CN114742449B - Electric automobile grouping scheduling method based on priority weight coefficient - Google Patents
Electric automobile grouping scheduling method based on priority weight coefficient Download PDFInfo
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Abstract
The electric automobile grouping scheduling method based on the priority weight coefficient comprises the following steps: step 1: determining the electric vehicle cluster scale participating in system scheduling, counting various data of electric vehicles willing to participate in system scheduling users, and fitting the travel time characteristics of the electric vehicles by using normal distribution; step 2: grouping electric vehicles, namely adding an electric vehicle priority weight coefficient by taking the minimum deviation between the average state of charge before traveling and the expected state of charge of the electric vehicle cluster as an objective function, and establishing an electric vehicle layer optimization scheduling model; step 3: and (3) importing the characteristic data of each electric automobile cluster into a dispatching model, and solving the electric automobile layer optimizing dispatching model established in the step (2) by applying an improved particle swarm optimizing algorithm to obtain a charging and discharging strategy of electric automobile optimizing dispatching in the cluster. The invention provides an electric vehicle grouping scheduling method based on priority weight coefficients, which provides a basis for the order of electric vehicles to participate in system scheduling preferentially.
Description
Technical Field
The invention relates to an electric automobile grouping scheduling method based on priority weight coefficients, and belongs to the technical field of electric automobile power distribution.
Background
In the prior art, the electric automobile not only provides a beneficial factor for improving the problems of environmental pollution and global climate warming, but also can participate in the dispatching of a comprehensive energy system due to the characteristics of the distributed energy storage units, and provides a new thought for relieving the operation pressure of a power grid and improving the environmental pollution.
Aiming at the electric vehicles participating in the regulation and control of the power grid, if a large-scale electric vehicle cluster is accessed to the power grid in a disordered way, adverse effects such as overload load and power quality reduction are caused, and a reasonable and ordered charge-discharge control strategy is adopted, so that the adverse effects can be avoided, the stability of the power grid can be effectively improved, peak clipping and valley filling are realized, the loss of the power grid is reduced, new energy consumption is promoted, and auxiliary services such as standby energy storage and frequency modulation are provided for the power grid.
When the electric automobile cluster is accessed to a power grid as a whole to participate in dispatching, the grouping principle of the electric automobile cluster comprises expected charging completion time, travel chains, maximum charging delay and the like; aiming at the problem of power distribution of electric vehicles in the clusters, the electric vehicle power distribution system has a charging and discharging system according to the charge state of the electric vehicles, the idle degree of the electric vehicles and the like. However, how to better and orderly guide the electric vehicles in the cluster to participate in the system scheduling is a technical problem to be solved currently under consideration of individual variability of the electric vehicles in the cluster.
Disclosure of Invention
In order to realize reasonable distribution of power in the electric automobile cluster and orderly charging and discharging of the electric automobiles. The invention provides an electric vehicle grouping scheduling method based on priority weight coefficients, which provides a basis for the order of electric vehicles to participate in system scheduling preferentially.
The technical scheme adopted by the invention is as follows:
The electric automobile grouping scheduling method based on the priority weight coefficient comprises the following steps:
Step 1: determining the electric vehicle cluster scale participating in system scheduling, counting various data of electric vehicles willing to participate in system scheduling users, and fitting the travel time characteristics of the electric vehicles by using normal distribution;
step 2: grouping electric vehicles, namely adding an electric vehicle priority weight coefficient by taking the minimum deviation between the average state of charge before traveling and the expected state of charge of the electric vehicle cluster as an objective function, and establishing an electric vehicle layer optimization scheduling model;
Step 3: and (3) importing the characteristic data of each electric automobile cluster into a dispatching model, and solving the electric automobile layer optimizing dispatching model established in the step (2) by applying an improved particle swarm optimizing algorithm to obtain a charging and discharging strategy of electric automobile optimizing dispatching in the cluster.
In the step 2, the established electric automobile layer optimization scheduling model takes the minimum deviation between the average state of charge SOC and the expected state of charge SOC of the electric automobile cluster before traveling as an objective function, segments the quadratic function thereof, introduces a continuous variable χ, and is expressed as:
0≤χ≤1；
Wherein: f is an objective function; q is the number of clusters; i is a cluster sequence; t is the number of time periods; average SOC for period t cluster i; Average SOC for period t-1 cluster i; SOC ex is the travel expectation of cluster i, and the SOC takes a fixed value of 0.5; χ is an auxiliary continuous variable.
In the step 2, the following method is adopted to define the priority weight coefficient:
s in, the state of charge of the electric automobile after the trip is finished; s out is the state of charge of the electric automobile meeting basic travel requirements before traveling at time t; p out is the discharge power of the electric automobile; t dt is the travel time of the P out electric vehicle; q is the rated charge capacity of the electric automobile; η is the discharge efficiency of the electric automobile.
