A method and system for evaluating flexible adjustment capability of a distributed power system user containing light storage and charging

Abstract

The present application relates to the technical field of power regulation capability evaluation, and discloses a kind of flexible regulation capability evaluation method and system of distribution transformer user containing light storage and filling, the willingness of electric vehicle charging user participating in power grid regulation is determined, and the charging capacity of electric vehicle charging user participating in power grid regulation is transferred to the next period of the current calculation period within a day, so as to reasonably arrange the charging scheme, and the regulation income maximization of distribution transformer user is taken as the target condition, and the electric vehicle charging capacity of the next period of the current calculation period within a day is taken as the maximum charging capacity margin of electric vehicle regulation, the target function of charging regulation is constructed, the optimal solution corresponding to the regulation capacity determined by optimizing and solving the target function of charging regulation is used to represent the flexible regulation capability of distribution transformer user, the regulation income of distribution transformer user is guaranteed, and the calculation flexibility and precision of the flexible regulation capability of distribution transformer user are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power regulation capability evaluation, in particular to a flexible regulation capability evaluation method and system for distribution transformer users with photovoltaic storage and charging. BACKGROUND

[0002] Electric vehicles have gradually become an inseparable part of life, and charging stations have also developed rapidly. A large number of electric vehicle charging and grid-connected new energy will impact the power grid, and photovoltaic storage and charging stations have become an effective solution to promote new energy consumption and smooth impact, and gradually become the mainstream form of development.

[0003] Because photovoltaic storage and charging stations mix photovoltaic resources, charging pile loads, and energy storage resources, they are connected to the grid in the form of a dedicated transformer in the distribution network. Current research on photovoltaic storage and charging stations mainly focuses on site selection, capacity configuration, and optimal scheduling. Existing research has optimized the configuration and scheduling of photovoltaic storage and charging stations based on time-of-use electricity prices. For photovoltaic output scenarios, regression models, kernel density estimation, Copula functions, and machine learning are commonly used for output prediction or clustering. For charging pile loads, Monte Carlo methods and queuing theory are commonly used to simulate user travel behavior and then calculate different charging load demands. In terms of regulation capability, user comfort is usually considered for user-side resources, and different regulation schemes are developed for different types of users to evaluate the potential of high, medium, and low response rates.

[0004] However, current research focuses on the potential evaluation of a single resource and does not consider the willingness of electric vehicle users, lacks evaluation of regulation benefits, and is prone to poor flexibility and precision in calculating the flexible regulation capability of distribution transformer users. SUMMARY

[0005] The present application provides a flexible regulation capability evaluation method and system for distribution transformer users with photovoltaic storage and charging, which solves the technical problem of current research focusing on the potential evaluation of a single resource, not considering the willingness of electric vehicle users, lacking evaluation of regulation benefits, and being prone to poor flexibility and precision in calculating the flexible regulation capability of distribution transformer users.

[0006] Therefore, the first aspect of the present application provides a flexible regulation capability evaluation method for distribution transformer users with photovoltaic storage and charging, comprising:

[0007] According to the electricity price sensitivity and electricity anxiety level of electric vehicle charging users, determine the electric vehicle charging users participating in grid regulation and not participating in grid regulation, respectively;

[0008] Shift the charging capacity of the electric vehicle charging users participating in grid regulation to the next time period of the current calculation period within the day, and determine the electric vehicle charging capacity of the next time period within the current calculation period within the day.

[0009] With the goal of maximizing the regulation revenue of distribution transformer users, the decision variables are the energy storage charging and discharging amount, regulation amount and grid purchase amount during the charging response period, and the electric vehicle charging amount in the next period of the current calculation period within the day is used as the maximum charging amount margin of electric vehicles. The objective function of charging regulation is constructed.

[0010] The objective function of the charging regulation is optimized to determine the regulation amount corresponding to the optimal solution. The regulation amount corresponding to the optimal solution is used to characterize the flexible regulation capability of the distribution transformer user.

