CN111469682B - Electric automobile real-time intelligent charging method based on day-ahead plan - Google Patents

Electric automobile real-time intelligent charging method based on day-ahead plan Download PDF

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CN111469682B
CN111469682B CN202010255394.0A CN202010255394A CN111469682B CN 111469682 B CN111469682 B CN 111469682B CN 202010255394 A CN202010255394 A CN 202010255394A CN 111469682 B CN111469682 B CN 111469682B
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时珊珊
张宇
方陈
王育飞
王皓靖
涂轶昀
刘舒
魏新迟
徐文法
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State Grid Shanghai Electric Power Co Ltd
East China Power Test and Research Institute Co Ltd
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Abstract

The invention relates to a real-time intelligent charging method for an electric automobile based on a day-ahead plan, which comprises the steps of adopting a pre-established day-ahead electric automobile optimized charging model, comprehensively considering the charging amount of the electric automobile, the charging cost of the electric automobile, load fluctuation and three-phase imbalance, and charging the electric automobile in the plan; and charging the unplanned electric automobile by adopting a pre-established real-time electric automobile optimization charging model according to the charging priority of the electric automobile user. Compared with the prior art, the intelligent regulation and optimization method realizes intelligent regulation and optimization of electric vehicle charging in different charging scenes, and has the advantages of strong adaptability, high reliability and the like.

Description

Electric automobile real-time intelligent charging method based on day-ahead plan
Technical Field
The invention relates to the field of optimized charging methods for electric automobiles, in particular to a real-time intelligent charging method for an electric automobile based on a day-ahead plan.
Background
Under the background of the increasing deterioration of global environment and the increasing shortage of petroleum resources, electric vehicles are receiving more and more attention as new energy vehicles, and more people start purchasing and using electric vehicles. However, the disordered charging behavior of a large number of electric vehicles will increase the load fluctuation, cause three-phase imbalance, and reduce the life of the transformer.
At present, the optimization thought of 'day ahead + real time' is mainly adopted for the research on the electric vehicle optimization charging problem, and due to the fact that the accuracy of a basic data prediction result of the day ahead is poor, a day ahead electric vehicle charging plan cannot be directly applied to a real-time charging scene, and needs to be adjusted in real time. However, the real-time optimization principle in the existing research is fixed and simplified, so that the adaptability of different charging scenes is poor. Therefore, how to realize intelligent regulation real-time optimization under different charging scenes is a big difficulty in optimizing the charging problem of the electric automobile at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the real-time intelligent charging method for the electric automobile based on the day-ahead plan, which can adjust and optimize in real time according to the requirements of different charging scenes.
The purpose of the invention can be realized by the following technical scheme:
a real-time intelligent charging method for an electric vehicle based on a day-ahead plan is characterized by adopting a pre-established day-ahead electric vehicle optimized charging model, comprehensively considering electric vehicle charging amount, electric vehicle charging cost, load fluctuation and three-phase imbalance and charging the electric vehicle in the plan; and charging the unplanned electric automobile by adopting a pre-established real-time electric automobile optimization charging model according to the charging priority of the electric automobile user.
Further, the objective function of the day-ahead electric vehicle optimized charging model is as follows:
Z＝min(ω1·Z1+ω2·Z2+ω3·Z3+ω4·Z4)
wherein Z is the calculation result of the day-ahead optimized charging model of the electric automobile, Z1As a component of electric vehicle charge, ω1Weight of charge quantity component for electric vehicle, Z2Component of charging cost, omega, for electric vehicles2Weighting the charging cost component of an electric vehicle, Z3As a component of load fluctuation, ω3As a weight of the load fluctuation component, Z4Being three-phase unbalanced components, ω4Is the weight of the three-phase unbalanced component.
Further, the calculation expression of the electric vehicle charge amount component is:
Figure GDA0003150987660000021
wherein N is the total number of the electric vehicles, SOCminIs the lower limit of the state of charge, SOCmaxIs the upper limit value of the state of charge of the battery,
Figure GDA0003150987660000022
is the initial state of charge, SOC, of the nth electric vehiclenAnd c is a weight factor.
Further, the calculation expression of the charging cost component of the electric vehicle is as follows:
Figure GDA0003150987660000023
in the formula, ckCharging price for electric vehicle in k-th period, Pi,j,kIs at k timeThe j phase load power of the charging station of the section electric vehicle, delta t is the duration of each time interval, Qn.needThe required electric quantity, omega, for charging the nth electric vehiclenThe charging efficiency of the electric vehicle n is shown.
