阅读说明：本技术 考虑用户选择的电动汽车优化调度方法及优化调度系统 (Electric vehicle optimal scheduling method and optimal scheduling system considering user selection ) 是由 刘乙 钱科军 谢鹰 朱超群 李亚飞 郑众 张晓明 于 2021-08-24 设计创作，主要内容包括：考虑用户选择的电动汽车优化调度方法及优化调度系统,方法包括：步骤1,采集调度区域内的历史负荷数据；步骤2,基于历史负荷数据对电动汽车和非电动汽车总负荷功率进行预测得到每个时间段t内的总负荷功率预测值；步骤3,建立待优化调度总时间段内电动汽车优化调度的目标函数；步骤4,设定电动汽车的约束条件；步骤5,实时采集调度区域内所有电动汽车的充放电数据并对目标函数进行求解得到最优调度所需要使用的充电功率系数与放电功率系数。本发明还公开了对应该方法的优化调度系统。本发明能够得到电动汽车最优的调度,在考虑用户充电选择的情况下,最小化负荷峰谷差以达到削峰填谷的效果。(An electric vehicle optimal scheduling method and an optimal scheduling system considering user selection are provided, and the method comprises the following steps: step 1, collecting historical load data in a dispatching area; step 2, predicting the total load power of the electric automobile and the non-electric automobile based on historical load data to obtain a total load power predicted value in each time period t; step 3, establishing an objective function of electric vehicle optimized dispatching in the total time period to be optimized dispatching; step 4, setting constraint conditions of the electric automobile; and 5, acquiring the charging and discharging data of all the electric vehicles in the dispatching area in real time, and solving the objective function to obtain the charging power coefficient and the discharging power coefficient required by the optimal dispatching. The invention also discloses an optimized dispatching system corresponding to the method. The method and the device can obtain the optimal scheduling of the electric automobile, and minimize the load peak-valley difference under the condition of considering the charging selection of the user so as to achieve the effect of peak clipping and valley filling.)

1. The electric vehicle optimal scheduling method considering user selection is characterized by comprising the following steps of:

step 1, collecting historical load data in a dispatching area;

step 2, predicting the total load power of the electric automobile and the non-electric automobile in each time period t in the total time period to be optimally scheduled based on the historical load data in the step 1 to obtain a total load power predicted valueT is the time period after T is equally divided,

step 3, establishing an objective function of electric vehicle optimized dispatching in the total time period to be optimized dispatching;

the objective function of the optimized dispatching of the electric automobile meets the following relational expression:

wherein G is^tThe electricity purchasing cost phi of the electric automobile in the time period t_iFor the battery loss cost, P, of the electric vehicle i in the dispatching process^maxIs the maximum value of the total load power, P, of the electric vehicle and the non-electric vehicle^minThe minimum value of the total load power of the electric automobile and the non-electric automobile; i is the total set of the electric automobile;

andrespectively representing the charging power coefficient and the discharging power coefficient of the electric automobile i in the time period t;

alpha, lambda and delta are each G^t、φ_iAnd (P)^max-P^min) The corresponding coefficients;

step 4, setting constraint conditions of the electric automobile;

and 5, acquiring charge and discharge data of the electric automobile in each time period t in real time, and solving the model in the step 3 by combining the charge and discharge data, the total load power predicted value in the step 2 and the constraint condition in the step 4 to obtain a charge power coefficient and a discharge power coefficient which are needed by optimal scheduling.

2. The electric vehicle optimal scheduling method considering user selection according to claim 1, wherein:

in step 1, the historical load data in the dispatching area comprises historical electric vehicle load power data and non-electric vehicle load power data.

3. The optimal scheduling method of an electric vehicle considering user selection according to claim 1 or 2, wherein:

in the step 3, when the electric automobile belongs to I_g2vElectric automobile i₁When the temperature of the water is higher than the set temperature,is composed ofRepresents an electric vehicle i₁A charging power coefficient at a time period t; when the electric automobile belongs to I_v2gElectric automobile i₂When the temperature of the water is higher than the set temperature,is composed ofRepresents an electric vehicle i₂The coefficient of the charging power over the period of time t,is composed ofRepresents an electric vehicle i₂Discharge power coefficient at time t; i is_g2vFor an electric vehicle set adopting G2V model, I_v2gThe electric automobiles adopting the V2G mode are combined to form a total set I of the electric automobiles.

