Vehicle network interactive regulation and control method and device considering user satisfaction and power grid problem treatment

Abstract

The invention provides a vehicle network interaction regulation and control method and device considering user satisfaction and power grid problem governance, and the method comprises the steps: constructing a forward heavy overload constraint condition and an electric vehicle charging constraint condition, and constructing a three-phase imbalance and user satisfaction objective function; and taking the obtained charging load data and non-charging load data as input, adopting a sequence least square quadratic programming optimization algorithm, taking constructed three-phase imbalance minimization and user satisfaction maximization as optimization targets, and combining forward heavy overload constraint conditions and electric vehicle charging constraint conditions to solve, so as to obtain an optimal solution. And outputting the optimized electric vehicle charging data. The objective function is more accurate, the evaluation indexes are more comprehensive, the optimization effect is better, the power grid problem can be effectively solved, and meanwhile the high user satisfaction degree is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electric vehicle charging load optimization and control, and particularly relates to a vehicle-to-grid interaction control method and device considering user satisfaction and power grid problem treatment. BACKGROUND

[0002] With the rapid popularization of electric vehicles, the influence of large-scale charging behavior on distribution network operation is increasingly significant. Electric vehicle charging load has the characteristics of spatial and temporal randomness and high power. If there is no reasonable control, it is easy to cause problems such as three-phase imbalance and overload in the distribution network, which threatens the safe and stable operation of the power grid. Traditional charging strategies mostly use simple time shifting or power limiting methods, which can alleviate some pressure on the power grid, but ignore the differences in user charging demand, resulting in a decrease in user satisfaction. In addition, the existing technology has a relatively single evaluation effect on the treatment of power grid problems, lacks quantitative indicators for comprehensive consideration of power grid operation optimization and user experience, and is difficult to meet the fine control requirements of vehicle-to-grid interaction.

[0003] The current vehicle-to-grid interaction control method mainly has two deficiencies: on the one hand, the objective function is too simplified, usually only taking the power grid side index or the user side index as the optimization target, and failing to consider the coordinated optimization of power grid problem treatment and user satisfaction improvement. In addition, the existing evaluation system mostly focuses on a single dimension, and it is difficult to fully reflect the comprehensive benefits of the control strategy. In view of the above problems, it is urgent to develop a vehicle-to-grid interaction control method that takes into account the power grid safety and user experience. This method needs to build a more accurate multi-objective optimization model, considering the treatment of power grid problems and the improvement of user satisfaction, and in addition, a comprehensive evaluation system covering power grid problem treatment effect, charging demand satisfaction, time efficiency and other multi-dimensional needs to be designed to provide quantitative basis for the optimization effect of the control strategy. Through such a method, the operation pressure of the distribution network can be relieved while the user charging experience is maximized, promoting the coordinated and sustainable development of electric vehicles and the power grid. SUMMARY

[0004] The purpose of the present application is to provide a vehicle-to-grid interaction control method and device considering user satisfaction and power grid problem treatment, which optimizes the electric vehicle charging load accurately, treats power grid problems and ensures high user satisfaction.

[0005] A vehicle-to-grid interaction control method considering user satisfaction and power grid problem treatment, comprising the following steps:

[0006] Constructing a positive overload constraint condition and an electric vehicle charging constraint condition, and constructing a three-phase imbalance and user satisfaction objective function;

[0007] The obtained charging load data and non-charging load data are taken as inputs, a sequence least square quadratic programming optimization algorithm is used, three-phase imbalance minimization and user satisfaction maximization constructed as optimization objectives, forward overload constraint conditions and electric vehicle charging constraint conditions are combined for solving, and the optimized electric vehicle charging data is output.

[0008] Further, it also includes evaluating the optimization effect, specifically:

[0009] An evaluation index system is constructed, the evaluation indexes include overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate and charging time reduction rate, based on the output optimized charging data, the optimization effect of power grid problem treatment and user satisfaction is quantitatively evaluated by the evaluation index quantitative evaluation algorithm, and the power grid problems include overload and three-phase imbalance.

