Layout planning method and device of component mobile charging facility

Abstract

The application relates to the technical field of transportation, in particular to a layout planning method and device for a component type mobile charging facility, wherein the method comprises the following steps: acquiring actual distribution conditions and actual charging standards of charging equipment of multiple operators; initializing the actual distribution conditions and the actual charging standards to obtain initial distribution conditions and initial charging standards, so as to calculate total revenue of the multiple operators based on the initial distribution conditions and the initial charging standards; and based on the total revenue, iteratively optimizing the initial distribution conditions and the initial charging standards by using a particle swarm algorithm and an evolutionary game strategy to obtain target distribution conditions meeting a preset iteration stop condition. Thus, the problems in the related art that due to the lack of effective pricing strategies, the fierce market competition cannot be effectively responded to, the market share is easily lost, the profit is reduced, and due to the unreasonable distribution of charging facilities, the utilization rate of the charging equipment is possibly reduced and the user experience is affected are solved.

Description

Technical Field

[0001] This application relates to the field of transportation technology, and in particular to a method and apparatus for planning the layout of modular mobile charging facilities. Background Technology

[0002] Driven by the "dual carbon" policy, the electric vehicle and its charging infrastructure industry has developed rapidly. However, while fixed charging facilities can provide stable charging services, they have limitations in terms of initial investment costs, geographical distribution adaptability, and flexibility.

[0003] In related technologies, modular mobile charging facilities can be utilized, comprising two parts: a socket assembly and a charger assembly. The socket assembly can be pre-installed at selected locations and connected to the local power grid to form a fixed charging point. The charger assembly can be connected to the socket assembly when needed to quickly establish a temporary charging station. When charging demand increases in a specific area, the charger assembly can be quickly deployed to meet immediate charging needs. Once demand decreases, the charger assembly can be rapidly removed and redistributed to other areas with higher demand, thereby maximizing resource utilization.

[0004] However, the lack of effective pricing strategies in related technologies may prevent them from effectively coping with fierce market competition, easily leading to the loss of market share and reduced profits. Furthermore, the unreasonable distribution of charging facilities results in an oversupply of charging facilities in some areas, while other areas experience a shortage, reducing the utilization rate of charging equipment and affecting user experience, which urgently needs improvement. Summary of the Invention

[0005] This application provides a layout planning method and apparatus for modular mobile charging facilities to solve the problems in related technologies, such as the lack of effective pricing strategies, the inability to effectively cope with fierce market competition, the easy loss of market share and reduced profits, and the possibility of reduced utilization of charging equipment and negative impact on user experience due to unreasonable distribution of charging facilities.

[0006] The first aspect of this application provides a layout planning method for modular mobile charging facilities, comprising the following steps: obtaining the actual distribution and actual charging standards of charging equipment from multiple operators; initializing the actual distribution and the actual charging standards to obtain an initial distribution and an initial charging standard, and calculating the total revenue of the multiple operators based on the initial distribution and the initial charging standard; and iteratively optimizing the initial distribution and the initial charging standard based on the total revenue using a particle swarm optimization algorithm and an evolutionary game strategy until a preset iteration stopping condition is reached to obtain a target distribution that satisfies the preset iteration stopping condition.

[0007] Optionally, in one embodiment of this application, calculating the total revenue of the multiple operators includes: quantifying the changes in charging demand in different time periods and regions using a time price elasticity matrix and a spatial price elasticity matrix based on price elasticity and changes in the number of charging facilities; adding the changes in charging demand to a preset baseline charging demand to obtain the total charging demand under different prices and different numbers of charging facilities; and obtaining the total revenue of the multiple operators based on the total charging demand and the utility of electric vehicle user charging behavior.

[0008] Optionally, in one embodiment of this application, the total benefit is:

[0009]

[0010] Where B represents the operator's daily revenue, and K represents the total number of regions. The charging price at charging station j within time period t. Let N be the electricity purchase price for time period t. k Let P be the number of charging stations in region k, T be the length of the large time period, Δt be the length of the hourly period, and P be the number of charging stations in region k. j The charging power of charging station j Let η be the number of electric vehicles in charging station j during time period i. j For charging efficiency, M j,t This represents the number of charging piles at charging station j within time period t. The maintenance cost per Δt for a single charging pile at a charging station.

[0011] Optionally, in one embodiment of this application, the preset iteration stopping condition is reaching a preset number of iterations or reaching a preset convergence condition, wherein the preset convergence condition is:

[0012]

[0013] Where, N v For matrix v iter Number of elements, ∈ v These are the preset parameters.