4. The electric vehicle optimal scheduling method considering user selection according to claim 3, wherein:

in the step 3, the electric vehicle has an electricity purchase cost G in a time period t^tThe following relation is satisfied:

wherein the content of the first and second substances,is of I_g2vElectric automobile i₁The rated charging power of the battery pack,is of I_v2gElectric automobile i₂The rated charging power of the battery pack,is of I_v2gElectric automobile i₂Rated discharge power; omega₁For electric vehicle charging price, omega, using G2V model₂For electric vehicle charging price, omega, using V2G mode₃For the discharge price, omega, of an electric vehicle adopting the V2G mode₂Ratio omega₁Is small.

5. The electric vehicle optimal scheduling method considering user selection according to claim 4, wherein:

belong to I_g2vElectric automobile i₁And belong to I_v2gElectric automobile i₂Respectively, the battery loss cost isAndif the electric automobile I belongs to I_g2vElectric vehicle of (1), then phi_iAndsimilarly, if the electric vehicle I belongs to I_v2gElectric vehicle of (1), then phi_iAndin the same way, the first and second,andthe following relation is satisfied:

wherein: epsilon₁Is epsilon₂The loss coefficients of the battery in the charging and discharging process respectively represent a power fluctuation coefficient and a power size coefficient;andrespectively represent electric vehicles i₁Or i₂A charging power coefficient at a time period t-1;represents an electric vehicle i₂Discharge power coefficient at time t-1;is of I_g2vElectric automobile i₁The rated charging power of the battery pack,is of I_v2gElectric automobile i₂The rated charging power of the battery pack,is of I_v2gElectric automobile i₂Rated discharge power.

6. The electric vehicle optimal scheduling method considering user selection according to claim 5, wherein:

the P is^maxAnd P^minThe following conditions are met:

wherein the content of the first and second substances,and predicting the total load power of the t-th time period.

7. The electric vehicle optimal scheduling method considering user selection according to claim 1, wherein:

in the step 4, the constraint conditions of the electric vehicle include an SOC constraint condition of the electric vehicle, a constraint condition for ensuring that charging reaches an ideal level, a maximum SOC limit in a charging and discharging process, a minimum SOC limit in a charging and discharging process, a charging and discharging rate limit before charging, and a charging and discharging power coefficient limit.

8. The electric vehicle optimal scheduling method considering user selection according to claim 7, wherein:

the SOC constraint conditions of the electric automobile are as follows:

wherein the content of the first and second substances,is the SOC when the electric vehicle i arrives at the charging station, the SOC_init,iIs the initial SOC of the electric vehicle i,andelectric automobile i in t time periods respectively₁Or i₂The SOC of (a) is determined,andare respectively an electric automobile i₁Or i₂The efficiency of the charging of (a) the charging,is an electric automobile i₂The efficiency of the discharge of (a) is,andare respectively an electric automobile i₁Or i₂The battery capacity of (a);

I_g2vfor an electric vehicle set adopting G2V model, I_v2gThe method is characterized in that a set of electric automobiles adopting a V2G mode jointly forms a total set I of the electric automobiles;

andrespectively represent electric vehicles i₁Or i₂A charging power coefficient at a time period t-1;represents an electric vehicle i₂Discharge power coefficient at time t-1.

9. The electric vehicle optimal scheduling method considering user selection according to claim 7, wherein:

the constraint condition for ensuring that the charging reaches the ideal level is as follows:

therein, SOC_desired,iIs the expected minimum SOC value of the electric automobile i,the SOC value is the SOC value when the electric vehicle i leaves the charging station.

