Electric vehicle alternating current pile orderly charging method and system based on transformer area autonomy

Abstract

The application discloses a kind of based on the orderly charging method and system of electric vehicle alternating current stake of transformer area autonomy, the corresponding transformer area orderly charging peak cutting strategy baseline band of next control period is acquired based on the historical load curve of transformer area in advance prediction, baseline band peak is determined according to baseline band;Acquire the preset judgment condition: 1) whether the total load of transformer area in current control period exceeds the baseline band peak;2) whether the load curve fluctuation rate exceeds the preset load curve fluctuation margin value;If at least one of the above conditions is yes, then execute orderly charging control strategy, modify the charging power provided by alternating current stake in transformer area in next control period;Otherwise, in next control period, charging service is provided for electric vehicle in transformer area according to disorderly charging mode.The advantage is: fully consider the charging experience of user, as far as possible in the case where transformer area is not expanded to guarantee the safety of transformer area during charging.

Description

TECHNICAL FIELD

[0001] The application relates to an electric vehicle AC pile orderly charging method and system based on a transformer area autonomy, and belongs to the technical field of electric vehicle orderly charging. BACKGROUND

[0002] With the increasingly serious problems of climate warming and energy crisis, it is more and more urgent to develop new energy vehicles represented by electric vehicles and realize the electrification transformation demand in the transportation field. However, the electric vehicle load is a high-power, nonlinear load. With the gradual increase of the number of electric vehicles owned by residents, the disordered charging mode of large-scale electric vehicles may generate a very high harmonic current and impact voltage, increase the fluctuation of the load power curve, intensify the peak-valley difference of power consumption, and bring great challenges to the safe and stable operation of the power grid.

[0003] Although there are many related researches on the regulation and control strategy of electric vehicle orderly charging in the academic field at present, some obvious defects are found when combined with actual application. The application scene of the strategy is relatively single, and the strategy is generally only applicable to DC fast charging piles. The algorithm is highly dependent on the state of charge (SOC) curve of the vehicle, and does not consider that the SOC curve of the vehicle cannot be obtained in the actual application of the AC charging pile. The algorithm does not fully consider the randomness of the electric vehicle charging behavior in time, and generally defaults to the reservation charging mode. Moreover, the algorithm does not consider the charging use satisfaction of users and the enthusiasm of users participating in orderly charging and other multiple factors. SUMMARY

[0004] The technical problem to be solved by the application is to overcome the defects of the prior art, provide an electric vehicle AC pile orderly charging method and system based on transformer area autonomy, provide algorithm support based on the actual situation that the AC charging pile cannot obtain the SOC curve of the vehicle, reduce the power fluctuation and cut the load peak value as much as possible under the premise of meeting the power constraint of the transformer area, and at the same time, the time randomness of the electric vehicle charging behavior is considered to the greatest extent, the charging experience of users is fully considered, and the safety of the transformer area during charging is guaranteed as much as possible without expansion of the transformer area.

[0005] To solve the above technical problems, the application provides an electric vehicle AC pile orderly charging method based on transformer area autonomy, comprising:

[0006] obtaining the total load of the transformer area in the current regulation period and the load curve fluctuation rate in the next regulation period according to the disordered charging mode, wherein the disordered charging mode refers to that each AC charging pile provides charging service for the connected electric vehicle according to the rated power;

[0007] obtaining the transformer area orderly charging peak cutting strategy baseline band corresponding to the next regulation period based on the historical load curve of the transformer area, and determining the baseline band peak value according to the baseline band;

[0008] obtaining a preset judgment condition, the judgment condition being:

[0009] 1) whether the total load of the current regulation period exceeds the baseline peak value;

[0010] 2) whether the load curve fluctuation rate exceeds the preset load curve fluctuation margin value;

[0011] If at least one of the above conditions is yes, an orderly charging control strategy is executed to obtain a target charging plan, and the charging power provided by the AC pile in the next regulation period is modified according to the target charging plan; otherwise, the electric vehicles in the area are provided with charging services in the next regulation period according to a disordered charging mode.

[0012] Further, the total load calculation formula of the area under the disordered charging mode is:

[0013]

[0014] Wherein, P free represents the total load of the area under the disordered charging mode; N ev represents the number of guns in the charging state of the area; p represents the rated charging power of the AC charging pile.

[0015] Further, the baseline band of the orderly charging peak shaving strategy of the area corresponding to the next regulation period is obtained based on the historical load curve of the area, including:

[0016] A BP neural network method based on similar days is adopted to predict a cluster of daily electricity load curves according to the historical load curve of the area, and the electricity load curve is taken as the baseline band of the orderly charging peak shaving strategy of the area.

[0017] Further, the calculation formula of the load curve fluctuation rate is:

[0018]

[0019] Wherein, P' is the load curve fluctuation rate, P is the total charging power in the current regulation period, and Δt is the length of the regulation period.

[0020] Further, the execution of the orderly charging control strategy to obtain the target charging plan includes:

[0021] determining a target function; wherein the target function is: min f = λ1f1+ λ2f2+ λ3f3;

[0022] Wherein, min indicates the minimum value of the function f as an optimization goal, f1 indicates a console area charging power fluctuation objective function, f2 indicates a substation orderly charging peak shaving strategy baseline regression function, f3 indicates a user charging satisfaction function, λ1, λ2, and λ3 are balance coefficients, and satisfy λ1+λ2+λ3=1;

[0023] The constraint condition includes an alternating current pile charging power constraint and a substation main transformer power constraint;

[0024] The target charging plan is obtained by solving the target function according to the target function and the constraint condition by using a particle swarm algorithm.

