A regional electric vehicle optimization scheduling method based on time-of-use electricity price

Abstract

A method for optimizing the dispatching of regional electric vehicles based on time-of-use electricity prices comprises the following steps: step 1, using the Monte Carlo method to establish a probability distribution model of electric vehicle charging behavior; step 2, establishing a price elasticity matrix of electric vehicle charging demand, dividing the time period of a day into three parts: peak, valley and flat based on the size of the basic electricity load, and using different electricity prices respectively; step 3, obtaining relevant information of electric vehicles that have been connected to the system during the initial adjustment period of the region, judging the category to which the electric vehicles that have been connected to the system belong, taking optimization dispatching measures for the electric vehicles based on the dual objective functions of reducing the peak-valley difference of electricity load and reducing charging costs, and calculating the optimized charging power and objective function; step 4, calculating and outputting the charging load curve after optimized dispatching through the basic load and the mobile load after optimized dispatching.

Description

Technical Field

[0001] The invention relates to the technical field of gas overhead pipelines, in particular to a method for optimizing the dispatching of regional electric vehicles based on time-of-use electricity prices. Background Art

[0002] In recent years, transportation electrification has been considered an effective measure to achieve energy conservation and emission reduction and improve energy efficiency. With the development of transportation electrification, electric vehicles will replace fuel vehicles as the main means of transportation. Electric vehicles are environmentally friendly because they consume electrical energy and produce almost zero pollutant emissions. Therefore, electric vehicles provide an alternative for the development of clean transportation systems and offer great potential for sustainable transportation development. New energy vehicles, especially electric vehicles, have been widely promoted and applied around the world, bringing about huge charging demand. With the increase in the penetration rate of electric vehicles, large-scale uncoordinated charging of electric vehicles will bring huge pressure to power supply, causing problems such as "peak on peak" and voltage drop, thus affecting the safety and stability of the power system. In order to solve this problem, it is necessary to optimize the charging of large electric vehicles, realize the benign interaction between electric vehicles and microgrids, and adopt time-of-use electricity prices to guide and motivate users to achieve the effect of optimizing scheduling.

[0003] A large number of research institutions and scholars have conducted research on the optimization and scheduling of electric vehicles, and have achieved many results. Some have constructed a two-layer optimization control model with the goal of reducing the variance of charging load and reducing the charging cost of electric vehicle users through a hierarchical architecture; some have the goal of minimizing the peak-to-valley difference of load, and through secondary planning optimization in each scheduling period, other periods are abandoned for optimization to achieve dynamic optimization of control strategies. However, some technologies do not reasonably consider the basic requirements of the electric vehicle user side. The patent with the application publication number CN116691414A and the name of a patent for an orderly charging business intelligent detection and management system based on minute-level collection is mainly through orderly charging sequence regulation, supplemented by disordered charging power regulation, to improve the orderly charging control system and improve the stability of power system operation; the patent with the application publication number CN105871029A and the name of a patent for an orderly charging intelligent management system and orderly charging control method for electric vehicles is a control system for orderly charging intelligent management of electric vehicles based on neural networks, which controls the charging and discharging of vehicles in the station, realizes peak shaving and valley filling of the power grid and maximizes the operating efficiency of the charging station. This type of technology that does not consider the travel electricity demand on the user side may reduce the participation of electric vehicle users in responding to scheduling. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a regional electric vehicle optimization scheduling method based on time-of-use electricity prices. The Monte Carlo method is used to establish a probability distribution model of electric vehicle charging behavior to predict the charging power; a demand price elasticity matrix of the relationship between electric vehicle charging demand and charging price is established, and the electricity price is formulated according to the three parts of peak, valley and flat during the day. The relevant information of the electric vehicles that have been connected to the system during the initial adjustment period of the region is obtained, the category of the electric vehicles that have been connected to the system is determined, and corresponding optimization scheduling measures are taken for the electric vehicles, thereby realizing peak shaving and valley filling of mobile loads and improving the safety and stability of system operation.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for optimizing the dispatch of regional electric vehicles based on time-of-use electricity prices, comprising the following steps:

[0006] Step 1: Using the Monte Carlo method, a probability distribution model of electric vehicle charging behavior is established;

[0007] Step 2: Establish a price elasticity matrix for electric vehicle charging demand, divide the time period of the day into three parts: peak, valley and flat based on the size of the basic electricity load, and use different electricity prices for each part;

[0008] Step 3: Obtain relevant information about electric vehicles that have been connected to the system during the initial adjustment period of the region, determine the category to which the electric vehicles that have been connected to the system belong, take optimal scheduling measures for the electric vehicles based on the dual objective functions of reducing the peak-to-valley difference in power load and reducing charging costs, and calculate the optimized charging power and objective function;

[0009] Step 4: Calculate and output the charging load curve after optimized scheduling through the basic load and the mobile load after optimized scheduling.

[0010] Preferably, in step 1, the probability distribution model of the electric vehicle charging behavior is composed of a charging start time model, a charging end time model, an initial state of charge probability model and a charging duration probability model;

[0011] The charging start time model is based on σ a Indicates the standard deviation of the charging start time; μ a represents the mean of the charging start time, and the constructed Probability model of

[0012] The charging end time model is based on σ l Indicates the standard deviation of the charging end time; μ l represents the mean of the charging end time, and the constructed Probability model of

[0013] The initial state of charge probability model is a probability model uniformly distributed in a certain state of charge interval represented by a probability density function;

[0014] The charging time probability model is based on the initial state of charge SOC of the electric vehicle. i 、Battery capacity S e , the battery state of charge (SOC) at the end of charging of electric vehicles h 、Charging power P of electric vehicles i And the charging efficiency η.

[0015] Preferably, in step 2, the specific steps of establishing the electric vehicle charging demand price elasticity matrix are as follows:

[0016] Step 2-1: Solve the change in charging demand, charging price change and elasticity coefficient;

[0017] Step 2-2: Establish a price demand elasticity matrix based on the required variables;

[0018] Step 2-3: Obtain the charging load demand formula in response to time-of-use electricity prices based on the price demand elasticity matrix.

[0019] Preferably, in step 2-1, the change in charging demand and the change in charging price are represented by the difference between the charging amount and the charging price at the end and the beginning of a certain time period, and the elasticity coefficient is represented by the quotient of the change in charging demand and the initial value of the charging amount divided by the quotient of the change in charging price and the initial value of the charging price.

[0020] Preferably, in step 2-2, a day is divided into T charging periods, and the charging demand change matrix is ​​obtained by multiplying the elasticity coefficient matrix by the price change matrix.

[0021] Preferably, in step 2-3, the charging load demand in response to the time-of-use electricity price is determined by the initial amount of charging demand, the amount of change in charging price and the elasticity coefficient. Based on the change in the amount of charging load demand for electric vehicles before and after the price change, a reasonable time-of-use electricity price policy is formulated, and a day is divided into three time periods: peak, valley and flat according to the size of the basic electricity load, and the corresponding electricity price is formulated, among which the peak time period has the highest electricity price, the valley time period has the lowest electricity price, and the flat time period has the middle electricity price.

[0022] Preferably, in step 3, the relevant information of the electric vehicles connected to the system during the initial adjustment period of the region includes: the charging start time of the electric vehicle, the charging end time of the electric vehicle, and the initial charge state of the charging vehicle; the electric vehicle is optimized for scheduling with the dual objective function of reducing the peak-to-valley difference of the power load and reducing the charging cost, and the optimized charging power and objective function are calculated. The specific steps are as follows:

[0023] Step 3-1: Model the dual objective function of reducing the peak-valley difference of electricity load and charging cost;

[0024] Step 3-2: Classify the electric vehicles connected to the system and take corresponding optimization dispatching measures for the electric vehicles that comply with the dispatching;

[0025] Step 3-3: Calculate the optimized charging power, and calculate the regional electric vehicle charging load and total electricity load by time period.

