Electric vehicle charging two-stage optimal coordination scheduling method

Abstract

The invention provides an electric vehicle charging two-stage optimal coordination scheduling method, and relates to the field of power system and energy management, and the method comprises the steps: constructing a power grid load fluctuation model, a power purchase model and an electric vehicle energy relaxation model of an area where a charging station is located, and carrying out the optimal coordination scheduling on the basis of the models; a power grid-charging station first-stage objective function and a charging station-electric vehicle second-stage objective function are constructed, the first-stage objective function takes minimization of power grid fluctuation and electricity purchase cost as target quantities, and the second-stage objective function takes maximization of minimum energy relaxation of all vehicles at the next moment as the target quantity; therefore, a two-stage charging scheduling scheme is formed. An effective coordination scheduling mechanism of the power grid, the charging station and the electric vehicle is realized on the software level, the fluctuation of the power grid is reduced, the electricity purchasing cost of the managed charging station is minimized, and the charging requirement of the electric vehicle is met to the greatest extent.

Description

Technical Field

[0001] The present invention belongs to the field of power system and energy management, and in particular relates to an optimal coordinated scheduling method for two-stage charging of electric vehicles. Background Art

[0002] Electric vehicle technology has made rapid progress in recent years, a trend that is mainly due to the global community's increased attention to reducing carbon emissions and promoting sustainable transportation modes. Electric vehicles have been widely praised for their excellent environmental performance. In the future, with the continuous evolution of technology and the gradual improvement of market mechanisms, electric vehicles are expected to become mainstream transportation tools around the world, driving the transportation industry towards a greener and more efficient direction.

[0003] However, with the improvement of lithium-ion battery efficiency, the penetration of electric vehicles in the market has increased rapidly. This change in the transportation system requires additional electric vehicle charging facilities to facilitate the operation of vehicles. However, due to problems such as low monetary profits and complex electricity costs, the deployment of electric vehicle charging facilities has been delayed, resulting in unreasonable scheduling and uneven distribution of charging ports at charging stations. Therefore, in addition to equipment upgrades and deployment in hardware, implementing an effective scheduling mechanism at the software level is also a necessary measure to reduce the load on the power grid and meet the charging needs of electric vehicles. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a two-stage optimal coordinated scheduling method for electric vehicle charging in view of the deficiencies of the above-mentioned prior art, which performs two-stage coordinated scheduling on the distribution network, charging station and electric vehicle in the area where the charging station is located, thereby reducing grid fluctuations and minimizing the electricity purchase cost of the charging station, while meeting the needs of users as much as possible.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a two-stage optimal coordination scheduling method for electric vehicle charging, comprising the following steps:

[0006] Step 1: For the distribution network, charging station and electric vehicles in the area where the charging station is located, a grid load fluctuation model, a power purchase model and an electric vehicle energy relaxation model are constructed to represent the area where the charging station is located;

[0007] The grid load fluctuation model represents the fluctuation relative to the average daily load curve, that is, the grid load at the current moment is subtracted from the average grid load of the past T days and then the difference is squared to measure the degree of grid fluctuation. The specific formula is:

[0008]

[0009] in, is all other grid loads in the area except the charging station load at time t, F a (P t ) is the grid load P of all charging stations at time t in the grid fluctuation model t The function of P t is the grid load of all charging stations at time t, is the average value of the grid load at time t in the past T days;

[0010] The power purchase model represents the power purchase cost that the charging station pays to the power grid according to the power grid load required at the current moment. It is a quadratic function of the power grid load of the charging station. The specific formula is:

[0011]

[0012] Among them, a0>0, b0≥0, c0≥0 are constants, m is the number of charging stations, is the time t The grid load of each charging station, For Electricity purchasing model when is a variable;

[0013] The electric vehicle energy relaxation model represents the urgency of electric vehicle charging, where the energy relaxation is l j,t , which means the amount of electricity that the j-th electric vehicle can obtain by charging at the maximum charging power during the remaining time at the charging station minus the amount of electricity required to fully charge. The specific formula is:

[0014]

[0015] in, is the maximum charging power of the jth electric vehicle, α j is the time when the jth electric car arrives at the charging station, d j is the expected departure time of the jth electric vehicle, t is the current time, E req,j,t is the power demand of the jth electric car at time t;

[0016] The specific formula for the energy relaxation at the next moment is:

[0017]

[0018] Among them, r ch,j,t is the charging power of electric vehicle j at time t;

[0019] Step 2: Construct a two-stage objective function for the electric vehicle charging process and establish constraints for the two-stage objective function;

[0020] The electric vehicle charging process is divided into two stages, where the first stage is represented as: power grid-charging station, and the second stage is represented as: charging station-electric vehicle;

[0021] The objective function of the first stage is to minimize the grid load fluctuation and the electricity purchase cost of the charging station, including the grid load fluctuation model and the electricity purchase model in step 1;

[0022] The objective function of the second stage is to maximize the minimum energy relaxation of all vehicles at the next moment, so as to achieve reasonable distribution of power to vehicles;

[0023] According to the grid load fluctuation model and the power purchase model, the first-stage objective function is obtained. The specific formula is:

[0024]

[0025] P t ∈P

[0026] Among them, γ ≥ 0 is the parameter for the trade-off between the goal of reducing grid fluctuations and the goal of a single charging station, p i,min and p i,max are empirical values, representing the minimum empirical power and maximum empirical power of the i-th charging station, respectively; P represents the power set of all charging stations at all times;

[0027] Among them, the grid load P of all charging stations at time t t The following formula should be satisfied:

[0028]

[0029] For a preset time range U={1,2,…,T} that divides a day into T time periods, V i,t is the set of electric vehicles charging at the i-th charging station at time t; it is assumed that the charging piles in the electric vehicle charging station can freely configure the charging power between the minimum charging power and the maximum charging power within a given time;

[0030] Maximizing the minimum energy relaxation of all electric vehicles at time t, the specific formula for the unique solution of electric vehicle charging power is:

[0031]

[0032] Where f is any twice continuously differentiable, strictly concave, and monotonically increasing function;

[0033] The solution is obtained based on the electric vehicle information at the current moment, and the second-stage objective function is approximated by continuously maximizing the relaxation, as follows:

[0034]

[0035] subject tor ch,j,t ≥0,j∈V i,t

[0036]

[0037] The above formula maximizes the feasible margin at time t by maximizing the minimum relaxation at time t+1; where l j,t+1 is the minimum energy relaxation of the j-th electric car at time t+1; req,j,t is the energy shortage of the jth electric car compared to the expected value, is the amount of electricity distributed by the power grid to the i-th charging station at time t;

[0038] The Karush-Kuhn-Tucker condition of the second-stage objective function is determined as follows:

[0039] r ch,j,t ≥0

[0040]

[0041] f′(l j,t+1 )-λ j +u j +v=0

[0042] λ j ≥0,u j ≥0,v≥0

[0043] λ j r ch,j,t =0

[0044]

[0045] Among them, λ j ,u j and v are respectively ch,j,t ≥0, formula Japanese style The dual variable of , consider three mutually exclusive cases:

[0046] (1)r ch,j,t = 0, which leads to u j = 0, and

[0047] (2) This leads to λ j =0 and u j = 0, and

[0048] (3) This leads to λ j = 0, and

[0049] By combining these three mutually exclusive situations, the solution of the second-stage objective function is obtained as follows:

[0050]

[0051] Step 3: For the two-stage coordinated scheduling problem of the distribution network, charging station and electric vehicles in the area where the charging station is located, the power scheduling plan of the first stage is iteratively updated until the optimal solution for the power distribution from the distribution network to the charging station in the area where the charging station is located is obtained; then, based on the set of electric vehicles and the battery capacity, expected arrival time, expected departure time, required power and power distribution information of each electric vehicle, the optimal solution of the objective function of the second stage is obtained, including the following steps:

[0052] Step 3.1: The first-stage power dispatching scheme is iteratively solved by the ADMM algorithm until the optimal solution for allocating power from the distribution network to the charging station in the area where the charging station is located is obtained, including the following steps:

[0053] Step 3.1.1: Obtain all other grid loads in the area except the power load of the charging station at time t, and the load information of the charging station in each time period in the past T days;

[0054] Step 3.1.2: Initialize the total power consumption P of all charging stations t = 0, initialize the grid to allocate power set P = {P1, P2, ...P m}, let the initial value of each element of the set be zero, and initialize the original error ∈ primal = 0 and dual error ∈ dual =0;

[0055] Step 3.1.3: Set the grid load value P for all charging stations t and P = {P1,P2,...P m} corresponds to a pair of primal error and dual error, forming a solution (P t ,P,∈ primal ,∈ dual ), using the alternating direction multiplier method ADMM to calculate P t and And iteratively solve the dual variables to obtain the iteratively updated charging scheduling plan (P t new , P new ,∈ primal new ,∈ dual new); when ∈ primal new ≤10 -3 and ∈ dual new ≤10 -5 Stop iteration when

[0056] Step 3.2: According to the Karush-Kuhn-Tucker (KKT) condition of the second-stage objective function, the optimal charging power of electric vehicles in each charging station is obtained;

[0057] According to the current set of electric vehicles in each charging station V = {v1,v2,...v m}, as well as the battery capacity, expected arrival time, expected departure time, required power and power information of the electric vehicles in each set, the optimal charging power of the electric vehicles in each charging station is obtained according to the Karush-Kuhn-Tucker condition of the second-stage objective function.

[0058] The beneficial effects of adopting the above technical solution are as follows: the present invention provides a two-stage optimal coordinated scheduling method for electric vehicle charging. First, for electric vehicles equipped with an intelligent charging control system, the charging scheduling of electric vehicles is decomposed into two stages: power grid-charging station and charging station-electric vehicle; target optimization functions are constructed for the two stages respectively, so as to obtain the optimal solution, which reduces power grid fluctuations and minimizes the power purchase cost of the managed charging stations, while taking into account user needs;

[0059] Secondly, the concept of energy relaxation is introduced, which can compensate for the inaccuracy of the information provided by electric vehicle users in advance to a certain extent, such as the actual energy required exceeds expectations; users can update their demand information to the charging station during the charging process, and the charging station will adjust the energy relaxation of the vehicle at the next moment according to the updated information, thereby increasing the charging urgency of the vehicle to ensure that it obtains more power. In this way, the charging process is more flexible and efficient, and can better meet the actual needs of users;

[0060] Finally, without increasing investment in grid expansion, by uniformly dispatching a large number of charging stations, the maximum utilization of existing grid resources is achieved and the total dispatch time is shortened. Through the intelligent dispatching system, the power distribution strategy of each charging station can be flexibly adjusted according to the grid load and charging demand, which not only avoids the risk of grid overload, but also improves the utilization efficiency of electric energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flow chart of an optimal coordinated scheduling method for two-stage charging of electric vehicles provided by an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of a charging process provided by an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of a load curve provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0065] In this embodiment, an optimal coordinated scheduling method for two-stage charging of electric vehicles is provided. Figure 1 As shown, the following steps are included:

[0066] Step 1: For the distribution network, charging station and electric vehicles in the area where the charging station is located, a grid load fluctuation model, a power purchase model and an electric vehicle energy relaxation model are constructed to represent the area where the charging station is located;

[0067] The grid load fluctuation model represents the fluctuation relative to the average daily load curve, that is, the grid load at the current moment is subtracted from the average grid load of the past T days and then the difference is squared to measure the degree of grid fluctuation. The specific formula is:

[0068]

[0069] in, is all other grid loads in the area except the charging station load at time t, F a (P t ) is the grid load P of all charging stations at time t in the grid fluctuation model t The function of P t is the grid load of all charging stations at time t, is the average value of the grid load at time t in the past T days;

[0070] The power purchase model represents the power purchase cost that the charging station pays to the power grid according to the power grid load required at the current moment. It is a quadratic function of the power grid load of the charging station. The specific formula is:

[0071]

[0072] Among them, a0>0, b0≥0, c0≥0 are constants, m is the number of charging stations, is the time t The grid load of each charging station, For Electricity purchasing model when is a variable;

[0073] The electric vehicle energy relaxation model represents the urgency of electric vehicle charging, where the energy relaxation is l j,t , which means the amount of electricity that the j-th vehicle can obtain by charging at the maximum charging power during the remaining time at the charging station minus the amount of electricity required for full charging. The specific formula is:

[0074]

[0075] in, is the maximum charging power of the jth electric vehicle, α j is the time when the jth vehicle arrives at the charging station, d j is the expected departure time of the jth vehicle, t is the current time, E req,j,t is the power demand of the jth vehicle at time t;

[0076] The specific formula for the energy relaxation at the next moment is:

[0077]

[0078] Among them, r ch,j,t is the charging power of electric vehicle j at time t;

[0079] Step 2: Construct a two-stage objective function for the electric vehicle charging process and establish constraints for the two-stage objective function;

[0080] The electric vehicle charging process is divided into two stages, such as Figure 2 As shown, the first stage is represented as: power grid-charging station, and the second stage is represented as: charging station-electric vehicle;

[0081] The objective function of the first stage is to minimize the grid load fluctuation and the electricity purchase cost of the charging station, including the grid load fluctuation model and the electricity purchase model in step 1;

[0082] The objective function of the second stage is to maximize the minimum energy relaxation of all vehicles at the next moment, so as to achieve reasonable distribution of power to vehicles;

[0083] According to the grid load fluctuation model and the power purchase model, the first-stage objective function is obtained. The specific formula is:

[0084]

[0085] P t ∈P

[0086] Among them, γ ≥ 0 is the parameter for the trade-off between the goal of reducing grid fluctuations and the goal of a single charging station, p i,min and p i,maxare empirical values, representing the minimum empirical power and maximum empirical power of the i-th charging station, respectively; P represents the power set of all charging stations at all times;

[0087] Among them, the grid load P of all charging stations at time t t The following formula should be satisfied:

[0088]

[0089] The data is transmitted from the charging station to the distribution network operator before time t. The objective function of the first stage is a convex function, and the optimal solution of the objective function of the first stage can be obtained. Moreover, the separability of the objective function can decompose the original problem into multiple minimization sub-problems, and then solve them alternately. Therefore, the alternating direction multiplier method (ADMM) can be used. This is a computational framework for solving separable convex optimization problems. Due to its fast processing speed and good convergence performance, the ADMM algorithm is suitable for solving distributed convex optimization problems; the ADMM algorithm can also be regarded as a combination of the dual decomposition method and the augmented Lagrange multiplier method, which makes the algorithm decomposable while ensuring good convergence. The algorithm first decomposes the original problem into several sub-problems that are relatively simple to the original problem, and then combines the solutions of the sub-problems to obtain the global solution of the original problem;

[0090] The actions performed by the charging station include charging, waiting and no action, where charging means that the charging station charges the vehicle according to the charging power allocated to the vehicle, waiting means that the electric vehicle is waiting for the charging port at the charging station, and no action means that the electric vehicle has been charged and the power supply has stopped;

[0091] For a preset time range U={1, 2T} where a day is divided into T time periods, V i,t is the set of electric vehicles being charged at the i-th charging station at time t; it is assumed that the charging piles in the electric vehicle charging station can freely configure the charging power between the minimum charging power and the maximum charging power within a given time;

