CN114039370B - Electric automobile and intelligent charging and discharging station resource optimization method based on V2G mode - Google Patents
Electric automobile and intelligent charging and discharging station resource optimization method based on V2G mode Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a resource optimization method of an electric automobile and an intelligent charging and discharging station based on a V2G mode, which comprises the following steps: the method comprises the steps of receiving first feedback information which is triggered and reported by an electric automobile user according to a dispatching demand instruction, and obtaining second feedback information of the electric automobile corresponding to the electric automobile user; if the battery charge state expected by a user when the vehicle leaves the network is met, an electric vehicle state matrix is constructed according to the first feedback information and the second feedback information, an electric vehicle charging and discharging station operation matrix is constructed according to the operation parameter information of all charging and discharging stations in the area, the electric vehicle state matrix is combined with the electric vehicle charging and discharging station operation matrix to respectively calculate and analyze the schedulable capacity, the user expected service station, the schedulable capacity and the user credibility, and a classification hierarchical scheduling method based on the category+K-Means and the mixed integer programming is adopted to form a vehicle pile optimal allocation scheme, so that the problem of mismatching of vehicle pile resources is solved, and the effective and full utilization of the vehicle pile double-layer resources is promoted.
Description
Technical Field
The invention relates to the technical field of V2G scheduling, in particular to a resource optimization method of an electric automobile and an intelligent charging and discharging station based on a V2G mode.
Background
The V2G (Vehicle to Grid) technology regards the electric automobile as a storage resource, and realizes the bidirectional interaction between the electric automobile and a power grid. The large-scale electric automobile participates in the power grid operation in the role of the distributed energy storage unit, and on the premise of meeting the basic running requirement of an electric automobile user, when the power grid needs to adjust the power grid load, the electric automobile can realize energy conversion (charge and discharge) with the power grid, so that the buffer is provided for power generation of the power grid and renewable energy sources, the permeability of the renewable energy sources is improved, the load fluctuation of the power grid is reduced, and the comprehensive operation efficiency of the power grid is improved.
Most of the prior V2G scheduling technologies accept scheduling at any time and any place by default users, but the randomness of the users participating in the V2G scheduling causes that the availability and the actual availability degree of energy storage resources of an electric automobile are difficult to grasp, and in the V2G scheduling planning, charge and discharge piles are important carriers for connection between the electric automobile and the electric network and are influenced by environmental resources, and the arrangement of charge and discharge facilities has regional differences, unbalanced distribution and different scales. Therefore, the user of the electric automobile participates in the V2G dispatching, the service will of the user and the distribution problem of the charging and discharging piles are comprehensively considered, and the problem that the resources of the vehicle and the charging and discharging piles are not fully utilized is avoided.
Disclosure of Invention
Aiming at the problems, the invention provides a resource optimization method of an electric automobile and an intelligent charging and discharging station based on a V2G mode, which aims to optimize allocation of automobile pile resources, improve the schedulability of a vehicle for rapidly responding to a power grid instruction, improve the executability of a V2G vehicle charging and discharging scheduling plan, achieve the effect of predicting peak regulation and frequency modulation, and solve the problems of mismatching automobile pile resources, low response degree and the like in the scheduling process.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a resource optimization method of an electric automobile and an intelligent charging and discharging station based on a V2G mode comprises the following steps:
the method comprises the steps of receiving first feedback information which is triggered and reported by an electric automobile user according to a scheduling demand instruction, wherein the first feedback information comprises expected SOC, scheduled participating service time and scheduled off-grid time, and obtaining second feedback information of the electric automobile corresponding to the electric automobile user, and the second feedback information comprises vehicle departure place position information, current SOC and vehicle state parameters;
if the electric automobile meets the battery charge state expected by a user when the electric automobile leaves the network in the planned residence time, an electric automobile state matrix is constructed according to the first feedback information and the second feedback information, an electric automobile charging and discharging station operation matrix is constructed according to the operation parameter information of all charging and discharging stations in the area, the electric automobile state matrix is combined with the electric automobile charging and discharging station operation matrix to respectively calculate and analyze the schedulable capacity, the user expected service station, the schedulable capacity and the user credibility, and a classification hierarchical scheduling method based on Canopy+K-Means and mixed integer programming is adopted to form a vehicle pile optimal allocation scheme between the electric automobile and the charging and discharging stations, and allocation service station information contained in the vehicle pile optimal allocation scheme is fed back to the electric automobile user.
The beneficial effects of the invention are as follows: by establishing the optimized allocation model of the pile resources of the vehicle, the problem that the energy storage resources of the electric vehicle and the pile resources of the vehicle are not matched due to the randomness of the decision of the user is solved, the problems of 'whether the vehicle exists a pile or not and whether the vehicle exists a pile or not' in the V2G scheduling process are solved, and the double-layer resources of the vehicle pile are promoted to be effectively and fully utilized.
