Charging scheduling method and device considering utilization rate of charging pile, equipment and medium

Abstract

The invention relates to the technical field of electric vehicle charging scheduling, in particular to a charging scheduling method, device and equipment considering the utilization rate of charging piles and a medium, and the method comprises the steps: building a charging station charging scheduling double-layer game architecture comprising a charging operator layer and a user layer; a user charging time cost and a charging economic cost are considered, and an electric vehicle user charging scheduling strategy of a user layer based on the comprehensive charging cost is established; the charging station utilization rate is considered, and a charging station charging dispatching model is established with the purpose of maximizing the charging station charging pile utilization rate; and based on an adaptive improved bat algorithm, solving the charging scheduling model to obtain an optimal scheduling method. According to the invention, the electric vehicles are guided to go to different charging stations to participate in the power system demand response, and the utilization efficiency of the charging piles of the charging stations is maximized. Compared with other types of mechanisms, the scheduling strategy is more flexible, the change of the demand capacity of the charging station can be reflected in a short time, and the method is more suitable for the demand response of the charging station aggregator.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging scheduling, and in particular to a charging scheduling method, device, equipment and medium that considers the utilization rate of charging piles. Background Art

[0002] The participation of electric vehicles in the demand response dispatch of the power system requires the use of reasonable means to guide and plan the charging behavior of electric vehicles. System-oriented demand-side management and market-oriented demand-side management are important means for the power system to dispatch and plan the charging behavior of electric vehicles.

[0003] The time-of-use electricity price mechanism is a market reward system designed based on the time value of electricity. According to the actual electricity consumption situation, a day is divided into three different electricity consumption periods: peak, flat and valley, and different charging electricity prices are specified for different periods of demand. The purpose of this mechanism is to give full play to the signal role of electricity prices, guide electric vehicle users to change the charging and discharging time of electric vehicles or adjust station selection strategies based on their own cost sensitivity, alleviate the overload phenomenon of the power system, improve the utilization rate of power equipment during the low load period, and promote the safe and stable operation of the power system.

[0004] However, the traditional time-of-use electricity price strategy is difficult to flexibly balance the contradiction between the growing demand for electric vehicle charging over time and the demand response of charging station aggregators. This strategy is generally set based on the overall demand of the power system and the charging situation of electric vehicle users, and remains unchanged for a certain period of time, and the re-evaluation interval is often one year or even many years. In the current free competition power market, electricity prices fluctuate significantly, and the traditional time-of-use electricity price strategy is difficult to follow the changes in load characteristics. At the same time, the use of traditional time-of-use electricity price strategies to guide electric vehicles to participate in demand response is prone to generate new charging load peaks during the low electricity price period or it is difficult to meet the actual capacity needs of aggregators.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a charging scheduling method, device, equipment and medium taking into account the utilization rate of charging piles, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a charging scheduling method considering the utilization rate of charging piles, comprising the following steps:

[0008] Establish a two-layer game architecture for charging station charging scheduling, including the charging operator layer and the user layer;

[0009] Based on the two-layer game architecture of charging station charging scheduling, the user charging time cost and charging economic cost are considered in the lower layer, and a charging scheduling strategy for electric vehicle users based on the comprehensive charging cost is established at the user layer;

[0010] Based on the two-layer game architecture of charging station charging scheduling, the charging station utilization rate and the charging scheduling strategy of the electric vehicle user are considered in the upper layer, and the charging station charging scheduling model is established with the goal of maximizing the utilization rate of charging piles at the charging station;

[0011] Based on the adaptive improved bat algorithm, the charging scheduling model is solved to obtain the optimal scheduling method.

[0012] Furthermore, the establishment of a two-layer game architecture for charging station charging scheduling including a charging operator layer and a user layer includes:

[0013] Establishing an upper leadership layer and a lower follower layer, wherein the upper leadership layer is a charging operator layer, and the lower follower layer is a user layer, wherein the upper leadership layer and the lower follower layer structurally constitute a Stackelberg game structure of a leader-follower;

[0014] Users with charging needs complete charging behavior according to the scheduling arrangement formulated by the upper-level leadership and the scheduling response strategy;

[0015] The upper leadership layer actively adjusts the scheduling strategy, and the lower follower layer receives the scheduling strategy and responds to the scheduling strategy.

[0016] Furthermore, the electric vehicle user charging scheduling strategy includes:

[0017] Considering the charging time cost and the charging economic cost, the two are weighted as the comprehensive charging cost. Electric vehicle users choose charging stations with lower comprehensive charging costs to charge.