10. The electric vehicle optimal scheduling method considering user selection according to claim 8, wherein:

the maximum SOC limit in the charging and discharging process is as follows:

therein, SOC_max,iThe maximum SOC value allowed by the electric vehicle I is when the electric vehicle I belongs to I_g2vWhen the temperature of the water is higher than the set temperature,is composed ofWhen the electric automobile I belongs to I_v2gWhen the temperature of the water is higher than the set temperature,is composed of

11. The electric vehicle optimal scheduling method considering user selection according to claim 10, wherein:

the charge and discharge rate before charging is limited as follows:

wherein the content of the first and second substances,represents an electric vehicle i₁The coefficient of the charging power before the charging,represents an electric vehicle i₂The coefficient of the charging power before the charging,represents an electric vehicle i₂Discharge power coefficient before charging.

12. The electric vehicle optimal scheduling method considering user selection according to claim 7, wherein:

the charge-discharge power coefficient limit comprises the following contents:

for the electric automobile adopting the G2V mode, the charging power coefficient is limited as follows:

wherein the content of the first and second substances,represents an electric vehicle i₁A charging power coefficient at a time period t; i is_g2vThe method is characterized in that the method is a set of electric automobiles adopting a G2V mode;

for the electric automobile adopting the V2G mode, the charging power coefficient is limited as follows:

wherein the content of the first and second substances,represents an electric vehicle i₂A charging power coefficient at a time period t;represents an electric vehicle i₂Discharge power coefficient at time t; i is_v2gThe method is characterized in that the method is a set of electric automobiles adopting a V2G mode;andthe charging flag bit and the discharging flag bit are respectively binary integer variables of 0 or 1, and at most only one value can be taken as 1, namely, the charging flag bit is charged1, discharge flag bitIs 0; during discharge, the discharge targetSign position1, charging flag bitIs 0.

13. The electric vehicle optimal scheduling method considering user selection according to any one of claims 1 to 12, wherein:

in the step 5, the collected charging and discharging data of the electric automobile comprises rated charging power of the electric automobile in a scheduling area within a time period t; rated discharge power of the electric vehicle; an electric vehicle charging selection mode, namely a G2V mode or a V2G mode; the charging efficiency and the discharging efficiency of the electric vehicle; an electric vehicle desired minimum state of charge value; a maximum allowable state of charge value of the electric vehicle; the minimum allowable state of charge value of the electric vehicle; battery capacity value of electric vehicle.

14. The electric vehicle optimal scheduling method considering user selection according to claim 13, wherein:

in the step 5, a solver including Cplex, Mosek and Gurobi may be directly used to solve the charging power coefficient and the discharging power coefficient of the electric vehicle in each time period in combination with the model in the step 3 and the constraint condition in the step 4.

15. The optimal scheduling system of the electric vehicle optimal scheduling method considering the user selection according to claim 14, comprising a historical data acquisition module, a charge and discharge data acquisition module, a load prediction module, an objective function module, a constraint condition module and an optimal scheduling parameter solving module, wherein:

the historical data acquisition module is used for acquiring historical load data in a dispatching area and inputting the acquired data to the load prediction module;

the charging and discharging data acquisition module acquires charging and discharging data of all electric vehicles in a scheduling area within a total time period T to be optimized and scheduled, and inputs the acquired charging and discharging data to the optimized scheduling parameter solving module;

the load prediction module predicts the total load power of the electric automobile and the non-electric automobile in the total time period T to be optimally scheduled, determines the predicted value of the total load power in each time period T,inputting the predicted total load power predicted value to the optimized scheduling parameter solving module;

the objective function module establishes an objective function of electric vehicle optimized dispatching in the total time period to be optimized dispatching, and inputs the objective function to the optimized dispatching parameter solving module;

the constraint condition module sets constraint conditions of the electric automobile and inputs the constraint conditions to the optimized scheduling parameter solving module;

and the optimal scheduling parameter solving module substitutes the charging and discharging data and the predicted total load power predicted value into a target function, and obtains a charging power coefficient and a discharging power coefficient which are required to be used for optimal scheduling according to the constraint condition.

Technical Field

The invention belongs to the technical field of electric vehicle optimized dispatching, and particularly relates to an electric vehicle optimized dispatching method and an optimized dispatching system considering user selection.

