CN111126765B - Electric automobile group joint optimization method and system - Google Patents
Electric automobile group joint optimization method and system Download PDFInfo
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- CN111126765B CN111126765B CN201911157307.1A CN201911157307A CN111126765B CN 111126765 B CN111126765 B CN 111126765B CN 201911157307 A CN201911157307 A CN 201911157307A CN 111126765 B CN111126765 B CN 111126765B
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Abstract
The application discloses a method and a system for jointly optimizing electric automobile groups, wherein the method comprises the following steps: respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve; and solving the objective function by adopting a genetic algorithm to obtain the optimal parameters. According to the method, the wind power resource consumption is maximized, the peak-valley load difference is minimized through the simultaneous consideration of a double-target optimization model, and the stability of the operation of the power grid can be improved from the two aspects of wind power consumption and peak load shifting.
Description
Technical Field
The application relates to the technical field of power grids, in particular to a method and a system for optimizing electric vehicle group combination.
Background
Because large-scale access of various intermittent renewable new energy sources such as wind energy and the like to a power grid inevitably generates power fluctuation, real-time balance between power generation and power utilization needs to be maintained.
In the prior art, most schemes adopt single-target optimization, for example, the power grid load peak-valley difference rate is maximized or the power standard deviation for measuring the total load fluctuation condition of the system is minimized, and a dual-target optimization model for maximizing wind power resource consumption and minimizing peak-valley load difference is not considered at the same time. In addition, many technologies take peak-to-valley load difference reduction as a primary optimization target, often neglect to regulate and control on the premise of not influencing the electric quantity requirement of daily driving of the electric automobile and not influencing the automobile using requirement and satisfaction degree of users in order to achieve the optimization purpose, otherwise, the basic benefit of the users is seriously influenced. In addition, the total load of the system in the region is the sum of the conventional load and the charging load of the electric automobile group, the comparison according to the difference of the daily load curve change trends of the conventional load in the region in the typical summer day and the typical winter day is also lacked in the prior art, and the analysis on whether the joint optimization of the charging load of the electric automobile group can meet the requirements of different seasons is lacked.
Disclosure of Invention
The embodiment of the application provides a method and a system for jointly optimizing an electric automobile group, so that the stability of power grid operation is improved.
In view of the above, a first aspect of the present application provides an electric vehicle group joint optimization method, including:
respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve;
and solving the objective function by adopting a genetic algorithm to obtain the optimal parameters.
Preferably, the specifically establishing the objective function with the minimum variance of the difference between the charging power of the electric vehicle group connected to the household charging pile and the wind power consumption requirement of the wind farm in the area as the minimum is as follows:
in the formula, the wind power consumption requirement of the wind power plant in the region is PWThe charging power of the household charging pile is simultaneously accessed at the t moment
Preferably, the establishing of the objective function with the minimum peak-to-valley difference of the daily load curve is specifically as follows:
wherein Δ P represents a peak-to-valley difference, Pp、PvRespectively the total load of the system
The maximum value and the minimum value of the charging power are that the charging power of the household charging pile is simultaneously accessed at the t moment
The normal load when the electric vehicle is not connected in the area is
Preferably, before the minimum variance of the difference between the charging power of the electric vehicle group connected to the household charging pile and the wind power consumption requirement of the regional wind farm is established and the minimum peak-to-valley difference of the daily load curve is established as the objective function, the method further comprises the following steps:
initializing parameters, and acquiring a charging demand model of the user electric vehicle; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of the genetic algorithm.
Preferably, the solving the objective function by using a genetic algorithm to obtain the optimal parameters specifically comprises:
generating an initial population according to the charging demand model;
judging whether the population meets constraint conditions;
if yes, calculating the target function and the population individual fitness;
selecting, crossing and mutating population individuals, and judging whether the results after selection, crossing and mutation reach the optimization target;
if the optimal target is reached, outputting an optimal solution, otherwise, selecting a good individual with fitness reaching a preset threshold as a population of the next iteration for iteration, and outputting the optimal solution until the maximum iteration times is reached or the optimal target is reached.
Preferably, the solving the objective function by using the genetic algorithm further comprises:
judging whether the electric automobile reaches the forced charging time or not;
if so, the electric automobile is forcibly charged, otherwise, whether the iteration number reaches the maximum iteration number or an optimal target is continuously judged.
Preferably, the generating an initial population according to the charging demand model specifically includes:
and generating an initial population according to the home arrival time, the away time and the driving mileage of the electric automobile.