[0018] The charging cost includes travel time on the road, waiting time in the charging station, and charging time;

[0019] The economic cost of charging includes the cost of recharging.

[0020] Furthermore, the travel time on the road is obtained by using the Dijkstra algorithm through the actual road network data of the section to obtain the fastest travel path, and its travel time is used as the travel time on the road; the waiting time in the charging station is estimated by historical data and the current status of the charging station; the charging time is estimated by the output power of the charging pile of the charging station and the current battery state of charge SOC of the vehicle.

[0021] Furthermore, the comprehensive charging cost of an electric vehicle at its current location is as follows:

[0022]

[0023] Where F(i) is the comprehensive charging cost of electric vehicle users going to charging station i for charging. The economic cost of charging for users, and the remaining items are the charging time costs, is the charging journey time, Waiting time in line for charging stations, is the charging time, and π(·) is the conversion function between the user's charging time cost and the user's charging economic cost.

[0024] Furthermore, the charging station utilization rate is considered, and a charging station charging scheduling model is established with the goal of maximizing the utilization rate of charging piles in the charging station, including:

[0025] The objective function to maximize the utilization rate of charging piles at charging stations is:

[0026]

[0027] In the formula, c csf is the economic cost of the charging station, N tp is the number of periodic updates, N sgcc is the number of charging stations under the self-operated operator; α uti U is the conversion coefficient between the utilization rate of charging piles and the economic cost of charging stations; i is the evaluation index of charging pile utilization rate of charging station i in the complete operation cycle;

[0028] The calculation formula of the evaluation index of electric pile utilization rate is as follows:

[0029]

[0030] Where, T gap For the update cycle; represents the number of charging piles at charging station i, For nth tp The start time of an update cycle; N cr The total amount of charging demand for electric vehicles; and They represent the start and end charging time of the kth electric vehicle with charging demand, Indicates that the car is charging at the current time t; is the indicator function, when argmin i When F(i,k)=i, the return value is 1, otherwise it is 0; is the indicator function, when If true, the return value is 1, otherwise 0;

[0031] The constraints of the charging station charging scheduling model are as follows:

[0032]

[0033] In the formula, and They respectively represent the lower and upper limits of the economic cost adjustment of charging stations.

[0034] Furthermore, the adaptively improved bat algorithm comprises the following steps:

[0035] Flight pulse frequency update:

[0036] The formula for calculating bat pulse frequency is as follows:

[0037]

[0038] Where f1 is the pulse frequency; c w is a constant for calculating the pulse frequency; ρ is a constant representing the average fitness weight of the bat population; F avg is the average fitness of the bat population; F best is the fitness of the optimal bat; ζ is a constant representing the weight of the current iteration number; gen is the current iteration number; gen max is the maximum number of iterations; f min is the constant for controlling the maximum value of pulse frequency;

[0039] Flight speed and position update:

[0040] Assuming that the bat's search space is D-dimensional, the flight speed and position of each bat are updated according to the following formula during iteration:

[0041]

[0042] in is the flying speed of bat i in the gen generation; k1 is a random number between 0 and 1; is the position of bat i in the gen generation; is the weight coefficient for position update; x* is the current optimal bat;

[0043] Local Search:

[0044] If the random number between 0 and 1 is greater than the bat's current pulse emission frequency R gen , then perform local search according to the following formula:

[0045]

[0046] Among them A genis the impulse loudness of the bats in the gen generation; d is the ratio of the upper and lower boundary distances of the feasible solution domain to the number of bat populations; rateG is the ratio of the current number of iterations to the maximum number of iterations; δ is a random number between -1 and 1;

[0047] Where g(rateG) is a piecewise function; the calculation method of g(rateG) is as follows:

[0048]

[0049] Pulse loudness and pulse emission frequency updated:

[0050] Update the pulse loudness and pulse emission frequency according to the following formula:

[0051] A gen+1 =εA gen ;

[0052] R gen+1 =R 0 [1-e -θgen ];

[0053] Among them A gen is the impulse loudness of bats in the gen generation; ε is the impulse loudness reduction factor; R gen is the pulse emission frequency of bats in the gen generation; R0 is the maximum pulse emission frequency; θ is the pulse emission frequency increase coefficient.