[0014] Optionally, in one embodiment of this application, the expression for the evolutionary game strategy is:

[0015] λ iter+1 ←γ1λ iter +(1-β1)λ iter+1

[0016] M iter+1 ←round[γ2M iter +(1-γ2)M iter+1 ]

[0017] Where, λiter M represents the pricing result of the charging station in the iterth iteration. iter This represents the distribution of chargers within the charging station in the iterth iteration, where γ1 and γ2 are preset parameters, and round[·] is the rounding function.

[0018] A second aspect of this application provides a layout planning device for modular mobile charging facilities, comprising: an acquisition module for acquiring the actual distribution and actual charging standards of charging equipment from multiple operators; a calculation module for initializing the actual distribution and the actual charging standards to obtain an initial distribution and an initial charging standard, and calculating the total revenue of the multiple operators based on the initial distribution and the initial charging standard; and an optimization module for iteratively optimizing the initial distribution and the initial charging standard based on the total revenue using a particle swarm optimization algorithm and an evolutionary game strategy until a preset iteration stopping condition is reached, thereby obtaining a target distribution that satisfies the preset iteration stopping condition.

[0019] Optionally, in one embodiment of this application, the calculation module includes: a quantification unit, used to quantify the changes in charging demand in different time periods and different regions based on price elasticity and changes in the number of charging facilities, using a time price elasticity matrix and a spatial price elasticity matrix; a first calculation unit, used to add the changes in charging demand to a preset benchmark charging demand to obtain the total charging demand under different prices and different numbers of charging facilities; and a second calculation unit, used to obtain the total revenue of the multiple operators based on the total charging demand and the utility of electric vehicle user charging behavior.

[0020] Optionally, in one embodiment of this application, the total benefit is:

[0021]

[0022] Where B represents the operator's daily revenue, and K represents the total number of regions. The charging price at charging station j within time period t. Let N be the electricity purchase price for time period t. k Let P be the number of charging stations in region k, T be the length of the large time period, Δt be the length of the hourly period, and P be the number of charging stations in region k. j The charging power of charging station j Let η be the number of electric vehicles in charging station j during time period i. j For charging efficiency, M j,t This represents the number of charging piles at charging station j within time period t. The maintenance cost per Δt for a single charging pile at a charging station.

[0023] Optionally, in one embodiment of this application, the preset iteration stopping condition is reaching a preset number of iterations or reaching a preset convergence condition, wherein the preset convergence condition is:

[0024]

[0025] Where, N v For matrix v iter Number of elements, ∈ v These are the preset parameters.

[0026] Optionally, in one embodiment of this application, the expression for the evolutionary game strategy is:

[0027] λ iter+1 ←γ1λ iter +(1-γ1)λ iter+1

[0028] M iter+1 ←round[γ2M iter +(1-γ2)M iter+1 ]

[0029] Where, λ iter M represents the pricing result of the charging station in the iterth iteration. iter This represents the distribution of chargers within the charging station in the iterth iteration, where γ1 and γ2 are preset parameters, and round[·] is the rounding function.

[0030] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the layout planning method for modular mobile charging facilities as described in the above embodiments.

[0031] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described layout planning method for modular mobile charging facilities.

[0032] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described layout planning method for modular mobile charging facilities.

[0033] This application embodiment can obtain the actual distribution and charging standards of charging equipment from multiple operators. Then, it initializes the initial distribution and initial charging standards to calculate the total revenue of multiple operators. Based on the total revenue, iterative optimization of the initial distribution and initial charging standards can be performed using particle swarm optimization and evolutionary game theory strategies until a preset iteration stopping condition is reached, resulting in a target distribution that satisfies the preset iteration stopping condition. This maximizes economic benefits and promotes the efficient utilization and rational layout of charging facilities. Therefore, it solves the problems in related technologies, such as the lack of effective pricing strategies, inability to effectively cope with fierce market competition, easy loss of market share, reduced profits, and the potential for reduced charging equipment utilization and negative user experience due to unreasonable charging facility distribution.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0036] Figure 1 This is a flowchart illustrating a layout planning method for a modular mobile charging facility according to an embodiment of this application;

[0037] Figure 2 This is a schematic diagram illustrating an application mode of a component-based mobile charging facility according to an embodiment of this application;

[0038] Figure 3 This is a flowchart illustrating the pricing and charging facility distribution adjustment based on a particle swarm optimization algorithm according to one embodiment of this application.