Drawings
Fig. 1 is a flow chart of a resource optimization method of an electric automobile and an intelligent charging and discharging station based on a V2G mode, which is disclosed in the embodiment of the invention;
FIG. 2 is a hierarchical model of a user's desired service site;
FIG. 3 is a first level clustering plan based on Canopy+K-Means;
FIG. 4 is a first-level clustering result of Canopy;
FIG. 5 is a first-order clustering result of K-Means;
FIG. 6 is a graph of projected power match variation simulating 100 scheduled workdays;
FIG. 7 is a graph modeling actual power match changes for 100 scheduled workdays;
FIG. 8 is a graph modeling total cost change for a system of 100 scheduled workdays;
fig. 9 is a graph of electric vehicle participation service reliability variation simulating 100 scheduled workdays.
Detailed Description
The present invention will be described in further detail with reference to the drawings and the detailed description below, in order to make the objects, technical solutions and advantages of the present invention more clear and distinct. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the matters related to the present invention are shown in the accompanying drawings.
As shown in fig. 1, this embodiment provides a resource optimization method for an electric vehicle and an intelligent charging and discharging station based on a V2G mode, including:
when the power grid needs to adjust the power grid load, a power grid dispatching center sends a demand instruction, the power grid dispatching center receives first feedback information which is triggered and reported by an electric vehicle user according to the dispatching demand instruction, the first feedback information comprises expected SOC, expected participation dispatching service time and planned off-grid time, second feedback information of the electric vehicle corresponding to the electric vehicle user is obtained, and the second feedback information comprises vehicle departure place position information, current SOC and vehicle state parameters;
if the electric automobile meets the battery charge state expected by a user when the electric automobile leaves the network in the planned residence time, an electric automobile state matrix is constructed according to the first feedback information and the second feedback information, an electric automobile charging and discharging station operation matrix is constructed according to the operation parameter information of all charging and discharging stations in the area, the electric automobile state matrix is combined with the electric automobile charging and discharging station operation matrix to respectively carry out comprehensive calculation and analysis on the schedulable capacity, the user expected service station, the schedulable capacity and the user reliability, a classification hierarchical scheduling method based on the Canopy+K-Means and the mixed integer programming is adopted to form a vehicle pile optimal allocation scheme between the electric automobile and the charging and discharging stations, and allocation service station information contained in the vehicle pile optimal allocation scheme is fed back to the electric automobile user.
The calculation method of the state matrix of the electric automobile comprises the following steps:
aiming at different electric vehicles, to embody fixed parameters, charge and discharge states of the electric vehicles and master expected information reported by users, a state matrix EV of the electric vehicles in a t period is defined i As shown in formula (1):
in the formula ,EVi The state matrix of the electric automobile in the t period is that I is the number of vehicles which are intentionally involved in the V2G scheduling service, and ID EV Numbering the electric automobile; cap is the rated battery capacity of the electric automobile; l is the rated endurance mileage of the electric automobile; b is the default rate of the electric automobile; var is the service time variance of the electric automobile; x and Y are two-dimensional geographic coordinates of the departure place of the electric automobile; SOC (State of Charge) a The current state of charge of the battery of the electric automobile; SOC (State of Charge) b The method comprises the steps of (1) expecting a expected battery charge state of the electric automobile after participating in service; t (T) a The time that can participate in the scheduling is predicted for the electric automobile; t (T) b The method is a planned off-grid time of the electric automobile.
To more accurately grasp the near real-time state of the vehicle, the daily schedule time is divided into 96 time periods, and assuming that the residence time of the electric vehicle is 15min as one time period and the state (participation or non-participation) of the electric vehicle in the schedule service is not changed in any time period, the duration of the planned grid connection of the vehicle is t=t b -T a . The system will determine if the electric vehicle can meet the battery state of charge expected by the user when the vehicle is off-grid within the planned residence time,
wherein ,PC 、P D Charging and discharging power of the electric automobile respectively; GD is the current demand of the power grid, gd=0 indicates that the power grid needs higher power load, electric vehicles are required to participate in charging service, gd=1 indicates that the power grid needs to reduce current power load, and electric vehicles are required to participate in discharging service. If the user expectations can be met, a state matrix is generated, and the electric automobile is brought into an alternative list; if the information cannot be met, the system returns the information of the electric automobile user to report the scheduling participation again until the requirement is met or the scheduling participation is abandoned.
The calculation method of the operation matrix of the charging and discharging station of the electric automobile comprises the following steps:
in the formula ,CSj An operation matrix of the electric automobile charging and discharging stations J in the t period is provided, J is the number of the electric automobile charging and discharging stations in the dispatching area,numbering the charging and discharging stations of the electric automobile; loc j Two-dimensional geographic coordinates of a charging and discharging station of the electric automobile; j->The number of charging and discharging piles in the j-th electric automobile charging and discharging station; q (Q) j The method comprises the steps that the electric quantity required by a jth electric automobile charging and discharging station for adjusting the electric load of a power grid in a t-th period is obtained; price i，j And the price that the ith vehicle participates in the charging or discharging at the jth charging and discharging station in the t period is set.
