CN114039370A - Resource optimization method for electric automobile and intelligent charging and discharging station based on V2G mode - Google Patents
Resource optimization method for electric automobile and intelligent charging and discharging station based on V2G mode Download PDFInfo
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- H—ELECTRICITY
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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: receiving first feedback information which is triggered and reported by an electric vehicle user according to a scheduling demand instruction, and acquiring second feedback information of an electric vehicle corresponding to the electric vehicle user; if the battery charge state expected by a user when the vehicle is off-grid 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 operation parameter information of all charging and discharging stations in the region, 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 credit, and a classification hierarchical scheduling method based on Canopy + K-Means and mixed integer programming is adopted to form a vehicle pile optimal distribution scheme, so that the problem of unmatched vehicle pile resources is solved, and the effective and full utilization of double-layer resources of the vehicle piles is promoted.
Description
Technical Field
The invention relates to the technical field of V2G scheduling, in particular to a resource optimization method for an electric automobile and an intelligent charging and discharging station based on a V2G mode.
Background
The V2G (Vehicle to Grid) technology regards an electric automobile as a load storage resource, and realizes bidirectional interaction between the electric automobile and a power Grid. The large-scale electric automobile participates in the operation of the power grid in the role of the distributed energy storage unit, on the premise of meeting the basic running requirement of an electric automobile user, when the power grid needs to adjust the load of the power grid, the electric automobile can realize energy conversion (charging and discharging) with the power grid, the buffer is provided for the power grid and the power generation of renewable energy sources, the permeability of the renewable energy sources is improved, the fluctuation of the load of the power grid is reduced, and the comprehensive operation efficiency of the power grid is improved.
Most of the existing V2G scheduling technologies default users to receive scheduling anytime and anywhere, but the randomness of the users participating in V2G scheduling causes that the availability and the actual availability of energy storage resources of electric vehicles by a power grid are difficult to grasp, and in V2G scheduling planning, charging and discharging piles are important carriers for connection between the electric vehicles and the power grid and are influenced by environmental resources, and the charging and discharging facilities and equipment layout has regional differences, unbalanced distribution and different scales. Therefore, the user of the electric automobile needs to comprehensively consider the user service will and the distribution problem of the charging and discharging piles when participating in the V2G scheduling, and the situation 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 vehicle and an intelligent charging and discharging station based on a V2G mode, aiming at optimizing allocation of vehicle stub resources, improving schedulability of the vehicle to quickly respond to a power grid instruction, improving performability of a V2G vehicle charging and discharging scheduling plan, achieving the effect of predicting peak-load and frequency modulation, and solving the problems of unmatched vehicle stub resources, low response degree and the like in a scheduling process.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a resource optimization method for an electric automobile and an intelligent charging and discharging station based on a V2G mode comprises the following steps:
receiving first feedback information which is triggered and reported by an electric vehicle user according to a scheduling demand instruction, wherein the first feedback information comprises an expected SOC (state of charge), a predicted scheduling service participation time and a planned off-network time, and acquiring second feedback information of an electric vehicle corresponding to the electric vehicle user, and the second feedback information comprises vehicle departure place position information, a current SOC and vehicle state parameters;
if the electric automobile meets the battery charge state expected by a user when the automobile is off-network within the planned stay 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 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 schedulable capacity, user expected service stations, schedulable capacity and user credibility, a classification hierarchical scheduling method based on Canopy + K-Means and mixed integer programming is adopted to form an optimal allocation scheme of the electric automobile and the charging and discharging stations, and allocation service station information contained in the optimal allocation scheme of the electric automobile is fed back to the electric automobile user.
The invention has the beneficial effects that: by establishing the vehicle pile resource optimal allocation model, the problem that the energy storage resources of the electric vehicle and the vehicle pile resources are not matched due to the randomness of decision of a user is solved, the problems of vehicle existence, pile existence and vehicle nonexistence in the V2G scheduling process are solved, and the effective and full utilization of the vehicle pile double-layer resources is promoted.