Background

The rapid development of electric vehicles puts higher demands on power systems. With the increase of the number of the electric automobiles connected to the power grid, the power load of the power system is inevitably increased, the electric automobiles bring great pressure to the power system under large-scale charging due to uncertainty of charging time and morning and evening tidal effects, load peak-valley difference is aggravated, stability of the power system is influenced, higher requirements are provided for equipment capacity of the power system, and design and maintenance of the power system are also difficult. If the accessed electric automobile is effectively scheduled, the load peak-valley difference can be effectively reduced, and the utilization rate of the power equipment is improved. When the electric automobile receives system dispatching, two modes are included: the 1 st mode is charging only but not discharging (G2V mode), and the 2 nd mode is both charging and discharging to the grid (V2G mode). The V2G mode is more effective in reducing the peak-to-valley difference than the G2V mode, but the battery loss for the electric vehicle is more serious.

Most of the existing electric vehicle dispatching methods only consider a single charging mode and do not consider the charging selection of a user. Meanwhile, the loss cost of the battery of the electric automobile user is not counted when the dispatching is carried out.

Disclosure of Invention

In order to solve the defects in the prior art, the invention aims to provide an electric vehicle optimal scheduling method and an optimal scheduling system considering user selection. On the basis of load prediction and electric vehicle charging data, a scheduling model of the electric vehicle is established according to charging selection of electric vehicle users (such as a G2V mode or a V2G mode), and the electric vehicle is optimally scheduled, so that peak-valley difference of the load is reduced. Firstly, load prediction and charging and discharging data acquisition of the electric automobile are carried out, and then the electric automobile is optimally scheduled according to the established optimal scheduling model. The model is solved by a commercial solver, and the charging power and the discharging power of the electric automobile in different time periods are obtained.

In order to solve the technical problems, the invention adopts the following technical scheme:

the electric vehicle optimal scheduling method considering user selection comprises the following steps:

step 1, collecting historical load data in a dispatching area;

step 2, predicting the total load power of the electric automobile and the non-electric automobile in each time period t in the total time period to be optimally scheduled based on the historical load data in the step 1 to obtain a total load power predicted valueT is the time period after T is equally divided,

step 3, establishing an objective function of electric vehicle optimized dispatching in the total time period to be optimized dispatching;

the objective function of the optimized dispatching of the electric automobile meets the following relational expression:

wherein G is^tThe electricity purchasing cost phi of the electric automobile in the time period t_iFor the battery loss cost, P, of the electric vehicle i in the dispatching process^maxIs the maximum value of the total load power, P, of the electric vehicle and the non-electric vehicle^minThe minimum value of the total load power of the electric automobile and the non-electric automobile; i is the total set of the electric automobile;

andrespectively representing the charging power coefficient and the discharging power coefficient of the electric automobile i in the time period t;

alpha, lambda and delta are each G^t、φ_iAnd (P)^max-P^min) The corresponding coefficients;

step 4, setting constraint conditions of the electric automobile;

and 5, acquiring charge and discharge data of the electric automobile in each time period t in real time, and solving the model in the step 3 by combining the charge and discharge data, the total load power predicted value in the step 2 and the constraint condition in the step 4 to obtain a charge power coefficient and a discharge power coefficient which are needed by optimal scheduling.

In step 1, the historical load data in the dispatching area comprises historical electric vehicle load power data and non-electric vehicle load power data.

In step 3, when the electric vehicle belongs to I_g2vElectric automobile i₁When the temperature of the water is higher than the set temperature,is composed ofRepresents an electric vehicle i₁A charging power coefficient at a time period t; when the electric automobile belongs to I_v2gElectric automobile i₂When the temperature of the water is higher than the set temperature,is composed ofRepresents an electric vehicle i₂The coefficient of the charging power over the period of time t,is composed ofRepresents an electric vehicle i₂Discharge power coefficient at time t; i is_g2vFor an electric vehicle set adopting G2V model, I_v2gThe electric automobiles adopting the V2G mode are combined to form a total set I of the electric automobiles.

In step 3, the electricity purchasing cost G of the electric automobile in the time period t^tThe following relation is satisfied:

wherein the content of the first and second substances,is of I_g2vElectric automobile i₁The rated charging power of the battery pack,is of I_v2gElectric automobile i₂The rated charging power of the battery pack,is of I_v2gElectric automobile i₂Rated discharge power; omega₁For electric vehicle charging price, omega, using G2V model₂For electric vehicle charging price, omega, using V2G mode₃For the discharge price, omega, of an electric vehicle adopting the V2G mode₂Ratio omega₁Is small.

