An electric vehicle charging and discharging resource optimization scheduling method, system and device

Abstract

The application discloses an electric vehicle charging and discharging resource optimization scheduling method, system and device, according to the state of charge of the electric vehicle and the spare charging piles of each charging and discharging station, the schedulable charging and discharging resources of each charging and discharging station are predicted, and an electric vehicle charging and discharging resource optimization scheduling model is constructed with the minimum power of a transformer area power grid, the minimum operation cost and the highest satisfaction of electric vehicle users as targets, so that the optimization scheduling strategy of the electric vehicle charging and discharging resource is obtained. The application fully excavates the potential energy storage resource utilization rate of the electric vehicle, corrects the scheduling instruction and scheduling plan in real time through the prediction of the charging and discharging resources of the electric vehicle in the station, eliminates the influence of the source and load uncertainty caused by the change of the number of electric vehicles in the charging station, improves the accuracy of the transformer area power grid scheduling strategy, increases the accommodation of the renewable distributed power and the economic benefits of the power grid.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power grid energy management, and particularly relates to an electric vehicle charging and discharging resource optimization scheduling method, system and device. BACKGROUND

[0002] At present, the rapid development of electric vehicles (EV) and renewable distributed generation (RDG) promotes the transformation of social energy to green energy, among which, the renewable distributed generation system represented by photovoltaic has been widely applied in distribution networks. However, due to the intermittent and fluctuating nature of renewable distributed generation during power supply, there is a large deviation between the actual output of the distribution network and the planned output of the dispatching demand, which brings great hidden dangers to the safe operation of the power grid, in addition, the randomness of electric vehicle charging and discharging behavior in urban distribution networks will exacerbate the unfavorable conditions of power supply. SUMMARY

[0003] The purpose of the application is to solve the above problems existing in the prior art, and provide an electric vehicle charging and discharging resource optimization scheduling method, system and device.

[0004] To achieve the above purpose, the technical scheme of the application is as follows:

[0005] In a first aspect, the application provides an electric vehicle charging and discharging resource optimization scheduling method, comprising:

[0006] S1, predicting the schedulable charging and discharging resources of each charging and discharging station according to the state of charge of the electric vehicle;

[0007] S2, based on the schedulable charging and discharging resources of each charging and discharging station, constructing an electric vehicle charging and discharging resource optimization scheduling model with the minimum distribution network power, the minimum operation cost and the highest electric vehicle user satisfaction as the target;

[0008] S3, inputting the schedulable charging and discharging resource data of each charging and discharging station, solving the electric vehicle charging and discharging resource optimization scheduling model, and obtaining the optimization scheduling strategy of the electric vehicle charging and discharging resource.

[0009] In the S2, the objective function of the electric vehicle charging and discharging resource optimization scheduling model comprises:

[0010] min J=αF1+βF2+γF3;

[0011] F1=P R，t+l -P L，t+l -P ch，t+l +P dis，t+l +PG，t+l ;

[0012]

[0013]

[0014] In the above formula, F1 is the power of the power grid in the transformer area, F2 is the operating cost, F3 is the negative value of user satisfaction, and a, β, γ are the weight coefficients of F1, F2, and F3, respectively; P R，t+ is the new energy power generation of the power grid in the transformer area at t+l, P L，t+l is the load power of the power grid in the transformer area at t+l, P ch，t+l is the total charging power of the battery of the electric vehicle at t+l, P dis，t+l is the total output power of the battery of the electric vehicle at t+l, P G，t+l is the electricity purchased from the outside at t+l; I is the number of electric vehicles actually participating in charging and discharging at the current time, N ch，i is the charging price of the i-th electric vehicle, N LH，i is the battery aging cost of the i-th electric vehicle per unit of charging and discharging, N dis，i is the discharging price of the i-th electric vehicle, P ch，i，t+l , P dis，i，t+l are the charging power and discharging power of the i-th electric vehicle at t+l, respectively, C G，t+l is the unit price of the electricity purchased from the external power grid at t+l; μ1, μ2 are the weight coefficients for measuring user satisfaction, t wait，i is the charging waiting time of the i-th electric vehicle, t wait-max，i is the maximum waiting time acceptable by the user of the i-th electric vehicle, P in-terrupt，i is the charging interruption probability of the i-th electric vehicle;

[0015] The constraint conditions of the electric vehicle charging and discharging resource optimization scheduling model include:

[0016]

[0017] S_EV min，i ≤S_EV i，t+l ≤S_EV max，i ;

[0018] E c，i，t+l =η c，i P c，i，t+l ;

[0019]

[0020] In the above formula, S_EV i,t+l is the state of charge of the i-th electric vehicle at t+l, j is the total time, and Ec，i，t+l 、E dis，i，t+l are the charging energy and discharging energy of the i-th electric vehicle at time t+1, E i is the initial storage capacity of the i-th electric vehicle, S_EV min，i 、S_EV max，i are the minimum state of charge and maximum state of charge of the i-th electric vehicle, η c，j ,η dis，i are the charging efficiency and discharging efficiency of the i-th electric vehicle, P c，i，t+l 、P dis，i，t+l They are respectively the charging power and discharging power of the i-th electric vehicle at time t+1 predicted at time t.

[0021] The S3 includes:

[0022] S31. Establish a state transition dynamic model for the electric vehicle charging and discharging resource system, including:

[0023] s t+l =f(s t ,u t ,ω t );

[0024] R(s t ,u t )=C ch (s t ,u t )-C dis (s t ,u t )-C LH (Δsoc t );

[0025] In the above formula, s t+l is the state of the electric vehicle charging and discharging system at time t+l, f is the state transfer function, s t is the state of the electric vehicle charging and discharging system at time t, i.e., the current time. The state includes the external electricity purchase price at the current time, the charging and discharging price of the electric vehicle, the renewable energy power generation power and load of the regional power grid, the charging and discharging power demand of the electric vehicle, and the predicted value of the dispatchable charging and discharging resources of each charging and discharging station; u t is the control input at time t, including the electric vehicle charging power, discharging power and charging and discharging start time; ω t is the external disturbance at time t, including fluctuations in renewable energy power generation, electricity purchase price, and electric vehicle charging and discharging price; R is the reward function of the state of the electric vehicle charging and discharging system at the current moment, C ch is the charging cost, C dis is the discharge gain, C LH is the battery aging cost, Δsoct is a variation of the state of charge of the battery at time t;

[0026] S32, based on the state transition model of the electric vehicle charging and discharging resource system, optimizing the control input at time t+l, and further obtaining the state model of the electric vehicle charging and discharging system at time t+l+1;

[0027]

[0028] s t+l+1 = f(s t+l , u t+l ), (l = 0, 1,..., N-1);

[0029] In the above formula, arg max is a function of parameters, and N is the number of steps;

[0030] S33, solving the state model of the electric vehicle charging and discharging system at time t+l+1 by the subtraction optimizer algorithm, to obtain the optimized scheduling strategy of the electric vehicle charging and discharging resource at time t+l+1.