[0039] Figure 4 A flowchart illustrating a pricing method for charging operators of modular mobile facilities within a city, according to an embodiment of this application;

[0040] Figure 5 This is a schematic diagram of the layout planning device for a modular mobile charging facility provided according to an embodiment of this application;

[0041] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0042] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0043] The following description, with reference to the accompanying drawings, illustrates a layout planning method and apparatus for modular mobile charging facilities according to embodiments of this application. Addressing the issues raised in the background section regarding the related technologies, such as the lack of effective pricing strategies leading to insufficient market competition, reduced market share, and decreased profits, as well as the potential for reduced charging equipment utilization and negative user experience due to unreasonable charging facility distribution, this application provides a layout planning method for modular mobile charging facilities. This method obtains the actual distribution and pricing standards of charging equipment from multiple operators, initializes the initial distribution and pricing standards, calculates the total revenue of multiple operators, and iteratively optimizes the initial distribution and pricing standards using particle swarm optimization and evolutionary game theory until a preset iteration stopping condition is met, resulting in a target distribution that satisfies the preset iteration stopping condition. This maximizes economic benefits and promotes efficient utilization and rational layout of charging facilities. Therefore, this method solves the problems in the related technologies, such as the lack of effective pricing strategies leading to insufficient market competition, reduced market share, decreased profits, and the potential for reduced charging equipment utilization and negative user experience due to unreasonable charging facility distribution.

[0044] Specifically, Figure 1 This is a schematic flowchart illustrating a layout planning method for a modular mobile charging facility provided in an embodiment of this application.

[0045] like Figure 1 As shown, the layout planning method for this modular mobile charging facility includes the following steps:

[0046] In step S101, the actual distribution of charging equipment and actual charging standards of multiple operators are obtained.

[0047] Understandably, the actual distribution refers to the location of all charging facilities operated by the operator, including fixed charging stations and modular mobile charging facilities, as well as the number of chargers at each charging station, combined with... Figure 2 As shown, the application model of modular mobile charging facilities can be obtained. The actual charging standard refers to the service fee price of each charging station of the operator, including the fixed charging facility price of operator B, and the charging service fee price of operator A itself at different time periods (such as peak, valley, and flat).

[0048] In one embodiment, this application can divide charging facility operators into two categories: Operator A, which holds a certain number of modular mobile charging facilities and a certain number of fixed charging facilities; and Operator B, which is the collection of all other operators, which only holds fixed charging facilities. Both parties adjust the charging service fee price with the goal of maximizing their own overall benefit. Operator A can also adjust the distribution of charger components at various charging stations under the constraint of the number of charger components and socket components. For Operator A, the number of particles is approximately 1.5 times the number of variables, including the service fee price setting and the distribution of charger component deployment. For each charging station, three service fees are set according to peak, valley, and flat rates, with the peak, valley, and flat rates implemented during the same time-of-use electricity pricing period.

[0049] This application embodiment obtains the actual distribution and charging standards of charging equipment from multiple operators, which helps to understand the actual distribution and charging information of charging equipment, and provides an information basis for subsequent planning of charging equipment layout.

[0050] In step S102, the actual distribution and actual charging standards are initialized to obtain the initial distribution and initial charging standards, so as to calculate the total revenue of multiple operators based on the initial distribution and initial charging standards.

[0051] Specifically, in the initialization phase of this application embodiment, the price variable part is... Randomly selected from among them, The average service fee is calculated. Regarding the distribution of charger components, the total number of charger components deployed in each characteristic time period meets the upper limit constraint, and the number of charger components in each characteristic time period and at each node does not exceed the number of socket components built at that node. Therefore, in the initialization phase of the number of charger components, firstly, if there are a total of N... MCS For each mobile charging station, then for each characteristic time period, in Intermittent extraction N MCS Given a set of distinct serial numbers, with 0 as the first element, the difference is used to obtain the number of charger components deployed. If this number exceeds the number of socket components to be built at the corresponding node, it is directly set to the latter. It is understandable that the particle velocity v is initialized to a zero vector.

[0052] It is worth noting that for operator B, only fixed charging facilities are available, and there is no issue of adjusting the distribution of charger components; only the position and velocity of particles are initialized for price.

[0053] In addition, combined Figure 3As shown, the embodiments of this application can perform particle fitness calculation, that is, calculate the total revenue of the operator. First, the total charging demand in the grid under the existing price and charger distribution is calculated based on the price elasticity and the number of chargers. Then, the charging load in each charging station in the grid is calculated based on the utility of electric vehicle charging behavior. Finally, the total revenue of the operator is settled.

[0054] This application embodiment, by initializing the actual distribution and actual charging standards, can provide a reasonable starting point for subsequent optimization, effectively reduce unnecessary iterations, accelerate the speed at which the preset iteration stopping conditions are reached, improve algorithm efficiency, and adapt to complex actual situations, thereby improving robustness.

