main bottlenecks restricting their development. Existing charging station planning methods mainly rely on simple regional divisions or empirical judgments, lacking in-depth analysis of building types and their characteristics, leading to low utilization rates of charging stations and increased construction and operating costs. Therefore, there is an urgent need for an EV charging station planning method based on building type characteristics, scientifically predicting charging demand, and combining optimal economic principles. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method, system, equipment and medium for planning electric vehicle charging piles to solve the problems of low utilization rate and unreasonable layout of charging piles in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] In a first aspect, the present invention provides a method for planning electric vehicle charging stations, comprising:

[0007] Acquire urban road data and establish a dynamic traffic network matrix;

[0008] Based on the dynamic traffic network matrix, considering the impact of different building types on traffic, the dynamic traffic network matrix is ​​corrected and driving parameters are calculated.

[0009] Based on the corrected dynamic traffic network matrix and driving parameters, the travel routes of electric vehicles are simulated and the charging demand of electric vehicles is predicted through path search and optimization algorithms and multi-dimensional traffic demand analysis technology.

[0010] Based on the charging demand of electric vehicles, an optimization model is established with the goal of maximizing the economic benefits of charging, and a charging station capacity configuration scheme is generated.

[0011] As a preferred embodiment of the electric vehicle charging pile planning method of the present invention, the step of establishing a dynamic traffic network matrix includes:

[0012] Acquire urban road data, and establish an urban road network model;

[0013] Define a traffic network set;

[0014] For each road segment in the traffic network set, a specific weight is assigned according to different time periods;

[0015] The connection relationship between nodes in the traffic network set is described using an adjacency matrix, and the matrix element values are dynamically adjusted according to the time period;

[0016] Integrate the traffic network matrix of each hour to generate a dynamic traffic network matrix.

[0017] As a preferred scheme of the electric vehicle charging pile planning method described in the application, wherein: the corrected dynamic traffic network matrix and the calculation of the driving parameters include:

[0018] Based on different types of buildings, the dynamic traffic network matrix is corrected by introducing a bias variable;

[0019] Using the corrected dynamic traffic network matrix, combined with the maximum driving speed, traffic capacity and real-time traffic flow of each road segment, the driving speed of each road segment in each time period is calculated;

[0020] Based on the calculated driving speed of each time period, a full-day driving speed matrix is constructed;

[0021] Combined with the distance of the road and the corresponding driving speed, the travel time of each road in each time period is calculated to form a total matrix.

[0022] The beneficial effects of the preferred technical scheme are that the accuracy of traffic simulation is effectively improved, the path planning and charging demand prediction are more close to the actual traffic conditions, and the layout and service efficiency of the electric vehicle charging pile are optimized.

[0023] As a preferred scheme of the electric vehicle charging pile planning method described in the application, wherein: the prediction of electric vehicle charging demand includes:

[0024] Based on the corrected dynamic traffic network matrix and the road travel time of each period, the multi-dimensional traffic demand analysis technology and Monte Carlo sampling are used to determine the starting point and endpoint of the electric vehicle trip;

[0025] Through path search and optimization algorithm, the shortest driving path of each vehicle is planned;

[0026] Based on the actual driving habits, a trip behavior model is established and applied;

[0027] According to the simulated electric vehicle driving trajectory and power change, the spatiotemporal distribution of charging load of each charging station is counted.

[0028] The beneficial effects of the preferred technical solution are that the accuracy of the electric vehicle travel path planning and the reliability of the charging demand prediction are improved, the layout and service efficiency of the charging station are optimized, and the resource utilization efficiency and user satisfaction are improved.

[0029] As a preferred scheme of the electric vehicle charging pile planning method provided by the application, the generation of the charging station capacity configuration scheme comprises:

[0030] The calculation method of each operation cost is determined;

[0031] The annualized construction cost is calculated

[0032] The annualized total cost is calculated by combining the operation cost and the annualized construction cost, and the charging station revenue is calculated according to the charging fee;

[0033] Based on the comprehensive analysis of the cost and the revenue, an optimization model is established with the maximum total profit of the electric vehicle charging station as an objective function;

[0034] The power distribution network constraint is set to obtain the optimal number, position, and number of charging piles and waiting positions in each charging station of the charging station.