Preferably, the constraint condition is:
at the latest moment t when the electric vehicle must be chargedFThe constraints of (2) are:
tF＝tL-Tch
wherein the expected value of the time of leaving home on work of the family user is tLThe charging time required by the battery is Tch。
A second aspect of the present application provides an electric vehicle fleet joint optimization system, the system comprising: an objective function establishing unit and a solving unit;
the target function establishing unit is used for respectively establishing a target function with the minimum variance of the difference value between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve;
the solving unit is used for solving the objective function by adopting a genetic algorithm to obtain the optimal parameters.
Preferably, the device further comprises an initialization unit;
the initialization unit is used for initializing parameters and acquiring a charging demand model of the user electric automobile; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of the genetic algorithm.
According to the technical scheme, the embodiment of the application has the following advantages:
in an embodiment of the present application, a method for jointly optimizing electric vehicle groups is provided, including: respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve; and solving the objective function by adopting a genetic algorithm to obtain optimal parameters.
According to the method, the wind power resource consumption is maximized, the peak-valley load difference is minimized through the simultaneous consideration of a double-target optimization model, and the stability of the operation of the power grid can be improved from the two aspects of wind power consumption and peak load shifting.
Drawings
FIG. 1 is a flowchart of a method of one embodiment of a group joint optimization method for electric vehicles according to the present application;
FIG. 2 is a flowchart of a method of another embodiment of a group-tie optimization method of electric vehicles according to the present application;
FIG. 3 is a system diagram illustrating an embodiment of an electric vehicle group joint optimization system according to the present application;
FIG. 4 is a flowchart of a genetic algorithm in an embodiment of a method for group-associative optimization of electric vehicles according to the present application;
FIG. 5 is a graph comparing the charging load curves of the electric vehicle group without optimization and the electric vehicle group combined optimization method in summer;
FIG. 6 is a graph comparing charging loads of electric vehicle groups after the electric vehicle group joint optimization method and optimization processing are adopted in winter;
FIG. 7 is a graph showing the comparison of daily load in the region without optimization in summer using the algorithm of the present application;
FIG. 8 is a graph showing the comparison of daily load in the area without optimization treatment by the algorithm of the present application in winter.
Detailed Description
According to the method, the wind power resource consumption is maximized, the peak-valley load difference is minimized through the simultaneous consideration of a double-target optimization model, and the stability of the operation of the power grid can be improved from the two aspects of wind power consumption and peak load shifting.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method of an embodiment of a method for jointly optimizing electric vehicle groups according to the present application, as shown in fig. 1, where fig. 1 includes:
101. and respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve.
It should be noted that, in order to maximally absorb the wind power output, a first objective function is established, where the minimum variance of a difference between the charging power of the electric vehicle group connected to the household charging pile and the wind power absorption requirement of the regional wind farm is the first objective function; in order to enhance the capacity of 'peak clipping and valley filling', the peak-valley difference of the daily load curve is minimized, and an objective function with the minimum peak-valley difference of the daily load curve is established.
102. And solving the objective function by adopting a genetic algorithm to obtain the optimal parameters.
It should be noted that the objective function can be solved by using a genetic algorithm, so that the wind power resource can be effectively consumed.
According to the electric automobile group joint optimization method, the wind power resource consumption maximization and the peak-valley load difference minimization are achieved through a double-target optimization model, and the operation stability of a power grid can be improved from the two aspects of wind power consumption and peak load shifting.
For easy understanding, please refer to fig. 2, fig. 2 is a flowchart of a method of another embodiment of a method for jointly optimizing electric vehicle groups according to the present application, and as shown in fig. 2, the method specifically includes:
201. initializing parameters, and acquiring a charging demand model of the user electric vehicle; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of a genetic algorithm.
It should be noted that the initialization parameters include parameters of the electric vehicle, parameters of the power grid load, and parameter settings of the genetic algorithm; the electric vehicle parameters comprise the time when the electric vehicle leaves the household charging pile, the time when the electric vehicle is connected into the charging pile, the probability density function of the daily mileage, expected values corresponding to the probability density function and standard deviations; the electric vehicle parameters also comprise electric vehicle performance parameters, and the electric vehicle performance parameters comprise power consumption per hundred kilometers, rated capacity of a battery, rated charging power, rated charging efficiency and the like; the power grid load parameters comprise conventional loads and wind power consumption corresponding to the conventional loads. The parameters of the genetic algorithm are set as dimensions, population size, maximum genetic algebra (maximum iteration number), repetition rate of filial generation and parent generation, cross rate, variation rate and the like.
