Urban multi-type charging facility site selection and pricing method considering traffic-power grid coupling network

Abstract

The invention relates to the technical field of charging facility planning, and discloses an urban multi-type charging facility site selection and pricing method considering a traffic-power grid coupling network, and the method comprises the steps: constructing an electric vehicle user traffic balance model according to the traffic network data and the types of travel electric vehicle users; constructing a power distribution network optimal power flow model based on the electric vehicle charging and discharging process and the power distribution network cost; constructing a traffic-power coupling network model based on the electric vehicle user traffic balance model and the optimal power flow model of the power distribution network; a double-layer mathematical programming model is constructed, an upper layer model in the double-layer mathematical programming model is an electric vehicle multi-type charging facility site selection model, and a lower layer model in the double-layer mathematical programming model is a traffic-power coupling network model; and solving the upper-layer model based on a genetic algorithm to obtain addresses of the multiple types of charging facilities, feeding back the obtained addresses to the lower-layer model, and solving the lower-layer model based on an alternating iterative algorithm. According to the invention, multiple types of charging facilities are considered, and a more flexible charging network is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of charging facility planning, and in particular to a method for site selection and pricing of urban multi-type charging facilities taking into account a transportation-grid coupling network. Background Art

[0002] As a green, environmentally friendly, energy-saving and efficient means of transportation, electric vehicles have been widely promoted and applied around the world. Especially in urban areas, the penetration rate of electric vehicles has increased year by year, and they have gradually become an important part of urban transportation, which not only alleviates the problem of urban environmental pollution, but also provides a new direction for optimizing the energy structure of cities. However, with the rapid growth of the number of electric vehicles, the planning and layout of charging facilities have become increasingly prominent. At present, the charging facilities for electric vehicles are mainly concentrated in several forms such as static charging stations, road charging piles and wireless power transmission roads. However, the layout of these facilities is still insufficient in coping with the diversified and complex charging needs of electric vehicles in the future, and it is difficult to achieve full coverage of charging services, especially during peak hours, which may lead to charging queues and low utilization of facilities.

[0003] Therefore, some preliminary research progress has been made on the planning and layout of electric vehicle charging facilities at home and abroad. For example: ① Considering the spatial charging demand distribution of electric vehicles, a static charging station site selection model based on charging demand prediction is proposed to optimize the coverage and service efficiency of charging stations; ② The wireless power transmission road layout optimization model based on the urban road network structure and traffic flow characteristics is studied, aiming to reduce the construction pressure of charging stations by reasonably laying out charging roads; ③ Combining the load distribution of the power grid with the charging and discharging behavior of electric vehicles, a comprehensive layout model for the coordinated optimization of electric vehicles and power grids is constructed to achieve efficient utilization of power resources and reasonable layout of the charging network.

[0004] However, the existing background technology still has the following defects: (1) Most studies only focus on a single type of charging facility and fail to fully consider the coordinated layout of multiple types of charging facilities, resulting in insufficient flexibility and adaptability of the overall charging network; (2) The research on the discharge behavior of electric vehicles and V2G (Vehicle-to-Grid) technology is not fully considered, and the potential of electric vehicles as mobile energy storage units is not fully utilized, which cannot effectively alleviate the peak load of the power grid; (3) There is a lack of coordinated optimization of charging facility layout and electricity price pricing strategy. Most existing studies are planned independently and fail to achieve the best coordination between electric vehicle charging network and power grid operation, which affects the economy and convenience of electric vehicle popularization; (4) The existing technology mostly relies on experience-weighted composite indicators in terms of optimization goals and evaluation indicators for charging facility layout, which are easily affected by subjective judgment and difficult to form a universal evaluation model; (5) When proposing specific charging facility layout strategies, existing studies lack a unified framework and solution algorithm, which is not conducive to practical operation and promotion and application. Summary of the invention

[0005] The present invention provides a method for site selection and pricing of urban multi-type charging facilities considering a traffic-grid coupling network, proposes a planning method for comprehensively considering the coordinated layout of multi-type charging facilities, so as to solve the problem of insufficient flexibility and adaptability of the overall charging network caused by the layout of a single type of charging facilities in the prior art, and at the same time, proposes an optimization model combining the discharge behavior of electric vehicles and V2G technology, fully utilizes the potential of electric vehicles as mobile energy storage units, effectively alleviates the peak load of the power grid, and improves the synergistic benefits of the power grid and the charging network, and proposes a method for coordinated optimization of the layout of charging facilities and the electricity price pricing strategy, through the overall planning of the layout of charging facilities and the electricity price strategy, to achieve the best coordination between the operation of the electric vehicle charging network and the power grid, enhance the economy and convenience of the popularization of electric vehicles, and at the same time, construct a charging facility layout effect evaluation model based on objective evaluation indicators, which can perform mathematical operations on multiple indicators with different dimensions using their own characteristics to form a comprehensive evaluation index, avoid the influence of subjective judgment, provide scientific and reasonable layout decision support, and provide a unified charging facility layout strategy use framework and solution algorithm to ensure high feasibility and promotion in actual operation, and overcome the problem of lack of a unified operation framework and solution algorithm in existing research.

[0006] The present invention is achieved through the following technical solutions:

[0007] A pricing method for urban multi-type charging facility location considering the transportation-grid coupling network, including:

[0008] Based on the traffic network data and the types of electric vehicle users, a traffic equilibrium model for electric vehicle users is constructed that takes into account the heterogeneity of electric vehicle users.

[0009] Based on the charging and discharging process of electric vehicles and the cost of distribution network, an optimal power flow model of distribution network considering the charging and discharging process of electric vehicles is constructed;

[0010] Constructing a traffic-power coupling network model based on the electric vehicle user traffic equilibrium model and the distribution network optimal power flow model;

[0011] A location selection model for multi-type charging facilities for electric vehicles is constructed with the minimum total cost of all expenses as the objective function;

[0012] Constructing a two-layer mathematical programming model, wherein the upper model in the two-layer mathematical programming model is the multi-type charging facility site selection model for electric vehicles, and the lower model in the two-layer mathematical programming model is a traffic-power coupling network model;

[0013] The upper model is solved based on a genetic algorithm to obtain the addresses of various types of charging facilities, and the addresses are fed back to the lower model. The lower model is solved based on an alternating iterative algorithm to ultimately obtain the optimal locations of various types of charging facilities, balanced flow distribution of electric vehicle traffic, optimal power flow distribution of the distribution network, and charging and discharging options for electric vehicle users.

[0014] As an optimization, the electric vehicle user traffic equilibrium model includes a first objective function and a first constraint set, wherein the first objective function is expressed as:

[0015]

[0016] in, represents the travel cost of the mth electric vehicle user who chooses path r and meets the electric vehicle user equilibrium condition; q * The traffic network flow allocation scheme represented by ; represents the traffic flow of the mth type of electric vehicle user passing through path r between OD pairs f under the electric vehicle user equilibrium condition; where OD pairs represent the starting and ending points of the traffic network flow of electric vehicle users; F represents the set of all OD pairs in the traffic network; M represents the total number of types of all electric vehicle users; r represents the travel path, R f,m represents the set of all travel paths between the OD pair f of the m-th electric vehicle user; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f;

[0017]

[0018] Among them, θ m is the time value coefficient of the mth type of electric vehicle user; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the mth electric vehicle user under OD does not pass through section a. represents the total time for the mth electric vehicle user to pass through section a under OD pair f; Ψ a represents the set of traffic sections in the traffic network, Ψ n represents the set of all nodes in the transportation network, It represents the charging and discharging delay time when the m-th electric vehicle user is charging and discharging at the state charging station j between the OD pair f; They represent the cost and benefit of the mth type of user electric vehicle user charging and discharging at the static charging station j and the wireless power transmission section a between the OD pair f respectively;

[0019] The first constraint set includes traffic flow conservation constraints, power balance constraints, road travel time constraints, wireless power transmission section charging and discharging behavior constraints, static charging station charging and discharging constraints, electric vehicle charging and discharging economic cost constraints, power consumption constraints, and initial power and power safety constraints, wherein:

[0020] The traffic flow conservation constraint is specifically:

[0021]

[0022] Among them, d f,m represents the transportation demand of the mth type of electric vehicle user in OD pair f; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f; Indicates whether the road segment a is on the available path r for the mth type of electric vehicle user between OD pair f. If so, Otherwise R f,m represents the set of available paths for the mth type of electric vehicle user between OD pair f, q a represents the traffic flow on road section a;

[0023] The power balance constraint is specifically:

[0024]

[0025] in, represents the power of the mth electric vehicle when it passes through node j between OD pair f; represents the power of the mth electric vehicle when passing through node i between OD pair f; represents the charge and discharge amount of the mth type of electric vehicle between OD pair f when passing through node j; represents the charge and discharge amount of the mth type electric vehicle when passing through section a between OD pair f; e s,a Indicates the power consumption of electric vehicles when passing through road section a; Ψ wcl represents the set of wireless power transmission sections in the traffic network; Ψ r Represents the set of all common road segments in the transportation network;

[0026] The road travel time constraints are specifically:

[0027]

[0028]

[0029] Among them, t a (q a ) represents the travel time of electric vehicle users through road section a; represents the free travel time of road section a under zero traffic flow; q a represents the traffic flow on road section a; c a It represents the road capacity of section a; represents the actual passing time of the mth electric vehicle user passing through the road section a between the OD pair f∈F;

[0030] The charging and discharging behavior constraints of the wireless power transmission section are specifically as follows:

[0031]

[0032] in, Indicates the charging power and discharging power of electric vehicles on the wireless power transmission section; represents the charging time and discharging time of the mth electric vehicle user between the OD pair f when passing through the wireless power transmission section a; represents a 0-1 variable, indicating whether the mth type of electric vehicle user charges and discharges on road section a between OD pair f; represents the charge and discharge amount of the mth type electric vehicle when passing through section a between OD pair f; t a,max It represents the longest time that electric vehicles travel on road section a;