[0031] The S1 comprises:

[0032] S11, taking the distance between the electric vehicle and each charging and discharging station and the charging price of each charging and discharging station as indexes, constructing a weighted standardized decision matrix of priority as follows:

[0033]

[0034] (Z kj )K×m=p kj *ω j ;

[0035]

[0036] In the above formula, Z' is the weighted standardized decision matrix of priority, (Z kj )K×m is the standardized decision matrix of priority, ω ch_risk_k is the probability evaluation coefficient of the electric vehicle user to the kth charging and discharging station for charging and discharging, p kj is the standardized matrix, ω j is the weight of the jth index, K is the total number of charging and discharging stations available for charging and discharging near the electric vehicle, m is the number of indexes, w D is the weight coefficient of distance, D st-k is the distance between the electric vehicle user and the kth charging and discharging station, w P-c is the weight coefficient of charging price, P st-c-k is the charging price of the kth charging and discharging station, w P-dis is the weight coefficient of discharging price, P st-dis-kis the discharge price of the kth charging and discharging station, w H is the weight of convenience, H st-k is the convenience of the kth charging and discharging station;

[0037] S12. Based on the weighted standardized decision matrix of priority, the following formula is used to calculate the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions:

[0038]

[0039]

[0040]

[0041]

[0042] In the above formula, are the distances between the electric vehicle’s charging solution and the positive and negative ideal solutions to the kth charging and discharging station, respectively. are positive and negative ideal solutions respectively, are the distances between the discharge scheme of the electric vehicle to the kth charging and discharging station and the positive and negative ideal solutions respectively;

[0043] S13, generating a priority for the electric vehicle to charge and discharge at each charging and discharging station based on the distances between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions;

[0044] S14. Based on the priority of electric vehicles charging at each charging and discharging station, the following formula is used to predict the dispatchable charging resources of each charging and discharging station:

[0045] P af_ck =P c -C ch_k *P rch ;

[0046] P c =P rch ×(L r +L m );

[0047] In the above formula, P af_ck is the dispatchable charging resource of the kth charging and discharging station at the next moment, P c is the remaining rechargeable resources in the current charging and discharging station, C ch_k The priority of electric vehicles to charge at the kth charging and discharging station, P rch is the rated charging power of the charging pile, L r is the number of available charging piles, L m The number of electric vehicles leaving the charging station;

[0048] Based on the priority of electric vehicles discharging to each charging and discharging station, the following formula is used to predict the dispatchable discharge resources of each charging and discharging station:

[0049] P af_dis =P d +C dis_k *P γdis ;

[0050]

[0051] In the above formula, P af_dis is the dispatchable discharge resource of the kth charging and discharging station at the next moment, P d is the remaining dischargeable resources in the charging and discharging station, C dis_k is the priority of electric vehicles to discharge to the kth charging and discharging station, P γdis is the rated discharge power of the electric vehicle, I is the number of electric vehicles discharged at the charging and discharging station, S i is the current state of charge of the i-th electric vehicle, S out_i is the state of charge of the i-th electric vehicle when it leaves the charging and discharging station.

[0052] In a second aspect, the present invention proposes an optimization scheduling system for electric vehicle charging and discharging resources, including a resource prediction module, a model building module, and a model solving module;

[0053] The resource prediction module is used to predict the schedulable charging and discharging resources of each charging and discharging station according to the state of charge of the electric vehicle;

[0054] The model building module is used to build an electric vehicle charging and discharging resource optimization scheduling model based on the dispatchable charging and discharging resources of each charging and discharging station, with the goals of minimizing the power of the power grid in the substation area, minimizing the operating cost and maximizing the satisfaction of electric vehicle users;

[0055] The model solving module is used to input the dispatchable charging and discharging resource data of each charging and discharging station, solve the electric vehicle charging and discharging resource optimization scheduling model, and obtain the electric vehicle charging and discharging resource optimization scheduling strategy.

[0056] The model construction module includes an objective function construction unit and a constraint condition construction unit;

[0057] The objective function construction unit is used to construct the objective function of the following electric vehicle charging and discharging resource optimization scheduling model:

[0058] mmJ=αF1+βF2+γF3;

[0059] F1=P R，t+l -P L，t+l -P ch，t+l +P dis，t+l +P G，t+l;

[0060]

[0061]

[0062] In the above formula, F1 is the power of the power grid in the transformer area, F2 is the operating cost, F3 is the negative value of user satisfaction, and a, β, and γ are the weight coefficients of F1, F2, and F3, respectively; P R，t+l is the new energy power generation of the power grid in the transformer area at t+1, P L，t+l is the load power of the power grid in the transformer area at t+1, P ch，t+l is the total charging power of the battery of the electric vehicle at t+1, P dis，t+l is the total output power of the battery of the electric vehicle at t+1, P G，t+l is the electricity purchased from the outside at t+1; I is the number of electric vehicles actually participating in charging and discharging at the current moment, N ch，i is the charging price of the i th electric vehicle, N LH，i is the battery aging cost of the i th electric vehicle per unit of charging and discharging electricity, N dis，i is the discharging price of the i th electric vehicle, P ch，i，t+l , P dis，i，t+l are the charging power and discharging power of the i th electric vehicle at t+1, respectively, C G，t+l is the unit price of the electricity purchased from the external power grid at t+1; μ1 and μ2 are the weight coefficients for measuring user satisfaction, t wait，i is the charging waiting time of the i th electric vehicle, t wait-max，i is the maximum waiting time acceptable by the user of the i th electric vehicle, P in-terrupt，i is the charging interruption probability of the i th electric vehicle;

[0063] The constraint condition construction unit is configured to construct the constraint conditions of the electric vehicle charging and discharging resource optimization scheduling model as follows:

[0064]

[0065] S_EV min，i ≤ S_EV i，t+l ≤ S_EV max，i ;

[0066] E c，i，t+l = η c，i P c，i，t+l ;

[0067]

[0068] In the above formula, S_EV i,t+l is the state of charge of the i th electric vehicle at t+1, j is the total time, and Ec，i，t+l 、E dis，i，t+l are the charging energy and discharging energy of the i-th electric vehicle at time t+1, E i is the initial storage capacity of the i-th electric vehicle, S_EV min，i 、S_EV max，i are the minimum state of charge and maximum state of charge of the i-th electric vehicle, η c，i ,η dis，i are the charging efficiency and discharging efficiency of the i-th electric vehicle, P c，i，t+l 、P dis，i，t+l They are respectively the charging power and discharging power of the i-th electric vehicle at time t+1 predicted at time t.

[0069] The model solving module includes a dynamic model building unit, a state model determining unit, and a state model solving unit;

[0070] The dynamic model establishment unit is used to establish the following state transition dynamic model of the electric vehicle charging and discharging resource system:

[0071] s t+l =f(s t ,u t ,ω t );

[0072] R(s t ,u t )=C ch (s t ,u t )-C dis (s t ,u t )-C LH (Δsoc t );

[0073] In the above formula, s t+l is the state of the electric vehicle charging and discharging system at time t+l, f is the state transfer function, s t is the state of the electric vehicle charging and discharging system at time t, i.e., the current time. The state includes the external electricity purchase price at the current time, the charging and discharging price of the electric vehicle, the renewable energy power generation power and load of the regional power grid, the charging and discharging power demand of the electric vehicle, and the predicted value of the dispatchable charging and discharging resources of each charging and discharging station; u t is the control input at time t, including the electric vehicle charging power, discharging power and charging and discharging start time; ω t is the external disturbance at time t, including fluctuations in renewable energy power generation, electricity purchase price, and electric vehicle charging and discharging price; R is the reward function of the state of the electric vehicle charging and discharging system at the current moment, C ch is the charging cost, C disis the discharge gain, C LH is the battery aging cost, Δsoc t is the change in battery state of charge at time t;

[0074] The state model determination unit is used to optimize the control input at time t+1 based on the state transition dynamic model of the electric vehicle charging and discharging resource system, thereby obtaining the following state model of the electric vehicle charging and discharging system at time t+1+1:

[0075]

[0076] s t+l+1 =f(s t+l ,u t+l ), (l=0, 1,..., N-1);

[0077] In the above formula, arg max is the function to find the parameter, and N is the number of steps;

[0078] The state model solving unit is used to solve the state model of the electric vehicle charging and discharging system at time t+l+1 by using a subtraction optimizer algorithm to obtain an optimized scheduling strategy for the electric vehicle charging and discharging resources at time t+l+1.