Belong to I_g2vElectric automobile i₁And belong to I_v2gElectric automobile i₂Respectively, the battery loss cost isAndif the electric automobile I belongs to I_g2vElectric vehicle of (1), then phi_iAndsimilarly, if the electric vehicle I belongs to I_v2gElectric vehicle of (1), then phi_iAndin the same way, the first and second,andthe following relation is satisfied:

wherein: epsilon₁Is epsilon₂The loss coefficients of the battery in the charging and discharging process respectively represent a power fluctuation coefficient and a power size coefficient;andrespectively represent electric vehicles i₁Or i₂A charging power coefficient at a time period t-1;represents an electric vehicle i₂Discharge power coefficient at time t-1;is of I_g2vElectric automobile i₁The rated charging power of the battery pack,is of I_v2gElectric automobile i₂The rated charging power of the battery pack,is of I_v2gElectric automobile i₂Rated discharge power.

P^maxAnd P^minThe following conditions are met:

wherein, P_t ^loadAnd predicting the total load power of the t-th time period.

In step 4, the constraint conditions of the electric vehicle include an SOC constraint condition of the electric vehicle, a constraint condition for ensuring that charging reaches an ideal level, a maximum SOC limit in a charging and discharging process, a minimum SOC limit in a charging and discharging process, a charging and discharging rate limit before charging, and a charging and discharging power coefficient limit.

The SOC constraint conditions of the electric automobile are as follows:

wherein the content of the first and second substances,is the SOC when the electric vehicle i arrives at the charging station, the SOC_init,iIs the initial SOC of the electric vehicle i,andelectric automobile i in t time periods respectively₁Or i₂The SOC of (a) is determined,andare respectively an electric automobile i₁Or i₂The efficiency of the charging of (a) the charging,is an electric automobile i₂The efficiency of the discharge of (a) is,andare respectively an electric automobile i₁Or i₂The battery capacity of (a);

I_g2vfor an electric vehicle set adopting G2V model, I_v2gThe method is characterized in that a set of electric automobiles adopting a V2G mode jointly forms a total set I of the electric automobiles;

andrespectively represent electric vehicles i₁Or i₂A charging power coefficient at a time period t-1;represents an electric vehicle i₂Discharge power coefficient at time t-1.

The constraint condition for ensuring that the charging reaches the ideal level is as follows:

therein, SOC_desired,iIs the expected minimum SOC value of the electric automobile i,the SOC value is the SOC value when the electric vehicle i leaves the charging station.

The maximum SOC limit during charging and discharging is:

therein, SOC_max,iThe maximum SOC value allowed by the electric vehicle I is when the electric vehicle I belongs to I_g2vWhen the temperature of the water is higher than the set temperature,is composed ofWhen the electric automobile I belongs to I_v2gWhen the temperature of the water is higher than the set temperature,is composed of

The charge and discharge rate before charging is limited to:

wherein the content of the first and second substances,represents an electric vehicle i₁The coefficient of the charging power before the charging,represents an electric vehicle i₂The coefficient of the charging power before the charging,represents an electric vehicle i₂Discharge power coefficient before charging.

The charge-discharge power coefficient limit includes the following:

for the electric automobile adopting the G2V mode, the charging power coefficient is limited as follows:

wherein the content of the first and second substances,represents an electric vehicle i₁A charging power coefficient at a time period t; i is_g2vThe method is characterized in that the method is a set of electric automobiles adopting a G2V mode;

for the electric automobile adopting the V2G mode, the charging power coefficient is limited as follows:

wherein the content of the first and second substances,represents an electric vehicle i₂A charging power coefficient at a time period t;represents an electric vehicle i₂Discharge power coefficient at time t; i is_v2gThe method is characterized in that the method is a set of electric automobiles adopting a V2G mode;andthe charging flag bit and the discharging flag bit are respectively binary integer variables of 0 or 1, and at most only one value can be taken as 1, namely, the charging flag bit is charged1, discharge flag bitIs 0; during discharging, the discharge flag bit1, charging flag bitIs 0.