Drawings
Fig. 1 is a schematic flowchart of a resource optimization method for an electric vehicle and an intelligent charging and discharging station based on a V2G mode according to an embodiment of the present invention;
FIG. 2 is a hierarchical analysis 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 Canopy first-level clustering result;
FIG. 5 shows the first-level clustering results of K-Means;
FIG. 6 is a graph of planned power match changes for 100 scheduled weekdays;
FIG. 7 is a graph of actual power match change for 100 scheduled weekdays;
FIG. 8 is a graph that models the total cost change for a system for 100 scheduled weekdays;
FIG. 9 is a graph that simulates the change in reliability of electric vehicle participation in service for 100 scheduled weekdays.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the following detailed description of the present invention is provided with reference to the accompanying drawings and detailed description. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
As shown in fig. 1, the present 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 load of the power grid, 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 an expected SOC (state of charge), predicted scheduling service participation 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, a current SOC and vehicle state parameters;
if the electric automobile meets the battery charge state expected by a user when the automobile is off-network within the planned stay 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 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 schedulable capacity, user expected service sites, schedulable capacity and user credit, a classification hierarchical scheduling method based on Canopy + K-Means and mixed integer programming is adopted to form an optimal allocation scheme of the electric automobile and the charging and discharging stations, and allocation service site information contained in the optimal allocation scheme of the electric automobile is fed back to the electric automobile user.
The method for calculating the state matrix of the electric vehicle comprises the following steps:
aiming at different electric vehicles, in order to embody fixed parameters and charge-discharge states of the electric vehicles and grasp expected information reported by users, a state matrix EV of the electric vehicles in the t-th time period is definediAs shown in formula (1):
in the formula ,EViFor the state matrix of the electric automobile in the t-th time period, I is the number of vehicles intentionally scheduling service to the V2G participating in the current time, IDEVNumbering the electric automobiles; 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; SOCaThe current battery charge state of the electric automobile; SOCbExpecting a desired battery state of charge for the electric vehicle after participation in the service; t isaPredicting a time that the electric vehicle can participate in scheduling; t isbThe planned off-grid time for the electric vehicle.
In order to more accurately grasp the near-real-time state of the vehicle, the scheduling time per day is divided into 96 time periods, the time for which the electric vehicle stays is 15min as one time period, and the state (participation or non-participation) of the scheduling service in any time period is not changed, so that the scheduled synchronization duration of the vehicle is T-Tb-Ta. The system will determine if the electric vehicle can meet the user-desired battery state of charge while the vehicle is off-grid for the planned dwell time,
wherein ,PC、PDThe charging and discharging power of the electric automobile are respectively; GD is the demand of the power grid this time, GD is 0 and means that the power grid needs higher power load, needs electricityThe electric vehicle participates in the charging service, and GD 1 means that the current power load of the power grid needs to be reduced, and the electric vehicle needs to participate in the discharging service. If the user expectation can be met, generating a state matrix, and bringing the electric automobile into an alternative list; if the requirement cannot be met, the system returns the information of the electric vehicle user to report the scheduling participation again until the requirement is met or the participation in the scheduling is abandoned.
The method for calculating the operation matrix of the electric automobile charging and discharging station comprises the following steps:
in the formula ,CSjIs an operation matrix of the charging and discharging stations J of the electric automobile in the t-th time period, wherein J is the number of the charging and discharging stations of the electric automobile in the dispatching area,
numbering the charging and discharging stations of the electric automobile; locjThe two-dimensional geographic coordinates of the electric vehicle charging and discharging station are obtained; j is a function of
The number of the charging and discharging piles in the jth charging and discharging station of the electric automobile is shown; qjThe electric quantity required by the jth electric vehicle charging and discharging station for adjusting the power load of the power grid in the tth time period; pricei，jAnd the price of the ith vehicle participating in the charging or discharging at the jth charging and discharging station in the t period is shown.
