CN115709665A - Electric vehicle alternating current pile ordered charging method and system based on platform area autonomy - Google Patents
Electric vehicle alternating current pile ordered charging method and system based on platform area autonomy Download PDFInfo
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Abstract
The invention discloses a method and a system for orderly charging an electric vehicle alternating current pile based on platform area autonomy, wherein a platform area orderly charging peak clipping strategy base line band corresponding to the next regulation and control time interval predicted in advance based on a platform area historical load curve is obtained, and a base line band peak value is determined according to the base line band; acquiring preset judgment conditions: 1) Whether the total load of the station area in the current regulation and control period exceeds the peak value of the baseline band or not; 2) Whether the fluctuation rate of the load curve exceeds a preset fluctuation margin value of the load curve or not; if at least one of the conditions is yes, executing an ordered charging control strategy, and modifying the charging power provided by the alternating current piles in the station area in the next regulation and control period; otherwise, in the next regulation and control time interval, the charging service is provided for the electric automobile in the platform area according to the disordered charging mode. The advantages are that: the charging experience of a user is fully taken care of, and the safety of the charging platform area is guaranteed under the condition that the platform area is not expanded as much as possible.
Description
Technical Field
The invention relates to an orderly charging method and system for an electric automobile alternating current pile based on platform area autonomy, and belongs to the technical field of orderly charging of electric automobiles.
Background
With the increasing severity of the problems of climate warming, energy crisis and the like, new energy automobiles represented by electric automobiles are vigorously developed, and the requirement of electrification transformation in the traffic field is more urgent. However, the electric vehicle load is a high-power nonlinear load, and as the holding capacity of the electric vehicle of residents increases gradually, the disordered charging mode of the large-scale electric vehicle may generate very high harmonic current and impulse voltage, increase the fluctuation of the load power curve, and aggravate the peak-valley difference of power consumption, thereby bringing about greater challenges to the safe and stable operation of the power grid.
Although many related researches on the regulation and control strategy of the orderly charging of the electric automobile exist in the academic community at present, some obvious defects are found when the regulation and control strategy is combined with practical application. The strategy application scene is single, and the method is generally only suitable for direct-current quick pile filling; the algorithm highly depends on a vehicle State of Charge (SOC) curve, and the fact that the SOC curve of the vehicle cannot be obtained by an alternating current charging pile in actual application is not considered; the randomness of the charging behavior of the electric automobile in time is not fully considered, and an appointed charging mode is generally adopted by an algorithm in a default mode; and does not consider the charging use satisfaction of the user and the enthusiasm of the user for participating in the ordered charging and other multiple factors.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide an orderly charging method and system for an electric vehicle alternating-current pile based on platform area autonomy, algorithm support is provided based on the actual condition that the alternating-current pile cannot obtain a vehicle SOC curve, power fluctuation is reduced as much as possible and a load peak value is reduced on the premise of meeting platform area power constraint, meanwhile, the time randomness of the electric vehicle charging behavior is considered to the greatest extent, the charging experience of a user is fully taken care of, and the platform area safety during charging is ensured as much as possible under the condition that the platform area is not expanded.
In order to solve the technical problem, the invention provides a method for orderly charging an electric vehicle alternating current pile based on platform district autonomy, which comprises the following steps:
acquiring the total load of a distribution area in the current regulation and control period and the fluctuation rate of a load curve in the next regulation and control period according to a disordered charging mode, wherein the disordered charging mode is that each alternating current charging pile provides charging service for an accessed electric automobile according to rated power;
acquiring a base line band of a platform area ordered charging peak clipping strategy corresponding to the next regulation and control time interval predicted in advance based on a platform area historical load curve, and determining a peak value of the base line band according to the base line band;
acquiring preset judgment conditions, wherein the judgment conditions are as follows:
1) Whether the total load of the station area in the current regulation and control period exceeds the peak value of the baseline band or not;
2) Whether the fluctuation rate of the load curve exceeds a preset fluctuation tolerance value of the load curve or not;
if at least one of the conditions is yes, executing an ordered charging control strategy to obtain a target charging plan, and modifying the charging power provided by the alternating current piles in the next regulation and control time interval zone according to the target charging plan; otherwise, in the next regulation and control period, the charging service is provided for the electric vehicles in the platform area according to the disordered charging mode.
Further, the calculation formula of the total load of the distribution room in the disordered charging mode is as follows:
wherein, P free Representing the total load of the distribution area when the charging is carried out according to disorder; n is a radical of hydrogen ev Indicating the number of guns in the charging state of the platform area; p represents the rated charging power of the ac charging pile.
Further, the obtaining of the station area ordered charging peak clipping strategy baseband corresponding to the next regulation and control period predicted in advance based on the station area historical load curve includes:
and predicting a cluster of daily power load curves according to the historical load curves of the transformer area by adopting a BP neural network method based on similar days, and taking the power load curves as the base line of the orderly charging peak clipping strategy of the transformer area.
Further, the calculation formula of the fluctuation rate of the load curve is as follows:
wherein, P' is the fluctuation rate of the load curve, P is the total charging power in the current regulation time interval, and delta t is the length of the regulation time interval.
Further, the executing the ordered charging control strategy to obtain the target charging plan includes:
determining an objective function; wherein the objective function is: min f = λ 1 f 1 +λ 2 f 2 +λ 3 f 3 ；
Wherein min represents the minimum of the function f as the optimization target, f 1 An objective function representing the fluctuation of the charging power of the control area, f 2 Representing the return function of the ordered charging peak clipping strategy baseline band, f 3 Representing the user charge satisfaction function, λ 1 、λ 2 、λ 3 Is a balance coefficient and satisfies lambda 1 +λ 2 +λ 3 ＝1；
Determining constraint conditions including charging power constraint of an alternating current pile and main transformer power constraint of a transformer area;
and solving the objective function by using a particle swarm algorithm according to the objective function and the constraint condition to obtain a target charging plan.