In the step 5, the collected charging and discharging data of the electric automobile comprises rated charging power of the electric automobile in a scheduling area within a time period t; rated discharge power of the electric vehicle; an electric vehicle charging selection mode, namely a G2V mode or a V2G mode; the charging efficiency and the discharging efficiency of the electric vehicle; an electric vehicle desired minimum state of charge value; a maximum allowable state of charge value of the electric vehicle; the minimum allowable state of charge value of the electric vehicle; battery capacity value of electric vehicle.

In step 5, a solver including Cplex, Mosek and Gurobi may be directly used to solve the charging power coefficient and the discharging power coefficient of the electric vehicle in each time period in combination with the model in step 3 and the constraint condition in step 4.

The invention also discloses an optimized dispatching system based on the electric vehicle optimized dispatching method considering user selection, which comprises a historical data acquisition module, a charging and discharging data acquisition module, a load prediction module, an objective function module, a constraint condition module and an optimized dispatching parameter solving module, and is characterized in that:

the historical data acquisition module is used for acquiring historical load data in the dispatching area and inputting the acquired data to the load prediction module;

the charging and discharging data acquisition module acquires charging and discharging data of all electric vehicles in a scheduling area within a total time period T to be optimized and scheduled, and inputs the acquired charging and discharging data to the optimized scheduling parameter solving module;

the load prediction module predicts the total load power of the electric automobile and the non-electric automobile in the total time period T to be optimally scheduled, determines the predicted value of the total load power in each time period T,inputting the predicted total load power predicted value to an optimized scheduling parameter solving module;

the target function module establishes a target function of electric vehicle optimized dispatching in the total time period to be optimized dispatching, and inputs the target function to the optimized dispatching parameter solving module;

the constraint condition module sets constraint conditions of the electric automobile and inputs the constraint conditions to the optimal scheduling parameter solving module;

and the optimal scheduling parameter solving module substitutes the charging and discharging data and the predicted total load power predicted value into a target function, and obtains a charging power coefficient and a discharging power coefficient which are required to be used for optimal scheduling according to the constraint condition.

Compared with the prior art, the invention has the beneficial effects that:

1. the technical scheme provided by the invention considers the charging selection of the user, uses different calculation methods according to the G2V charging mode or the V2G charging mode, and simultaneously considers the battery loss cost of the user, so that the algorithm is more accurate;

2. the algorithm in the technical scheme of the invention is a self-developed optimization algorithm, and a mathematical model solved according to the proposed limiting conditions is quicker and more accurate than the algorithm in the prior art;

3. the mathematical model established by the method is a quadratic programming model, and is simpler and more convenient than the model in the prior art in solving; the model can directly call a commercial solver to carry out efficient solving, obtains the optimal scheduling method of the electric automobile, and can minimize the load peak-valley difference under the condition of considering the charging selection of a user so as to achieve the effect of peak clipping and valley filling.

Drawings

Fig. 1 is a schematic flow chart of an electric vehicle optimal scheduling method considering user selection according to the present invention.

Detailed Description

The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.

Step 1: collecting historical load data in a scheduling area;

the historical load data in the dispatch area includes historical electric vehicle load power data and non-electric vehicle load power data. In this embodiment, the historical load data is obtained from the records of the dispatch center.

Step 2: predicting the total load power of the electric automobile and the non-electric automobile in each time period t in the total time period to be optimally scheduled based on the historical load data in the step 1 to obtain a total load power predicted value P_t ^loadT is a time period after T is equally divided,

before the electric automobile is optimally scheduled, the total load power of the electric automobile and the non-electric automobile is predicted based on historical load data, and the load sizes of different time periods are determined, so that peak clipping and valley filling can be performed during the optimal scheduling of the electric automobile, and the peak-valley difference of the load is reduced;

the total load power prediction may select conventional prediction techniques, including neural network prediction.