[0079] The resource prediction module includes a decision matrix construction unit, a distance calculation unit, a priority generation unit, a dispatchable charging resource prediction unit, and a dispatchable discharging resource prediction unit;

[0080] The decision matrix construction unit is used to construct the following priority weighted standardized decision matrix based on the distance between the electric vehicle and each charging and discharging station and the charging price of each charging and discharging station as indicators;

[0081]

[0082] (Z kj )K×m=pk j *ω j ;

[0083]

[0084] In the above formula, Z′ is the weighted normalized decision matrix of priority, (Z kj ) K×m is the standardized decision matrix of priority, ω ch_risk_k is the probability evaluation coefficient of electric vehicle users going to the kth charging and discharging station for charging and discharging, p kj is the normalized matrix, ω j is the weight of the jth indicator, K is the total number of charging and discharging stations available for charging and discharging near electric vehicles, m is the number of indicators, and w D is the weight coefficient of distance, Dst-k is the distance between the electric vehicle user and the kth charging and discharging station, w P-c is the weight coefficient of charging price, P st-c-k is the charging price of the kth charging and discharging station, w P-dis is the weight coefficient of the discharge price, P st-dis-k is the discharge price of the kth charging and discharging station, w H is the weight of convenience, H st-k is the convenience of the kth charging and discharging station;

[0085] The distance calculation unit is used to calculate the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions based on the weighted standardized decision matrix of the priority using the following formula:

[0086]

[0087]

[0088]

[0089]

[0090] In the above formula, are the distances between the electric vehicle’s charging solution and the positive and negative ideal solutions to the kth charging and discharging station, respectively. are positive and negative ideal solutions respectively, are the distances between the discharge scheme of the electric vehicle to the kth charging and discharging station and the positive and negative ideal solutions respectively;

[0091] The priority generation unit is used to generate the priority of charging and discharging of the electric vehicle at each charging and discharging station based on the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions;

[0092] The dispatchable charging resource prediction unit is used to predict the dispatchable charging resources of each charging and discharging station based on the priority of electric vehicles charging at each charging and discharging station using the following formula:

[0093] P af_ck =P c -C ch_k *P rch ;

[0094] P c =P rch ×(L r +L m );

[0095] In the above formula, P af_ck is the dispatchable charging resource of the kth charging and discharging station at the next moment, P cis the remaining rechargeable resources in the current charging and discharging station, C ch_k The priority of electric vehicles to charge at the kth charging and discharging station, P rch is the rated charging power of the charging pile, L r is the number of available charging piles, L m The number of electric vehicles leaving the charging station;

[0096] The dispatchable discharge resource prediction unit is used to predict the dispatchable discharge resources of each charging and discharging station based on the priority of electric vehicles discharging to each charging and discharging station using the following formula:

[0097] P af_dis =P d +C dis_k *P γdis ;

[0098]

[0099] In the above formula, P af_dis is the dispatchable discharge resource of the kth charging and discharging station at the next moment, P d is the remaining dischargeable resources in the charging and discharging station, C dis_k is the priority of electric vehicles to discharge to the kth charging and discharging station, P γdis is the rated discharge power of the electric vehicle, I is the number of electric vehicles discharged at the charging and discharging station, S i is the current state of charge of the i-th electric vehicle, S out_i is the state of charge of the i-th electric vehicle when it leaves the charging and discharging station.

[0100] In a third aspect, the present invention provides an optimized scheduling device for electric vehicle charging and discharging resources, comprising a processor and a memory;

[0101] The memory is used to store computer program code and transmit the computer program code to the processor;

[0102] The processor is used to execute the aforementioned method for optimizing the scheduling of electric vehicle charging and discharging resources according to the instructions in the computer program code.

[0103] In a fourth aspect, the present invention provides a computer storage medium having a computer program stored thereon;

[0104] When the computer program is executed by a processor, the steps of the aforementioned method for optimizing the scheduling of charging and discharging resources of electric vehicles are implemented.

[0105] Compared with the prior art, the present invention has the following beneficial effects:

[0106] 1. The present invention proposes a method, system and equipment for optimizing the scheduling of electric vehicle charging and discharging resources. The method first predicts the dispatchable charging and discharging resources of each charging and discharging station according to the charge state of the electric vehicle. Then, based on the dispatchable charging and discharging resources of each charging and discharging station, an optimization scheduling model for electric vehicle charging and discharging resources is constructed with the goals of minimizing the power of the power grid in the substation, minimizing the operating cost and maximizing the satisfaction of electric vehicle users. Finally, by inputting the dispatchable charging and discharging resource data of each charging and discharging station, the optimization scheduling model for electric vehicle charging and discharging resources is solved to obtain the optimization scheduling strategy for electric vehicle charging and discharging resources. On the one hand, this method improves the accuracy of the substation power grid dispatching strategy by predicting the charging and discharging resources of electric vehicles in the station and correcting the dispatching instructions and dispatching plans in real time, thereby solving the impact of the random flow of electric vehicles on the substation power grid; on the other hand, this method fully considers the dispatching potential of electric vehicles outside the station while taking into account the dual uncertainty of source and load, and constructs an electric vehicle charging and discharging resource optimization dispatching model with the goal of minimizing the power of the substation power grid, minimizing the operating cost and maximizing the satisfaction of electric vehicle users, eliminating the impact of source and load uncertainty caused by changes in the number of electric vehicles at the charging station, increasing the absorption of renewable distributed power sources, and improving the economic benefits of the power grid, providing technical support for subsequent research on multiple uncertainty issues in the energy dispatch process.

[0107] 2. The present invention proposes a method, system and equipment for optimizing the scheduling of electric vehicle charging and discharging resources. This method aims to sort electric vehicles with multiple indicators and multiple samples, and then predict the dispatchable charging and discharging resources of each charging and discharging station. The probability evaluation coefficient of electric vehicle users to each charging and discharging station and the positive and negative ideal solutions are introduced to eliminate the influence of different dimensions of influencing factors, thereby obtaining a more reasonable charging and discharging priority ranking and more accurately predicting the charging and discharging resources of electric vehicles in the station. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 This is the load forecast diagram of the substation power grid described in Example 1.

[0109] Figure 2 This is the output forecast diagram of the substation power grid described in Example 1.

[0110] Figure 3 The figure is an overall flow chart of the method of the present invention.

[0111] Figure 4 1 is a structural diagram of the system of the present invention.

[0112] Figure 5 This is a structural diagram of the equipment described in Example 3. DETAILED DESCRIPTION

[0113] The present invention will be further described in detail below with reference to specific embodiments and the accompanying drawings.

[0114] The present invention proposes a method, system and equipment for optimizing the scheduling of electric vehicle charging and discharging resources. According to the charge state of electric vehicles and the available charging piles at each charging and discharging station, the priority of electric vehicles within a certain geographical range outside the station to go to different charging and discharging stations is ranked, and the dispatchable charging and discharging resources of each charging and discharging station are predicted. With the goals of minimizing the power of the power grid in the substation, minimizing the operating cost and maximizing the satisfaction of electric vehicle users, an optimization scheduling model for electric vehicle charging and discharging resources is constructed to obtain an optimized scheduling strategy for electric vehicle charging and discharging resources, fully tap the potential energy storage resource utilization rate of electric vehicles, improve the accuracy of the substation power grid scheduling strategy, increase the absorption of renewable distributed power sources and the economic benefits of the power grid.

[0115] Example 1:

[0116] This embodiment takes the power grid system of a certain city's residential area as the research object, and conducts a case analysis on the basic operation of the system on a certain day. There are 3 charging stations in the power grid of this area, and the detailed parameters are shown in Table 1; three different types of vehicles, A, B, and C, are connected to the power grid in one day, and their detailed parameters are shown in Table 2; the market peak and valley electricity prices of this city are shown in Table 3. During the normal load period, the electricity price is low, and the energy storage system and electric vehicles are charged to reduce the operating costs of the power grid. During the peak load period, the electricity price is high, and the energy storage system and electric vehicles are dispatched to support the power grid to reduce the amount of electricity purchased by the power grid; the maximum discharge power of the energy storage configured in the area is 150KW, the maximum charging power is 200KW, the photovoltaic installed capacity is 200kW, and the photovoltaic output forecast obeys the normal distribution. In the forecast period of one day, the load forecast situation from 11:00 to 13:00 is as follows Figure 1 As shown in the figure, the output forecast is as follows Figure 2 shown.