[0025] Further, the console area charging power fluctuation objective function is:

[0026]

[0027] Wherein, α1 and α2 are balance coefficients, and satisfy α1+α2=1; P'(t,t+1) indicates a change rate of expected charging power in a t+1 period set in a t period; P'(t,t+2) indicates a change rate of expected charging power in a t+2 period set in a t period; P(t,t+1) indicates expected charging power in a t+1 period set in a t period; P(t,t+2) indicates expected charging power in a t+2 period set in a t period; P(t,t) indicates actual total charging power of the substation in the t period; and P(t,t+1) and P(t,t+2) are selected as decision variables of the particle swarm algorithm;

[0028] The substation orderly charging peak shaving strategy baseline regression function is:

[0029] f2=α3[P(t,t+1)-M(t+1)] 2 +α4[P(t,t+2)-M(t+2)] 2

[0030] Wherein, α3 and α4 are balance coefficients, and satisfy α3+α4=1; M(t+1) is a peak value of the substation orderly charging peak shaving strategy baseline band in a t+1 period; and M(t+2) is a peak value of the substation orderly charging peak shaving strategy baseline band in a t+2 period;

[0031] The user charging satisfaction function is:

[0032] f3=β[P(t,t+1)-P free (t+1)] 2

[0033] Wherein, β is a user satisfaction coefficient, and P free (t+1) is total load of the substation in a t+1 period in a disorderly charging mode.

[0034] Further, the AC pile charging power constraint is:

[0035]

[0036] Wherein, p max is the maximum charging power that the AC charging pile in the transformer area can output, N ev (t) is the number of guns in the charging state in the transformer area;

[0037] The transformer area main transformer power constraint is:

[0038]

[0039] Wherein, P max is the upper limit of the transformer area main transformer power; P base (t) is the basic load of the transformer area at time period t; P allow (t) is the power margin reserved to ensure the safety of the transformer area main transformer at time period t.

[0040] Further, the optimal charging plan is obtained by solving the objective function according to the objective function and the constraint condition by using the particle swarm algorithm, comprising:

[0041] a) According to the determined objective function and constraint condition, set the fitness value calculation function;

[0042] b) Initialize the population, including population size, maximum iteration number, constraint range, and randomly assign values to the initial population;

[0043] c) Calculate the fitness value of the individual members of the population by using the fitness value calculation function;

[0044] d) Enter the iteration stage, if the iteration stop condition is met, jump to f), output the transformer area autonomous power of the global optimal individual member as the result; otherwise, for each member individual in the group:

[0045] iv. Save the historical optimal fitness value of the member individual in the last iteration period;

[0046] v. Reallocate the search task of the member individual, update the speed and position information of the member individual, and calculate the fitness value function to update the historical target fitness value of the member individual;

[0047] vi. If the historical target fitness value of the member individual is better than the historical optimal fitness value in the last iteration period, execute the global target updating strategy to update the global target value information;

[0048] e) The iteration step number is increased by 1, and return to d);

[0049] f) iteration ends, the global target individual information is saved as the optimal charging plan output, and the target charging plan is represented as:

[0050]

[0051] Further, the modification of the charging power provided by the AC pile in the target charging plan is as follows:

[0052] According to the target charging plan, the actual total charging power P(t+1, t+1) of the station area in the t+1 period is set to the expected charging power P(t, t+1) set in the t period, and the rated charging power P(t+1) of the charging gun in the t+1 period is set to the actual total charging power P(t+1, t+1) of the station area in the t+1 period divided by the number N of guns in the charging state in the t period. ev (t).

[0053] A station area autonomous electric vehicle AC pile orderly charging system, comprising:

[0054] A first acquisition module is configured to acquire the total load of the station area in the current control period and the load curve fluctuation rate in the next control period according to a disorderly charging mode, wherein the disorderly charging mode refers to that each AC charging pile provides charging service to the electric vehicle connected according to the rated power.

[0055] A second acquisition module is configured to acquire a station area orderly charging peak clipping strategy baseline band corresponding to the next control period based on the historical load curve of the station area, and determine a baseline band peak value according to the baseline band.

[0056] A third acquisition module is configured to acquire a preset judgment condition, and the judgment condition is:

[0057] 1) whether the total load of the station area in the current control period exceeds the baseline band peak value;

[0058] 2) whether the load curve fluctuation rate exceeds a preset load curve fluctuation margin value;

[0059] A judgment module is configured to determine whether at least one of the above conditions is true, and if so, execute an orderly charging control strategy to obtain a target charging plan, and modify the charging power provided by the AC pile in the next control period according to the target charging plan; otherwise, the electric vehicle in the station area is provided with charging service according to the disorderly charging mode in the next control period.

[0060] Further, the total load calculation formula of the station area in the disorderly charging mode is as follows:

[0061]

[0062] P(t+1) = P(t+1, t+1) / Nfree N represents the total load of the transformer area when charging in disorder. ev P represents the number of guns in the charging state at the transformer area; p represents the rated charging power of the alternating current charging pile.

[0063] Further, the second acquisition module is used to

[0064] The BP neural network method based on similar days is adopted, the daily power consumption load curve is predicted according to the historical load curve of the transformer area, and the power load curve is taken as the baseline of the transformer area orderly charging peak shaving strategy.