[0026] Preferably, in step 3-1, the first objective function for reducing the peak-to-valley difference of the power load is represented by the difference between the peak value and the valley value of the total power load; the second objective function for reducing the charging cost is represented by integrating the product of the charging cost and the charging power with time as a microelement;

[0027] In step 3-2, the electric vehicles are classified according to their different charging times and initial charge states, and appropriate charging powers are adopted at different time periods.

[0028] Preferably, in step 3-3, the charging power of a certain time period is the sum of the charging powers corresponding to all charging electric vehicles in the time period; the time period load is determined by the time period power and the interval duration; and the total power load of a certain time period is determined by the basic load of the time period and the vehicle charging load.

[0029] Preferably, in step 4, the charging load curve after optimized scheduling is calculated and outputted, which is expressed in the form of a load curve based on the mobile load in each time period after optimized scheduling. The charging load curve after optimized scheduling can be obtained by linearly superimposing the optimized mobile load curve and the basic electricity load curve of the residents.

[0030] The present invention provides a method for optimizing the dispatching of regional electric vehicles based on time-of-use electricity prices, which has the following beneficial effects:

[0031] (1) Based on the time-of-use electricity price, corresponding optimization scheduling strategies are adopted for electric vehicles with different charge states and charging times to ensure the travel needs of users.

[0032] (2) It takes into account that electric vehicle users need to have a certain threshold charge state at the end of charging, which increases the enthusiasm of users to participate in the response.

[0033] (3) The price-demand elasticity matrix reflects the relationship between changes in electricity prices and changes in charging demand, making the scheduling strategy more feasible and more in line with the actual situation.

[0034] (4) In the scheduling process, charging is prioritized in the order of valley and peak periods, which realizes the shifting of peak loads, achieves the effect of peak shaving and valley filling, and improves the safety and stability of power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0036] Figure 1 is a flow chart of the system of the present invention;

[0037] Figure 2 is a flow chart of the power algorithm of the present invention;

[0038] Figure 3 is a diagram of ordered and disordered moving loads in an example of the present invention;

[0039] Figure 4 is the basic load diagram in the example of the present invention;

[0040] Figure 5 It is the ordered and disordered total power load diagram of the present invention;

[0041] Figure 6 It is the ordered and disordered charging cost diagram of the present invention. DETAILED DESCRIPTION

[0042] A regional electric vehicle optimization scheduling method based on time-of-use electricity prices, such as Figure 1 As shown, the method comprises the following steps:

[0043] Step 1: Use MCM to establish a probability distribution model of electric vehicle charging behavior. The probability distribution model of electric vehicle charging behavior consists of a charging start time model, a charging end time model, an initial state of charge probability model, and a charging duration probability model.

[0044] The charging start time model is:

[0045]

[0046] Where: t a Indicates the charging start time; μ a Indicates the charging start time t a The mean of a Indicates the charging start time t a The standard deviation of f(t a ) represents the charging start time t a The probability function of .

[0047] The charging end time model is:

[0048]

[0049] Where: t l Indicates the charging end time; μ l Indicates the charging end time tl The mean of a Indicates the charging end time t l The standard deviation of f(t a ) represents the charging end time t l The probability function of .

[0050] The initial state of charge probability model is:

[0051]

[0052] In the formula: x represents the minimum state of charge that the electric vehicle user needs to charge; y represents the maximum state of charge that the electric vehicle user needs to charge; SOC i is the initial state of charge of the i-th electric vehicle; f(SOC i ) is the probability function of the initial state of charge of the electric vehicle user.

[0053] The charging time probability model is:

[0054]

[0055] Where: T i is the charging time of the i-th electric car; S e is the battery capacity of electric vehicles; SOC d The state of charge of the battery at the end of charging the electric vehicle; P i is the charging power of the electric vehicle; η is the charging efficiency of the electric vehicle.

