Multi-objective Optimization Method, Device and Equipment Based on Flexible Resources of Charging Stations

Abstract

The present invention provides a multi-objective optimization method, device and equipment based on flexible resources of a charging station, which can be applied to the technical field of new energy vehicles. The method includes: in response to a scheduling request for flexible resources of a charging station, obtaining preset comprehensive resource trading parameters, expected charging amounts, charging durations and historical charging information; obtaining dynamic resource trading parameters according to the expected charging amounts and charging durations; obtaining the respective initial discharge willingness of a target object in N time periods according to the historical charging information, preset membership functions and fuzzy sets; taking the lowest operation consumption of the charging station and the highest discharge exchange value of new energy vehicles as objective functions, and based on objective constraint conditions and a two-layer decision-making model constructed according to the initial discharge willingness and dynamic resource trading parameters, adjusting the initial resource trading parameters and the initial discharge willingness to obtain the respective target resource trading parameters in N time periods and the respective target discharge willingness of the target object in N time periods.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicles, and more particularly to a multi-objective optimization method, device and equipment based on flexible resources of charging stations. Background Art

[0002] As a core component of new energy vehicles, with the continuous maturity of battery technology, the number of new energy vehicles is gradually increasing. The progress of battery technology has significantly improved the performance and reliability of new energy vehicles. For example, the acceleration of the battery charging speed has reduced the charging duration and provided convenience for new energy vehicle users.

[0003] In the process of implementing the present invention, the inventors found through research that due to frequent charging and discharging operations that will accelerate battery degradation, new energy vehicle users have a repulsive attitude towards the vehicle-to-grid (V2G) mode, which often ignores the dynamic changes in the willingness of new energy vehicle users when quantifying the flexible resources of new energy vehicles, resulting in low schedulability of flexible resources and low operational robustness of charging stations. Summary of the Invention

[0004] In view of the above problems, the present invention provides a multi-objective optimization method, device and equipment based on flexible resources of charging stations.

[0005] According to a first aspect of the present invention, there is provided a multi-objective optimization method based on flexible resources of a charging station, including: in response to a scheduling request for the flexible resources of the charging station, obtaining preset comprehensive resource trading parameters, the expected charging amount of a new energy vehicle, the charging duration corresponding to the expected charging amount, and historical charging information, wherein the preset comprehensive resource trading parameters include M time periods and the resource trading parameters of each of the M time periods, and M is an integer greater than 1; adjusting the M time periods and the resource trading parameters of each of the M time periods according to the expected charging amount and the charging duration to obtain N time periods of dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods, wherein N is an integer greater than M; obtaining the initial discharge willingness of the target object in each of the N time periods according to the historical charging information, a preset membership function, and a fuzzy set, wherein the fuzzy set is obtained by performing fuzzy processing on the historical charging information; constructing a two-layer decision model according to the initial discharge willingness of the target object in each of the N time periods, the N time periods of dynamic resource trading parameters, and the initial resource trading parameters of each of the N time periods; taking the lowest operating consumption of the charging station and the highest discharge exchange value of the new energy vehicle as objective functions, and based on objective constraint conditions and the two-layer decision model, adjusting the initial resource trading parameters of each of the N time periods and the initial discharge willingness of the target object in each of the N time periods to obtain the target resource trading parameters of each of the N time periods and the target discharge willingness of the target object in each of the N time periods.

[0006] According to the multi-objective optimization method, device and equipment based on the flexibility resources of charging stations provided by the present invention, by adjusting the M time periods of the preset comprehensive resource trading parameters and the resource trading parameters of each of the M time periods, the N time periods of the dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods are obtained, and with the lowest operating consumption of the charging station and the highest discharge exchange value of new energy vehicles as the objective functions, based on the objective constraint conditions and the two-layer decision-making model, the initial resource trading parameters of each of the N time periods and the initial discharge willingness of the target object in each of the N time periods are adjusted to obtain the target resource trading parameters of each of the N time periods and the target discharge willingness of the target object in each of the N time periods. Based on the two-layer decision-making model, the discharge exchange value of the target object and the operating consumption of the charging station can be considered simultaneously, achieving the balance of the loads of the charging station and the power grid, avoiding the formation of new load peaks, improving the schedulability of flexibility resources and the operation robustness of the charging station, and further achieving the purposes of peak shaving and valley filling, load optimization, and maximizing the discharge exchange value of the target object. Brief Description of the Drawings

[0007] Through the following description of the embodiments of the present invention with reference to the drawings, the above content and other objects, features and advantages of the present invention will become clearer. In the drawings:

[0008] Figure 1 Shows an application scenario diagram of the multi-objective optimization method based on the flexibility resources of charging stations according to an embodiment of the present invention.