[0033] The static charging station charging and discharging constraints are specifically:

[0034]

[0035] in, Indicates the charging power and discharging power of a static charging station; represents the charging and discharging time of the mth electric vehicle user at the static charging station j between the OD pair f; They respectively represent whether the mth type of electric vehicle user between OD pair f is charging and discharging at node j; Indicates the charging and discharging delay time when electric vehicle users are charging and discharging at a static charging station; s Represents a collection of static charging stations;

[0036] The economic cost constraints of electric vehicle charging and discharging are specifically:

[0037]

[0038] in, represents the cost and benefit of the mth electric vehicle user when charging and discharging at the static charging station j and the wireless power transmission section a between the OD pair f; It represents the unit cost of electric vehicle users to charge and discharge using static charging stations; It represents the unit cost of charging and discharging electric vehicle users on the wireless power transmission section; They respectively indicate whether the mth type of electric vehicle user is charging and discharging on the wireless power transmission section a between the OD pair f;

[0039] The power consumption constraint is specifically:

[0040]

[0041] e s,a represents the power consumption of electric vehicle users on road section a; h represents the power consumption of electric vehicles per kilometer, in kWh / km; l a It represents the distance length of road section a in the traffic network, in km;

[0042] The initial power and power safety constraints are specifically as follows:

[0043]

[0044] Among them, s m,o represents the initial power of the m-th electric vehicle; ι represents the minimum SOC threshold power of the electric vehicle when it reaches the destination; O represents the set of starting points in the starting and ending points, D represents the set of end points in the starting and ending points, Ψ n Represents the set of all nodes in the transportation network.

[0045] As an optimization, the distribution network optimal power flow model includes a second objective function, and the second objective function is specifically:

[0046] f D +FZ +F L

[0047]

[0048] Among them, F D Indicates the power generation cost of the generator; F Z F represents the cost of purchasing electricity from the main grid; L Node load reduction penalty cost; α 2,g , α 1,g represents the coefficient of the quadratic term and the coefficient of the linear term of the power generation cost function of the generator; represents the active power of the generator at node n; represents the active power injected from the main grid; χ0 represents the unit power purchase cost of the distribution network from the main grid; κ represents the load reduction penalty coefficient; represents the active and reactive power reduction of conventional load at node n; Φ0, Φ n Represents balanced nodes and unbalanced nodes in the distribution network.

[0049] As an optimization, the traffic-power coupling network model is specifically expressed as:

[0050]

[0051] in, represents the charge and discharge of the electric vehicle at the distribution network node n, represents the coupling connection relationship between traffic node i and distribution network node n. If traffic node i is coupled with power grid node n, then The value is 1, otherwise the value is 0; represents the coupling connection relationship between traffic section a and power grid node n. If traffic section a is coupled with power grid node n, then The value is 1, otherwise the value is 0; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f; It represents the charge and discharge amount of the mth electric vehicle when passing through node i between OD pair f.

[0052] As an optimization, the upper model is specifically:

[0053] minF=F TN +F ST +F PN +F EW +F EB

[0054]

[0055] Where F represents the total system cost; FTN F represents the total travel time cost of the travelers in the transportation system; ST represents the time cost of charging and discharging delay when electric vehicle users use static charging facilities; F PN F represents the power generation cost of the distribution network supplying power to the charging facilities; EW represents the total cost of charging facility construction; F EB represents the total daily operating cost of the charging facility; θ represents the average time value coefficient; α represents the investment return rate; δ represents the operating life of the charging facility; Η s represents a set of candidate nodes for static charging facilities; a represents the set of candidate nodes for wireless power transmission segment; k j represents the construction cost of building a static charging station at node j; k wcl represents the construction cost per unit length of wireless power transmission section; g cs represents the annual operating cost of a single static charging facility; g wcl represents the annual operating cost per unit length of wireless power transmission section; z j Indicates whether to build a static charging station at candidate node j; z a Indicates whether to construct a wireless power transmission section on the candidate section a; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the mth type of electric vehicle user under OD does not pass through section a; χ0 represents the unit power purchase cost of the distribution network from the main grid; Represents the active power injected from the main grid; α 2,g , α 1,g represents the quadratic coefficient and the linear coefficient of the generator power generation cost function; κ represents the load reduction penalty coefficient; represents the active and reactive curtailment power of conventional load at node n; Indicates the active power injected from the main grid; represents the charge and discharge amount of the mth type of electric vehicle when passing through node j between OD pair f; this paper adopts the directed graph G(Φ N ,Φ L ) represents a radial power network, where Φ N represents the set of power network nodes, Φ L Represents the collection of power network lines. The distribution network nodes can be divided into balancing nodes Φ0 and other nodes Φ n , that is, Φ N =Φ0∪Φ n . represents the active load of node n; Ψnode That is Ψ n .

[0056] As an optimization, based on the normal operation of the charging facilities, additional service fees are charged to electric vehicle users who use the charging facilities for charging services, thereby converting the traffic-power coupling network model into an electric vehicle user charging fee unit price model as the lower-level model, and the lower-level model is specifically:

[0057] μ j,c =LMP j +μ sf

[0058] μ a,c =LMP a +μ wf

[0059] Among them, μ j,c represents the unit charging cost of electric vehicle users at static charging station j; μ a,c It represents the unit charging cost of electric vehicle users at wireless power transmission section a; LMP j represents the node marginal electricity price of the grid node corresponding to the static charging facility j; LMP a represents the node marginal electricity price of the grid node corresponding to the wireless power transmission section a; μ sf Represents the additional charging service fee required for electric vehicle users to charge at static charging stations; μ wf It refers to the additional charging service fee required for electric vehicle users to charge on wireless power transmission sections.

[0060] As an optimization, the two-level mathematical programming model is solved by using an improved genetic algorithm. The specific process is as follows:

[0061] Step 1, initializing relevant parameters of the genetic algorithm, wherein the relevant parameters include population size, number of iterations, mutation probability, and crossover probability;

[0062] Step 2: Generate an initial population, using binary code to represent the locations of all static charging stations and wireless charging sections in the upper model to generate an initial population that meets the constraint conditions;

[0063] Step 3: parsing the individual codes of the population, obtaining the location of the static charging station and the wireless power transmission section by parsing the codes, and feeding back the location of the charging facilities to the lower model;

[0064] Step 4: Solve the traffic-power coupling network model using an alternating iterative algorithm to obtain the electric vehicle traffic flow distribution and the optimal power flow distribution of the distribution network under the location layout of the charging facilities;

[0065] Step 5, performing selection, crossover and mutation operations: feeding back the electric vehicle traffic flow distribution and the optimal power flow distribution of the distribution network under the layout of the charging facilities to the upper model, calculating the total system cost and converting it into an initial fitness value, adjusting the initial fitness value, retaining the individual with the best fitness, updating the crossover probability and mutation probability, and then performing crossover, mutation and other operations to generate new individuals to form a new population;

[0066] Step 6: Determine whether the maximum number of iterations has been reached. If so, the iteration is terminated. Otherwise, return to step 3.

[0067] Step 7: Output the optimal charging facility location, balanced flow distribution of electric vehicle traffic, optimal power flow distribution of the distribution network, charging and discharging choices of electric vehicle users, and the latest marginal electricity price.

[0068] As an optimization, the specific process of step 4 is:

[0069] Step 4.1, initialize the model parameters of the traffic-power coupling network model, the model parameters include basic data of traffic network roads, basic data of power network, coupling network relationship information, and travel demand of electric vehicle users, and set the number of iterations n = 0; at the same time, generate the initial strategy of the optimal power flow model of the distribution network, and let the electric vehicle charge and discharge charge is 0, solving the optimal power flow model of the distribution network to calculate the initial node marginal electricity price LMP0;

[0070] Step 4.2, solving the electric vehicle user traffic equilibrium model: after the upper model gives the location of the charging facility, combined with the initial node marginal electricity price LMP0, the electric vehicle user traffic equilibrium model is solved using the SRA algorithm to obtain the travel paths and charging and discharging selection results of all electric vehicle users under the charging facility network location layout and at the electricity price, thereby obtaining the traffic flow distribution TF that meets the user equilibrium condition. n+1 ;

[0071] Step 4.3: Solve the optimal power flow model of the distribution network, and calculate the traffic flow distribution TF under the user equilibrium condition. n+1 Calculate the charging and discharging charge of electric vehicle users based on the charging and discharging selection results The optimal power flow model of the distribution network is solved by the interior point method to obtain the optimal power flow of the distribution network and update the node marginal electricity price χ n+1 ;

[0072] Step 4.4: Determine whether the traffic flow distribution and the node marginal electricity price meet the convergence conditions. If so, output the optimal power flow of the distribution network, the traffic flow distribution and the node marginal electricity price, and then proceed to step 5. Otherwise, return to step 4.2.