According to the demand of a power grid dispatching instruction, when a power grid needs higher power load, the electric automobile participating in dispatching service is charged in the power grid; when the current power load of the power grid needs to be reduced, the electric automobiles participating in the dispatching service discharge to the power grid. The schedulable capacity of the electric automobile is used for representing that the electric automobile allows a power grid to perform energy dynamic bidirectional balance with the power grid on the premise of meeting basic requirements of users, and the capacity state and the charging and discharging behaviors of the electric automobile are accepted and scheduled to the maximum extent. Can be divided into a schedulable charging capacity and a schedulable discharging capacity, as shown in formula (4):
the calculation method of the schedulable capacity comprises the following steps:
in the formula ,EiThe schedulable capacity of the ith electric automobile when participating in the scheduling service; etaC、ηDRespectively the charge and discharge efficiency of the electric automobile; GD is the current requirement of the power grid; when GD is 0, and the electric automobile is required to participate in the charging service by the power grid, the schedulable capacity is the schedulable charging capacity, GD is 0 to indicate that the power grid needs to increase the power load, and GD is 1 to indicate that the power grid needs to decrease the power load. Otherwise, the electric network does not need the electric automobile to participate in the charging and discharging service, and the schedulable capacity is 0. The obtained schedulable capacity provides a capacity quantitative evaluation of each electric vehicle for the assignment model. Furthermore, the time of the dispatching command sent by the power grid can be current, or the dispatching command can be sent out hours before or day before the power grid needs,
the electric automobile user expectation service station adopts an analytic hierarchy process to consider the electricity price, distance cost and time cost of the electric automobile participating in the scheduling service, the three factors are used as a criterion layer, each charge-discharge station in a scheduling area is used as a scheme layer to establish a hierarchical analysis 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, the distance cost and the time cost of the electric automobile participating in the scheduling service as a criterion layer, taking each charging and discharging station in a scheduling area as a scheme layer, and establishing a first-level analysis model;
for n elements of the same level, an intermediate judgment matrix C is obtained:
the judgment matrix herein uses capital letter C, which is intended to describe how elements in the judgment matrix of the improved hierarchical analysis model are constructed, and has no actual reference, and can be changed into other alphabetical symbols.
wherein ,
and has Cp，p1, the characteristic elements have the same relative importance, and then the importance ranking index r of the elements is calculatedi，
Get
bm＝rmax/rminFinally, the elements of the middle judgment matrix C are solved, and a final judgment matrix B is constructed:
wherein ,
price in the electric vehicle user expectation service station adopts Price in the parameter matrix of the electric vehicle charging and discharging stationi，j(ii) a The distance cost of the electric vehicle is the Euclidean distance from the electric vehicle i to the charging and discharging station j, namely
The time cost of the electric automobile considers two parts, namely the time spent by the electric automobile i to the charging and discharging station j on the route, and the time compensation of power consumption on the route, namely the time cost of the expected service station of the user is as follows:
wherein V is the average running speed of the electric automobile, PC、PDRespectively, the charge and discharge power of the electric vehicle, Di，jThe Euclidean distance from the electric automobile i to the charging and discharging station j is obtained;
the time cost is calculated to obtain the score of the jth electric vehicle charge and discharge station of the ith electric vehicle, the charge and discharge station with phi before the score is taken as a user expected service station of the electric vehicle i participating in the scheduling service, and the index of the user side is provided for model evaluation, wherein phi is more than or equal to 1 (for example, phi is 1, 2, … J, and phi is less than or equal to J).
The calculation method of the schedulable capacity comprises the following steps:
using the battery capacity Cap and the current battery state of charge SOC of the electric automobileaAnd planning to access the power grid for a duration T as a criterion layer, responding to the served electric vehicle as a scheme layer, establishing a second-level analysis model,
normalizing each index value of the criterion layer:
wherein ,gmax and gminThe maximum value and the minimum value of the data g are respectively; for forward or reverse indices, g*Is the minimum or maximum value of data g; g is the normalized value of the data. The battery capacity of the electric automobile and the duration of planned access to the power grid are forward indexes. When the power grid needs the electric automobile to participate in the discharging service, the current charge state of the electric automobile is a forward index; when the power grid needs the electric vehicle to participate in the charging service, the current charge state of the electric vehicle is a reverse index. And the analytic hierarchy process gives a numerical value KWeight of the schedulable capability of each electric automobile, and is used for clustering and dividing the electric automobiles in the first-level clustering.
The calculation method of the user credit comprises the following steps:
product of vehicle using habit of electric vehicle user and participation in scheduling serviceThe difference of polarity and the like causes 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 is established for the electric automobile useri. The credit of the electric automobile user is based on historical service data of the electric automobile participating in the scheduling service, the model stores data information of the distributed electric automobile participating in the scheduling service after distribution is completed each time and the electric automobile is actually connected to 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 this scheduling service; break indicates whether the distributed electric automobile participates in the scheduling service, 0 indicates that the electric automobile participates in the service, and 1 indicates that the service is cancelled; the 'ST' and the 'ET' respectively represent planned grid-connection time and grid-disconnection time reported by an electric vehicle user, namely Ta and Tb(ii) a The RST and the RET respectively represent the actual grid connection time and the actual grid disconnection time of the electric automobile and are marked as Ta′ and Tb′。
The credit of the user of the electric automobile considers three factors of default times, historical time length difference and responsiveness of the electric automobile according to historical service data of the electric automobile, and the weight omega is calculated as [0.637, 0.258 and 0.105] according to the importance degree of the default times, the historical time length difference and the responsiveness.