Wherein, the time that electric automobile leaves domestic electric pile of filling, the time of inserting electric pile of filling are normal distribution, and the mileage of traveling on a day is close lognormal distribution, and its probability density function that corresponds is f respectivelyL(xL)、fA(xA) And fD(d)：
Wherein the expected value of the probability density function is mu, the standard deviation is sigma, and d represents the driving mileage.
In addition, the state of charge after the running task of the electric automobile is finished can be obtained according to the power consumption per hundred kilometers and the rated capacity of the battery as follows:
in the formula, set up SafterIndicating the state of charge after the nth electric vehicle has finished its driving task, SbeforeThe state of charge before the start of the driving task is shown, and the power consumption per hundred kilometers of each electric automobile is shown as E100The battery rated capacity is represented as BnThen the above equation represents the state of charge and the mileage dnThe relationship (2) of (c).
The real-time charging power of the battery obtained according to the rated charging power and the rated charging efficiency is as follows:
in the formula (I), the compound is shown in the specification,
the state of the nth electric vehicle at the t moment is represented by a value of 1 representing the charging state and a value of 0 generationThe table is in the off state. t is tA、tLRespectively, the charging start time and the charging end time, PrIndicating rated charging power, ηrSince the nominal charging efficiency is shown, the above expression represents the real-time charging power at which the nth electric vehicle battery is charged at time t.
According to the rated capacity of the battery, the charge state of the electric automobile after the driving task is finished and the charge state of the electric automobile before the driving task, the charging time of the battery of the electric automobile can be obtained as follows:
in the formula, PnRepresenting a constant charging power of the electric vehicle battery.
The charging model of the electric automobile group can be obtained as follows:
wherein N represents the total number of the electric vehicles which can be managed and controlled,
the charging power of the household charging pile is accessed at the t moment;
represents the total system load in the area at time t,
indicating a normal load when the electric vehicle is not connected in the area.
202. And respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve.
It should be noted that, in order to maximally absorb wind power output, a first objective function is established with the minimum variance of the difference between the charging power of the electric vehicle group connected to the household charging pile and the wind power absorption requirement of the regional wind farm; in order to enhance the capacity of 'peak clipping and valley filling', the peak-valley difference of the daily load curve is minimized, and an objective function with the minimum peak-valley difference of the daily load curve is established.
The method specifically comprises the following steps of establishing a target function of minimum variance of a difference value between charging power of an electric automobile group connected to a household charging pile and wind power consumption requirements of a wind power plant in an area:
in the formula, the wind power consumption requirement of the wind power plant in the region is PWThe charging power of the household charging pile is simultaneously accessed at the t moment
The specific steps of establishing the target function with the minimum peak-valley difference of the daily load curve are as follows:
wherein Δ P represents a peak-to-valley difference, Pp、PvRespectively the total load of the system
The maximum value and the minimum value of the charging power are that the charging power of the household charging pile is simultaneously accessed at the t moment
The normal load when the electric vehicle is not connected in the area is
203. And solving the objective function by adopting a genetic algorithm to obtain the optimal parameters.
It should be noted that the objective function can be solved by using a genetic algorithm, so that the wind power resource can be effectively consumed.
The method comprises the following specific steps of solving an objective function by adopting a genetic algorithm to obtain optimal parameters:
generating an initial population according to the charging demand model; judging whether the population meets constraint conditions; if yes, calculating a target function and the individual fitness of the population; selecting, crossing and mutating population individuals, and judging whether the results after selection, crossing and mutation reach the optimization target; if the optimal target is reached, outputting an optimal solution, otherwise, selecting a good individual with fitness reaching a preset threshold as a population of the next iteration for iteration, and outputting the optimal solution until the maximum iteration times is reached or the optimal target is reached.