[0056] Step 2: Establish the price elasticity matrix of electric vehicle charging demand. Based on the size of the basic electricity load, divide the time period of the day into three parts: peak, valley and flat, and use different electricity prices for each part. This includes the following steps:

[0057] Step 2-1: The formula for the elasticity coefficient that reflects the change in electricity price and the change in charge capacity is:

[0058]

[0059]

[0060]

[0061] Where: tt is the elastic coefficient; and e t are the charging amounts at the beginning and end of period t respectively; and m t are the charging prices at the beginning and end of period t respectively; Δe t is the change in charge capacity during period t; Δmt is the change in charging price during period t.

[0062] Step 2-2: Divide a day into T charging periods, and get the matrix:

[0063]

[0064] Step 2-3: From the price demand elasticity matrix, the charging load demand formula in response to the time-of-use electricity price is obtained:

[0065]

[0066] Step 3: Obtain relevant information about electric vehicles that have been connected to the system during the initial adjustment period of the region, determine the category of the electric vehicles that have been connected to the system, and take optimization scheduling measures for electric vehicles based on the dual objective functions of reducing the peak-to-valley difference in power load and reducing charging costs, and calculate the optimized charging power and objective function, including the following steps:

[0067] Step 3-1: The objective function formula for reducing the peak-to-valley difference of power load is:

[0068]

[0069] Where: H(L) is the peak-to-valley difference function of power load; is the maximum value of the power load; The minimum value of the power load.

[0070] The second objective function formula to reduce charging cost is:

[0071]

[0072] Where: M is the total charging cost of the electric vehicles participating in the dispatch; m(t) and P(t) are the charging electricity price and charging power at time t respectively; t is ,t ie are the start and end times of charging for the i-th electric vehicle.

[0073] Step 3-2: Determine the category of electric vehicles that have been connected to the system, and take the reduction of peak-to-valley difference in power load and charging cost as the dual objective function, and take corresponding optimization scheduling measures for electric vehicles that obey the scheduling, such as Figure 2 As shown, the specific steps are:

[0074] 1) Set the initial value SOC h , S e ,Δp,P s , P f ;

[0075] 2) Obtain relevant information about electric vehicles and divide the charging time of the vehicle into three time periods according to valley, flat and peak periods, marked as t 1 ,t 2 ,t 3 ;

[0076] 3) Let i = 1, P 0 =0,t 0 =0;

[0077] 4) Let P n =P s ;

[0078] 5) Determine P n ≤P f Is it true? If so, proceed to step 6); if not, proceed to step 12);

[0079] 6) Determine P i-1 =P f Is it true? If so, proceed to step 7); if not, proceed to step 9);

[0080] 7) Judgment Is it true? If so, proceed to step 11); if not, proceed to step 8);

[0081] 8) Let P n =P n +Δp, and return to step 6);

[0082] 9) Judgment If yes, proceed to step 11), if no, proceed to step 10);

[0083] 10) Order And return to step 8);

[0084] 11) Output P i =P n ;

[0085] 12) Output P i =P f ;

[0086] 13) Let i=i+1;

[0087] 14) Determine whether i≤3 is true, if so, return to step 4), if not, proceed to step 15);

[0088] 15)End.

[0089] Among them: SOC h is the charge threshold; S e is the battery capacity; Δp is the rising power; P s is the slow charging power; Pf is the fast charging power; t 1 ,t 2 ,t 3 are the charging time periods at the valley and peak respectively; 1 , P 2 , P 3 are the charging powers at valley, peak and level, respectively.

[0090] The above steps are repeated N times to obtain the charging power of N electric vehicles at different time periods.

