CN117458477A - Electric vehicle scheduling method considering participation of load aggregators in grouping optimization mode - Google Patents
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Abstract
The invention belongs to the technical field of electric automobile dispatching, and particularly relates to an electric automobile dispatching method considering participation of load aggregators in a grouping optimization mode; step S1, simulating the position of an electric vehicle charging station according to the local population distribution condition, and drawing a charging station distribution scatter diagram; s2, clustering the electric vehicles in groups by adopting a k-means method, distributing the electric vehicles to each aggregator for management, and providing charge and discharge prediction power of the electric vehicles in the range of jurisdiction by each aggregator; s3, constructing an electric vehicle optimal scheduling model to obtain a total electric vehicle scheduling plan; on the basis of grouping the electric automobiles, distributing an optimized dispatching plan according to the proportion of the predicted power of each aggregator in the total predicted power; the electric vehicle dispatching method considering participation of load aggregators in the grouping optimization mode can conveniently and rapidly group electric vehicles, further effectively reduce user cost, stabilize net load fluctuation and establish personalized dispatching plans for different aggregators.
Description
Technical Field
The invention belongs to the technical field of electric automobile dispatching, and particularly relates to an electric automobile dispatching method considering participation of load aggregators in a grouping optimization mode.
Background
The electric automobile is a vehicle using electric energy as driving energy, and has the advantages of environmental protection, economy and the like. With the rapid increase of the number of electric vehicles, the management scale and the management difficulty of the electric vehicles are gradually increased. Meanwhile, the permeability of the electric automobile at the power grid side is continuously increased, and the uncertainty impact of the electric automobile on the power grid is gradually enhanced. How to manage large-scale electric vehicles and effectively utilize the flexibility advantage of the electric vehicles becomes a problem to be solved in the aspect of optimal scheduling of the electric vehicles.
At present, a plurality of electric automobile grouping optimization scheduling methods and results exist at home and abroad. In the aspect of electric automobile clustering, documents [ Zhu Xinyue, yue Yuntao, li Binghua and the like ] an electric automobile ordered charge-discharge grouping scheduling strategy [ J ]. Scientific technology and engineering, 2021,21 (19): 8023-8030 ] divide electric automobile clusters according to response will and response capability of an automobile owner; electric automobile long-time scale charging and discharging scheduling [ J ] under price and excitation combined demand response [ Hou Hui, tang Jun I, wang Yifan ] electric power system automation, 2022,46 (15): 46-55 ] electric automobiles are clustered and scheduled according to price type demand response and excitation type demand response; the electric automobile in each period is dynamically divided into a conventional automobile group and a regulation and control automobile group for management and scheduling according to battery constraint, time constraint and charge-discharge conversion frequency constraint of the electric automobile by adopting a grouping optimization electric automobile and power grid interaction scheduling strategy [ J ]. Electric power automation equipment 2020,40 (05): 77-85.DOI:10.16081/j.epae.202004010 ]; document [ Sun Yi, ge Mingyang, wang Xianchun ] electric vehicles are classified into a respondable cluster and a non-respondable cluster based on the residual capacity reachable principle and the idle time redundancy principle, taking into consideration a multi-region optimized scheduling strategy [ J/OL ] of electric vehicles for compensation excitation; the power distribution network is divided into different areas based on regional decoupling space-time double-scale electric vehicle optimization scheduling [ J/OL ]. 1-13[2023-04-24] comprehensive consideration of electric characteristics, production life characteristics and electric vehicle travel characteristics, and electric vehicles are optimally scheduled in the areas respectively. The above-mentioned documents, while classifying and managing electric vehicles in consideration of electric characteristics and user characteristics, have a large data size, frequent data update, small actual variation range, high acquisition difficulty, and difficulty in application to actual scheduling.