[0065] Further, the calculation formula of the load curve fluctuation rate is:

[0066]

[0067] Wherein, P' is the load curve fluctuation rate, P is the total charging power in the current regulation period, and Δt is the length of the regulation period.

[0068] Further, the judging module is used to

[0069] Determine the objective function; wherein the objective function is: min f = λ1f1+ λ2f2+ λ3f3;

[0070] Wherein, min represents the minimum value of the function f as the optimization target, f1 represents the objective function of controlling the transformer area charging power fluctuation, f2 represents the transformer area orderly charging peak shaving strategy baseline band regression function, f3 represents the user charging satisfaction function, λ1, λ2, λ3 are equalization coefficients, and satisfy λ1+ λ2+ λ3=1;

[0071] Determine the constraint condition including alternating current pile charging power constraint and transformer area main transformer power constraint;

[0072] According to the objective function and the constraint condition, the particle swarm algorithm is used to solve the objective function to obtain the target charging plan.

[0073] Further, the objective function of controlling the transformer area charging power fluctuation is:

[0074]

[0075] Wherein, α1, α2 are balance coefficients, and satisfy, α1+α2=1; P'(t, t+1) represents the change rate of the expected charging power at t+1 period set by t period; P'(t, t+2) represents the change rate of the expected charging power at t+2 period set by t period; P(t, t+1) represents the expected charging power at t+1 period set by t period; P(t, t+2) represents the expected charging power at t+2 period set by t period; P(t, t) represents the actual total charging power of the substation at t period; P(t, t+1) and P(t, t+2) are selected as the decision variables of the particle swarm algorithm;

[0076] The baseline regression function of the substation orderly charging peak clipping strategy is:

[0077] f2=α3[P(t, t+1)-M(t+1)] 2 +α4[P(t, t+2)-M(t+2)] 2

[0078] Wherein, α3, α4 are balance coefficients, and satisfy, α3+α4=1; M(t+1) is the peak value of the substation orderly charging peak clipping strategy baseline at t+1 period; M(t+2) is the peak value of the substation orderly charging peak clipping strategy baseline at t+2 period;

[0079] The user charging satisfaction function is:

[0080] f3=β[P(t, t+1)-P free (t+1)] 2

[0081] Wherein, β is the user satisfaction coefficient, P free (t+1) is the total load of the substation under the unordered charging mode at t+1 period.

[0082] Further,

[0083] The alternating current pile charging power constraint is:

[0084]

[0085] Wherein, p max is the maximum charging power that the alternating current charging pile in the substation can output, N ev (t) is the number of guns in the charging state of the substation;

[0086] The substation main transformer power constraint is:

[0087]

[0088] Wherein, P max is the upper limit of the substation main transformer power; P base(t) is the base load of the transformer area at time period t; P allow (t) is the power margin reserved for ensuring the safety of the transformer area main transformer at time period t.

[0089] Further, the judging module is used for

[0090] a) setting an fitness value calculation function according to the determined objective function and constraint condition;

[0091] b) initializing a population, including population size, maximum iteration number, constraint range, and randomly assigning values to the initial population;

[0092] c) calculating the fitness value of each member of the population by using the fitness value calculation function;

[0093] d) entering an iteration stage, if the iteration stop condition is met, jumping to f) and outputting the transformer area autonomous power of the global target member as the result; otherwise, for each member of the population:

[0094] iv. saving the historical target fitness value of the member in the last iteration period;

[0095] v. reassigning the search task of the member, updating the speed and position information of the member, and calculating the fitness value function and updating the historical target fitness value of the member;

[0096] vi. if the historical target fitness value of the member is better than the historical target fitness value in the last iteration period, performing a global target updating strategy to update the global target value information;

[0097] e) increasing the iteration step number by 1 and returning to d);

[0098] f) ending the iteration and outputting the global target member information as the optimal charging plan, wherein the target charging plan is represented as:

[0099]

[0100] Further, the method of modifying the charging power provided by the AC pile in the transformer area in the next control period according to the target charging plan comprises:

[0101] setting the actual total charging power P(t+1, t+1) of the transformer area at time period t+1 according to the target charging plan as the expected charging power P(t, t+1) of time period t+1 set at time period t, and setting the rated charging power P(t+1) of the charging gun in the transformer area at time period t+1 as the actual total charging power P(t+1, t+1) of the transformer area at time period t+1 divided by the number N ev (t) of guns in the charging state of the transformer area at time period t.

[0102] A computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods described.

[0103] A computing device, comprising,

[0104] One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods described.

[0105] The beneficial effects achieved by this invention are as follows:

[0106] To fill the gaps in existing research, this invention provides algorithmic support based on the fact that AC charging piles cannot obtain the vehicle's SOC curve. Under the premise of meeting the power constraints of the charging station area, it minimizes power fluctuations and reduces load peaks, while taking into account the temporal randomness of electric vehicle charging behavior to the greatest extent, fully taking care of the user's charging experience, and ensuring the safety of the charging station area during charging as much as possible without expanding the capacity of the charging station area. Attached Figure Description

[0107] Figure 1 A schematic diagram of an orderly charging method for AC charging piles for electric vehicles based on the autonomous operation of the substation area is provided in one embodiment of the present invention.