[0073] As an optimization, the specific process of using the SRA algorithm to solve the electric vehicle user traffic equilibrium model is to convert the first objective function into a sub-problem and a sub-problem constraint set of minimizing the travel cost of the m-th user between the OD pair f, wherein the sub-problem is specifically:

[0074]

[0075] The sub-problem constraint set is specifically:

[0076]

[0077]

[0078] Among them, O represents the set of starting points in the starting and ending points, and D represents the set of end points in the starting and ending points. represents a 0-1 variable, indicating whether the path of the mth user under OD pair f passes through the segment (i, j); It means that if the road segment a is on the best path for the mth user between the OD pair f∈F, then The value is 0, otherwise The domain of is unconstrained; M represents a very large constant; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. A value of 0 means that the path of the mth type of electric vehicle user under OD does not pass through the road section;

[0079] The specific solution process is:

[0080] B1. Initialize the parameters of the SRA algorithm, including setting the iteration parameter n = 1, the convergence parameter

[0081] B2. Solve the traffic equilibrium model of electric vehicle users, calculate the sub-problem of the m-th electric vehicle user between OD pairs f, obtain the travel path and charging and discharging selection results of the electric vehicle users of this type, and then repeat the above process (calculate the sub-problem of the m-th electric vehicle user between OD pairs f in this step) until the travel path and charging and discharging selection results of all electric vehicle users are obtained, and on this basis, calculate the current traffic network flow TF n ;

[0082] B3. Perform a direction search and use the BPR function to update the travel impedance of all road sections in the traffic network. At the same time, update the current vehicle travel time of the road sections in the traffic network. Use the all-or-nothing allocation method again to obtain the new traffic flow distribution of OD to f. Search direction is TF f -TF n TF f represents the traffic flow between OD pair f;

[0083] B4. Update the step size and path flow, let λ n is the auxiliary variable in the nth iteration process, λ1 is a given value, λ n According to the following formula, the step length in the nth iteration is The formula for updating the path traffic flow is:

[0084]

[0085] in, represents the traffic flow distribution of OD to f in the nth iteration, λ n-1 represents the auxiliary variable in the n-1th iteration process, represents the traffic flow distribution of OD to f in the n-1th iteration; η a , η b An auxiliary parameter for adjusting the step size during the flow distribution process; TF n Indicates the current traffic network flow result of the nth traffic allocation, which is a set containing the traffic flow conditions of each path; B5, check whether it converges, if Then the final result is output to obtain the traffic flow distribution of electric vehicles; otherwise, n=n+1, and go to B3 to continue iteration.

[0086] As an optimization, in step 4.4, the specific criteria for judging whether the traffic flow distribution and the node marginal electricity price meet the convergence conditions are:

[0087] If the traffic flow distribution TF n and node marginal electricity price χ n Satisfy {||TF n+1 -TF n ||,||LMP n+1 -LMP n ||}<ξ, then the traffic flow distribution TF is determined n and node marginal electricity price χ n Convergence, where LMP n Represents the node marginal electricity price during the nth iteration.

[0088] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0089] (1) Optimize the flexibility and adaptability of the layout of charging facilities. The present invention builds a more flexible and adaptable charging network by comprehensively considering the coordinated layout of multiple types of charging facilities (such as road charging, charging piles, and battery swap stations). By introducing the layout planning of different types of facilities, the adaptability of the charging network to diversified charging needs can be effectively improved, thereby improving the reliability of the entire system and user experience.

[0090] (2) Enhance the synergistic benefits between electric vehicles and the power grid. The optimization model proposed in this invention combines the discharge behavior of electric vehicles with V2G technology, allowing electric vehicles to feed back electric energy during peak load periods of the power grid and effectively balance the load fluctuations of the power grid. Through the rational layout of charging facilities and the optimization of electricity price strategies, electric vehicles can not only be energy consumption terminals, but also active participants in power regulation, thereby enhancing the synergistic benefits between the charging network and the power grid.

[0091] (3) Scientific evaluation of charging facility layout effects. This paper introduces objective evaluation indicators to construct a multi-index comprehensive charging facility layout effect evaluation model. Using the concept of information entropy, the effects of different layout schemes in improving network reliability, cost-effectiveness, service level, etc. are quantitatively evaluated, overcoming the shortcomings of strong subjectivity in traditional layout evaluation methods and ensuring the scientificity and rationality of the evaluation results.

[0092] (4) Unified layout strategy framework and solution algorithm. The layout strategy proposed in this invention uses a framework and solution algorithm to ensure the uniformity and operability of the layout process of multiple types of charging facilities. By introducing the optimization algorithm, the multi-objective optimization problem can be effectively solved. The generated layout plan not only considers the construction cost, but also takes into account factors such as user charging convenience and grid load balance, providing effective technical support for the planning and implementation of actual charging facilities.

[0093] (5) Systematically improve the overall benefits of the charging network. Based on the above-mentioned optimized layout and evaluation model, the charging facility layout plan formed by the present invention can optimize resource allocation, reduce construction and operation costs, and ultimately achieve efficient operation of the charging network while ensuring the quality of charging services. Through in-depth analysis and model optimization of electric vehicle charging behavior, the present invention effectively improves the feasibility of popularizing electric vehicles and lays the foundation for the development of future intelligent transportation and green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0095] Figure 1 It is a framework diagram of the traffic-electricity coupling network model described in the present invention;

[0096] Figure 2 It is a flow chart of the alternating iteration algorithm of the present invention;

[0097] Figure 3 It is a schematic diagram of the modified IEEE 33-node power distribution system topology;

[0098] Figure 4 Schematic diagram of the planning results of charging facilities for the Nguyen-Dupuis network;

[0099] Figure 5 It is the coupling relationship diagram between charging facilities and distribution network;

[0100] Figure 6 It is the curve diagram of voltage amplitude between nodes in the distribution network;

[0101] Figure 7 A schematic diagram comparing the transportation-electricity coupling network costs under different scenarios; Figure 8 This is a flow chart of a method for site selection and pricing of urban multi-type charging facilities taking into account a transportation-grid coupling network according to the present invention. DETAILED DESCRIPTION

[0102] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0103] This embodiment 1 provides a method for site selection and pricing of urban multi-type charging facilities taking into account a traffic-grid coupling network, which mainly includes the following steps:

[0104] S1. Based on the traffic network data and the types of electric vehicle users, a traffic equilibrium model for electric vehicle users is constructed that takes into account the heterogeneity of electric vehicle users.

[0105] S2. Based on the charging and discharging process of electric vehicles and the cost of distribution network, an optimal power flow model of distribution network considering the charging and discharging process of electric vehicles is constructed;

[0106] S3, constructing a traffic-power coupling network model based on the electric vehicle user traffic equilibrium model and the distribution network optimal power flow model;

[0107] S4. Construct a location selection model for multi-type charging facilities for electric vehicles with the minimum total cost of all expenses as the objective function;

[0108] S5. Construct a two-layer mathematical programming model, wherein the upper model in the two-layer mathematical programming model is the site selection model for multi-type charging facilities for electric vehicles, and the lower model in the two-layer mathematical programming model is a traffic-power coupling network model;

[0109] S6. Solve the upper model based on the genetic algorithm to obtain the addresses of various types of charging facilities, and feed the obtained addresses back to the lower model, and solve the lower model based on the alternating iterative algorithm to finally obtain the optimal locations of various types of charging facilities, the balanced flow distribution of electric vehicle traffic, the optimal power flow distribution of the distribution network, and the charging and discharging selection of electric vehicle users.

[0110] Next, the implementation process of each step is introduced in detail.

[0111] S1. Based on the traffic network data and the types of electric vehicle users, a traffic equilibrium model for electric vehicle users is constructed that takes into account the heterogeneity of electric vehicle users.

[0112] It mainly includes two steps: quantifying the travel costs of electric vehicle users with user heterogeneity and building a traffic equilibrium model for electric vehicle users.

[0113] 1) Quantifying the travel costs of electric vehicle users with heterogeneity

[0114] The travel cost of electric vehicle users mainly includes travel time cost, charging and discharging time cost, charging fee and discharging income. Travelers may have different time values ​​due to the nature of their work and the urgency of their travel needs. Therefore, the present invention divides electric vehicle users into m categories, and each category of users will have different time value coefficients.

[0115] The OD pair f∈F between the m-th type of electric vehicle user travel cost quantification index c is set by the present invention. f,m as follows:

[0116]

[0117] θ m is the time value coefficient of the mth type of electric vehicle user; is a 0-1 variable, indicating whether the path of the mth user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the mth electric vehicle user under OD does not pass through section a. represents the total time for the mth electric vehicle user to pass through section a under OD pair f, Ψ a represents the set of traffic sections in the traffic network, Ψ n represents the set of all nodes in the transportation network, It represents the charging and discharging delay time when the m-th electric vehicle user is charging and discharging at the state charging station j between the OD pair f; They represent the cost and benefit of the mth user electric vehicle user charging and discharging at the static charging station j and the wireless power transmission section a between the OD pair f.

[0118] 2) Constructing a traffic equilibrium model for electric vehicle users

[0119] The present invention uses variational inequality to describe the first objective function of the electric vehicle user traffic equilibrium model:

[0120]

[0121] in, represents the travel cost of the mth user who satisfies the electric vehicle user equilibrium (UE) condition and chooses path r; q * The traffic network flow allocation scheme represented by ; represents the traffic flow of the mth type of electric vehicle user through path r between OD pairs f∈F under the (UE) condition; OD pairs represent the starting and ending points of the traffic network flow of electric vehicle users; F represents the set of all OD pairs in the traffic network; M represents the total number of types of all electric vehicle users; r represents the travel path, R f,m represents the set of all travel paths between the OD pair f of the m-th electric vehicle user; represents the traffic flow of the mth electric vehicle user on path r between OD pair f.

[0122] The electric vehicle equilibrium model considering user heterogeneity should first satisfy the traffic flow conservation constraint. At the same time, in order to ensure that electric vehicles can reach their destination safely, the constraints of the electric vehicle travel and charging and discharging selection model (the first constraint set) mainly include power conservation constraints, battery charging and discharging cost constraints, charging and discharging selection and other related constraints:

[0123] ① Traffic flow conservation constraints:

[0124]

[0125]

[0126] d f,m represents the traffic demand of the mth user in OD pair f∈F; represents the traffic flow of the mth user on path r between OD pair f∈F; Indicates whether the road segment a is on the available path r for the mth user between the OD pair f∈F. If so, Otherwise R f,mrepresents the set of available paths for the mth user between OD pairs f∈F, q a Represents the traffic flow on road section a.

[0127] ②Power balance constraint

[0128]

[0129] represents the power of the mth electric vehicle when it passes through node j between the OD pair f∈F; represents the power of the mth electric vehicle when passing through node i between OD pair f; represents the charge and discharge amount of the mth type of electric vehicle when passing through node j between the OD pair f∈F; represents the charge and discharge amount of the mth type electric vehicle when passing through section a between the OD pair f∈F; e s,a Indicates the power consumption of electric vehicles when passing through road section a; Ψ wcl represents the set of wireless power transmission sections in the traffic network; Ψ r Represents the set of all common road segments in the transportation network.