[0010] Further, the forward overload constraint condition and the electric vehicle charging constraint condition are constructed, and a target function with three-phase imbalance minimization and user satisfaction maximization as the target is constructed, specifically including:

[0011] User satisfaction is quantified by charging power, the greater the charging power, the shorter the charging time, and the higher the user satisfaction; the user satisfaction calculation formula is as follows:

[0012] ;

[0013] f1 represents the user satisfaction calculation result, wherein n represents the total number of piles, i represents the i-th pile, t represents the hour number, P i,t is the decision variable of charging pile i in period t, MP i represents the maximum power allowed by charging pile i. When charging optimization is performed, the optimized maximum charging power is not allowed to exceed the maximum charging power before optimization.

[0014] When regulating, attention should be paid to the balance of three-phase power, and the variance of the sum of three-phase power is used to quantify the target of three-phase imbalance, and the three-phase imbalance calculation formula is as follows:

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] f2 represents the three-phase imbalance calculation result, wherein t represents the current time point, The variance of ABC three-phase power, The power sum of all piles under phase A, The power sum of all piles under phase B, The power sum of all piles under phase C, t The mean value of three-phase power at t time, the greater the variance, the more serious the three-phase imbalance, and vice versa;

[0021] When regulating the charging power of electric vehicles, the total capacity of the transformer area needs to be considered to ensure that the total charging power and the total non-charging power at each time point do not overload, and the positive overload constraint condition calculation formula is as follows:

[0022] ;

[0023] Where L t represents the non-charging load at t time, represents the charging load of the i-th pile at t time, Z max represents the maximum load that the transformer area can withstand, the sum of the charging load and the non-charging load of each time period needs to be less than the maximum load that the transformer area can withstand;

[0024] When regulating, ensure that the amount of electricity before regulation and the amount of electricity after regulation of a single pile in a day are as consistent as possible, and the electric vehicle charging constraint condition calculation formula is as follows:

[0025] ;

[0026] Where T i is the set of effective charging periods of charging pile i, E i is the total charging amount of a charging pile in a day without regulation.

[0027] Further, the specific process of the sequence least squares quadratic programming optimization algorithm is as follows:

[0028] 1) Determine the optimization objective function and constraint condition;

[0029] 2) Convert the inequality constraint into a penalty function;

[0030] 3) Data initialization, select an initial solution;

[0031] 4) Construct the Lagrange function, the specific calculation method is as follows:

[0032] ;

[0033] Where f(x) is the objective function, g(x) is the constraint condition, is the Lagrange multiplier;

[0034] 5) Minimize the Lagrangian function Solve for x to get the updated x value;

[0035] 6) Update the penalty function according to the current x value and the condition of the constraint g(x);

[0036] 7) Determine whether the current solution meets the termination condition, whether the change of the objective function is less than the corresponding threshold value, and whether the maximum number of iterations is reached;

[0037] 8) If the termination condition is not met, return to step 4) to continue iterative updating;

[0038] 9) When the termination condition is met, output the optimal solution variable.

[0039] Further, the calculation formulas of the heavy overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate, and charging time reduction rate are as follows:

[0040] The heavy overload reduction rate is used to evaluate the management effect of heavy overload after charging optimization. The calculation formula of the heavy overload reduction rate is as follows:

[0041] ;

[0042] Where y1 represents the heavy overload reduction rate, o is the number of heavy overload occurrences in a day, is the number of heavy overload occurrences in a day after optimization;

[0043] The three-phase imbalance reduction rate is used to evaluate the management effect of three-phase imbalance after charging optimization. The calculation formula of the three-phase imbalance reduction rate is as follows:

[0044] ;

[0045] Where y2 represents the three-phase imbalance reduction rate, represents the power variance of all piles under ABC three-phase before regulation, represents the power variance of all piles under ABC three-phase after regulation;