[0108] Figure 2 The charging power curves provided by the AC charging piles in the transformer area for each time period according to the present invention;

[0109] Figure 3 This is a comparison curve of the total load of the charging station area under ordered charging and the total load of the charging station area under disordered charging according to the present invention. Detailed Implementation

[0110] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0111] like Figure 1 As shown, an orderly charging method for electric vehicles using AC charging stations based on district autonomy includes:

[0112] The 96-point daily load curve method is used, dividing a 24-hour day into 96 equal time periods, each lasting 15 minutes. Within each time period, charging and basic electrical information for the transformer substations are collected to determine the appropriate charging method for the next time period if unordered charging is implemented.

[0113] (1) whether the charging load value exceeds the baseline peak value of the orderly charging peak shaving strategy corresponding to the next control period;

[0114] (2) whether the load curve fluctuation rate exceeds the load curve fluctuation margin value.

[0115] If at least one of the above conditions is met, the orderly charging control strategy based on the particle swarm algorithm is executed to modify the charging power provided by the AC pile in the next control period; otherwise, the electric vehicles in the substation are provided with charging services in the next control period according to the disorderly charging mode.

[0116] The charging information of the current period of the substation includes the number of guns in the charging state, the rated charging power of the AC charging pile and the real-time total charging power.

[0117] The basic electrical information of the substation includes the upper limit of the power of the main transformer of the substation, the power margin reserved for the safety of the main transformer of the substation, etc.

[0118] The disorderly charging mode means that each AC charging pile provides charging services for the connected electric vehicles according to the rated power:

[0119]

[0120] wherein, P free represents the real-time total charging power of the substation according to the disorderly charging, which is [98, 119, 133, 154, 182, 217, 210, 182, 161, 119, 105, 77] respectively; N ev represents the number of guns in the charging state of the substation, which is [14, 17, 19, 22, 26, 31, 30, 26, 23, 17, 15, 11] respectively; p represents the rated charging power of the AC charging pile, which is 7kW.

[0121] The baseline peak of the orderly charging peak shaving strategy of the substation adopts a BP neural network method based on similar days, which predicts a cluster of possible daily power load curves according to the historical load curve of the substation. This cluster of power load curves forms the baseline peak of the orderly charging peak shaving strategy of the substation.

[0122] The load curve fluctuation rate is calculated by the change rate of the total charging load value of the substation in the next control period according to the disorderly charging and the charging load value in the current control period:

[0123]

[0124] wherein, P' is the change rate of the charging load value, P is the total charging power in the current control period, Δt is the length of the period, which is 15min, and the total period number is 12.

[0125] The ordered charging control strategy based on the particle swarm algorithm has the following specific steps:

[0126] Step 1, determining a target function:

[0127] minf=λ1f1+λ2f2+λ3f3

[0128] Wherein, min represents the minimum value of the function as an optimization goal, f1 represents a target function of controlling console area charging power fluctuation, f2 represents a baseline regression function of the ordered charging peak shaving strategy of the console area, f3 represents a user charging satisfaction function, λ1, λ2, λ3 are balance coefficients, and satisfy λ1+λ2+λ3=1, and λ1=0.5, λ2=0.2, λ3=0.3 are taken here.

[0129] Step 2, determining a constraint condition, mainly including an alternating pile charging power constraint and a console area main transformer power constraint:

[0130] Step 3, calling an improved particle swarm algorithm to solve the target.

[0131] Preferably, the target function of controlling console area charging power fluctuation in step 1 is as follows:

[0132]

[0133] Wherein, α1, α2 are balance coefficients, and satisfy α1+α2=1, and α1=0.8, α2=0.2 are taken here; P'(t,t+1) represents a change rate of expected charging power set at t+1 period of t period; P'(t,t+2) represents a change rate of expected charging power set at t+2 period of t period; P(t,t+1) represents expected charging power set at t+1 period of t period; P(t,t+2) represents expected charging power set at t+2 period of t period; P(t,t) represents actual total charging power of the console area at t period.

[0134] In particular, P(t,t+1) and P(t,t+2) are selected as decision variables of the particle swarm algorithm.

[0135] The baseline regression function of the ordered charging peak shaving strategy of the console area is as follows:

[0136] f2=α3[P(t,t+1)-M(t+1)] 2 +α4[P(t,t+2)-M(t+2)] 2

[0137] Wherein, α3, α4 are equalization coefficients, and satisfy; α3+α4=1, here α3=0.7, α4=0.3; M(t+1) is the peak value of the ordered charging peak shaving strategy baseline of the transformer area at time period t+1; M(t+2) is the peak value of the ordered charging peak shaving strategy baseline of the transformer area at time period t+2.

[0138] The user charging satisfaction function is as follows:

[0139] f3=β[P(t,t+1)-P free (t+1)] 2

[0140] Wherein, β is the user satisfaction coefficient, here it is 2.

[0141] Preferably, the alternating current pile charging power constraint in step 2 is as follows:

[0142]

[0143] Wherein, p max is the maximum charging power that the alternating current charging pile in the transformer area can output.

[0144] Preferably, the transformer main transformer power constraint in step 2 is as follows:

[0145]

[0146] Wherein, P max is the upper limit of the transformer main transformer power, here it is 630kVA; P base (t) is the basic load of the transformer area at time period t, here it is [357.3, 367.5, 383.6, 394.2, 411.6, 427.2, 424.7, 414.7, 403.1, 394.5, 386.3, 376.8]; P allow (t) is the power margin reserved to ensure the safety of the transformer main transformer at time period t, here it is 0.1P base (t).