[0035] As a preferred scheme of the electric vehicle charging pile planning method provided by the application, the modified dynamic traffic network matrix is represented as:

[0036] W=[α1D1,α2D2,…,α 24 D 24 ]

[0037] Wherein, α is a deviation variable calculated according to the influence of the building property on traffic.

[0038] As a preferred scheme of the electric vehicle charging pile planning method provided by the application, the objective function is represented as:

[0039]

[0040] Wherein, N is the number of charging stations.

[0041] In a second aspect, the application provides an electric vehicle charging pile planning system, comprising:

[0042] An acquisition module is configured to acquire city road data and establish a dynamic traffic network matrix;

[0043] A correction module is configured to correct the dynamic traffic network matrix and calculate the driving parameters based on the dynamic traffic network matrix and considering the influence of different building types on traffic;

[0044] The charging demand prediction module is configured to simulate the travel path of the electric vehicle and predict the charging demand of the electric vehicle by a path search and optimization algorithm and a multi-dimensional traffic demand analysis technology based on the corrected dynamic traffic network matrix and the travel parameter.

[0045] The charging station capacity configuration optimization model is configured to establish an optimization model with the maximum charging economic benefit as the target based on the charging demand of the electric vehicle, and generate a charging station capacity configuration scheme.

[0046] In a third aspect, the present application provides an electronic device, comprising:

[0047] A memory is configured to store a program.

[0048] A processor is configured to execute the computer executable instructions, and the computer executable instructions are configured to implement the steps of the electric vehicle charging pile planning method when executed by the processor.

[0049] In a fourth aspect, the present application provides a computer readable storage medium, comprising: the program is executed by the processor, and the steps of the electric vehicle charging pile planning method are implemented.

[0050] The present application has the following beneficial effects: the present application introduces a bias variable to correct the dynamic traffic network matrix based on the building type, and combines the maximum travel speed, the traffic capacity and the real-time traffic flow of the road section to calculate the travel speed of each time period, thereby constructing the all-day travel speed matrix and the total traffic time matrix, which effectively improves the accuracy of traffic simulation, and makes the path planning and charging demand prediction more close to the actual traffic condition; the starting point and the ending point of the electric vehicle are determined by using the corrected dynamic traffic network matrix and the road traffic time of each time period, combining the multi-dimensional traffic demand analysis technology and the Monte Carlo sampling, the shortest travel path of each vehicle is planned by using the path search and optimization algorithm, and the travel behavior model is established based on the actual driving habit, thereby enhancing the accuracy of path planning and the reliability of charging demand prediction; the charging load space-time distribution of each charging station is counted according to the simulated electric vehicle travel trajectory and the electric quantity change, thereby realizing the optimization of charging station layout and service efficiency, improving the resource utilization efficiency and user satisfaction; the technical solutions work together, which not only solves the problems of low charging pile utilization rate and unreasonable layout in the prior art, but also improves the charging efficiency and the load balance of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0052] Figure 1 A basic flowchart of an electric vehicle charging pile planning method provided for an embodiment of the present application is shown in the figure.

[0053] Figure 2 A traffic network structure diagram of an electric vehicle charging pile planning method provided for an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0055] Embodiment 1, refer to Figure 1 For an embodiment of the present application, an electric vehicle charging pile planning method is provided, comprising:

[0056] S100: Obtain city road data and establish a dynamic traffic network matrix;

[0057] S200: Based on the dynamic traffic network matrix, consider the influence of different building types on traffic, correct the dynamic traffic network matrix and calculate the driving parameters;

[0058] S300: Based on the corrected dynamic traffic network matrix and driving parameters, simulate the travel path of electric vehicles through path search and optimization algorithm and multi-dimensional traffic demand analysis technology, and predict the charging demand of electric vehicles;

[0059] S400: Based on the charging demand of electric vehicles, establish an optimization model with the goal of maximizing charging economic benefits, and generate a charging station capacity configuration scheme.