[0011] Preferably, the step of determining which electric vehicle charging users participate in grid regulation and which do not, based on the price sensitivity and power anxiety of the electric vehicle charging users, specifically includes:

[0012] Construct a membership system for the electricity price sensitivity of electric vehicle charging users;

[0013] Construct membership degrees for the level of battery anxiety among electric vehicle charging users;

[0014] Based on the membership degree of the electricity price sensitivity and the membership degree of the power anxiety, the willingness of electric vehicle charging users to participate in grid regulation is determined.

[0015] The willingness of electric vehicle charging users to participate in grid regulation is compared with a preset willingness threshold, and the electric vehicle charging users corresponding to participating in grid regulation and not participating in grid regulation are determined respectively.

[0016] Preferably, the step of transferring the charging volume of electric vehicle charging users participating in grid regulation to the next time period within the day, and determining the electric vehicle charging volume for the next time period within the day, specifically includes:

[0017] The charging amount of electric vehicle charging users participating in grid regulation is summed with the charging amount of electric vehicle charging users not participating in grid regulation in the next time period of the current calculation period within the day to determine the electric vehicle charging amount in the next time period of the current calculation period within the day.

[0018] Preferably, before the step of constructing the objective function for charging regulation, which takes maximizing the regulation revenue of distribution transformer users as the objective condition, energy storage charging and discharging volume, regulation power volume, and grid-purchased power volume during the charging response period as decision variables, and electric vehicle charging volume in the next period of the current calculation period as the maximum charging volume margin for electric vehicles, the following method is further included:

[0019] By sampling historical charging data, charging parameters for different time periods within the day can be obtained;

[0020] The amount of electric vehicle charging in each time period of the day is determined by using the charging parameters in each time period of the day.

[0021] With maximizing the daily revenue of distribution transformer users as the objective condition, the energy storage charging and discharging volume and the grid purchase volume in each time period of the day as decision variables, the balance of energy storage charging and discharging volume as a constraint, and the electric vehicle charging volume in each time period of the day as the maximum charging margin for electric vehicles, an objective function for energy storage charging and discharging is constructed. The objective function and constraints for energy storage charging and discharging are as follows:

[0022]

[0023] In the formula, F represents the daily revenue of the distribution transformer user, and t is the time period. For charging electricity price, For the grid purchase price of electricity, To regulate energy storage costs, The amount of electricity charged for electric vehicles, Purchase electricity for the power grid For energy storage charging capacity, This is the predicted value for photovoltaic power output. For energy storage charging and discharging capacity, The energy storage discharge amount is represented by st, which is a constraint symbol.

[0024] The objective function for energy storage charging and discharging is optimized to determine the maximum daily revenue for distribution transformer users.

[0025] Preferably, the prediction process for photovoltaic power output specifically includes:

[0026] Wavelet decomposition was used to decompose the historical photovoltaic power output data of distribution transformer users to obtain smooth components and detail components;

[0027] A training set is constructed based on historical photovoltaic power output data and its corresponding smooth and detail components.

[0028] A photovoltaic power output prediction model is obtained by training the training set based on an integrated neural network.

[0029] The photovoltaic output prediction model is used to predict the photovoltaic output within a preset time period in the future, and the predicted photovoltaic output value is obtained.

[0030] Preferably, the step of sampling historical charging data to obtain charging parameters for different time periods within a day includes:

[0031] Historical charging data is divided according to time scale, including the number of vehicles in the station, SOC status, and stopping time.

[0032] The historical charging data was sampled using the Gibbs sampling method to obtain the charging parameters for each time period of the day.

[0033] Preferably, the objective function for charging regulation is:

[0034]

[0035] In the formula, To adjust the returns for distribution transformer users, a and b represent the start and end times of the adjustment, respectively. To adjust the maximum charging capacity margin for electric vehicles. To purchase electricity from the adjusted power grid, To regulate electricity through energy storage, In order to regulate power consumption, To regulate electricity pricing based on electricity consumption, The amount of electric vehicle charging and discharging within time period t. For adjusted photovoltaic output.