It should be noted that, specifically, the generating of the initial population according to the charging demand model is as follows: and generating an initial population according to the home arrival time, the away time and the driving mileage of the electric automobile. After the initial population is generated, whether the population meets constraint conditions needs to be judged, wherein the constraint conditions are as follows: latest moment t for starting charging of electric automobileFThe constraint of (2) is:
tF＝tL-Tch
wherein the expected value of the time of leaving home on work of the family user is tLThe charging time required by the battery is Tch。
If the constraint condition is not met, discarding the population; and if the constraint conditions are met, calculating the two objective functions, calculating the fitness of the individuals, and selecting the individuals meeting the fitness to perform selection, crossing and mutation operations. Judging whether the results after selection, crossing and variation reach the optimization target; if the optimal target is reached, outputting an optimal solution, otherwise, selecting a good individual with fitness reaching a preset threshold as a population for next iteration to perform iteration, wherein the iteration is a process of judging whether a new generation population meets constraint conditions, calculating a target function and individual fitness, and performing selection, crossing and variation; then judging whether the electric automobile reaches the forced charging time, if so, carrying out forced charging, otherwise, judging whether the maximum genetic algebra is reached; if so, outputting the optimal solution, otherwise, performing the selecting, crossing and mutation operations again, and referring to fig. 4 for a specific flowchart.
According to the embodiment of the application, the operation stability of the power grid can be improved from two aspects of wind power consumption and peak load shifting by simultaneously considering a double-target optimization model which enables wind power resource consumption to be maximized and peak load difference to be minimized; in addition, the constraint condition of the latest moment when charging must be started is introduced, so that the full charge of the electric automobile before traveling is met, and the daily traveling electric quantity requirement of the electric automobile and the traveling requirement and satisfaction of a user are guaranteed.
The application also provides a specific embodiment of the electric vehicle group joint optimization method, which specifically comprises the following steps:
s1: initializing parameters, and acquiring a charging demand model of the electric automobile of a user; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of a genetic algorithm.
Wherein, electric automobile parameter includes:
the time and the daily mileage of the electric automobile leaving the household charging pile and accessing the charging pile are provided, and the three expected values of the probability density function are muleave＝7.06，μarrive＝18.32，μdistance3.06; three standard deviations σleave＝3.26，σarrive＝3.42，σdistance＝0.6。
Performance parameters of the electric vehicle:
the power grid load parameters comprise parameters of conventional load power and wind power consumption demand power:
the parameters of the algorithm are set as follows:
s2: and respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve.
The method specifically comprises the following steps of establishing a target function of minimum variance of a difference value between charging power of an electric automobile group connected into a household charging pile and wind power consumption requirements of a wind power plant in an area:
in the formula, the wind power consumption requirement of the wind power plant in the region is PWThe charging power of the household charging pile is simultaneously accessed at the t moment
The specific steps of establishing the target function with the minimum peak-valley difference of the daily load curve are as follows:
wherein Δ P represents a peak-to-valley difference, Pp、PvRespectively the total load of the system
Maximum and minimum values ofThe charging power of the household charging pile is simultaneously accessed at the t moment
The normal load when the electric vehicle is not connected in the area is
S3: and solving the objective function by adopting a genetic algorithm to obtain the optimal parameters.
It should be noted that the objective function can be solved by using a genetic algorithm, so that the wind power resource can be effectively consumed.
The method comprises the following specific steps of solving an objective function by adopting a genetic algorithm to obtain optimal parameters:
the generating of the initial population according to the charging demand model specifically comprises the following steps: and generating an initial population according to the home arrival time, the away time and the driving mileage of the electric automobile. After the initial population is generated, whether the population meets constraint conditions needs to be judged, wherein the constraint conditions are as follows: latest moment t for starting charging of electric automobileFThe constraints of (2) are:
tF＝tL-Tch
wherein the expected value of the time of leaving home on work of the family user is tLThe required charging time of the battery is Tch。
If the constraint condition is not met, discarding the population; and if the constraint conditions are met, calculating the two objective functions and calculating the fitness of the individual. Which calculates the individual fitness f (x)i(t)) the procedure is:
and carrying out dynamic linear calibration on the target function to obtain a fitness function, wherein in the dynamic linear calibration method, a function expression is set as follows:
f＝atF+bt
in the formula: f is a fitness function, F is a target function value, and t is an iteration number. Is provided with
Is the smallest order of the tth generation individualScalar value, for maximization problem, order
In the formula: parameter xitOn one hand, the wide area search range is widened to keep population diversity, and on the other hand, the local area search is refined to keep convergence, so that the worst individuals still have the possibility of breeding. It is desirable that the preferred function need not be too strong in the early stages of the iteration, and that the more excellent individuals remain in the later stages of the iteration, it is desirable to strengthen the preferred function. XitAs the number of iterations t increases, it decreases, which can be adjusted by changing M and c:
The transformation formula of the finally obtained dynamic linear calibration is as follows:
let the fitness of the current position of each particle be f (x)i(t)), the fitness function set by the present application may therefore be:
after the fitness is calculated, selecting individuals meeting the fitness to perform selection, crossing and mutation operations, and specifically comprising the following steps of:
the selection operation is as follows: firstly, the proportion of the fitness of the individual in the total fitness of all the individuals in the population is calculated, and for the population with the size of 50, P is { X ═ X1,X2,...,Xi,...,X50The survival probability of the individual i is }
A random number δ e (0,1) is generated and the wheel is made by accumulating the survival probabilities from the first individual until equation (3.8) is satisfied and the mth individual is taken out to the next generation population. The selection operation itself does not lead to new individuals, and the subsequent crossover and mutation operations introduce new individuals into the population.