[0117] Table 1 Parameters of charging stations in the power grid of the substation

[0118] Charging station serial number Charging Station 1 Charging Station 2 Charging Station 3 Number of charging piles 30 25 35 Charging power / KW 45 / 90 / 100 60 / 90 90 / 120 Rated charging power 2350 1950 3750 ;

[0119] Table 2 Parameters of electric vehicles connected to the grid

[0120] type A B C Battery capacity / kWh 45 50 60 Rated charge / discharge power / kW 9 / 6 12 / 8 15 / 12 Vehicles connected to the grid 120 90 160 Minimum charging power / kW 3 4 5 ;

[0121] Table 3 Market peak and valley electricity prices

[0122] Time Electricity purchase price (yuan / kwh) Valley hours (00:00-08:00, 22:00-24:00) 0.395 Normal hours (08:00-14:00, 16:00-19:00) 0.729 Peak hours (14:00-16:00, 19:00-22:00) 1.063 .

[0123] like Figure 3 As shown, a method for optimizing the scheduling of electric vehicle charging and discharging resources is performed in the following steps:

[0124] 1. Based on the state of charge of electric vehicles and the available charging piles at each charging and discharging station, the priority of electric vehicles within a certain geographical range outside the station to go to different charging and discharging stations is sorted;

[0125] The number of nearby charging stations available for charging is obtained based on the current battery status of the electric vehicle. A weighted standardized decision matrix of priority is constructed based on the distance between the electric vehicle and each charging and discharging station and the charging price of each charging and discharging station. The weighted standardized decision matrix is ​​evaluated by considering the probability of electric vehicle users charging and discharging at each charging and discharging station.

[0126]

[0127] (Z kj )K×m=p kj *ω j ;

[0128]

[0129] In the above formula, Z′ is the weighted normalized decision matrix of priority, (Z kj ) K×m is the standardized decision matrix of priority, ω ch_risk_k is the probability evaluation coefficient of the electric vehicle user going to the kth charging and discharging station for charging and discharging. A positive value indicates that the electric vehicle user prefers a low-risk solution, and a negative value indicates that the electric vehicle user accepts high risks in exchange for possible high returns. kj is the normalized matrix, ω j is the weight of the jth indicator, K is the total number of charging and discharging stations available for charging and discharging near electric vehicles, m is the number of indicators, and w D is the weight coefficient of distance, D st-k is the distance between the electric vehicle user and the kth charging and discharging station, w P-c is the weight coefficient of charging price, P st-c-k is the charging price of the kth charging and discharging station, w P-dis is the weight coefficient of the discharge price, P st-dis-k is the discharge price of the kth charging and discharging station, w H is the weight of convenience, H st-k is the convenience of the kth charging and discharging station;

[0130] Based on the weighted standardized decision matrix of priority, the following formula is used to calculate the distance between each charging and discharging scheme of electric vehicles and their positive and negative ideal solutions:

[0131]

[0132]

[0133]

[0134]

[0135] In the above formula, are the distances between the electric vehicle’s charging solution and the positive and negative ideal solutions to the kth charging and discharging station, respectively. are positive and negative ideal solutions respectively, are the distances between the discharge scheme of the electric vehicle to the kth charging and discharging station and the positive and negative ideal solutions respectively;

[0136] Based on the distance between each charging and discharging scheme of electric vehicles and their positive and negative ideal solutions, the priority of electric vehicles to charge and discharge at each charging and discharging station is generated;

[0137]

[0138]

[0139] In the above formula, C ch_k 、C dis_k are the priorities for electric vehicles to charge and discharge at the kth charging and discharging station, respectively. The value range is [0, 1]. The closer to 1, the better the sample score, the higher the priority, and the greater the probability that the owner will go there to charge or discharge.

[0140] 2. Based on the priority of electric vehicles charging at each charging and discharging station, predict the dispatchable charging and discharging resources of each charging and discharging station;

[0141] Based on the priority of electric vehicles charging at each charging and discharging station, the following formula is used to predict the dispatchable charging resources of each charging and discharging station:

[0142] P af_ck =P c -C ch_k *P rch ;

[0143] P c =P rch ×(L r +L m );

[0144] In the above formula, P af_ck is the dispatchable charging resource of the kth charging and discharging station at the next moment, P c is the remaining rechargeable resources in the current charging and discharging station, C ch_k The priority of electric vehicles to charge at the kth charging and discharging station, P rch is the rated charging power of the charging pile, L r is the number of available charging piles, L m The number of electric vehicles leaving the charging station;

[0145] Based on the priority of electric vehicles discharging to each charging and discharging station, the following formula is used to predict the dispatchable discharge resources of each charging and discharging station:

[0146] P af_dis =P d +C dis_k *P γdis ;

[0147]

[0148] In the above formula, P af_dis is the dispatchable discharge resource of the kth charging and discharging station at the next moment, P d is the remaining dischargeable resources in the charging and discharging station, C dis_k is the priority of electric vehicles to discharge to the kth charging and discharging station, P γdis is the rated discharge power of the electric vehicle, I is the number of electric vehicles discharged at the charging and discharging station, S i is the current state of charge of the i-th electric vehicle, S out_i is the state of charge of the i-th electric vehicle when it leaves the charging and discharging station.

[0149] 3. Based on the dispatchable charging and discharging resources of each charging and discharging station, with the goal of minimizing grid power, minimizing operating costs, and maximizing electric vehicle user satisfaction, an optimal scheduling model for electric vehicle charging and discharging resources is constructed;

[0150] The optimization goal is to ensure that the sum of the output power of distributed power generation and electric vehicles and the power purchased from the external power grid is close to the predicted value of the power grid load in the substation, the maintenance and operation costs of electric vehicles and the operation costs of the substation power grid are minimized, and user satisfaction is maximized.

[0151] The objective function of the electric vehicle charging and discharging resource optimization scheduling model includes:

[0152] min J = αF1 + βF2 + γF3;

[0153] F1=P R，t+l -P L，t+l -P ch，t+l +P dis，t+l +P G，t+l ;

[0154]

[0155]

[0156] In the above formula, F1 is the power of the power grid in the substation, F2 is the operating cost, F3 is the negative value of user satisfaction, α, β, and γ are the weight coefficients of F1, F2, and F3 respectively. The weight determination of each indicator is an iterative and multi-faceted process. Decision makers can determine the weights based on the importance and conflict degree of the power grid, operating cost, and user satisfaction, as well as the decision makers' goals. R，t+l is the renewable energy power generation power of the grid in the substation at time t+1, P L，t+l is the load power of the power grid in the substation at time t+1, P ch，t+l is the total charging power of the electric vehicle battery at time t+l, P dis，t+l is the total output power of the electric vehicle battery at time t+1, P G，t+l is the amount of electricity purchased from the outside at time t+1; I is the number of electric vehicles actually involved in charging and discharging at the current moment, N ch，i is the charging price of the i-th electric vehicle, N LH，i is the battery aging cost per unit charge and discharge of the i-th electric vehicle, N dis，i is the discharge price of the i-th electric vehicle, P ch，i，t+l 、P dis，i，t+l are the charging power and discharging power of the i-th electric vehicle at time t+l, C G，t+ , is the unit price of electricity purchased from the external grid at time t+1; μ1 and μ2 are weight coefficients for measuring user satisfaction, which can be determined based on the importance of waiting time, charging interruption probability, and the decision maker's goals. wait，i is the charging waiting time of the i-th electric vehicle, t wait-max，i is the maximum waiting time acceptable to the user of the i-th electric vehicle, P in-terrupt，i is the charging interruption probability of the i-th electric vehicle;

