A charging station recommendation policy-based electric vehicle charging guidance method

Abstract

The application provides a kind of electric vehicle charging guidance method based on charging station recommendation strategy, comprising: obtaining real-time related data when user generates charging demand and regular data required by system;Build charging station recommendation model, provide charging strategy ranking based on time cost and economic cost for user;Obtain the charging decision of all charging demand users based on charging station recommendation strategy in a time period, and obtain the charging load of each charging station in the time period by statistics;Build real-time electricity price guidance model, use the charging load of each charging station as the model input of real-time electricity price guidance model, solve the objective function of real-time electricity price guidance model using simulated annealing algorithm, obtain the ideal charging load of each charging station in the next time period, and output the electricity price of each charging station in the next time period.The application accurately guides user charging behavior, realizes charging station load curve peak clipping and valley filling and improves charging equipment utilization, and provides effective basis for electric vehicle charging station site selection.

Description

Technical Field

[0001] This invention relates to the technical field of electric vehicle charging scheduling, specifically a method for guiding electric vehicle charging based on a charging station recommendation strategy. Background Technology

[0002] Electric vehicles (EVs) offer a promising market prospect as an effective solution to the current energy crisis and environmental pollution problems. However, the influx of EVs onto roads not only puts significant pressure on urban transportation systems but also poses new challenges to the economic efficiency and security of the power grid. Against this backdrop, it is necessary to study EV charging guidance strategies, providing charging station recommendations to users with charging needs. The goal is to minimize the time and financial costs of charging for users, thereby improving user satisfaction. By guiding relevant EV charging services, the aim is to alleviate grid load pressure through peak shaving and valley filling, improve the utilization rate of charging equipment, and ultimately achieve overall coordination and optimization of the interests of the power grid and users.

[0003] There is currently a wealth of research on charging guidance strategies, which can be divided into two categories based on the guidance method: one is direct charging guidance based on guidance strategies, and the other is indirect charging guidance through time-of-use pricing. For example, patent application number CN202010027578.1, titled "A Method for Recommending Electric Vehicle Charging Stations Considering Multiple Factors and Scenarios," intelligently provides users with recommended solutions by collecting the SOC and location information of electric vehicles to be charged, taking into account multiple objectives such as the shortest charging time, the lowest charging cost, and the most balanced grid load. This patent comprehensively considers the interests of multiple parties to provide users with charging station choices; however, users are primarily driven by their own interests, and this method cannot reflect the charging choices from the user's perspective. Another example is patent application number CN202110154195.5, titled "An Electric Vehicle Charging Guidance Method Based on Time Sensitivity." This method obtains the attributes and current status of charging piles in the area to be analyzed, constructs a calculation model of the overall charging time for users, solves the calculation model, and calculates the user's charging pile selection effect, thereby guiding vehicles in the area to be analyzed to charge. This patent can effectively reduce the charging time of electric vehicles. However, it only considers the interests of users and does not take into account the impact of charging vehicles on the grid load. It ignores the relationship of mutual coordination and mutual constraints between the interests of users, charging station operators and the grid.

[0004] The latter, such as patent application number CN201711021969.7, titled "An Orderly Charging Control Method for Electric Vehicles Based on Peak-Valley Time-of-Use Electricity Pricing," establishes a two-stage optimization model based on peak-valley time-of-use electricity pricing, aiming to minimize the total charging cost of electric vehicles and the peak-valley difference in the distribution network load. This ensures the stability of the power grid. However, optimizing with the goal of minimizing the total charging cost of electric vehicles does not represent the interests of every user and cannot achieve a good guiding effect. Patent application number CN201910910606, titled "An Electric Vehicle Charging Guidance Method Considering Road-Network-Vehicle," proposes the concept of equivalent road length, comprehensively considers factors such as road traffic conditions and the overall charging time, and introduces a micro-traffic distribution model to describe the charging demand and travel patterns of electric vehicle users. Furthermore, this patent uses a time-of-use and zone-based electricity pricing method to guide user charging, achieving a significant reduction in the electricity purchase cost of charging stations and improving the safety and economy of power system operation. However, the characteristic quantities involved cannot accurately simulate the driving behavior of electric vehicle users, thus failing to obtain an accurate spatiotemporal distribution of charging demand.