[0091] Step 3-3: The calculation formula for calculating the optimized charging power is:

[0092]

[0093] Where: N is the total number of trams; P i is the charging power of the i-th tram; P T The charging power of the Tth interval among the 96 intervals divided into one day; As the determining factor, When, it means that the i-th electric car is at T i Charging during the period; When the i-th electric car is not in T i Charge during the period.

[0094] The calculation formula for the regional electric vehicle charging load during period T is:

[0095] L T =P T ×Δt (13)

[0096] Where: L T is the charging load of the electric vehicle during period T; Δt is the equally divided charging time interval.

[0097] The calculation formula for the total power load of regional electric vehicles during period T is:

[0098] L a =L T +L b (14)

[0099] Where: L a is the total power load; L b It is the basic load of residential electricity.

[0100] Step 4: According to the mobile load of each period after the optimized scheduling obtained in step 3, it is expressed in the form of a load curve. The optimized mobile load curve is linearly superimposed with the basic electricity load curve of the residents to obtain the charging load curve after the optimized scheduling as shown in Figure 5 .

[0101] Example:

[0102] In this example, the CPC uses 1,000 electric vehicles, and the information of the electric vehicles used is as follows (EV i represents the i-th tram, SOC i represents the state of charge of the i-th tram):

[0103] Table 1 Charging Electric Vehicle Information Table

[0104]

[0105] In the example simulation, set μ a and σ a are 18 and 3.33 respectively; set μ l and σ l 8 and 3.33 respectively; set x and y to 0.1 and 0.5 respectively; set S e is 60AH, η is 0.9. In the example, a day is divided into 96 time periods, that is, 15 minutes is a time period. Based on the changes in the demand for electric vehicle charging load before and after the price change, the time-of-use electricity price policy is formulated as follows: peak electricity price 1.2 yuan / kWh, valley electricity price 0.8 yuan / kWh, and normal electricity price 1 yuan / kWh. The set threshold state of charge is 0.8, the peak time periods are 9:00-12:00, 18:00-23:00; the valley time periods are 0:00-7:00, 23:00-24:00; the normal time periods are 7:00-9:00, 12:00-18:00; the slow charging power is 15kW, the fast charging power is 35kW, and the rising power is 5kW. The basic electricity load of the city is as follows Figure 3 shown.

[0106] After simulation, the present invention obtains the following Figure 4 The comparison chart of disordered charging and ordered charging load after optimized scheduling is shown. As can be seen from the figure, for mobile loads, the charging load after optimized scheduling is mainly concentrated in the valley period, that is, 0:00-7:00 and 23:00-24:00; on the contrary, there is only a small amount of charging load in the peak period, that is, 9:00-12:00 and 18:00-23:00, which has better completed the shift of the peak period.

[0107] After simulation, the present invention obtains the following Figure 5The total load of disordered charging and ordered charging after optimized scheduling is shown. As can be seen from the figure, for the total power load, the peak maximum value of the power load after optimized scheduling is 7499kW, the valley minimum value is 4674.25kW, and the peak-to-valley difference is 2824.75kW; the peak maximum value of disordered power load is 7836.25kW, the valley minimum value is 4520kW, and the peak-to-valley difference is 3316.25kW. The peak-to-valley difference is significantly reduced, basically achieving the goal of shaving peaks and filling valleys and improving the stability of power system operation. From the simulation results Figure 6 From the comparison of ordered and disordered charging costs shown in the figure, it can be seen that the total cost of ordered charging of the simulated 1,000 electric vehicles is 13,687.5 yuan; the total cost of disordered charging is 21,962.3 yuan, and the percentage of charging cost reduction is 37.68%, which effectively reduces the charging cost of users.

