CN105068419A - Residential community electric automobile charging and discharging control method - Google Patents

Abstract

The invention discloses a charging and discharging control method for electric vehicles in a residential area. It adopts a double-layer discrete particle swarm algorithm to solve the charging and discharging plan of each electric vehicle satisfying all constraint conditions through the bottom layer particle swarm algorithm, and then optimizes it by using the top layer particle swarm algorithm. A method for controlling the charging and discharging power of all electric vehicles outside the residential area. This method can not only effectively improve the load characteristic index of the residential area, improve the utilization rate of the grid equipment in the residential area, but also significantly reduce the charging cost of the electric vehicle, and is easy to implement and popularize.

Description

Charging and discharging control method for electric vehicles in residential quarters

technical field

The invention relates to the field of optimal control of electric vehicles, in particular to the field of energy interaction between electric vehicles and intelligent communities.

Background technique

Electric vehicles have the advantages of energy saving and emission reduction, and are emerging industries that countries are competing to develop. Breakthroughs in key technologies of electric vehicles have promoted the large-scale application of electric vehicles. The disorderly charging of large-scale electric vehicles will cause severe fluctuations in grid load, increase the expansion cost of electrical equipment, and reduce the utilization rate of electrical equipment; the energy storage potential of electric vehicles is not fully utilized, which is not conducive to the construction of active distribution networks. Formulate the charging and discharging control method of electric vehicles in residential areas, on the premise of meeting the main driving needs of electric vehicles, use time-of-use electricity prices to guide users to participate in the orderly charging and discharging of electric vehicles, improve the load characteristics of residential areas, improve the reliability of power supply, and delay The expansion period of electrical equipment can be shortened, and the user's electricity bill can be reduced.

The existing charging and discharging scheduling methods for electric vehicles in residential quarters are mainly integer programming, mixed integer programming algorithms and intelligent optimization algorithms. Integer programming and mixed integer programming algorithms cannot deal with nonlinear problems, are prone to fall into local optimum and produce "curse of dimensionality" problems. Intelligent optimization algorithms include particle swarm optimization algorithm, genetic algorithm and improved algorithm based on basic optimization algorithm. These algorithms can achieve more ideal optimization results, but it is difficult to deal with the charging and discharging control method of electric vehicles in residential areas with equality constraints.

Particle Swarm Optimization (PSO) has the advantages of easy implementation, fast calculation speed, and good convergence. It has good adaptability in solving high-dimensional, inequality-constrained, nonlinear problems. To solve the optimization problem with equality constraints, it is necessary to use the penalty function to change the equality constraints into unconstrained, or to change the equality constraints into two inequality constraints, and then construct a new particle swarm optimization solution. These methods have difficulty in particle position. Problems that satisfy the equality constraints and affect the convergence accuracy.

Contents of the invention

The purpose of the present invention is to provide a charging and discharging control method for electric vehicles in residential quarters. The method uses particle swarm algorithm to solve the problem, which can significantly reduce the peak-valley difference of electric load in residential quarters, increase the load rate of electric power, and reduce the fluctuation of electric load. , to reduce the cost of charging electric vehicles.

The technical method adopted by the present invention to achieve the purpose of the invention is a method for controlling the charging and discharging of electric vehicles in residential quarters, the steps of which are as follows:

A. Record the power battery capacity E _i , the maximum charging power P _max,i , and the available capacity ratio k _i of electric vehicle i; It is defined as the first travel time t _i,s of electric vehicle i on the next day, the last return time t _i,e , and the daily power consumption S _i ; where i represents the number of the electric vehicle, i=1,2,3...I; I is the total number of electric vehicles in the residential area;

B. Based on the historical basic power load data of the community, predict the basic load L _j in the jth hour (j=1, 2, 3...24) of the next day, and then obtain the daily load rate F _2min and times The peak-to-valley difference F _1max of the daily base load, and the fluctuation mean square error F _3max of the next day's base load;

C. Calculate the maximum total cost of the car owner on the next day F _4max

The electric vehicle i is charged at the last return time t _i,e of the next day, that is, charged with the maximum charging power P _max,i until it is fully charged, and the total cost generated is defined as the maximum total cost F _4max of the owner of the next day:

Among them: cp _j is the charging electricity price for the jth hour; t _i,c is the charging hours required for the electric vehicle i to fully charge the power battery at one time under the maximum charging power P _max,i , which can be calculated by the following formula:

Among them: [] is rounded off;

D. Determination of charging and discharging power constraints and objective functions

Assuming that the time-sharing charging and discharging power of electric vehicle i in the jth hour of the next day is p _i,j , then it satisfies the constraint conditions (3)-(4):

Among them, H is a set of high electricity price periods, and Z is an integer set;

The meaning of formula (3) is: only electric vehicle i is allowed to discharge during the period of high electricity price; the meaning of formula (4) is: the sum of net discharge capacity and daily power consumption of electric vehicle i at any time cannot exceed the available capacity of electric vehicle i capacity;

The objective function is: the deviation fitness between the net charge of the electric vehicle and the daily power consumption:

E. Taking the minimum value of formula (5) as the objective function and formulas (3)-(4) as constraints, iteratively calculate the charging and discharging power p _i,j of electric vehicle i in the jth hour of the next day through the particle swarm optimization algorithm The iterative value P _i,j of electric vehicle i, then the set of all charging and discharging power iteration values P _i,j of electric vehicle i constitutes the charging and discharging particle P _i of electric vehicle i, P _i =[P _i,1 ,P _i,2 … P _i,j ...P _i,24 ]; the collection of all electric vehicle charging and discharging particles P _i in the residential area constitutes the charging and discharging particles P of the community, P=[P ₁ ,P ₂ ...P _i ...P _I ];

F. Calculation of charging and discharging parameters for the next day

F1. Calculate the normalized load peak-to-valley difference F ₁ ^* of the next day:

The sum of the basic load L _j of the community in the j hour of the next day plus the charge and discharge power p _i,j of all electric vehicles i in the j hour is the load L′ _j of the j hour of the L′ _j community,

Comparing the hourly load L′ _j of the next day, the maximum load L′ _max and the minimum load L′ _min of the next day can be obtained,

In the formula: max means to take the maximum value, and min means to take the minimum value;

Then get the peak-to-valley difference F ₁ of the next day's load,

F ₁ =L' _max -L' _min (8)

Normalize the peak-to-valley difference F ₁ of the next day's load with the peak-to-valley difference of the base load F _1max of the next day to obtain the normalized peak-to-valley difference of the next day's load F ₁ ^* , F ₁ ^* = F ₁ /F _1max ;

F2. Calculate the normalized load rate F ₂ ^* of the next day:

Calculate the average load L′ _av of the next day,

Calculate the ratio of the average load L' _av of the next day to the maximum load L' _max of the next day, that is, the daily load rate F ₂ of the next day,

The daily load rate F ₂ of the next day is normalized by the daily load rate F _2min of the base load of the next day to obtain the normalized daily load rate F ₂ ^* of the next day, F ₂ ^* = F _2min /F ₂ ;

F3. Calculate the normalized mean square error F ₃ ^* of the load fluctuation of the next day:

Normalize the mean square error of the load fluctuation F ₃ of the next day with the daily load rate F _3max of the basic load of the next day to obtain the normalized mean square error of the load fluctuation F ₃ ^* of the next day, F ₃ ^* = F ₃ /F _3max ;

F4. Calculating the normalized next-day owner's fee F ₄ ^* :

In the formula: u _i,j , v _i,j are the charging and discharging identifiers of the i-th electric vehicle at the jth hour respectively: when charging, u _i,j =1, v _i,j =0, when discharging, u _{i ,j} ＝0, v _i,j ＝1; cp _j , dp _j are charging electricity price and discharging electricity price in the jth hour respectively;

Carry out normalization processing on the next day's car owner's fee F ₄ with the next day's car owner's maximum total fee F _4max to obtain the next day's normalized car owner's fee F ₄ ^* , F ₄ ^* = F ₄ /F _4max ;