[0009] Figure 2 Shows a flowchart of the multi-objective optimization method based on the flexibility resources of charging stations according to an embodiment of the present invention.

[0010] Figure 3A Shows a membership degree schematic diagram of net discharge compensation consumption according to an embodiment of the present invention.

[0011] Figure 3B Shows a membership degree schematic diagram of the dischargeable margin according to an embodiment of the present invention.

[0012] Figure 3C Shows a membership degree schematic diagram of the overtime occupancy duration according to an embodiment of the present invention.

[0013] Figure 4 Shows a framework diagram of the two-layer decision-making model according to an embodiment of the present invention.

[0014] Figure 5 Shows a schematic diagram of the dynamic resource trading parameters according to an embodiment of the present invention.

[0015] Figure 6 Shows a peak shaving schematic diagram of the load of the charging station by the dynamic resource trading parameters according to an embodiment of the present invention.

[0016] Figure 7A Shows a schematic diagram of the flexibility supply - demand relationship without considering V2G according to an embodiment of the present invention.

[0017] Figure 7B Shows a schematic diagram of the flexibility supply - demand relationship considering V2G according to an embodiment of the present invention.

[0018] Figure 8 Shows a schematic diagram of the discharge flexibility region under different discharge willingness thresholds according to an embodiment of the present invention.

[0019] Figure 9 Shows a block diagram of a multi - objective optimization device based on the flexibility resources of a charging station according to an embodiment of the present invention.

[0020] Figure 10 Shows a block diagram of an electronic device suitable for implementing a multi - objective optimization method based on the flexibility resources of a charging station according to an embodiment of the present invention. Detailed implementation manners

[0021] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well - known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present invention.

[0022] The terms used herein are merely for describing specific embodiments and are not intended to limit the present invention. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but is not limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0025] In the technical solution of the present invention, the user information involved (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties. Moreover, the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure, and application, all comply with relevant laws, regulations, and standards, adopt necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or reject.

[0026] In the scenario of making automated decisions using personal information, the methods, devices, and systems provided by the embodiments of the present invention all provide corresponding operation entrances for users to choose to agree or reject the results of automated decisions; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision" here refers to the activity of automatically analyzing and evaluating an individual's behavior habits, hobbies, or economic, health, credit status, etc. through a computer program and making decisions. The expression "expert decision" here refers to the activity of making decisions by personnel who are engaged in work in a specific field, have specialized experience, knowledge, and skills, and have reached a certain professional level.

[0027] In the process of implementing the present invention, in related technologies, frequent charge and discharge operations will accelerate the attenuation of the battery, and new energy vehicle users have a repulsive attitude towards the V2G mode, resulting in the dynamic changes of the willingness of new energy vehicle users being often ignored when quantifying the flexibility resources of new energy vehicles, leading to relatively low schedulability of flexibility resources and operational robustness of charging stations.

[0028] In view of this, an embodiment of the present invention provides a multi-objective optimization method based on flexible resources of a charging station. In response to a scheduling request for the flexible resources of the charging station, preset comprehensive resource trading parameters, an estimated charging amount of a new energy vehicle, a charging duration corresponding to the estimated charging amount, and historical charging information are obtained. The preset comprehensive resource trading parameters include M time periods and resource trading parameters for each of the M time periods, where M is an integer greater than 1. The M time periods and the resource trading parameters for each of the M time periods are adjusted according to the estimated charging amount and the charging duration to obtain N time periods of dynamic resource trading parameters and initial resource trading parameters for each of the N time periods, where N is an integer greater than M. Initial discharge willingnesses of the target object in each of the N time periods are obtained according to the historical charging information, a preset membership function, and a fuzzy set, where the fuzzy set is obtained by fuzzifying the historical charging information. A two-layer decision model is constructed according to the initial discharge willingnesses of the target object in each of the N time periods, the N time periods of the dynamic resource trading parameters, and the initial resource trading parameters for each of the N time periods. With the lowest operating consumption of the charging station and the highest discharge exchange value of the new energy vehicle as the objective functions, based on the objective constraint conditions and the two-layer decision model, the initial resource trading parameters for each of the N time periods and the initial discharge willingnesses of the target object in each of the N time periods are adjusted to obtain target resource trading parameters for each of the N time periods and target discharge willingnesses of the target object in each of the N time periods.