[0055] Optionally, in one embodiment of this application, calculating the total revenue of multiple operators includes: quantifying the changes in charging demand in different time periods and regions using a time price elasticity matrix and a spatial price elasticity matrix based on price elasticity and changes in the number of charging facilities; adding the changes in charging demand to a preset baseline charging demand to obtain the total charging demand under different prices and different numbers of charging facilities; and obtaining the total revenue of multiple operators based on the total charging demand and the utility of electric vehicle user charging behavior.

[0056] It is understood that the preset benchmark charging demand refers to the basic level of electric vehicle charging demand in a specific time period and area under the condition that there are no changes in price and the number of charging facilities. The specific setting can be made by those skilled in the art according to the actual situation, and there are no restrictions here.

[0057] Specifically, in combination Figure 3 As shown, in this embodiment of the application, for grid k (k = 1...K indicates that there are K grids in which modular charging facilities are deployed), the following baseline values ​​and variable symbols are given, namely, the charging price λ. k Number of charging facilities (M) k and charging demand f k Specifically, it can be seen as follows:

[0058]

[0059] Where, λ k,0 M k,0 f k,0 This is the baseline value for the corresponding variable.

[0060] Average benchmark charging price and average charging price The calculation methods are shown in equations (1) and (2):

[0061]

[0062] Where, N k Let k be the number of charging stations in region k.

[0063] Furthermore, in equations (3) and (4), the embodiments of this application can calculate the corresponding changes in charging demand based on the time price elasticity matrix and the spatial price elasticity matrix, respectively. Specifically, it can be shown as follows:

[0064]

[0065]

[0066] in, Let t be the change in charging demand in region k caused by price changes in region l during time period t. For time price elasticity, f pu As the base value for charging demand, Let be the change in charging demand during period t caused by the price change during period s within region k. Let be the change in charging demand caused by the change in the total number of charging facilities within the grid in region k and time period t.

[0067] In this embodiment, the total charging demand f under the influence of price and the number of charging facilities can be obtained by summing the baseline charging demand and the change in charging demand. k Specifically, it can be seen as follows:

[0068]

[0069] Therefore, the embodiments of this application can obtain the relationship between the total charging demand in each time period within the grid and the price of electric vehicle charging facilities and the number of electric vehicle charging facilities in operation.

[0070] In some embodiments, this application can select charging facilities for electric vehicle users within a grid. For a grid, based on the calculation of charging demand within the grid, the charging behavior of electric vehicle users within that grid is treated as a game, and a calculation method is given for allocating charging demand to each charging station. For a grid, traffic network factors are ignored, and a location factor is uniformly used to describe the superiority of charging facility locations. The queuing situation of electric vehicles in charging facilities is considered. A unified parameter index is used to measure the status of electric vehicles.

[0071] Among these, electric vehicle users can choose charging facilities based on the following three assumptions:

[0072] Assumption 1: Since each grid area is small, it is assumed that the electric vehicles with charging needs can reach all charging facilities, and the distance to charging stations has no impact on the choice of electric vehicle users.

[0073] Assumption 2: It is assumed that all electric vehicle charging needs are met.

[0074] Assumption 3: It is assumed that all electric vehicles' choice of charging facilities depends solely on the utility of the charging behavior.

[0075] For grid k, the utility of selecting charging facility j within the sub-period i of time period t is:

[0076]

[0077]

[0078] in, This represents the energy cost of traveling to charging facility j. This represents the time cost of traveling to charging facility j, ignoring the time required for the vehicle to reach the facility. In fact, data shows that electric vehicle users tend to take detours to avoid higher charging costs and waiting times, indicating that the cost of traveling to a charging facility has a low weight. Furthermore, for a small grid, the difference in travel time cost between different charging facilities is small and negligible. Additionally, VOT represents the unit time cost, and t... c This indicates the time taken for the charging process. R represents the queuing time for going to charging facility j during hourly segment i. j Here, θ is the time cost conversion factor, and r is a preset parameter. j The attractiveness factor for buildings near the charging station. SOC is the state of charge during the charging process, E B For battery capacity, η j For charging efficiency, P j The charging power of charging station j.