G. Determination of optimization target value:

The four normalization parameters obtained in step F are linearly weighted and summed to obtain the optimization target value F:

In the formula: w ₁ , w ₂ , and w ₃ are the weight coefficients of load peak-to-valley difference F ₁ , daily load rate F ₂ , and fluctuation mean square error F ₃ respectively, and their values are all 0.2, and w ₄ is the total cost of the vehicle owner F ₄ The weight coefficient of , whose value is 0.4;

H. First iteration

Repeat step E- to step F for L times to obtain L optimization target values F and L cell charge and discharge particles P for the first iteration, let the lth optimization target value be F ^1,l , and the lth cell The charging and discharging particles are P ^1,l , and the charging and discharging particles P ¹ ,l in each cell are the set of the time-sharing charging power p _i,j of all electric vehicles corresponding to the lth operation, that is, P ^1,l = [ P ₁ ^1,l ,P ₂ ^1,l ...P _i ^1,l ...P _I ^1,l ]; the cell charging and discharging particle P ^1,l corresponding to the smallest F ^1,l value is the global optimal cell charging The discharge particle P ^gbest completes the first iteration;

I. The second and subsequent iterations

I1, compare the F ^k,l value in the previous iterations of the charging and discharging particles in the l-th cell, if the F ^{k' in the k'th iteration, the l} value is the smallest, then the P ^{k' in the k'th iteration, l} is the first l individual optimal cell charging and discharging particles P ^l,pbest ;

I2, with the formula (5) in step D as the objective function, with the formulas (3)-(4) as the constraint conditions, the global optimal cell charging and discharging particles P ^gbest and the lth individual of the last k iterations The optimal cell charging and discharging particles P ^l,pbest are substituted into the particle swarm optimization algorithm, and iteratively solved to obtain the k+1th time, that is, the cell charging and discharging particles P ^k+1,l of this iteration, and then the charging and discharging particles of electric vehicle i are obtained P _i ^k+1,l , from [P _i,1 ,P _i,2 ,…P _i,j …P _i,24 ]=P _i ^k+1,l to get Iterative value P _i,j of charging and discharging power;

I3, re-substituting the iterative value P _i,j into step F and step G, to obtain the optimization target value F ^k+1,l of the charging and discharging particle P ^k+1,l of the l-th cell;

I4. Repeat steps I1-I3 for L times, the cell charge and discharge particle P ^k+1,l corresponding to the smallest F ^k+1,l value is the global optimal cell charge and discharge particle P ^gbest , and complete this iteration;

I5. Repeat steps I1-I4 for K times to obtain the global optimal cell charge and discharge particle P ^gbest during K iterations; the next day, electric vehicle i is controlled according to the charge and discharge power of electric vehicle i in the jth hour of the next day equal to P The value of the element p _i,j of ^gbest is charged and discharged.

Further, in the step D of the present invention, the first iteration value of p _i,j is iteratively calculated by using the particle swarm optimization algorithm with formula (5) as the objective function and formulas (3)-(4) as constraints The parameters used are: the weight factor is 0.9, the learning factor is 2.02, and the particle size is 20;

Compared with prior art, the beneficial effect of the present invention is:

1. The present invention adopts a double-layer discrete particle swarm algorithm, solves the charging and discharging plan of each electric vehicle satisfying all constraint conditions through the bottom layer particle swarm algorithm, and then uses the top layer particle swarm optimization algorithm to optimize the control method for the charging and discharging power of all electric vehicles in the residential area , compared with the intelligent optimization algorithm using penalty function, this algorithm optimizes the charging and discharging plan of electric vehicles in residential quarters more accurately.

2. The charging and discharging control method for electric vehicles in residential quarters of the present invention can not only effectively improve the load characteristic indicators of residential quarters, improve the utilization rate of power grid equipment in residential quarters, but also significantly reduce the charging costs of electric vehicles, and is easy to implement and popularize.

The present invention will be further described in detail below in combination with specific embodiments.