[0130] A node is a node in a road network, such as Figure 4 The numbers in represent the node numbers.

[0131] ③Road travel time constraints

[0132] In an actual traffic network, the road travel time will be affected by the traffic volume and the road carrying capacity. The present invention adopts the Federal Highway Administration function (BPR function) to describe the relationship between the road section travel time and the traffic volume.

[0133]

[0134]

[0135] t a (q a ) represents the travel time of the user through section a; represents the free travel time of road section a under zero traffic flow; q a represents the traffic flow on road section a; c a It represents the road capacity of section a; represents the actual passing time of the mth electric vehicle user passing through the road section a between the OD pair f∈F; r Represents the set of all common road sections in the traffic network; Ψ a Represents a set of traffic segments in a traffic network.

[0136] ③ Constraints on charging and discharging behavior in wireless power transmission sections

[0137] When using wireless power transmission sections for charging and discharging, electric vehicle users can slow down when passing through wireless power transmission sections to obtain more charging or discharging capacity. Considering the safety of electric vehicle batteries, electric vehicle users cannot perform charging and discharging operations at the same time on wireless power transmission sections during travel. The charging and discharging capacity and time of electric vehicle users on wireless power transmission sections meet the following relationship:

[0138]

[0139]

[0140] Indicates the charging power and discharging power of electric vehicles on the wireless power transmission section; represents the charging and discharging time of the mth electric vehicle user when passing through the wireless power transmission section a between the OD pair f∈F; represents a 0-1 variable, indicating whether the m-th EV user charges and discharges on road section a between the OD pair f∈F; represents the charge and discharge amount of the mth type electric vehicle when passing through section a between OD pair f; t a,max represents the maximum time for electric vehicles to travel on road section a. In order to ensure the safety of electric vehicles, the present invention assumes that the maximum safe travel time t a,max is the road section travel time t calculated by the BPR function a (q a ) times.

[0141] ④Charging and discharging constraints of static charging stations

[0142] When electric vehicle users charge and discharge at a static charging station, they need to wait on the spot for the electric vehicle to complete charging and discharging. The charging and discharging time is mainly related to the charging and discharging amount and charging and discharging power of the electric vehicle.

[0143]

[0144] Indicates the charging power and discharging power of a static charging station; represents the charging and discharging time of the mth user at the static charging station j between the OD pair f∈F; represents a 0-1 variable, whether the m-th EV user charges and discharges at node j between the OD pair f∈F; Indicates the charging and discharging delay time when electric vehicle users are charging and discharging at a static charging station; s Represents a collection of static charging stations; represents the charge and discharge amount of the mth type of electric vehicle between OD pair f when passing through node j; It represents the charge and discharge amount of the mth electric vehicle when passing through node j between OD pair f.

[0145] ⑤ Economic cost of charging and discharging electric vehicles

[0146] When electric vehicles are charged at static charging stations or wireless power transmission sections, they need to pay charging fees to the charging facility operators. Conversely, when electric vehicles are discharged at static charging stations or wireless power transmission sections, the charging facility operators need to provide discharge subsidies to electric vehicle users.

[0147]

[0148] represents the cost and benefit of the mth electric vehicle user charging and discharging at the static charging station j and the wireless power transmission section a between the OD pair f∈F; when the user discharges, When it is positive, it indicates the benefit obtained from discharging. When the user charges, A negative value indicates the cost of charging.

[0149] It represents the unit cost of electric vehicle users to charge and discharge using static charging stations; It represents the unit cost of charging and discharging electric vehicle users on the wireless power transmission section; They respectively indicate whether the mth electric vehicle user between the OD pair f is charging and discharging on the wireless power transmission section a.

[0150] ⑥Power consumption constraints

[0151] The present invention assumes that the power consumption of an electric vehicle is directly proportional to the driving mileage.

[0152]

[0153] e s,a represents the power consumption of electric vehicle users on road section a; h represents the power consumption of electric vehicles per kilometer, in kWh / km; l a It represents the distance of road section a in the traffic network, in km.

[0154] ⑦ Initial power and power safety constraints

[0155] To ensure the safety of electric vehicles, electric vehicle users must not run out of power during driving, otherwise they will not be able to reach the destination and will cause significant irreversible damage to the battery. At the same time, considering the convenience of electric vehicle users' travel, the electric vehicle must have a power level greater than the minimum guaranteed power level at the destination to facilitate subsequent travel.

[0156]

[0157] s m,o represents the initial power of the m-th electric vehicle; ι represents the minimum SOC threshold power of the electric vehicle when it reaches the destination; O represents the set of starting points in the starting and ending points, D represents the set of end points in the starting and ending points, Ψ n Represents the set of all nodes in the transportation network.

[0158] The present invention proposes a sub-problem (SP) to solve the path and charging and discharging selection problem of electric vehicle users. The objective function of the sub-problem is to minimize the travel cost of the m-th type of user between the OD pair f∈F.

[0159]

[0160] The constraints of the sub-problems mainly include the following constraints:

[0161]

[0162] represents a 0-1 variable, and Equivalently, it indicates whether the path of the mth user under OD pair f passes through the segment (i, j), that is, It means that if the road segment a is on the best path for the mth user between the OD pair f∈F, then The value is 0, otherwise The domain of is unconstrained; M represents a very large constant.

[0163] 3) Solution algorithm

[0164] The present invention adopts the adaptive average algorithm (SRA algorithm) to solve the traffic flow distribution problem. The specific solution process is as follows.

[0165] B1: Initialization. Set the iteration parameter n = 1, the convergence parameter

[0166] B2: Solve the electric vehicle user equilibrium model. Calculate the m-th user subproblem (SP) between the OD pair f∈F, obtain the travel path and charging and discharging selection results of this type of user, and then repeat the above process until the travel path and charging and discharging selection results of all electric vehicle users are obtained, and on this basis, calculate the current traffic network flow TF n .

[0167] B3: Direction search. Use the BPR function to update the travel impedance of all road sections in the traffic network, and update other relevant information in the traffic network at the same time, and use the all-or-nothing allocation method again to obtain a new traffic flow distribution. Search direction is TF f -TF n .

[0168] B4: Update step size and path flow. n is an auxiliary variable, λ1 is a given value, λ n Calculate according to formula (3-44). Step length The formula for updating the path traffic flow is:

[0169]

[0170] B5: Check whether it converges. Then the final result is output to obtain the traffic flow distribution of electric vehicles; otherwise, n=n+1, and go to B3 to continue iteration.

[0171] S2. Based on the charging and discharging process of electric vehicles and the cost of distribution networks, an optimal power flow model of the distribution network considering the charging and discharging process of electric vehicles is constructed.

[0172] The distribution network model established by the present invention taking into account the charging and discharging process of electric vehicles has an objective function (second objective function) to minimize the power generation cost:

[0173] f D +F Z +F L

[0174] The power generation cost of the distribution network mainly includes the power generation cost of the generator, the power purchase cost from the main grid, and the node load reduction penalty cost.

[0175]

[0176] F D F represents the cost of power generation by the generator; Z F represents the cost of electricity purchased by the distribution network from the main grid; L represents the load reduction penalty cost; α 2,g , α 1,g represents the coefficient of the quadratic term and the coefficient of the linear term of the power generation cost function of the generator; represents the active power of the generator at node n; represents the active power injected from the main grid; χ0 represents the unit power purchase cost of the distribution network from the main grid; κ represents the load reduction penalty coefficient; represents the active and reactive curtailment power of conventional load at node n.

[0177] Urban charging facilities are generally powered by a distribution network with a radial structure. The urban distribution network is generally composed of a busbar and branches.N ,Φ L ) represents a radial power network, where Φ N represents the set of power network nodes, Φ L Represents the collection of power network lines. Distribution network nodes can be divided into balancing nodes Φ0 and other nodes Φ n , that is, Φ N =Φ0∪Φ n .

[0178] The balancing node of the power grid refers to the node used for power balancing in the power system flow calculation. The voltage amplitude and phase of the balancing node are given, while the active power and reactive power injected by it are the quantities to be determined. There is only one balancing node in the system, and there must be one balancing node. It plays a role in power balancing the system, providing the system with missing power and absorbing excess power in the system.

[0179] S3, constructing a traffic-power coupling network model based on the electric vehicle user traffic equilibrium model and the distribution network optimal power flow model, such as Figure 1 shown.

[0180] 1) Model building

[0181] The present invention establishes a traffic-power coupling network that considers the charging and discharging behavior of electric vehicles. The model consists of a distribution network optimal power flow model that considers the charging and discharging process of electric vehicles and an electric vehicle user traffic equilibrium model. When the traffic-power coupling network that considers the charging and discharging behavior of electric vehicles reaches a balanced state, all electric vehicle users in the traffic network cannot reduce travel costs by changing travel routes and charging and discharging plans, and the distribution network reaches the optimal power flow. At this time, the power generation cost is the lowest and the generator output distribution reaches the optimal state, and the power network safe operation constraints are met, and no faults such as voltage overlimit will occur.

[0182] The transportation network and the distribution network are connected through the static charging station of electric vehicles and the wireless power transmission section. The charging and discharging charge of the electric vehicle at the node n of the distribution network can be considered as There is a linear mapping relationship between the traffic flow of electric vehicles and the charging and discharging charges in the traffic network, and the relationship between the two can be expressed by the following formula.

[0183]

[0184] in, represents the charge and discharge of the electric vehicle at the distribution network node n, represents the coupling connection relationship between traffic node i and distribution network node n. If traffic node i is coupled with power grid node n, then The value is 1, otherwise the value is 0; represents the coupling connection relationship between traffic section a and power grid node n. If traffic section a is coupled with power grid node n, then The value is 1, otherwise the value is 0; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f; represents the charge and discharge amount of the mth type electric vehicle when passing through node i between OD pair f; It represents the charge and discharge amount of the mth type electric vehicle when passing through the road section a between the OD pair f; represents the traffic flow of the mth electric vehicle user on path r between OD pair f.