[0046] User satisfaction is quantified and evaluated by total charging capacity retention rate and charging time reduction rate. The higher the total charging capacity retention rate, the higher the user satisfaction, and the greater the charging time reduction rate, the greater the user satisfaction;

[0047] The calculation formula of the total charging capacity retention rate is as follows:

[0048] ;

[0049] Where y3 represents the total charging capacity retention rate, b represents the sum of charging capacities of all piles before regulation, represents the sum of charging capacities of all piles after regulation;

[0050] The charging time reduction rate calculation formula is as follows:

[0051] ;

[0052] Wherein y4 represents the charging time reduction rate, T represents the total charging time of all piles before regulation, The total charging time of all piles after regulation.

[0053] A vehicle-to-grid interaction regulation device considering user satisfaction and power grid problem treatment, characterized in that it comprises:

[0054] A constraint condition and objective function construction module for constructing forward overload constraint conditions and electric vehicle charging constraint conditions, and simultaneously constructing three-phase imbalance and user satisfaction objective functions;

[0055] The obtained charging load data and non-charging load data are taken as inputs, and a sequential least squares quadratic programming optimization algorithm is used to solve the constructed three-phase imbalance minimization and user satisfaction maximization as the optimization objective, combined with the forward overload constraint condition and the electric vehicle charging constraint condition, to output the optimized electric vehicle charging data.

[0056] Further, it further comprises an optimization effect evaluation module for:

[0057] An evaluation index system is constructed, and the evaluation indexes include overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate and charging time reduction rate; based on the output of the optimized charging data, the optimization effect of power grid problem treatment and user satisfaction is quantitatively evaluated by the evaluation index quantitative evaluation algorithm, and the power grid problems include overload and three-phase imbalance.

[0058] Further, the constraint condition and objective function construction module constructs forward overload constraint conditions and electric vehicle charging constraint conditions, and simultaneously constructs three-phase imbalance and user satisfaction objective functions, specifically comprising:

[0059] User satisfaction is quantified using charging power, and the greater the charging power, the shorter the charging time and the higher the user satisfaction; the user satisfaction calculation formula is as follows:

[0060] ;

[0061] f1 represents the user satisfaction calculation result, wherein n represents the total number of piles, i represents the i-th pile, t represents the number of hours, P i,t is the decision variable of charging pile i in period t, MP i represents the maximum power allowed by charging pile i. When performing charging optimization, the maximum charging power after optimization is not allowed to exceed the maximum charging power before optimization.

[0062] In the regulation, attention should be paid to the balance of three-phase power, and the variance of the sum of three-phase power is used to quantify the target of three-phase imbalance, and the calculation formula of three-phase imbalance is as follows:

[0063] ;

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] f2 represents the calculation result of three-phase imbalance, where t represents the current time point, represents the variance of ABC three-phase power, represents the sum of the power of all piles under phase A, represents the sum of the power of all piles under phase B, represents the sum of the power of all piles under phase C, u t represents the mean value of three-phase power at t period, the larger the variance is, the more serious the three-phase imbalance is, and vice versa;

[0069] When regulating the charging power of electric vehicles, the limitation of the total capacity of the transformer area needs to be considered to ensure that the total charging power and the total non-charging power at each time point do not cause overload, and the calculation formula of the positive overload constraint condition is as follows:

[0070] ;

[0071] Where L t represents the non-charging load at t, represents the charging load of the i-th pile at t, Z max represents the maximum load that the transformer area can bear, and the sum of the charging load and the non-charging load at each time period needs to be less than the maximum load that the transformer area can bear;

[0072] When regulating, it is necessary to ensure that the amount of electricity before regulation and the amount of electricity after regulation of a single pile in a day are as consistent as possible, and the calculation formula of the electric vehicle charging constraint condition is as follows:

[0073] ;

[0074] Where T i is the set of effective charging periods of the charging pile i, E i is the total charging amount of the charging pile in a day without regulation.