The invention discloses an electric automobile grouping scheduling method based on priority weight coefficients, which has the following technical effects:
1) The invention provides an electric vehicle grouping scheduling method based on priority weight coefficients by utilizing an electric vehicle layer optimization scheduling model and an electric vehicle grouping criterion, and provides a basis for making a proper electric vehicle charging and discharging plan.
2) According to the invention, by defining the priority weight coefficient of the charging and discharging of the electric vehicles in the cluster, the individual demand levels of the charging and discharging are ordered, the power is more reasonably distributed in the cluster, the influence on the users of the electric vehicles during system scheduling is reduced to the minimum, and win-win situation of the users and the scheduling layer is formed.
Drawings
Fig. 1 is a flow chart of electric vehicle grouping scheduling based on priority weight coefficients.
Fig. 2 is a user travel period profile.
Fig. 3 is a user end travel period profile.
Fig. 4 is a graph showing charge and discharge power distribution at each time of the electric vehicle cluster.
Fig. 5 is a graph of system energy fluctuation.
Fig. 6 is a state of charge graph of an electric vehicle.
Detailed Description
According to the electric vehicle group scheduling method based on the priority weight coefficient, firstly, each item of data willing to participate in system scheduling of user electric vehicle traveling is counted, and the characteristics of the electric vehicle group traveling are matched and participated in scheduling by using the front-end distribution. Secondly, grouping electric vehicles participating in dispatching, establishing an electric vehicle layer optimization dispatching model, taking the minimum deviation between the average state of charge before traveling and the expected state of charge of the electric vehicle clusters as an objective function, adding an electric vehicle charging and discharging priority weight coefficient, solving the model by using an improved particle swarm optimization algorithm, and making a charging and discharging plan of each electric vehicle cluster. Finally, the accuracy and the effectiveness of the electric automobile grouping scheduling method based on the priority weight coefficient are shown by the analysis of an embodiment.
The scheduling method of the invention comprises the following steps, as shown in fig. 1:
step S1: determining the electric vehicle cluster scale participating in system scheduling, counting various data of electric vehicles willing to participate in the system scheduling users, fitting the travel time characteristics of the electric vehicles by using normal distribution, and enabling the travel time T out (unit: hours) of the users to obey the normal distribution, wherein the probability density is as follows:
wherein mu out is an expected value at the starting moment of travel, and the unit is hours; x out is travel time; σ out is the standard deviation.
The probability density of the user obeys normal distribution at the travel end time T in (unit: hour) of the same day is as follows:
Wherein mu in is an expected value at the end time of the return stroke, and the unit is hour; x in is the return time; σ in is the standard deviation.
The user's time of day T dt (unit: hours) obeys a normal distribution with a probability density of:
Wherein: mu dt is the expected value of travel time in hours; σ dt is the standard deviation.
The number d (unit: kilometer) of travel of a user per day tends to be lognormal distribution, and the probability density is:
Wherein mu d is an expected value for forming a driving distance in kilometers; x represents the mileage; σ d is the standard deviation. The required charge duration T ct (unit: hours) can be obtained from the mileage on the day:
Wherein d is the driving mileage; w 100 is the power consumption of the electric automobile for 100 km; p c is the charging power.
Step S2: the criterion for grouping electric vehicles is based on three discrimination indexes: travel time T out, return end time T in, required charge duration T ct. And selecting the automobiles with the same starting time period and ending time period as a cluster, taking the minimum deviation between the average state of charge before traveling and the expected state of charge of the electric automobile cluster as an objective function, giving each electric automobile a dynamic priority weight coefficient, and establishing an electric automobile layer optimization scheduling model.
The objective function expression is specifically as follows:
0≤χ≤1 (7)
Wherein: average SOC for period t cluster i; Average SOC for t-1 period aggregation weight i;
SOC ex is the travel expected SOC of cluster i; χ is an auxiliary continuous variable.
The average state of charge before the electric automobile cluster goes out is:
Wherein: p i,t is the net charge-discharge power of cluster i in period t; the battery capacity of cluster i.
The electric automobile layer optimization scheduling model comprises the following constraint conditions:
1) Charging and discharging power constraint of electric automobile clusters:
wherein: p c,max and P c,min are respectively the maximum and minimum values of the charging power of the electric automobile; p d,max and P d,min are respectively the maximum and minimum discharge power values of the electric automobile.
2) Load state constraint of electric automobile cluster i in scheduling period:
Wherein: the upper and lower limits of the average state of charge of cluster i, respectively.