[0185] 2) Traffic-power coupling network alternating iterative algorithm

[0186] like Figure 2 shown.

[0187] Step 1: Initialization. Input the basic data of traffic network roads, basic data of power network, coupling network relationship information, electric vehicle travel demand and other related data, and set the number of iterations n = 0.

[0188] Step 2: Generate initial strategy. Charge and discharge the electric vehicle is 0, and the optimal power flow model is solved to calculate the initial node marginal electricity price LMP0.

[0189] Step 3: Solve the electric vehicle traffic equilibrium problem. According to the marginal electricity price LMP of the power network node n , the SRA algorithm is used to solve the electric vehicle traffic equilibrium problem, and the travel path and charging and discharging selection results of electric vehicle users under the electricity price are obtained to achieve the equilibrium state of electric vehicle users in the transportation network.

[0190] Step 4: Solve the optimal power flow model. Using the traffic network flow distribution TF under UE conditions n+1 and the charging and discharging selection results, calculate the charging and discharging charge of the electric vehicle Then solve the optimal power flow model of the distribution network to obtain the optimal power flow and update the node marginal electricity price χ n+1 .

[0191] Step 5: Convergence judgment. Determine the traffic flow distribution TF n and node marginal electricity price χ n Convergence, if {||TF n+1 -TF n ||,||LMP n+1 -LMP n ||}<ξ, then the iteration ends and goes to step 6 of this process; if the convergence condition is not met, set n=n+1, jump to step 3 of this process and continue the iterative process.

[0192] Step 6: Output the optimal power flow of the distribution network, traffic flow distribution and node marginal electricity price.

[0193] S4. Construct a location selection model for multi-type charging facilities for electric vehicles with the minimum total cost of all expenses as the objective function;

[0194] S5. Construct a two-layer mathematical programming model, wherein the upper model in the two-layer mathematical programming model is the site selection model for multi-type charging facilities for electric vehicles, and the lower model in the two-layer mathematical programming model is a traffic-power coupling network model;

[0195] S6. Solve the upper model based on the genetic algorithm to obtain the addresses of various types of charging facilities, and feed the obtained addresses back to the lower model, and solve the lower model based on the alternating iterative algorithm to finally obtain the optimal locations of various types of charging facilities, the balanced flow distribution of electric vehicle traffic, the optimal power flow distribution of the distribution network, and the charging and discharging selection of electric vehicle users.

[0196] 1) Model building

[0197] This section will establish a two-level mathematical programming model, in which the upper model is a site selection model for multi-type charging facilities for electric vehicles, and the lower model is a traffic-power coupling network equilibrium model that takes into account the charging and discharging behavior of electric vehicles.

[0198] ① Upper model - electric vehicle multi-type charging facility site selection model

[0199] The upper model is a multi-type charging facility site selection and planning model, which takes minimizing system costs as a constraint and mainly considers related costs such as the total cost of the transportation system, the power generation cost of the distribution network, and the construction and operation costs of charging facilities.

[0200] minF=F TN +F ST +F PN +F EW +F EB

[0201]

[0202] Indicates the total system cost; F TN F represents the total travel time cost of the travelers in the transportation system; ST represents the time cost of charging and discharging delay when electric vehicle users use static charging facilities; F PN F represents the power generation cost of the distribution network supplying power to the charging facilities; EW represents the total cost of charging facility construction; F EBrepresents the total daily operating cost of the charging facility; θ represents the average time value coefficient; α represents the investment return rate; r represents the operating life of the charging facility; Η s represents a set of candidate nodes for static charging facilities; a represents the set of candidate nodes for wireless power transmission segment; k j represents the construction cost of building a static charging station at node j; k wcl represents the construction cost per unit length of wireless power transmission section; g cs represents the annual operating cost of a single static charging facility; g wcl represents the annual operating cost per unit length of wireless power transmission section; z j Indicates whether to build a static charging station at candidate node j; z a Indicates whether to construct a wireless power transmission section on the candidate section a; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the mth type of electric vehicle user under OD does not pass through section a; χ0 represents the unit power purchase cost of the distribution network from the main grid; Represents the active power injected from the main grid; α 2,g , α 1,g represents the quadratic coefficient and the linear coefficient of the generator power generation cost function; κ represents the load reduction penalty coefficient; represents the active and reactive curtailment power of conventional load at node n; Indicates the active power injected from the main grid; represents the charge and discharge amount of the mth type of electric vehicle when passing through node j between OD pair f; this paper adopts the directed graph G(Φ N ,Φ L ) represents a radial power network, where Φ N represents the set of power network nodes, Φ L Represents the collection of power network lines. The distribution network nodes can be divided into balancing nodes Φ0 and other nodes Φ n , that is, Φ N =Φ0∪Φ n . represents the active load of node n; LMP represents the marginal power price; LMPn represents the marginal power price at node n. node That is Ψ n .

[0203] ② Lower model - traffic-power coupling network equilibrium model considering the charging and discharging behavior of electric vehicles

[0204] Based on the normal operation of the charging facilities, additional service fees are charged to electric vehicle users who use the charging facilities for charging services, thereby converting the traffic-power coupling network model into an electric vehicle user charging fee unit price model as the lower-level model. That is, the lower-level model is a traffic-power coupling network model that takes into account the charging and discharging behavior of electric vehicles. In order to ensure the normal operation of the charging facilities, the charging facility management agency will charge additional service fees for the charging services of electric vehicle users, and the charging fee unit price of electric vehicle users satisfies the following formula:

[0205] μ j,c =LMP j +μ sf

[0206] μ a,c =LMP a +μ wf

[0207] μ j,c represents the unit charging cost of electric vehicle users at static charging station j; μ a,c It represents the unit charging cost of electric vehicle users at wireless power transmission section a; LMP j represents the node marginal electricity price of the grid node corresponding to the static charging facility j; LMP a represents the node marginal electricity price of the grid node corresponding to the wireless power transmission section a; μ sf Represents the additional charging service fee required for electric vehicle users to charge at static charging stations; μ wf It refers to the additional charging service fee required for electric vehicle users to charge on wireless power transmission sections.

[0208] 2) Model transformation and solution algorithm design

[0209] Genetic algorithm is a parallel random search optimization algorithm with the characteristics of mature theory, practical efficiency, strong robustness, etc. However, it also has related problems such as relatively slow convergence speed. Therefore, the present invention uses an improved genetic algorithm to calculate the planning problem of multi-type charging facilities for electric vehicles.

[0210] Step 1, initializing relevant parameters of the genetic algorithm, wherein the relevant parameters include population size, number of iterations, mutation probability, and crossover probability;

[0211] Step 2: Generate an initial population, using binary code to represent the locations of all static charging stations and wireless charging sections in the upper model to generate an initial population that meets the constraint conditions;

[0212] Step 3: parsing the individual codes of the population, obtaining the location of the static charging station and the wireless power transmission section by parsing the codes, and feeding back the location of the charging facilities to the lower model;

[0213] Step 4: Solve the traffic-power coupling network model using an alternating iterative algorithm to obtain the electric vehicle traffic flow distribution and the optimal power flow distribution of the distribution network under the location layout of the charging facilities;

[0214] Step 4.1, initialize the model parameters of the traffic-power coupling network model, the model parameters include basic data of traffic network roads, basic data of power network, coupling network relationship information, and travel demand of electric vehicle users, and set the number of iterations n = 0; at the same time, generate the initial strategy of the optimal power flow model of the distribution network, and let the electric vehicle charge and discharge charge is 0, solving the optimal power flow model of the distribution network to calculate the initial node marginal electricity price LMP0;

[0215] The basic data of the traffic network roads include the road section capacity, road section length, and road section free flow travel time;

[0216] The basic data of the power network include the maximum current amplitude, the minimum / maximum voltage amplitude, the maximum active / reactive power of the balancing node, the maximum active / reactive power of the generator, the maximum active / reactive power of the wireless charging section, the power purchase cost coefficient of the upper power grid, the power reduction penalty coefficient, and the impedance of the line; among them, active power is the power actually consumed by the electrical equipment, which is the power that converts electrical energy into other forms of energy (such as mechanical energy, light energy, thermal energy, etc.). For example, when current passes through a resistor, all electrical energy is converted into thermal energy, and the power at this time is active power. Reactive power is mainly used to establish and maintain an alternating magnetic field. In an AC circuit, inductors and capacitors store and release energy. For an inductor, when current passes through, it will establish a magnetic field and store electrical energy in the form of magnetic field energy; for a capacitor, it will establish an electric field and store electrical energy in the form of electric field energy.

[0217] For example, in a circuit containing inductance, such as the winding of an electric motor, a magnetic field is generated when current passes through the winding, which is necessary for the normal operation of the motor. However, the process of establishing and maintaining this magnetic field is not directly converted into other useful energy forms like the electrical energy consumed by resistance. The power involved in this process is reactive power.

[0218] The coupling network relationship information mainly includes whether a static charging station is built at the road node and whether the road section is a wireless charging and discharging circuit section;

[0219] The travel demand of electric vehicle users mainly includes the flow demand at the travel starting point and the departure point, that is, the travel volume at the starting point and the arrival volume at the departure point, with the unit of XX vehicles / h.