[0108] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A regional electric vehicle optimization scheduling method based on time-of-use electricity prices, characterized in that: The following steps are involved: Step 1: Using the Monte Carlo method, a probability distribution model of electric vehicle charging behavior is established; Step 2: Establish a price elasticity matrix for electric vehicle charging demand, divide the time period of the day into three parts: peak, valley and flat based on the size of the basic electricity load, and use different electricity prices for each part; Step 3: Obtain relevant information about electric vehicles that have been connected to the system during the initial adjustment period of the region, determine the category to which the electric vehicles that have been connected to the system belong, take optimal scheduling measures for the electric vehicles based on the dual objective functions of reducing the peak-to-valley difference in power load and reducing charging costs, and calculate the optimized charging power and objective function; Step 4: Calculate and output the charging load curve after optimized scheduling through the basic load and the mobile load after optimized scheduling; In step 1, the probability distribution model of the electric vehicle charging behavior is composed of a charging start time model, a charging end time model, an initial state of charge probability model and a charging duration probability model; The charging start time model is: (1)； Where: Indicates the charging start time; Indicates charging start time The mean of Indicates charging start time The standard deviation of Indicates charging start time The probability function of The charging end time model is: (2)； Where: Indicates the charging end time; Indicates the charging end time The mean of Indicates the charging end time The standard deviation of Indicates the charging end time The probability function of The initial state of charge probability model is: (3)； Where: Indicates that the electric vehicle user has reached the minimum state of charge that requires charging; Indicates that the electric vehicle user has reached the highest state of charge that requires charging; is the initial state of charge of the i-th electric vehicle; is the probability function of the initial state of charge of the electric vehicle user; The charging time probability model is: (4)； Where: is the charging time of the i-th electric car; is the battery capacity of the electric vehicle; The state of charge of the battery at the end of charging the electric vehicle; Charging power for electric vehicles; Charging efficiency for electric vehicles; The charging start time model is based on represents the standard deviation of charging start time; represents the mean of the charging start time, and the constructed Probability model of The charging end time model is based on Indicates the standard deviation of charging end time; represents the mean of the charging end time, and the constructed Probability model of The initial state of charge probability model is a probability model uniformly distributed in a certain state of charge interval represented by a probability density function; The charging time probability model is based on the initial charge state of the electric vehicle , Battery capacity , the state of charge of the battery at the end of charging of electric vehicles 、Charging power of electric vehicles And charging efficiency constituted by; In step 2, the specific steps of establishing the electric vehicle charging demand price elasticity matrix are as follows: Step 2-1: Solve the change in charging demand, charging price change and elasticity coefficient; Step 2-2: Establish a price demand elasticity matrix based on the required variables; Step 2-3: Obtain the charging load demand formula in response to the time-of-use electricity price based on the price demand elasticity matrix; In step 2-1, the change in charging demand and the change in charging price are represented by the difference between the charging amount and the charging price at the end and the beginning of a certain period, and the elasticity coefficient is represented by the quotient of the change in charging demand and the initial value of charging amount divided by the quotient of the change in charging price and the initial value of charging price; The formula for the elasticity coefficient that reflects the change in electricity price and the change in charge capacity is: (5)； (6)； (7)； Where: is the elastic coefficient; and are the charging amounts at the beginning and end of period t respectively; and mt are the charging prices at the beginning and end of period t respectively; is the change in charging capacity during period t; is the change in charging price during period t; In step 2-2, a day is divided into T charging periods, and the charging demand change matrix is ​​obtained by multiplying the elasticity coefficient matrix by the price change matrix; Dividing a day into T charging periods, we can get the matrix: (8)； In step 2-3, the formula for obtaining the charging load demand in response to the time-of-use electricity price is obtained from the price demand elasticity matrix: (9)； The charging load demand in response to the time-of-use electricity price is determined by the initial amount of charging demand, the change in charging price and the elasticity coefficient; based on the change in the charging load demand of electric vehicles before and after the price