Further, the calculation expression of the load fluctuation component is:
Figure GDA0003150987660000024
in the formula, Pi,j,kThe j phase load power a of the electric vehicle charging station in the k time periodj,nNumber of electric vehicles, Δ P, charged for the j-th phase of the charging stationj,nThe maximum charging power difference value T of the adjacent time period of the j-th phase n-th electric vehiclej,nFor the charging time of the jth electric vehicle, Δ t is the time duration of each time interval.
Further, the calculation expression of the three-phase unbalance component is as follows:
Figure GDA0003150987660000031
in the formula, Pi,j,kFor the j phase load power of the electric vehicle charging station i in the k period, j is 1,2,3, N is the total number of electric vehicles, PEV,maxN maximum charging power for the electric vehicle, dk,nMaking a charging decision for the electric vehicle n in the k period when dk,nWhen 1, the electric vehicle n is charged in the k-th period, and when dk,nWhen 0, the electric vehicle n is not charged in the k-th period.
Further, the real-time optimized charging model of the electric vehicle comprises a charging capacity priority sub-model, and the expression of the charging capacity priority sub-model is as follows:
Figure GDA0003150987660000032
in the formula, protity1(n, t) is the charging capacity priority of the electric vehicle n at the time t, SOCn,tIs the state of charge, SOC, of the electric vehicle n at the time tminIs the lower limit of the state of charge, SOCmaxIs the upper limit of the state of charge of the battery, BnIs the battery capacity of the electric vehicle n; t isn,stayThe n parking time of the electric automobile, M is a constant and is 105。
Further, the real-time optimized charging model of the electric vehicle comprises a charging cost priority submodel, and the expression of the charging cost priority submodel is as follows:
Figure GDA0003150987660000033
in the formula, protity2(n, t) is charging fee priority of the electric vehicle n at the time t, ctCharging price for electric vehicle at time t, PEV,n,tCharging power for an electric vehicle n at time t, tmaxIs the maximum charge time.
Further, the real-time electric vehicle optimized charging model comprises a charging load fluctuation priority submodel, and the expression of the charging load fluctuation priority submodel is as follows:
priority3(n,t)＝-PEV,n,t
wherein the priority3(n, t) is a priority value of load fluctuation of the electric vehicle n during charging at the time t, PEV,n,tAnd charging power for the electric automobile n at the time t.
Further, the real-time electric vehicle optimized charging model comprises a three-phase unbalanced priority submodel during charging, and the expression of the three-phase unbalanced priority submodel during charging is as follows:
priority4(n,t)＝L'imbalance(n,t)-Limbalance(n,t)
wherein the priority4(n, t) is the three-phase imbalance priority of the electric vehicle n during charging at the time t; l'imbalance(n, t) is the three-phase unbalance degree when the electric automobile n does not participate in charging at the moment t; l isimbalance(n, t) three-phase imbalance when the electric vehicle n participates in charging at time tAnd (4) degree.
The method comprises the steps that charging scenes are changed continuously along with the participation of unplanned electric vehicles in charging, the requirements of different charging scenes on electric vehicle charging quantity, charging cost, load fluctuation and three-phase imbalance are different, and the arrangement sequence of mixed priority indexes is adjusted continuously according to the requirements of different charging scenes in the real-time optimal charging process of the electric vehicles.
Compared with the prior art, the invention has the following advantages:
(1) the invention relates to a real-time intelligent charging method of an electric automobile based on a day-ahead plan, which is used for charging the electric automobile in the plan by adopting a day-ahead electric automobile optimized charging model, wherein the day-ahead electric automobile optimized charging model takes the factors of electric automobile charging quantity, electric automobile charging cost, load fluctuation and three-phase imbalance into consideration, and the factors can change continuously along with different charging scenes, so that intelligent regulation and optimization under different charging scenes are realized; and for the unplanned electric automobile, charging by adopting a real-time electric automobile optimized charging model based on the charging priority level, and adjusting the electric automobile optimized charging model in real time in the day ahead to obtain an optimal electric automobile charging scheme.
(2) In the real-time optimized charging model for the electric vehicle, the factors for measuring the charging priority of the electric vehicle also include: the charging electric quantity, the charging cost, the load fluctuation and the three-phase imbalance of the electric automobile correspond to factors considered by a day-ahead electric automobile optimized charging model, intelligent adjustment and optimization under different charging scenes are achieved, and the electric automobile intelligent charging system has high adaptability.