[0005] Previous studies have been limited in two ways: firstly, they cannot accurately describe the charging decision-making behavior of electric vehicle users; secondly, the time-of-use pricing method for guiding users to charge relies on day-ahead data, which cannot cope with frequent and abnormal load fluctuations. Therefore, it is almost impossible for its charging guidance and scheduling to achieve global optimization. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an electric vehicle charging guidance method based on a charging station recommendation strategy. This method is entirely user-centric, comprehensively considering both time and economic costs to provide users with high-quality charging station recommendations. Furthermore, compared to load forecasting methods, using real-time statistical charging station load data as the basis for setting charging prices for the next time period allows for more accurate guidance of user charging behavior based on the charging station recommendation system, achieving peak shaving and valley filling of the charging station load curve and improving the utilization rate of charging equipment. This method balances the needs of both users and the power grid, forming an overall coordination and optimization of the interests of both the power grid and users.

[0007] The technical solution adopted in this invention is as follows:

[0008] A method for guiding electric vehicle charging based on a charging station recommendation strategy includes the following steps:

[0009] A. Acquire data, including real-time relevant data when users generate charging needs, as well as regular data required by the system;

[0010] B. Construct a charging station recommendation strategy from the user's perspective, and establish a comprehensive evaluation index for charging stations through the charging station recommendation strategy to provide users with charging station recommendations based on comprehensive time and economic costs;

[0011] C. Obtain the charging decisions of all users with charging needs within a certain time period based on the charging station recommendation strategy, and then statistically obtain the charging load of each charging station within that time period.

[0012] D. Construct a real-time electricity price guidance model. Use the charging load of each charging station statistically analyzed in step C as the model input of the real-time electricity price guidance model. Use the simulated annealing algorithm to solve the objective function of the real-time electricity price guidance model, obtain the ideal charging load of each charging station in the next time period, and output the electricity price of each charging station in the next time period based on the real-time electricity price formulation strategy.

[0013] Furthermore, in step A, real-time relevant data when a user generates a charging demand and the routine data required by the system are obtained. The real-time relevant data when a user generates a charging demand includes the location information of the electric vehicle generating the charging demand, the SOC data and charging standard of the electric vehicle, the remaining mileage of the electric vehicle, the charging capacity set by the owner, and the maximum tolerable guided distance of the user. The routine data required by the system includes the geographical location data of each charging station in the jurisdiction, the model, quantity and operating status of each charging pile in the station, the time that the charging station operator is willing to reserve the applied charging pile for the user, and the historical traffic information of the road.

[0014] Furthermore, step B involves constructing a charging recommendation strategy from the user's perspective. This strategy uses the data obtained in step A as input parameters, calculates a comprehensive evaluation index for feasible charging stations, ranks the evaluation indicators, and returns them to the user. This allows the user to make the optimal charging choice based on time and economic costs. Specifically:

[0015] First, based on the constraints, a set of feasible charging stations {S1,S2,…,S} is selected. n Specifically:

[0016] Determine whether each charging station is available based on the model and operating status of its charging piles.

[0017] Determine whether there is a charging station within the reachable range based on the electric vehicle's current location, remaining mileage, and the user's maximum tolerable guided distance;

[0018] Whether the charging station operator is willing to reserve the applied charging pile for a user for a period of time that is no less than the time it takes for the user to arrive at the charging station;

[0019] Secondly, a comprehensive evaluation index for feasible charging stations is established, the expression of which is:

[0020]

[0021] Where, σ i This serves as a comprehensive evaluation indicator for charging station i within the jurisdiction. This provides an evaluation metric for the time cost of a user choosing to charge their electric vehicle at charging station i.

[0022]

[0023] Where t o This refers to the time during which the charging station operator is willing to reserve the charging station for the user; p,k The distance l represents the travel time from the location where the vehicle k needs charging to the charging station i. k and driving speed v k Related to; t c,k The charging time for a vehicle to go from its current battery level to the user's required battery level is related to the charging capacity Q. k Charging power P i,k and charging efficiency e k Related to; t w The waiting time for the vehicle to arrive at the charging station and for charging to begin is the minimum remaining charging time for all charging stations at that station.