[0157] The constraints of the electric vehicle charging and discharging resource optimization scheduling model include:

[0158]

[0159] S_EV min，i ≤S_EV i，t+l ≤S_EV max，i ;

[0160] E c，i，t+l =η c，i P c，i，t+l ;

[0161]

[0162] In the above formula, S_EV i,t+l is the state of charge of the i-th electric vehicle at time t+l, j is the total time, E c，i，t+l 、Edis，i，t+l are the charging energy and discharging energy of the i-th electric vehicle at time t+1, E i is the initial storage capacity of the i-th electric vehicle, S_EV min，i 、S_EV max，i are the minimum state of charge and maximum state of charge of the i-th electric vehicle, η c，i ,η dis，i are the charging efficiency and discharging efficiency of the i-th electric vehicle, P c，i，t+l 、P dis，i，t+l They are respectively the charging power and discharging power of the i-th electric vehicle at time t+1 predicted at time t.

[0163] 4. Input the dispatchable charging and discharging resource data of each charging and discharging station, solve the electric vehicle charging and discharging resource optimization scheduling model, and obtain the optimal scheduling strategy for electric vehicle charging and discharging resources;

[0164] Predict the fluctuations in electricity purchase prices, electric vehicle charging and discharging prices, renewable energy generation power and load in the power grid, and electric vehicle charging and discharging power requirements for the next hour, and establish a dynamic state transition model for the electric vehicle charging and discharging resource system, including:

[0165] s t+l =f(s t ,u t ,ω t );

[0166] R(s t ,u t )=C ch (s t ,u t )-C dis (s t ,u t )-C LH (Δsoc t );

[0167] In the above formula, s t+l is the state of the electric vehicle charging and discharging system at time t+l, f is the state transfer function, s t is the state of the electric vehicle charging and discharging system at time t, i.e., the current time. The state includes the current external electricity purchase price, the charging and discharging price of the electric vehicle, the renewable energy power generation power and load of the regional power grid, the charging and discharging power demand of the electric vehicle, and the predicted value of the dispatchable charging and discharging resources of each charging and discharging station; u t is the control input at time t, including the electric vehicle charging power, discharging power and charging and discharging start time; ω tis the external disturbance at time t, including fluctuations in renewable energy power generation, electricity purchase price, and electric vehicle charging and discharging price; R is the reward function of the state of the electric vehicle charging and discharging system at the current moment, C ck is the charging cost, C dis is the discharge gain, C LH is the battery aging cost, Δsoc t is the change in battery state of charge at time t;

[0168] Based on the state transition dynamic model of the electric vehicle charging and discharging resource system, the control input at time t+1 is optimized, and then the state model of the electric vehicle charging and discharging system at time t+1+1 is obtained;

[0169]

[0170] s t+l+1 =f(s t+l ,u t+l ), (l=0, 1, ..., N-1);

[0171] In the above formula, arg max is the function to find the parameter, and N is the number of steps;

[0172] The state model of the electric vehicle charging and discharging system at time t+l+1 is solved by the subtraction optimizer algorithm, and the optimal scheduling strategy of the electric vehicle charging and discharging resources at time t+l+1 is obtained;

[0173] The subtraction optimizer algorithm is based on the principle of subtraction averaging. It updates and optimizes the position of the search agent by calculating the differences between individuals and the differences from the objective function value. If the fitness value of the new position is improved, the new position is accepted, otherwise it remains unchanged. It has the characteristics of strong optimization ability and fast convergence speed, which gives it significant advantages in solving optimization problems, including:

[0174] The following formula is used to calculate the value of the target decision variable of the target search agent in the search space:

[0175] x i，d =lb d +r i，d (ub d -lb d ),i=1,...,N,d=1,...,m;

[0176] In the above formula, x i，d is the value of the d-th decision variable of the i-th search agent in the search space, ub d lb d are the upper and lower limits of the particle value, r i，d is the random coefficient, N is the total number of particles, and m is the dimension;

[0177] The v-subtraction between search agent B and search agent A is defined using the following formula:

[0178]

[0179] In the above formula, sign is the sign function, F(A) is the objective function value of the search agent A, and F(B) is the objective function value of the search agent B. A random number generated for an m-dimensional vector [1, 2];

[0180] In the subtraction optimizer algorithm, any search agent x i The displacement in the search space is calculated by each search agent x j The new proposed position for each search agent in the subtraction optimizer algorithm is calculated using the arithmetic mean of the "-v" subtractions:

[0181]

[0182] In the above formula, For the i-th search agent X i The new proposed location of is a random value that follows a normal distribution, X i 、X j The location of each search agent;

[0183] Construct the constraints for updating the proposed position:

[0184]

[0185] In the above formula, for The objective function value, F i For X i The objective function value of .

[0186] Example 2:

[0187] like Figure 4 As shown, an optimization scheduling system for electric vehicle charging and discharging resources includes a resource prediction module, a model building module, and a model solving module;

[0188] The resource prediction module is used to predict the schedulable charging and discharging resources of each charging and discharging station according to the state of charge of the electric vehicle;

[0189] The model building module is used to build an electric vehicle charging and discharging resource optimization scheduling model based on the dispatchable charging and discharging resources of each charging and discharging station, with the goals of minimizing the power of the power grid in the substation area, minimizing the operating cost and maximizing the satisfaction of electric vehicle users;

[0190] The model solving module is used to input the dispatchable charging and discharging resource data of each charging and discharging station, solve the electric vehicle charging and discharging resource optimization scheduling model, and obtain the electric vehicle charging and discharging resource optimization scheduling strategy.

[0191] The resource prediction module includes a decision matrix construction unit, a distance calculation unit, a priority generation unit, a dispatchable charging resource prediction unit, and a dispatchable discharging resource prediction unit;

[0192] The decision matrix construction unit is used to construct the following priority weighted standardized decision matrix based on the distance between the electric vehicle and each charging and discharging station and the charging price of each charging and discharging station as indicators;

[0193]

[0194] (Z kj ) K×m =p kj *ω j ;

[0195]

[0196] In the above formula, Z is the weighted standardized decision matrix of priority, (Z kj ) K×m is the standardized decision matrix of priority, ω ch_risk_k is the probability evaluation coefficient of electric vehicle users going to the kth charging and discharging station for charging and discharging, p kj is the normalized matrix, ω j is the weight of the jth indicator, K is the total number of charging and discharging stations available for charging and discharging near electric vehicles, m is the number of indicators, and w D is the weight coefficient of distance, D st-k is the distance between the electric vehicle user and the kth charging and discharging station, w P-c is the weight coefficient of charging price, P st-c-k is the charging price of the kth charging and discharging station, w P-dis is the weight coefficient of the discharge price, P st-dis-k is the discharge price of the kth charging and discharging station, w H is the weight of convenience, H st-k is the convenience of the kth charging and discharging station;

[0197] The distance calculation unit is used to calculate the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions based on the weighted standardized decision matrix of the priority using the following formula:

[0198]

[0199]

[0200]

[0201]

[0202] In the above formula, are the distances between the electric vehicle’s charging solution and the positive and negative ideal solutions to the kth charging and discharging station, respectively. are positive and negative ideal solutions respectively, are the distances between the discharge scheme of the electric vehicle to the kth charging and discharging station and the positive and negative ideal solutions respectively;

[0203] The priority generation unit is used to generate the priority of charging and discharging of the electric vehicle at each charging and discharging station based on the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions;

[0204]

[0205]

[0206] In the above formula, C ch_k 、C dis_k are the priorities for electric vehicles to charge and discharge at the kth charging and discharging station, respectively. The value range is [0, 1]. The closer to 1, the better the sample score, the higher the priority, and the greater the probability that the owner will go there to charge or discharge.