[0060] It should be noted that electric vehicle charging pile planning faces many challenges, including dynamic changes in traffic flow, uncertainty of user travel behavior, dual constraints of road network and power distribution network, and uneven distribution of charging demand in time and space. These challenges make it difficult for traditional static planning methods to meet the needs of practical applications, which can easily lead to unreasonable charging pile layout, low utilization rate, or power grid overload. In this context, in-depth analysis of building types and their characteristics is particularly important.

[0061] Therefore, in view of the low utilization rate and unreasonable layout of the charging pile in the prior art, through the steps S100-S400, a dynamic traffic network matrix is constructed based on road traffic parameters and construction states, a road resistance model is corrected combined with building function characteristics, and the influence of traffic flow in different periods on driving speed is quantified; a path search and optimization algorithm and a multi-dimensional traffic demand analysis technology are used to simulate electric vehicle travel path distribution and accurately predict the space-time charging demand condition associated with building types; an optimization model is established with the maximum charging economic benefit as the target, a charging station capacity configuration scheme is generated, the limitations of traditional experience planning are broken through, and efficient allocation of charging resources is realized through coupling analysis of building characteristics and traffic dynamics.

[0062] Embodiment 2, refer to Figure 2 For an embodiment of the present application, a kind of electric vehicle charging pile planning method is provided based on the last embodiment, comprising:

[0063] In the embodiment of the present application, the city road data is acquired in step S100, and a dynamic traffic network matrix is established, comprising the following steps:

[0064] According to the model of the city road network, the road data is counted, and the road network model of the city is established. Since the city traffic flow will change with time, one hour is selected as a time period for modeling, and the dynamic traffic network is represented as:

[0065]

[0066] Wherein, G is a traffic network set; R is a set of all nodes in the traffic network, a total of u; E is a set of road sections in the traffic network; H is a set of divided time periods, T=24; K is a set of road section weights; r i is the i th node in the traffic network; r ij is the road section connecting the i th node and the j th traffic network node; k ij (t) is the weight of the road section r ij in the t period. The connection relationship between each node in the traffic network set G is described by the adjacency matrix D. The expression of the element d ij of the matrix D is:

[0067]

[0068] At this time, the traffic network information has been stored in the matrix D. For different time periods, the road traffic will change due to different construction conditions, and the matrix D will change accordingly. The traffic network matrix of each hour is calculated separately:

[0069] W=[D1,D2,…,D i ,…,D 24 ]

[0070] wherein W represents a traffic network matrix for 24 hours, which contains 24 small matrices D i , i = 1, 2, …, 24, represents the road condition of each hour of the 24 hours of the day.

[0071] In an optional embodiment, the multi-dimensional traffic demand analysis technique in step S200 can be an OD analysis method, can also be a space-time data analysis method, and can also be a GIS analysis method.

[0072] In an optional embodiment, the OD analysis method is used to determine the origin and destination of the electric vehicle by multi-dimensional traffic demand analysis and Monte Carlo sampling, and the optimal path is planned using a path optimization algorithm to simulate travel behavior and predict charging demand, in combination with the corrected dynamic traffic network matrix and real-time travel time.

[0073] In an optional embodiment, the space-time data analysis method is used to determine the origin and destination of the electric vehicle by identifying charging demand patterns in different time periods and regions and Monte Carlo sampling, and the optimal path is planned using a path optimization algorithm to simulate travel behavior and predict charging demand, based on the corrected dynamic traffic network matrix and real-time travel time.

[0074] In an optional embodiment, the GIS analysis method is used to determine the origin and destination of the electric vehicle by integrating geographic information data to assess regional charging demand and Monte Carlo sampling, and the optimal path is planned using a path optimization algorithm to simulate travel behavior and predict charging demand, based on the corrected dynamic traffic network matrix and real-time travel time.