[0024] This provides users with economic cost evaluation metrics to choose when charging their electric vehicles at charging station i.

[0025]

[0026] Where s c The charging cost for a vehicle to go from its current battery level to the user's required battery level, in addition to the charging time t. c,k and real-time electricity price ρ i,j Related; s t The cost of the journey from the location where the charging demand is generated to the charging station i is s. p The additional parking fee for a vehicle waiting to be charged at the charging station is related to the length of time the vehicle stays at the charging station, and the values ​​of θ are as follows:

[0027]

[0028] λ1 and λ2 are the weight coefficients corresponding to each evaluation indicator, respectively;

[0029] Finally, the comprehensive evaluation index of all feasible charging stations in the jurisdiction is calculated, and they are sorted according to the numerical value of the index calculation results. The sorted results are returned to the user as the charging station recommendation ranking.

[0030] Furthermore, in step C, the user makes a charging decision based on the charging station recommendation strategy in step B. The user's charging decision behavior is represented by a set. Among the elements For the selection of charging stations, elements For the selection of the charging start time, the element For route selection to the charging station, elements For the selection of departure time, elements Select the charging capacity;

[0031] Obtain the charging decisions of all users with charging needs within time period j based on the charging station recommendation strategy, and statistically analyze the charging load L generated by charging station i in the jurisdiction due to electric vehicle charging within time period j. i,j The time period is divided into 24 time periods, each consisting of one hour. j=1 represents the time period from 0:00 to 1:00, and so on. The charging load L of charging station i within the jurisdiction of time period j is... i,j The specific statistical methods are as follows:

[0032] First, based on the elements element The estimated time when the user arrives at the charging station and begins charging, based on the element element Estimated charging time for the vehicle;

[0033] Secondly, compare the expected start time of charging with the element The selected charging start time is used to determine whether the user can arrive at the charging station and begin charging before the selected charging start time. If so, the arrival time is used as the charging start time to update the element. Otherwise keep the elements The charging start time selected in the settings;

[0034] Finally, based on the elements element And the estimated charging time calculation period j, the charging load L generated by electric vehicle charging at charging stations i within the jurisdiction. i,j The calculation method is as follows:

[0035]

[0036] Where m is the number of electric vehicles that start charging across time periods within time period j, and t is the number of electric vehicles that start charging across time periods within time period j. k,j Let t be the charging time of electric vehicle k within time period j. k,j+1 Let P be the charging time of electric vehicle k within time period j+1. i,k Let k be the charging power of electric vehicle k at charging station i.

[0037] Furthermore, the real-time electricity price guidance model described in step D is used to reduce the peak-valley difference of the power grid load and improve the utilization rate of power grid equipment. The output of the real-time electricity price guidance model is the electricity price ρ of each charging station in the next time period. i,j+1 The objective function expression of the model is:

[0038] min F=η1ε1+η2ε2

[0039] Wherein, ε1 is an indicator for evaluating the differences in the utilization rate of charging piles at each charging station, and the expression for ε1 is as follows, where C i The number of charging piles at charging station i:

[0040]

[0041] ε2 is an index used to evaluate the smoothness of the daily charging load curve of all charging stations in the jurisdiction. The expression for ε2 is as follows, where L i，j The charging load of charging station i during time period j:

[0042]

[0043] η1 and η2 are the corresponding weight coefficients;

[0044] Finding the optimal value of the objective function of the above model is essentially a problem of finding the minimum value of an n-variable function within the domain of its independent variables. The simulated annealing algorithm is used to solve the objective function of the real-time electricity price guidance model, and its optimal solution is taken as the ideal charging load for each charging station in the next time period. The steps of using the simulated annealing algorithm to solve the objective function of the real-time electricity price guidance model are as follows:

[0045] Step 1: Input the real-time statistical load L of each charging station i,j ;

[0046] Step 2: Set the algorithm parameters, namely the annealing start T. b Termination temperature T e , cooling rate r, maximum number of iterations n, probability coefficient S1 of accepting the difference solution;

[0047] Step 3: Randomly generate initial solution ω(L) 1,j+1 ,L 2,j+1 ,…,L n,j+1 ), calculate the objective function F(ω);

[0048] Step 4: Perturbation generates a new solution ω′(L′) 1,j+1 ,L′ 2,j+1 ,…,L′ n,j+1 ), calculate the objective function F(ω′) and the measure ΔF = F(ω′) - F(ω);

[0049] Step 5: Use the Metropolis criterion to determine whether to accept the new solution: if ΔF < 0, then accept ω′ as the new current solution ω; otherwise, accept ω′ as the new current solution S with probability exp(-ΔF / T).