[0075] Further, the sequence least square quadratic programming optimization algorithm specific process as follows:

[0076] 1) determine the optimization objective function and constraint conditions;

[0077] 2) the inequality constraint into a penalty function;

[0078] 3) data initialization, select an initial solution;

[0079] 4) construct the Lagrange function, the specific calculation method as follows:

[0080] ;

[0081] Where f(x) is the objective function, g(x) is the constraint, The Lagrange multiplier;

[0082] 5) by minimizing the Lagrange function Solve for x, get the updated x value;

[0083] 6) according to the current x value and the constraint condition g(x) of the situation, update the penalty function;

[0084] 7) judge whether the current solution meets the termination condition, the change of objective function is less than the corresponding threshold value, whether to reach the maximum iteration number;

[0085] 8) if not meet the termination condition, return to 4) continue iteration update;

[0086] 9) when the termination condition is met, output the optimal solution variable.

[0087] Further, the calculation formula of the heavy overload reduction rate, three phase imbalance reduction rate, total charge retention rate and charging time reduction rate is as follows:

[0088] Heavy overload reduction rate is used to evaluate the management effect of heavy overload after charging optimization, heavy overload reduction rate calculation formula as follows:

[0089] ;

[0090] Where y1 represents the heavy overload reduction rate, o is the number of heavy overload in a day, The number of heavy overload in a day after optimization;

[0091] Three phase imbalance reduction rate is used to evaluate the management effect of three phase imbalance after charging optimization, three phase imbalance reduction rate calculation formula as follows:

[0092] ;

[0093] Wherein y2 represents a three-phase imbalance reduction rate, represents the power variance of all piles under ABC three-phase before regulation, represents the power variance of all piles under ABC three-phase after regulation.

[0094] User satisfaction is quantified and evaluated by total charging capacity retention rate and charging time reduction rate respectively, the higher the total charging capacity retention rate, the higher the user satisfaction, and the greater the charging time reduction rate, the greater the user satisfaction.

[0095] The total charging capacity retention rate calculation formula is as follows:

[0096] ;

[0097] Wherein y3 represents the total charging capacity retention rate, b represents the sum of charging capacity of all piles before regulation, represents the sum of charging capacity of all piles after regulation.

[0098] The charging time reduction rate calculation formula is as follows:

[0099] ;

[0100] Wherein y4 represents the charging time reduction rate, T represents the sum of charging time of all piles before regulation, represents the sum of charging time of all piles after regulation.

[0101] The present application has the following beneficial effects: The present application takes the regulation of electric vehicle charging curve as the object, constructs the mathematical model and related constraint conditions of power grid problem governance and user satisfaction, based on the mathematical model and constraint conditions, proposes a vehicle network interaction regulation method considering user satisfaction and power grid problem governance, compared with other methods, the method has better optimization effect, more comprehensive evaluation index, and can provide effective support for charging planning of power grid. BRIEF DESCRIPTION OF DRAWINGS

[0102] Figure 1 is a flow chart of a vehicle network interaction regulation method considering user satisfaction and power grid problem governance according to an embodiment of the present application;

[0103] Figure 2 is an optimization result graph according to an embodiment of the present application; DETAILED DESCRIPTION

[0104] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0105] Embodiment 1

[0106] As shown in Figure 1 , the embodiment of the present application provides a vehicle-to-grid interactive regulation method considering user satisfaction and power grid problem governance, comprising the following steps:

[0107] A target function of three-phase imbalance and user satisfaction is constructed, and forward overload constraint and electric vehicle charging constraint are constructed; charging load data and non-charging load data are taken as input, and a sequential least squares quadratic programming optimization algorithm is used to minimize the maximum user satisfaction and the minimum three-phase imbalance, considering the forward overload constraint condition and the charging constraint condition, to output the optimized charging data; a heavy overload reduction rate, a three-phase imbalance reduction rate, a total charging amount retention rate and a charging time reduction rate evaluation index are constructed, each evaluation index of the optimized charging data is calculated, and the optimization effect is evaluated.