When the system is operated for the first time, because all the electric automobiles have no history service data record, the credit rating of all the electric automobiles is set to be 1, after one-time scheduling service, the credit rating which does not participate in the scheduling service and is newly added into the electric automobiles is designated as the average value of the credit rating of the electric automobiles adopting the existing history service data at present;
for the electric automobile i, K (K is more than or equal to 1) historical service data records and breach times are countediIs shown as
When K is equal to Breaktime, namely when the electric automobile is in default every time of participating in the scheduling service before, the historical time difference of the electric automobile is set to be 0, and when K is equal to Breaktime, the historical time difference is expressed as
Responsibility Response of electric automobile participating in scheduling serviceiIs shown as
Wherein SysDay is the number of days of system operation, when normalization processing is carried out on default times, historical time length difference and responsiveness, the default times and the historical time length difference are reverse indexes, the responsiveness is a forward index, the weight is calculated according to the importance degrees of the default times, the historical time length difference and the responsiveness, and the user credit degree is expressed as
Wherein the superscript denotes the normalized value, ω1、ω2 and ω3Weights representing the number of violations, historical time length differences, and responsiveness, respectively. The credit degree of the electric vehicle user reflects the vehicle using habit of the electric vehicle user and the enthusiasm for participating in scheduling service, and provides data support for a credit priority strategy.
The classification and hierarchy level scheduling method comprises the steps of constructing a first-level clustering model and a second-level assignment model,
the large-scale electric automobile has strong randomness and dispersity, and when the large-scale electric automobile enters a V2G dispatching system, if a mixed integer linear programming model is distributed according to a traditional vehicle pile, dynamic big data of the automobile are difficult to process in a short time and an optimal solution of the model is difficult to obtain, and the solving difficulty is exponentially increased. Therefore, the proposed classification and hierarchical scheduling mode aims at quickly generating a vehicle pile allocation scheme after a large-scale electric vehicle enters a scheduling system and solving a 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 changes in the results, and K-Means is very sensitive to outliers. Canopy clustering does not require the prior specification of k values, and has great advantages in speed compared with other clustering algorithms although the precision is low.
Thus, the construction of the first-level cluster includes: roughly clustering 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, finely clustering the first feedback information and the second feedback information by using a K-Means clustering algorithm, and calculating the Euclidean distance D from the electric vehicle i to a charge and discharge station j in the K-Means clustering algorithmi，jThe distance weight of the charging and discharging station is set to be 1, and the schedulable capacity is used as the distance weight of the electric automobile to form a first-level optimization scheme. The large-scale electric vehicle data are 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 automobiles are generally contained, and different charging and discharging stations have different numbers of charging and discharging piles. The second-level assignment model solves the problem of distribution of single-quantity electric vehicles to electric vehicle charging and discharging stations.
xi，j(I-1, 2, …, I; J-1, 2, …, J) indicates whether the ith electric vehicle is assigned to the jth electric vehicle charge and discharge station
EVCosti，j(t) (I is 1, 2, …, I; J is 1, 2, …, J) represents the cost consumption of the ith electric vehicle to the jth electric vehicle charge and discharge station during the t period, and all EVCosti，j(t) forming a cost matrix [ EVCost ] of electric vehicles participating in scheduling service in t time period and charging and discharging stations of each electric vehiclei，j(t)]；
QjAnd (t) represents the required scheduling electric quantity of the jth electric automobile charge and discharge station in the tth period.
The overall objective considers three aspects, namely the minimum total scheduling distribution cost, the maximum satisfaction of the requirements of the electric automobile charging and discharging station, and the maximum credit degree of the electric automobile users participating in scheduling.
Thus, the construction of the second level assignment model includes: forming a second-stage optimization scheme by establishing a total target min F
Wherein, EVCosti，j(t) (I ═ 1, 2, …, I; (J ═ 1, 2, …, J) represents cost consumption of the ith electric vehicle to the jth electric vehicle charge-discharge station during the t period, xi，jIndicating whether the ith electric vehicle is assigned to the jth electric vehicle charging and discharging station, QjAnd (t) represents the required scheduling electric quantity of the jth electric automobile charge and discharge station in the tth period.