[0036] Secondly, the present invention also provides a system for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic storage and charging, comprising:

[0037] The user willingness determination module is used to determine the electric vehicle charging users who participate in grid regulation and those who do not, based on their electricity price sensitivity and power anxiety.

[0038] The charging amount transfer module is used to transfer the charging amount of electric vehicle charging users participating in grid regulation to the next time period of the current calculation period within the day, and to determine the electric vehicle charging amount of the next time period of the current calculation period within the day.

[0039] The charging control module is used to construct the objective function of charging control with the goal of maximizing the adjustment benefits of distribution transformer users, the decision variables being the energy storage charging and discharging amount, the control amount and the grid purchase amount during the charging response period, and the electric vehicle charging amount in the next period of the current calculation period within the day as the maximum charging amount margin of electric vehicles.

[0040] The regulation capability determination module is used to optimize the objective function of the charging regulation and determine the regulation capacity corresponding to the optimal solution. The regulation capacity corresponding to the optimal solution is used to characterize the flexible regulation capability of the distribution transformer user.

[0041] Thirdly, the present invention also provides an electronic device, the electronic device including a memory and a processor;

[0042] The memory is used to store programs;

[0043] The processor executes the program to implement the above-mentioned method for evaluating the flexible regulation capability of distribution transformer users, which includes photovoltaic, energy storage, and charging.

[0044] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the flexible adjustment capability of distribution transformer users, including photovoltaic storage and charging.

[0045] As can be seen from the above technical solutions, the present invention has the following advantages:

[0046] This invention determines the willingness of electric vehicle (EV) charging users to participate in grid regulation by assessing their price sensitivity and energy anxiety. It then transfers the charging volume of EVs participating in grid regulation to the next time period within the day, thereby rationally arranging charging schemes. With maximizing the regulation benefits for distribution transformer users as the objective condition, and using the energy storage charging and discharging volume, regulation volume, and grid-purchased volume during the charging response period as decision variables, and the EV charging volume in the next time period within the day as the maximum regulation charging margin for EVs, a target function for charging regulation is constructed. By optimizing this target function, the regulation volume corresponding to the optimal solution is determined to characterize the flexible regulation capability of distribution transformer users, ensuring their regulation benefits and improving the computational flexibility and accuracy of their flexible regulation capability. Attached Figure Description

[0047] Figure 1 A flowchart of a method for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic storage and charging, provided as an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of a system for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic storage and charging, provided in an embodiment of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0051] Because photovoltaic (PV) and energy storage (ESS) charging stations integrate photovoltaic resources, charging pile loads, and energy storage resources, they are connected to the power distribution network via dedicated transformers. Current research on PV-ESS charging stations mainly focuses on site selection, capacity allocation, and optimized scheduling. Existing studies have optimized the configuration and scheduling of PV-ESS charging stations based on time-of-use pricing. For PV output scenarios, regression models, kernel density estimation, Copula functions, and machine learning are typically used for output prediction or clustering. For charging pile loads, Monte Carlo methods and queuing theory are commonly used to simulate user travel behavior and calculate different charging load demands. Regarding regulation capabilities, user comfort is usually considered for user-side resources, and different regulation schemes are developed for different types of users, with potential assessments of high, medium, and low response levels.

[0052] However, current research focuses on the potential assessment of a single resource, without taking into account the willingness of electric vehicle users and lacking an assessment of the adjustment benefits. This can easily lead to poor flexibility and accuracy in calculating the flexible adjustment capabilities of distribution transformer users.

[0053] In view of this, embodiments of the present invention provide a method for evaluating the flexible regulation capability of distribution transformer users in the form of photovoltaic storage and charging. Embodiments of the present invention are applicable to the situation of evaluating the flexible regulation capability of distribution transformer users. The method can be executed by a device for evaluating the flexible regulation capability of distribution transformer users. The device for evaluating the flexible regulation capability of distribution transformer users can be implemented in hardware and / or software and can be configured in a computer device.

[0054] For easier understanding, please refer to Figure 1 , Figure 1 The flowchart illustrates the process of a method for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic storage and charging, provided by the present invention.