[0050] Step 6: Termination condition determination: Current temperature T is less than the lowest temperature T e Or the number of iterations is less than 0;

[0051] Step 7: If the termination condition is met, take the current solution ω as the optimal solution and output the ideal charging load L of charging station i for the next time period. i,j+1 End the program;

[0052] After solving the objective function using the simulated annealing algorithm, the optimal solution ω is the ideal charging load L of charging station i in the next time period. i,j+1 The power grid operator can adjust the electricity price ρ for the next time period. i,j+1 To achieve the ideal charging load: In step B, the real-time electricity price ρ i,j+1 The impact on the comprehensive evaluation indicators of charging stations is reflected in the economic cost assessment indicators. The above means that fluctuations in the real-time electricity price of each charging station will affect the charging station recommendation ranking. Therefore, updating the charging station recommendation ranking will guide users who will have charging needs in the next time period. The real-time electricity price setting strategy for charging station i is as follows:

[0053]

[0054] ρ i,j Let ρ be the time-of-use electricity price for charging station i during time period j. i,cs The service fee for charging station i This is the adjustment coefficient for charging station i.

[0055] The present invention has the following beneficial effects:

[0056] Compared to disordered charging, the charging station using this invention exhibits a smaller peak-to-valley difference in its total load curve and a more balanced load across different time periods, thus mitigating user waiting issues that arise during disordered charging. The electric vehicle charging guidance system proposed in this invention can provide a valid basis for further electric vehicle charging station site selection and other charging-related services. Attached Figure Description

[0057] Figure 1 This is a flowchart of one embodiment of the electric vehicle charging guidance method based on charging station recommendation strategy of the present invention;

[0058] Figure 2 This is a detailed flowchart of the present invention;

[0059] Figure 3 This is a flowchart illustrating the process of solving the objective function of the model using the simulated annealing algorithm in this invention;

[0060] Figure 4 This is a road network map of a certain jurisdiction;

[0061] Figure 5 This invention guides the average daily load of each charging station before and after its implementation.

[0062] Figure 6 The present invention guides the total load curve of the charging station before and after. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] like Figure 1 and Figure 2 The present invention provides an electric vehicle charging guidance method based on a charging station recommendation strategy, comprising the following steps:

[0065] A. Acquire data, including real-time relevant data when users generate charging needs, as well as regular data required by the system;

[0066] B. Construct a charging station recommendation strategy from the user's perspective, and establish a comprehensive evaluation index for charging stations through the charging station recommendation strategy to provide users with charging station recommendations based on comprehensive time and economic costs;

[0067] C. Obtain the charging decisions of all users with charging needs within a certain time period based on the charging station recommendation strategy, and then statistically obtain the charging load of each charging station within that time period.

[0068] D. Construct a real-time electricity price guidance model. Use the charging load of each charging station statistically analyzed in step C as the model input of the real-time electricity price guidance model. Use the simulated annealing algorithm to solve the objective function of the real-time electricity price guidance model, obtain the ideal charging load of each charging station in the next time period, and output the electricity price of each charging station in the next time period based on the real-time electricity price formulation strategy.

[0069] Step A involves acquiring real-time relevant data when a user generates a charging demand, as well as the general data required by the system. The real-time relevant data includes the location information of the electric vehicle when the charging demand is generated, the electric vehicle's SOC data and charging standard, the remaining mileage of the electric vehicle, the charging capacity set by the owner, and the user's maximum tolerable guided distance. The general data required by the system includes the geographical location data of each charging station in the jurisdiction, the model, quantity, and operating status of each charging pile in the station, the time that the charging station operator is willing to reserve the requested charging pile for the user, and historical traffic information of the road.