Crossover operation refers to the probability of gene recombination according to the parent chromosome, i.e., crossover rate PcRandomly selecting a pair of parent chromosomes to carry out gene recombination to generate new filial individuals, and promoting the population to evolve towards the direction of the optimal solution. PcThe probability of directly copying to the next generation is (1-P)c) In particular, if PcIf 0, all parents are directly copied to next generation, if PcAll parents were genetically recombined at 1. The cross rate selected in this application is Pc＝0.9。
The mutation operation is to randomly change one or more gene values on the selected parent chromosomes, and the effect is to add new gene values to the gene library, thereby generating new chromosomes, maintaining genetic diversity of the population, and preventing too many similar individuals in the population from converging to a local optimal solution or prematurely converging to a suboptimal solution. Mutation Rate P for probability of Gene ChangemIndicates that the mutation rate selected in this application is Pm＝0.01。
After selection, crossing and variation, judging whether the results of the selection, crossing and variation reach the optimal target; if the optimal target is reached, outputting an optimal solution, otherwise, selecting a good individual with fitness reaching a preset threshold as a population of next iteration for iteration, wherein the iteration is a process of judging whether a new generation population meets constraint conditions, calculating a target function and individual fitness, and performing selection, crossing and variation; and then judging whether the electric automobile reaches the forced charging time, if so, carrying out forced charging, wherein the judgment process of carrying out forced charging is as follows:
the power demand of a user is ensured to be met, the EV can be fully charged before leaving home, and the charging is started at the latest time tforceIn time, the EV needs to exit regulation, and there is a constraint:
tforce＝tleave-Tcharge
before and after the EV arrives at home, the EV does not participate in regulation, and there are:
it should be noted that, the user has regular work leaving time, and the expected value is tleave。
If the forced charging time is not reached, judging whether the maximum genetic algebra is reached; if so, outputting the optimal solution, otherwise, performing selection, crossing and mutation operations again.
Through the parameters, data of typical days in summer and winter are respectively selected, the method is compared with the charging load of the electric automobile group without optimization processing and the daily load in the region, and the results are shown in fig. 5-8 through experimental comparison.
As shown in fig. 5 to 6, when there is no optimization process, the electric vehicle starts charging immediately after being connected to the home charging pile, and only a small amount of charging power can be used to continuously consume the wind power resources. In addition, the phenomenon of peak-to-peak is caused when the power grid is accessed in the peak period of the conventional load, and the stable operation of the power grid is interfered and impacted. After the genetic algorithm is adopted for optimization, the charging power of the electric automobile group well follows the change of the wind power consumption required power, and the charging power distribution is relatively dispersed. Therefore, the scheme of the application can furthest consume the wind power resource.
As shown in fig. 7-8, with a large-scale electric vehicle group connected to a power grid, a total load of the system has an obvious load peak within a range of 18-21 hours, and after optimization by a genetic algorithm, for typical days in summer and typical days in winter, on one hand, the load in the peak hours and the maximum load in the day are both reduced, and the peak-valley difference rate in the typical days in two seasons is respectively reduced from 38.31% to 34.75% and from 57.59% to 52.22%, which shows that the load peak-valley difference can be effectively reduced, and the scheme of the application realizes a significant peak clipping and valley filling effect; on the other hand, the daily load rate is increased, and the daily load fluctuation mean square error is reduced, which shows that the daily load is relatively average, and the invention can stabilize the load curve of the region.
Therefore, the method and the device can improve the operation stability of the power grid from two aspects of wind power consumption and peak load shifting by simultaneously considering a double-target optimization model which maximizes wind power resource consumption and minimizes peak-to-valley load difference; the constraint condition of the latest moment when charging is started is introduced, so that the full charge of the electric automobile before traveling is met, and the daily traveling electric quantity requirement of the electric automobile and the traveling requirement and satisfaction of a user are guaranteed; in addition, the charging loads of the electric automobile groups are jointly optimized to meet the requirements of different seasons by comparing the variation trends of the daily load curves of the conventional loads in the typical summer days and the typical winter days.