change, a reasonable time-of-use electricity price policy is formulated, and a day is divided into three time periods: peak, valley and flat according to the size of the basic electricity load, and the corresponding electricity price is formulated, among which the peak time period has the highest electricity price, the valley time period has the lowest electricity price, and the flat time period has the middle price; In step 3, the relevant information of the electric vehicles connected to the system during the initial adjustment period of the region includes: the charging start time of the electric vehicle, the charging end time of the electric vehicle, and the initial charge state of the charging vehicle; the electric vehicle is optimized for scheduling with the dual objective function of reducing the peak-to-valley difference of the power load and reducing the charging cost, and the optimized charging power and objective function are calculated. The specific steps are as follows: Step 3-1: Model the dual objective function of reducing the peak-valley difference of electricity load and charging cost; Step 3-2: Classify the electric vehicles connected to the system and take corresponding optimization dispatching measures for the electric vehicles that comply with the dispatching; Step 3-3: Calculate the optimized charging power, and calculate the regional electric vehicle charging load and total electricity load by time period; In step 3-1, the objective function 1 for reducing the peak-to-valley difference of the power load is represented by the difference between the peak value and the valley value of the total power load; the objective function 2 for reducing the charging cost is represented by integrating the product of the charging cost and the charging power with time as a microelement; The objective function formula for reducing the peak-to-valley difference of power load is: (10)； Where: is the peak-to-valley difference function of power load; is the maximum value of the power load; is the minimum value of the power load; The second objective function formula to reduce charging cost is: (11)； Where: The total charging cost of the electric vehicles participating in the dispatch; , They are Charging electricity price and charging power at the moment; , Respectively The start and end time of charging of electric vehicles; In step 3-2, the electric vehicles are classified according to their different charging times and initial charge states, and appropriate charging powers are adopted at different time periods; Determine the category of electric vehicles that have been connected to the system, take reducing the peak-to-valley difference of power load and charging cost as the dual objective function, and take corresponding optimization dispatching measures for electric vehicles that obey the dispatch. The specific steps are: 1) Set the initial value , , , , ; 2) Obtain relevant information about electric vehicles and divide the charging time of the vehicle into three time periods according to the valley, flat and peak periods, marked as , , ; 3) Order , , ; 4) Order ; 5) Judgment Is it true? If so, proceed to step 6); if not, proceed to step 12); 6) Judgment Is it true? If so, proceed to step 7); if not, proceed to step 9); 7) Judgment Is it true? If so, proceed to step 11); if not, proceed to step 8); 8) Order , and return to step 6); 9) Judgment , if yes, proceed to step 11); if no, proceed to step 10); 10) Order , and return to step 8); 11) Output ; 12) Output ; 13) Order ; 14) Judgment Is it true? If so, return to step 4); if not, proceed to step 15); 15) End; in: is the charge threshold; is the battery capacity; is the rising power; It is the slow charging power; is the fast charging power; , , They are the charging time periods located at the valley peak; , , are the charging powers of valley, flat and peak respectively; The above steps are repeated N times to obtain the charging power of N electric vehicles at different time periods. In step 3-3, the charging power of a certain period is the sum of the corresponding charging powers of all charging electric vehicles in the period; the period load is determined by the period power and the interval length; the total power load of a certain period is determined by the basic load of the period and the vehicle charging load; The calculation formula for the optimized charging power is: (12)； Where: N is the total number of trams; is the charging power of the i-th tram; The charging power of the Tth interval among the 96 intervals divided into one day; As the determining factor, =1, it means that the i-th electric car is charged in the Ti period; =0, it means that the i-th electric vehicle is not charged in the Ti period; Regional Electric Vehicles The calculation formula for charging load in a period is: (13)； Where: For electric vehicles Charging load during the time period; The charging time intervals are equally divided; Regional Electric Vehicles The calculation formula for the total power load during a period is: (14)； Where: is the total electricity load; The base load for residential electricity consumption; In step 4, the charging load curve after optimized scheduling is calculated and outputted, which is expressed in the form of a load curve based on the mobile load in each time period after optimized scheduling. The charging load curve after optimized scheduling can be obtained by linearly superimposing the optimized mobile load curve and the basic electricity load curve of the residents.

Images