[0092] Since the least slack first (LLF) algorithm starts charging from the electric vehicle with the least slack; however, the least slack first algorithm can also lead to excessive preemption and oscillation of the charging rate, which may reduce the life of some batteries (such as lithium-ion batteries). In order to solve this problem, the minimum energy slack of all electric vehicles at time t is maximized, and the specific formula for the unique solution is obtained as follows:

[0093]

[0094] Where f is any twice continuously differentiable, strictly concave, monotonically increasing function

[0095] Since the above equation is an offline problem that requires all vehicle information, in order to obtain an (online) solution based only on the current electric vehicle information without complete electric vehicle information, the second-stage objective function is approximated by continuously maximizing the relaxation, as follows:

[0096]

[0097] subject tor ch,j,t ≥0,j∈V i,t

[0098]

[0099] The above formula maximizes the feasible margin at time t by maximizing the minimum relaxation at time t+1; where l j,t+1 is the minimum energy relaxation of the j-th electric car at time t+1; req,j,t is the energy shortage of the jth electric car compared to the expected value, is the amount of electricity distributed by the power grid to the i-th charging station at time t;

[0100] The Karush-Kuhn-Tucker condition of the second-stage objective function is determined as follows:

[0101] r ch,j,t ≥0

[0102]

[0103] f′(l j,t+1 )-λ j +u j +v=0

[0104] λ j ≥0,u j ≥0,v≥0

[0105] λ j r ch,j,t =0

[0106]

[0107] Among them, λ j ,u j and v are respectively ch,j,t ≥0, formula Japanese style The dual variable of , consider three mutually exclusive cases:

[0108] (1)r ch,j,t = 0, which leads to u j = 0, and

[0109] (2) This leads to λ j =0 and u j = 0, and

[0110] (3) This leads to λ j = 0, and

[0111] Among them, because f is strictly concave, strictly increasing and twice continuously differentiable, the inverse of the function f exists. Therefore, by combining these three mutually exclusive cases, the solution of the second stage objective function is obtained as follows:

[0112]

[0113] in, Represents the projection of a scalar onto the interval [a,b].

[0114] Step 3: For the two-stage coordinated scheduling problem of the distribution network, charging stations and electric vehicles in the area where the charging station is located, the power scheduling plan of the first stage is iteratively updated until the optimal solution for the power distribution from the distribution network to the charging station in the area where the charging station is located is obtained; then, based on the set of electric vehicles and the battery capacity, expected arrival time, expected departure time, required power and power information of each electric vehicle, the optimal solution of the objective function of the second stage is obtained. This process aims to reduce grid fluctuations and minimize the power purchase cost of the managed charging stations, while meeting the needs of users as much as possible; compared with not performing charging guidance, this scheduling method can effectively reduce load fluctuations, such as Figure 3 As shown, the following steps are included:

[0115] Step 3.1: The first-stage power dispatching scheme is iteratively solved by the ADMM algorithm until the optimal solution for allocating power from the distribution network to the charging station in the area where the charging station is located is obtained, including the following steps:

[0116] Step 3.1.1: Obtain all other grid loads in the area except the power load of the charging station at time t, and the load information of the charging station in each time period in the past T days;

[0117] Step 3.1.2: Initialize the total power consumption P of all charging stations t = 0, initialize the grid to allocate power set P = {P1, P2, ...P m}, let the initial value of each element of the set be zero, and initialize the original error ∈ primal = 0 and dual error ∈ dual=0;

[0118] Step 3.1.3: Set the grid load value P for all charging stations t and P = {P1,P2,...P m} corresponds to a pair of primal error and dual error, forming a solution (P t ,P,∈ primal ,∈ dual ), using the alternating direction multiplier method ADMM to calculate P t and And iteratively solve the dual variables to obtain the iteratively updated charging scheduling plan (P t new , P new ,∈ primal new ,∈ dual new ); when ∈ primal new ≤10 -3 and ∈ dual new ≤10 -5 Stop iteration when