Further, the objective function of the charging power fluctuation of the console area is as follows:
wherein alpha is 1 、α 2 To equalize the coefficients, and satisfy, α 1 +α 2 =1; p' (t, t + 1) represents a rate of change of expected charging power for a t +1 period set for the t period; p' (t, t + 2) represents a rate of change of expected charging power for a t +2 period set for the t period; p (t, t + 1) represents a t +1 period expected charging power set for the t period; p (t, t + 2) represents a t + 2-period expected charging power set for the t period; p (t, t) represents the total actual charging power of the platform area in the period t; p (t, t + 1) and P (t, t + 2) are selected as decision variables of the particle swarm algorithm;
the basis line return function of the ordered charging peak clipping strategy of the distribution room is as follows:
f 2 ＝α 3 [P(t,t+1)-M(t+1)] 2 +α 4 [P(t,t+2)-M(t+2)] 2
wherein alpha is 3 、α 4 To equalize the coefficients, and satisfy, α 3 +α 4 =1; m (t + 1) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 1; m (t + 2) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 2;
the user charging satisfaction function is:
f 3 ＝β[P(t,t+1)-P free (t+1)] 2
where β is the user satisfaction coefficient, P free And (t + 1) is the total load of the platform area in the disordered charging mode at the time period of t + 1.
Further, the charging power constraint of the ac pile is as follows:
wherein p is max Maximum charging power N that can be output by the AC charging pile in the transformer area ev (t) the number of guns in the charging state in the platform area;
the main transformer power constraint of the transformer area is as follows:
wherein, P max The upper limit of main transformer power of the transformer area is set; p base (t) the base load of the station area in time period t; p allow And (t) ensuring the reserved power margin required by the safety of the main transformer of the transformer area in the time period t.
Further, the solving the objective function by using the particle swarm optimization according to the objective function and the constraint condition to obtain the optimal charging plan includes:
a) Setting a fitness value calculation function according to the determined target function and the constraint condition;
b) Initializing a population, comprising: randomly assigning values to the initial population according to the population scale, the maximum iteration times and the constraint range;
c) Calculating the fitness value of each individual member of the population by using the fitness value calculation function;
d) Entering an iteration stage, if an iteration cutoff condition is met, skipping to f), and outputting the district autonomous power of the globally optimal individual member as a result; otherwise, for each member individual in the population:
saving the historical optimal fitness value of the member individual in the last iteration period;
v, redistributing the search task of the member individual, updating the speed and position information of the member individual, calculating a fitness value function, and updating the historical target fitness value of the member individual;
if the historical target adaptability value of the member individual is better than the historical optimal adaptability value of the last iteration period, executing a global target updating strategy and updating global target value information;
e) Adding 1 to the number of iteration steps, and returning to d);
f) And after iteration is finished, storing and outputting the global target individual information as an optimal charging plan, wherein the target charging plan is represented as:
further, the modifying the charging power provided by the ac piles in the next regulation and control time interval zone according to the target charging plan includes:
setting the actual total charging power P (t +1 ) of the station area in the period t +1 as the expected charging power P (t, t + 1) in the period t +1 set in the period t according to the target charging plan; and the rated charging power P (t + 1) of the charging gun in the station area in the period of t +1 is set as the actual charging total power P (t +1 ) of the station area in the period of t +1 divided by the number of guns N in the charging state of the station area in the period of t ev (t)。
The utility model provides an electric automobile exchanges orderly charging system of stake based on platform district is autonomic, includes:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring the total load of a distribution room in the current regulation and control period and the fluctuation rate of a load curve in the next regulation and control period according to a disordered charging mode, and the disordered charging mode refers to that each alternating current charging pile provides charging service for an accessed electric automobile according to rated power;
the second acquisition module is used for acquiring a base line band of the station area ordered charging peak clipping strategy corresponding to the next regulation and control time interval predicted based on the historical load curve of the station area in advance, and determining a peak value of the base line band according to the base line band;
a third obtaining module, configured to obtain a preset determination condition, where the determination condition is:
1) Whether the total load of the station area in the current regulation and control period exceeds the peak value of the baseline band or not;
2) Whether the fluctuation rate of the load curve exceeds a preset fluctuation margin value of the load curve or not;
the judging module is used for judging whether at least one of the conditions is yes, executing an ordered charging control strategy to obtain a target charging plan, and modifying the charging power provided by the alternating current piles in the next regulation and control time interval station area according to the target charging plan; otherwise, in the next regulation and control time interval, the charging service is provided for the electric automobile in the platform area according to the disordered charging mode.
Further, the calculation formula of the total load of the distribution room in the disordered charging mode is as follows:
wherein, P free Representing the total load of the distribution area when the charging is carried out according to disorder; n is a radical of ev Indicating the number of guns in the charging state of the platform area; p represents the rated charging power of the ac charging pile.
Further, the second obtaining module is configured to obtain the second data
And predicting a cluster of daily electric load curves according to the historical load curves of the distribution room by adopting a BP neural network method based on similar days, and taking the electric load curves as a distribution room ordered charging peak clipping strategy base line band.
Further, the calculation formula of the fluctuation rate of the load curve is as follows:
wherein, P' is the fluctuation rate of the load curve, P is the total charging power in the current regulation time interval, and delta t is the length of the regulation time interval.