[0054] Furthermore, solving the charging scheduling model based on the adaptive improved bat algorithm includes the following steps:

[0055] Step 1: Generate typical charging scenarios based on the impact of charging efficiency and charging costs on the charging behavior of electric vehicle users;

[0056] Step 2: Initialize the parameters of the bat population, including the number of bat populations, the dimension of the problem, the number of iterations, the pulse loudness, the pulse loudness reduction factor, the maximum pulse emission frequency, and the pulse emission frequency increase factor, and encode the bats to represent the economic cost of charging at the charging station;

[0057] Step 3: Use a recursive method to obtain the initial bat position matrix that satisfies the constraints; randomly generate uniformly distributed binary strings as the initial charging station charging economic cost random matrix B (b1, b2, b3…b D ) T , and then use the dot product type of matrix B to obtain the matrix P (p1, p2, p3…p D ) T Get the initial bat position matrix X(x1,x2,x3…x D ) T, the value of each dimension in the initial bat position matrix corresponds to the type of electric vehicle charging station, the subscript of each dimension corresponds to the location of the charging station, and the recursive termination condition is the constraints of the distribution network AC power flow model and the time-sharing charging price adjustment model; then the fitness calculation is performed to obtain the fitness of each bat population, that is, the objective function value of the charging pile utilization rate in the complete operation cycle of all typical scenarios, and the initial optimal bat population is obtained, recorded as x*;

[0058] Step 4: Update the flight pulse frequency, update the flight speed and position of each bat, and then calculate the fitness of all populations;

[0059] Step 5: Determine whether a single bat performs local search; if the random number between 0 and 1 is greater than the bat's current pulse emission frequency, perform local search, and then calculate the bat's fitness;

[0060] Step 6: If the random number between 0 and 1 is less than the pulse loudness of the current bat, and the fitness of the bat is less than x*, then accept the newly acquired bat; update the pulse loudness and pulse emission frequency, and search for the optimal bat again;

[0061] Step 7: Determine whether the termination condition is met. If so, stop the iteration and output the optimal solution. Otherwise, go to step 4.

[0062] The present invention also includes a charging scheduling device that takes into account the utilization rate of charging piles. Using the above method, the scheduling device includes:

[0063] A two-layer game unit, used to establish a two-layer game architecture for charging station charging scheduling, including a charging operator layer and a user layer;

[0064] A user strategy unit is used to establish a user-level charging scheduling strategy for electric vehicle users based on the comprehensive charging cost by considering the user's charging time cost and charging economic cost in the lower layer based on the two-layer game architecture of charging station charging scheduling;

[0065] A scheduling model building unit is used to establish a charging station charging scheduling model based on the charging station charging scheduling two-layer game architecture, taking into account the charging station utilization rate and the electric vehicle user charging scheduling strategy in the upper layer, with the goal of maximizing the charging station charging pile utilization rate;

[0066] The solving unit is used to solve the charging scheduling model based on the adaptive improved bat algorithm to obtain the optimal scheduling method.

[0067] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0068] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0069] The beneficial effects of the present invention are as follows: the present invention changes the station selection strategy of electric vehicle users by adjusting the scheduling arrangement of charging stations within the management scope of charging station aggregators, guides electric vehicles to go to different charging stations to participate in the power system demand response, and maximizes the utilization efficiency of charging piles at charging stations. Compared with other types of mechanisms, the scheduling strategy is more flexible and can reflect the changes in the demand capacity of charging stations in a shorter period of time, which is more suitable for the demand response of charging station aggregators. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 is a flow chart of the method in Example 1;

[0072] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0073] Figure 3 is a flow chart of the method in Example 2;

[0074] Figure 4 This is a schematic diagram of a two-layer game architecture in Example 2;

[0075] Figure 5 This is a result diagram of the charging scheduling example in Example 2;

[0076] Figure 6 This is a heat map of charging station utilization using the scheduling mechanism in Example 2;

[0077] Figure 7 This is a heat map of charging station utilization under the traditional peak-valley electricity price mechanism;

[0078] Figure 8 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0080] Embodiment 1:

[0081] like Figure 1 As shown: A charging scheduling method considering the utilization rate of charging piles includes the following steps:

[0082] Establish a two-layer game architecture for charging station charging scheduling, including the charging operator layer and the user layer;

[0083] Based on the two-layer game architecture of charging station charging scheduling, the user's charging time cost and charging economic cost are considered in the lower layer, and the charging scheduling strategy for electric vehicle users based on the comprehensive charging cost is established at the user level;

[0084] Based on the two-layer game architecture of charging station charging scheduling, the charging station utilization rate and electric vehicle user charging scheduling strategy are considered in the upper layer, and the charging station charging scheduling model is established with the goal of maximizing the utilization rate of charging piles at the charging station;

[0085] Based on the adaptive improved bat algorithm, the charging scheduling model is solved and the optimal scheduling method is obtained.