(3) The optimization charging model of the electric automobile in the day ahead is provided with the constraint that each electric automobile can be charged only in one charging station, the charging power constraint of the electric automobile and the constraint that the load power of the charging station of the electric automobile cannot exceed the maximum power of the charging station, is considered comprehensively, and improves the reliability of the real-time intelligent charging method of the electric automobile.
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Fig. 1 is a flow chart of an electric vehicle real-time intelligent charging method based on a day-ahead plan according to the invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
As shown in fig. 1, the embodiment provides a real-time intelligent charging method for an electric vehicle based on a day-ahead plan, which comprehensively considers the charging electric quantity, the charging cost, the load fluctuation and the three-phase imbalance of the electric vehicle, and establishes an optimized charging model for the day-ahead electric vehicle; and providing real-time charging hybrid optimization indexes of the electric automobile, establishing a corresponding real-time electric automobile optimized charging model, and adjusting the optimized charging plan of the electric automobile in real time in the future according to the combination sequence of the real-time hybrid optimization indexes required by different charging scenes.
The method works out the combination sequence of the optimization indexes according to the requirements of different charging scenes, effectively overcomes the defect that the adjustment principle is fixed and single in the real-time optimization charging of the electric automobile, can be suitable for the optimization charging problem of the electric automobiles in different charging scenes, and has strong adaptability.
The following describes the optimization charging model of the electric vehicle before the day, the optimization charging model of the real-time electric vehicle and the simulation experiment in detail.
Day-ahead electric automobile optimized charging model
Comprehensively considering the charging electric quantity, the charging cost, the load fluctuation and the three-phase imbalance of the electric automobile, establishing a day-ahead electric automobile optimized charging model, wherein the objective function and the constraint condition of the day-ahead electric automobile optimized charging model are as follows:
1) objective function
The expression of the objective function is:
Z＝min(ω1·Z1+ω2·Z2+ω3·Z3+ω4·Z4)
wherein Z is the calculation result of the day-ahead optimized charging model of the electric automobile, Z1As a component of electric vehicle charge, ω1Weight of charge quantity component for electric vehicle, Z2Component of charging cost, omega, for electric vehicles2Weighting the charging cost component of an electric vehicle, Z3As a component of load fluctuation, ω3As a weight of the load fluctuation component, Z4Being three-phase unbalanced components, ω4Is the weight of the three-phase unbalanced component. The expressions for the four components are respectively as follows:
Figure GDA0003150987660000051
Figure GDA0003150987660000052
Figure GDA0003150987660000061
Figure GDA0003150987660000062
in the formula: n is the total number of the electric automobiles; SOCmin，SOCmaxRespectively a lower limit value and an upper limit value of the state of charge of the battery;
Figure GDA0003150987660000063
the initial charge state of the nth electric vehicle is obtained; c is a weight factor, the value is 0.1, and the SOC represents that the battery of the electric automobile is charged to the minimum state of charge (SOC)minThe importance of is charging to a maximum state of charge SOCmax10 times the importance of; c. CkCharging the electric automobile in the kth time period by using the electricity price; pi,j,kThe j phase load power of the electric vehicle charging station is the k phase; Δ t is the duration of each time period; omeganThe charging efficiency of the electric vehicle n; qn.needThe required electric quantity is charged for the nth electric automobile; a isj,nThe number of electric vehicles charging the jth phase of the charging station; delta Pj,nThe maximum charging power difference value of the nth electric vehicle of the j phase in the adjacent time period; t isj,nCharging time of the jth electric automobile of the jth phase; pEV,n,maxN maximum charging power for the electric vehicle; dk,nMaking a charging decision for the electric vehicle n in the k period when dk,nWhen 1, the electric vehicle n is charged in the k-th period, and when dk,nWhen 0, the electric vehicle n is not charged in the k-th period.
2) Constraint conditions
The constraints are as follows:
2.1) each electric vehicle can only be charged at one charging station, and a first constraint is carried out, wherein the expression of the first constraint is as follows:
Figure GDA0003150987660000064
in the formula: xi,nCharging command for electric vehicle n at charging station i, when Xi,nWhen the charging time is 1, the electric vehicle n is charged at a charging station i, and when the charging time is Xi,nWhen 0, the electric vehicle n is not charged at the charging station i.