[0108] Embodiment 2

[0109] The vehicle-to-grid interactive regulation method considering user satisfaction and power grid problem governance in this embodiment, the optimization result is as shown in Figure 2

[0110] As can be seen from Figure 2 (a), the optimized load is improved in the early morning, the charging load is reduced in the early morning, the charging load before and after optimization is close in the afternoon, and the charging load is reduced in the peak period at night.

[0111] In Figure 2 (b), the blue curve is the total load curve before optimization, the red dashed line is the total load curve after optimization, and the black dashed line is the upper limit of the transformer capacity.

[0112] As can be seen from Figure 2 (c) and Figure 2 (d), the overload condition of the total load after optimization is significantly improved, the blue load curve before optimization presents obvious fluctuation in the 24-hour cycle, at least in three periods, breaks through the capacity upper limit of 300 kVA at about the 7th hour, and forms a typical overload peak section. The red curve after optimization always maintains a more stable state, the peak load is generally lowered than before optimization, only approaches the capacity upper limit at the 20th hour at the same time node, and the duration is significantly shortened.​

[0113] Before optimization, persistent overload occurs in the night load rebound stage, and after optimization, the load value is stabilized in the capacity upper limit of 90%-95% through load balancing adjustment, and the number of overload is reduced from 7 times before optimization to 0 times after optimization.

[0114] Embodiment 3

[0115] The vehicle-to-grid interaction control method considering user satisfaction and power grid problem treatment in the embodiment is compared with other optimization algorithms as shown in Table 1.

[0116] Table 1

[0117]

[0118] In Table 1, SLSQP is a sequential least squares quadratic programming optimization algorithm, trust-constr is a trust region constraint optimization algorithm, and GA is a genetic algorithm.

[0119] As can be seen from Table 1, SLSQP and GA have good effects on handling overload, completely eliminating the risk of overload, and trust-constr has good effects on charge retention rate. The overload reduction rate, total charge retention rate, charge time reduction rate and three-phase imbalance reduction rate of SLSQP are 100%, 94.6%, 32.9% and 67.6% respectively. The overload reduction rate, total charge retention rate, charge time reduction rate and three-phase imbalance reduction rate of trust-constr are 28%, 96.2%, 16.7% and 64.8% respectively. The overload reduction rate, total charge retention rate, charge time reduction rate and three-phase imbalance reduction rate of GA are 100%, 80.8%, 0 and 24.8% respectively.

[0120] The average of each evaluation index is taken as the total evaluation index of each algorithm, and the total index of SLSQP is calculated as 0.738, the total index of trust-constr is 0.516, and the total index of GA is 0.514. Therefore, SLSQP is optimal.

[0121] The embodiment of the application also provides a vehicle-to-grid interaction control device considering user satisfaction and power grid problem treatment, comprising:

[0122] The constraint condition and objective function construction module is used for constructing the positive overload constraint condition and the electric vehicle charging constraint condition, and simultaneously constructing the three-phase imbalance and user satisfaction objective function;

[0123] The acquired charging load data and non-charging load data are taken as inputs, a sequence least square quadratic programming optimization algorithm is used, three-phase imbalance minimization and user satisfaction maximization constructed are taken as optimization objectives, forward overload constraint conditions and electric vehicle charging constraint conditions are combined for solving, and the optimized electric vehicle charging data are output.

[0124] The conversion device can further include an optimization effect evaluation module configured to:

[0125] An evaluation index system is constructed, the evaluation indexes include overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate, and charging time reduction rate; based on the output optimized charging data, the optimization effect of power grid problem treatment and user satisfaction is quantitatively evaluated by the evaluation index quantitative evaluation algorithm, and the power grid problems include overload and three-phase imbalance.