Detailed ways:

Example

A specific embodiment of the present invention is a method for controlling charging and discharging of an electric vehicle in a residential area, the steps of which are:

A. Record the power battery capacity E _i , the maximum charging power P _max,i , and the available capacity ratio k _i of electric vehicle i; It is defined as the first travel time t _i,s of electric vehicle i on the next day, the last return time t _i,e , and the daily power consumption S _i ; where i represents the number of the electric vehicle, i=1,2,3...I; I is the total number of electric vehicles in the residential area;

B. Based on the historical basic power load data of the community, predict the basic load L _j in the jth hour (j=1, 2, 3...24) of the next day, and then obtain the daily load rate F _2min and times The peak-to-valley difference F _1max of the daily base load, and the fluctuation mean square error F _3max of the next day's base load;

C. Calculate the maximum total cost of the car owner on the next day F _4max

The electric vehicle i is charged at the last return time t _i,e of the next day, that is, charged with the maximum charging power P _max,i until it is fully charged, and the total cost generated is defined as the maximum total cost F _4max of the owner of the next day:

Among them: cp _j is the charging electricity price for the jth hour; t _i,c is the charging hours required for the electric vehicle i to fully charge the power battery at one time under the maximum charging power P _max,i , which can be calculated by the following formula:

Among them: [] is rounded off;

D. Determination of charging and discharging power constraints and objective functions

Assuming that the time-sharing charging and discharging power of electric vehicle i in the jth hour of the next day is p _i,j , then it satisfies the constraint conditions (3)-(4):

Among them, H is a set of high electricity price periods, and Z is an integer set;

The meaning of formula (3) is: only electric vehicle i is allowed to discharge during the period of high electricity price; the meaning of formula (4) is: the sum of net discharge capacity and daily power consumption of electric vehicle i at any time cannot exceed the available capacity of electric vehicle i capacity;

The objective function is: the deviation fitness between the net charge of the electric vehicle and the daily power consumption:

E. Taking the minimum value of formula (5) as the objective function and formulas (3)-(4) as constraints, iteratively calculate the charging and discharging power p _i,j of electric vehicle i in the jth hour of the next day through the particle swarm optimization algorithm The iterative value P _i,j of electric vehicle i, then the set of all charging and discharging power iteration values P _i,j of electric vehicle i constitutes the charging and discharging particle P _i of electric vehicle i, P _i =[P _i,1 ,P _i,2 … P _i,j ...P _i,24 ]; the collection of all electric vehicle charging and discharging particles P _i in the residential area constitutes the charging and discharging particles P of the community, P=[P ₁ ,P ₂ ...P _i ...P _I ];

F. Calculation of charging and discharging parameters for the next day

F1. Calculate the normalized load peak-to-valley difference F ₁ ^* of the next day:

The sum of the basic load L _j of the community in the j hour of the next day plus the charge and discharge power p _i,j of all electric vehicles i in the j hour is the load L′ _j of the j hour of the L′ _j community,

Comparing the hourly load L′ _j of the next day, the maximum load L′ _max and the minimum load L′ _min of the next day can be obtained,

In the formula: max means to take the maximum value, and min means to take the minimum value;

Then get the peak-to-valley difference F ₁ of the next day's load,

F ₁ =L' _max -L' _min (8)

Normalize the peak-to-valley difference F ₁ of the next day's load with the peak-to-valley difference of the base load F _1max of the next day to obtain the normalized peak-to-valley difference of the next day's load F ₁ ^* , F ₁ ^* = F ₁ /F _1max ;

F2. Calculate the normalized load rate F ₂ ^* of the next day:

Calculate the average load L′ _av of the next day,

Calculate the ratio of the average load L' _av of the next day to the maximum load L' _max of the next day, that is, the daily load rate F ₂ of the next day,

The daily load rate F ₂ of the next day is normalized by the daily load rate F _2min of the base load of the next day to obtain the normalized daily load rate F ₂ ^* of the next day, F ₂ ^* = F _2min /F ₂ ;

F3. Calculate the normalized mean square error F ₃ ^* of the load fluctuation of the next day:

Normalize the mean square error of the load fluctuation F ₃ of the next day with the daily load rate F _3max of the basic load of the next day to obtain the normalized mean square error of the load fluctuation F ₃ ^* of the next day, F ₃ ^* = F ₃ /F _3max ;

F4. Calculating the normalized next-day owner's fee F ₄ ^* :

In the formula: u _i,j , v _i,j are the charging and discharging identifiers of the i-th electric vehicle at the jth hour respectively: when charging, u _i,j =1, v _i,j =0, when discharging, u _{i ,j} ＝0, v _i,j ＝1; cp _j , dp _j are charging electricity price and discharging electricity price in the jth hour respectively;

Carry out normalization processing on the next day's car owner's fee F ₄ with the next day's car owner's maximum total fee F _4max to obtain the next day's normalized car owner's fee F ₄ ^* , F ₄ ^* = F ₄ /F _4max ;

G. Determination of optimization target value:

The four normalization parameters obtained in step F are linearly weighted and summed to obtain the optimization target value F:

F=w ₁ F ₁ ^* +w ₂ F ₂ ^* +w ₃ F ₃ ^* +w ₄ F ₄ ^* (13)

In the formula: w ₁ , w ₂ , and w ₃ are the weight coefficients of load peak-to-valley difference F ₁ , daily load rate F ₂ , and fluctuation mean square error F ₃ respectively, and their values are all 0.2, and w ₄ is the total cost of the vehicle owner F ₄ The weight coefficient of , whose value is 0.4;

H. First iteration

Repeat step E- to step F for L times to obtain L optimization target values F and L cell charge and discharge particles P for the first iteration, let the lth optimization target value be F ^1,l , and the lth cell The charging and discharging particles are P ^1,l , and the charging and discharging particles P ¹ ,l in each cell are the set of the time-sharing charging power p _i,j of all electric vehicles corresponding to the lth operation, that is, P ^1,l = [ P ₁ ^1,l ,P ₂ ^1,l ...P _i ^1,l ...P _I ^1,l ]; the cell charging and discharging particle P ^1,l corresponding to the smallest F ^1,l value is the global optimal cell charging The discharge particle P ^gbest completes the first iteration;

I. The second and subsequent iterations

I1, compare the F ^k,l value in the previous iterations of the charging and discharging particles in the l-th cell, if the F ^{k' in the k'th iteration, the l} value is the smallest, then the P ^{k' in the k'th iteration, l} is the first l individual optimal cell charging and discharging particles P ^l,pbest ;

I2, with the formula (5) in step D as the objective function, with the formulas (3)-(4) as the constraint conditions, the global optimal cell charging and discharging particles P ^gbest and the lth individual of the last k iterations The optimal cell charging and discharging particles P ^l,pbest are substituted into the particle swarm optimization algorithm, and iteratively solved to obtain the k+1th time, that is, the cell charging and discharging particles P ^k+1,l of this iteration, and then the charging and discharging particles of electric vehicle i are obtained P _i ^k+1,l , from [P _i,1 ,P _i,2 ,…P _i,j …P _i,24 ]=P _i ^k+1,l to get Iterative value P _i,j of charging and discharging power;

I3, re-substituting the iterative value P _i,j into step F and step G, to obtain the optimization target value F ^k+1,l of the charging and discharging particle P ^k+1,l of the l-th cell;

I4. Repeat steps I1-I3 for L times, the cell charge and discharge particle P ^k+1,l corresponding to the smallest F ^k+1,l value is the global optimal cell charge and discharge particle P ^gbest , and complete this iteration;

I5. Repeat steps I1-I4 for K times to obtain the global optimal cell charge and discharge particle P ^gbest during K iterations; the next day, electric vehicle i is controlled according to the charge and discharge power of electric vehicle i in the jth hour of the next day equal to P The value of the element p _i,j of ^gbest is charged and discharged.