[0207] The dispatchable charging resource prediction unit is used to predict the dispatchable charging resources of each charging and discharging station based on the priority of electric vehicles charging at each charging and discharging station using the following formula:

[0208] P af_ck =P c -C ch_k *P rch ;

[0209] P c =P rch ×(L r +L m );

[0210] In the above formula, P af_ck is the dispatchable charging resource of the kth charging and discharging station at the next moment, P c is the remaining rechargeable resources in the current charging and discharging station, C ch_k The priority of electric vehicles to charge at the kth charging and discharging station, P rch is the rated charging power of the charging pile, L r is the number of available charging piles, K m The number of electric vehicles leaving the charging station;

[0211] The dispatchable discharge resource prediction unit is used to predict the dispatchable discharge resources of each charging and discharging station based on the priority of electric vehicles discharging to each charging and discharging station using the following formula:

[0212] P af_dis =P d +C dis_k *P γdis ;

[0213]

[0214] In the above formula, P af_dis is the dispatchable discharge resource of the kth charging and discharging station at the next moment, P d is the remaining dischargeable resources in the charging and discharging station, C dis_k is the priority of electric vehicles to discharge to the kth charging and discharging station, P rdis is the rated discharge power of the electric vehicle, I is the number of electric vehicles discharged at the charging and discharging station, S i is the current state of charge of the i-th electric vehicle, S out_i is the state of charge of the i-th electric vehicle when it leaves the charging and discharging station.

[0215] The model construction module includes an objective function construction unit and a constraint condition construction unit;

[0216] The objective function construction unit is used to construct the objective function of the following electric vehicle charging and discharging resource optimization scheduling model:

[0217] min J = αF1 + βF2 + γF3;

[0218] F1=P R，t+l -P L，t+l -P ch，t+l +P dis，t+l +P G，t+l ;

[0219]

[0220]

[0221] In the above formula, F1 is the power of the power grid in the substation area, F2 is the operating cost, F3 is the negative value of user satisfaction, α, β, and γ are the weight coefficients of F1, F2, and F3 respectively; P R，t+l is the renewable energy power generation power of the grid in the substation at time t+1, P L，t+l is the load power of the power grid in the substation at time t+1, P ch，t+l is the total charging power of the electric vehicle battery at time t+l, P dis，t+l is the total output power of the electric vehicle battery at time t+1, P G，t+lis the amount of electricity purchased from the outside at time t+1; I is the number of electric vehicles actually involved in charging and discharging at the current moment, N ch，i is the charging price of the i-th electric vehicle, N LH，i is the battery aging cost per unit charge and discharge of the i-th electric vehicle, N dis，i is the discharge price of the i-th electric vehicle, P ch，i，t+l 、P dis，i，t+l are the charging power and discharging power of the i-th electric vehicle at time t+l, C G，t+l is the unit price of electricity purchased from the external power grid at time t+1; μ1 and μ2 are the weight coefficients for measuring user satisfaction, t wait，i is the charging waiting time of the i-th electric vehicle, t wait-max，i is the maximum waiting time acceptable to the user of the i-th electric vehicle, P in-terrupt，i is the probability of charging interruption of the i-th electric vehicle;

[0222] The constraint condition construction unit is used to construct the following constraint conditions of the electric vehicle charging and discharging resource optimization scheduling model:

[0223]

[0224] S_EV min，i ≤S_EV i，t+l ≤S_EV max，i ;

[0225] E c，i，t+l =η c，i P c，i，t+l ;

[0226]

[0227] In the above formula, S_EV i,t+l is the state of charge of the i-th electric vehicle at time t+l, j is the total time, E c，i，t+l 、E dis，i，t+l are the charging energy and discharging energy of the i-th electric vehicle at time t+1, E i is the initial storage capacity of the i-th electric vehicle, S_EV min，i 、S_EV max，i are the minimum state of charge and maximum state of charge of the i-th electric vehicle, η c，j ,η dis：i are the charging efficiency and discharging efficiency of the i-th electric vehicle, P c，i，t+l 、P dis，i，t+l They are respectively the charging power and discharging power of the i-th electric vehicle at time t+1 predicted at time t.

[0228] The model solving module includes a dynamic model building unit, a state model determining unit, and a state model solving unit;

[0229] The dynamic model establishment unit is used to establish the following state transition dynamic model of the electric vehicle charging and discharging resource system:

[0230] s t+l =f(s t ,u t ,ω t );

[0231] R(s t ,u t )=C ch (s t ,u t )-C dis (s t ,u t )-C LH (Δsoc t );

[0232] In the above formula, s t+l is the state of the electric vehicle charging and discharging system at time t+l, f is the state transfer function, s t is the state of the electric vehicle charging and discharging system at time t, i.e., the current time. The state includes the external electricity purchase price at the current time, the charging and discharging price of the electric vehicle, the renewable energy power generation power and load of the regional power grid, the charging and discharging power demand of the electric vehicle, and the predicted value of the dispatchable charging and discharging resources of each charging and discharging station; u t is the control input at time t, including the electric vehicle charging power, discharging power and charging and discharging start time; ω t is the external disturbance at time t, including fluctuations in renewable energy power generation, electricity purchase price, and electric vehicle charging and discharging price; R is the reward function of the state of the electric vehicle charging and discharging system at the current moment, C ch is the charging cost, C dis is the discharge gain, C LH is the battery aging cost, Δsoc t is the change in battery state of charge at time t;

[0233] The state model determination unit is used to optimize the control input at time t+1 based on the state transition dynamic model of the electric vehicle charging and discharging resource system, thereby obtaining the following state model of the electric vehicle charging and discharging system at time t+1+1:

[0234]

[0235] s t+l+1 =f(s t+l ,u t+l), (l=0, 1,..., N-1);

[0236] In the above formula, arg max is the function to find the parameter, and N is the number of steps;

[0237] The state model solving unit is used to solve the state model of the electric vehicle charging and discharging system at time t+l+1 by using a subtraction optimizer algorithm to obtain an optimized scheduling strategy for the electric vehicle charging and discharging resources at time t+l+1.

[0238] Example 3:

[0239] like Figure 5 As shown, an optimization scheduling device for electric vehicle charging and discharging resources includes a processor and a memory;

[0240] The memory is used to store computer program code and transmit the computer program code to the processor;

[0241] The processor is used to execute the method for optimizing the scheduling of electric vehicle charging and discharging resources described in Example 1 according to the instructions in the computer program code.

[0242] Example 4:

[0243] A computer storage medium having a computer program stored thereon;

[0244] When the computer program is executed by a processor, the steps of the method for optimizing the scheduling of electric vehicle charging and discharging resources described in this solution are implemented.