3) Rotation standby constraint of electric automobile:
Wherein: And The method comprises the steps of respectively providing up-rotation and down-rotation for a cluster i in a period t for standby; And Respectively carrying out upward rotation and downward rotation required by the system in the period t for later use; q is the number of clusters;
In addition, according to the different states of charge of each electric automobile, defining a dynamic priority weight coefficient of the electric automobile:
S in, the state of charge of the electric automobile after the trip is finished; s out is the state of charge of the electric automobile meeting basic travel requirements before traveling at time t; p out is the discharge power of the electric automobile; t dt is travel time of the electric automobile; q is the rated charge capacity of the electric automobile; η is the discharge efficiency of the electric automobile.
When lambda is less than 1, the electric automobile is in an unscheduled mode, charging treatment is needed, and the smaller lambda is, the higher priority is. When lambda is more than or equal to 1, the electric automobile is in a schedulable mode, can be charged and discharged according to scheduling or is in an idle state, and the priority is lower, and the higher lambda is, the lower the priority is.
Step S3: and importing the characteristic data of each electric automobile cluster into a dispatching model, wherein the characteristic data of each electric automobile cluster comprises a travel time characteristic number, an end travel time characteristic number and a travel mileage characteristic number. And (3) solving the model established in the step (S2) by applying an improved particle swarm optimization algorithm to obtain a charging and discharging strategy of the optimization scheduling of the electric vehicles in the cluster. The improved particle swarm optimization algorithm uses an external archive to store non-inferior solutions and uses the non-inferior solutions as disturbance terms to update the speed and position of particles.
The improved particle swarm optimization algorithm comprises the following steps:
(1) And fuzzifying the objective function of each solution in the Pareto non-inferior solution set by adopting a piecewise linear membership function. The j-th objective function of the i-th solution has a membership degree δ i,j of:
Wherein: f i,j is the jth target value of the ith solution; The maximum value and the minimum value of the jth objective function in the Pareto non-inferior solution set are obtained.
(2) The membership delta i of each solution in each Pareto non-inferior solution set is calculated:
Wherein: n p is the number of non-inferior solutions in the Pareto non-inferior solution set; n o is the number of objective functions.
(3) And evaluating according to the membership degree, and selecting an optimal compromise solution.
Examples:
The invention describes the travel characteristics of an electric automobile on a working day by using related data in a 2019 automobile survey report of a user in a certain area, and a specific implementation mode is described by taking an electric automobile cluster containing 1000 electric automobiles willing to participate in system optimization scheduling as an example.
According to step S1, counting various data of each electric vehicle traveling in the electric vehicle cluster, taking traveling time and traveling ending time as examples, fitting probability distribution of the electric vehicle traveling starting time and traveling ending time in the area as shown in fig. 2 and 3, and obtaining characteristic number mu out＝7.68(h),σout =2.96 of the traveling time; characteristic number μ in＝17.68(h),σin =3.16 at the end of travel; the feature number mu dt＝0.89(h),σdt =1.68 of the travel time is obtained in the same way; characteristic number μ d＝2.94(km),σd =1.2 of the driving range.
According to step S2, electric vehicles are clustered, the electric vehicles with the same starting period and ending period are divided into a cluster, the initial SOC and ending SOC in the cluster obey normal distribution, the expected travel SOC is 0.6, the travel mileage d obeys log normal distribution N (2.94,1.2 2), in addition, the battery capacity of the electric vehicles is uniformly 20 kw.h, the rated charge and discharge power is 4kw, the discharge efficiency is 95%, and the cluster division is shown in table 1 and table 2 according to the clustering principle.
Table 1 travel time period cluster partitioning
Table 2 end period cluster partitioning
According to step S3, the clusters are regarded as a whole, a priority weight coefficient is defined to represent the charge and discharge requirement level of the electric vehicles, the vehicles in each cluster are ordered according to the weight coefficient, the average charge state before traveling and the expected charge state deviation before traveling of the electric vehicles are taken as objective functions, and the charge and discharge power of each cluster in each period is solved through an improved particle swarm algorithm. Calculated, the clusters are found at 0:00 to 10: 00. 12:00 to 16: 00. 20:00 to 24: the charging power for the 00 time period is 10.54MW in total; at 10:00 to 12: 00. 16:00 to 20: the total discharge power of the 00 time period is 3.65MW, the functions of peak clipping, valley filling, standby capacity providing and the like can be realized, and the charge and discharge power of each time period is shown in fig. 4.
The method comprises the steps of setting three modes of unordered charging and discharging of the electric automobile, ordered charging and discharging based on priority weight coefficients, adopting prediction values of uncertain quantities such as charging and discharging power of the electric automobile and the like to conduct an algorithm for day-ahead dispatching optimization of an energy system, and comparing system energy fluctuation conditions and electric automobile charge condition conditions of the three modes.