Set the constraint of the overall target to
wherein ,A1Is a column vector of size I × 1 elements all 1, A1＝[1，1，…，1]T；A2Is a row vector of 1 × J elements all 1, A2＝[1，1，…，1]；PjThe number of the charging and discharging piles in the jth charging and discharging station of the electric automobile is shown. The constraint condition (1) enables each electric automobile to participate in service only at one electric automobile charging and discharging station at the same time; the constraint condition (2) ensures that the number of the electric vehicles accepted by each electric vehicle charging and discharging station does not exceed the number of the charging and discharging piles owned by the electric vehicle charging and discharging station; the constraint condition (3) enables the requirement of each charging station on the schedulable capacity of the electric vehicle to be met.
Further, the method also comprises the following steps of constructing evaluation indexes of a second-level optimization scheme:
the evaluation indexes comprise the matching degree of the expected service site, the total cost of the dispatching service, the planned power matching degree of the electric vehicle charging and discharging station, the actual power matching degree of the electric vehicle charging and discharging station and the reliability of the electric vehicle participating in the service, and if the second-stage optimization scheme meets all the evaluation indexes, the second-stage optimization scheme is output to the power grid dispatching center.
According to the invention, by establishing the vehicle pile resource optimal allocation model, the problem that the energy storage resources of the electric vehicle are uncertain due to the randomness of decision of a user is solved, and the problems of' no vehicle pile and no vehicle pile in the scheduling process of V2G are solved, so that the vehicle pile double-layer resources are effectively and fully utilized.
The method for calculating the matching degree of the expected service site comprises the following steps:
the matching degree of the expected service site reflects the satisfaction degree expected by the electric vehicle user, the higher the matching degree is, the higher the user satisfaction degree is, but the expected matching of the user cannot be completely achieved by considering factors such as cost and power grid requirements. The formula of the expected serving site matching degree α is as follows:
wherein a is the total number of the electric vehicles actually participating in the scheduling service; b is the number of the electric automobiles with the actual service sites matched with the expected service sites of the electric automobiles; alpha is the matching degree of the expected service site;
the method for calculating the total cost of the scheduling service comprises the following steps:
the cost of the dispatch service may be a distance cost, a time cost, etc. for the user side to participate in the dispatch service, or a cost for the grid side to attract the electric vehicle to participate in the dispatch service. The total cost of the dispatch service is expressed as
TotalCost＝∑i∈I∑j∈JEVCosti，jxi，j (21)
The method for calculating 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 plan requirement satisfaction degree is 1, and the best performance is achieved; the requirements of the charging and discharging station with the value less than 1 cannot be completely met; a value greater than 1 would result in a waste of resources. The formula of the planned demand satisfaction degree beta of the electric vehicle charging and discharging station is as follows:
the method for calculating the actual power matching degree of the electric automobile charging and discharging station comprises the following steps:
due to the randomness of the electric automobile, the schedulable capacity of the electric automobile cannot be completely released, so that in the actual scheduling service, the actual power requirement of the electric automobile charging and discharging station may not be completely met, the actual power matching degree gamma is less than or equal to 1, and the closer the value is to 1, the better the performance is. The charging and discharging station of 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 scheduling service, 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 electric automobile participating in the service comprises the following steps:
the reliability theta of the electric automobile participating in the service is embodied by the average credit degree of the electric automobile participating in the service, the higher the average credit degree of the electric automobile participating in the dispatching service is, the closer the electric automobile is to various information reported by electric automobile users in the actual service is, the possibility that the users cancel the dispatching service or leave 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 vehicle participating in the dispatching service is as follows:
case analysis-user desired service site screening
Electric vehicle ID for a certain electric vehicle to participate in charging serviceEVLet 1 assume that there are 5 charge-discharge stations s1，s2，s3，s4，s5And optionally, screening according to the proposed service electricity price, distance cost and time cost as criteria. Constructing a comparison matrix for criterion layer elements
Calculating the importance ranking index as r1＝3，r2＝5，r3When 1, then rmax＝5，rmin＝1，bmWhen 5/1 is 5, the decision matrix is obtained by equation (8)
The eigenvector of the judgment matrix is calculated as u ═ 0.261, 0.633, 0.106]TMaximum eigenvalue is λmax3.039, consistency index
The result is found from the RI value table of consistency check, RI is 0.58, and consistency ratio
This indicates that the judgment matrix constructed by the criterion layer satisfies the consistency requirement, and the corresponding obtained feature vector is valid.