Aiming at the scheduling problem of electric vehicles, documents [ Zhang Pan, xie Lirong, ma Ruizhen and the like, taking into consideration a multi-main two-stage low-carbon optimizing operation strategy [ J ] of the scheduling capability of an electric vehicle cluster, a power grid technology, 2022,46 (12): 4809-4825.DOI:10.13335/] and documents [ Li Dongdong, zhang Kai, yao Yin and the like, wherein the electric vehicle cluster based on the information gap decision theory has a day-before demand response scheduling strategy [ J ]. Electric power system protection and control, 2022,50 (24): 101-111.DOI:10.19783/] respectively quantizes the scheduling capability of the electric vehicles by using a Monte Carlo method and the information gap decision theory, and the scheduling capability of the quantized electric vehicle cluster is utilized to improve the scheduling efficiency; the dynamic time-sharing electricity price is utilized to establish a multi-objective optimal scheduling model with minimum charge and discharge cost of the electric automobile, minimum power grid loss caused by electric automobile access and minimum influence on voltage stability; the document [ Zhu Lei, yellow river, gaosheng, he Yuhuan, yu ] electric automobile load optimization configuration research [ J ] for wind power consumption, china motor engineering report, 2021,41 (S1): 194-203.DOI:10.13334/] considers the change of wind power output in dynamic time-of-use electricity price, and the established scheduling model considers the user electricity cost and wind power consumption; the method is characterized in that short-time scheduling strategies and response excitation designs [ J ] of electric automobile aggregators of road traffic models are considered, the power grid technology is considered, 2022,46 (07): 2699-2712.DOI:10.13335/] is used for considering the cost of a user in the driving process and the charging and discharging process, response uncertainty caused by the electric automobile scoring, the user participation rate and the distance is comprehensively considered, a response excitation method of two-step pricing is designed, and the electric automobile aggregator scheduling strategy under the short time scale (15 min) is established. The above document considers dynamic time-of-use electricity prices in a scheduling policy and performs optimal scheduling for different scheduling targets, but the scheduling flow is complicated, the execution is difficult, and the study on scheduling plan allocation problems of each aggregation merchant in the case of packet scheduling is lacking.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an electric automobile scheduling method considering participation of load aggregators in a grouping optimization mode.
The purpose of the invention is realized in the following way: the electric automobile dispatching method considering participation of load aggregators in the grouping optimization mode comprises the following steps:
s1, simulating the position of an electric vehicle charging station according to the local population distribution condition, and drawing a charging station distribution scatter diagram;
s2, binding the electric vehicles with the electric vehicle charging stations by utilizing the characteristic that the charging and discharging behaviors of the electric vehicles are stable in a certain area on the basis of the step S1, converting the grouping management of the scattered electric vehicles into the grouping management of the electric vehicle charging stations with fixed positions, clustering the electric vehicles in groups by adopting a k-means method, distributing the clustered electric vehicles to each aggregator for management, and providing the charging and discharging predicted power of the electric vehicles in the range of jurisdiction by each aggregator;
s3, taking the lowest electricity cost of a user and the lowest load fluctuation as an optimal scheduling target, and constructing an electric vehicle optimal scheduling model by considering the influence of user demand constraint, equipment operation constraint and time-of-use electricity price to obtain a total electric vehicle scheduling plan; on the basis of grouping the electric vehicles, the optimized dispatching plans are distributed according to the proportion of the predicted power of each aggregator in the total predicted power.
Further, the step S2 includes clustering electric vehicles by k-means method, and the specific implementation steps are as follows:
1) Determining sample set charging pile x i (1 is equal to or more than i is equal to or less than N) and the number N of clustering centers; randomly selecting N initial values from the available sample set as a cluster center c r (R), wherein k=1, 2,.. and determining the iteration times R;
2) Calculating the distance between each charging pile and the center of each cluster in the sample set, and distributing the data object to the cluster U closest to the center k ；
3) For each cluster, the cluster center is recalculated, the calculation formula is as follows,
calculating the cluster judgment standard function as
4) Judging whether the D is converged or not, and if so, ending iteration; otherwise, returning to the step 2).
Further, the concrete implementation model of the step S3 includes:
s31, establishing a charging and discharging model of the electric automobile
Electric automobile charge-discharge constraint:
wherein p is i,t Charging and discharging power at t moment of ith electric automobile, P char And P dis Respectively the maximum value of charging power and discharging power of the electric automobile, N is the total amount of the electric automobile, N V2G In order to accept the total amount of electric vehicles scheduled,and->Respectively representing the charge and discharge start time and the charge and discharge end time of the ith electric automobile;
user satisfaction and electric vehicle SOC constraints:
in the method, in the process of the invention,for the initial electric quantity of the ith electric automobile, delta t is the charge-discharge duration, eta is the charge-discharge efficiency,/>Expected electric quantity for ith electric automobile and SOC i,t And SOC (System on chip) i,t-1 The charge states of the ith electric automobile at the moment t and the moment t-1 respectively,maximum capacity of the ith electric automobile;
s32, establishing a time-of-use electricity price model based on real-time load change
C(P t )＝a+bP t
Wherein P is t The total load at the moment t is given by,for the base load at time t, < >>Is used for all electric automobilestotal scheduled power at time t, C (P t ) For real-time electricity price, a and b are intercept and slope after linearization processing is carried out on the function relationship between the electricity price and the load by utilizing Taylor expansion;
s33, establishing a scheduling plan distribution model of each load aggregator
In the method, in the process of the invention,scheduling power for kth aggregator t time plan, P k,t Predicting power for the kth aggregation quotient t moment, wherein M is the aggregation quotient quantity;
s34, on the basis of considering the degradation loss cost of the battery of the electric automobile, constructing a scheduling model by taking the total cost of electricity consumption of the user of the electric automobile, the peak-valley difference of the comprehensive load and the minimum fluctuation of the comprehensive load as targets
(1) Total cost of electric automobile user is minimum
Wherein T is a scheduling period;
(2) Minimum total load fluctuation
Wherein P is av Average load in a scheduling period;
s35, single-target processing for multi-target optimization
Converting the multiple targets into single targets by adopting a linear weighting method:
wherein omega is 1 、ω 2 For each target weight, the corner mark B represents each target amount initial value.