According to the power grid dispatching instruction demand, when the power grid needs higher power load, the electric automobile participating in dispatching service is added into the power grid for charging; when the current power load of the power grid needs to be reduced, the electric automobile participating in the dispatching service discharges to the power grid. The schedulable capacity of the electric automobile is used for representing that the electric automobile allows the power grid and the electric automobile to carry out energy dynamic bidirectional balance in real time on the premise that the basic requirements of users are met, and the capacity state and the charging and discharging behaviors of the schedulable electric automobile are accepted to the greatest extent. The capacity can be classified into a schedulable charge capacity and a schedulable discharge capacity as shown in formula (4):
the calculation method of the schedulable capacity comprises the following steps:
in the formula ,Ei The capacity of the ith electric automobile which can be scheduled when participating in the scheduling service at the time is set; η (eta) C 、η D Charging and discharging efficiencies of the electric automobile respectively; GD is the current demand of the power grid; when gd=0, the electric network needs the electric automobile to participate in chargingWhen in service, the schedulable capacity is the schedulable charging capacity, gd=0 indicates that the power grid needs to increase the power load, and gd=1 indicates that the power grid needs to decrease the power load. Otherwise, the electric network does not need the electric automobile to participate in the charge and discharge service, and the schedulable capacity is 0. The resulting schedulable capacity provides a quantitative assessment of the capacity of each electric car for the assignment model. Furthermore, the time when the power grid sends out the dispatching instruction can be current, can also be several hours before the power grid needs or the day before,
the electric automobile user expects the service site to adopt the analytic hierarchy process to consider the electricity price, distance cost and time cost of the electric automobile participating in the dispatching service, the three factors are taken as a criterion layer, each charge and discharge station in the dispatching area is taken as a scheme layer to establish an analytic hierarchy model, and an improved three-scale analytic hierarchy process is adopted to construct a judgment matrix.
The screening method of the user expected service site comprises the following steps: taking the electricity price, distance cost and time cost of the electric automobile participating in the scheduling service as a criterion layer, taking each charge and discharge station in a scheduling area as a scheme layer, and establishing a first analytic hierarchy process model;
for n elements of the same layer, an intermediate judgment matrix C is obtained:
the judgment matrix here uses capital letter C, which is intended to describe how elements in the judgment matrix of the improved analytic hierarchy model are constructed, and is not actually referred to, but can be changed to other letter symbols.
wherein ,
and has C p，p =1, characterizing the element itself to be of the same importance, and then calculating the importance ranking index r of the element i ，
Taking outb m ＝r max /r min Finally, the elements of the intermediate judgment matrix C are obtained, and a final judgment matrix B is constructed:
wherein ,
electric Price in electric automobile user expected service station adopts Price in electric automobile charging and discharging station parameter matrix i，j The method comprises the steps of carrying out a first treatment on the surface of the The distance cost of the electric automobile is the Euclidean distance from the electric automobile i to the charging and discharging station j, namelyThe time cost of the electric automobile considers two parts, namely, the time cost of the electric automobile i to the charging and discharging station j on the way is considered, and the time compensation of the consumed electric quantity on the way is considered, namely, the time cost of a user expected service station is as follows:
wherein V is the average running speed of the electric automobile, and P C 、P D Respectively charging and discharging power of electric automobile, D i，j The Euclidean distance from the electric automobile i to the charging and discharging station j;
the time cost is calculated to obtain the grade of the charging and discharging station of the J electric automobile in the ith electric automobile, the charging and discharging station of the phi position before the grade is taken as a user expected service station of the electric automobile i in the dispatching service, and indexes of the user side are provided for model evaluation, wherein phi is more than or equal to 1, (e.g. phi=1, 2, … J; phi is less than or equal to J).
The calculation method of the schedulable capacity comprises the following steps:
with the battery capacity Cap and the current battery state of charge SOC of the electric automobile a And planning the duration T of the power grid access as a criterion layer, taking the electric automobile responding to the service as a scheme layer, and establishing a second analytic hierarchy model,
normalizing the index values of the criterion layer:
wherein ,gmax and gmin Respectively the maximum value and the minimum value of the data g; for forward or reverse index, g * Is the minimum or maximum value of data g; g is the normalized value of the data. The battery capacity and the planned access duration of the electric automobile are both forward indicators. When the electric network needs the electric automobile to participate in discharging service, the current charge state of the electric automobile is a forward index; when the electric network needs the electric automobile to participate in charging service, the current state of charge of the electric automobile is a reverse index. The analytic hierarchy process gives a value KWIght of the schedulable capacity of each electric automobile, and the electric automobiles are clustered and divided in a first-level clustering.