[0075] It should be noted that the OD analysis method can directly reflect the traffic flow between different regions by constructing a detailed origin-destination (OD) matrix, and can efficiently plan the shortest travel path for each electric vehicle by combining path search and optimization algorithms (such as Floyd algorithm), ensuring the accuracy and real-time nature of path selection. In contrast, the space-time data analysis method can identify charging demand patterns in different time periods and regions, but its accuracy in specific path planning is not as good as the OD analysis method. The GIS analysis method focuses more on the integration of geographic information data and spatial analysis, and has limited ability for microscopic path optimization. Therefore, the OD analysis method can provide more accurate path planning and charging demand prediction when dealing with complex traffic networks and dynamically changing traffic flow, thereby optimizing charging pile layout and service efficiency. Therefore, the present application has unique advantages in accurately simulating individual travel behavior and optimizing paths using the OD analysis method.

[0076] In an optional embodiment, the path search and optimization algorithm in step S200 can be a Floyd algorithm, can also be a Dijkstra algorithm, and can also be an A*(A-Star) algorithm.

[0077] In an alternative embodiment, Floyd algorithm is used to plan the optimal driving path for each electric vehicle based on the corrected dynamic traffic network matrix and real-time travel time by iteratively calculating the shortest path between all nodes.

[0078] In an alternative embodiment, Dijkstra algorithm is used to plan the optimal driving path for each electric vehicle based on the corrected dynamic traffic network matrix and real-time travel time by calculating the shortest path from the starting point of each vehicle to other nodes.

[0079] In an alternative embodiment, A* algorithm is used to plan the optimal driving path for each electric vehicle based on the corrected dynamic traffic network matrix and real-time travel time by heuristic search to calculate the shortest path from the starting point to the end point.

[0080] It should be noted that Floyd algorithm performs one-time calculation and storage of the shortest path between all nodes in the entire traffic network through dynamic programming, which facilitates fast query of the optimal path between any starting point and end point when simulating a large number of electric vehicle travel paths, and improves the overall path planning efficiency. In contrast, Dijkstra algorithm is more suitable for single-source shortest path calculation, and needs to be repeated when facing large-scale vehicle path simulation, which is less efficient. Although A* algorithm has high search efficiency under heuristic search, it depends on the design of heuristic function, and may not guarantee global optimality under complex urban road network or dynamic traffic conditions. Therefore, the use of Floyd algorithm in the present document for large-scale, multi-period path planning based on OD matrix has stronger stability and applicability, and is more conducive to achieving accurate charging demand prediction and charging pile layout optimization.

[0081] In the embodiments of the present application, the step of correcting the dynamic traffic network matrix and calculating the driving parameters in step S200 includes the following steps:

[0082] According to the different properties of the building, such as Figure 2 As shown, the building is divided into three areas: living area, residential area and commercial area; the living area is divided into living area 1 and living area 2, which helps to analyze the traffic flow and travel demand between different functional areas and provides basic data support for the planning of electric vehicle charging piles; according to the influence of each type of area on the traffic matrix, the matrix W is corrected:

[0083] W=[α1D1,α2D2,…,α 24 D 24 ]

[0084] Wherein, a is a deviation variable calculated according to the influence of the building property on the traffic. According to the traffic network matrix, the most concerned by the vehicle owner is the travel time of each road. The travel speed of the vehicle is closely related to the carrying capacity of the road, the traffic volume of the time period and other factors, and is a dynamic variable changing with time. The travel speed v ij The expression is:

[0085]

[0086] Wherein, v ij.max is the maximum travel speed of the road section ij; Z ij is the traffic capacity of the road section ij, which is determined by the road grade and divided into first-class and second-class roads; U ij (t) is the traffic volume of the road section ij at time t; U ij (t) and Z ij is the ratio of U 1 (t) and Z 2 is the saturation of the road section at time t; p, q, are adaptive coefficients of the road, which are determined by the grade of the road.