The invention has the beneficial effects that: the invention relates to an electric vehicle dispatching method considering participation of load aggregators in a grouping optimization mode, which comprises the following steps of S1, simulating the position of an electric vehicle charging station according to the local population distribution condition, and drawing a charging station distribution scatter diagram; s2, binding the electric vehicles with the electric vehicle charging stations by utilizing the characteristic that the charging and discharging behaviors of the electric vehicles are stable in a certain area on the basis of the step S1, converting the grouping management of the scattered electric vehicles into the grouping management of the electric vehicle charging stations with fixed positions, clustering the electric vehicles in groups by adopting a k-means method, distributing the clustered electric vehicles to each aggregator for management, and providing the charging and discharging predicted power of the electric vehicles in the range of jurisdiction by each aggregator; s3, taking the lowest electricity cost of a user and the lowest load fluctuation as an optimal scheduling target, and constructing an electric vehicle optimal scheduling model by considering the influence of user demand constraint, equipment operation constraint and time-of-use electricity price to obtain a total electric vehicle scheduling plan; on the basis of grouping the electric automobiles, distributing an optimized dispatching plan according to the proportion of the predicted power of each aggregator in the total predicted power; the electric vehicle dispatching method considering participation of load aggregators in the grouping optimization mode can conveniently and rapidly group electric vehicles, further effectively reduce user cost, stabilize net load fluctuation, and formulate personalized dispatching plans for different aggregators so as to provide reference for dispatching plans of dispatching centers.
Drawings
Fig. 1 is a geographical distribution scatter diagram of an electric vehicle charging station.
Fig. 2 is a schematic diagram of a clustering result.
FIG. 3 is a schematic diagram of a typical daily load profile.
Fig. 4 is a schematic diagram of a total load optimization scheduling result.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
In order to better understand the technical solution of the present invention, the following detailed description is given by way of specific examples.
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The following description of the technical solutions in the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention relates to an electric automobile dispatching method considering participation of load aggregators in a grouping optimization mode, which comprises the following steps:
s1, simulating the position of an electric vehicle charging station according to the local population distribution condition, and drawing a charging station distribution scatter diagram;
s2, binding the electric vehicles with the electric vehicle charging stations by utilizing the characteristic that the charging and discharging behaviors of the electric vehicles are stable in a certain area on the basis of the step S1, converting the grouping management of the scattered electric vehicles into the grouping management of the electric vehicle charging stations with fixed positions, clustering the electric vehicles in groups by adopting a k-means method, distributing the clustered electric vehicles to each aggregator for management, and providing the charging and discharging predicted power of the electric vehicles in the range of jurisdiction by each aggregator;
s3, taking the lowest electricity cost of a user and the lowest load fluctuation as an optimal scheduling target, and constructing an electric vehicle optimal scheduling model by considering the influence of user demand constraint, equipment operation constraint and time-of-use electricity price to obtain a total electric vehicle scheduling plan; on the basis of grouping the electric vehicles, the optimized dispatching plans are distributed according to the proportion of the predicted power of each aggregator in the total predicted power.
The distribution of the charging and discharging behaviors of the electric vehicle in space and time has strong dispersity, the scale is increased increasingly, centralized management and scheduling are difficult, but the position of the charging station of the electric vehicle has fixity. In actual life, it can be found that in a certain area, the charging and discharging behaviors of the electric vehicle user have stability, namely, the user is used to charge the electric vehicle at a fixed charging station, and the electric vehicle charging station can be bound by utilizing the characteristics, so that the grouping management of the electric vehicles with different dispersion is converted into the grouping management of the electric vehicle charging stations with fixed positions.