In step D of this example, the first iteration value of p _i,j is iteratively calculated by the particle swarm optimization algorithm with equation (5) as the objective function and equations (3)-(4) as constraints The parameters used are: the weight factor is 0.9, the learning factor is 2.02, and the particle size is 20;

In the step I2 described in this example, with the formula (5) in the step D as the objective function, and with the formulas (3)-(4) as the constraint conditions, the global optimal cell charging and discharging particle P of the last k iterations is ^gbest and the lth individual optimal cell charging and discharging particles P ^l,pbest are substituted into the particle swarm optimization algorithm, and iteratively solved to obtain the k+1th time, that is, the specific steps of the cell charging and discharging particles P ^k+1,l in this iteration are as follows: :

I21. The lth individual optimal cell charge and discharge particle P ^l,pbest and the global optimal cell charge and discharge particle P ^gbest of k iterations are decomposed into individual optimal electric vehicle charge and discharge particles [P ₁ ^l,pbest ,P ₂ ^{l ,pbest} ...P _i ^l,pbest ...P _I ^l,pbest ]=P ^l,pbest , global optimal electric vehicle charge and discharge particles [P ₁ ^gbest ,P ₂ ^gbest ...P _i ^gbest ...P _I ^gbest ]=P ^gbest , Taking the individual optimal electric vehicle charging and discharging particle P _i ^l,pbest as the individual optimal position, and the global optimal electric vehicle charging and discharging particle P _i ^gbest as the global optimal position, the first calculation of the particle swarm algorithm is performed to obtain the kth The initial value P _i ^k+1,l,0 of the charging and discharging particles of the i-th electric vehicle in the +1 iteration;

I22. Take the initial value P _i ^k+1,l,0 of the charging and discharging particles of the i-th electric vehicle in the k+1 iteration as the individual optimal position of the i-th electric vehicle, and take the minimum of the formula (5) The initial value of the charging and discharging particles of the i-th electric vehicle is taken as the global optimal position. By using formula (5) as the objective function and formulas (3)-(4) as constraints, the particle swarm algorithm is used to iteratively calculate the i-th electric vehicle The charging and discharging particles P _i ^k+1,l of electric vehicles; and then get the charging and discharging particles P ^k+1,l of the lth cell in the k+1th iteration;

Simulation:

1. The parameters and conditions of the simulation test are as follows:

There are 389 households in the residential area, and the total number of cars is 300. Assume that the start time of their travel follows a normal distribution:

Among them: μ _L =7.2, σ _L =2.1(h);

The final trip end time follows a normal distribution:

Among them: μ _L =17.6, σ _L =3.4(h);

The daily mileage follows a lognormal distribution:

Where: μ _D = 3.2, σ _L = 0.88 (miles);

The daily load of the residential area on a certain day in January 2014 is shown in Table 1:

Table 1 Residential area load on a certain day in January in winter 2014

According to Table 1, the daily load rate, peak-to-valley difference, and load mean square deviation of the next day’s base load can be calculated, and the time-of-use electricity price for Shenzhen residents is used as shown in Table 2:

Table 2 Electricity Price List for Residents in Shenzhen in Winter

2. Simulation test results:

Table 1 is the simulation test result under the disorderly charging mode (the car owner charges with maximum power immediately after the last trip, until the state of charge reaches the maximum allowable value); Table 2 is the simulation test result of the inventive method. Penetration is the ratio of the number of electric vehicles to the total number of vehicles.

Table 1 Simulation test results of disordered charging mode

The simulation test result of the inventive method of table 2

The penetration rate in the table is the ratio of the number of electric vehicles to the total number of vehicles. As can be seen from Table 1 and Table 2: under the same penetration rate (number of electric vehicles), each index obtained by the simulation test of the inventive method is better than the index of disorderly charging. When the penetration rate is 50% (i.e. electric cars are 150): the electric charge that the owner of the electric car needs to pay only needs 395.4042 yuan by the method of the present invention, and 899.7931 yuan for disorderly charging, and the daily load by the method of the present invention The fluctuation is 71.2883kW, the daily peak load is 469.4kW, and the daily load peak-to-valley difference is 205.8kW, while the disordered charging is as high as 167.3819kW; 666.8kW, 548.8kW respectively.