[0087] The travel speed of all roads can be calculated:

[0088]

[0089] V=[v 1 ,v 2 ,…,v 24 ]

[0090] Wherein, represents the travel speed of the vehicle from the i-th node to the j-th node at time t, and 0 is used to represent when the two nodes are not connected by a road; n represents the total number of nodes of the traffic network; V represents the total matrix of the travel speed of each road in each hour of the whole day 24 hours; the travel time of the road can be calculated by combining the road matrix and the travel speed of the road:

[0091]

[0092] T=[t 1 ,t 2 ,…,t 24 ]

[0093] Wherein, T represents the travel time of the vehicle of each road in each time period.

[0094] In the embodiment of the application, the step S300 of predicting the charging demand of the electric vehicle includes the following steps:

[0095] Based on the traffic network model established in S200 and the road travel time in each time period, the driving path of electric vehicles is simulated. First, the OD analysis method is used to analyze the starting point and end point of electric vehicles. The OD matrix commonly used in the field of transportation can represent the traffic volume between each road node, which is shown as follows:

[0096]

[0097] wherein, represents the number of vehicles from node o to node d at time t. By combining the OD matrix with the Monte Carlo sampling method, the starting point and destination of each electric vehicle can be determined. According to the driving habits of drivers in reality, it is assumed that each electric vehicle owner will choose the route with the shortest time. Floyd algorithm is used to plan the travel path of electric vehicles, select the path with the shortest travel time for each vehicle, and record the corresponding distance. The steps of Floyd algorithm are as follows:

[0098] 1) Determine the shortest time from node i to node j directly, and record it as

[0099] 2) Determine the shortest time from node i to node j through a node, and compare it with , the shorter one is recorded as

[0100] 3) Continue in this way until all nodes in the planning area are traversed, and the final shortest distance D ij is obtained.

[0101] The basic situation of electric vehicles is modeled, and the initial travel time of electric vehicles is determined. The travel time of electric vehicles is determined by working hours and travel habits. Through the collection and analysis of electric vehicle data, the distribution probability of electric vehicle travel time can be obtained:

[0102]

[0103] wherein, λ1 = 0.389, α1 = 7.046, β1 = 1.086, λ2 = 0.066, α2 = 15.610, β2 = 9.667.

[0104] The parking time and initial electric quantity of electric vehicles are greatly influenced by the driving habits of the owners and the use of the vehicles, so they have certain randomness. We can assume that the distribution of electric vehicles obeys normal distribution, and the corresponding probability density function is obtained as follows:

[0105]

[0106] Wherein, T is the parking time of the electric vehicle, unit h; μ is the mean value; σ is the standard deviation. According to the actual use habits of the electric vehicle, different parameter values can be selected to match the corresponding distribution situation.

[0107] According to the travel trajectory of the electric vehicle, it is considered that the electric vehicle will go to the charging station for charging when the electric quantity is lower than 30%, and then the charging load space-time distribution of each charging station can be counted.

[0108] In the embodiment of the application, the charging station capacity configuration scheme is generated in step S400, including the following steps:

[0109] The planning method comprehensively considers the characteristics of buildings in the traffic network and the total income and construction cost of the charging station and other factors, establishes an optimization model with the maximum total profit of the electric vehicle charging station as the objective function, and obtains the optimal number, position, and the number of charging piles and waiting positions in each charging station under the constraint conditions of the traffic network and the power distribution network.

[0110] The operation cost of the electric vehicle fast charging station mainly includes the operation cost I e of purchasing electricity from the power grid, q the penalty cost I r of rejecting electric vehicles due to fullness, is the penalty cost I iw of idle charging piles and waiting positions, and the annualized construction cost I a .

[0111] The operation cost I e (t) of purchasing electricity from the power grid in the t time interval is represented as:

[0112] I e (t) = c e × B(t)

[0113] Wherein, c e is the purchase price per unit of electricity required for charging each charging pile.

[0114] The penalty cost I q (t) of electric vehicles queuing for charging in the t time interval is represented as:

[0115] I q (t) = c q × L q (t)

[0116] Wherein, c q is the penalty factor of the electric vehicle in the waiting state. B(t) is the number of charging piles working in the time interval.