Further, the judging module is used for
Determining an objective function; wherein the objective function is: min f = λ 1 f 1 +λ 2 f 2 +λ 3 f 3 ；
Wherein min represents the minimum of the function f as the optimization target, f 1 An objective function representing the fluctuation of the charging power of the control area, f 2 Base line band regression of ordered charging peak clipping strategy for representing transformer areaFunction, f 3 Function representing the degree of satisfaction of the user charging, λ 1 、λ 2 、λ 3 Is a balance coefficient and satisfies lambda 1 +λ 2 +λ 3 ＝1；
Determining constraint conditions including charging power constraint of an alternating current pile and main transformer power constraint of a transformer area;
and solving the objective function by using a particle swarm algorithm according to the objective function and the constraint condition to obtain a target charging plan.
Further, the objective function of the charging power fluctuation of the console area is as follows:
wherein alpha is 1 、α 2 To equalize the coefficients, and satisfy 1 +α 2 =1; p' (t, t + 1) represents a rate of change of expected charging power for a t +1 period set for the t period; p' (t, t + 2) represents a rate of change of expected charging power for a t +2 period set for the t period; p (t, t + 1) represents a t +1 period expected charging power set for the t period; p (t, t + 2) represents a t +2 period expected charging power set for the t period; p (t, t) represents the total actual charging power of the platform area in the period t; p (t, t + 1) and P (t, t + 2) are selected as decision variables of the particle swarm algorithm;
the return function of the ordered charging peak clipping strategy baseline band of the transformer area is as follows:
f 2 ＝α 3 [P(t,t+1)-M(t+1)] 2 +α 4 [P(t,t+2)-M(t+2)] 2
wherein alpha is 3 、α 4 To equalize the coefficients, and satisfy, α 3 +α 4 =1; m (t + 1) is the peak value of a station area ordered charging peak clipping strategy base line band in a time period t + 1; m (t + 2) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 2;
the user charging satisfaction function is as follows:
f 3 ＝β[P(t,t+1)-P free (t+1)] 2
where β is the user satisfaction coefficient, P free (t + 1) is t +1 period of time noneAnd the total load of the platform area in the sequential charging mode.
Further, in the above-mentioned case,
the charging power constraint of the alternating current pile is as follows:
wherein p is max Maximum charging power N that can be output by the AC charging pile in the transformer area ev (t) the number of guns in the charging state of the platform area;
the main transformer power constraint of the transformer area is as follows:
wherein, P max The upper limit of main transformer power of the transformer area is set; p is base (t) base load of the platform area in time period t; p allow And (t) ensuring the reserved power margin required by the safety of the main transformer of the transformer area in the time period t.
Further, the judging module is configured to
a) Setting a fitness value calculation function according to the determined target function and the constraint condition;
b) Initializing a population, comprising: randomly assigning values to the initial population according to the population scale, the maximum iteration times and the constraint range;
c) Calculating the fitness value of each individual member of the population by using the fitness value calculation function;
d) Entering an iteration stage, if an iteration cutoff condition is met, skipping to f), and outputting the district autonomous power of the global target individual member as a result; otherwise, for each member individual in the population:
saving the historical target fitness value of the member individual in the last iteration period;
v, redistributing the search task of the member individual, updating the speed and position information of the member individual, calculating a fitness value function, and updating the historical target fitness value of the member individual;
if the historical target adaptability value of the member individual is better than that of the previous iteration period, executing a global target updating strategy and updating global target value information;
e) Adding 1 to the number of the iteration steps, and returning to the step d);
f) And after iteration is finished, saving and outputting global target individual information as an optimal charging plan, wherein the target charging plan is represented as:
further, the modifying the charging power provided by the ac piles in the next regulation and control time interval zone according to the target charging plan includes:
setting the actual total charging power P (t +1 ) of the station area in the period t +1 as the expected charging power P (t, t + 1) in the period t +1 set in the period t according to the target charging plan; and the rated charging power P (t + 1) of the charging gun in the station area in the period of t +1 is set as the actual charging total power P (t +1 ) of the station area in the period of t +1 divided by the number of guns N in the charging state of the station area in the period of t ev (t)。
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods.
A computing device, comprising, in combination,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods.
The invention achieves the following beneficial effects:
the invention provides algorithm support for filling the defects of the existing research, based on the fact that the alternating current charging pile cannot obtain the vehicle SOC curve, reduces power fluctuation as much as possible and reduces the load peak value on the premise of meeting the power constraint of the transformer area, simultaneously considers the time randomness of the charging behavior of the electric vehicle to the greatest extent, fully takes care of the charging experience of users, and ensures the transformer area safety during charging under the condition that the transformer area is not expanded as much as possible.
Drawings
Fig. 1 is a schematic flow chart of an orderly charging method for an electric vehicle alternating-current pile based on platform area autonomy according to an embodiment of the present invention;
FIG. 2 is a graph of the charging power provided by the AC piles in the distribution room at each time interval according to the present invention;
fig. 3 is a comparison curve of the total load of the ordered charging distribution area and the total load of the disordered charging distribution area according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, an orderly charging method for ac piles of an electric vehicle based on platform area autonomy includes:
the daily load curve method of 96 points is adopted, and 24 hours a day is divided into 96 time periods, and each time period is controlled for 15 minutes. In each regulation and control time interval, the charging information and the basic electrical information of the current time interval of the transformer area are collected, and if the charging is carried out according to the disordered charging mode in the next regulation and control time interval:
(1) Whether the charging load value exceeds the base line peak value of the ordered charging peak clipping strategy of the distribution room corresponding to the next regulation and control time interval or not;
(2) Whether the fluctuation rate of the load curve exceeds the fluctuation margin value of the load curve.
If at least one of the conditions is met, executing an ordered charging control strategy based on a particle swarm algorithm, and modifying the charging power provided by the alternating current piles in the next regulation and control time interval area; otherwise, in the next regulation and control period, the charging service is provided for the electric vehicles in the platform area according to the disordered charging mode.