To screen out a service site desired by a user from the 5 charging and discharging stations, the service price, distance cost and time cost of the 5 charging and discharging stations for the electric vehicle need to be compared respectively. The distance and time cost were calculated from the price information of each charge and discharge station and equation (9), as shown in table 1
TABLE 1 electric vehicle IDEVExpected site screening criteria tier value of 1
Constructing a comparison matrix according to the principle that the higher the price or the cost is, the lower the importance of the charging and discharging station is, wherein the comparison matrix of the service price is
Calculating the importance ranking index as rmax＝9，rmin＝1，bmWhen 9/1 is 9, the decision matrix of service price is
The judgment matrix of distance cost and time cost calculated in the same way is
The corresponding weight vector is obtained by calculation
ω1＝[0.035，0.188，0.068，0.521，0.188]
ω2＝[0.503，0.134，0.26，0.068，0.035]
ω3＝[0.503，0.134，0.26，0.035，0.068]
By inspection, B1，B2，B3All meet the requirement of consistency. Finally, calculating the expected score of each charging and discharging station,
namely, ID of each charging and discharging station to the electric automobileEVUser desired ranking of 1: s1＞s3＞s4＞s2＞s5. The first two digits are screened as service sites expected by the user, that is, the charging and discharging station 1 and the charging and discharging station 3 are expected service sites of the electric vehicle i.
Case analysis two-electric vehicle user credit calculation
When a scheduling task is started, the system receives the ID of the electric vehicle participating in the discharging serviceEVObtain the history information of its participation in the dispatch service as the corresponding information of 1, as shown in table 2
TABLE 2 electric vehicle IDEV1 historical service data
| 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 equation (11), electric vehicle IDEVNumber of default times of 1
Calculated according to equation (12), the historical time length difference is
Calculated according to equation (13), electric vehicle IDEVResponsibility for participation in scheduling is 1
The value after normalization processing with each attribute value of other electric vehicles responding to the dispatching service is
Calculated, electric vehicle IDEVA confidence of 1 is
The Credit of other electric vehicles participating in the current scheduling may also be calculated through the above calculation process, and the electric vehicle Credit set Credit of the current scheduling is [0.737, 0.635, 0.719, …, 0.598 ].
Case analysis three-vehicle-pile resource optimization model first-level clustering
The case is based on Python3.7 coding and solved by a CPLEX solver; computer configuration: model-PC Dell OptiPlex 7060, CPU-Intel (R) core (TM) i7-8700, Memory-3.20 GHZ Memory 32GB, operating system-Windows 10 OS.
And performing first-level clustering on the electric automobile and the charging and discharging stations by using a Canopy + K-Means algorithm to form a first-level optimization scheme for V2G system scheduling, and performing coarse clustering processing for second-level fine scheduling. The clustering results are shown in FIGS. 4 and 5.
Case analysis four-vehicle-pile resource optimization model second-level assignment model
Setting parameters:
charging power P c30 kW; charging efficiency ηC99%; discharge power P D30 kW; discharge efficiency ηD98 percent; the average running speed V is 50 km/h;
three different vehicle types:
1) the capacity of the Biediyuan new energy battery is 42kWh endurance mileage 305 km;
2) ES8 Ulmaria with a battery capacity of 70kWh endurance mileage of 355 km;
3) long-ampere-free new energy battery capacity of 45kWh endurance mileage of 300km
[ci，j(t)]: cost matrix of electric automobile participating in scheduling service to electric automobile charging and discharging station
Ei: electric automobile schedulable capacity participating in scheduling service at this time
Ei＝[11.09，20.29，23.13，29.16，…，17.42，22.32]
Pj: the number of charging and discharging piles in each charging and discharging station of the electric automobile
Pj＝[7，8，7，9，8，10，8，9，10，7，10，9]
Qj(t): the dispatching electric quantity required by each electric automobile charge and discharge station
Qj(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
Case analysis five-model evaluation index result
A V2G system consisting of 100 electric cars and 12 electric car charging and discharging stations was simulated in a 10km x 10km area, and this case was performed for 100 scheduled working days. The results of the evaluation indexes of the model are shown in the attached 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 system operation days, and the actual power matching degree is increased from 90% to 95%. The total running cost of the system is reduced, and the reliability of the electric automobile participating in the dispatching service is gradually improved.