Claims (2)

1. A charging and discharging control method for an electric vehicle in a residential area, the steps of which are: A. Record the power battery capacity E _i , the maximum charging power P _max,i , and the available capacity ratio k _i of electric vehicle i; It is defined as the first travel time t _i,s of electric vehicle i on the next day, the last return time t _i,e , and the daily power consumption S _i ; where i represents the number of the electric vehicle, i=1,2,3...I; I is the total number of electric vehicles in the residential area; B. Based on the historical basic power load data of the community, predict the basic load L _j in the jth hour (j=1, 2, 3...24) of the next day, and then obtain the daily load rate F _2min and times The peak-to-valley difference F _1max of the daily base load, and the fluctuation mean square error F _3max of the next day's base load; C. Calculate the maximum total cost of the car owner on the next day F _4max The electric vehicle i is charged at the last return time t _i,e of the next day, that is, charged with the maximum charging power P _max,i until it is fully charged, and the total cost generated is defined as the maximum total cost F _4max of the owner of the next day: ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&CenterDot;</span>{\mathrm{<span\; class="notranslate">\; cp</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ Among them: cp _j is the charging electricity price for the jth hour; t _i,c is the charging hours required for the electric vehicle i to fully charge the power battery at one time under the maximum charging power P _max,i , which can be calculated by the following formula: ${\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\frac{{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ Among them: [] is rounded off; D. Determination of charging and discharging power constraints and objective functions Assuming that the time-sharing charging and discharging power of electric vehicle i in the jth hour of the next day is p _i,j , then it satisfies the constraint conditions (3)-(4): Among them, H is a set of high electricity price periods, and Z is an integer set; $<span\; class="notranslate">\; \{</span>\begin{array}{c}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ <span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$ The meaning of formula (3) is: only electric vehicle i is allowed to discharge during the period of high electricity price; the meaning of formula (4) is: the sum of net discharge capacity and daily power consumption of electric vehicle i at any time cannot exceed the available capacity of electric vehicle i capacity; The objective function is: the deviation fitness between the net charge of the electric vehicle and the daily power consumption: $\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; e</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; |</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \&lsqb;</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; |</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ E. Taking the minimum value of formula (5) as the objective function and formulas (3)-(4) as constraints, iteratively calculate the charging and discharging power p _i,j of electric vehicle i in the jth hour of the next day through the particle swarm optimization algorithm The iterative value P _i,j of electric vehicle i, then the set of all charging and discharging power iteration values P _i,j of electric vehicle i constitutes the charging and discharging particle P _i of electric vehicle i, P _i =[P _i,1 ,P _i,2 … P _i,j ...P _i,24 ]; the collection of all electric vehicle charging and discharging particles P _i in the residential area constitutes the charging and discharging particles P of the community, P=[P ₁ ,P ₂ ...P _i ...P _I ]; F. Calculation of charging and discharging parameters for the next day F1. Calculate the normalized load peak-to-valley difference of the next day The sum of the basic load L _j of the community in the j hour of the next day plus the charge and discharge power p _i,j of all electric vehicles i in the j hour is the load L′ _j of the j hour of the L′ _j community, Comparing the hourly load L′ _j of the next day, the maximum load L′ _max and the minimum load L′ _min of the next day can be obtained, ${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$ ${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \&lsqb;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ...</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$ In the formula: max means to take the maximum value, and min means to take the minimum value; Then get the peak-to-valley difference F ₁ of the next day's load, F ₁ =L' _max -L' _min (8) Normalize the peak-to-valley difference F ₁ of the next day's load with the peak-to-valley difference of the next day's base load F _1max to obtain the normalized peak-to-valley difference of the next day's load $f_1^{*}=f_1/f_{1\max };$ F2. Calculate the normalized load rate of the next day Calculate the average load L′ _av of the next day, ${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&rsqb;</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$ Calculate the ratio of the average load L' _av of the next day to the maximum load L' _max of the next day, that is, the daily load rate F ₂ of the next day, ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}^{<span\; class="notranslate">\; \&prime;</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; 100</span>}\mathrm{<span\; class="notranslate">\; \%</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$ The next day's daily load rate F ₂ is normalized by the next day's base load daily load rate F _2min to obtain the normalized next day's daily load rate $f_2^{*}{=f}_{2mi\mathrm{no}}/f_2;$ F3. Calculate the normalized mean square error of the load fluctuation of the next day ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$ Normalize the mean square error of the next day's load fluctuation F ₃ with the daily load rate of the next day's base load F _3max to obtain the normalized mean square error of the next day's load fluctuation $f_3^{*}=f_3/f_{3max};$ F4. Calculation of the normalized next-day owner's fee ${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; I</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; twenty\; four</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; cp</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}}{\mathrm{<span\; class="notranslate">\; dp</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$ In the formula: u _i,j , v _i,j are the charging and discharging identifiers of the i-th electric vehicle at the jth hour respectively: when charging, u _i,j =1, v _i,j =0, when discharging, u _{i ,j} ＝0, v _i,j ＝1; cp _j , dp _j are charging electricity price and discharging electricity price in the jth hour respectively; Carry out normalization processing on the next day's car owner's fee F ₄ with the next day's car owner's maximum total fee F _4max to obtain the next day's normalized car owner's fee $f_4^{*}=f_4/f_{4max};$ G. Determination of optimization target value: The four normalized parameters obtained in step F are linearly weighted and summed to obtain the optimization target value F: $\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$ In the formula: w ₁ , w ₂ , and w ₃ are the weight coefficients of load peak-to-valley difference F ₁ , daily load rate F ₂ , and fluctuation mean square error F ₃ respectively, and their values are all 0.2, and w ₄ is the total cost of the vehicle owner F ₄ The weight coefficient of , whose value is 0.4; H. The first iteration Repeat step E- to step F for L times to obtain L optimization target values F and L cell charge and discharge particles P for the first iteration, let the lth optimization target value be F ^1,l , and the lth cell The charging and discharging particle is P ^1,l , and the charging and discharging particle P ¹ ,l in each cell is the collection of the next day’s time-sharing charging power p _i,j of all electric vehicles corresponding to the lth operation, that is The cell charging and discharging particle P ^1,l corresponding to the smallest F ^1,l value is the global optimal cell charging and discharging particle P ^gbest , and the first iteration is completed; I. The second and subsequent iterations I1, compare the F ^{k, l} value in the previous iterations of the charging and discharging particles in the l-th cell, if the F ^{k ', l} value is the smallest in the k' iteration, then the P ^{k' in the k' iteration, l} is the first l individual optimal cell charging and discharging particles P ^l,pbest ; I2, with the formula (5) in step D as the objective function, with the formulas (3)-(4) as the constraint conditions, the global optimal cell charging and discharging particles P ^gbest and the lth individual of the last k iterations The optimal cell charging and discharging particles P ^l,pbest are substituted into the particle swarm optimization algorithm, and iteratively solved to obtain the k+1th time, that is, the cell charging and discharging particles P ^k+1,l of this iteration, and then the charging and discharging particles of electric vehicle i are obtained Depend on Obtain the iteration value P _i,j of the charging and discharging power of electric vehicle i in the jth hour of this iteration; I3, re-substituting the iterative value P _i,j into step F and step G, to obtain the optimization target value F ^k+1,l of the charging and discharging particle P ^k+1,l of the l-th cell; I4. Repeat steps I1-I3 for L times, the cell charge and discharge particle P ^k+1,l corresponding to the smallest F ^k+1,l value is the global optimal cell charge and discharge particle P ^gbest , and complete this iteration; I5. Repeat steps I1-I4 for K times to obtain the global optimal cell charge and discharge particle P ^gbest during K iterations; the next day, electric vehicle i is controlled according to the charge and discharge power of electric vehicle i in the jth hour of the next day equal to P The value of the element p _i,j of ^gbest is charged and discharged. 2. a kind of district electric vehicle charging and discharging control method according to claim 1 is characterized in that: in described step D, by particle swarm optimization algorithm with formula (5) as objective function, with formula (3)-(4 ) Calculate the first iteration value of p _i,j for the constraint iteration The parameters used are: the weight factor is 0.9, the learning factor is 2.02, and the particle size is 20.