The charging information of the current time period of the platform area comprises the number of guns in a charging state, the rated charging power of the alternating current charging pile and the total real-time charging power.
The basic electrical information of the transformer area comprises an upper limit of main transformer power of the transformer area, a power margin which is reserved for ensuring the safety of the main transformer of the transformer area and the like.
The disordered charging mode refers to that each alternating-current charging pile provides charging service for the connected electric automobile according to rated power:
wherein, P free The total real-time charging power of the distribution room is [98,119,133,154,182,217,210,182,161,119,105,77 ] when the random charging is performed]；N ev The number of guns representing the charging state of the platform zone is [14,17,19,22,26,31,30,26,23,17,15,11](ii) a p represents the rated charging power of the alternating-current charging pile, and is 7kW.
And predicting a cluster of possible daily power utilization load curves according to the historical load curves of the distribution area by adopting a BP neural network method based on similar days, wherein the cluster of possible daily power utilization load curves forms the distribution area ordered charging peak clipping strategy base line band.
The fluctuation rate of the load curve is calculated by adopting the change rate of the total charging load value of the distribution room and the charging load value of the current regulation and control time interval when the next regulation and control time interval is charged according to disorder:
wherein P' is the change rate of the charging load value, P is the total charging power in the current regulation and control time interval, delta t is the time interval length, 15min is taken, and the total time interval number is 12.
The ordered charging control strategy based on the particle swarm optimization specifically comprises the following steps:
minf＝λ 1 f 1 +λ 2 f 2 +λ 3 f 3
where min represents the minimum of the function to doTo optimize the objective, f 1 An objective function representing the fluctuation of the charging power of the control area, f 2 Representing the return function of the ordered charging peak clipping strategy baseline band, f 3 Function representing the degree of satisfaction of the user charging, λ 1 、λ 2 、λ 3 Is a balance coefficient and satisfies lambda 1 +λ 2 +λ 3 =1, here take λ 1 ＝0.5、λ 2 ＝0.2、λ 3 ＝0.3。
and 3, calling an improved particle swarm algorithm to solve the target.
Preferably, the objective function of the charging power fluctuation of the console area in step 1 is as follows:
wherein alpha is 1 、α 2 Is an equalization coefficient and satisfies; alpha is alpha 1 +α 2 =1, here taken as α 1 ＝0.8、α 2 =0.2; p' (t, t + 1) represents a rate of change of expected charging power for a t +1 period set for the t period; p' (t, t + 2) represents a rate of change of expected charging power for a t +2 period set for the t period; p (t, t + 1) represents a t +1 period expected charging power set for the t period; p (t, t + 2) represents a t +2 period expected charging power set for the t period; p (t, t) represents the total actual charging power of the platform area in the period t.
In particular, P (t, t + 1) and P (t, t + 2) are selected as decision variables for the particle swarm algorithm.
The control console area ordered charging peak clipping strategy baseline band regression function is as follows:
f 2 ＝α 3 [P(t,t+1)-M(t+1)] 2 +α 4 [P(t,t+2)-M(t+2)] 2
wherein alpha is 3 、α 4 Is an equalization coefficient and satisfies; alpha is alpha 3 +α 4 =1, herein take alpha 3 ＝0.7、α 4 =0.3; m (t + 1) is a base line band of ordered charging peak clipping strategy in transformer areaA peak at time period t + 1; m (t + 2) is the peak value of the base line band of the station area ordered charging peak clipping strategy in the time period t + 2.
The user charging satisfaction function is as follows:
f 3 ＝β[P(t,t+1)-P free (t+1)] 2
where β is the user satisfaction coefficient, here taken to be 2.
Preferably, the ac pile charging power constraint in step 2 is as follows:
wherein p is max The maximum charging power which can be output by the alternating-current charging pile in the transformer area is obtained.
Preferably, the main transformer power constraint of the transformer area in step 2 is as follows:
wherein, P max Taking 630kVA for the upper limit of main power of the transformer area; p base (t) is the base load of the station region in time period t, here [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) the reserved power margin required for ensuring the safety of the main transformer of the transformer area in the time period t is taken as 0.1P base (t)。
Preferably, the improved particle swarm algorithm in step 3 comprises the following steps:
a) Acquiring related data, and defining a fitness value calculation function according to the target function in the step 1 and the constraint condition in the step 2;
b) Initializing a population, including the population scale, the maximum iteration times, the constraint range and the like, and randomly assigning values to the initial population;
c) Calculating the fitness value of the individual members of the population by using a defined fitness value calculation function;
d) Entering an iteration stage, if an iteration cutoff condition is met, skipping to f), and outputting the district autonomous power of the global target individual member as a result; otherwise, for each member of the population:
i. saving the individual historical target of the previous iteration period;
redistributing the search task of the member individual, updating the speed and position information of the member individual, calculating a fitness value function, and updating historical target fitness value information of the individual;
if the historical target value of the member individual is better than the historical target value of the last iteration cycle, executing a global target updating strategy;
executing a global target information perturbation strategy, and updating global target value information;
e) Adding 1 to the iteration step number, and returning to d);
f) And after the iteration is finished, saving and outputting the global target individual information as a target charging plan.
Further, the method for modifying the charging power provided by the ac piles in the station area in the next regulation and control period is as follows:
according to calculation, the charging power provided by the alternating current piles in the platform area in each time interval is [7.0, 6.8.8, 5.5,4.0,2.8,3.0,3.9,4.9,6.8,7.0 ], and the charging power curve is drawn as shown in fig. 2.