[0086] By adjusting the scheduling of charging stations within the management scope of charging station aggregators, the station selection strategy of electric vehicle users can be changed, and electric vehicles can be guided to different charging stations to participate in the power system demand response, so as to maximize the utilization efficiency of charging piles at charging stations. Compared with other types of mechanisms, the scheduling strategy is more flexible and can respond to changes in the demand capacity of charging stations in a shorter period of time, which is more suitable for demand response of charging station aggregators.

[0087] In this embodiment, a two-layer game architecture for charging station charging scheduling including a charging operator layer and a user layer is established, including:

[0088] Establish an upper leadership layer and a lower follower layer. The upper leadership layer is the charging operator layer, and the lower follower layer is the user layer. The upper leadership layer and the lower follower layer constitute a Stackelberg game structure of leaders and followers.

[0089] Users who need to charge complete their charging behavior according to the scheduling arrangements made by the upper-level leadership and the scheduling response strategy;

[0090] The upper leadership layer proactively adjusts the scheduling strategy, and the lower follower layer receives and responds to the scheduling strategy.

[0091] The charging scheduling strategies for electric vehicle users include:

[0092] Considering the charging time cost and the charging economic cost, the two are weighted as the comprehensive charging cost. Electric vehicle users choose charging stations with lower comprehensive charging costs to charge.

[0093] Charging costs include travel time, waiting time in queues at charging stations, and charging time;

[0094] The economic cost of charging includes the cost of recharging.

[0095] Among them, the travel time on the road is estimated by using the Dijkstra algorithm through the actual road network data of the section to obtain the fastest travel path, and its travel time is used as the travel time on the road; the waiting time in the charging station is estimated by historical data and the current status of the charging station; the charging time is estimated by the output power of the charging station charging pile and the current battery state of charge SOC of the vehicle.

[0096] The comprehensive charging cost of an electric vehicle at the current location is as follows:

[0097]

[0098] Where F(i) is the comprehensive charging cost of electric vehicle users going to charging station i for charging. The economic cost of charging for users, and the remaining items are the charging time costs, is the charging journey time, Waiting time in line for charging stations, is the charging time, and π(·) is the conversion function between the user's charging time cost and the user's charging economic cost.

[0099] In this embodiment, the utilization rate of the charging station is taken into consideration, and the charging scheduling model of the charging station is established with the goal of maximizing the utilization rate of the charging piles in the charging station, including:

[0100] The objective function to maximize the utilization rate of charging piles at charging stations is:

[0101]

[0102] In the formula, c csf is the economic cost of the charging station, N tn is the number of periodic updates, N sgcc is the number of charging stations under the self-operated operator; α uti U is the conversion coefficient between the utilization rate of charging piles and the economic cost of charging stations; i is the evaluation index of charging pile utilization rate of charging station i in the complete operation cycle;

[0103] The calculation formula of the evaluation index of electric pile utilization rate is as follows:

[0104]

[0105] Where, T gap For the update cycle; represents the number of charging piles at charging station i, For nth tp The start time of an update cycle; Ncr The total amount of charging demand for electric vehicles; and They represent the start and end charging time of the kth electric vehicle with charging demand, Indicates that the car is charging at the current time t; is the indicator function, when argmin i When F(i,k)=i, the return value is 1, otherwise it is 0; is the indicator function, when If true, the return value is 1, otherwise 0;

[0106] The constraints of the charging station charging scheduling model are as follows:

[0107]

[0108] In the formula, and They respectively represent the lower and upper limits of the economic cost adjustment of charging stations.

[0109] As a preferred embodiment of the above, based on the adaptive improved bat algorithm, the following steps are included:

[0110] Flight pulse frequency update:

[0111] The formula for calculating bat pulse frequency is as follows:

[0112]

[0113] Where f1 is the pulse frequency; c w is a constant for calculating the pulse frequency; ρ is a constant representing the average fitness weight of the bat population; F avg is the average fitness of the bat population; F best is the fitness of the optimal bat; ζ is a constant representing the weight of the current iteration number; gen is the current iteration number; gen max is the maximum number of iterations; f min is the constant for controlling the maximum value of pulse frequency;

[0114] Flight speed and position update:

[0115] Assuming that the bat's search space is D-dimensional, the flight speed and position of each bat are updated according to the following formula during iteration:

[0116]

[0117] in is the flying speed of bat i in the gen generation; k1 is a random number between 0 and 1; is the position of bat i in the gen generation; is the weight coefficient for position update; x* is the current optimal bat;