The method for calculating the user confidence level comprises the following steps:
the difference of the using habit of the electric automobile user, the enthusiasm for participating in the dispatching service and the like leads to the difference of the reliability of the electric automobile when participating in the dispatching service, and in order to measure and screen the electric automobile which is more suitable for participating in the dispatching service and has stronger reliability, the Credit Credit is established for the electric automobile user i . The user credibility of the electric automobile is based on historical service data of the electric automobile participating in the dispatching service, the model stores data information of the distributed electric automobile participating in the dispatching service after completing distribution each time and the electric automobile is actually connected with the network and disconnected from the network, and the data information has the following format:
(f，23，20210430，{′Break′：0，′RST′：36，′RET′：72，′ST′：34，′ET′：72})
wherein f is a data reading pointer; 23 is the number of the electric automobile; 20210430 is the time of the present dispatch service; break indicates whether the allocated electric automobile participates in the scheduling service, 0 indicates that the electric automobile participates in the service, and 1 indicates that the service is canceled; 'ST', 'ET' respectively represent planned grid-connected and grid-disconnected time reported by the electric automobile user, namely T a and Tb The method comprises the steps of carrying out a first treatment on the surface of the RST and RET respectively represent actual grid-connected and grid-disconnected time of the electric automobile and are marked as T a′ and Tb ′。
The user credibility of the electric automobile considers three factors of the number of times of default, the difference of historical time length and the responsiveness of the electric automobile according to the historical service data of the electric automobile, and the weight omega= [0.637,0.258,0.105] is calculated according to the importance degree of the number of times of default, the difference of historical time length and the responsiveness.
When the system is operated for the first time, as all electric vehicles have no history service data record, the credit of all electric vehicles is set to be 1, and after one-time dispatch service, the credit which does not participate in the overstock service and is added into the electric vehicles newly is designated as the average value of the credit of the electric vehicles which adopts the existing history service data currently;
for the electric automobile i, K (K is more than or equal to 1) historical service data records are added, and the number of violations is Breaktime i Represented as
When k=break time, i.e. when the electric vehicle has been in default of each time of participation in the scheduling service before, the historical time length difference of the electric vehicle is set to 0, and when K is not equal to break time, the historical time length difference is expressed as
Responsivity Response of electric automobile participating in dispatch service i Represented as
When the SysDay is the number of days of system operation and the number of times of default, the difference of historical time length and the responsivity are normalized, the number of times of default and the difference of historical time length are reverse indexes, the responsivity is a forward index, the weight is calculated according to the number of times of default, the difference of historical time length and the importance degree of responsivity, and the user credibility is expressed as follows
Wherein, superscript indicates the value after normalization treatment, ω 1 、ω 2 and ω3 Weights for the number of violations, the historical time difference, and the responsiveness are represented, respectively. The reliability of the electric automobile user reflects the habit of the electric automobile user and the enthusiasm of the electric automobile user for participating in dispatch service, and data support is provided for the credit priority strategy.
The classification level scheduling method comprises constructing a first-level clustering model and a second-level assignment model,
the large-scale electric automobile has strong randomness and dispersibility, when the large-scale electric automobile enters the V2G scheduling system, if a mixed integer linear programming model is distributed according to a traditional automobile pile, the dynamic large data of the automobile is difficult to process in a short time, the optimal solution of the model is obtained, and the solution difficulty rises exponentially. Therefore, the proposed classification hierarchical scheduling mode aims to quickly generate a vehicle pile allocation scheme after a large-scale electric vehicle enters a scheduling system and solve the dimension disaster caused by model operation.
The K-Means clustering algorithm is a prototype-based, partitioned distance technique that attempts to find a user-specified number K of clusters, but the K-value of K-Means needs to be pre-specified, different settings for the K-value can cause a change in the result, and K-Means is very sensitive to outliers. The Canopy cluster does not need to specify a k value in advance, and has a great advantage in speed compared with other clustering algorithms, although the Canopy cluster is lower in accuracy.
Thus, the construction of the first-level cluster includes: coarse clustering is firstly carried out on the first feedback information and the second feedback information by using a Canopy clustering algorithm to obtain a K value and a central point corresponding to the K value, then fine clustering is carried out on the first feedback information and the second feedback information by using a K-Means clustering algorithm, and Euclidean distance D from the electric automobile i to the charging and discharging station j calculated in the K-Means clustering algorithm i，j And the distance weight of the charging and discharging station is 1, and the schedulable capacity is used as the distance weight of the electric automobile, so that a first-stage optimization scheme is formed. The large-scale electric automobile data is divided into a plurality of parts, and early preparation is made for second-stage fine scheduling.
In the first-level clustering, a clustering result comprising the electric automobile and the electric automobile charging and discharging station is obtained. For each cluster, a plurality of charging and discharging stations and a large number of electric vehicles are generally contained, and different charging and discharging stations have different numbers of charging and discharging piles. The second-stage assignment model solves the problem of distribution of a single electric vehicle to an electric vehicle charging and discharging station.
x i，j (i=1, 2, …, I; j=1, 2, …, J) indicates whether the ith electric vehicle is assigned to the jth electric vehicle charging/discharging station
EVCost i，j (t) (i=1, 2, …, I; j=1, 2, …, J) represents the cost consumption of the ith electric vehicle in the t period if going to the jth electric vehicle charging and discharging station, all EVCost i，j (t) the cost matrix [ EVCost ] of the electric vehicle and each electric vehicle charging and discharging station participating in the dispatching service in the t period is formed i，j (t)]；
Q j And (t) represents the scheduled electric quantity required by the jth electric automobile charging and discharging station in the t-th period.
The overall goal is to consider three aspects, namely, the overall dispatching allocation cost is minimum, the requirements of the charging and discharging stations of the electric automobile are met maximally, and the reliability of users of the electric automobile participating in dispatching is maximized.