[0220] Step 4.2, solving the electric vehicle user traffic equilibrium model: after the upper model gives the location of the charging facility, combined with the initial node marginal electricity price LMP0, the electric vehicle user traffic equilibrium model is solved using the SRA algorithm to obtain the travel paths and charging and discharging selection results of all electric vehicle users under the charging facility network location layout and at the electricity price, thereby obtaining the traffic flow distribution TF that meets the user equilibrium condition. n+1 and charging and discharging selection results;

[0221] In some embodiments, it can be known from the foregoing content that the specific process of using the SRA algorithm to solve the electric vehicle user traffic equilibrium model is to convert the first objective function into a sub-problem and a sub-problem constraint set of minimizing the travel cost of the m-th type of user between the OD pair f, wherein the sub-problem is specifically:

[0222]

[0223] The sub-problem constraint set is specifically:

[0224]

[0225] Among them, O represents the set of starting points in the starting and ending points, and D represents the set of end points in the starting and ending points. represents a 0-1 variable, indicating whether the path of the mth user under OD pair f passes through the segment (i, j); It means that if the road segment a is on the best path for the mth user between the OD pair f∈F, then The value is 0, otherwise The domain of is unconstrained; M represents a very large constant; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the m-th electric vehicle user under OD does not pass through the road section; a Represents the set of traffic sections in the traffic network;

[0226] The specific solution process is:

[0227] B1. Initialize the parameters of the SRA algorithm, including setting the iteration parameter n = 1, the convergence parameter

[0228] B2. Solve the traffic equilibrium model of electric vehicle users, calculate the sub-problem of the mth type of electric vehicle users between OD pairs f, obtain the travel path and charging and discharging selection results of the electric vehicle users of this type, and then repeat the above process until the travel path and charging and discharging selection results of all electric vehicle users are obtained, and on this basis, calculate the current traffic network flow TF n The above process refers to calculating the sub-problem of the m-th type of electric vehicle users between the OD pair f, obtaining the travel path and charging and discharging selection results of the electric vehicle users of this type, and calculating from the first type to the last type.

[0229] With the travel path selection of each type of user, that is, which road each car takes, the traffic flow of each road can naturally be counted, that is, the traffic network flow TF n It's calculated.

[0230] B3. Perform a direction search and use the BPR function to update the travel impedance of all road sections in the traffic network. At the same time, update other relevant information in the traffic network (the current vehicle travel time of the road section). Use the all-or-nothing allocation method again to obtain the new traffic flow distribution of OD to f. Search direction is TF f -TF n ;

[0231] The traffic impedance (travel impedance) of the road is a basic parameter of the road network and will not be elaborated on here.

[0232] TF f represents the traffic flow between OD pair f; it is a set that aggregates the different traffic conditions on the road section between OD pair f;

[0233]

[0234] B4. Update the step size and path flow, let λ n is the auxiliary variable in the nth iteration process, λ1 is a given value (generally ranging from 5 to 50), λ n According to the following formula, the step length in the nth iteration is The formula for updating the path traffic flow is:

[0235]

[0236] in, represents the traffic flow distribution of OD to f in the nth iteration, λ n-1 represents the auxiliary variable in the n-1th iteration process, represents the traffic flow distribution of OD pair f in the n-1th iteration;

[0237] η a , η b It is an auxiliary parameter for adjusting the step length in the process of flow distribution. Its value is adjusted according to the degree of change of each flow distribution result to obtain a suitable step length. Generally, η a Greater than 1, η b It is 0 to 1.

[0238] TF n It represents the current traffic network flow result of the nth traffic distribution, which is a set including the traffic flow situation of each path.

[0239] B5. Check whether it converges. If Then the final result is output to obtain the traffic flow distribution of electric vehicles; otherwise, n=n+1, and go to B3 to continue iteration.

[0240] Step 4.3: Solve the optimal power flow model of the distribution network, and calculate the traffic flow distribution TF under the user equilibrium condition. n+1 Calculate the charging and discharging charge of electric vehicle users based on the charging and discharging selection results The optimal power flow model of the distribution network is solved by the interior point method to obtain the optimal power flow of the distribution network and update the node marginal electricity price χ n+1 ;

[0241] The node marginal price is the selling price, and χ0 is the purchase price of electricity purchased by the distribution network. This is equivalent to the purchase price (χ0) and selling price (node ​​marginal price) of supermarket goods being the same.

[0242] Traffic flow distribution TF n+1 Represents the traffic flow state of the traffic network, which is a traffic flow set.

[0243] Step 4.4: Determine whether the traffic flow distribution and the node marginal electricity price meet the convergence conditions. If so, output the optimal power flow of the distribution network, the traffic flow distribution and the node marginal electricity price, and then proceed to step 5. Otherwise, return to step 4.2.

[0244] In some embodiments, the specific criteria for judging whether the traffic flow distribution and the node marginal electricity price meet the convergence condition are:

[0245] If the traffic flow distribution TF n and node marginal electricity price χ n Satisfy {||TF n+1 -TF n ||,||LMP n+1 -LMP n ||}<ξ, then the traffic flow distribution TF is determined n and node marginal electricity price χ n Convergence, where LMPn It represents the marginal electricity price of the node during the nth iteration; ξ represents the allowed convergence value, which is a given value. The specific value depends on your actual needs, usually a few percent or a few thousandths.

[0246] Step 5, performing selection, crossover and mutation operations: feeding back the electric vehicle traffic flow distribution and the optimal power flow distribution of the distribution network under the layout of the charging facilities to the upper model, calculating the total system cost and converting it into an initial fitness value, adjusting the initial fitness value, retaining the individual with the best fitness, updating the crossover probability and mutation probability, and then performing crossover, mutation and other operations to generate new individuals to form a new population;

[0247] Step 6: Determine whether the maximum number of iterations has been reached. If so, the iteration is terminated. Otherwise, return to step 3.

[0248] Step 7: Output the optimal charging facility location, balanced electric vehicle traffic flow distribution, optimal power distribution of the distribution network, charging and discharging options for electric vehicle users, and the latest marginal electricity price. See the detailed process for details. Figure 8 .

[0249] The optimal power flow distribution of the distribution network is Figure 8 The output in the right cycle, and then the charge and discharge charge in the right cycle is one of the parameters for quantifying the charge and discharge selection behavior, which can represent the user's charge and discharge selection behavior.

[0250] The present invention conducts case analysis on a coupled network consisting of the Nguyen-Dupuis traffic network and the IEEE 33-node distribution network. In terms of traffic network, this section adopts the Nguyen-Dupuis traffic network, and the relevant parameters such as nodes and section lengths are set as shown in Table 1.

[0251] This section sets four types of electric vehicles. The relevant parameters and values ​​of electric vehicles are shown in Table 2. In terms of distribution network, this section adopts the modified IEEE 33-node distribution network, whose topology is as follows: Figure 3 As shown in the figure, node {1} is a balancing node. Considering the power matching problem, this example adjusts the node load to twice the original value. At the same time, distributed power sources are set at distribution network nodes 10, 18, and 33, with a capacity of 3MW. Other distribution network related parameter settings are shown in Table 3. The adjusted distribution network line and node load and other specific parameter settings are shown in Table 4. This section assumes that all charging facilities will be generated from the candidate set. The modified Nguyen-Dupuis network static charging station candidate nodes and wireless power transmission section candidate sections corresponding to the power network relationship table are shown in Table 5 and Table 6.

[0252] Assume that the government charges an additional service fee of 0.15 and 0.35 USD per kilowatt-hour for charging services provided to electric vehicle users at static charging stations and wireless power transmission sections, respectively. The government charging facility construction investment cost budget is 25 million USD, the construction cost of a single electric vehicle static charging station is 800,000 USD, the annual operating cost of a static charging station is 400,000 USD, the construction cost of a wireless power transmission section is 1 million USD / km, and the annual operating cost of a wireless power transmission section is 6*104 USD / km. The road capacity impact factor of the wireless power transmission section is 0.9. The investment return rate α is set to 0.08, and the operating life of the charging facility δ is set to 20 years. In terms of algorithm parameter settings, the population size is set to 20, the crossover probability parameters are set to k1=0.5, k2=0.7, the mutation probability parameters are set to k3=0.01, k4=0.05, and the maximum number of iterations of the algorithm is 200.

[0253] Table 1 Nguyen-Dupuis transportation network related data

[0254]

[0255] Table 2 Electric vehicle types

[0256]

[0257] Table 3 Values ​​of distribution network related parameters

[0258]

[0259] Table 4 Modified IEEE 33-bus distribution system parameters

[0260]

[0261]

[0262] Table 5 Nguyen-Dupuis network static charging station candidate nodes corresponding to the power network relationship table

[0263]

[0264] Table 6 Nguyen-Dupuis network wireless power transmission section candidate section corresponding to the power network relationship table

[0265]

[0266] 1) Planning results

[0267] The above scenario is planned using the proposed method for site selection and pricing of urban multi-type charging facilities in the transportation-grid coupling network. The final planning result of the charging facilities in the Nguyen-Dupuis network is as follows: Figure 4 The relationship between the charging facility planning results and the distribution network is as follows: Figure 5 The government will build static charging stations at nodes 10 and 12, and wireless power transmission sections at sections 5 and 14. The total system cost is 52,624$ / h, of which the total transportation system cost is 51,718$ / h, the distribution network power generation cost is 592$ / h, and the charging facility construction and operation cost is 314$ / h. Table 7 shows the travel paths and charging and discharging choices of all electric vehicle users in the Nguyen-Dupuis network under this charging facility network.

[0268] Table 7 Electric vehicle travel routes and charging and discharging options

[0269]

[0270]

[0271] Table 8 shows the marginal electricity price of the power network nodes of the distribution network. It can be seen that the marginal electricity price at the 24th node of the distribution network is the highest, which is 165.95 ($ / MWH), and its corresponding charging facility is the wireless power transmission section 5. Table 9 shows the output of the generator sets of the distribution network. It can be seen from the table that the distribution network purchases the most electricity from the main grid, totaling 5.47MW, and the overall loss of the distribution network is 0.308MW.

[0272] Table 8 Marginal electricity prices at distribution network nodes

[0273]

[0274] Table 9 Output of generator sets in distribution network

[0275]

[0276] 2) Optimization effect analysis

[0277] In order to demonstrate the superiority of the planning results of the present invention, three types of scenarios are set up for comparative analysis based on the above planning results. The scenario settings are as follows:

[0278] Scenario 1: The transportation network and the power network operate independently of each other. The V2G discharge behavior of electric vehicles is not considered, and the charging and discharging electricity prices of electric vehicles are fixed.