[0118] Local Search:

[0119] If the random number between 0 and 1 is greater than the bat's current pulse emission frequency Rgen, a local search is performed according to the following formula:

[0120]

[0121] Among them A gen is the impulse loudness of the bats in the gen generation; d is the ratio of the upper and lower boundary distances of the feasible solution domain to the number of bat populations; rateG is the ratio of the current number of iterations to the maximum number of iterations; δ is a random number between -1 and 1;

[0122] Where g(rateG) is a piecewise function; the calculation method of g(rateG) is as follows:

[0123]

[0124] Pulse loudness and pulse emission frequency updated:

[0125] Update the pulse loudness and pulse emission frequency according to the following formula:

[0126] A gen+1 =εA gen ;

[0127] R gen+1 =R 0 [1-e -θgen ];

[0128] Among them A gen is the impulse loudness of bats in the gen generation; ε is the impulse loudness reduction factor; R gen is the pulse emission frequency of bats in the gen generation; R0 is the maximum pulse emission frequency; θ is the pulse emission frequency increase coefficient.

[0129] As a preferred embodiment of the above embodiment, based on the adaptive improved bat algorithm, solving the charging scheduling model includes the following steps:

[0130] Step 1: Generate typical charging scenarios based on the impact of charging efficiency and charging costs on the charging behavior of electric vehicle users;

[0131] Step 2: Initialize the parameters of the bat population, including the number of bat populations, the dimension of the problem, the number of iterations, the pulse loudness, the pulse loudness reduction factor, the maximum pulse emission frequency, and the pulse emission frequency increase factor, and encode the bats to represent the economic cost of charging at the charging station;

[0132] Step 3: Use a recursive method to obtain the initial bat position matrix that satisfies the constraints; randomly generate uniformly distributed binary strings as the initial charging station charging economic cost random matrix B (b1, b2, b3…b D ) T , and then use the dot product type of matrix B to obtain the matrix P (p1, p2, p3…p D ) T Get the initial bat position matrix X(x1,x2,x3…x D ) T , the value of each dimension in the initial bat position matrix corresponds to the type of electric vehicle charging station, the subscript of each dimension corresponds to the location of the charging station, and the recursive termination condition is the constraints of the distribution network AC power flow model and the time-sharing charging price adjustment model; then the fitness calculation is performed to obtain the fitness of each bat population, that is, the objective function value of the charging pile utilization rate in the complete operation cycle of all typical scenarios, and the initial optimal bat population is obtained, recorded as x*;

[0133] Step 4: Update the flight pulse frequency, update the flight speed and position of each bat, and then calculate the fitness of all populations;

[0134] Step 5: Determine whether a single bat performs local search; if the random number between 0 and 1 is greater than the bat's current pulse emission frequency, perform local search, and then calculate the bat's fitness;

[0135] Step 6: If the random number between 0 and 1 is less than the pulse loudness of the current bat, and the fitness of the bat is less than x*, then accept the newly acquired bat; update the pulse loudness and pulse emission frequency, and search for the optimal bat again;

[0136] Step 7: Determine whether the termination condition is met. If so, stop the iteration and output the optimal solution. Otherwise, go to step 4.

[0137] like Figure 2 As shown, this embodiment also includes a charging scheduling device that takes into account the utilization rate of the charging pile. Using the above method, the scheduling device includes:

[0138] A two-layer game unit, used to establish a two-layer game architecture for charging station charging scheduling, including a charging operator layer and a user layer;

[0139] The user strategy unit is used to establish a user-level charging scheduling strategy for electric vehicle users based on the comprehensive charging cost, taking into account the user's charging time cost and charging economic cost in the lower layer based on the two-layer game architecture of charging station scheduling;

[0140] The scheduling model building unit is used to establish a charging station charging scheduling model based on a two-layer game architecture of charging station charging scheduling, taking into account the charging station utilization rate and the charging scheduling strategy of electric vehicle users in the upper layer, with the goal of maximizing the utilization rate of charging piles at the charging station;

[0141] The solving unit is used to solve the charging scheduling model based on the adaptive improved bat algorithm to obtain the optimal scheduling method.

[0142] Embodiment 2:

[0143] like Figure 3 As shown, this embodiment discloses a charging station charging scheduling strategy method considering the utilization rate of charging piles, including:

[0144] A two-layer Stackelberg game architecture for charging station charging scheduling is established, which includes the charging operator layer and the user layer. The game architecture is as follows: Figure 4 As shown;

[0145] Considering the user's charging time cost and user's charging economic cost, a charging scheduling strategy for electric vehicle users based on the comprehensive charging cost is established at the user level;

[0146] Considering the utilization rate of charging stations and taking maximizing the utilization rate of charging piles in charging stations as the goal, a charging scheduling model solving algorithm for charging stations based on the adaptive improved bat algorithm is established.