The optimized allocation method for the vehicle pile resources can quickly allocate the electric vehicles meeting the conditions to the corresponding electric vehicle charging and discharging stations when large-scale electric vehicles enter the dispatching system, and provides accurate power matching for the electric vehicle charging and discharging stations. The optimal allocation model of the vehicle pile resources considers the service willingness of the electric vehicle user, reduces the cost of the user service, and simultaneously reduces the total running cost of the system. The vehicle pile resource optimization distribution model can screen out electric vehicles with strong scheduling capability and high credit degree, and provides accurate resource matching for 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 the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (10)
1. A resource optimization method for an electric automobile and an intelligent charging and discharging station based on a V2G mode is characterized by comprising the following steps:
receiving first feedback information which is triggered and reported by an electric vehicle user according to a scheduling demand instruction, wherein the first feedback information comprises an expected SOC (state of charge), a predicted scheduling service participation time and a planned off-network time, and acquiring second feedback information of an electric vehicle corresponding to the electric vehicle user, and the second feedback information comprises vehicle departure place position information, a current SOC and vehicle state parameters;
if the electric automobile meets the battery charge state expected by a user when the automobile is off-network within the planned stay 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 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 schedulable capacity, user expected service stations, schedulable capacity and user credibility, a classification hierarchical scheduling method based on Canopy + K-Means and mixed integer programming is adopted to form an optimal allocation scheme of the electric automobile and the charging and discharging stations, and allocation service station information contained in the optimal allocation scheme of the electric automobile is fed back to the electric automobile user.
2. The resource optimization method for the V2G mode-based electric vehicle and the intelligent charging and discharging station as claimed in claim 1, wherein the calculation method for the electric vehicle state matrix is as follows:
in the formula ,EViFor the state matrix of the electric automobile in the t-th time period, I is the number of vehicles intentionally scheduling service to the V2G participating in the current time, IDEVNumbering the electric automobiles; 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; SOCaThe current battery charge state of the electric automobile; SOCbExpecting a desired battery state of charge for the electric vehicle after participation in the service; t isaPredicting a time that the electric vehicle can participate in scheduling; t isbThe planned off-grid time for the electric vehicle.
3. The resource optimization method for the electric automobile and the intelligent charging and discharging station based on the V2G mode as claimed in claim 2, wherein the calculation method for the operation matrix of the charging and discharging station of the electric automobile is as follows:
in the formula ,CSjAn operation matrix of an electric vehicle charging and discharging station J in the t-th time period, wherein J is in dispatchingThe number of electric vehicle charging and discharging stations in the region,
numbering the charging and discharging stations of the electric automobile; locjThe two-dimensional geographic coordinates of the electric vehicle charging and discharging station are obtained;
the number of the charging and discharging piles in the jth charging and discharging station of the electric automobile is shown; qjThe electric quantity required by the jth electric vehicle charging and discharging station for adjusting the power load of the power grid in the tth time period; pricei，jAnd the price of the ith vehicle participating in the charging or discharging at the jth charging and discharging station in the t period is shown.
4. The resource optimization method for the V2G mode-based electric automobile and the intelligent charging and discharging station as claimed in claim 3, wherein the calculation method for the schedulable capacity is as follows:
in the formula ,EiThe schedulable capacity of the ith electric automobile when participating in the scheduling service; etaC、ηDRespectively the charge and discharge efficiency of the electric automobile; GD is the current requirement of the power grid; when GD is 0, and the electric automobile is required to participate in the charging service by the power grid, the schedulable capacity is the schedulable charging capacity, GD is 0 to indicate that the power grid needs to increase the power load, and GD is 1 to indicate that the power grid needs to decrease the power load.
5. The resource optimization method for the V2G-based mode electric automobile and the intelligent charging and discharging station as claimed in claim 4, wherein the method for screening the user desired service sites comprises the following steps: taking the electricity price, the distance cost and the time cost of the electric automobile participating in the scheduling service as a criterion layer, taking each charging and discharging station in a scheduling area as a scheme layer, and establishing a first-level analysis model;
for n elements of the same level, an intermediate judgment matrix C is obtained:
wherein ,
and has Cp，p1, the characteristic elements have the same relative importance, and then the importance ranking index r of the elements is calculatedi，
Get
bm＝rmax/rminFinally, the elements of the intermediate judgment matrix C are solved, and a final judgment matrix B is constructed:
wherein ,
the time cost of the user desiring to service the site is:
wherein V is of an electric automobileAverage running speed, PC、PDRespectively, the charge and discharge power of the electric vehicle, Di，jThe Euclidean distance from the electric automobile i to the charging and discharging station j is obtained;
and calculating the time cost to obtain the score of the jth electric vehicle charging and discharging station of the ith 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.