[0279] Scenario 2: The transportation network and the power network form a coupled network and operate in coordination. The V2G discharge behavior of electric vehicles is not considered. The charging price of electric vehicles adopts a combination of node marginal electricity price and charging service fee, and the discharge price adopts a fixed value.

[0280] Scenario 3: The transportation network and the power network form a coupled network and operate in coordination. The charging and discharging behavior of electric vehicles is taken into consideration. The charging price of electric vehicles adopts a combination of the node marginal electricity price and the charging service fee, and the discharge price adopts a fixed value.

[0281] The node voltage results under three different scenarios are as follows: Figure 6 It can be seen that the voltage amplitudes of most nodes in the distribution network in scenario 3 are higher than those in scenarios 2 and 1. It can be found that the disordered charging of a large number of electric vehicles will affect the safe operation of the power grid and may cause the voltage of the distribution network nodes to exceed the limit. Therefore, attracting electric vehicle users to participate in V2G discharge behavior through the method proposed by the present invention can reduce the impact of electric vehicle charging on the power grid to a certain extent, improve the power supply quality of the power grid, and reduce the risk of voltage exceeding the limit.

[0282] from Figure 6 It can be seen that the voltage amplitudes of most nodes in the distribution network in scenario 3 are higher than those in scenarios 2 and 1. It can be found that the disordered charging of a large number of electric vehicles will affect the safe operation of the power grid and may cause the voltage of the distribution network nodes to exceed the limit. By attracting electric vehicle users to participate in V2G discharge behavior, the impact of electric vehicle charging on the power grid can be reduced to a certain extent, the power supply quality of the power grid can be improved, and the risk of voltage exceeding the limit can be reduced.

[0283] Figure 7 The comparison results of the total operation cost of the traffic-power coupling network in different scenarios (including the total operation cost of the distribution network, the total cost of traffic travel time, and the charging delay time cost caused by electric vehicle users using static charging stations). By analyzing the data, it can be seen that the total operation cost of the traffic-power coupling network in scenario 3 is the smallest. Compared with scenario 2 and scenario 1, the total operation cost of the distribution network in scenario 2 and scenario 1 is almost the same, and the traffic travel time cost decreases by about 1.4%, indicating that the coordinated operation of the traffic-power coupling network in scenario 2 can effectively reduce the travel time cost of the traffic system, and the node marginal electricity price can guide the charging and discharging choices of electric vehicle users. Compared with scenario 3 and scenario 2, the total operation cost of the traffic-power coupling network system in scenario 3 is lower than that in scenario 2, among which the total operation cost of the distribution network in scenario 3 decreases by 19%, while the travel time cost of the traffic system increases slightly. The reason is that some electric vehicle users participate in V2G discharge services, which effectively reduces the power load of the distribution network, and also causes a small amount of congestion on some sections.

[0284] In summary, the traffic-power coupling network model established by the present invention taking into account the charging and discharging behavior of electric vehicles can effectively reduce the operating cost of the distribution network and the time cost of the traffic system, thereby reducing the total system cost.

[0285] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for site selection and pricing of urban multi-type charging facilities considering the traffic-grid coupling network, characterized in that: include: Based on the traffic network data and the types of electric vehicle users, a traffic equilibrium model for electric vehicle users is constructed that takes into account the heterogeneity of electric vehicle users. Based on the charging and discharging process of electric vehicles and the cost of distribution network, an optimal power flow model of distribution network considering the charging and discharging process of electric vehicles is constructed; Constructing a traffic-power coupling network model based on the electric vehicle user traffic equilibrium model and the distribution network optimal power flow model; A location selection model for multi-type charging facilities for electric vehicles is constructed with the minimum total cost of all expenses as the objective function; Constructing a two-layer mathematical programming model, wherein the upper model in the two-layer mathematical programming model is the multi-type charging facility site selection model for electric vehicles, and the lower model in the two-layer mathematical programming model is a traffic-power coupling network model; The upper model is solved based on a genetic algorithm to obtain the addresses of various types of charging facilities, and the addresses are fed back to the lower model. The lower model is solved based on an alternating iterative algorithm to ultimately obtain the optimal locations of various types of charging facilities, balanced flow distribution of electric vehicle traffic, optimal power flow distribution of the distribution network, and charging and discharging options for electric vehicle users.

2. A method for site selection and pricing of urban multi-type charging facilities considering the traffic-grid coupling network according to claim 1, characterized in that: The electric vehicle user traffic equilibrium model includes a first objective function and a first constraint set, wherein the first objective function is expressed as: in, represents the travel cost of the mth electric vehicle user who chooses path r and meets the electric vehicle user equilibrium condition; q * The traffic network flow allocation scheme represented by ; represents the traffic flow of the mth type of electric vehicle user passing through path r between OD pairs f under the electric vehicle user equilibrium condition; where OD pairs represent the starting and ending points of the traffic network flow of electric vehicle users; F represents the set of all OD pairs in the traffic network; M represents the total number of types of all electric vehicle users; r represents the travel path, R f,m represents the set of all travel paths between the OD pair f of the m-th electric vehicle user; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f; Among them, θ m is the time value coefficient of the mth type of electric vehicle user; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the mth electric vehicle user under OD does not pass through section a. represents the total time for the mth electric vehicle user to pass through section a under OD pair f; Ψ a represents the set of traffic sections in the traffic network, Ψ n represents the set of all nodes in the transportation network, It represents the charging and discharging delay time when the m-th electric vehicle user is charging and discharging at the state charging station j between the OD pair f; They represent the cost and benefit of the mth type of user electric vehicle user charging and discharging at the static charging station j and the wireless power transmission section a between the OD pair f respectively; The first constraint set includes traffic flow conservation constraints, power balance constraints, road travel time constraints, wireless power transmission section charging and discharging behavior constraints, static charging station charging and discharging constraints, electric vehicle charging and discharging economic cost constraints, power consumption constraints, and initial power and power safety constraints, wherein: The traffic flow conservation constraint is specifically: Among them, d f,m represents the transportation demand of the mth type of electric vehicle user in OD pair f; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f; Indicates whether the road segment a is on the available path r for the mth type of electric vehicle user between OD pair f. If so, Otherwise R f,m represents the set of available paths for the mth type of electric vehicle user between OD pair f, q a represents the traffic flow on road section a; The power balance constraint is specifically: in, represents the power of the mth electric vehicle when it passes through node j between OD pair f; represents the power of the mth electric vehicle when passing through node i between OD pair f; represents the charge and discharge amount of the mth type of electric vehicle between OD pair f when passing through node j; represents the charge and discharge amount of the mth type electric vehicle when passing through section a between OD pair f; e s,a Indicates the power consumption of electric vehicles when passing through road section a; Ψ wcl represents the set of wireless power transmission sections in the traffic network; Ψ r Represents the set of all common road segments in the transportation network; The road travel time constraints are specifically: Among them, t a (q a ) represents the travel time of electric vehicle users through road section a; represents the free travel time of road section a under zero traffic flow; q a represents the traffic flow on road section a; c a It represents the road capacity of section a; represents the actual passing time of the mth electric vehicle user passing through the road section a between the OD pair f; The charging and discharging behavior constraints of the wireless power transmission section are specifically as follows: in, Indicates the charging power and discharging power of electric vehicles on the wireless power transmission section; represents the charging time and discharging time of the mth electric vehicle user between the OD pair f when passing through the wireless power transmission section a; represents a 0-1 variable, indicating whether the mth type of electric vehicle user charges and discharges on road section a between OD pair f; represents the charge and discharge amount of the mth type electric vehicle when passing through section a between OD pair f; t a,max It represents the longest time that electric vehicles travel on road section a; The static charging station charging and discharging constraints are specifically: in, Indicates the charging power and discharging power of a static charging station; represents the charging and discharging time of the mth electric vehicle user at the static charging station j between the OD pair f; They respectively represent whether the mth type of electric vehicle user between OD pair f is charging and discharging at node j; Indicates the charging and discharging delay time when electric vehicle users are charging and discharging at a static charging station; s Represents a collection of static charging stations; represents the charge and discharge amount of the mth type of electric vehicle between OD pair f when passing through node j; The economic cost constraints of electric vehicle charging and discharging are specifically: in, represents the cost and benefit of the mth electric vehicle user when charging and discharging at the static charging station j and the wireless power transmission section a between the OD pair f; It represents the unit cost of electric vehicle users to charge and discharge using static charging stations; It represents the unit cost of charging and discharging electric vehicle users on the wireless power transmission section; They respectively indicate whether the mth type of electric vehicle user is charging and discharging on the wireless power transmission section a between the OD pair f; The power consumption constraint is specifically: e s,a represents the power consumption of electric vehicle users on road section a; h represents the power consumption of electric vehicles per kilometer, in kWh / km; l a It represents the distance length of road section a in the traffic network, in km; The initial power and power safety constraints are specifically as follows: Among them, s m,o represents the initial power of the m-th electric vehicle; ι represents the minimum SOC threshold power of the electric vehicle when it reaches the destination; O represents the set of starting points in the starting and ending points, D represents the set of end points in the starting and ending points, Ψ n Represents the set of all nodes in the transportation network.

3. A method for site selection and pricing of urban multi-type charging facilities considering the traffic-grid coupling network according to claim 1, characterized in that: The distribution network optimal power flow model includes a second objective function, which is specifically: minF D +F Z +F L Among them, F D Indicates the power generation cost of the generator; F Z F represents the cost of purchasing electricity from the main grid; L Node load reduction penalty cost; α 2,g , α 1,g represents the coefficient of the quadratic term and the coefficient of the linear term of the power generation cost function of the generator; represents the active power of the generator at node n; represents the active power injected from the main grid; χ0 represents the unit power purchase cost of the distribution network from the main grid; κ represents the load reduction penalty coefficient; represents the active and reactive power reduction of conventional load at node n; Φ0, Φ n Represents balanced nodes and unbalanced nodes in the distribution network.