Through calculation, the total charging power in each time interval in the platform 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 the orderly charging is adopted, the total power load of each time period of the platform 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], the total power load of each time period of the platform area after the orderly charging is adopted and the total load of the platform area under the unordered charging are drawn on the same coordinate plane for comparison, as shown in a figure 3, as can be seen from the figure, the orderly charging method provided by the invention can obviously reduce the total power load of the platform area in a power utilization peak period, reduce power fluctuation and reduce a load peak value as much as possible on the premise of meeting the power constraint of the platform area, simultaneously consider the time randomness of the charging action of the electric vehicle to the greatest extent, fully take care of the charging experience of users, and ensure the safety of the platform area during charging as much as possible under the condition that the platform area is not expanded.
Correspondingly, the invention also provides an orderly charging system for the alternating current piles of the electric vehicle based on the platform area autonomy, which comprises the following components:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring the total load of a distribution room in the current regulation and control period and the fluctuation rate of a load curve in the next regulation and control period according to a disordered charging mode, and the disordered charging mode refers to that each alternating current charging pile provides charging service for an accessed electric automobile according to rated power;
the second acquisition module is used for acquiring a base line band of the platform area ordered charging peak clipping strategy corresponding to the next regulation and control time interval predicted in advance based on the platform area historical load curve, and determining a peak value of the base line band according to the base line band;
a third obtaining module, configured to obtain a preset determination condition, where the determination condition is:
1) Whether the total load of the station area in the current regulation and control period exceeds the peak value of the baseline band or not;
2) Whether the fluctuation rate of the load curve exceeds a preset fluctuation margin value of the load curve or not;
the judging module is used for judging whether at least one of the conditions is yes, executing an ordered charging control strategy to obtain a target charging plan, and modifying the charging power provided by the alternating current piles in the next regulation and control time interval station area according to the target charging plan; otherwise, in the next regulation and control time interval, the charging service is provided for the electric automobile in the platform area according to the disordered charging mode.
Further, the calculation formula of the total load of the distribution room in the disordered charging mode is as follows:
wherein, P free Show by disorderWhen charging, the total load of the transformer area; n is a radical of ev Indicating the number of guns in the charging state of the platform area; p represents the rated charging power of the ac charging pile.
Further, the second obtaining module is configured to obtain the second data
And predicting a cluster of daily electric load curves according to the historical load curves of the transformer area by adopting a BP neural network method based on similar days, and taking the electric load curves as the base line of the orderly charging peak clipping strategy of the transformer area.
Further, the calculation formula of the fluctuation rate of the load curve is as follows:
wherein, P' is the fluctuation rate of the load curve, P is the total charging power in the current regulation and control time interval, and delta t is the length of the regulation and control time interval.
Further, the judging module is configured to
Determining an objective function; wherein the objective function is: min f = λ 1 f 1 +λ 2 f 2 +λ 3 f 3 ；
Wherein min represents the minimum of the function f as the optimization target, f 1 An objective function representing the fluctuation of the charging power of the control area, f 2 Representing the return function of the ordered charging peak clipping strategy baseline band, f 3 Function representing the degree of satisfaction of the user charging, λ 1 、λ 2 、λ 3 Is a balance coefficient and satisfies lambda 1 +λ 2 +λ 3 ＝1；
Determining constraint conditions including charging power constraint of an alternating current pile and main transformer power constraint of a transformer area;
and solving the objective function by using a particle swarm algorithm according to the objective function and the constraint condition to obtain a target charging plan.
Further, the objective function of the charging power fluctuation of the console area is as follows:
wherein alpha is 1 、α 2 To equalize the coefficients, and satisfy, α 1 +α 2 =1; p' (t, t + 1) represents a rate of change of expected charging power for a t +1 period set for the t period; p' (t, t + 2) represents a rate of change of the expected charging power for a t +2 period set for the t period; p (t, t + 1) represents a t + 1-period expected charging power set for the t period; p (t, t + 2) represents a t +2 period expected charging power set for the t period; p (t, t) represents the total actual charging power of the platform area in the period t; p (t, t + 1) and P (t, t + 2) are selected as decision variables of the particle swarm algorithm;
the return function of the ordered charging peak clipping strategy baseline band of the transformer area is as follows:
f 2 ＝α 3 [P(t,t+1)-M(t+1)] 2 +α 4 [P(t,t+2)-M(t+2)] 2
wherein alpha is 3 、α 4 To equalize the coefficients, and satisfy, α 3 +α 4 =1; m (t + 1) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 1; m (t + 2) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 2;
the user charging satisfaction function is:
f 3 ＝β[P(t,t+1)-P free (t+1)] 2
wherein beta is a user satisfaction coefficient, P free And (t + 1) is the total load of the platform area in the disordered charging mode at the time period of t + 1.
In a further aspect of the present invention,
the charging power constraint of the alternating current pile is as follows:
wherein p is max Maximum charging power N that can be output by the AC charging pile in the transformer area ev (t) the number of guns in the charging state of the platform area;
the main transformer power constraint of the transformer area is as follows:
wherein, P max The upper limit of main transformer power of the transformer area is set; p base (t) the base load of the station area in time period t; p allow And (t) ensuring the reserved power margin required by the safety of the main transformer of the transformer area in the time period t.