The K-means clustering algorithm takes the distance as a clustering judgment standard, considers that the smaller the distance of the clustered objects is, the more similar the distance is, and finally forms a mutually exclusive cluster formed by the objects with small distances. At present, the method is mainly used for extracting a scene of typical wind and light output in a power system, and because the method can objectively distribute objects to each cluster according to the distance and accords with the clustering requirement of the electric automobile, the invention clusters the electric automobile by adopting a k-means method.
The K-means algorithm first sets N central values, each representing a cluster. In the process of continuous iteration, other objects are clustered, and each object is clustered to the cluster closest to the object.
Further, the step S2 includes clustering electric vehicles by k-means method, and the specific implementation steps are as follows:
1) Determining sample set charging pile x i (1 is equal to or more than i is equal to or less than N) and the number N of clustering centers; randomly selecting N initial values from the available sample set as a cluster center c r (R), wherein k=1, 2,.. and determining the iteration times R;
2) Calculating the distance between each charging pile and the center of each cluster in the sample set, and distributing the data object to the cluster U closest to the center k ；
3) For each cluster, the cluster center is recalculated, the calculation formula is as follows,
calculating the cluster judgment standard function as
4) Judging whether the D is converged or not, and if so, ending iteration; otherwise, returning to the step 2).
As a user side flexible resource with increasingly large scale, the scheduling difficulty of the electric automobile is also increasing. Since the load changes in real time affect the supply and demand relationship of electric energy and thus the electricity price, it is necessary to consider the role of dynamic time-sharing electricity price in scheduling. In addition, the charging and discharging behaviors of the electric vehicles in the respective controlled ranges of the load aggregators have certain specificity, and the scheduling tasks are distributed by combining the specificity and the proportion of the aggregators in the total amount, so that the execution of the total scheduling plan can be more accurate.
Further, the concrete implementation model of the step S3 includes:
s31, establishing a charging and discharging model of the electric automobile
Electric automobile charge-discharge constraint:
wherein p is i,t Charging and discharging power at t moment of ith electric automobile, P char And P dis Respectively the maximum value of charging power and discharging power of the electric automobile, N is the total amount of the electric automobile, N V2G In order to accept the total amount of electric vehicles scheduled,and->Respectively representing the charge and discharge start time and the charge and discharge end time of the ith electric automobile;
user satisfaction and electric vehicle SOC constraints:
in the method, in the process of the invention,for the initial electric quantity of the ith electric automobile, delta t is the charge-discharge duration, eta is the charge-discharge efficiency,/>Expected electric quantity for ith electric automobile and SOC i,t And SOC (System on chip) i,t-1 The charge states of the ith electric automobile at the moment t and the moment t-1 respectively,maximum capacity of the ith electric automobile;
s32, establishing a time-of-use electricity price model based on real-time load change
C(P t )＝α+bP t
Wherein P is t The total load at the moment t is given by,for the base load at time t, < >>For all electric vehicles, the power is totally scheduled at the t moment, C (P t ) For real-time electricity price, a and b are intercept and slope after linearization processing is carried out on the function relationship between the electricity price and the load by utilizing Taylor expansion;
s33, establishing a scheduling plan distribution model of each load aggregator
In the method, in the process of the invention,scheduling power for kth aggregator t time plan, P k,t Predicting power for the kth aggregation quotient t moment, wherein M is the aggregation quotient quantity;
s34, on the basis of considering the degradation loss cost of the battery of the electric automobile, constructing a scheduling model by taking the total cost of electricity consumption of the user of the electric automobile, the peak-valley difference of the comprehensive load and the minimum fluctuation of the comprehensive load as targets
(1) Total cost of electric automobile user is minimum
Wherein T is a scheduling period;
(2) Minimum total load fluctuation
Wherein P is av Average load in a scheduling period;
s35, single-target processing for multi-target optimization
Converting the multiple targets into single targets by adopting a linear weighting method:
wherein omega is 1 、ω 2 For each target weight, the corner mark B represents each target amount initial value.
The invention is further illustrated with specific examples.
And selecting certain local data as simulation example input data. The population of the area is 16 ten thousand, and the occupied area is about 41km 2 In order to better embody the effectiveness of the model built by the invention, the invention assumes that the ground lead achieves the aim of 2:1 of vehicle-to-vehicle pile ratio, the occupation amount of electric vehicles per person is 0.05 vehicle/person, the area has 200 distributed electric vehicle charging stations, each charging station comprises 20 charging piles on average, and 4000 charging piles supply power for 8000 electric vehicles.