[0126] In the above manner, the present application takes the regulation of electric vehicle charging curve as an object, constructs a mathematical model of power grid problem treatment and user satisfaction and related constraint conditions, proposes a vehicle-grid interaction regulation method considering user satisfaction and power grid problem treatment based on the mathematical model and constraint conditions, and compared with other methods, the method has better optimization effect and more comprehensive evaluation indexes, and can provide effective support for charging planning of the power grid.

[0127] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the above examples, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered in the protection scope of the claims of the present application.

Claims

1. A vehicle-to-grid interaction regulation method considering user satisfaction and power grid problem governance, characterized in that, The method comprises the following steps: forward overload constraint conditions and electric vehicle charging constraint conditions are constructed, and a target function with minimized three-phase imbalance and maximized user satisfaction is constructed; charging load data and non-charging load data are taken as inputs, a sequential least square quadratic programming optimization algorithm is used, the constructed minimized three-phase imbalance and maximized user satisfaction are taken as optimization targets, the forward overload constraint conditions and the electric vehicle charging constraint conditions are combined to solve, and the optimized electric vehicle charging data is output.

2. The vehicle-grid interaction regulation method considering user satisfaction and power grid problem treatment according to claim 1, characterized in that, Further comprising: The optimization effect is evaluated, specifically: an evaluation index system is constructed, the evaluation indexes include overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate and charging time reduction rate, based on the output optimized charging data, the optimization effect of power grid problem treatment and user satisfaction is quantitatively evaluated by the evaluation index quantitative evaluation algorithm, and the power grid problems include overload and three-phase imbalance. 3.The vehicle-grid interaction regulation method considering user satisfaction and power grid problem treatment according to claim 1, wherein, The forward overload constraint conditions and the electric vehicle charging constraint conditions are constructed, and the target function with minimized three-phase imbalance and maximized user satisfaction is constructed, specifically comprising: The user satisfaction is quantified by using charging power, the greater the charging power is, the shorter the charging time is, and the higher the user satisfaction is; the user satisfaction calculation formula is as follows: ； f1 represents the user satisfaction calculation result, where n represents the total number of piles, i represents the i-th pile, t represents the number of hours, P i,t is the decision variable of the charging pile i in the time period t, MP i represents the maximum power allowed by the charging pile i. When the charging optimization is performed, the optimized maximum charging power is not allowed to exceed the maximum charging power before optimization; When regulating, the balance of three-phase power is paid attention to, the variance of the sum of three-phase power is used to quantify the three-phase imbalance target, and the three-phase imbalance calculation formula is as follows: ； ； ； ； ； f2 represents the three-phase imbalance calculation result, where t represents the current time point, represents the variance of ABC three-phase power, represents the total power of all piles under phase A, represents the total power of all piles under phase B, represents the total power of all piles under phase C, t represents the mean value of t period three-phase power, the greater the variance, the more serious the three-phase imbalance, otherwise the three-phase is balanced; When regulating the electric vehicle charging power, the limitation of the total capacity of the transformer area also needs to be considered, the total charging power and the total non-charging power at each time point are ensured not to be overloaded, the forward overload constraint condition calculation formula is as follows: ； wherein L t represents the non-charging load at time t, represents the charging load of the i-th pile at time t, Z max represents the maximum load that the transformer can bear, and the sum of the charging load and the non-charging load of each time period needs to be less than the maximum load that the transformer can bear; When regulating, the electric quantity before regulation and the electric quantity after regulation of a single pile in one day are ensured to be consistent as much as possible, the electric vehicle charging constraint condition calculation formula is as follows: ； Wherein T i is the set of effective charging time periods of charging pile i, E i is the total charging amount of charging pile i in a day without regulation.