[0070] Step B involves constructing a charging recommendation strategy from the user's perspective. This strategy uses the data obtained in Step A as input parameters, calculates a comprehensive evaluation index for feasible charging stations, ranks the evaluation indicators, and returns them to the user. This allows the user to make the optimal charging choice based on time and economic costs. Specifically:

[0071] First, based on the constraints, a set of feasible charging stations {S1,S2,…,S} is selected. n Specifically:

[0072] 1. Determine whether each charging station's charging piles are available based on their model and operating status;

[0073] 2. Determine whether there are charging stations within the reachable range based on the electric vehicle's current location, remaining mileage, and the user's maximum tolerable guided distance;

[0074] 3. Whether the charging station operator is willing to reserve the applied charging pile for a period of time that is no less than the time it takes for the user to arrive at the charging station;

[0075] Secondly, a comprehensive evaluation index for feasible charging stations is established, the expression of which is:

[0076]

[0077] Where, σ i This serves as a comprehensive evaluation indicator for charging station i within the jurisdiction. This provides an evaluation metric for the time cost of a user choosing to charge their electric vehicle at charging station i.

[0078]

[0079] Where t o This refers to the time during which the charging station operator is willing to reserve the charging station for the user; p,k The distance l represents the travel time from the location where the vehicle k needs charging to the charging station i. k and driving speed v k Related to; t c,k The charging time for a vehicle to go from its current battery level to the user's required battery level is related to the charging capacity Q. k Charging power P i,k and charging efficiency e k Related to; t w The waiting time for the vehicle to arrive at the charging station and for charging to begin is the minimum remaining charging time for all charging stations at that station.

[0080] This provides users with economic cost evaluation metrics to choose when charging their electric vehicles at charging station i.

[0081]

[0082] Where s c The charging cost for a vehicle to go from its current battery level to the user's required battery level, in addition to the charging time t. c,k and real-time electricity price ρ i,j Related; s t The cost of the journey from the location where the charging demand is generated to the charging station i is s. p The additional parking fee for a vehicle waiting to be charged at the charging station is related to the length of time the vehicle stays at the charging station, and the values ​​of θ are as follows:

[0083]

[0084] λ1 and λ2 are the weight coefficients corresponding to each evaluation indicator, respectively;

[0085] Finally, a comprehensive evaluation index for all feasible charging stations within the jurisdiction is calculated, and these stations are ranked according to their numerical values. The ranked results are then returned to users as a charging station recommendation ranking. Users can then book a charging station based on this ranking and arrive at the designated station within the specified time to charge their devices.

[0086] In step C, the user makes a charging decision based on the charging station recommendation strategy in step B. The user's charging decision behavior is represented by a set. Among the elements For the selection of charging stations, elements For the selection of the charging start time, the element For route selection to the charging station, elements For the selection of departure time, elements Select the charging capacity;

[0087] Obtain the charging decisions of all users with charging needs within time period j based on the charging station recommendation strategy, and statistically analyze the charging load L generated by charging station i in the jurisdiction due to electric vehicle charging within time period j. i,j The time period is divided into 24 time periods, each consisting of one hour. j=1 represents the time period from 0:00 to 1:00, and so on. The charging load L of charging station i within the jurisdiction of time period j is... i,j The specific statistical methods are as follows:

[0088] First, based on the elements element The estimated time when the user arrives at the charging station and begins charging, based on the element element Estimated charging time for the vehicle;

[0089] Secondly, compare the expected start time of charging with the element The selected charging start time is used to determine whether the user can arrive at the charging station and begin charging before the selected charging start time. If so, the arrival time is used as the charging start time to update the element. Otherwise keep the elements The charging start time selected in the settings;

[0090] Finally, based on the elements element And the estimated charging time calculation period j, the charging load L generated by electric vehicle charging at charging stations i within the jurisdiction. i,j The calculation method is as follows:

[0091]

[0092] Where m is the number of electric vehicles that start charging across time periods within time period j, and t is the number of electric vehicles that start charging across time periods within time period j. k,j Let t be the charging time of electric vehicle k within time period j. k,j+1 Let P be the charging time of electric vehicle k within time period j+1. i,k Let k be the charging power of electric vehicle k at charging station i.