Simulating a geographical distribution scatter diagram of the electric vehicle charging station according to the regional population distribution as shown in fig. 1; because the charging positions of the electric vehicles of the users are relatively fixed in the actual process, the clustering distribution of the electric vehicles according to the clustering method provided by the invention can be equivalent to the clustering distribution of the electric vehicle charging stations to electric vehicle aggregators, and the clustering result is shown in figure 2.
As can be seen from fig. 2, the clustering model sorts the disordered charging stations into four aggregates, the obtained clustering result is accurate to each charging station, and a scheduling range is allocated to each aggregator, so that not only is the scheduling accuracy improved, but also the load scheduling difficulty of the electric automobile is reduced.
The present invention was simulated based on the data of the typical daily load of the day 2022, which is shown in fig. 3. And inquiring related data, and combining the actual investigation data to obtain simulation model parameters as shown in the following table.
TABLE 1 simulation model part parameters
The typical daily load data and the parameters shown in the table are brought into the model for simulation calculation, and the obtained total optimal scheduling result is shown in fig. 4.
As can be seen from the graph, in the first load peak period, namely 9:00 to 12:00, the electric automobile responds to the scheduling command to discharge the power grid, and the net load in each period of the load peak period is reduced to different degrees; in the load valley period, namely 13:00 to 18:00, the electric automobile responds to the scheduling instruction to charge, so that the electricity consumption of the load valley period is improved; in the second load peak period, namely 20:00 to 23:00, the electric automobile responds to the dispatching instruction to discharge, so that the load peak value is reduced, but the electric automobile is limited by the charge and discharge requirements of users, the dispatching instruction is blocked from being executed, the peak clipping potential of the electric automobile cannot be fully exerted, and the electric automobile is consistent with the actual dispatching condition. The quantification of the optimization results of the user cost and the total load before and after the optimization scheduling is shown in the following table:
table 2 comparison of data before and after example optimization
| Optimizing pre-scheduling | After optimizing the dispatching | Optimization rate | |
| Total cost of user | 108000 yuan | 67327.2 yuan | 37.66％ |
| Variance of load fluctuation | 3728.87 | 3106.60 | 16.69％ |
The strategy provided by the invention can save 37.66% of cost for users, and simultaneously, the load fluctuation is reduced by 16.69%.
Regarding carbon emission reduction, taking an electric vehicle with a range of 350km for example, a daily driving range of about 60km, the daily power consumption is calculated according to the range and the battery capacity of the vehicle. The currently accepted electric automobile consumes about 15 degrees of electricity per hundred kilometers, namely about 0.15 degree of electricity per kilometer, and the average one-day driving mileage of the electric automobile is assumed to be 60km, the electricity consumption is about 9 degrees, and the electricity consumption is controlled according to the 2022 electric network carbon emission factor 0.5810tCO 2 According to the calculation of/Mwh, the carbon emission can be reduced by about 62.272t, and the carbon emission reduction benefit is obvious.
In summary, the rapid development of electric vehicles brings impact to the power grid, and the unordered charge and discharge of the electric vehicles can increase peak-valley differences of regional loads, so that the power supply pressure of the power grid is increased. Aiming at the problems that the quantity of electric vehicles is gradually increased, disorder and disorder electric vehicles are difficult to uniformly manage and schedule, and the accuracy and rationality of scheduling instructions are difficult to guarantee, the invention provides an electric vehicle grouping optimization scheduling method and establishes a related model. Taking a place related data as an example, clustering and grouping the electric vehicles, and generating a total dispatching result and dispatching instructions of all aggregations by combining the local typical daily load data. The conclusion is as follows:
1. by utilizing the stability of the charging and discharging behaviors of the electric vehicles in a certain area, the electric vehicles are distributed to each electric vehicle load aggregator to perform unified management and scheduling by using a K-means clustering method, the problems that the data requirements are large and the data are difficult to acquire in time in the conventional clustering method are avoided, and the electric vehicle load scheduling difficulty is effectively reduced.
2. The electric automobile scheduling model built by the invention can reduce the user cost, stabilize the net load fluctuation, improve the net load curve, allocate the scheduling plan according to the predicted load data of each aggregator, improve the accuracy of the execution of the scheduling plan and provide reference for the formulation and allocation of the electric automobile scheduling plan while meeting the user demand.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 foregoing embodiments are only for illustrating the present invention, wherein the structures, connection modes, manufacturing processes, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solutions of the present invention should not be excluded from the protection scope of the present invention.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims.