Thus, the construction of the second level assignment model includes: by establishing a total target min F, a second-stage optimization scheme is formed
Wherein EVCost i，j (t) (i=1, 2, …, I; j=1, 2, …, J) represents the cost consumption of the ith electric vehicle in the t period if going to the jth electric vehicle charging and discharging station, x i，j Indicating whether the ith electric car is allocated to the jth electric car charging and discharging station, Q j And (t) represents the scheduled electric quantity required by the jth electric automobile charging and discharging station in the t-th period.
Setting constraint conditions of the overall targets as
wherein ,A1 Is a column vector with the size of I multiplied by 1 elements being 1, A 1 ＝[1，1，…，1] T ；A 2 Is a row vector with the size of 1 XJ elements being 1, A 2 ＝[1，1，…，1]；P j Is the j-th electric automobile charging and discharging deviceThe number of charging and discharging piles in the power station. The constraint condition (1) enables each electric automobile to only participate in service from one electric automobile charging and discharging station at the same time; constraint conditions (2) ensure that the number of electric vehicles received by each electric vehicle charging and discharging station does not exceed the number of charging and discharging piles owned by the electric vehicles; constraint (3) allows the demand of each charging station for the dispatchable capacity of the electric vehicle to be satisfied.
Further, the method also comprises the step of constructing an evaluation index of a second-level optimization scheme:
the evaluation indexes comprise expected service station matching degree, total cost of dispatching service, planned power matching degree of an electric vehicle charging and discharging station, actual power matching degree of the electric vehicle charging and discharging station and reliability of electric vehicle participation service, and if the second-level optimization scheme meets all the evaluation indexes, the second-level optimization scheme is output to a power grid dispatching center.
According to the invention, by establishing the optimized allocation model of the pile resources of the vehicle, the problem that the energy storage resources of the electric vehicle are uncertain due to the randomness of the decision of the user is solved, the problem of 'whether the vehicle exists or not and whether the vehicle exists or not' in the V2G scheduling process is solved, and the double-layer resources of the vehicle pile are effectively and fully utilized.
The calculation method of the expected service site matching degree comprises the following steps:
the matching degree of the expected service site reflects the satisfaction degree of the expected service site for the electric automobile user, and the higher the matching degree is, the higher the user satisfaction degree is, but the expected matching for the user cannot be completely achieved by considering factors such as cost, power grid demand and the like. The formula for the desired serving site match α is as follows:
a is the total number of electric vehicles actually participating in the service of the scheduling service; b is the number of electric vehicles of which the actual service sites and the expected service sites are matched; alpha is the matching degree of the expected service site;
the total cost calculation method of the scheduling service comprises the following steps:
the cost of the dispatching service considers the distance cost, time cost and the like of the user side participating in the dispatching service, and also can consider the cost and the like of the electric network side for attracting the electric automobile to participate in the dispatching service. The total cost of the dispatch service is expressed as
TotalCost＝∑ i∈I ∑ j∈J EVCost i，j x i，j (21)
The calculation method of the planned power matching degree of the electric automobile charging and discharging station comprises the following steps:
the planned power matching degree beta of the electric vehicle charging and discharging station reflects the condition that the power demand of the electric vehicle charging and discharging station is met by the distribution scheme given by the model. The planned demand satisfaction degree is 1 and is best represented; the requirement of a charging and discharging station with the value less than 1 is not fully satisfied; a value greater than 1 will result in waste of resources. The formula of the planned demand satisfaction degree beta of the electric automobile charging and discharging station is as follows:
the calculation method of the actual electric power matching degree of the electric automobile charging and discharging station comprises the following steps:
because the randomness of the electric automobile can not completely release the schedulable capacity of the electric automobile, in the actual scheduling service, the actual power demand of the electric automobile charging and discharging station can not be completely met, and the actual power matching degree gamma is smaller than or equal to 1, and the value is closer to 1 and better performs. Charging and discharging station for electric automobile. The formula of the actual power matching degree γ is as follows:
the REi is a scheduling capacity of the electric vehicle actually participating in the service in the current scheduling service, and is the same as the schedulable capacity, and is further divided into an actual charging capacity and an actual discharging capacity.
The method for calculating the reliability of the participation service of the electric automobile comprises the following steps:
the reliability theta of the electric automobile participation service is represented by the average reliability of the electric automobile participating in the service, and the higher the average reliability of the electric automobile participating in the dispatching service is, the more the electric automobile is close to various information reported by the electric automobile user in the actual service, so that the possibility that the user cancels the participation in the dispatching service or the user leaves the network in advance is reduced, and the stability and reliability of the actual dispatching service are improved. The formula of the reliability theta of the electric automobile participating in the dispatching service is as follows:
case analysis a user desired service site screening
For a certain electric automobile ID to participate in charging service EV =1, assuming 5 charging and discharging stations s 1 ，s 2 ，s 3 ，s 4 ，s 5 Alternatively, the screening is performed by taking the proposed service electricity price, distance cost and time cost as criteria. Constructing a comparison matrix for criterion layer elements
Calculating an importance ranking index r 1 ＝3，r 2 ＝5，r 3 =1, then r max ＝5，r min ＝1，b m =5/1=5, and the judgment matrix is obtained by using the formula (8) as
The eigenvector of the judgment matrix is calculated as u= [0.261,0.633,0.106 ]] T Maximum eigenvalue is lambda max = 3.039, consistency index
From the consistency check RI table, ri=0.58, consistency rate
This illustrates that the judgment matrix constructed by the criterion layer meets the consistency requirement, and the corresponding obtained feature vector is valid.