[0055] This invention provides a method for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic storage and charging, comprising steps S1 to S4:

[0056] Step S1: Based on the price sensitivity and power anxiety of electric vehicle charging users, determine the electric vehicle charging users who participate in grid regulation and those who do not.

[0057] It should be noted that, considering the different levels of user sensitivity to electricity prices and energy consumption anxiety, there are users who are willing to participate in grid regulation and users who are not. By analyzing the electricity price sensitivity and energy consumption anxiety of electric vehicle charging users, we can determine the electric vehicle charging users who participate in grid regulation and users who do not participate in grid regulation.

[0058] Specifically, step S1 includes the following steps S101~S104:

[0059] Step S101: Construct the membership degree of the electricity price sensitivity of electric vehicle charging users.

[0060] The membership function for electricity price sensitivity is:

[0061]

[0062] In the formula, m 1,r Let r be the membership function for electricity price sensitivity, r = 1, 2, 3, 4, corresponding to low electricity consumption, relatively low electricity consumption, relatively high electricity consumption, and high electricity consumption, respectively, with threshold ranges of [0, 2.5], [2.5, 5], [5, 7.5], and [7.5, 10], respectively; SOC is the initial state of charge. Take 2.5; Standard deviation The values ​​for low battery, lower battery, higher battery, and high battery are 0, 3.33, 6.67, and 10, respectively.

[0063] Step S102: Construct the membership degree of electric vehicle charging users' battery anxiety level.

[0064] The membership function for battery anxiety is:

[0065]

[0066] In the formula, m 2,w Let w = 1, 2, 3, 4, which represent no anxiety, low anxiety, medium anxiety, and high anxiety, respectively, with threshold ranges of [0, 2.5], [2.5, 5], [5, 7.5], and [7.5, 10], respectively; Sen represents the anxiety state. Take 2.5; Standard deviation The corresponding values ​​for no anxiety, low anxiety, moderate anxiety, and high anxiety are 0, 3.33, 6.67, and 10, respectively.

[0067] Step S103: Determine the willingness of electric vehicle charging users to participate in grid regulation based on the membership degree of electricity price sensitivity and the membership degree of electricity anxiety.

[0068] Step S104: Compare the willingness of electric vehicle charging users to participate in grid regulation with the preset willingness threshold, and determine the electric vehicle charging users who participate in grid regulation and those who do not.

[0069] Specifically, the user's willingness to adjust the charging amount is obtained by using the membership output rules:

[0070]

[0071] In the formula, V=1 indicates that the user participates in grid regulation and control, and V=0 indicates that the user does not participate in grid regulation and control.

[0072] Step S2: Transfer the charging amount of electric vehicle charging users participating in grid regulation to the next time period of the current calculation period within the day, and determine the electric vehicle charging amount of the next time period of the current calculation period within the day.

[0073] Specifically, by calculating the adjustable and non-adjustable electric vehicle charging amounts in each time period of the day, the charging amounts of electric vehicle charging users participating in grid regulation are transferred to the next time period of the current calculation period within the day, thereby enabling orderly regulation, improving the average charging amount in each time period of the day, and maximizing the regulation benefits for distribution transformer users.

[0074] Specifically, the charging amount of electric vehicle charging users participating in grid regulation is summed with the charging amount of electric vehicle charging users not participating in grid regulation in the next time period of the current calculation period within the day to determine the electric vehicle charging amount in the next time period of the current calculation period within the day.

[0075] The electric vehicle charging volume for the next time period within the current calculation period of the day is as follows:

[0076]

[0077] In the formula, The amount of electric vehicle charging during time period t. This represents the charging amount of electric vehicle charging users participating in grid regulation during time period t-1. This represents the charging amount of electric vehicle charging users who do not participate in grid regulation during time period t.

[0078] Step S3: With the goal of maximizing the regulation revenue of distribution transformer users, the energy storage charging and discharging amount, regulation amount and grid purchase amount during the charging response period are used as decision variables, and the electric vehicle charging amount in the next period of the current calculation period within the day is used as the maximum charging amount margin of electric vehicles, construct the objective function for charging regulation.