[0093] In step D, the charging load L of each charging station is calculated based on the statistics in step C. ij A real-time electricity price guidance model is constructed, aiming to reduce the peak-valley difference in grid load and improve the utilization rate of grid equipment. Its output is the electricity price ρ for each charging station in the next time period. i,j+1 The objective function expression of the model is:

[0094] min F=η1ε1+η2ε2

[0095] Wherein, ε1 is an indicator for evaluating the differences in the utilization rate of charging piles at each charging station, and the expression for ε1 is as follows, where C i The number of charging piles at charging station i:

[0096]

[0097] ε2 is an index used to evaluate the smoothness of the daily charging load curve of all charging stations in the jurisdiction. The expression for ε2 is as follows, where L i，j The charging load of charging station i during time period j:

[0098]

[0099] η1 and η2 are the corresponding weight coefficients.

[0100] The aforementioned optimization model is essentially a problem of finding the minimum value of an n-variable function within its domain. This invention introduces a simulated annealing algorithm to solve the objective function of this model, and uses the optimal solution as the ideal charging load for each charging station in the next time period. The simulated annealing algorithm is highly efficient and robust in solving complex nonlinear optimization problems. The main steps of the solution are as follows (e.g.) Figure 3 As shown):

[0101] Step 1: Input the real-time statistical load L of each charging station. i,j ;

[0102] Step 2 sets the algorithm parameters, namely the annealing start T. b Termination temperature T e , cooling rate r, maximum number of iterations n, probability coefficient S1 of accepting the difference solution;

[0103] Step 3: Randomly generate the initial solution ω(L) 1,j+1 ,L 2,j+1 ,…,L n,j+1 ), calculate the objective function F(ω);

[0104] Step 4: The perturbation generates a new solution ω′(L′) 1,j+1 ,L′ 2,j+1 ,…,L′ n,j+1 ), calculate the objective function F(ω′) and the measure ΔF = F(ω′) - F(ω);

[0105] Step 5: Use the Metropolis criterion to determine whether to accept the new solution: if ΔF < 0, then accept ω′ as the new current solution ω; otherwise, accept ω′ as the new current solution S with probability exp(-ΔF / T).

[0106] Step 6 Termination condition determination: Current temperature T is less than the lowest temperature T e Or the number of iterations is less than 0;

[0107] Step 7: If the termination condition is met, take the current solution ω as the optimal solution and output the ideal load L of charging station i for the next time period. i,j+1 End the program.

[0108] After solving the objective function using the simulated annealing algorithm, the optimal solution ω is the ideal charging load L of charging station i in the next time period. i,j+1 The power grid operator can adjust the electricity price ρ for the next time period. i,j+1 To achieve the ideal load value: In step B, the electricity price ρ i,j+1 The impact on the comprehensive evaluation indicators of charging stations is reflected in the economic cost assessment indicators. The above means that fluctuations in the real-time electricity price of each charging station will affect the charging station recommendation ranking. Therefore, updating the charging station recommendation ranking guides users who will have charging needs in the next time period. The real-time electricity price setting strategy for charging station i is as follows:

[0109]

[0110] ρ i,j Let ρ be the time-of-use electricity price for charging station i during time period j. i,cs The service fee for charging station i This is the adjustment coefficient for charging station i.

[0111] When simulating an electric vehicle charging guidance method based on a charging station recommendation strategy, taking a certain jurisdiction as an example, its traffic network map is shown below. Figure 4 Given the following parameters: (1) The simulation time is 0:00-24:00, divided into 24 time periods, each time period lasting 1 hour; (2) There are 5 charging stations in the jurisdiction, each charging station has 10 fast charging piles, the location of each charging station is shown in the figure, and the charging price of each charging station at time J is shown in Table 1. There are 1000 electric vehicles, all of which are constant power fast charging, with a charging power of 60KW; (3) The time when electric vehicle users generate charging demand conforms to a uniform distribution, the location where electric vehicles generate charging demand is randomly selected, and users select the charging station with the highest comprehensive evaluation index for charging; (4) When electric vehicle users generate charging demand, the remaining distance is greater than the distance between them and any charging station in the jurisdiction, and the charging time is 1 hour; (5) The charging cost is only related to the real-time electricity price, and the travel time is only related to the travel length; (6) The weight coefficient ε1 is 0.6, ε2 is 0.4, the weight coefficient λ1 is 0.6, and λ2 is 0.4.