[0079] The queuing time can be calculated by estimating the number of queuing rounds, as shown in equations (12) to (15):

[0080]

[0081] in, Let I be the number of electric vehicles in charging station j during time period i. i,j Let N be a binary variable representing whether a queue exists; it is 1 if a queue exists and 0 otherwise. k Let be the number of charging stations within the grid, Δt be the hourly segment length, T be the long period length, and β be a preset parameter. Since the expression contains x... i,j ×I i,j The nonlinear part is therefore linearized as follows:

[0082]

[0083]

[0084] Among them, variables were introduced. Multiply by I on both sides of equation (15) i,j Equation (12b) is obtained. Since for a binary variable, I i,j =I i,j 2 This holds true for all equations, therefore, equation (12b) can be transformed into equation (12c). Equations (12d) to (12h) introduce a large positive number Q. EV Perform big M linearization.

[0085] Since the relationship between the utility value of electric vehicle charging behavior and the number of electric vehicles in the grid is discontinuous, the minimum of the norm sum of the differences between the utility value of each charging behavior node in the grid and the average value is used as the equilibrium index for electric vehicle users to select charging facilities, as shown in Equation (16):

[0086]

[0087] Therefore, embodiments of this application can construct a mathematical model describing the spatiotemporal distribution of charging demand at each charging station and the relationship between charging station pricing and the number of chargers.

[0088] It is worth noting that charging facility operators' revenue comes from service fees. For operators who own a certain number of charging stations, if the total number of their charging stations is Ω... CS The operating revenue is shown in equation (17).

[0089] Optionally, in one embodiment of this application, the total benefit is:

[0090]

[0091] Where B represents the operator's daily revenue, and K represents the total number of regions. The charging price at charging station j within time period t. Let N be the electricity purchase price for time period t. k Let P be the number of charging stations in region k, T be the length of the large time period, Δt be the length of the hourly period, and P be the number of charging stations in region k. j The charging power of charging station j Let η be the number of electric vehicles in charging station j during time period i. j For charging efficiency, M j,t This represents the number of charging piles at charging station j within time period t. The maintenance cost per Δt for a single charging pile at a charging station.

[0092] Therefore, this application embodiment provides the operator revenue settlement result under a certain pricing and charger component distribution. Furthermore, the operator revenue settlement result is used as the fitness of a particle. If the current fitness is better than the particle's historical best fitness value, the historical fitness is updated to that value, and the particle's historical best position is updated to the current position. For the entire particle swarm, the global best fitness value and the global best particle position are updated according to the same rules.

[0093] In step S103, based on the total revenue, the initial distribution and initial fee standard are iteratively optimized using the particle swarm optimization algorithm and evolutionary game strategy until the preset iteration stopping condition is reached, thus obtaining the target distribution that satisfies the preset iteration stopping condition.

[0094] It is understood that the preset iteration stopping condition refers to the pre-set standard used in particle swarm optimization and evolutionary game strategies to determine when to stop the iteration process, including reaching a preset number of iterations or reaching a preset convergence condition. The preset number of iterations is also the maximum number of iterations, which can be 1000. The preset convergence condition can be that the change in total payoff between two consecutive iterations is within a certain threshold. The specific settings can be made by those skilled in the art according to the actual situation, and are not limited here.

[0095] Specifically, in combination Figure 3 As shown, the embodiments of this application can perform particle velocity updates, wherein the particle velocity includes an inertial velocity component, a globally optimal velocity component, and an individual optimal velocity component, as shown in equation (18):

[0096] v iter+1 =ω iter v iter +α1(x best,h -x iter )+α2(x best,p -x iter )#(18)

[0097]

[0098] Among them, v iter Let ω be the velocity value in the iterth iteration. iter ω is an inertial parameter. Since we want the particle to have greater exploratory power in the early iterations to reduce the possibility of getting trapped in local optima, ω is therefore... iter The value is relatively large, and the calculation method is shown in equation (19). best,h For the global optimal position of all particles, x iter x represents the current particle position. best,p The optimal position in the particle's history is represented by α1 and α2, which are pre-set parameters.

[0099] For the price part of the variable The embodiments of this application can first calculate at the current speed. If it exceeds If the price constraint range is met, then the corresponding particle position is set at the corresponding boundary, and the correction result is... Then update the speed component of the price part.

[0100] For the variable of the number of charger components and velocity components If the value of a velocity component element is greater than 0.5, it is set to 1; if the value of a velocity component element is less than -0.5, it is set to -1; otherwise, it is set to 0. Furthermore, it can be calculated... If the constraint on the number of node socket components is not met, the corresponding boundary value is set, and the corresponding velocity value is corrected. The particle position correction result is as follows: Furthermore, it is calculated whether the charger component quantity constraint is satisfied. If not, the velocity values ​​corresponding to the charger component quantity distribution of the corresponding particle within this characteristic time period are all set to 0, finally obtaining the updated velocity component of the charger component quantity part.