6. The resource optimization method for the V2G mode-based electric automobile and the intelligent charging and discharging station as claimed in claim 5, wherein the calculation method for the schedulable capacity is as follows:
using the battery capacity Cap and the current battery state of charge SOC of the electric automobileaAnd planning to access the power grid for a duration T as a criterion layer, responding to the served electric vehicle as a scheme layer, establishing a second-level analysis model,
and normalizing the index values of the criterion layer:
wherein ,gmax and gminThe maximum value and the minimum value of the data g are respectively; for forward or reverse indices, g*Is the minimum or maximum value of data g; g is the normalized value of the data.
7. The resource optimization method for the V2G-mode-based electric automobile and the intelligent charging and discharging station as claimed in claim 6, wherein the calculation method for the user credit comprises the following steps:
when the system is operated for the first time, the credit rating of all electric vehicles is set to be 1, after one-time scheduling service, the credit rating which does not participate in the scheduling service and newly joins in the electric vehicle is designated as the average value of the credit rating of the electric vehicle adopting the existing historical service data at present;
for the electric automobile i, K (K is more than or equal to K)1) Historical service data record, breach timesiIs shown as
Setting the historical time length difference of the electric automobile to be 0 when K ≠ Breaktime, namely when the electric automobile is violated before each time of participating in the scheduling service, and expressing the historical time length difference as 0 when K ≠ Breaktime
The responsibility Response of the electric automobile participating in the dispatching serviceiIs shown as
SysDay is the number of system operation days, when normalization processing is carried out on the default times, the historical time length difference and the responsiveness, the default times and the historical time length difference are reverse indexes, the responsiveness is a forward index, the weight is calculated according to the importance degrees of the default times, the historical time length difference and the responsiveness, and the user credit degree is expressed as
Wherein the superscript denotes the normalized value, ω1、ω2 and ω3Weights representing the number of violations, historical time length differences, and responsibilities, respectively.
8. The resource optimization method for the V2G-based mode electric vehicle and the intelligent charging and discharging station as claimed in claim 7, wherein the classification and hierarchy scheduling method comprises constructing a first-level clustering model and a second-level assignment model,
the construction of the first-level cluster comprises the following steps: roughly clustering 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, finely clustering the first feedback information and the second feedback information by using a K-Means clustering algorithm, and calculating the Euclidean distance D from the electric vehicle i to a charge and discharge station j in the K-Means clustering algorithmi，jThe distance weight of a charging and discharging station is set to be 1, and the schedulable capacity is used as the distance weight of the electric automobile to form a first-level optimization scheme;
the construction of the second-level assignment model comprises the following steps: forming a second-stage optimization scheme by establishing a total target min F
Wherein, EVCosti，j(t) represents the cost consumption of the ith electric vehicle to the jth electric vehicle charging and discharging station in the t period, xi，jIndicating whether the ith electric vehicle is assigned to the jth electric vehicle charging and discharging station, Qj(t) represents the scheduling electric quantity required by the jth electric vehicle charging and discharging station in the tth time period;
setting the constraint condition of the total target as
wherein ,A1Is a column vector of size I × 1 elements all 1, A1＝[1，1，…，1]T；A2Is a row vector of 1 × J elements all 1, A2＝[1，1，…，1]；PjThe number of the charging and discharging piles in the jth charging and discharging station of the electric automobile is shown.
9. The resource optimization method for the electric vehicle and the intelligent charging and discharging station based on the V2G model as claimed in claim 8, further comprising the steps of constructing an evaluation index of the second-stage optimization scheme:
the evaluation indexes comprise expected service site 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 participating in service, and if the second-stage optimization scheme meets all the evaluation indexes, the second-stage optimization scheme is output to a power grid dispatching center.
10. The resource optimization method for the electric vehicle and the intelligent charging and discharging station based on the V2G model as claimed in claim 9, wherein the calculation method for the matching degree of the expected service site is as follows:
wherein a is the total number of the electric vehicles actually participating in the scheduling service; b is the number of the electric automobiles with the actual service sites matched with the expected service sites of the electric automobiles; alpha is the matching degree of the expected service site;
the method for calculating the total cost of the scheduling service comprises the following steps:
TotalCost＝∑i∈I∑j∈JEVCosti，jxt，j
the method for calculating the planned power matching degree of the electric automobile charging and discharging station comprises the following steps:
the method for calculating the actual power matching degree of the electric automobile charging and discharging station comprises the following steps:
wherein REi is the scheduling capacity of the electric automobile actually participating in the service in the scheduling service;
the method for calculating the reliability of the electric automobile participating in the service comprises the following steps:
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