[0112] Table 1. Charging electricity price at each charging station at time J.

[0113]

[0114] To demonstrate the guiding effect of this invention, the results of guided charging are compared with the results of disordered charging. Figure 5 As can be seen, under the charging guidance of this invention, compared with disordered charging, the average daily load of charging stations 1, 3, and 4 decreased, while the average daily load of charging stations 2 and 5 increased. Because the number of vehicles in the area is fixed, the charging guidance method of this invention guides vehicles that were originally charging at charging stations 1, 3, and 4 to charging station 2 or 5, making the load on charging stations with the same capacity more balanced, thereby improving the overall utilization rate of charging piles.

[0115] from Figure 6As can be seen, under the charging guidance of this invention, compared with disordered charging, the peak-valley difference of the total load curve of the charging station is smaller, the load is more balanced in each time period, and the user waiting phenomenon in disordered charging is alleviated.

[0116] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1.A method for electric vehicle charging guidance based on charging station recommendation strategy, characterized in that: Includes the following steps: A. Acquire data, including real-time relevant data when users generate charging needs, as well as regular data required by the system; B. Construct a charging station recommendation strategy from the user's perspective, and establish a comprehensive evaluation index for charging stations through the charging station recommendation strategy to provide users with charging station recommendations based on comprehensive time and economic costs; C. Obtain the charging decisions of all users with charging needs within a certain time period based on the charging station recommendation strategy, and then statistically obtain the charging load of each charging station within that time period. D. Construct a real-time electricity price guidance model. Use the charging load of each charging station statistically analyzed in step C as the model input of the real-time electricity price guidance model. Use the simulated annealing algorithm to solve the objective function of the real-time electricity price guidance model, obtain the ideal charging load of each charging station in the next time period, and output the electricity price of each charging station in the next time period based on the real-time electricity price formulation strategy. Step A involves acquiring real-time relevant data when a user generates a charging demand, as well as the general data required by the system. The real-time relevant data includes the location information of the electric vehicle when the charging demand is generated, the SOC data and charging standard of the electric vehicle, the remaining range of the electric vehicle, the charging capacity set by the owner, and the maximum tolerable guided distance of the user. The general data required by the system includes the geographical location data of each charging station in the jurisdiction, the model, quantity, and operating status of each charging pile in the station, the time that the charging station operator is willing to reserve the applied charging pile for the user, and the historical traffic information of the road. Step B involves constructing a charging recommendation strategy from the user's perspective. This strategy uses the data obtained in Step A as input parameters, calculates a comprehensive evaluation index for feasible charging stations, ranks the evaluation indicators, and returns them to the user. This allows the user to make the optimal charging choice based on time and economic costs. Specifically: First, the feasible charging station set is selected according to the constraint conditions , specifically: Determine whether each charging station is available based on the model and operating status of its charging piles. Determine whether there is a charging station within the reachable range based on the electric vehicle's current location, remaining mileage, and the user's maximum tolerable guided distance; Whether the charging station operator is willing to reserve the applied charging pile for a user for a period of time that is no less than the time it takes for the user to arrive at the charging station; Secondly, a comprehensive evaluation index for feasible charging stations is established, the expression of which is: ； in, This serves as a comprehensive evaluation indicator for charging station i within the jurisdiction. This provides an evaluation metric for the time cost of a user choosing to charge their electric vehicle at charging station i. ； The time during which the charging station operator is willing to reserve the charging pile for the user; The distance traveled by the vehicle k from the location where it needs to charge to the charging station i is calculated as follows: and driving speed related; The charging time for a vehicle to go from its current battery level to the user's required battery level, in relation to the amount of electricity being charged. Charging power and charging efficiency related; The waiting time for the vehicle to arrive at the charging station and for charging to begin is the minimum remaining charging time for all charging stations at that station. This provides users with economic cost evaluation metrics to choose when charging their electric vehicles at charging station i. ； The charging cost for a car to go from its current battery level to the user's required battery level, in addition to the charging time. and real-time electricity price related; The cost of the journey from the location where the charging demand is generated to the charging station i. Additional parking fees for vehicles waiting to be charged at the charging station are related to the length of time the vehicle remains at the station. The possible values ​​are