[0101] Therefore, the embodiments of this application have completed the correction of particle velocity, which can ensure that the variables satisfy the relevant constraints in each iteration, and the velocity value can be as shown in equation (21):

[0102]

[0103] Among them, v iter This represents the example velocity in the iterth iteration, including the price velocity component. and the number of charger components and speed components

[0104] Furthermore, this embodiment of the application can perform particle position updates, wherein the current particle position is calculated by summing the particle position and particle velocity of the previous iteration, as shown below:

[0105] x iter+1 =x iter +v iter+1 #(twenty two)

[0106] Where, x iter+1 x represents the current particle position. iter v represents the particle position from the previous iteration. iter+1 This represents the particle velocity.

[0107] Furthermore, embodiments of this application can terminate the iteration process when a preset iteration stop condition is reached. Optionally, in one embodiment of this application, the preset iteration stop condition is reaching a preset number of iterations or reaching a preset convergence condition, wherein the preset convergence condition is:

[0108]

[0109] Where, N v For matrix v iter Number of elements, ∈ v These are the preset parameters.

[0110] Meanwhile, for operator B who does not own modular mobile charging facilities, the embodiments of this application can also use a similar particle swarm optimization algorithm to determine the optimal pricing of its own charging stations, but only the price part is calculated during the particle initialization and speed update stages.

[0111] It is worth noting that, combined Figure 4 As shown, in the multi-round iteration process, in each round, operator B first adjusts its own pricing based on the current pricing and charger quantity distribution of each charging station. Then, operator A adjusts its own pricing and charger quantity distribution based on the information updated by operator B, thus completing one round of iteration. A moving average strategy is used in each round of operator information updates. Optionally, in one embodiment of this application, the expression of the evolutionary game strategy is:

[0112] λ iter+1 ←γ1λ iter +(1-γ1)λ iter+1 #(twenty four)

[0113] M iter+1 ←round[γ2M iter +(1-γ2)M iter+1 ]#(25)

[0114] Where, λ iter M represents the pricing result of the charging station in the iterth iteration. iter This represents the distribution of chargers within the charging station in the iterth iteration, where γ1 and γ2 are preset parameters, and round[·] is the rounding function.

[0115] Therefore, embodiments of this application can establish an evolutionary game process based on particle swarm optimization algorithm, including pricing strategies for mobile facility charging operators and adjustment strategies for charger components.

[0116] Based on total revenue, this application's embodiments utilize particle swarm optimization and evolutionary game theory to iteratively optimize the initial distribution and initial charging standards. This enables rapid adjustments to prices and charging facility distribution, facilitating responses to market changes, effectively improving charging facility utilization, reducing operating costs, and increasing the operator's total revenue.

[0117] The modular mobile charging facility layout planning method proposed in this application can obtain the actual distribution and charging standards of charging equipment from multiple operators. Then, an initial distribution and initial charging standards are initialized to calculate the total revenue of multiple operators. Based on the total revenue, particle swarm optimization and evolutionary game theory strategies can be used to iteratively optimize the initial distribution and initial charging standards until a preset iteration stopping condition is reached, resulting in a target distribution that satisfies the preset iteration stopping condition. This maximizes economic benefits and promotes the efficient utilization and rational layout of charging facilities. Therefore, it solves the problems in related technologies, such as the lack of effective pricing strategies, inability to effectively cope with fierce market competition, easy loss of market share and reduced profits, and the potential for reduced charging equipment utilization and negative user experience due to unreasonable charging facility distribution.

[0118] Next, referring to the accompanying drawings, a layout planning device for a modular mobile charging facility according to an embodiment of this application is described.

[0119] Figure 5 This is a block diagram of a layout planning device for a modular mobile charging facility according to an embodiment of this application.

[0120] like Figure 5 As shown, the layout planning device 10 for the modular mobile charging facility includes: an acquisition module 100, a calculation module 200, and an optimization module 300.

[0121] Specifically, module 100 is used to obtain the actual distribution of charging equipment and actual charging standards of multiple operators;

[0122] The calculation module 200 is used to initialize the actual distribution and actual charging standards to obtain the initial distribution and initial charging standards, and to calculate the total revenue of multiple operators based on the initial distribution and initial charging standards.

[0123] The optimization module 300 is used to iteratively optimize the initial distribution and initial fee standard based on the total revenue using the particle swarm optimization algorithm and evolutionary game strategy until a preset iteration stopping condition is reached, thus obtaining the target distribution that satisfies the preset iteration stopping condition.