Further, the judging module is configured to
a) Setting a fitness value calculation function according to the determined target function and the constraint condition;
b) Initializing a population, comprising: randomly assigning values to the initial population according to the population scale, the maximum iteration times and the constraint range;
c) Calculating the fitness value of each individual member of the population by using the fitness value calculation function;
d) Entering an iteration stage, if an iteration cutoff condition is met, skipping to f), and outputting the district autonomous power of the global target individual member as a result; otherwise, for each member individual in the population:
i. saving the member individual historical target fitness value of the previous iteration period;
redistributing the search task of the member individual, updating the speed and position information of the member individual, calculating a fitness value function, and updating the historical target fitness value of the member individual;
if the historical target adaptability value of the member individual is better than that of the previous iteration cycle, executing a global target updating strategy and updating global target value information;
e) Adding 1 to the number of iteration steps, and returning to d);
f) And finishing iteration, and saving and outputting the global target individual information as a target charging plan, wherein the target charging plan is expressed as:
further, the modifying the charging power provided by the ac piles in the next regulation and control time interval zone according to the target charging plan includes:
setting the actual charging total power P (t +1 ) of the station area at the t +1 time period as the expected charging power P (t, t + 1) at the t +1 time period set at the t time period according to the target charging plan; and the rated charging power P (t + 1) of the charging gun in the station area in the period of t +1 is set as the total power P (t +1 ) of the actual charging in the station area in the period of t +1 divided by the number of guns in the charging state in the station area in the period of t N ev (t)。
The present invention accordingly also provides a computer readable storage medium storing one or more programs, wherein the one or more programs include instructions, which when executed by a computing device, cause the computing device to perform any of the methods described.
The invention also provides a computing device, comprising,
the invention accordingly also provides one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to 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 flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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 flow or flows and/or block diagram block or blocks.
These computer program instructions may 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 instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may 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 flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, it is possible to make various improvements and modifications without departing from the technical principle of the present invention, and those improvements and modifications should be considered as the protection scope of the present invention.
Claims (20)
1. An electric vehicle alternating current pile ordered charging method based on platform area autonomy is characterized by comprising the following steps:
acquiring the total load of a distribution area in the current regulation and control period and the fluctuation rate of a load curve in the next regulation and control period according to a disordered charging mode, wherein the disordered charging mode is that each alternating current charging pile provides charging service for an accessed electric automobile according to rated power;
acquiring a base line band of a platform area ordered charging peak clipping strategy corresponding to the next regulation and control time interval predicted in advance based on a platform area historical load curve, and determining a peak value of the base line band according to the base line band;
acquiring preset judgment conditions, wherein the judgment conditions are as follows:
1) Whether the total load of the station area in the current regulation and control period exceeds the peak value of the baseline band or not;
2) Whether the fluctuation rate of the load curve exceeds a preset fluctuation margin value of the load curve or not;
if at least one of the conditions is yes, executing an ordered charging control strategy to obtain a target charging plan, and modifying the charging power provided by the alternating current piles in the next regulation and control time interval zone according to the target charging plan; otherwise, in the next regulation and control time interval, the charging service is provided for the electric automobile in the platform area according to the disordered charging mode.
2. The area-autonomous-based orderly charging method for alternating-current piles of electric vehicles according to claim 1, wherein a calculation formula of total load of the area in the unordered charging mode is as follows:
wherein, P free Representing the total load of the distribution area when the charging is carried out according to disorder; n is a radical of ev Indicating the number of guns in the charging state of the platform area; p represents the rated charging power of the ac charging pile.
3. The method for orderly charging alternating-current piles of the electric vehicle based on the self-governing of the transformer substation according to claim 1, wherein the step of obtaining the base line band of the orderly charging peak clipping strategy of the transformer substation corresponding to the next regulation and control period predicted in advance based on the historical load curve of the transformer substation comprises the following steps:
and predicting a cluster of daily power load curves according to the historical load curves of the transformer area by adopting a BP neural network method based on similar days, and taking the power load curves as the base line of the orderly charging peak clipping strategy of the transformer area.
4. The method for orderly charging the alternating-current piles of the electric vehicle based on the self-governing of the transformer district as claimed in claim 2, wherein the calculation formula of the fluctuation rate of the load curve is as follows:
wherein, P' is the fluctuation rate of the load curve, P is the total charging power in the current regulation time interval, and delta t is the length of the regulation time interval.
5. The method for orderly charging alternating-current piles of electric vehicles based on self-governing of a transformer area according to claim 1, wherein the executing of the orderly charging control strategy to obtain the target charging plan comprises:
determining an objective function; wherein the objective function is: minf = λ 1 f 1 +λ 2 f 2 +λ 3 f 3 ；
Wherein min represents the minimum of the function f as the optimization target, f 1 An objective function representing the fluctuation of the charging power of the control area, f 2 Representing the return function of the ordered charging peak clipping strategy baseline band, f 3 Function representing the degree of satisfaction of the user charging, λ 1 、λ 2 、λ 3 Is a balance coefficient and satisfies lambda 1 +λ 2 +λ 3 ＝1；
Determining constraint conditions including charging power constraint of an alternating current pile and main transformer power constraint of a transformer area;
and solving the objective function by using a particle swarm algorithm according to the objective function and the constraint condition to obtain a target charging plan.
6. The electric vehicle alternating-current pile orderly charging method based on platform district autonomy as claimed in claim 5,
the objective function of the charging power fluctuation of the console area is as follows:
wherein alpha is 1 、α 2 To equalize the coefficients, and satisfy, α 1 +α 2 =1; p' (t, t + 1) represents a rate of change of expected charging power for a t +1 period set for the t period; p' (t, t + 2) represents a rate of change of expected charging power for a t +2 period set for the t period; p (t, t + 1) represents a t +1 period expected charging power set for the t period; p (t, t + 2) represents a t + 2-period expected charging power set for the t period; p (t, t) represents the total actual charging power of the platform area in the period t; p (t, t + 1) and P (t, t + 2) are selected as decision variables of the particle swarm algorithm;
the return function of the ordered charging peak clipping strategy baseline band of the transformer area is as follows:
f 2 ＝α 3 [P(t,t+1)-M(t+1)] 2 +α 4 [P(t,t+2)-M(t+2)] 2
wherein alpha is 3 、α 4 To equalize the coefficients, and satisfy, α 3 +α 4 =1; m (t + 1) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 1; m (t + 2) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 2;
the user charging satisfaction function is:
f 3 ＝β[P(t,t+1)-P free (t+1)] 2
where β is the user satisfaction coefficient, P free And (t + 1) is the total load of the platform area in the disordered charging mode at the time period of t + 1.