[0119] Step 3.2: According to the Karush-Kuhn-Tucker (KKT) condition of the second-stage objective function, the optimal charging power of electric vehicles in each charging station is obtained;

[0120] According to the current set of electric vehicles in each charging station V = {v1,v2,...v m}, as well as the battery capacity, expected arrival time, expected departure time, required power and power information allocated by the power grid of each electric vehicle in each set, and the Karush-Kuhn-Tucker (KKT) condition of the second-stage objective function is used to obtain the optimal charging power for electric vehicles in each charging station.

[0121] Users can also update their demand information to the charging station during the charging process. The charging station will adjust the energy relaxation of the vehicle at the next moment based on the updated information, thereby increasing the charging urgency of the vehicle to ensure that it gets more power. In this way, the charging process is more flexible and efficient, and can better meet the actual needs of users.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A two-stage optimal coordination scheduling method for electric vehicle charging, characterized by: The following steps are involved: Step 1: For the distribution network, charging station and electric vehicles in the area where the charging station is located, a grid load fluctuation model, a power purchase model and an electric vehicle energy relaxation model are constructed to represent the area where the charging station is located; Step 2: Construct a two-stage objective function for the electric vehicle charging process and establish constraints for the two-stage objective function; Step 3: For the two-stage coordinated scheduling problem among the distribution network, charging stations and electric vehicles in the area where the charging station is located, the power scheduling plan of the first stage is iteratively updated until the optimal solution for the power allocation from the distribution network in the area where the charging station is located to the charging station is obtained; then, based on the set of electric vehicles and the battery capacity, expected arrival time, expected departure time, required power and power information of each electric vehicle, the optimal solution for the objective function of the second stage is obtained.

2. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 1 is characterized by: The grid load fluctuation model represents the fluctuation relative to the average daily load curve, that is, the grid load at the current moment is subtracted from the average grid load of the past T days and then the difference is squared to measure the degree of grid fluctuation. The specific formula is: in, is all other grid loads in the area except the charging station load at time t, F a (P t ) is the grid load P of all charging stations at time t in the grid fluctuation model t The function of P t is the grid load of all charging stations at time t, is the average value of the grid load at time t over the past T days.

3. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 2 is characterized by: The power purchase model represents the power purchase cost that the charging station pays to the power grid according to the power grid load required at the current moment. It is a quadratic function of the power grid load of the charging station. The specific formula is: Among them, a0>0, b0≥0, c0≥0 are constants, m is the number of charging stations, is the time t The grid load of each charging station, For Electricity purchasing model when is a variable.

4. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 1 is characterized in that: The electric vehicle energy relaxation model represents the urgency of electric vehicle charging, where the energy relaxation is l j,t , which means the amount of electricity that the j-th electric vehicle can obtain by charging at the maximum charging power during the remaining time at the charging station minus the amount of electricity required to fully charge. The specific formula is: in, is the maximum charging power of the jth electric vehicle, α j is the time when the jth electric car arrives at the charging station, d j is the expected departure time of the jth electric vehicle, t is the current time, E req,j,t is the power demand of the jth electric car at time t; The specific formula for the energy relaxation at the next moment is:

5. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 1 is characterized by: The step 2 divides the electric vehicle charging process into two stages, wherein the first stage is represented as: power grid-charging station, and the second stage is represented as: charging station-electric vehicle; The objective function of the first stage is to minimize the grid load fluctuation and the electricity purchase cost of the charging station, including the grid load fluctuation model and the electricity purchase model in step 1; The objective function of the second stage is to maximize the minimum energy relaxation of all vehicles at the next moment to achieve reasonable distribution of power to vehicles.

6. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 1 is characterized by: The method of constructing the first-stage objective function and its constraints in step 2 is: According to the grid load fluctuation model and the power purchase model, the first-stage objective function is obtained. The specific formula is: P t ∈P Among them, γ ≥ 0 is the parameter for the trade-off between the goal of reducing grid fluctuations and the goal of a single charging station, p i,min and p i,max are empirical values, representing the minimum empirical power and maximum empirical power of the i-th charging station, respectively; P represents the power set of all charging stations at all times; Among them, the grid load P of all charging stations at time t t The following formula should be satisfied:

7. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 1 is characterized by: The method for constructing the second-stage objective function and its constraints in step 2 is: For a preset time range U={1,2,…,T} that divides a day into T time periods, V i,t is the set of electric vehicles charging at the i-th charging station at time t; it is assumed that the charging piles in the electric vehicle charging station can freely configure the charging power between the minimum charging power and the maximum charging power within a given time; Maximizing the minimum energy relaxation of all electric vehicles at time t, the specific formula for the unique solution of electric vehicle charging power is: Where f is any twice continuously differentiable, strictly concave, and monotonically increasing function; The solution is obtained based on the electric vehicle information at the current moment, and the second-stage objective function is approximated by continuously maximizing the relaxation, as follows: subject tor ch,j,t ≥0,j∈V i,t The above formula maximizes the feasible margin at time t by maximizing the minimum relaxation at time t+1; where l j,t+1 is the minimum energy relaxation of the j-th electric car at time t+1; req,j,t is the energy shortage of the jth electric car compared to the expected value, is the amount of electricity distributed by the power grid to the i-th charging station at time t; The Karush-Kuhn-Tucker condition of the second-stage objective function is determined as follows: r ch,j,t ≥0 f′(l j,t+1 )-λ j +u j +v=0 l j ≥0,u j ≥0,v≥0 l j r ch,j,t =0 Among them, λ j ,u j and v are respectively ch,j,t ≥0, formula Japanese style The dual variable of , consider three mutually exclusive cases: (1)r ch,j,t = 0, which leads to u j = 0, and (2) This leads to λ j =0 and u j = 0, and (3) This leads to λ j = 0, and By combining these three mutually exclusive situations, the solution of the second-stage objective function is obtained as follows:

8. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 7 is characterized by: The step 3 comprises: Step 3.1: The first-stage power dispatching scheme is iteratively solved by the ADMM algorithm until the optimal solution for allocating power from the distribution network to the charging station in the area where the charging station is located is obtained, including the following steps: Step 3.2: According to the Karush-Kuhn-Tucker condition of the second-stage objective function, the optimal charging power of electric vehicles in each charging station is obtained; According to the current set of electric vehicles in each charging station V = {v1,v2,...v m }, as well as the battery capacity, expected arrival time, expected departure time, required power and power information of the electric vehicles in each set, the optimal charging power of the electric vehicles in each charging station is obtained according to the Karush-Kuhn-Tucker condition of the second-stage objective function.

9. The two-stage optimal coordination scheduling method for electric vehicle charging according to claim 8 is characterized by: The step 3.1 comprises: Step 3.1.1: Obtain all other grid loads in the area except the power load of the charging station at time t, and the load information of the charging station in each time period in the past T days; Step 3.1.2: Initialize the total power consumption P of all charging stations t = 0, initialize the grid to allocate power set P = {P1, P2, ...P m }, let the initial value of each element of the set be zero, and initialize the original error ∈ primal = 0 and dual error ∈ dual =0; Step 3.1.3: Set the grid load value P for all charging stations t and P = {P1,P2,...P m } corresponds to a pair of primal error and dual error, forming a solution (P t ,P,∈ primal ,∈ dual ), using the alternating direction multiplier method ADMM to calculate P t and And iteratively solve the dual variables to obtain the iteratively updated charging scheduling plan (P t new , P new ,∈ primal new ,∈ dual new ); when ∈ primal new ≤10 -3 and ∈ dual new ≤10 -5 Stop iteration when .

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