[0124] Optionally, in one embodiment of this application, the calculation module 200 includes: a quantization unit, a first calculation unit, and a second calculation unit.

[0125] The quantification unit is used to quantify the changes in charging demand in different time periods and regions based on price elasticity and changes in the number of charging facilities, using time price elasticity matrix and spatial price elasticity matrix.

[0126] The first calculation unit is used to add the change in charging demand to the preset baseline charging demand to obtain the total charging demand under different prices and different numbers of charging facilities.

[0127] The second calculation unit is used to obtain the total revenue of multiple operators based on the total charging demand and the utility of electric vehicle users' charging behavior.

[0128] Optionally, in one embodiment of this application, the total benefit is:

[0129]

[0130] Where B represents the operator's daily revenue, and K represents the total number of regions. The charging price at charging station j within time period t. Let N be the electricity purchase price for time period t. k Let P be the number of charging stations in region k, T be the length of the large time period, Δt be the length of the hourly period, and P be the number of charging stations in region k. j The charging power of charging station j Let η be the number of electric vehicles in charging station j during time period i. j For charging efficiency, M j,t This represents the number of charging piles at charging station j within time period t. The maintenance cost per Δt for a single charging pile at a charging station.

[0131] Optionally, in one embodiment of this application, the preset iteration stopping condition is reaching a preset number of iterations or reaching a preset convergence condition, wherein the preset convergence condition is:

[0132]

[0133] Where, N v For matrix v iter Number of elements, ∈ v These are the preset parameters.

[0134] Optionally, in one embodiment of this application, the expression for the evolutionary game strategy is:

[0135] λ iter+1 ←γ1λ iter +(1-γ1)λ iter+1

[0136] M iter+1 ←round[γ2M iter +(1-γ2)Miter+1 ]

[0137] Where, λ iter M represents the pricing result of the charging station in the iterth iteration. iter This represents the distribution of chargers within the charging station in the iterth iteration, where γ1 and γ2 are preset parameters, and round[·] is the rounding function.

[0138] It should be noted that the explanation of the above-described method for planning the layout of modular mobile charging facilities also applies to the layout planning device for modular mobile charging facilities in this embodiment, and will not be repeated here.

[0139] The modular mobile charging facility layout planning device proposed in this application can obtain the actual distribution and charging standards of charging equipment from multiple operators. Then, it initializes the initial distribution and initial charging standards to calculate the total revenue of multiple operators. Based on the total revenue, it iteratively optimizes the initial distribution and initial charging standards using particle swarm optimization and evolutionary game theory until a preset iteration stopping condition is reached, obtaining a target distribution that satisfies the preset iteration stopping condition. This maximizes economic benefits and promotes the efficient utilization and rational layout of charging facilities. Therefore, it solves the problems in related technologies, such as the lack of effective pricing strategies, inability to effectively cope with fierce market competition, easy loss of market share and reduced profits, and the potential for reduced charging equipment utilization and negative user experience due to unreasonable charging facility distribution.

[0140] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0141] The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0142] When the processor 602 executes the program, it implements the layout planning method for the component-based mobile charging facility provided in the above embodiments.

[0143] Furthermore, electronic devices also include:

[0144] Communication interface 603 is used for communication between memory 601 and processor 602.

[0145] The memory 601 is used to store computer programs that can run on the processor 602.

[0146] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0147] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0148] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0149] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0150] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described layout planning method for modular mobile charging facilities.

[0151] This application also provides a computer program product that can run computer instructions, which, when executed by a processor, implement the above-described layout planning method for modular mobile charging facilities.