[0117] Penalty cost of queuing for an EV in time interval t r (t) is given by:

[0118] I r (t) = c r × R(t)

[0119] where c r is the penalty factor of rejecting an EV.

[0120] Penalty cost of idle charging posts in time interval t is (t) and penalty cost of idle waiting locations in time interval t iw (t) are given by:

[0121] I is (t) = c s × ID s (t)

[0122] I iw (t) = c w × ID w (t)

[0123] where c s and c w are the penalty factors of idle charging posts and idle waiting locations, respectively.

[0124] The construction cost of an EV fast charging station includes the construction cost of charging posts and waiting locations, as well as other fixed construction cost such as necessary housing, road renovation, etc. Therefore, the annualized construction cost I a is given by:

[0125]

[0126] where i c is the purchase unit price of a charging post; i w is the purchase unit price of a waiting location; I f is the total price of other fixed construction cost; m is the planned service life of the EV fast charging station; and r is the depreciation rate.

[0127] The annualized total cost I cs of an EV fast charging station is given by:

[0128]

[0129] The profit of a charging station is given by:

[0130] Re(t) = c p ×∑B(t)

[0131] where c pThe cost to be paid for charging an electric vehicle at a fast charging station. B(t) is the number of charging piles working in the time interval.

[0132] The optimization objective can be expressed as

[0133]

[0134] Where N is the number of charging stations.

[0135] Constraints are set to ensure the stability of the optimization model; the constraint of the distribution network is that when all the charging piles of the electric vehicle fast charging station are working, the voltage fluctuation of the distribution node where the electric vehicle fast charging station is located is not more than 5% of the range required for stable operation of the power grid, expressed as:

[0136] 0.95 p.u.≤V l ≤1.05 p.u.

[0137] At the same time, the fast charging load is not more than the total capacity C sub of the transformer substation, expressed as:

[0138]

[0139] Embodiment 3, which is an embodiment of the present application, differs from the first embodiment in that an electric vehicle charging pile planning system is provided.

[0140] It should be noted that the technical scheme of the electric vehicle charging pile planning system is the same as the technical scheme of the electric vehicle charging pile planning method described above, and the technical scheme of the electric vehicle charging pile planning system in this embodiment is not described in detail. The contents, which can be seen from the description of the technical scheme of the electric vehicle charging pile planning method described above.

[0141] The electric vehicle charging pile planning system in this embodiment comprises:

[0142] The acquisition module is configured to acquire urban road data and establish a dynamic traffic network matrix.

[0143] The correction module is configured to correct the dynamic traffic network matrix and calculate driving parameters based on the dynamic traffic network matrix and considering the influence of different building types on traffic.

[0144] The charging demand prediction module is configured to simulate the travel path of the electric vehicle and predict the charging demand of the electric vehicle based on the corrected dynamic traffic network matrix and driving parameters, through path search and optimization algorithm and multi-dimensional traffic demand analysis technology.

[0145] The charging station capacity configuration optimization model is used for establishing an optimization model based on charging demand of electric vehicles and maximizing charging economic benefits to generate a charging station capacity configuration scheme.

[0146] The embodiment also provides an electronic device suitable for the case of the electric vehicle charging pile planning method, and the electronic device comprises the following:

[0147] The memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the electric vehicle charging pile planning method proposed in the above embodiment.

[0148] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the electric vehicle charging pile planning method proposed in the above embodiment.

[0149] The storage medium proposed in the embodiment and the electric vehicle charging pile planning method proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that the present application can be realized by means of software and necessary general hardware, and of course can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH memory, a hard disk or an optical disk, etc., including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the present application, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for electric vehicle charging pile planning, characterized in that, The method comprises the following steps: Obtaining urban road data and establishing a dynamic traffic network matrix; Based on the dynamic traffic network matrix, considering the influence of different building types on traffic, correcting the dynamic traffic network matrix and calculating the driving parameters; Based on the corrected dynamic traffic network matrix and driving parameters, through path search and optimization algorithm and multi-dimensional traffic demand analysis technology, the travel path of the electric vehicle is simulated, and the charging demand of the electric vehicle is predicted; Based on the charging demand of the electric vehicle, an optimization model is established to maximize the charging economic benefit, and a charging station capacity configuration scheme is generated.