[0079] The objective function for charging regulation is:

[0080]

[0081] In the formula, To adjust the returns for distribution transformer users, a and b represent the start and end times of the adjustment, respectively. To adjust the maximum charging capacity margin for electric vehicles. To purchase electricity from the adjusted power grid, To regulate electricity through energy storage, In order to regulate power consumption, To regulate electricity pricing based on electricity consumption, The amount of electric vehicle charging and discharging within time period t. For the adjusted photovoltaic output, max:F represents the maximum daily revenue for distribution transformer users.

[0082] Step S4: Optimize the objective function of charging regulation to determine the regulation amount corresponding to the optimal solution. The regulation amount corresponding to the optimal solution is used to characterize the flexible regulation capability of distribution transformer users.

[0083] The optimization method can be a group optimization algorithm. By optimizing the objective function of charging regulation, the regulation capacity corresponding to the maximum regulation benefit of distribution transformer users is determined, which is the flexible regulation capability of distribution transformer users, thus ensuring the regulation benefit of distribution transformer users.

[0084] It should be noted that this invention determines the willingness of electric vehicle (EV) charging users to participate in grid regulation by assessing their price sensitivity and energy anxiety. The charging volume of EVs participating in grid regulation is then transferred to the next time period within the day, thereby rationally arranging charging schemes. The objective condition is to maximize the regulation benefits for distribution transformer users. The decision variables are the energy storage charging and discharging volume, regulation volume, and grid-purchased volume during the charging response period. The charging volume of EVs in the next time period within the day is used as the maximum regulation charging margin for EVs. An objective function for charging regulation is constructed. By optimizing this objective function, the regulation volume corresponding to the optimal solution is determined to characterize the flexible regulation capability of distribution transformer users, ensuring their regulation benefits and improving the computational flexibility and accuracy of their flexible regulation capability.

[0085] In one specific embodiment, the method further includes the following step before step S3:

[0086] Step S301: Sample the historical charging data to obtain the charging parameters for each time period of the day.

[0087] Specifically, step S301 includes:

[0088] Step S3011: Divide the historical charging data according to the time scale. The historical charging data includes the number of vehicles in the station, SOC status, and stopping time.

[0089] This involves dividing historical charging data into units of time, such as one day, and assigning time tags to the divided historical charging data, including holidays, weekdays, and weekends. Then, the historical charging data can be further divided into intraday time periods, where each intraday time period can be one hour.

[0090] Step S3012: Use the Gibbs sampling method to sample historical charging data to obtain charging parameters for each time period of the day.

[0091] Historical charging data is defined as follows:

[0092]

[0093] In the formula, X is the charging data set, X EV X SOC X P X T These are the number of vehicles in the charging station, SOC status, parking time, and time period within the day.

[0094] Choose any initial value For each value, repeat the following steps:

[0095] 1) Constructing a conditional distribution: The historical charging data set includes different combinations of the number of vehicles at charging stations, SOC status, parking time, and intraday time periods, along with their frequencies. First, calculate the combination of the number of vehicles at a specific charging station, SOC status, parking time, and intraday time period, then calculate X. i The frequency of possible values ​​in historical data is used to calculate X. i The conditional distribution can be obtained by considering the frequency of occurrence given the current values ​​of other variables.

[0096] 2) Extracting a new value: Suppose we extract a new value X. EV The value of X is fixed. The current values ​​of other variables are fixed, and only X is considered. EV The conditional probability distribution. First, generate a random number in the interval [0,1]. Second, based on X... EV The conditional probability distribution is used to find the probability or probability interval corresponding to each possible value. Finally, the generated random number is used to select X. EV The new value, specifically, is to find the probability interval within which the random number falls, and the corresponding X value for that interval. EV The value is the newly selected value.