Claims (3)
1. The electric automobile dispatching method considering participation of load aggregators in the grouping optimization mode is characterized by comprising the following steps:
s1, simulating the position of an electric vehicle charging station according to the local population distribution condition, and drawing a charging station distribution scatter diagram;
s2, binding the electric vehicles with the electric vehicle charging stations by utilizing the characteristic that the charging and discharging behaviors of the electric vehicles are stable in a certain area on the basis of the step S1, converting the grouping management of the scattered electric vehicles into the grouping management of the electric vehicle charging stations with fixed positions, clustering the electric vehicles in groups by adopting a k-means method, distributing the clustered electric vehicles to each aggregator for management, and providing the charging and discharging predicted power of the electric vehicles in the range of jurisdiction by each aggregator;
s3, taking the lowest electricity cost of a user and the lowest load fluctuation as an optimal scheduling target, and constructing an electric vehicle optimal scheduling model by considering the influence of user demand constraint, equipment operation constraint and time-of-use electricity price to obtain a total electric vehicle scheduling plan; on the basis of grouping the electric vehicles, the optimized dispatching plans are distributed according to the proportion of the predicted power of each aggregator in the total predicted power.
2. The electric vehicle dispatching method considering participation of load aggregators in a clustering optimization mode as claimed in claim 1, wherein the step S2 comprises clustering electric vehicles in a clustering manner by adopting a k-means method, and the specific implementation steps are as follows:
1) Determining sample set charging pile x i (1 is equal to or more than i is equal to or less than N) and the number N of clustering centers; randomly selecting N initial values from the available sample set as a cluster center c r (R), wherein k=1, 2,.. and determining the iteration times R;
2) Calculating the distance between each charging pile and the center of each cluster in the sample set, and distributing the data object to the cluster U closest to the center k ；
3) For each cluster, the cluster center is recalculated, the calculation formula is as follows,
calculating the cluster judgment standard function as
4) Judging whether the D is converged or not, and if so, ending iteration; otherwise, returning to the step 2).
3. The electric vehicle dispatching method considering participation of load aggregators in the grouping optimization mode as claimed in claim 1, wherein the specific implementation model of the step S3 comprises:
s31, establishing a charging and discharging model of the electric automobile
Electric automobile charge-discharge constraint:
wherein p is i,t Charging and discharging power at t moment of ith electric automobile, P char And P dis Respectively the maximum value of charging power and discharging power of the electric automobile, N is the total amount of the electric automobile, N V2G In order to accept the total amount of electric vehicles scheduled,and->Respectively representing the charge and discharge start time and the charge and discharge end time of the ith electric automobile;
user satisfaction and electric vehicle SOC constraints:
in the method, in the process of the invention,for the initial electric quantity of the ith electric automobile, delta t is the charge-discharge duration, eta is the charge-discharge efficiency,/>Expected electric quantity for ith electric automobile and SOC i,t And SOC (System on chip) i,t-1 The charge states of the ith electric automobile at the moment t and the moment t-1 are respectively +.>Maximum capacity of the ith electric automobile;
s32, establishing a time-of-use electricity price model based on real-time load change
C(P t )＝a+bP t
Wherein P is t The total load at the moment t is given by,for the base load at time t, < >>For all electric vehicles, the power is totally scheduled at the t moment, C (P t ) For real-time electricity price, a and b are intercept and slope after linearization processing is carried out on the function relationship between the electricity price and the load by utilizing Taylor expansion;
s33, establishing a scheduling plan distribution model of each load aggregator
In the method, in the process of the invention,scheduling power for kth aggregator t time plan, P k,t Predicting power for the kth aggregation quotient t moment, wherein M is the aggregation quotient quantity;
s34, on the basis of considering the degradation loss cost of the battery of the electric automobile, constructing a scheduling model by taking the total cost of electricity consumption of the user of the electric automobile, the peak-valley difference of the comprehensive load and the minimum fluctuation of the comprehensive load as targets
(1) Total cost of electric automobile user is minimum
Wherein T is a scheduling period;
(2) Minimum total load fluctuation
Wherein P is av Average load in a scheduling period;
s35, single-target processing for multi-target optimization
Converting the multiple targets into single targets by adopting a linear weighting method:
wherein omega is 1 、ω 2 For each target weight, the corner mark B represents each target amount initial value.
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