4. The vehicle-grid interaction regulation method considering user satisfaction and power grid problem treatment according to claim 1, characterized in that, The specific process of the sequential least square quadratic programming optimization algorithm is as follows: 1) determine the optimization target function and the constraint conditions; 2) convert the inequality constraint into a penalty function; 3) initialize the data, select an initial solution; 4) construct the Lagrange function, and the specific calculation method is as follows: ； where f(x) is the objective function, g(x) is the constraint condition, is the Lagrange multiplier; 5) by minimizing the Lagrangian function Solve for x to get the updated value of x; 6) update the penalty function according to the current x value and the constraint condition g(x) condition; 7) judge whether the current solution meets the termination condition, whether the change of the target function is less than the corresponding threshold value, and whether the maximum iteration number is reached; 8) if the termination condition is not met, return to 4) to continue iteration and update; 9) when the termination condition is met, the variable of the optimal solution is output.

5. The vehicle-grid interaction regulation method considering user satisfaction and power grid problem treatment according to claim 2, characterized in that, The calculation formulas of the overload reduction rate, the three-phase imbalance reduction rate, the total charging capacity retention rate and the charging time reduction rate are as follows: The overload reduction rate is used to evaluate the treatment effect of overload after charging optimization, and the overload reduction rate calculation formula is as follows: ； where y1 represents the heavy overload reduction rate, o is the number of times of heavy overload occurrence in a day, to optimize the number of heavy overload occurrences in the following day; The three-phase imbalance reduction rate is used to evaluate the treatment effect of three-phase imbalance after charging optimization, and the three-phase imbalance reduction rate calculation formula is as follows: ； wherein y2 represents a three-phase imbalance reduction rate, denotes the power variance of all the piles under the ABC three-phase before regulation, denotes the power variance of all the piles under the ABC three-phase after regulation; The user satisfaction is quantified and evaluated by the total charging capacity retention rate and the charging time reduction rate, the higher the total charging capacity retention rate is, the higher the user satisfaction is, and the greater the charging time reduction rate is, the greater the user satisfaction is; The total charging capacity retention rate calculation formula is as follows: ； wherein y3 represents the total charge amount retention rate, b represents the sum of the charge amounts of all the piles before regulation, represents the sum of the charge amounts of all the piles after regulation; The charging time reduction rate calculation formula is as follows: ； wherein y4 represents a charging time reduction rate, T represents a total of charging times of all piles before regulation, represents a total of charging times of all piles after regulation.