[0097] From X iExtracting from conditional distribution ,Right now The conditional distribution probability This refers to the proportion of that value within the same type of data, i.e.:

[0098]

[0099] Among them, nub( ) is the data type Xi Number of occurrences, nub(X) i ) is a data type X i Data size;

[0100] Repeat the above steps until convergence or the required number of samples is reached;

[0101] After obtaining the conditional distribution, it is approximately assumed that the distribution is close to the target distribution, and samples of charging parameters during intraday periods can be directly extracted.

[0102] Step S302: Determine the electric vehicle charging amount for each time period of the day using the charging parameters for each time period of the day.

[0103] Among them, the amount of electric vehicle charging during different time periods throughout the day :

[0104]

[0105] In the formula, P x The charging power of the charging unit, S a This represents the total battery capacity of electric vehicles within the station.

[0106] Step S303: Taking the maximization of daily revenue for distribution transformer users as the objective condition, the energy storage charging and discharging volume and grid-purchased electricity volume in each time period of the day as decision variables, the balance of energy storage charging and discharging volume as a constraint, and the electric vehicle charging volume in each time period of the day as the maximum charging margin for electric vehicles, construct the objective function for energy storage charging and discharging. The objective function and constraints for energy storage charging and discharging are as follows:

[0107]

[0108] In the formula, F represents the daily revenue of the distribution transformer user, and t is the time period. For charging electricity price, For the grid purchase price of electricity, To regulate energy storage costs, The amount of electricity charged for electric vehicles, Purchase electricity for the power grid For energy storage charging capacity, This is the predicted value for photovoltaic power output. For energy storage charging and discharging capacity, The energy storage discharge amount is represented by st, which is a constraint symbol.

[0109] Step S304: Optimize the objective function of energy storage charging and discharging to determine the maximum daily revenue of distribution transformer users.

[0110] Among them, the optimal solution can be obtained through swarm optimization algorithms.

[0111] In one specific embodiment, the prediction process for photovoltaic power output specifically includes:

[0112] Step S311: Use wavelet decomposition to decompose the historical photovoltaic power output data of the distribution transformer user to obtain smooth components and detail components.

[0113] Among them, the smooth component and detail component of photovoltaic power output are decomposed using wavelet decomposition, resulting in:

[0114]

[0115] In the formula, X(t) represents the approximate function value for time period t, the scale is j, the shift is k, M is the maximum scale value of the approximate coefficient, N is the maximum shift value of the approximate coefficient, and c j,k Let d be a smooth component of scale j and translation amount k. j,k Let j be the detail coefficients for scale j and translation k. , All are wavelet functions.

[0116] Step S312: Construct a training set based on historical photovoltaic power output data and its corresponding smoothing and detail components.

[0117] The training set contains a large amount of historical photovoltaic power output data and the mapping relationship between its corresponding smoothing component and detail component.

[0118] Step S313: Train the training set based on the integrated neural network to obtain the photovoltaic power output prediction model.

[0119] The smoothing component and detail component are used as inputs to the ensemble neural network for training and prediction. The ensemble neural network is a collection of multiple sub-modules, including but not limited to convolutional neural networks and recurrent neural networks. Finally, the predicted output of photovoltaic power is obtained through an averaging layer.

[0120] Step S314: Use the photovoltaic power output prediction model to predict the photovoltaic power output within a preset time period in the future, and obtain the photovoltaic power output prediction value.

[0121] The predicted total daily photovoltaic output data is obtained by superimposing the photovoltaic output forecasts for each time period:

[0122]

[0123] In the formula, This represents the predicted photovoltaic power output for time period t.

[0124] The above is a detailed description of an embodiment of a method for evaluating the flexible regulation capability of distribution transformer users with photovoltaic, energy storage and charging functions provided by the present invention. The following is a detailed description of an embodiment of a system for evaluating the flexible regulation capability of distribution transformer users with photovoltaic, energy storage and charging functions provided by the present invention.

[0125] For easier understanding, please refer to Figure 2 The present invention also provides a system for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic storage and charging, comprising:

[0126] The user willingness determination module 100 is used to determine the electric vehicle charging users who participate in grid regulation and those who do not, based on the electricity price sensitivity and power anxiety of the electric vehicle charging users.