Claims

1. A method for optimizing the scheduling of electric vehicle charging and discharging resources, characterized in that: The method comprises: S1. Predict the dispatchable charging and discharging resources of each charging and discharging station based on the state of charge of electric vehicles; S2. Based on the dispatchable charging and discharging resources of each charging and discharging station, with the goal of minimizing grid power, minimizing operating costs, and maximizing electric vehicle user satisfaction, an optimal scheduling model for electric vehicle charging and discharging resources is constructed; The objective function of the electric vehicle charging and discharging resource optimization scheduling model includes: minJ=αF1+βF2+γF3; F1=P R，t+l -P L，t+l -P ch，t+l +P dis，t+l +P G，t+l ; In the above formula, F1 is the power of the power grid in the substation area, F2 is the operating cost, F3 is the negative value of user satisfaction, α, β, and γ are the weight coefficients of F1, F2, and F3 respectively; P R，t+l is the renewable energy power generation power of the grid in the substation at time t+1, P L，t+l is the load power of the power grid in the substation at time t+1, P ch，t+l is the total charging power of the electric vehicle battery at time t+l, P dis，t+l is the total output power of the electric vehicle battery at time t+1, P G，t+l is the amount of electricity purchased from the outside at time t+1; I is the number of electric vehicles actually involved in charging and discharging at the current moment, N ch，i is the charging price of the i-th electric vehicle, N LH，i is the battery aging cost per unit charge and discharge of the i-th electric vehicle, N dis，i is the discharge price of the i-th electric vehicle, P ch,i,t+l 、P dis，i，t+l are the charging power and discharging power of the i-th electric vehicle at time t+l, C G，t+l is the unit price of electricity purchased from the external power grid at time t+1; μ1 and μ2 are the weight coefficients for measuring user satisfaction, t wait，i is the charging waiting time of the i-th electric vehicle, t wait-max，i is the maximum waiting time acceptable to the user of the i-th electric vehicle, P in-terrupt，i is the charging interruption probability of the i-th electric vehicle; The constraints of the electric vehicle charging and discharging resource optimization scheduling model include: SLOPE min，i ≤S_EV i，t+l ≤S_eV max，i ; E c，i，t+l =the c，i P c，i，t+l ; In the above formula, S_EV i，t+l is the state of charge of the i-th electric vehicle at time t+l, j is the total time, E c，i，t+l 、E dis，i，t+l are the charging energy and discharging energy of the i-th electric vehicle at time t+1, E i is the initial storage capacity of the i-th electric vehicle, S_EV min，i 、S_EV max，i are the minimum state of charge and maximum state of charge of the i-th electric vehicle, η c，i ,η dis，i are the charging efficiency and discharging efficiency of the i-th electric vehicle, P c，i，t+l 、P dis，i，t+l are respectively the charging power and discharging power of the i-th electric vehicle at time t+1 predicted at time t; S3. Input the dispatchable charging and discharging resource data of each charging and discharging station, solve the electric vehicle charging and discharging resource optimization scheduling model, and obtain the optimal scheduling strategy for the electric vehicle charging and discharging resources.

2. The method for optimizing the scheduling of electric vehicle charging and discharging resources according to claim 1, characterized in that: The S3 includes: S31. Establish a state transition dynamic model for the electric vehicle charging and discharging resource system, including: s t+l =f(s t ,u t ,ω t ); R(s t ,u t )=C ch (s t ,u t )-C dis (s t ,u t )-C LH (Δsoc t ); In the above formula, s t+l is the state of the electric vehicle charging and discharging system at time t+l, f is the state transfer function, s t is the state of the electric vehicle charging and discharging system at time t, i.e., the current time. The state includes the external electricity purchase price at the current time, the charging and discharging price of the electric vehicle, the renewable energy power generation power and load of the regional power grid, the charging and discharging power demand of the electric vehicle, and the predicted value of the dispatchable charging and discharging resources of each charging and discharging station; u t is the control input at time t, including the electric vehicle charging power, discharging power and charging and discharging start time; ω t is the external disturbance at time t, including fluctuations in renewable energy power generation, electricity purchase price, and electric vehicle charging and discharging price; R is the reward function of the state of the electric vehicle charging and discharging system at the current moment, C ch is the charging cost, C dis is the discharge gain, C LH is the battery aging cost, Δsoc t is the change in battery state of charge at time t; S32. Based on the state transition dynamic model of the electric vehicle charging and discharging resource system, optimize the control input at time t+1, and then obtain the state model of the electric vehicle charging and discharging system at time t+1+1; s t+l+1 =f(s t+l ,u t+l ),(l=0,1,...,N-1); In the above formula, arg max is the function to find the parameter, and N is the number of steps; S33. Solve the state model of the electric vehicle charging and discharging system at time t+l+1 by using a subtraction optimizer algorithm to obtain the optimal scheduling strategy of the electric vehicle charging and discharging resources at time t+l+1.

3. The method for optimizing the scheduling of electric vehicle charging and discharging resources according to claim 1, characterized in that: Said S1 comprises: S11. Using the distance between the electric vehicle and each charging and discharging station and the charging price of each charging and discharging station as indicators, construct the following priority weighted standardized decision matrix; (Z kj ) K×m =p kj *ω j ; In the above formula, Z′ is the weighted normalized decision matrix of priority, (Z kj ) K×m is the standardized decision matrix of priority, ω ch_risk_k is the probability evaluation coefficient of electric vehicle users going to the kth charging and discharging station for charging and discharging, p kj is the normalized matrix, ω j is the weight of the jth indicator, K is the total number of charging and discharging stations available for charging and discharging near electric vehicles, m is the number of indicators, and w D is the distance weight coefficient, D st-k is the distance between the electric vehicle user and the kth charging and discharging station, w P-c is the weight coefficient of charging price, P st-c-k is the charging price of the kth charging and discharging station, w P-dis is the weight coefficient of the discharge price, P st-dis-k is the discharge price of the kth charging and discharging station, w H is the weight of convenience, H st-k is the convenience of the kth charging and discharging station; S12. Based on the weighted standardized decision matrix of priority, the following formula is used to calculate the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions: In the above formula, are the distances between the electric vehicle’s charging solution and the positive and negative ideal solutions to the kth charging and discharging station, respectively. are positive and negative ideal solutions respectively, are the distances between the discharge scheme of the electric vehicle to the kth charging and discharging station and the positive and negative ideal solutions respectively; S13, generating a priority for the electric vehicle to charge and discharge at each charging and discharging station based on the distances between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions; S14. Based on the priority of electric vehicles charging at each charging and discharging station, the following formula is used to predict the dispatchable charging resources of each charging and discharging station: P af_ck =P c -C ch_k *P rch ; P c =P rch ×(L r +L m ); In the above formula, P af_ck is the dispatchable charging resource of the kth charging and discharging station at the next moment, P c is the remaining rechargeable resources in the current charging and discharging station, C ch_k The priority of electric vehicles to charge at the kth charging and discharging station, P rch is the rated charging power of the charging pile, L r is the number of available charging piles, L m The number of electric vehicles leaving the charging station; Based on the priority of electric vehicles discharging to each charging and discharging station, the following formula is used to predict the dispatchable discharge resources of each charging and discharging station: P af_dis =P d +C dis_k *P rdis ; In the above formula, P af_dis is the dispatchable discharge resource of the kth charging and discharging station at the next moment, P d is the remaining dischargeable resources in the charging and discharging station, C dis_k is the priority of electric vehicles to discharge to the kth charging and discharging station, P rdis is the rated discharge power of the electric vehicle, I is the number of electric vehicles discharged at the charging and discharging station, S i is the current state of charge of the i-th electric vehicle, S out_i is the state of charge of the i-th electric vehicle when it leaves the charging and discharging station.