[0147] The specific implementation is as follows:

[0148] S1. Select the urban area road data and the current pricing of each charging station;

[0149] The selected data is real road data, and the elements included in the object are intersection nodes, roads and charging station nodes. The economic cost data of the charging station includes two parts: charging electricity price and charging service fee.

[0150] S2, generating charging demand;

[0151] Within the region, regional charging demand is generated by probability based on the POI typical charging demand curve according to regional POI attributes and time. The longitude and latitude of the charging demand generating point are obtained by random sampling. Take the charging demand of 3,500 vehicles as an example.

[0152] S3, using the adaptive improved bat algorithm to complete the charging scheduling of charging stations during the period;

[0153] The solution process of the electric vehicle scheduling model is as follows:

[0154] Step 1: Generate typical charging scenarios based on the impact of charging efficiency and charging costs on the charging behavior of electric vehicle users.

[0155] Step 2: Initialize the parameters of the bat population, including the number of bat populations, the dimension of the problem, the number of iterations, the pulse loudness, the pulse loudness reduction coefficient, the maximum pulse emission frequency, the pulse emission frequency increase coefficient, etc., and encode the bats to represent the economic cost of charging at the charging station.

[0156] Step 3: Use a recursive method to obtain the initial bat position matrix that satisfies the constraints. Randomly generate uniformly distributed binary strings as the initial charging and service fee random matrix B (b1, b2, b3…b D ) T , and then use the dot product type of matrix B to obtain the matrix P (p1, p2, p3…p D ) T Get the initial bat position matrix X(x1,x2,x3…x D ) T , the value of each dimension in the initial bat position matrix corresponds to the type of electric vehicle charging station, the subscript of each dimension corresponds to the location of the charging station, and the recursive end condition is the distribution network AC power flow model constraint and the time-sharing charging price adjustment model constraint. Then the fitness calculation is performed to obtain the fitness of each bat population, that is, the objective function value of the charging pile utilization rate in all typical scenarios within the complete operation cycle, and the initial optimal bat population is obtained, which is recorded as x*.

[0157] Step 4: Update the flight pulse frequency, update the flight speed and position of each bat, and then calculate the fitness of all populations.

[0158] Step 5: Determine whether a single bat performs a local search; if the random number between 0 and 1 is greater than the bat's current pulse emission frequency, a local search is performed, and then the bat's fitness is calculated.

[0159] Step 6: If the random number between 0 and 1 is less than the pulse loudness of the current bat, and the fitness of the bat is less than x*, then accept the newly acquired bat; update the pulse loudness and pulse emission frequency, and search for the optimal bat again;

[0160] Step 7: Determine whether the termination condition is met. If so, stop the iteration and output the optimal solution. Otherwise, go to step 4.

[0161] S3, user response;

[0162] Considering the charging time cost and charging economic cost, the two are weighted as the comprehensive charging cost. Electric vehicle users choose to charge at stations with lower comprehensive charging costs.

[0163] The charging time cost includes the travel time on the road, the waiting time in the charging station queue and the charging time. Among them, the travel time on the road is obtained by using the Dijkstra algorithm through the actual road network data of the section to obtain the fastest travel path, and its travel time is used as the travel time on the road; the waiting time in the charging station queue is estimated by historical data and the current status of the charging station; the charging time is estimated by the output power of the charging pile at the charging station and the current SOC of the vehicle.

[0164] The comprehensive charging cost of an electric vehicle at the current location is as follows:

[0165]

[0166] Among them, F(i) is the comprehensive charging cost of electric vehicle users going to charging station i to charge. is the economic cost of charging, and the remaining items are the time cost of charging. is the charging journey time, Waiting time in line for charging stations, is the charging time, and π(·) is the conversion function between the charging time cost and the charging economic cost.

[0167] An urban road section in the central area of ​​Wuxi was selected for example analysis. The map data was derived from the open source platform OpenStreetMap and processed through the open source geographic information system QGIS.

[0168] The results of the economic cost optimization of the charging station are as follows: Figure 5 The heat map of the utilization rate of charging stations under the charging scheduling mechanism of the present invention is shown as follows. Figure 6 The heat map of charging station utilization rate in this example under the traditional peak-valley electricity price mechanism is as follows: Figure 7 The results show that this method significantly improves the utilization rate of charging piles in charging stations.