Method 1, disordered charge and discharge of an electric automobile: the electric automobile is used as a schedulable unit in the system, and is charged or discharged immediately after being connected into the system until the state of charge of each automobile is charged again to meet the expected travel SOC, and the state of charge of each automobile is not lower than the expected travel SOC.
Method 2, orderly charging and discharging of the electric automobile: the electric automobile is used as a schedulable unit in the system, and is connected into the system for charging and discharging in time sequence until the state of charge of each automobile meets the expected travel SOC during charging, and the state of charge is not lower than the expected travel SOC during discharging.
Method 3, charging and discharging strategies of the electric automobile based on priority coefficient weights: the electric vehicles are used as schedulable units in the system, and are sequenced to be charged and discharged according to the weight coefficient after being sequenced to be connected into the system until the state of charge of each vehicle meets the expected travel SOC during charging, and the state of charge is not lower than the expected travel SOC during discharging.
The initial state of charge of the electric automobile in three modes is 0.5, and the system energy fluctuation and cluster state of charge change caused by each method are shown in fig. 5 and 6:
under the unordered charging and discharging strategy, the electric automobile clusters can participate in scheduling in the system, but waste of electric energy in the system is caused, and the peak value of the system load and the system load are increased after traveling is finished; compared with the prior art, the ordered charge-discharge policy can enable the electric vehicles to be reasonably charged and discharged, peak clipping and valley filling are realized, electric load fluctuation is effectively restrained, an electric load curve is obviously improved, the travel SOC is expected, and meanwhile, the effect of energy scheduling is achieved, but the difference of individual charge states is not considered, when the electric vehicles are out, the electric vehicles with different charge states can only be divided into two clusters for charging and discharging to participate in scheduling, and the priority order inside the clusters is not provided, so that the electric vehicles cannot meet some emergency demands of individuals; and based on the charging and discharging strategies of the priority weight coefficients, the individual demands for charging and discharging are ordered, and the influence on the users of the electric automobile is minimized while the power is more reasonably distributed in the cluster, so that win-win situation between the users and the dispatching system is formed.
Claims (1)
1. The electric automobile grouping scheduling method based on the priority weight coefficient is characterized by comprising the following steps of:
Step 1: determining the electric vehicle cluster scale participating in system scheduling, counting various data of electric vehicles willing to participate in system scheduling users, and fitting the travel time characteristics of the electric vehicles by using normal distribution;
step 2: grouping electric vehicles, namely adding an electric vehicle priority weight coefficient by taking the minimum deviation between the average state of charge before traveling and the expected state of charge of the electric vehicle cluster as an objective function, and establishing an electric vehicle layer optimization scheduling model;
Step 3: importing the characteristic data of each electric automobile cluster into a dispatching model, and solving the electric automobile layer optimizing dispatching model established in the step 2 by applying an improved particle swarm optimizing algorithm to obtain a charging and discharging strategy of electric automobile optimizing dispatching in the cluster;
In the step 2, the established electric automobile layer optimization scheduling model takes the minimum deviation between the average state of charge (SOC) before traveling and the expected state of charge (SOC) of the electric automobile cluster as an objective function, segments the quadratic function of the objective function, and introduces continuous variables Expressed as:
；
；
Wherein: Is an objective function; Is the number of clusters; Is a cluster sequence; Is the number of time periods; Is that Average SOC of period cluster i; Is that Period clusterAverage SOC of (a); Travel expectations for cluster i; is an auxiliary continuous variable;
In the step 2, the following method is adopted to define the priority weight coefficient:
；
Wherein: The state of charge of the electric automobile after the trip is finished; Is in the electric automobile The state of charge meeting basic travel requirements before traveling at any moment; discharging power for the electric automobile; Is that Travel time of the electric automobile; rated charge capacity of the electric automobile; The discharge efficiency of the electric automobile is achieved.
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| CN112366740A (en) * | 2020-11-13 | 2021-02-12 | 深圳供电局有限公司 | Electric vehicle cluster scheduling method |
| CN112467767A (en) * | 2020-11-02 | 2021-03-09 | 国网内蒙古东部电力有限公司呼伦贝尔供电公司 | Electric automobile grid-connected cooperative control method in comprehensive energy system environment |
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| CN112467767A (en) * | 2020-11-02 | 2021-03-09 | 国网内蒙古东部电力有限公司呼伦贝尔供电公司 | Electric automobile grid-connected cooperative control method in comprehensive energy system environment |
| CN112366740A (en) * | 2020-11-13 | 2021-02-12 | 深圳供电局有限公司 | Electric vehicle cluster scheduling method |
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