[0147] Preferably, the improved particle swarm algorithm in step 3 includes the following steps:

[0148] a) Obtain relevant data, define the fitness value calculation function according to the objective function in step 1 and the constraint condition in step 2;

[0149] b) Initialize the population, including population size, maximum iteration number, constraint range, etc., and randomly assign values to the initial population;

[0150] c) Calculate the fitness value of the individual members of the population using the defined fitness value calculation function;

[0151] d) entering an iteration phase, if the iteration stop condition is met, jumping to f), outputting the autonomous power of the substation area of the global target individual member as a result; otherwise, for each member in the group:

[0152] i. saving the individual historical target of the last iteration period;

[0153] ii. reassigning the search task of the member individual, updating the speed and position information of the member individual, and calculating the fitness value function, and updating the individual historical target fitness value information;

[0154] iii. if the historical target value of the member individual is better than the historical target value of the last iteration period, performing a global target updating strategy;

[0155] iv. performing a global target information disturbance strategy to update the global target value information;

[0156] e) the iteration step is incremented by 1, and returning to d);

[0157] f) the iteration is ended, and the global target individual information is saved as the target charging plan and output.

[0158] Further, the modification method of the charging power provided by the AC pile in the next control period in the substation area is as follows:

[0159]

[0160] After calculation, the charging power provided by the AC pile in the substation area in each period is [7.0, 6.8 6.8, 5.5, 4.0, 2.8, 3.0, 3.9, 4.9, 6.8, 7.0, 7.0], and the charging power curve is as shown in Figure 2 As can be seen from the figure, although the ordered charging method provided by the application reduces the charging power to a certain extent, it can still meet the AC charging demand of normal electric vehicles.

[0161] After calculation, the total charging power of each period in the substation area is [98.0, 115.8, 129.4, 120.9, 103.5, 87.9, 90.4, 100.4, 112.0, 115.8, 105.0, 77.0]; after adopting the ordered charging, the total power load of each period in the substation area is [455.3, 483.3, 513.0, 515.1, 515.1, 515.1, 515.1, 515.1, 515.1, 510.3, 491.3, 453.8], and the total power load of each period in the substation area after adopting the ordered charging is compared with the total load of the substation area under unordered charging on the same coordinate plane, as shown in Figure 3As shown, it can be seen from the figure that the orderly charging method provided by the application can significantly reduce the total power load of the transformer area during the power consumption peak period, reduce the power fluctuation and cut the load peak value as much as possible under the premise of meeting the power constraint of the transformer area, and at the same time, the time randomness of the electric vehicle charging behavior is considered to the greatest extent, the charging experience of the user is fully considered, and the safety of the transformer area during charging is guaranteed as much as possible without expanding the transformer area.

[0162] Correspondingly, the application also provides an electric vehicle alternating current pile orderly charging system based on transformer area autonomy, comprising:

[0163] The first acquisition module is used to acquire the total load of the transformer area in the current control period and the load curve fluctuation rate in the next control period according to the disorderly charging mode, wherein the disorderly charging mode refers to that each alternating current charging pile provides charging service for the electric vehicle connected according to the rated power.

[0164] The second acquisition module is used to acquire the transformer area orderly charging peak cutting strategy baseline band corresponding to the next control period based on the historical load curve of the transformer area, and determine the baseline band peak value according to the baseline band.

[0165] The third acquisition module is used to acquire the preset judgment condition, and the judgment condition is:

[0166] 1) whether the total load of the transformer area in the current control period exceeds the baseline band peak value;

[0167] 2) whether the load curve fluctuation rate exceeds the preset load curve fluctuation margin value;

[0168] The judgment module is used to judge that if at least one of the above conditions is yes, the orderly charging control strategy is executed to obtain the target charging plan, and the charging power provided by the alternating current pile in the transformer area in the next control period is modified according to the target charging plan; otherwise, the electric vehicle in the transformer area is provided with charging service according to the disorderly charging mode in the next control period.

[0169] Further, the total load calculation formula of the transformer area under the disorderly charging mode is:

[0170]

[0171] Wherein, P free represents the total load of the transformer area according to the disorderly charging; N ev represents the number of guns in the charging state of the transformer area; and p represents the rated charging power of the alternating current charging pile.

[0172] Further, the second acquisition module is used to

[0173] The BP neural network method based on similar days is adopted to predict a cluster daily power consumption curve according to historical power consumption curves of the transformer area, and the power consumption curve is taken as a baseline of the transformer area orderly charging peak shaving strategy.

[0174] Further, the formula for calculating the load curve fluctuation rate is:

[0175]

[0176] Wherein, P' is the load curve fluctuation rate, P is the total charging power in the current regulation period, and Δt is the length of the regulation period.

[0177] Further, the judging module is configured to

[0178] determine a target function; wherein the target function is: min f = λ1f1 + λ2f2 + λ3f3;

[0179] Wherein, min represents the minimum value of the function f as the optimization target, f1 represents the target function of controlling the transformer area charging power fluctuation, f2 represents the transformer area orderly charging peak shaving strategy baseline band regression function, f3 represents the user charging satisfaction function, λ1, λ2, and λ3 are balance coefficients, and satisfy λ1 + λ2 + λ3 = 1;

[0180] determine constraint conditions including AC pile charging power constraints and transformer area main transformer power constraints;

[0181] Solve the target function by using a particle swarm algorithm according to the target function and the constraint conditions to obtain a target charging plan.