To screen out the service sites desired by the user from the 5 charge and discharge stations, it is necessary to compare the service prices, distance costs and time costs of the 5 charge and discharge stations for the electric vehicle, respectively. Calculating the distance and time costs based on the price information of each charging and discharging station and (9), as shown in Table 1
Table 1 electric car ID EV Expected site screening criteria layer value of =1
According to the principle that the higher the price or the cost is, the lower the importance of the charging and discharging station is, a comparison matrix is constructed, and the comparison matrix of the service price is
Calculating an importance ranking index r max ＝9，r min ＝1，b m The judgment matrix of the service price is 9/1=9
The judgment matrix of the distance cost and the time cost calculated by the same method is
The corresponding weight vector is obtained by calculation as
ω 1 ＝[0.035，0.188，0.068，0.521，0.188]
ω 2 ＝[0.503，0.134，0.26，0.068，0.035]
ω 3 ＝[0.503，0.134，0.26，0.035，0.068]
Through inspection, B 1 ，B 2 ，B 3 All meet the consistency requirement. Finally, calculating the user expected score of each charging and discharging station,
namely, each charging and discharging station is used for charging and discharging the ID of the electric automobile EV User desired ranking of =1: s is(s) 1 ＞s 3 ＞s 4 ＞s 2 ＞s 5 . Screening the first two service stations as service stations expected by users, namely the charging and discharging station 1 and the charging and discharging station 3 are electric vehicles iIs a desired service site of (c).
Case analysis two-electric-vehicle user confidence calculation
When a scheduling task starts, the system receives the ID of the electric automobile participating in the discharging service EV Corresponding information of=1, obtain history information of participation in scheduling service, as shown in table 2
Table 2 electric car ID EV Historical service data of =1
| Date | Break | RST | RET | ST | ET |
| 20210403 | 0 | 36 | 72 | 34 | 72 |
| 20210405 | 0 | 36 | 70 | 36 | 71 |
| 20210406 | 1 | 0 | 0 | 0 | 0 |
| 20210409 | 0 | 72 | 90 | 70 | 90 |
Calculated according to formula (11), electric automobile ID EV Number of violations of =1 is
Calculated according to the formula (12), the difference of the historical time length is
Calculated according to formula (13), the electric automobile ID EV The responsivity of the =1 participation schedule is
The value after normalization processing is carried out on the attribute values of the electric automobile responding to the dispatching service of the present time and the attribute values of other electric automobiles is thatCalculated, electric automobile ID EV The confidence level of =1 is
The reliability of other electric vehicles participating in the current dispatching can also be calculated through the calculation process, and an electric vehicle reliability set Credit= [0.737,0.635,0.719, …,0.598] of the current dispatching is formed.
First-level clustering of case analysis three-vehicle pile resource optimization model
The case is based on Python3.7 coding, and a CPLEX solver solves; computer configuration: model-PC Dell OptiPlax 7060, CPU-Intel (R) Core (TM) i7-8700, memory-3.20GHZ Memory 32GB, operating System-Windows 10 OS.
And performing first-level clustering on the electric automobile and the charging and discharging station by using a Canopy+K-Means algorithm to form a first-level optimization scheme of the V2G system scheduling, and performing coarse clustering treatment on the second-level fine scheduling. The clustering results are shown in figures 4 and 5.
Second-stage assignment model of case analysis four-vehicle pile resource optimization model
Parameter setting:
charging power P c =30kw; charging efficiency eta C =99%; discharge power P D =30kw; discharge efficiency eta D =98%; average travel speed v=50 km/h;
three different models:
1) The capacity of the Biediyuan new energy battery is 42kWh, and the endurance mileage is 305km;
2) The battery capacity of the ULAES 8 is 70kWh, and the endurance mileage is 355km;
3) Long-safety new energy battery capacity 45kWh endurance mileage 300km
[c i，j (t)]: cost matrix of electric automobile participating in dispatching service for charging and discharging station of electric automobile
E i : electric automobile schedulable container participating in scheduling service at this timeMeasuring amount
E i ＝[11.09，20.29，23.13，29.16，…，17.42，22.32]
P j : the number of charging and discharging piles in each electric automobile charging and discharging station
P j ＝[7，8，7，9，8，10，8，9，10，7，10，9]
Q j (t): scheduling electric quantity required by charging and discharging stations of electric vehicles
Q j (t)＝[46，41，34，45，48，39，48，33，40，40，50，48]
Model assignment results:
the distribution results obtained by the model calculation processing are shown in table 3.