[0169] See also Figure 8 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0170] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0171] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0172] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0173] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0175] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0176] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0177] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0178] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0179] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A charging scheduling method considering the utilization rate of charging piles, characterized in that: The steps include: Establish a two-layer game architecture for charging station charging scheduling, including the charging operator layer and the user layer; Based on the two-layer game architecture of charging station charging scheduling, the user charging time cost and charging economic cost are considered in the lower layer, and a charging scheduling strategy for electric vehicle users based on the comprehensive charging cost is established at the user layer; Based on the two-layer game architecture of charging station charging scheduling, the charging station utilization rate and the charging scheduling strategy of the electric vehicle user are considered in the upper layer, and the charging station charging scheduling model is established with the goal of maximizing the utilization rate of charging piles at the charging station; Based on the adaptive improved bat algorithm, the charging scheduling model is solved to obtain the optimal scheduling method.

2. The charging scheduling method considering the utilization rate of charging piles according to claim 1 is characterized in that: The establishment of a two-layer game architecture for charging station charging scheduling including a charging operator layer and a user layer includes: Establishing an upper leadership layer and a lower follower layer, wherein the upper leadership layer is a charging operator layer, and the lower follower layer is a user layer, wherein the upper leadership layer and the lower follower layer structurally constitute a Stackelberg game structure of a leader-follower; Users with charging needs complete charging behavior according to the scheduling arrangement formulated by the upper-level leadership and the scheduling response strategy; The upper leadership layer actively adjusts the scheduling strategy, and the lower follower layer receives the scheduling strategy and responds to the scheduling strategy.

3. The charging scheduling method considering the utilization rate of charging piles according to claim 1 is characterized in that: The electric vehicle user charging scheduling strategy includes: Considering the charging time cost and the charging economic cost, the two are weighted as the comprehensive charging cost. Electric vehicle users choose the charging station with the lowest comprehensive charging cost to charge. The charging cost includes travel time on the road, waiting time in the charging station, and charging time; The economic cost of charging includes the cost of recharging.

4. The charging scheduling method considering the utilization rate of charging piles according to claim 3 is characterized in that: The travel time on the road is obtained by using the Dijkstra algorithm through the actual road network data of the section to obtain the fastest travel path, and the travel time is used as the travel time on the road; The waiting time in the charging station queue is estimated by historical data and the current state of the charging station; the charging time is estimated by the output power of the charging pile of the charging station and the current battery state of charge SOC of the vehicle.

5. The charging scheduling method considering the utilization rate of charging piles according to claim 4 is characterized in that: The comprehensive charging cost of an electric vehicle at the current location is as follows: Where F(i) is the comprehensive charging cost of electric vehicle users going to charging station i for charging. The economic cost of charging for users, and the remaining items are the charging time costs, is the charging journey time, Waiting time in line for charging stations, is the charging time, and π(·) is the conversion function between the user's charging time cost and the user's charging economic cost.

6. The charging scheduling method considering the utilization rate of charging piles according to claim 1, characterized in that: The charging station utilization rate is considered, and the charging station charging scheduling model is established with the goal of maximizing the utilization rate of charging piles in the charging station, including: Establish an objective function to maximize the utilization rate of charging piles at charging stations: In the formula, c csf is the economic cost of the charging station, N tp is the number of periodic updates, N sgcc is the number of charging stations under the self-operated operator; α uti U is the conversion coefficient between the utilization rate of charging piles and the economic cost of charging stations; i is the evaluation index of charging pile utilization rate of charging station i in the complete operation cycle; The calculation formula of the evaluation index of electric pile utilization rate is as follows: Where, T gap For the update cycle; represents the number of charging piles at charging station i, For nth tp The start time of an update cycle; N cr The total amount of charging demand for electric vehicles; and They represent the start and end charging time of the kth electric vehicle with charging demand, Indicates that the car is charging at the current time t; is the indicator function, when argmin i When F(i,k)=i, the return value is 1, otherwise it is 0; is the indicator function, when If true, the return value is 1, otherwise 0; Constraints for establishing charging station charging scheduling model: In the formula, and They respectively represent the lower and upper limits of the economic cost adjustment of charging stations.