[0182] Further, the target function of controlling the transformer area charging power fluctuation is:

[0183]

[0184] Wherein, α1 and α2 are balance coefficients, and satisfy α1 + α2 = 1; P'(t, t+1) represents the change rate of the expected charging power in the t+1 period set in the t period; P'(t, t+2) represents the change rate of the expected charging power in the t+2 period set in the t period; P(t, t+1) represents the expected charging power in the t+1 period set in the t period; P(t, t+2) represents the expected charging power in the t+2 period set in the t period; P(t, t) represents the actual total charging power of the transformer area in the t period; P(t, t+1) and P(t, t+2) are selected as decision variables of the particle swarm algorithm;

[0185] The transformer area orderly charging peak shaving strategy baseline band regression function is:

[0186] f2 = α3[P(t, t+1) - M(t+1)] 2+ a4[P(t, t+2) - M(t+2)] 2

[0187] wherein a3, a4 are equalization coefficients, and satisfy a3+a4=1; M(t+1) is the peak value of the substation orderly charging peak shaving strategy baseline at time period t+1; M(t+2) is the peak value of the substation orderly charging peak shaving strategy baseline at time period t+2;

[0188] The user charging satisfaction function is:

[0189] f3=β[P(t, t+1) - P(t, t+2)] free 2

[0190] wherein β is a user satisfaction coefficient, P(t, t+1) is the total load of the substation under the disordered charging mode at time period t+1. free

[0191] Further,

[0192] The alternating current pile charging power constraint is:

[0193]

[0194] wherein p max is the maximum charging power that the alternating current charging pile in the substation can output, N ev (t) is the number of guns in the charging state of the substation;

[0195] The substation main transformer power constraint is:

[0196]

[0197] wherein P max is the upper limit of the substation main transformer power; P base (t) is the basic load of the substation at time period t; P allow (t) is the power margin reserved for ensuring the safety of the substation main transformer at time period t.

[0198] Further, the judging module is used for

[0199] a) setting an fitness value calculation function according to the determined objective function and constraint condition;

[0200] b) initializing a population, including population size, maximum iteration number, constraint range, and randomly assigning values to the initial population;

[0201] c) calculating the fitness value of the population individual members by using the fitness value calculation function;

[0202] ​​d) entering an iteration phase, if the iteration termination condition is met, jumping to f), outputting the autonomous power of the substation area of the global target individual member as a result; otherwise, for each member individual in the group:

[0203] i. saving the historical target fitness value of the member individual in the last iteration period;

[0204] ii. reassigning the search task of the member individual, updating the speed and position information of the member individual, and calculating the fitness value function, and updating the historical target fitness value of the member individual;

[0205] iii. if the historical target fitness value of the member individual is better than the historical target fitness value in the last iteration period, performing a global target updating strategy to update the global target value information;

[0206] e) incrementing the iteration step number by 1 and returning to d);

[0207] f) ending the iteration, and outputting the global target individual information as a target charging plan, wherein the target charging plan is represented as:

[0208]

[0209] Further, the method further comprises:

[0210] According to the target charging plan, the actual total charging power P(t+1, t+1) of the substation in the t+1 period is set to the expected charging power P(t, t+1) of the t+1 period set in the t period, and the rated charging power P(t+1) of the charging gun in the t+1 period is set to the actual total charging power P(t+1, t+1) of the substation in the t+1 period divided by the number N of guns in the charging state in the t period ev (t).

[0211] Correspondingly, the application also provides a computer readable storage medium storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods.

[0212] Correspondingly, the application also provides a computing device, comprising,

[0213] Correspondingly, the application also provides one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the methods.

[0214] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0215] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0216] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0217] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0218] The above description is only preferred embodiments of the application. It should be pointed out that for those skilled in the art, some improvements and modifications can be made without departing from the technical principles of the application, and these improvements and modifications should also be considered as falling within the scope of the application.

Claims

1. A method for orderly charging of an electric vehicle alternating current pile based on a transformer area autonomy, characterized in that, The method comprises the following steps: obtaining the total load of the current control period and the load curve fluctuation rate of the next control period in the unordered charging mode, wherein the unordered charging mode refers to that each AC charging pile provides charging service for the electric vehicles according to the rated charging power; obtaining the ordered charging peak shaving strategy baseline band of the next control period based on the historical load curve of the substation, and determining the baseline band peak value according to the baseline band; obtaining the preset judgment condition, wherein the judgment condition is: 1) whether the total load of the current control period exceeds the baseline band peak value; 2) whether the load curve fluctuation rate exceeds the preset load curve fluctuation margin value; if at least one of the above conditions is yes, an ordered charging control strategy is executed to obtain a target charging plan, and the charging power provided by the AC pile in the next control period is modified according to the target charging plan; otherwise, the electric vehicles in the substation are provided with charging service in the next control period in the unordered charging mode; the step of obtaining the ordered charging peak shaving strategy baseline band of the next control period based on the historical load curve of the substation comprises the following steps: a BP neural network method based on similar days is adopted to predict a cluster of daily electricity load curves according to the historical load curve of the substation, and the electricity load curve is taken as the ordered charging peak shaving strategy baseline band; the calculation formula of the load curve fluctuation rate is: ； Wherein, P' is the load curve fluctuation rate, P free The total load of the transformer area is represented as P, the total charging power in the current control period is represented as P, and the length of the control period is represented as Δt.