as follows: ； These are the weighting coefficients corresponding to each evaluation indicator; Finally, the comprehensive evaluation index of all feasible charging stations in the jurisdiction is calculated, and they are sorted according to the numerical value of the index calculation results. The sorted results are returned to the user as the charging station recommendation ranking. In step C, the user makes a charging decision based on the charging station recommendation strategy in step B. The user's charging decision behavior is represented by a set. , of which elements For the selection of charging stations, elements For the selection of the charging start time, the element For choosing a route to the charging station, elements For the selection of departure time, elements Select the charging capacity; Obtain the charging decisions of all users with charging needs within time period j based on the charging station recommendation strategy, and statistically analyze the charging load generated by charging station i in the jurisdiction due to electric vehicle charging within time period j. The time period is divided by dividing one day into 24 time periods, each consisting of one hour. This represents the time period from 0:00 to 1:00, and so on, representing the charging load of charging station i within the jurisdiction of time period j. The specific statistical methods are as follows: First, based on the elements ,element The estimated time when the user arrives at the charging station and begins charging, based on the element ,element Estimated charging time for the vehicle; Secondly, compare the expected start time of charging with the element The selected charging start time is used to determine whether the user can arrive at the charging station and start charging before the selected charging start time; if so, the arrival time is used as the charging start time to update the element. Otherwise, keep the element The charging start time selected in the settings; Finally, based on the elements ,element And the estimated charging time calculation period j, the charging load generated by charging stations i within the jurisdiction due to electric vehicle charging. The calculation method is as follows: ； Where m represents the number of electric vehicles that start charging across time periods within time period j. The charging time of electric vehicle k within time period j. Let k be the charging time of electric vehicle k within time period j+1. The charging power of electric vehicle k at charging station i; The real-time electricity price guidance model described in step D is used to reduce the peak-valley difference of the power grid load and improve the utilization rate of power grid equipment. The output of the real-time electricity price guidance model is the electricity price of each charging station in the next time period. The objective function expression of the model is: ； in, To assess the differences in the utilization rate of charging piles at various charging stations, The expression is as follows, where The number of charging piles at charging station i: ； To evaluate the smoothness of the daily charging load curve of all charging stations in the jurisdiction, The expression is as follows, where The charging load of charging station i during time period j: ； These are the corresponding weight coefficients; Finding the optimal value of the objective function of the above model is essentially a problem of finding the minimum value of an n-variable function within the domain of its independent variables. The simulated annealing algorithm is used to solve the objective function of the real-time electricity price guidance model, and its optimal solution is taken as the ideal charging load for each charging station in the next time period. The steps of using the simulated annealing algorithm to solve the objective function of the real-time electricity price guidance model are as follows: Step 1: Input the real-time load statistics for each charging station ; Step 2: Set the algorithm parameters, namely the annealing start... Termination temperature Cooling rate r, maximum number of iterations n, probability coefficient of accepting the difference solution ; Step 3: Randomly generate initial solutions Calculate the objective function ; Step 4: Perturbation generates new solutions ( ), calculate the objective function and measure value ; Step 5: Determine whether the new solution is acceptable using the Metropolis criterion: If Then accept As the new current solution Otherwise, based on probability accept As the new current solution S; Step 6: Termination condition determination: Current temperature T is less than the lowest temperature Or the number of iterations is less than 0; Step 7: If the termination condition is met, then use the current solution. As the optimal solution, output the ideal charging load of charging station i in the next time period. End the program; After solving the objective function using the simulated annealing algorithm, the optimal solution is obtained. The ideal charging load for charging station i in the next time period The power grid operator can adjust the electricity price for the next time period. To achieve the ideal charging load: real-time electricity price in step B. The impact on the comprehensive evaluation indicators of charging stations is reflected in the economic cost assessment indicators. The above means that fluctuations in the real-time electricity price of each charging station will affect the charging station recommendation ranking. Therefore, updating the charging station recommendation ranking will guide users who will have charging needs in the next time period. The real-time electricity price setting strategy for charging station i is as follows: ； Let i be the time-of-use electricity price for charging station i during time period j. The service fee for charging station i, This is the adjustment coefficient for charging station i.

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