The above embodiment is an embodiment of an electric vehicle group joint optimization method according to the present application, the present application further includes an embodiment of an electric vehicle group joint optimization system, specifically as shown in fig. 3, fig. 3 is a system schematic diagram of an embodiment of an electric vehicle group joint optimization system according to the present application, and specifically includes:
an objective function establishing unit 301 and a solving unit 302.
The objective function establishing unit 301 is configured to respectively establish an objective function with a minimum variance of a difference between charging power of an electric vehicle group connected to the home charging pile and a wind power consumption requirement of a regional wind farm, and a minimum peak-to-valley difference of a daily load curve.
The solving unit 302 is configured to solve the objective function by using a genetic algorithm to obtain an optimal parameter.
Specifically, still include: an initialization unit; the initialization unit is used for initializing parameters and acquiring a charging demand model of the user electric automobile; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of a genetic algorithm.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "comprises," "comprising," and "having," and any variations thereof, in this application are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, a module of the unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (8)
1. A method for jointly optimizing electric vehicle groups is characterized by comprising the following steps:
initializing parameters, and acquiring a charging demand model of the user electric vehicle; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of a genetic algorithm;
respectively establishing a target function with the minimum variance of the difference between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve;
solving the objective function by adopting a genetic algorithm to obtain optimal parameters, which specifically comprise the following steps:
generating an initial population according to the charging demand model;
judging whether the population meets constraint conditions;
if yes, calculating the target function and the population individual fitness;
selecting, crossing and mutating population individuals, and judging whether the results after selection, crossing and mutation reach the optimization target;
if the optimal target is reached, outputting an optimal solution, otherwise, selecting a good individual with fitness reaching a preset threshold as a population of the next iteration for iteration, and outputting the optimal solution until the maximum iteration times is reached or the optimal target is reached.
2. The electric vehicle group joint optimization method according to claim 1, wherein the step of respectively establishing the objective function with the minimum variance of the difference between the charging power of the electric vehicle group connected to the household charging pile and the wind power consumption requirement of the regional wind farm is specifically as follows:
in the formula, the wind power consumption requirement of the wind power plant in the region is PWThe charging power of the household charging pile is simultaneously accessed at the t moment
3. The electric vehicle group joint optimization method according to claim 1, wherein the establishing of the objective function with the minimum peak-to-valley difference of the daily load curve is specifically as follows:
wherein Δ P represents a peak-to-valley difference, Pp、PvRespectively the total load of the system
The maximum value and the minimum value of the charging power are that the charging power of the household charging pile is simultaneously accessed at the t moment
Routine when no electric vehicle is connected in areaLoaded with
4. The electric vehicle group joint optimization method according to claim 1, wherein the solving the objective function by using the genetic algorithm further comprises:
judging whether the electric automobile reaches the forced charging time or not;
if so, the electric automobile is forcibly charged, otherwise, whether the iteration number reaches the maximum iteration number or an optimal target is continuously judged.
5. The electric vehicle group joint optimization method according to claim 1, wherein the generating of the initial group according to the charging demand model specifically comprises:
and generating an initial population according to the home arrival time, the away time and the driving mileage of the electric automobile.
6. The electric vehicle group joint optimization method according to claim 1, wherein the constraint condition is:
latest moment t for starting charging of electric automobileFThe constraints of (2) are:
tF＝tL-Tch
wherein the expected value of the time of leaving home on duty of the home user is tLThe charging time required by the battery is Tch。
7. An electric vehicle group joint optimization system, comprising: an objective function establishing unit and a solving unit;
the target function establishing unit is used for respectively establishing a target function with the minimum variance of the difference value between the charging power of the electric automobile group connected to the household charging pile and the wind power consumption requirement of the wind power plant in the region and the minimum peak-valley difference of the daily load curve;
the solving unit is used for solving the objective function by adopting a genetic algorithm to obtain the optimal parameters.
8. The electric vehicle group joint optimization system of claim 7, further comprising an initialization unit;
the initialization unit is used for initializing parameters and acquiring a charging demand model of the user electric automobile; the parameters comprise electric vehicle parameters, power grid load parameters and initial parameters of the genetic algorithm.
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