[0127] The charging amount transfer module 200 is used to transfer the charging amount of electric vehicle charging users participating in grid regulation to the next time period of the current calculation period within the day, and to determine the electric vehicle charging amount of the next time period of the current calculation period within the day.

[0128] The charging control module 300 is used to construct the objective function of charging control with the goal of maximizing the adjustment benefits of distribution transformer users, the decision variables being the energy storage charging and discharging amount, the control amount and the grid purchase amount during the charging response period, and the electric vehicle charging amount in the next period of the current calculation period within the day as the maximum charging amount margin of electric vehicles.

[0129] The regulation capability determination module 400 is used to optimize the objective function of charging regulation and determine the regulation capacity corresponding to the optimal solution. The regulation capacity corresponding to the optimal solution is used to characterize the flexible regulation capability of the distribution transformer user.

[0130] The present invention also provides an electronic device, which includes a memory and a processor;

[0131] Memory is used to store programs;

[0132] The processor executes a program to implement the aforementioned method for evaluating the flexible regulation capability of distribution transformer users, which includes photovoltaic, energy storage, and charging.

[0133] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for evaluating the flexible regulation capability of distribution transformer users, including photovoltaic, energy storage, and charging.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, electronic devices, and computer storage media described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] In the several embodiments provided by this invention, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0136] In the embodiments provided by this invention, it should be understood that the disclosed systems, electronic devices, computer storage media, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the flexible regulation capability of distribution transformer users that includes photovoltaic, energy storage, and charging systems, characterized in that, include: Based on the electricity price sensitivity and power anxiety of electric vehicle charging users, determine the electric vehicle charging users who participate in grid regulation and those who do not participate in grid regulation respectively; The charging volume of electric vehicle charging users participating in grid regulation is transferred to the next time period of the current calculation period within the day, and the charging volume of electric vehicles in the next time period of the current calculation period within the day is determined. With the goal of maximizing the regulation revenue of distribution transformer users, the decision variables are the energy storage charging and discharging amount, regulation amount and grid purchase amount during the charging response period, and the electric vehicle charging amount in the next period of the current calculation period within the day is used as the maximum charging amount margin of electric vehicles. The objective function of charging regulation is constructed. Before the step of constructing the objective function for charging regulation, which takes maximizing the regulation revenue of distribution transformer users as the objective condition, energy storage charging and discharging volume, regulation power volume, and grid-purchased power volume during the charging response period as decision variables, and electric vehicle charging volume in the next period of the current calculation period as the maximum charging volume margin for electric vehicles, the following steps are also included: By sampling historical charging data, charging parameters for different time periods within the day can be obtained. The amount of electric vehicle charging in each time period of the day is determined by using the charging parameters in each time period of the day. With maximizing the daily revenue of distribution transformer users as the objective condition, the energy storage charging and discharging volume and the grid purchase volume in each time period of the day as decision variables, the balance of energy storage charging and discharging volume as a constraint, and the electric vehicle charging volume in each time period of the day as the maximum charging margin for electric vehicles, an objective function for energy storage charging and discharging is constructed. The objective function and constraints for energy storage charging and discharging are as follows: ； In the formula, F represents the daily revenue of the distribution transformer user, and t is the time period. For charging electricity price, For the grid purchase price of electricity, To regulate energy storage costs, The amount of electricity charged for electric vehicles, Purchase electricity for the power grid For energy storage charging capacity, This is the predicted value for photovoltaic power output. For energy storage charging and discharging capacity, The energy storage discharge amount is represented by st, which is a constraint symbol. The objective function for energy storage charging and discharging is optimized to determine the maximum daily revenue for distribution transformer users; The objective function for charging regulation is: ； In the formula, To adjust the returns for distribution transformer users, a and b represent the start and end times of the adjustment, respectively. To adjust the maximum charging capacity margin for electric vehicles. To purchase electricity from the adjusted power grid, To regulate electricity through energy storage, In order to regulate power consumption, To regulate electricity pricing based on electricity consumption, The amount of electric vehicle charging and discharging within time period t. For the adjusted photovoltaic output; The objective function of the charging regulation is optimized to determine the regulation amount corresponding to the optimal solution. The regulation amount corresponding to the optimal solution is used to characterize the flexible regulation capability of the distribution transformer user.