2. The electric vehicle charging station planning method of claim 1, wherein: The establishment of the dynamic traffic network matrix comprises: Obtaining urban road data and establishing a city road network model; Defining a traffic network set; For each road segment in the traffic network set, a specific weight is assigned according to different time periods; Use the adjacency matrix to describe the connection relationship between nodes in the traffic network set, and dynamically adjust the matrix element value according to the time period; Integrate the traffic network matrix of each hour to generate a dynamic traffic network matrix.

3. The electric vehicle charging station planning method of claim 1 or 2, wherein: The correction of the dynamic traffic network matrix and the calculation of the driving parameters comprise: Based on the different types of buildings, the dynamic traffic network matrix is corrected by introducing a bias variable; Using the corrected dynamic traffic network matrix, combined with the maximum driving speed, traffic capacity and real-time traffic flow of each road segment, the driving speed of each road segment in each time period is calculated; Based on the calculated driving speed of each time period, a whole-day driving speed matrix is constructed; Combined with the distance of the road and the corresponding driving speed, the travel time of each road in each time period is calculated to form a total matrix.

4. The electric vehicle charging station planning method of claim 3, wherein: The prediction of the charging demand of the electric vehicle comprises: Based on the corrected dynamic traffic network matrix and the road travel time of each period, the multi-dimensional traffic demand analysis technology and Monte Carlo sampling are used to determine the starting point and ending point of the electric vehicle; Through the path search and optimization algorithm, the shortest driving path of each vehicle is planned; Based on the actual driving habits, a travel behavior model is established and applied; According to the simulated electric vehicle driving trajectory and the change of electric quantity, the space-time distribution of the charging load of each charging station is calculated.

5. The electric vehicle charging station planning method of claim 4, wherein: The generation of the charging station capacity configuration scheme comprises: Clearly define the calculation method of each operating cost; Calculate the annual construction cost Combined with the operating cost and the annual construction cost, the calculation of the annual total cost is formed, and the charging station revenue is calculated according to the charging fee; Based on the comprehensive analysis of cost and benefit, an optimization model is established with the maximum total profit of electric vehicle charging station as the objective function; Set the power distribution network constraint to get the optimal number, location, and number of charging piles and waiting positions in each charging station.

6. The electric vehicle charging station planning method of claim 5, wherein: The corrected dynamic traffic network matrix is represented as: W = [α1D1, α2D2, …, αnDn] 24 D 24 ] Wherein, α is a bias variable calculated according to the influence of building properties on traffic.

7. The electric vehicle charging station planning method of claim 6, wherein: The objective function is represented as: Wherein, N is the number of charging stations.

8. An electric vehicle charging station planning system applying the method of any one of claims 1-7, characterized by, The method comprises the following steps: An acquisition module is configured to obtain urban road data and establish a dynamic traffic network matrix; A correction module is configured to correct the dynamic traffic network matrix based on the dynamic traffic network matrix and consider the influence of different building types on traffic, and calculate driving parameters; The charging demand prediction module is configured to simulate the travel path of the electric vehicle and predict the charging demand of the electric vehicle based on the corrected dynamic traffic network matrix and the travel parameter through a path search and optimization algorithm and a multi-dimensional traffic demand analysis technique. The charging station capacity configuration optimization model is configured to establish an optimization model with the maximum charging economic benefit as a target based on the charging demand of the electric vehicle, and generate a charging station capacity configuration scheme.

9. An electronic device, comprising: The program is stored in the memory and includes the following steps: The processor is configured to load the program to execute the steps of the method according to any one of claims 1-7. The program is executed by the processor to implement the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a program, characterized in that, ​

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