7. The method for orderly charging the AC piles of the electric vehicle based on the autonomous region of the transformer area of claim 6,
the charging power constraint of the alternating current pile is as follows:
wherein p is max Maximum charging power N that can be output by the AC charging pile in the transformer area ev (t) the number of guns in the charging state of the platform area;
the main transformer power constraint of the transformer area is as follows:
wherein, P max The upper limit of main transformer power of the transformer area is set; p base (t) the base load of the station area in time period t; p allow And (t) ensuring the reserved power margin required by the safety of the main transformer of the transformer area in the time period t.
8. The method for orderly charging alternating-current piles of electric vehicles based on self-governing of a transformer area according to claim 7, wherein the solving of the objective function by the particle swarm algorithm according to the objective function and the constraint condition to obtain the optimal charging plan comprises the following steps:
a) Setting a fitness value calculation function according to the determined target function and the constraint condition;
b) Initializing a population, comprising: randomly assigning values to the initial population according to the population scale, the maximum iteration times and the constraint range;
c) Calculating the fitness value of each individual member of the population by using the fitness value calculation function;
d) Entering an iteration stage, if an iteration cutoff condition is met, skipping to f), and outputting the district autonomous power of the globally optimal individual member as a result; otherwise, for each member individual in the population:
i. saving the historical optimal fitness value of the member individuals in the previous iteration period;
redistributing the search task of the member individual, updating the speed and position information of the member individual, calculating a fitness value function, and updating the historical target fitness value of the member individual;
if the historical target adaptability value of the member individual is better than the historical optimal adaptability value of the last iteration period, executing a global target updating strategy and updating global target value information;
e) Adding 1 to the number of iteration steps, and returning to d);
f) And after iteration is finished, storing and outputting the global target individual information as an optimal charging plan, wherein the target charging plan is represented as:
9. the method for orderly charging the AC piles of the electric vehicle based on the district autonomy of claim 8, wherein the step of modifying the charging power provided by the AC piles in the district at the next regulation and control period according to the target charging plan comprises the following steps:
setting the actual charging total power P (t +1 ) of the station area at the t +1 time period as the expected charging power P (t, t + 1) at the t +1 time period set at the t time period according to the target charging plan; and the rated charging power P (t + 1) of the charging gun in the station area in the period of t +1 is set as the total power P (t +1 ) of the actual charging in the station area in the period of t +1 divided by the number of guns in the charging state in the station area in the period of t N ev (t)。
10. The utility model provides an electric automobile exchanges orderly charging system of stake based on platform district is autonomic which characterized in that includes:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring the total load of a distribution room in the current regulation and control period and the fluctuation rate of a load curve in the next regulation and control period according to a disordered charging mode, and the disordered charging mode refers to that each alternating current charging pile provides charging service for an accessed electric automobile according to rated power;
the second acquisition module is used for acquiring a base line band of the station area ordered charging peak clipping strategy corresponding to the next regulation and control time interval predicted based on the historical load curve of the station area in advance, and determining a peak value of the base line band according to the base line band;
a third obtaining module, configured to obtain a preset determination condition, where the determination condition is:
1) Whether the total load of the station area in the current regulation and control period exceeds the peak value of the baseline band or not;
2) Whether the fluctuation rate of the load curve exceeds a preset fluctuation margin value of the load curve or not;
the judging module is used for judging whether at least one of the conditions is yes or not, executing an ordered charging control strategy to obtain a target charging plan, and modifying the charging power provided by the alternating current piles in the next regulation and control time interval table area according to the target charging plan; otherwise, in the next regulation and control period, the charging service is provided for the electric vehicles in the platform area according to the disordered charging mode.
11. The area-autonomous-based orderly charging system for alternating-current piles of electric vehicles according to claim 10, wherein a calculation formula of total load of the area in the unordered charging mode is as follows:
wherein, P free Representing the total load of the distribution area when the charging is carried out according to disorder; n is a radical of ev Indicating the number of guns in the charging state of the platform area; p represents the rated charging power of the ac charging pile.
12. The system for orderly charging ac piles of electric vehicles based on self-government of transformer district as claimed in claim 11, wherein said second obtaining module is configured to obtain the charging voltage of ac piles of electric vehicles
And predicting a cluster of daily electric load curves according to the historical load curves of the transformer area by adopting a BP neural network method based on similar days, and taking the electric load curves as the base line of the orderly charging peak clipping strategy of the transformer area.
13. The electric vehicle alternating-current pile orderly charging system based on the district autonomy of claim 11, wherein the load curve fluctuation rate is calculated by the formula:
wherein, P' is the fluctuation rate of the load curve, P is the total charging power in the current regulation time interval, and delta t is the length of the regulation time interval.
14. The system for orderly charging AC piles of electric vehicles based on self-government of transformer district as claimed in claim 10, wherein said judging module is used for judging whether the AC piles are in order
Determining an objective function; wherein the objective function is: minf = λ 1 f 1 +λ 2 f 2 +λ 3 f 3 ；
Wherein min represents the minimum of the function f as the optimization target, f 1 An objective function representing the fluctuation of the charging power of the control area, f 2 Representing the return function of the ordered charging peak clipping strategy baseline band, f 3 Function representing the degree of satisfaction of the user charging, λ 1 、λ 2 、λ 3 Is a balance coefficient and satisfies lambda 1 +λ 2 +λ 3 ＝1；
Determining constraint conditions including charging power constraint of an alternating current pile and main transformer power constraint of a transformer area;
and solving the objective function by using a particle swarm algorithm according to the objective function and the constraint condition to obtain a target charging plan.