2. The method for evaluating the flexible regulation capability of distribution transformer users including photovoltaic storage and charging as described in claim 1, characterized in that, The step of determining which electric vehicle charging users participate in grid regulation and which do not, based on their electricity price sensitivity and power anxiety levels, specifically includes: Construct a membership system for the electricity price sensitivity of electric vehicle charging users; Construct membership degrees for the level of battery anxiety among electric vehicle charging users; Based on the membership degree of the electricity price sensitivity and the membership degree of the power anxiety, the willingness of electric vehicle charging users to participate in grid regulation is determined. The willingness of electric vehicle charging users to participate in grid regulation is compared with a preset willingness threshold, and the electric vehicle charging users corresponding to participating in grid regulation and not participating in grid regulation are determined respectively.

3. The method for evaluating the flexible regulation capability of distribution transformer users including photovoltaic storage and charging as described in claim 1, characterized in that, The step of transferring the charging volume of electric vehicle charging users participating in grid regulation to the next time period within the day, and determining the electric vehicle charging volume for the next time period within the day, specifically includes: The charging amount of electric vehicle charging users participating in grid regulation is summed with the charging amount of electric vehicle charging users not participating in grid regulation in the next time period of the current calculation period within the day to determine the electric vehicle charging amount in the next time period of the current calculation period within the day.

4. The method for evaluating the flexible regulation capability of distribution transformer users including photovoltaic storage and charging as described in claim 1, characterized in that, The forecasting process for photovoltaic power output specifically includes: Wavelet decomposition was used to decompose the historical photovoltaic power output data of distribution transformer users to obtain smooth components and detail components; A training set is constructed based on historical photovoltaic power output data and its corresponding smooth and detail components. A photovoltaic power output prediction model is obtained by training the training set based on an integrated neural network. The photovoltaic output prediction model is used to predict the photovoltaic output within a preset time period in the future, and the predicted photovoltaic output value is obtained.

5. The method for evaluating the flexible regulation capability of distribution transformer users including photovoltaic storage and charging as described in claim 1, characterized in that, The step of sampling historical charging data to obtain charging parameters for different time periods within a day includes: Historical charging data is divided according to time scale, including the number of vehicles in the station, SOC status, and stopping time. The historical charging data was sampled using the Gibbs sampling method to obtain the charging parameters for each time period of the day.

6. A system for assessing the flexible regulation capability of distribution transformer users with photovoltaic, energy storage, and charging capabilities, comprising the method for assessing the flexible regulation capability of distribution transformer users with photovoltaic, energy storage, and charging capabilities as described in any one of claims 1 to 5, characterized in that, include: The user willingness determination module is used to determine the electric vehicle charging users who participate in grid regulation and those who do not, based on their electricity price sensitivity and power anxiety. The charging amount transfer module is used to transfer the charging amount of electric vehicle charging users participating in grid regulation to the next time period of the current calculation period within the day, and to determine the electric vehicle charging amount of the next time period of the current calculation period within the day. The charging control module is used to construct the objective function of charging control with the goal of maximizing the adjustment benefits of distribution transformer users, the decision variables being the energy storage charging and discharging amount, the control amount and the grid purchase amount during the charging response period, and the electric vehicle charging amount in the next period of the current calculation period within the day as the maximum charging amount margin of electric vehicles. The regulation capability determination module is used to optimize the objective function of the charging regulation and determine the regulation capacity corresponding to the optimal solution. The regulation capacity corresponding to the optimal solution is used to characterize the flexible regulation capability of the distribution transformer user.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor; The memory is used to store programs; The processor executes the program to implement the method for evaluating the flexible regulation capability of distribution transformer users, including photovoltaic, energy storage and charging, as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for evaluating the flexible regulation capability of distribution transformer users, including photovoltaic storage and charging, as described in any one of claims 1 to 5.

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