2. The method according to claim 1, wherein, the calculation formula of the total load of the substation in the unordered charging mode is: ； Wherein, P free Indicates the total load of the transformer area; N ev Indicates the number of guns in the charging state of the transformer area; p indicates the rated charging power of the alternating current charging pile. 3.The method of claim 1, wherein, the step of executing the ordered charging control strategy to obtain the target charging plan comprises the following steps: determining a target function, wherein the target function is min f = λ1f1 + λ2f2 + λ3f3; wherein min represents that the minimum value of the function f is taken as the optimization target, f1 represents a target function for controlling the fluctuation of the substation charging power, f2 represents a baseline band regression function of the ordered charging peak shaving strategy, f3 represents a user charging satisfaction function, λ1, λ2 and λ3 are balance coefficients, and λ1 + λ2 + λ3 = 1 is satisfied; determining constraint conditions including AC pile charging power constraints and substation main transformer power constraints; the target charging plan is obtained by solving the target function by using a particle swarm algorithm according to the target function and the constraint conditions.

4. The ordered charging method of the AC pile for the electric vehicles based on the autonomy of the substation according to claim 3, wherein the target function for controlling the fluctuation of the substation charging power is: ； wherein α1 and α2 are balance coefficients, and α1 + α2 = 1 is satisfied; P'(t, t+1) represents the change rate of the expected charging power from t period to t+1 period; P'(t, t+2) represents the change rate of the expected charging power from t period to t+2 period; P(t, t+1) represents the expected charging power from t period to t+1 period; P(t, t+2) represents the expected charging power from t period to t+2 period; P(t, t) represents the actual total charging power of the substation in t period; P(t, t+1) and P(t, t+2) are selected as the decision variables of the particle swarm algorithm; the baseline band regression function of the ordered charging peak shaving strategy is: f2 = a3 [P(t,t+1) - M(t+1)] 2 + a4 [P(t,t+2) - M(t+2)] 2 ; Wherein, α3, α4 are equalization coefficients, and satisfy, α3+α4=1; M(t+1) is the peak value of the substation orderly charging peak shaving strategy baseline in period t+1; M(t+2) is the peak value of the substation orderly charging peak shaving strategy baseline in period t+2; The user charging satisfaction function is: f3=β[P(t,t+1)-P free (t+1)] 2 ; Wherein, β is the user satisfaction coefficient, P free (t+1) is the total load of the transformer area under the unordered charging mode at t+1 period.

5. The substation autonomous electric vehicle alternating current (AC) pile orderly charging method according to claim 4, characterized in that, The AC pile charging power constraint is: ； Wherein, p(t+1) represents the set charging power of the charging gun in the station area at t+1 period, p max is the maximum charging power that the AC charging pile in the station area can output, N ev (t) is the number of guns in the charging state in the station area; The substation main transformer power constraint is: ； Wherein, P max is the upper limit of the main transformer power of the transformer area; P base (t) is the basic load of the transformer area at time period t; P allow (t) is the power margin reserved for ensuring the safety of the main transformer of the transformer area at time period t.

6. The method according to claim 5, wherein, The optimal charging plan is obtained by solving the objective function according to the objective function and the constraint condition, comprising: a) setting the fitness value calculation function according to the determined objective function and the constraint condition; b) initializing the population, including the population size, the maximum number of iterations, the constraint range, and randomly assigning values to the initial population; c) calculating the fitness value of the population individual members using the fitness value calculation function; d) entering the iteration stage, if the iteration stop condition is met, jumping to f), the substation autonomous power of the global optimal individual member is taken as the result output; otherwise, for each member individual in the group: i. saving the historical optimal fitness value of the member individual in the last iteration period; ii. reassigning the search task of the member individual, updating the speed and position information of the member individual, and calculating the fitness value function, updating the historical target fitness value of the member individual; iii. if the historical target fitness value of the member individual is better than the historical optimal fitness value in the last iteration period, executing the global target updating strategy to update the global target value information; e) the iteration step number is incremented by 1, and the process returns to d); f) the iteration is completed, and the global target individual information is saved as the optimal charging plan output, and the target charging plan is represented as: ； P(t+1, t+1) is the actual total charging power of the substation in period t+1.

7. The method according to claim 6, wherein, The charging power provided by the AC pile in the substation in the next control period is modified according to the target charging plan, comprising: According to the target charging plan, the actual total charging power P(t+1, t+1) of the substation in the t+1 period is set to the expected charging power P(t, t+1) set in the t period, and the set charging power p(t+1) of the charging gun in the t+1 period is set to the actual total charging power P(t+1, t+1) of the substation in the t+1 period divided by the number N of guns in the charging state in the t period ev (t).

8. A system for orderly charging of an electric vehicle alternating current pile based on a transformer area autonomy, characterized in that, including: A first acquisition module is configured to acquire the total load of the substation in the current control period and the load curve fluctuation rate of the substation in the next control period according to a disorderly charging mode, wherein the disorderly charging mode refers to each AC charging pile providing charging service to the electric vehicle connected according to the rated charging power; A second acquisition module is configured to acquire a baseline band of the substation orderly charging peak shaving strategy corresponding to the next control period predicted based on the historical load curve of the substation, and determine a baseline band peak value according to the baseline band; A third acquisition module is configured to acquire a predetermined judgment condition, wherein the judgment condition is: 1) whether the total load of the substation in the current control period exceeds the baseline band peak value; 2) whether the load curve fluctuation rate exceeds a preset load curve fluctuation margin value; A judgment module is configured to determine whether at least one of the above conditions is true, and if so, execute an orderly charging control strategy to obtain a target charging plan, and modify the charging power provided by the AC pile in the substation in the next control period according to the target charging plan; otherwise, the electric vehicles in the substation are provided with charging service in the next control period according to the disorderly charging mode; The second acquisition module is configured to The BP neural network method based on similar days is adopted to predict a cluster daily power consumption curve according to historical power consumption curves of the transformer area, and the power consumption curve is taken as a baseline of the transformer area orderly charging peak shaving strategy; The formula for calculating the fluctuation rate of the load curve is: ； Wherein, P' is the load curve fluctuation rate, P free It represents the total load of the transformer area when charging in disorder, P is the total charging power in the current control period, and Δt is the length of the control period.