4. An optimization scheduling system for electric vehicle charging and discharging resources, characterized in that: The system includes a resource prediction module, a model building module, and a model solving module; The resource prediction module is used to predict the schedulable charging and discharging resources of each charging and discharging station according to the state of charge of the electric vehicle; The model building module is used to build an electric vehicle charging and discharging resource optimization scheduling model based on the dispatchable charging and discharging resources of each charging and discharging station, with the goals of minimizing the power of the power grid in the substation area, minimizing the operating cost, and maximizing the satisfaction of electric vehicle users. It includes an objective function building unit and a constraint condition building unit. The objective function construction unit is used to construct the objective function of the following electric vehicle charging and discharging resource optimization scheduling model: minJ=αF1+βF2+γF3; F1=P R，t+l -P L,t+l -P ch，t+l +P dis，t+l +P G，t+l ; In the above formula, F1 is the power of the power grid in the substation area, F2 is the operating cost, F3 is the negative value of user satisfaction, α, β, and γ are the weight coefficients of F1, F2, and F3 respectively; P R，t+l is the renewable energy power generation power of the grid in the substation at time t+1, P L，t+l is the load power of the power grid in the substation at time t+1, P ch，t+l is the total charging power of the electric vehicle battery at time t+l, P dis，t+l is the total output power of the electric vehicle battery at time t+1, P G，t+l is the amount of electricity purchased from the outside at time t+1; I is the number of electric vehicles actually involved in charging and discharging at the current moment, N ch,i is the charging price of the i-th electric vehicle, N LH，i is the battery aging cost per unit charge and discharge of the i-th electric vehicle, N dis,i is the discharge price of the i-th electric vehicle, P ch，i，t+l 、P dis，i，t+l are the charging power and discharging power of the i-th electric vehicle at time t+l, C G，t+l is the unit price of electricity purchased from the external power grid at time t+1; μ1 and μ2 are the weight coefficients for measuring user satisfaction, t wait，i is the charging waiting time of the i-th electric vehicle, t wait-max，i is the maximum waiting time acceptable to the user of the i-th electric vehicle, P in-terrupt，i is the charging interruption probability of the i-th electric vehicle; The constraint condition construction unit is used to construct the following constraint conditions of the electric vehicle charging and discharging resource optimization scheduling model: SLOPE min，i ≤S_EV i，t+l ≤S_EV max，i ; E c，i，t+l =the c，i P c，i，t+l ; In the above formula, S_EV i，t+l is the state of charge of the i-th electric vehicle at time t+l, j is the total time, E c，i，t+l 、E dis，i，t+l are the charging energy and discharging energy of the i-th electric vehicle at time t+1, E i is the initial storage capacity of the i-th electric vehicle, S_EV min，i 、S_EV max，i are the minimum state of charge and maximum state of charge of the i-th electric vehicle, η c，i ,η dis，i are the charging efficiency and discharging efficiency of the i-th electric vehicle, P c，i，t+l 、P dis，i，t+l are respectively the charging power and discharging power of the i-th electric vehicle at time t+1 predicted at time t; The model solving module is used to input the dispatchable charging and discharging resource data of each charging and discharging station, solve the electric vehicle charging and discharging resource optimization scheduling model, and obtain the electric vehicle charging and discharging resource optimization scheduling strategy.

5. The optimization scheduling system for electric vehicle charging and discharging resources according to claim 4, characterized in that: The model solving module includes a dynamic model building unit, a state model determining unit, and a state model solving unit; The dynamic model establishment unit is used to establish the following state transition dynamic model of the electric vehicle charging and discharging resource system: s t+l =f(s t ,u t ,ω t ); R(s t ,u t )=C ch (s t ,u t )-C dis (s t ,u t )-C LH (Δsoc t ); In the above formula, s t+l is the state of the electric vehicle charging and discharging system at time t+l, f is the state transfer function, s t is the state of the electric vehicle charging and discharging system at time t, i.e., the current time. The state includes the external electricity purchase price at the current time, the charging and discharging price of the electric vehicle, the renewable energy power generation power and load of the regional power grid, the charging and discharging power demand of the electric vehicle, and the predicted value of the dispatchable charging and discharging resources of each charging and discharging station; u t is the control input at time t, including the electric vehicle charging power, discharging power and charging and discharging start time; ω t is the external disturbance at time t, including fluctuations in renewable energy power generation, electricity purchase price, and electric vehicle charging and discharging price; R is the reward function of the state of the electric vehicle charging and discharging system at the current moment, C ch is the charging cost, C dis is the discharge gain, C LH is the battery aging cost, Δsoc t is the change in battery state of charge at time t; The state model determination unit is used to optimize the control input at time t+1 based on the state transition dynamic model of the electric vehicle charging and discharging resource system, and then obtain the state model of the electric vehicle charging and discharging system at time t+1+1; S t+l+1 =f(s t+l ,u t+l ),(l=0,1,...,N-1); In the above formula, arg max is the function to find the parameter, and N is the number of steps; The state model solving unit is used to solve the state model of the electric vehicle charging and discharging system at time t+l+1 by using a subtraction optimizer algorithm to obtain an optimized scheduling strategy for the electric vehicle charging and discharging resources at time t+l+1.

6. The optimization scheduling system for electric vehicle charging and discharging resources according to claim 4, characterized in that: The resource prediction module includes a decision matrix construction unit, a distance calculation unit, a priority generation unit, a dispatchable charging resource prediction unit, and a dispatchable discharging resource prediction unit; The decision matrix construction unit is used to construct the following priority weighted standardized decision matrix based on the distance between the electric vehicle and each charging and discharging station and the charging price of each charging and discharging station as indicators; (Z kj ) K×m =p kj *ω j ; In the above formula, Z′ is the weighted normalized decision matrix of priority, (Z kj ) K×m is the standardized decision matrix of priority, ω ch_risk_k is the probability evaluation coefficient of electric vehicle users going to the kth charging and discharging station for charging and discharging, p kj p kj is the normalized matrix, ω j is the weight of the jth indicator, K is the total number of charging and discharging stations available for charging and discharging near electric vehicles, m is the number of indicators, and w D is the distance weight coefficient, D st-k is the distance between the electric vehicle user and the kth charging and discharging station, w P-c is the weight coefficient of charging price, P st-c-k is the charging price of the kth charging and discharging station, w P-dis is the weight coefficient of the discharge price, P st-dis-k is the discharge price of the kth charging and discharging station, w H is the weight of convenience, H st-k is the convenience of the kth charging and discharging station; The distance calculation unit is used to calculate the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions based on the weighted standardized decision matrix of the priority using the following formula: In the above formula, are the distances between the electric vehicle’s charging solution and the positive and negative ideal solutions to the kth charging and discharging station, respectively. are positive and negative ideal solutions respectively, are the distances between the discharge scheme of the electric vehicle to the kth charging and discharging station and the positive and negative ideal solutions respectively; The priority generation unit is used to generate the priority of charging and discharging of the electric vehicle at each charging and discharging station based on the distance between each charging and discharging scheme of the electric vehicle and its positive and negative ideal solutions; The dispatchable charging resource prediction unit is used to predict the dispatchable charging resources of each charging and discharging station based on the priority of electric vehicles charging at each charging and discharging station using the following formula: P af_ck =P c -C ch_k *P rch ; P c =P rch ×(L r +L m ); In the above formula, P af_ck is the dispatchable charging resource of the kth charging and discharging station at the next moment, P c is the remaining rechargeable resources in the current charging and discharging station, C ch_k The priority of electric vehicles to charge at the kth charging and discharging station, P rch is the rated charging power of the charging pile, L r is the number of available charging piles, L m The number of electric vehicles leaving the charging station; The dispatchable discharge resource prediction unit is used to predict the dispatchable discharge resources of each charging and discharging station based on the priority of electric vehicles discharging to each charging and discharging station using the following formula: P af_dis =P d +C dis_k *P rdis ; In the above formula, P af_dis is the dispatchable discharge resource of the kth charging and discharging station at the next moment, P d is the remaining dischargeable resources in the charging and discharging station, C dis_k is the priority of electric vehicles to discharge to the kth charging and discharging station, P rdis is the rated discharge power of the electric vehicle, I is the number of electric vehicles discharged at the charging and discharging station, S i is the current state of charge of the i-th electric vehicle, S out_i is the state of charge of the i-th electric vehicle when it leaves the charging and discharging station.

7. An optimization and scheduling device for electric vehicle charging and discharging resources, characterized in that: including a processor and a memory; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute the method for optimizing the scheduling of electric vehicle charging and discharging resources according to any one of claims 1 to 3 according to the instructions in the computer program code.

8. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the scheduling of charging and discharging resources of electric vehicles according to any one of claims 1 to 3 are implemented.

Images