And step 3: establishing an objective function of electric vehicle optimized dispatching in the total time period to be optimized dispatching;

the mathematical model of the optimized dispatching of the electric automobile comprises the following objective functions:

the objective function comprises three components of total cost of electricity purchase, total loss cost of the battery and peak-valley difference. Wherein: i is the general set of electric vehicles, item one G^tThe electricity purchasing cost of the electric automobile in the time period t is calculated; phi in the second term_iBattery loss cost of the electric automobile i in the dispatching process; item III P^maxIs the maximum value of the total load power, P, of the electric vehicle and the non-electric vehicle^minThe minimum value of the total load power of the electric automobile and the non-electric automobile;andrespectively representing the charging power coefficient and the discharging power coefficient of the electric automobile i in the time period t; when the electric automobile belongs to I_g2vElectric automobile i₁When the temperature of the water is higher than the set temperature,is composed ofRepresents an electric vehicle i₁A charging power coefficient at a time period t; when the electric automobile belongs to I_v2gElectric automobile i₂When the temperature of the water is higher than the set temperature,is composed ofRepresents an electric vehicle i₂The coefficient of the charging power over the period of time t,is composed ofRepresents an electric vehicle i₂Discharge power coefficient at time t; i is_g2vFor an electric vehicle set adopting G2V model, I_v2gThe method is characterized in that a set of electric automobiles adopting a V2G mode jointly forms a total set I of the electric automobiles;

alpha, lambda and delta are each G^t、φ_iAnd (P)^max-P^min) And the three coefficients determine the importance degrees of the three terms, the larger the value is, the more important the corresponding parameter is proved to be, and the specific value can be determined according to the actual situation.

The electricity purchase cost of the electric automobile in the time period t meets the following relational expression:

wherein:to adopt I_g2vMode of electric vehicle i₁The rated charging power of the battery pack,to adopt I_v2gMode of electric vehicle i₂The rated charging power of the battery pack,to adopt I_v2gMode of electric vehicle i₂Rated discharge power. I is_g2vFor an electric vehicle set adopting G2V model, I_v2gThe electric automobiles adopting the V2G mode are combined to form a total set I of the electric automobiles. Omega₁For electric vehicle charging price, omega, using G2V model₂For electric vehicle charging price, omega, using V2G mode₃The discharge price of the electric automobile adopting the V2G mode is obtained. Because the V2G mode is more battery-draining than the G2V mode, ω₂Then ratio omega₁Is small.

Belong to I_g2vElectric automobile i₁And belong to I_v2gElectric automobile i₂Respectively, the battery loss cost isAndif the electric automobile I adopts I_g2vElectric vehicle of model, then phi_iAndsimilarly, if the electric vehicle I belongs to I_v2gElectric vehicle of (1), then phi_iAndin the same way, the first and second,andthe following relation is satisfied:

wherein: epsilon₁Is epsilon₂The loss coefficients of the battery in the charging and discharging process respectively represent a power fluctuation coefficient and a power size coefficient.Andrespectively represent electric vehicles i₁Or i₂A charging power coefficient at a time period t;andrespectively represent electric vehicles i₁Or i₂A charging power coefficient at a time period t-1;electric steamVehicle i₂Discharge power coefficient at time t;represents an electric vehicle i₂Discharge power coefficient at time t-1.

Wherein P is^maxAnd P^minThe following conditions are met:

wherein, P_t ^loadAnd predicting the total load power of the t-th time period.

And 4, step 4: setting constraint conditions of the electric automobile;

the constraint conditions of the electric automobile comprise an SOC constraint condition of the electric automobile, a constraint condition for ensuring that charging reaches an ideal level, a maximum SOC limit in the charging and discharging process, a minimum SOC limit in the charging and discharging process, a charging and discharging rate limit before charging and a charging and discharging power coefficient limit.

The SOC constraint conditions of the electric automobile are as follows:

wherein the content of the first and second substances,is the SOC when the electric vehicle i arrives at the charging station, the SOC_init,iThe initial SOC of the electric vehicle i,andelectric automobile i in t time periods respectively₁Or i₂The SOC of (a) is determined,andare respectively an electric automobile i₁Or i₂The efficiency of the charging of (a) the charging,is an electric automobile i₂The efficiency of the discharge of (a) is,andare respectively an electric automobile i₁Or i₂The battery capacity of (a).