9. The orderly charging system for electric vehicle AC piles based on the autonomy of transformer area according to claim 8, characterized in that, The formula for calculating the total load of the transformer area in the unordered charging mode is: ； Wherein, P free represents the total load of the transformer area; N ev represents the number of guns in the charging state of the transformer area; p represents the rated charging power of the alternating current charging pile.

10. The system according to claim 8, wherein, The judgment module is configured to determine a target function; wherein the target function is: minf = λ1f1 + λ2f2 + λ3f3; wherein min represents the minimum value of the function f as the optimization target, f1 represents a target function for controlling the charging power fluctuation of the transformer area, f2 represents a baseline band regression function of the transformer area orderly charging peak shaving strategy, f3 represents a user charging satisfaction function, λ1, λ2, and λ3 are balance coefficients, and λ1 + λ2 + λ3 = 1 is satisfied; determine constraint conditions including AC pile charging power constraints and transformer area main transformer power constraints; obtain a target charging plan by solving the target function using a particle swarm algorithm according to the target function and the constraint conditions.

11. The transformer area autonomous electric vehicle AC pile orderly charging system according to claim 10, wherein the target function for controlling the charging power fluctuation of the transformer area is: ； wherein α1 and α2 are balance coefficients, and α1 + α2 = 1 is satisfied; P'(t, t+1) represents the change rate of the expected charging power in the t+1 time period set in the t time period; P'(t, t+2) represents the change rate of the expected charging power in the t+2 time period set in the t time period; P(t, t+1) represents the expected charging power in the t+1 time period set in the t time period; P(t, t+2) represents the expected charging power in the t+2 time period set in the t time period; P(t, t) represents the actual total charging power of the transformer area in the t time period; P(t, t+1) and P(t, t+2) are selected as decision variables of the particle swarm algorithm; the baseline band regression function of the transformer area orderly charging peak shaving strategy is: f2 = a3 [P(t,t+1) - M(t+1)] 2 + a4 [P(t,t+2) - M(t+2)] 2 ; wherein α3 and α4 are balance coefficients, and α3 + α4 = 1 is satisfied; M(t+1) is the peak value of the baseline band of the transformer area orderly charging peak shaving strategy in the t+1 time period; M(t+2) is the peak value of the baseline band of the transformer area orderly charging peak shaving strategy in the t+2 time period; the user charging satisfaction function is: f3=β[P(t,t+1)-P free (t+1)] 2 ; Wherein, β is the user satisfaction coefficient, P free (t+1) is the total load of the transformer area under the unordered charging mode at the t+1 period.

12. The transformer area autonomous electric vehicle AC pile orderly charging system according to claim 11, wherein the AC pile charging power constraint is: ； Wherein, p(t+1) represents the set charging power of the charging gun in the station area at t+1 period, p max is the maximum charging power that the AC charging pile in the station area can output, N ev (t) is the number of guns in the charging state in the station area; the transformer area main transformer power constraint is: ； Wherein, P max is the upper limit of the main transformer power of the transformer area; P base (t) is the basic load of the transformer area at time period t; P allow (t) is the power margin reserved for ensuring the safety of the main transformer of the transformer area at time period t.

13. The orderly charging system for electric vehicle AC piles based on the autonomy of transformer area according to claim 12, characterized in that, The judgment module is configured to a) set a fitness value calculation function according to the determined target function and constraint conditions; b) initialize a population, including population size, maximum iteration number, and constraint range, and randomly assign values to the initial population; c) calculate the fitness value of each member of the population using the fitness value calculation function; d) enter the iteration stage, if the iteration stop condition is met, jump to f) and output the transformer area autonomous power of the global optimal individual member as the result; otherwise, for each member individual in the population: i. save the historical optimal fitness value of the member individual in the last iteration period; ii. Reallocate the search task of the member individual, update the speed, position information of the member individual, and calculate the fitness value function, and update the historical target fitness value of the member individual; iii. If the historical target fitness value of the member individual is better than the historical target fitness value of the last iteration period, a global optimal update strategy is executed to update the global target value information; e) The iteration step number is incremented by 1, and the process returns to d); f) The iteration is ended, and the global target individual information is saved as an optimal charging plan and output, wherein the target charging plan is represented as: ； P(t+1, t+1) is the actual total charging power of the transformer area at the t+1 period.

14. The orderly charging system for electric vehicle AC piles based on the autonomy of transformer area according to claim 13, characterized in that, The method according to the target charging plan modifies the charging power provided by the AC pile in the transformer area in the next regulation period, comprising: According to the target charging plan, the actual total charging power P(t+1, t+1) of the substation in the t+1 period is set to the expected charging power P(t, t+1) set in the t period, and the set charging power p(t+1) of the charging gun in the t+1 period is set to the actual total charging power P(t+1, t+1) of the substation in the t+1 period divided by the number N of guns in the charging state in the t period ev (t).

15. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions for: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods according to claims 1-7.

16. A computing device, comprising: including, one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for performing any of the methods according to claims 1-7.

Images