6. A vehicle-to-grid interaction regulation device considering user satisfaction and power grid problem governance, characterized in that, including: The constraint condition and objective function construction module is used for constructing forward overload constraint conditions and electric vehicle charging constraint conditions, and simultaneously constructing three-phase imbalance and user satisfaction objective functions; The obtained charging load data and non-charging load data are taken as inputs, a sequential least square quadratic programming optimization algorithm is used, three-phase imbalance minimization and user satisfaction maximization constructed as optimization objectives, forward overload constraint conditions and electric vehicle charging constraint conditions are combined for solving, and optimized electric vehicle charging data is output. 7.The vehicle-grid interaction regulation device considering user satisfaction and grid problem governance according to claim 6, wherein, Further comprising: The optimization effect evaluation module is used for: An evaluation index system is constructed, the evaluation indexes include overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate and charging time reduction rate, and based on the output optimized charging data, the optimization effect of power grid problem treatment and user satisfaction is quantitatively evaluated by the evaluation index quantitative evaluation algorithm, and the power grid problems include overload and three-phase imbalance. 8.The vehicle-grid interaction regulation device considering user satisfaction and power grid problem treatment according to claim 6, wherein, The constraint condition and objective function construction module constructs forward overload constraint conditions and electric vehicle charging constraint conditions, and simultaneously constructs three-phase imbalance and user satisfaction objective functions, and specifically includes: User satisfaction is quantified by using charging power, the greater the charging power is, the shorter the charging time is, and the higher the user satisfaction is; and a user satisfaction calculation formula is as follows: ； f1 represents the user satisfaction calculation result, where n represents the total number of piles, i represents the i-th pile, t represents the number of hours, P i,t is the decision variable of the charging pile i in the time period t, MP i represents the maximum power allowed by the charging pile i. When the charging optimization is performed, the optimized maximum charging power is not allowed to exceed the maximum charging power before optimization; When regulating, attention should be paid to the balance of three-phase power, and the variance of three-phase power sum is used to quantify the three-phase imbalance target, and a three-phase imbalance calculation formula is as follows: ； ； ； ； ； f2 represents the three-phase imbalance calculation result, where t represents the current time point, represents the variance of ABC three-phase power, represents the total sum of power of all piles under phase A, represents the total sum of power of all piles under phase B, represents the total sum of power of all piles under phase C, t represents the mean value of t period three-phase power, the greater the variance, the more serious the three-phase imbalance, and vice versa. When regulating the electric vehicle charging power, the limitation of total capacity of the transformer area also needs to be considered, so as to ensure that the total charging power and the total non-charging power at each time point do not cause overload, and a forward overload constraint condition calculation formula is as follows: ； wherein L t represents the non-charging load at time t, represents the charging load of the i-th pile at time t, Z max represents the maximum load that the transformer can bear, and the sum of the charging load and the non-charging load of each time period needs to be less than the maximum load that the transformer can bear; When regulating, the electric quantity before regulation and the electric quantity after regulation of a single pile in one day should be ensured to be as consistent as possible, and an electric vehicle charging constraint condition calculation formula is as follows: ； Wherein T i is the set of effective charging periods of charging pile i, E i is the total charging amount of charging pile i in a day without regulation. 9.The vehicle-grid interaction regulation device considering user satisfaction and power grid problem treatment according to claim 6, wherein, The specific process of the sequential least square quadratic programming optimization algorithm is as follows: 1) Determine the optimization objective function and constraint conditions; 2) Convert the inequality constraint into a penalty function; 3) Data initialization, select an initial solution; 4) Construct the Lagrange function, and the specific calculation method is as follows: ； where f(x) is the objective function, g(x) is the constraint condition, is the Lagrange multiplier; 5) by minimizing the Lagrangian function Solve for x to get the updated value of x; 6) Update the penalty function according to the current x value and the constraint condition g(x) condition; 7) Judge whether the current solution meets the termination condition, whether the change of the objective function is less than the corresponding threshold value, and whether the maximum iteration number is reached; 8) If the termination condition is not met, return to 4) to continue iteration and update; 9) When the termination condition is met, the variable of the optimal solution is output. 10.The vehicle-grid interaction regulation device considering user satisfaction and power grid problem treatment according to claim 7, wherein, The calculation formulas of the overload reduction rate, three-phase imbalance reduction rate, total charging capacity retention rate and charging time reduction rate are as follows: The overload reduction rate is used to evaluate the treatment effect of overload after charging optimization, and an overload reduction rate calculation formula is as follows: ； where y1 represents the heavy overload reduction rate, o is the number of times of heavy overload occurrence in a day, to optimize the number of heavy overload occurrences in the following day; The three-phase imbalance reduction rate is used to evaluate the treatment effect of three-phase imbalance after charging optimization, and a three-phase imbalance reduction rate calculation formula is as follows: ； wherein y2 represents a three-phase imbalance reduction rate, denotes the power variance of all the poles under the ABC three-phase before regulation, denotes the power variance of all the poles under the ABC three-phase after regulation; User satisfaction is quantified and evaluated by using total charging capacity retention rate and charging time reduction rate, the higher the total charging capacity retention rate is, the higher the user satisfaction is, and the greater the charging time reduction rate is, the greater the user satisfaction is; The total charging capacity retention rate calculation formula is as follows: ； wherein y3 represents the total charge amount retention rate, b represents the sum of the charge amounts of all the piles before regulation, represents the sum of the charge amounts of all the piles after regulation; The charging time reduction rate is calculated by the following formula: ； wherein y4 represents a charging time reduction rate, T represents the total charging time of all piles before regulation, represents the total charging time of all piles after regulation.

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