TABLE 3 model assignment results
Five model evaluation index results of case analysis
A V2G system consisting of 100 electric vehicles and 12 electric vehicle charging and discharging stations is simulated in an area of 10km x 10km, and 100 scheduling days are performed in this case. The results of each evaluation index of the model are shown in figures 6-9.
The result shows that the planned power matching degree of the charging and discharging station of the electric automobile can reach 100% +/-1% along with the increase of the operation days of the system, and the actual power matching degree is increased from 90% to 95%. The total running cost of the system tends to be reduced, and the reliability of the electric automobile participating in the dispatching service is gradually improved.
According to the vehicle pile resource optimization allocation method, electric vehicles meeting the conditions can be rapidly allocated to the corresponding electric vehicle charging and discharging stations when large-scale electric vehicles enter the dispatching system, and accurate electric power matching is provided for the electric vehicle charging and discharging stations. The vehicle pile resource optimization allocation model considers the service wish of the electric vehicle user, reduces the cost of user service, and simultaneously reduces the total running cost of the system. The electric vehicle with strong dispatching capability and high credit degree can be screened out by the vehicle pile resource optimization distribution model, and accurate resource matching is provided for the vehicle piles so as to ensure effective implementation of a distribution plan.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the essence of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. The resource optimization method of the electric automobile and the intelligent charging and discharging station based on the V2G mode is characterized by comprising the following steps:
the method comprises the steps of receiving first feedback information which is triggered and reported by an electric automobile user according to a scheduling demand instruction, wherein the first feedback information comprises expected SOC, scheduled participating service time and scheduled off-grid time, and obtaining second feedback information of the electric automobile corresponding to the electric automobile user, and the second feedback information comprises vehicle departure place position information, current SOC and vehicle state parameters;
if the electric automobile meets the battery charge state expected by a user when the electric automobile leaves the network in the planned residence time, constructing an electric automobile state matrix according to the first feedback information and the second feedback information, constructing an electric automobile charging and discharging station operation matrix according to the operation parameter information of all charging and discharging stations in the area, respectively carrying out calculation and analysis on the schedulable capacity, the user expected service station, the schedulable capacity and the user reliability by combining the electric automobile charging and discharging station operation matrix, and forming a vehicle pile optimal allocation scheme between the electric automobile and the charging and discharging stations by adopting a classification hierarchical scheduling method based on Canopy+K-Means and mixed integer programming, and feeding back allocation service station information contained in the vehicle pile optimal allocation scheme to the electric automobile user;
the calculation method of the state matrix of the electric automobile comprises the following steps:
in the formula ,EVi The state matrix of the electric automobile in the t period is that I is the number of vehicles which are intentionally involved in the V2G scheduling service, and ID EV Numbering the electric automobile; cap is the rated battery capacity of the electric automobile; l is the rated endurance mileage of the electric automobile; b is the default rate of the electric automobile; var is the service time variance of the electric automobile; x and Y are two-dimensional geographic coordinates of the departure place of the electric automobile; SOC (State of Charge) a The current state of charge of the battery of the electric automobile; SOC (State of Charge) b The method comprises the steps of (1) expecting a expected battery charge state of the electric automobile after participating in service; t (T) a The time that can participate in the scheduling is predicted for the electric automobile; t (T) b The planned off-grid time of the electric automobile is;
the calculation method of the operation matrix of the charging and discharging station of the electric automobile comprises the following steps:
in the formula ,CSj An operation matrix of the electric automobile charging and discharging stations J in the t period is provided, J is the number of the electric automobile charging and discharging stations in the dispatching area,numbering the charging and discharging stations of the electric automobile; loc j Two-dimensional geographic coordinates of a charging and discharging station of the electric automobile;the number of charging and discharging piles in the j-th electric automobile charging and discharging station; q (Q) j The method comprises the steps that the electric quantity required by a jth electric automobile charging and discharging station for adjusting the electric load of a power grid in a t-th period is obtained; price i，j The price of the ith vehicle participating in the charge or discharge at the jth charge and discharge station in the t period;
the calculation method of the schedulable capacity comprises the following steps:
in the formula ,Ei The capacity of the ith electric automobile which can be scheduled when participating in the scheduling service at the time is set; η (eta) c 、η D Charging and discharging efficiencies of the electric automobile respectively; GD is the current demand of the power grid; when the electric automobile is required to participate in charging service by the power grid, the schedulable capacity is the schedulable charging capacity, gd=0 indicates that the power load of the power grid needs to be increased, and gd=1 indicates that the power load of the power grid needs to be reduced;
the screening method of the user expected service site comprises the following steps: taking the electricity price, distance cost and time cost of the electric automobile participating in the scheduling service as a criterion layer, taking each charge and discharge station in a scheduling area as a scheme layer, and establishing a first analytic hierarchy process model;
for n elements of the same layer, an intermediate judgment matrix C is obtained:
wherein ,
and has C p，p =1, characterizing the element itself to be of the same importance, and then calculating the importance ranking index r of the element i ，
Taking outb m ＝r max /r min Finally, the element of the intermediate judgment matrix c is obtained to construct a final judgment matrixB：
wherein ,