4. A method for site selection and pricing of urban multi-type charging facilities considering the traffic-grid coupling network according to claim 1, characterized in that: The traffic-power coupling network model is specifically expressed as: in, represents the charge and discharge of the electric vehicle at the distribution network node n, represents the coupling connection relationship between traffic node i and distribution network node n. If traffic node i is coupled with power grid node n, then The value is 1, otherwise the value is 0; l an represents the coupling connection relationship between traffic section a and power grid node n. If traffic section a is coupled with power grid node n, then l an The value is 1, otherwise the value is 0; represents the traffic flow of the mth type of electric vehicle user on path r between OD pair f; It represents the charge and discharge amount of the mth electric vehicle when passing through node i between OD pair f.

5. A method for site selection and pricing of urban multi-type charging facilities considering the transportation-grid coupling network according to claim 2, characterized in that: The upper model is specifically: minF=F TN +F ST +F PN +F EW +F EB Where F represents the total system cost; F TN F represents the total travel time cost of the travelers in the transportation system; ST represents the time cost of charging and discharging delay when electric vehicle users use static charging facilities; F PN F represents the power generation cost of the distribution network supplying power to the charging facilities; EW represents the total cost of charging facility construction; F EB represents the total daily operating cost of the charging facility; θ represents the average time value coefficient; α represents the investment return rate; δ represents the operating life of the charging facility; Η s represents a set of candidate nodes for static charging facilities; a represents the set of candidate nodes for wireless power transmission segment; k j represents the construction cost of building a static charging station at node j; g cs represents the annual operating cost of a single static charging facility; g wcl represents the annual operating cost per unit length of wireless power transmission section; z j Indicates whether to build a static charging station at candidate node j; z a Indicates whether to construct a wireless power transmission section on the candidate section a; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. 0 means that the path of the mth type of electric vehicle user under OD does not pass through section a; χ0 represents the unit power purchase cost of the distribution network from the main grid; Represents the active power injected from the main grid; α 2,g , α 1,g represents the quadratic coefficient and the linear coefficient of the generator power generation cost function; κ represents the load reduction penalty coefficient; represents the active and reactive curtailment power of conventional load at node n; Indicates the active power injected from the main grid; represents the charge and discharge amount of the mth type of electric vehicle when passing through node j between OD pair f; this paper adopts the directed graph G(Φ N ,Φ L ) represents a radial power network, where Φ N represents the set of power network nodes, Φ L Represents the collection of power network lines. The distribution network nodes can be divided into balancing nodes Φ0 and other nodes Φ n , that is, Φ N =Φ0∪Φ n . represents the active load of node n; Ψ node That is Ψ n 。 6. A method for site selection and pricing of urban multi-type charging facilities considering the transportation-grid coupling network according to claim 5, characterized in that: Based on the normal operation of the charging facilities, additional service fees are charged to electric vehicle users who use the charging facilities for charging services, thereby converting the traffic-power coupling network model into an electric vehicle user charging fee unit price model as the lower-level model, and the lower-level model is specifically: m j,c =LMP j +m sf m a,c =LMP a +m wf Among them, μ j,c represents the unit charging cost of electric vehicle users at static charging station j; μ a,c It represents the unit charging cost of electric vehicle users at wireless power transmission section a; LMP j represents the node marginal electricity price of the grid node corresponding to the static charging facility j; LMP a represents the node marginal electricity price of the grid node corresponding to the wireless power transmission section a; μ sf Represents the additional charging service fee required for electric vehicle users to charge at static charging stations; μ wf It refers to the additional charging service fee required for electric vehicle users to charge on wireless power transmission sections.

7. A method for site selection and pricing of urban multi-type charging facilities considering the transportation-grid coupling network according to claim 6, characterized in that: The improved genetic algorithm is used to solve the two-level mathematical programming model. The specific process is as follows: Step 1, initializing relevant parameters of the genetic algorithm, wherein the relevant parameters include population size, number of iterations, mutation probability, and crossover probability; Step 2: Generate an initial population, using binary code to represent the locations of all static charging stations and wireless charging sections in the upper model to generate an initial population that meets the constraint conditions; Step 3: parsing the individual codes of the population, obtaining the location of the static charging station and the wireless power transmission section by parsing the codes, and feeding back the location of the charging facilities to the lower model; Step 4: Solve the traffic-power coupling network model using an alternating iterative algorithm to obtain the electric vehicle traffic flow distribution and the optimal power flow distribution of the distribution network under the location layout of the charging facilities; Step 5, performing selection, crossover and mutation operations: feeding back the electric vehicle traffic flow distribution and the optimal power flow distribution of the distribution network under the layout of the charging facilities to the upper model, calculating the total system cost and converting it into an initial fitness value, adjusting the initial fitness value, retaining the individual with the best fitness, updating the crossover probability and mutation probability, and then performing crossover, mutation and other operations to generate new individuals to form a new population; Step 6: Determine whether the maximum number of iterations has been reached. If so, the iteration is terminated. Otherwise, return to step 3. Step 7: Output the optimal charging facility location, balanced flow distribution of electric vehicle traffic, optimal power flow distribution of the distribution network, charging and discharging choices of electric vehicle users, and the latest marginal electricity price.

8. A method for site selection and pricing of urban multi-type charging facilities considering the traffic-grid coupling network according to claim 7, characterized in that: The specific process of step 4 is: Step 4.1, initialize the model parameters of the traffic-power coupling network model, the model parameters include basic data of traffic network roads, basic data of power network, coupling network relationship information, and travel demand of electric vehicle users, and set the number of iterations n = 0; at the same time, generate the initial strategy of the optimal power flow model of the distribution network, and let the electric vehicle charge and discharge charge is 0, solving the optimal power flow model of the distribution network to calculate the initial node marginal electricity price LMP0; Step 4.2, solving the electric vehicle user traffic equilibrium model: after the upper model gives the location of the charging facility, combined with the initial node marginal electricity price LMP0, the electric vehicle user traffic equilibrium model is solved using the SRA algorithm to obtain the travel paths and charging and discharging selection results of all electric vehicle users under the charging facility network location layout and at the electricity price, thereby obtaining the traffic flow distribution TF that meets the user equilibrium condition. n+1 ; Step 4.3: Solve the optimal power flow model of the distribution network, and calculate the traffic flow distribution TF under the user equilibrium condition. n+1 Calculate the charging and discharging charge of electric vehicle users based on the charging and discharging selection results The optimal power flow model of the distribution network is solved by the interior point method to obtain the optimal power flow of the distribution network and update the node marginal electricity price χ n+1 ; Step 4.4: Determine whether the traffic flow distribution and the node marginal electricity price meet the convergence conditions. If so, output the optimal power flow of the distribution network, the traffic flow distribution and the node marginal electricity price, and then proceed to step 5. Otherwise, return to step 4.

2.

9. A method for site selection and pricing of urban multi-type charging facilities considering the traffic-grid coupling network according to claim 8, characterized in that: The specific process of solving the electric vehicle user traffic equilibrium model using the SRA algorithm is to convert the first objective function into a sub-problem and a sub-problem constraint set of minimizing the travel cost of the m-th user between OD pairs f, wherein the sub-problem is specifically: The sub-problem constraint set is specifically: Among them, O represents the set of starting points in the starting and ending points, and D represents the set of end points in the starting and ending points. represents a 0-1 variable, indicating whether the path of the mth user under OD pair f passes through the segment (i, j); It means that if the road segment a is on the best path for the mth user between the OD pair f∈F, then The value is 0, otherwise The domain of is unconstrained; M represents a very large constant; is a 0-1 variable, indicating whether the path of the mth electric vehicle user under OD pair f passes through section a; 1 means that the path of the m-th electric vehicle user under OD pair f must pass through section a. A value of 0 means that the path of the mth type of electric vehicle user under OD does not pass through the road section; The specific solution process is: B1. Initialize the parameters of the SRA algorithm, including setting the iteration parameter n = 1, the convergence parameter B2. Solve the traffic equilibrium model of electric vehicle users, calculate the sub-problem of the m-th type of electric vehicle users between OD pairs f, and obtain the travel path and charging and discharging selection results of the electric vehicle users of this type. Then repeat the sub-problem of calculating the m-th type of electric vehicle users between OD pairs f in this step until the travel paths and charging and discharging selection results of all electric vehicle users are obtained, and on this basis, calculate the current traffic network flow TF n ; B3. Perform a direction search and use the BPR function to update the travel impedance of all road sections in the traffic network. At the same time, update the current vehicle travel time of the road sections in the traffic network. Use the all-or-nothing allocation method again to obtain the new traffic flow distribution of OD to f. Search direction is TF f -TF n TF f represents the traffic flow between OD pair f; B4. Update the step size and path flow, let λ n is the auxiliary variable in the nth iteration process, λ1 is a given value, and the step length in the nth iteration process The path traffic flow update formula is: in, represents the traffic flow distribution of OD to f in the nth iteration, λ n-1 represents the auxiliary variable in the n-1th iteration process, represents the traffic flow distribution of OD pair f in the n-1th iteration, η a , η b An auxiliary parameter for adjusting the step size during the flow distribution process; TF n The current traffic network flow result of the nth traffic allocation is a set containing the traffic flow conditions of each path; B5. Check whether it converges. If Then the final result is output to obtain the traffic flow distribution of electric vehicles; otherwise, n=n+1, and go to B3 to continue iteration.

10. A method for site selection and pricing of urban multi-type charging facilities considering the transportation-grid coupling network according to claim 8, characterized in that: In step 4.4, the specific criteria for judging whether the traffic flow distribution and the node marginal electricity price meet the convergence conditions are: If the traffic flow distribution TF n and node marginal electricity price χ n Satisfy {||TF n+1 -TF n ||,||LMP n+1 -LMP n ||}<ξ, then the traffic flow distribution TF is determined n and node marginal electricity price χ n Convergence, where LMP n represents the node marginal electricity price during the nth iteration, and ξ represents the allowed convergence value.

Images