[0152] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0153] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0154] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0155] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0156] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0157] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0158] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0159] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A layout planning method for modular mobile charging facilities, characterized in that, Includes the following steps: The actual distribution and pricing of charging equipment from multiple operators are obtained. Charging facility operators are divided into two categories: Operator A, which owns a certain number of modular mobile charging facilities and a certain number of fixed charging facilities; and Operator B, which is the collection of all other operators and owns only fixed charging facilities. Both operators adjust their charging service fees to maximize their overall benefit. Operator A also adjusts the distribution of charger components at various charging stations under the constraint of the number of charger and socket components. For Operator A, variables include service fee settings and charger component deployment distribution. The actual distribution refers to the location of all operator's charging facilities, including fixed charging stations and modular mobile charging facilities, as well as the number of chargers at each charging station. The actual pricing refers to the service fee price at each operator's charging station, including the fixed charging facility price of Operator B and Operator A's own charging service fee price at different times. Initialize the actual distribution and the actual charging standard to obtain an initial distribution and an initial charging standard, and calculate the total revenue of the multiple operators based on the initial distribution and the initial charging standard; Based on the total revenue, the initial distribution and the initial charging standard are iteratively optimized using particle swarm optimization and evolutionary game strategy until a preset iteration stopping condition is reached, thus obtaining the target distribution that satisfies the preset iteration stopping condition. The calculation of the total revenue of the multiple operators includes: Based on price elasticity and changes in the number of charging facilities, the changes in charging demand in different time periods and regions are quantified using time price elasticity matrices and spatial price elasticity matrices. The change in charging demand is added to the preset baseline charging demand to obtain the total charging demand under different prices and different numbers of charging facilities. Based on the total charging demand and the utility of electric vehicle user charging behavior, the total revenue of the multiple operators is obtained. The total revenue is: in, For the operator's daily revenue, This represents the total number of regions. For time period charging station The charging price within the area, For time period The electricity purchase price, For the region The number of charging stations in the country For a long time period, The length of the hour segment. For charging stations The charging power, For time period At the charging station The number of electric vehicles in the country For charging efficiency, For time period charging station Number of internal charging stations For charging stations Single pile per Operation and maintenance costs.

2. The method according to claim 1, characterized in that, The preset iteration stopping condition is reaching a preset number of iterations or reaching a preset convergence condition, wherein the preset convergence condition is: in, For matrix Number of elements These are the preset parameters.

3. The method according to claim 1, characterized in that, The expression for the evolutionary game strategy is: in, Indicates the first The pricing results for charging stations in this iteration. Indicates the first The results of charger distribution within the charging station in the next iteration. , These are preset parameters. This is a rounding function.

4. A layout planning device for modular mobile charging facilities, characterized in that, include: The acquisition module is used to acquire the actual distribution and actual charging standards of charging equipment from multiple operators. Charging facility operators are divided into two categories: Operator A, which owns a certain number of modular mobile charging facilities and a certain number of fixed charging facilities; and Operator B, which is the collection of all other operators and only owns fixed charging facilities. Both operators adjust charging service fees to maximize their overall benefit. Operator A also adjusts the distribution of charger components at various charging stations under the constraint of the number of charger components and socket components. For Operator A, variables include service fee settings and charger component deployment distribution. The actual distribution refers to the location of all charging facilities of the operator, including fixed charging stations and modular mobile charging facilities, as well as the number of chargers at each charging station. The actual charging standards refer to the service fee prices of each charging station of the operator, including the fixed charging facility prices of Operator B and the charging service fee prices of Operator A itself at different time periods. The calculation module is used to initialize the actual distribution and the actual charging standard to obtain the initial distribution and the initial charging standard, so as to calculate the total revenue of the multiple operators based on the initial distribution and the initial charging standard; The optimization module is used to iteratively optimize the initial distribution and the initial charging standard based on the total revenue using particle swarm optimization and evolutionary game strategy until a preset iteration stopping condition is reached, thereby obtaining a target distribution that satisfies the preset iteration stopping condition. The computing module includes: The quantification unit is used to quantify the changes in charging demand in different time periods and regions based on price elasticity and changes in the number of charging facilities, using time price elasticity matrix and spatial price elasticity matrix. The first calculation unit is used to add the change in charging demand to a preset benchmark charging demand to obtain the total charging demand under different prices and different numbers of charging facilities. The second calculation unit is used to obtain the total revenue of the multiple operators based on the total charging demand and the utility of electric vehicle user charging behavior. The total revenue is: in, For the operator's daily revenue, This represents the total number of regions. For time period charging station The charging price within the area, For time period The electricity purchase price, For the region The number of charging stations in the country For a long time period, The length of the hour segment. For charging stations The charging power, For time period At the charging station The number of electric vehicles in the country For charging efficiency, For time period charging station Number of internal charging stations For charging stations Single pile per Operation and maintenance costs.

5. The apparatus according to claim 4, characterized in that, The preset iteration stopping condition is reaching a preset number of iterations or reaching a preset convergence condition, wherein the preset convergence condition is: in, For matrix Number of elements These are the preset parameters.

6. The apparatus according to claim 4, characterized in that, The expression for the evolutionary game strategy is: in, Indicates the first The pricing results for charging stations in this iteration. Indicates the first The results of charger distribution within the charging station in the next iteration. , These are preset parameters. This is a rounding function.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the layout planning method for modular mobile charging facilities as described in any one of claims 1-3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the layout planning method for modular mobile charging facilities as described in any one of claims 1-3.

9. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the layout planning method for modular mobile charging facilities as described in any one of claims 1-3.

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