7. The charging scheduling method considering the utilization rate of charging piles according to claim 1, characterized in that: The method based on adaptive improved bat algorithm comprises the following steps: Flight pulse frequency update: The formula for calculating bat pulse frequency is as follows: Where f1 is the pulse frequency; c w is a constant for calculating the pulse frequency; ρ is a constant representing the average fitness weight of the bat population; F avg is the average fitness of the bat population; F best is the fitness of the optimal bat; ζ is a constant representing the weight of the current iteration number; gen is the current iteration number; gen max is the maximum number of iterations; f min is the constant for controlling the maximum value of pulse frequency; Flight speed and position update: Assuming that the bat's search space is D-dimensional, the flight speed and position of each bat are updated according to the following formula during iteration: in is the flying speed of bat i in the gen generation; k1 is a random number between 0 and 1; is the position of bat i in the gen generation; is the weight coefficient for position update; x* is the current optimal bat; Local Search: If the random number between 0 and 1 is greater than the bat's current pulse emission frequency R gen , then perform local search according to the following formula: Among them A gen is the impulse loudness of the bats in the gen generation; d is the ratio of the upper and lower boundary distances of the feasible solution domain to the number of bat populations; rateG is the ratio of the current number of iterations to the maximum number of iterations; δ is a random number between -1 and 1; Where g(rateG) is a piecewise function; the calculation method of g(rateG) is as follows: Pulse loudness and pulse emission frequency updated: Update the pulse loudness and pulse emission frequency according to the following formula: A gen+1 =εA gen ; R gen+1 =R 0 [1-e -θgen ]; Among them A gen is the pulse loudness of the bat in the gen generation; ε is the pulse loudness reduction factor; R gen is the pulse emission frequency of bats in the gen generation; R0 is the maximum pulse emission frequency; θ is the pulse emission frequency increase coefficient.

8. The charging scheduling method considering the utilization rate of charging piles according to claim 7 is characterized in that: The method of solving the charging scheduling model based on the adaptive improved bat algorithm includes the following steps: Step 1: Generate typical charging scenarios based on the impact of charging efficiency and charging costs on the charging behavior of electric vehicle users; Step 2: Initialize the parameters of the bat population, including the number of bat populations, the dimension of the problem, the number of iterations, the pulse loudness, the pulse loudness reduction factor, the maximum pulse emission frequency, and the pulse emission frequency increase factor, and encode the bats to represent the economic cost of charging at the charging station; Step 3: Use a recursive method to obtain the initial bat position matrix that satisfies the constraints; randomly generate uniformly distributed binary strings as the initial charging station charging economic cost random matrix B (b1, b2, b3…b D ) T , and then use the dot product type of matrix B to obtain the matrix P (p1, p2, p3…p D ) T Get the initial bat position matrix X(x1,x2,x3…x D ) T , the value of each dimension in the initial bat position matrix corresponds to the type of electric vehicle charging station, the subscript of each dimension corresponds to the location of the charging station, and the recursive termination condition is the distribution network AC power flow model constraint and the time-sharing charging price adjustment model constraint; then the fitness calculation is performed to obtain the fitness of each bat population, that is, the charging pile utilization objective function value within the complete operation cycle of all typical scenarios, and the initial optimal bat population is obtained, denoted as x*; Step 4: Update the flight pulse frequency, update the flight speed and position of each bat, and then calculate the fitness of all populations; Step 5: Determine whether a single bat performs local search; if the random number between 0 and 1 is greater than the bat's current pulse emission frequency, perform local search, and then calculate the bat's fitness; Step 6: If the random number between 0 and 1 is less than the pulse loudness of the current bat, and the fitness of the bat is less than x*, then accept the newly acquired bat; update the pulse loudness and pulse emission frequency, and search for the optimal bat again; Step 7: Determine whether the termination condition is met. If so, stop the iteration and output the optimal solution. Otherwise, go to step 4.

9. A charging scheduling device considering the utilization rate of charging piles, characterized in that: Using the method according to any one of claims 1 to 8, the scheduling device comprises: A two-layer game unit, used to establish a two-layer game architecture for charging station charging scheduling, including a charging operator layer and a user layer; A user strategy unit is used to establish a user-level charging scheduling strategy for electric vehicle users based on the comprehensive charging cost by considering the user's charging time cost and charging economic cost in the lower layer based on the two-layer game architecture of charging station charging scheduling; A scheduling model building unit is used to establish a charging station charging scheduling model based on the charging station charging scheduling two-layer game architecture, taking into account the charging station utilization rate and the electric vehicle user charging scheduling strategy in the upper layer, with the goal of maximizing the charging station charging pile utilization rate; The solving unit is used to solve the charging scheduling model based on the adaptive improved bat algorithm to obtain the optimal scheduling method.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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