2.2) carrying out electric automobile charging power constraint, wherein the expression of the electric automobile charging power constraint is as follows:
0≤PEV,n≤PEV,max
in the formula: pEV,nThe charging power of the electric automobile n is obtained.
2.3) the load power of the electric vehicle charging station cannot exceed the maximum power of the charging station, and carrying out second constraint, wherein the expression of the second constraint is as follows:
Figure GDA0003150987660000065
in the formula: pi,j,tCharging station ith phase load power for t moment; pstation,i,maxThe maximum power of the charging station i.
Second, real-time electric automobile optimizes the model of charging
And obtaining a day-ahead electric vehicle charging plan according to the day-ahead electric vehicle optimized charging model, and charging the electric vehicles in the plan according to the day-ahead charging plan. And arranging the unplanned electric vehicles to be charged in order according to the charging priority of the electric vehicle users. Factors for measuring the charging priority of the electric vehicle comprise: the method comprises the following steps of respectively establishing a priority model of electric automobile charging quantity, charging cost, load fluctuation and three-phase imbalance as follows:
Figure GDA0003150987660000071
in the formula: prority1(n, t) is a priority value of the charging capacity of the electric automobile n at the moment t; SOCn,tThe charge state of the electric vehicle n at the time t; b isnIs the battery capacity of the electric vehicle n; t isn,stayThe n parking time of the electric automobile, M is a constant and is 105。
Figure GDA0003150987660000072
In the formula: prority2(n, t) is a priority value of the charging fee of the electric vehicle n at the time t; c. CtCharging the electric automobile at the time t; pEV,n,tCharging power for the electric automobile n at the time t;
priority3(n,t)＝-PEV,n,t
in the formula: priority3(n, t) is a priority value of load fluctuation of the electric vehicle n during charging at the time t
priority4(n,t)＝L'imbalance(n,t)-Limbalance(n,t)
In the formula: priority4(n, t) is a priority value of three-phase unbalance of the electric automobile n during charging at the moment t; l'imbalance(n, t) is the three-phase unbalance degree when the electric automobile n does not participate in charging at the moment t; l isimbalanceAnd (n, t) is the three-phase unbalance degree when the electric automobile n participates in charging at the moment t.
The method comprises the steps that charging scenes are changed continuously along with the participation of unplanned electric vehicles in charging, the requirements of different charging scenes on electric vehicle charging quantity, charging cost, load fluctuation and three-phase imbalance are different, and the arrangement sequence of mixed priority indexes is adjusted continuously according to the requirements of different charging scenes in the real-time optimal charging process of the electric vehicles.
Third, simulation experiment
In order to verify the correctness and the effectiveness of the real-time intelligent charging method for the electric vehicle based on the day-ahead plan, the intelligent optimized charging method for the electric vehicle provided by the embodiment is compared with the traditional 'day-ahead + real-time' optimized charging method for the electric vehicle, simulation experiments are performed for a plurality of times in different scenes, namely different deviation degrees Rand between the charging plan for the electric vehicle and the real-time charging of the electric vehicle before the day, and an average value is taken as a final result, which is shown in table 1.
Table 1 data results for different charging scenarios
Figure GDA0003150987660000081
From the analysis of table 1 it can be found: compared with the traditional 'day ahead + real-time' electric vehicle charging method, the electric vehicle intelligent charging method provided by the embodiment can effectively improve the charge state of the electric vehicle when the electric vehicle leaves, reduce load fluctuation and three-phase imbalance, and has more obvious effect along with the continuous increase of the deviation degree of the day ahead electric vehicle charging plan and real-time charging.