15. The area-based autonomous electric vehicle ac pole orderly charging system of claim 14,
the objective function of the charging power fluctuation of the console area is as follows:
wherein alpha is 1 、α 2 To equalize the coefficients, and satisfy, α 1 +α 2 =1; p' (t, t + 1) represents a rate of change of expected charging power for a t +1 period set for the t period; p' (t, t + 2) represents a rate of change of expected charging power for a t +2 period set for the t period; p (t, t + 1) represents a t +1 period expected charging power set for the t period; p (t, t + 2) represents a t +2 period expected charging power set for the t period; p (t, t) represents the total actual charging power of the platform area in the period t; p (t, t + 1) and P (t, t)+ 2) selecting as a decision variable of the particle swarm algorithm;
the basis line return function of the ordered charging peak clipping strategy of the distribution room is as follows:
f 2 ＝α 3 [P(t,t+1)-M(t+1)] 2 +α 4 [P(t,t+2)-M(t+2)] 2
wherein alpha is 3 、α 4 To equalize the coefficients, and satisfy, α 3 +α 4 =1; m (t + 1) is the peak value of the ordered charging peak clipping strategy base line band in the station area at the time period t + 1; m (t + 2) is the peak value of the sequential charging peak clipping strategy base line band in the time period t + 2;
the user charging satisfaction function is:
f 3 ＝β[P(t,t+1)-P free (t+1)] 2
where β is the user satisfaction coefficient, P free And (t + 1) is the total load of the platform area in the disordered charging mode at the time period of t + 1.
16. The area-based autonomous electric vehicle ac pole orderly charging system of claim 15,
the charging power constraint of the alternating current pile is as follows:
wherein p is max Maximum charging power N that can be output by the AC charging pile in the transformer area ev (t) the number of guns in the charging state of the platform area;
the main transformer power constraint of the transformer area is as follows:
wherein, P max The upper limit of main transformer power of the transformer area is set; p base (t) the base load of the station area in time period t; p allow And (t) ensuring the reserved power margin required by the safety of the main transformer of the transformer area in the time period t.
17. The system for orderly charging ac piles of electric vehicles based on self-government of transformer district as claimed in claim 16, wherein said judging module is configured to determine whether the charging is required
a) Setting a fitness value calculation function according to the determined target function and the constraint condition;
b) And initializing a population, including: randomly assigning values to the initial population according to the population scale, the maximum iteration times and the constraint range;
c) Calculating the fitness value of each individual member of the population by using the fitness value calculation function;
d) Entering an iteration stage, if an iteration cutoff condition is met, skipping to f), and outputting the district autonomous power of the global target individual member as a result; otherwise, for each member individual in the population:
i. saving the member individual historical target fitness value of the previous iteration period;
redistributing the search task of the member individual, updating the speed and position information of the member individual, calculating a fitness value function, and updating the historical target fitness value of the member individual;
if the historical target adaptability value of the member individual is better than that of the previous iteration cycle, executing a global target updating strategy and updating global target value information;
e) Adding 1 to the number of iteration steps, and returning to d);
f) And after iteration is finished, storing and outputting the global target individual information as an optimal charging plan, wherein the target charging plan is represented as:
18. the system of claim 17, wherein the modifying the charging power provided by the ac piles in the distribution room for the next regulation period according to the target charging plan comprises:
setting the actual total charging power P (t +1 ) of the station area in the period t +1 as the expected charging power P (t, t + 1) in the period t +1 set in the period t according to the target charging plan; and the rated charging power P (t + 1) of the charging gun in the station area in the period of t +1 is set as the actual charging total power P (t +1 ) of the station area in the period of t +1 divided by the number of guns N in the charging state of the station area in the period of t ev (t)。
19. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-9.
20. A computing device, comprising,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-9.
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| CN116512969A (en) * | 2023-07-04 | 2023-08-01 | 四川金信石信息技术有限公司 | Ordered charging power regulation and control method, system, terminal and medium for alternating-current charging pile |
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| CN116979513A (en) * | 2023-07-20 | 2023-10-31 | 一能充电科技(深圳)股份有限公司 | Processing method for dynamic regulation and control of charging power |
| CN116979513B (en) * | 2023-07-20 | 2024-05-31 | 一能充电科技(深圳)股份有限公司 | Processing method for dynamic regulation and control of charging power |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116667366A (en) * | 2023-05-25 | 2023-08-29 | 一能充电科技(深圳)股份有限公司 | Virtual power plant scheduling system |
| CN116667366B (en) * | 2023-05-25 | 2024-05-03 | 一能充电科技(深圳)股份有限公司 | Virtual power plant scheduling system |
| CN116512969A (en) * | 2023-07-04 | 2023-08-01 | 四川金信石信息技术有限公司 | Ordered charging power regulation and control method, system, terminal and medium for alternating-current charging pile |
| CN116512969B (en) * | 2023-07-04 | 2023-09-05 | 四川金信石信息技术有限公司 | Ordered charging power regulation and control method, system, terminal and medium for alternating-current charging pile |
| CN116979513A (en) * | 2023-07-20 | 2023-10-31 | 一能充电科技(深圳)股份有限公司 | Processing method for dynamic regulation and control of charging power |
| CN116979513B (en) * | 2023-07-20 | 2024-05-31 | 一能充电科技(深圳)股份有限公司 | Processing method for dynamic regulation and control of charging power |
| CN116691414A (en) * | 2023-08-03 | 2023-09-05 | 国网安徽省电力有限公司合肥供电公司 | Ordered charging service intelligent monitoring management system based on minute-level acquisition |
| CN116691414B (en) * | 2023-08-03 | 2023-10-31 | 国网安徽省电力有限公司合肥供电公司 | Ordered charging service intelligent monitoring management system based on minute-level acquisition |
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