the time cost of the user expected service site is:
wherein V is the average running speed of the electric automobile, and P C 、P D Respectively charging and discharging power of electric automobile, D i，j The Euclidean distance from the electric automobile i to the charging and discharging station j;
calculating the time cost to obtain the score of the j electric vehicle charging and discharging station of the i electric vehicle, and taking the charging and discharging station phi before scoring as a user expected service station of the electric vehicle i participating in the scheduling service, wherein phi is more than or equal to 1;
the calculation method of the schedulable capacity comprises the following steps:
with rated battery capacity Cap and current battery state of charge SOC of electric automobile a And planning the duration T of the power grid access as a criterion layer, taking the electric automobile responding to the service as a scheme layer, and establishing a second analytic hierarchy model,
normalizing the index values of the criterion layer:
wherein ,gmax and gmin Respectively the maximum value and the minimum value of the data g; for forward or reverse index, g * Is the minimum of data gA value or maximum; g is the value after normalization of the data;
the method for calculating the user confidence level comprises the following steps:
when the system is operated for the first time, the credit of all electric vehicles is set to be 1, and after one-time dispatching service, the credit which does not participate in the over-dispatching service and is added into the electric vehicles newly is designated as the average value of the credit of the electric vehicles which adopts the existing historical service data currently;
for the electric automobile i, K historical service data records are counted, wherein K is more than or equal to 1, and the number of times of default Breaktime is greater than or equal to 1 i Represented as
When k=break time, i.e. when the electric vehicle has violated each time the electric vehicle participates in the scheduling service before, the historical time difference of the electric vehicle is set to 0, break represents whether the distributed electric vehicle participates in the scheduling service, and when K is not equal to break time, the historical time difference is represented as
Responsivity Response of electric automobile participating in dispatch service i Represented as
When the SysDay is the number of days of system operation and the number of times of default, the difference of historical time length and the responsivity are normalized, the number of times of default and the difference of historical time length are reverse indexes, the responsivity is a forward index, the weight is calculated according to the number of times of default, the difference of historical time length and the importance degree of responsivity, and the user credibility is expressed as follows
Wherein, superscript indicates the value after normalization treatment, ω 1 、ω 2 and ω3 Weights for the number of violations, historical time difference, and responsiveness are represented, respectively.
2. The method for optimizing resources of an electric vehicle and an intelligent charging and discharging station based on a V2G mode according to claim 1, wherein the classification hierarchical scheduling method comprises constructing a first-level clustering model and a second-level assignment model,
the first-level clustering construction comprises the following steps: coarse clustering is firstly carried out on the first feedback information and the second feedback information by using a Canopy clustering algorithm to obtain a K value and a central point corresponding to the K value, then fine clustering is carried out on the first feedback information and the second feedback information by using a K-Means clustering algorithm, and Euclidean distance D from the electric automobile i to the charging and discharging station j calculated in the K-Means clustering algorithm i，j The distance weight of the charging and discharging station is 1, and the schedulable capacity is used as the distance weight of the electric automobile to form a first-stage optimization scheme;
the constructing of the second-stage assignment model includes: by establishing a total target min F, a second-stage optimization scheme is formed
Wherein EVCost i，j (t) represents the cost consumption of the ith electric vehicle in the t period if going to the jth electric vehicle charging and discharging station, x i，j Indicating whether the ith electric car is allocated to the jth electric car charging and discharging station, Q j (t) represents the scheduled power required by the jth electric automobile charging and discharging station in the t-th period;
setting the constraint condition of the total target as follows
wherein ,A1 Is a column vector with the size of I multiplied by 1 elements being 1, A 1 ＝[1，1，…，1] T ；A 2 Is a row vector with the size of 1 XJ elements being 1, A 2 ＝[1，1，…，1]；P j Is the number of charging and discharging piles in the j-th electric automobile charging and discharging station.
3. The resource optimization method of the electric automobile and the intelligent charging and discharging station based on the V2G mode as set forth in claim 2, further comprising the steps of constructing an evaluation index of the second-stage optimization scheme:
the evaluation indexes comprise expected service station matching degree, total cost of dispatching service, planned power matching degree of an electric vehicle charging and discharging station, actual power matching degree of the electric vehicle charging and discharging station and reliability of electric vehicle participation service, and if the second-level optimization scheme meets all the evaluation indexes, the second-level optimization scheme is output to a power grid dispatching center.
4. The resource optimization method of the electric automobile and the intelligent charging and discharging station based on the V2G mode as claimed in claim 3, wherein the calculation method of the matching degree of the expected service site is as follows:
a is the total number of electric vehicles actually participating in the service of the scheduling service; b is the number of electric vehicles of which the actual service sites and the expected service sites are matched; alpha is the matching degree of the expected service site;
the total cost calculation method of the scheduling service comprises the following steps:
TotalCost＝∑ i∈I ∑ j∈J EVCost i,j x i,j
the calculation method of the planned power matching degree of the electric automobile charging and discharging station comprises the following steps:
the calculation method of the actual power matching degree of the electric automobile charging and discharging station comprises the following steps:
the REi is the scheduling capacity of the electric automobile actually participating in the service in the current scheduling service;
the method for calculating the reliability of the participation service of the electric automobile comprises the following steps:
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