Simulation case study shows that: the intelligent charging method for the electric automobile can be suitable for different charging scenes, and has the advantages of being more obvious along with the continuous complexity of the charging scenes and stronger adaptability.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (5)

1. A real-time intelligent charging method for an electric automobile based on a day-ahead plan is characterized by comprising the steps of adopting a pre-established day-ahead electric automobile optimized charging model, comprehensively considering electric automobile charging amount, electric automobile charging cost, load fluctuation and three-phase imbalance, and charging the electric automobile in the plan; charging an unplanned electric vehicle by adopting a pre-established real-time electric vehicle optimized charging model according to the charging priority of an electric vehicle user;
the objective function of the day-ahead electric vehicle optimization charging model is as follows:
Z＝min(ω1·Z1+ω2·Z2+ω3·Z3+ω4·Z4)
wherein Z is the calculation result of the day-ahead optimized charging model of the electric automobile, Z1As a component of electric vehicle charge, ω1Weight of charge quantity component for electric vehicle, Z2Component of charging cost, omega, for electric vehicles2Weighting the charging cost component of an electric vehicle, Z3As a component of load fluctuation, ω3As a weight of the load fluctuation component, Z4Being three-phase unbalanced components, ω4Weights for three-phase imbalance components;
the real-time electric automobile optimized charging model comprises a charging electric quantity priority submodel, a charging expense priority submodel, a load fluctuation priority submodel during charging and a three-phase unbalance priority submodel during charging,
the expression of the charging electric quantity priority submodel is as follows:
Figure FDA0003181539740000011
in the formula, protity1(n, t) is the charging capacity priority of the electric vehicle n at the time t, SOCn,tIs the state of charge, SOC, of the electric vehicle n at the time tminIs the lower limit of the state of charge, SOCmaxIs the upper limit of the state of charge of the battery, BnIs the battery capacity of the electric vehicle n; t isn,stayN parking time of the electric vehicle, M is a constant, PEV,n,maxN maximum charging power for the electric vehicle;
the expression of the charging fee priority submodel is as follows:
Figure FDA0003181539740000012
in the formula, protity2(n, t) is charging fee priority of the electric vehicle n at the time t, ctCharging price for electric vehicle at time t, PEV,n,tCharging power for an electric vehicle n at time t, tmaxIs the maximum value of the charging time;
the expression of the load fluctuation priority submodel during charging is as follows:
priority3(n,t)＝-PEV,n,t
wherein the priority3(n, t) is a priority value of load fluctuation of the electric vehicle n during charging at the time t, PEV,n,tCharging power for the electric automobile n at the time t;
the expression of the three-phase unbalanced priority submodel during charging is as follows:
priority4(n,t)＝L'imbalance(n,t)-Limbalance(n,t)
wherein the priority4(n, t) is the three-phase imbalance priority of the electric vehicle n during charging at the time t; l'imbalance(n, t) is the three-phase unbalance degree when the electric automobile n does not participate in charging at the moment t; l isimbalanceAnd (n, t) is the three-phase unbalance degree when the electric automobile n participates in charging at the moment t.
2. The real-time intelligent charging method for the electric vehicle based on the day-ahead plan as set forth in claim 1, wherein the calculation expression of the electric vehicle charging quantity component is as follows:
Figure FDA0003181539740000021
wherein N is the total number of the electric vehicles, SOCminIs the lower limit of the state of charge, SOCmaxIs the upper limit value of the state of charge of the battery,
Figure FDA0003181539740000022
is the initial state of charge, SOC, of the nth electric vehiclenAnd c is a weight factor.
3. The real-time intelligent charging method for the electric vehicle based on the day-ahead plan as set forth in claim 1, wherein the calculation expression of the charging cost component of the electric vehicle is as follows:
Figure FDA0003181539740000023
in the formula, ckCharging price for electric vehicle in k-th period, Pi,j,kFor the j phase load power of the electric vehicle charging station in the k time period, delta t is the duration of each time period, Qn.needThe required charging quantity of the nth electric automobile is omega'nThe charging efficiency of the electric vehicle n is shown.
4. The real-time intelligent charging method for the electric automobile based on the day-ahead plan as set forth in claim 1, wherein the calculation expression of the load fluctuation component is as follows:
Figure FDA0003181539740000031
in the formula, Pi,j,kFor the kth period of time, i (th) of an electric vehicle charging stationLoad power of j phase, aj,nNumber of electric vehicles, Δ P, charged for the j-th phase of the charging stationj,nThe maximum charging power difference value T of the adjacent time period of the j-th phase n-th electric vehiclej,nFor the charging time of the jth electric vehicle, Δ t is the time duration of each time interval.
5. The real-time intelligent charging method for the electric automobile based on the day-ahead plan as set forth in claim 1, wherein the calculation expression of the three-phase imbalance component is as follows:
Figure FDA0003181539740000032
in the formula, Pi,j,kFor the j phase load power of the electric vehicle charging station i in the k period, j is 1,2,3, N is the total number of electric vehicles, PEV,n,maxN maximum charging power for the electric vehicle, dk,nMaking a charging decision for the electric vehicle n in the k period when dk,nWhen 1, the electric vehicle n is charged in the k-th period, and when dk,nWhen 0, the electric vehicle n is not charged in the k-th period.
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