The constraint condition for ensuring that the charging reaches the ideal level is as follows:

therein, SOC_desired,iIs the expected minimum SOC value of the electric automobile i,the SOC value is the SOC value when the electric vehicle i leaves the charging station.

The maximum SOC limit during charging and discharging is:

therein, SOC_max,iThe maximum SOC value allowed by the electric vehicle I is when the electric vehicle I belongs to I_g2vWhen the temperature of the water is higher than the set temperature,is composed ofWhen the electric automobile I belongs to I_v2gWhen the temperature of the water is higher than the set temperature,is composed of

The minimum SOC limit during charging and discharging is:

therein, SOC_min,iThe minimum SOC value allowed by the electric automobile i.

The charge and discharge rate before charging is limited to:

wherein the content of the first and second substances,represents an electric vehicle i₁The coefficient of the charging power before the charging,represents an electric vehicle i₂The coefficient of the charging power before the charging,represents an electric vehicle i₂Discharge power coefficient before charging.

The charge-discharge power coefficient limit includes the following:

for the electric automobile adopting the G2V mode, the charging power coefficient is limited as follows:

for the electric automobile adopting the V2G mode, the charging power coefficient is limited as follows:

whereinAndthe charging flag bit and the discharging flag bit are respectively binary integer variables of 0 or 1, and at most only one value can be taken as 1, namely, the charging flag bit is charged1, discharge flag bitIs 0; during discharging, the discharge flag bit1, charging flag bitIs 0.

And 5: acquiring charge and discharge data of the electric automobile in each time period t in real time, and solving the model in the step 3 by combining the charge and discharge data, the total load power predicted value in the step 2 and the constraint condition in the step 4 to obtain a charge power coefficient and a discharge power coefficient which are required by optimal scheduling of each time period;

the method mainly comprises the steps of collecting charging and discharging data of the electric automobile, wherein the charging and discharging data mainly comprise rated charging power of the electric automobile in a scheduling area within a time period t, rated discharging power of the electric automobile, a charging selection mode of the electric automobile, namely a G2V mode or a V2G mode, charging efficiency and discharging efficiency of the electric automobile, an expected minimum state of charge value of the electric automobile, a maximum allowable state of charge value of the electric automobile, a minimum allowable state of charge value of the electric automobile and a battery capacity value E of the electric automobile.

And the data of the electric automobile is directly uploaded to the optimized dispatching center by an electric automobile user.

And 3, only the third term of the target function is a quadratic function, the other terms are linear functions, and the constraint conditions belong to linear constraints, so that the model belongs to a mixed integer quadratic programming problem, and can be directly used for efficiently solving by a commercial solver to find the optimal scheduling strategy of the electric automobile, namely the charging power coefficient and the discharging power coefficient of the electric automobile in each time period. Solvers include Cplex, Mosek, Gurobi.

The invention also discloses an optimized dispatching system of the electric vehicle optimized dispatching method based on the consideration of user selection, which comprises a historical data acquisition module, a charging and discharging data acquisition module, a load prediction module, an objective function module, a constraint condition module and an optimized dispatching parameter solving module.

The historical data acquisition module is used for acquiring historical load data in the dispatching area and inputting the acquired data to the load prediction module;

the charging and discharging data acquisition module acquires charging and discharging data of all electric vehicles in a scheduling area within a total time period T to be optimized and scheduled, and inputs the acquired charging and discharging data to the optimized scheduling parameter solving module;

the load prediction module predicts the total load power of the electric automobile and the non-electric automobile in the total time period T to be optimally scheduled, determines the predicted value of the total load power in each time period T,inputting the predicted total load power predicted value to an optimized scheduling parameter solving module;

the target function module establishes a target function of electric vehicle optimized dispatching in the total time period to be optimized dispatching, and inputs the target function to the optimized dispatching parameter solving module;

the constraint condition module sets constraint conditions of the electric automobile and inputs the constraint conditions to the optimal scheduling parameter solving module;

and the optimal scheduling parameter solving module substitutes the charging and discharging data and the predicted total load power predicted value into a target function, and obtains a charging power coefficient and a discharging power coefficient which are required to be used for optimal scheduling according to the constraint condition.

The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.