[0029] Figure 1 FIG. 4 shows an application scenario diagram of the multi-objective optimization method based on flexible resources of a charging station according to an embodiment of the present invention.

[0030] As Figure 1 shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0031] Users may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0032] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop computers, desktop computers, and the like.

[0033] The server 105 can be a server that provides various services, such as a background management server (only for example) that supports the websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server can analyze and process data such as user requests received, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0034] It should be noted that the multi-objective optimization method based on the flexibility resources of the charging station provided by the embodiments of the present invention can generally be executed by the server 105. Correspondingly, the multi-objective optimization device based on the flexibility resources of the charging station provided by the embodiments of the present invention can generally be set in the server 105. The multi-objective optimization method based on the flexibility resources of the charging station provided by the embodiments of the present invention can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the multi-objective optimization device based on the flexibility resources of the charging station provided by the embodiments of the present invention can also be set in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105.

[0035] It should be understood that Figure 1 the numbers of the first terminal device, the second terminal device, the third terminal device, the network, and the server in

[0036] Figure 2 are merely illustrative. According to the implementation requirements, there can be any number of first terminal devices, second terminal devices, third terminal devices, networks, and servers.

[0037] As Figure 2 shown, the multi-objective optimization method 200 based on the flexibility resources of the charging station in this embodiment includes operations S210 to S250.

[0038] In operation S210, in response to a scheduling request for the flexibility resources of the charging station, obtain preset comprehensive resource trading parameters, the expected charging amount of the new energy vehicle, the charging duration corresponding to the expected charging amount, and historical charging information.

[0039] In operation S220, according to the predicted charging amount and charging duration, the resource trading parameters for M time periods and their respective M time periods are adjusted to obtain N time periods of dynamic resource trading parameters and the initial resource trading parameters for their respective N time periods, where N is an integer greater than M.

[0040] In operation S230, according to the historical charging information, the preset membership function, and the fuzzy set, the initial discharge willingness of the target object for each of the N time periods is obtained.

[0041] In operation S240, according to the initial discharge willingness of the target object for each of the N time periods, the N time periods of dynamic resource trading parameters, and the initial resource trading parameters for their respective N time periods, a two-layer decision-making model is constructed.

[0042] In operation S250, with the lowest operating consumption of the charging station and the highest discharge exchange value of new energy vehicles as the objective function, based on the objective constraint conditions and the two-layer decision-making model, the initial resource trading parameters for each of the N time periods and the initial discharge willingness of the target object for each of the N time periods are adjusted to obtain the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods.

[0043] According to an embodiment of the present invention, a charging station can be characterized as a facility that provides electrical energy replenishment for new energy vehicles. New energy vehicles are new energy vehicles that use unconventional vehicle fuels as the power source. For example, new energy vehicles can include types such as pure electric vehicles and hybrid vehicles. Flexible resources can include grid power supply flexibility resources, photovoltaic device power supply flexibility resources, and energy storage device power supply flexibility resources.

[0044] According to an embodiment of the present invention, the preset comprehensive resource trading parameters can represent the preset time-sharing resource trading parameters. The time-sharing resource trading parameters refer to the resource trading parameters that are divided into different grades according to the resource demands in different time periods of a day, so as to improve the utilization efficiency of resources. The preset comprehensive resource trading parameters can include the resource trading parameters for M time periods and their respective M time periods, where M is an integer greater than 1. For example, 24 hours of a day can be divided into 24 time periods, and each time period or several time periods can correspond to a resource trading parameter.

[0045] For example, 24 hours within a day can be divided into 4 time periods: peak, flat, valley, and deep valley. That is, the preset comprehensive resource trading parameters include 4 time periods and the resource trading parameters for their respective 4 time periods. The charging process of new energy vehicles may involve one or more of the peak, flat, valley, and deep valley time periods. Near the supply-demand balance point, there is a linear relationship between the charging demand of new energy vehicles and the resource trading parameters, as shown in the following formula (1).

[0046] (1)

[0047] wherein, represents the charging demand in the i-th time period, represents the resource trading parameter in the i-th time period, represents the peak time period, represents the normal time period, represents the valley time period, represents the deep valley time period, , represents the slope in the i-th time period, represents the intercept in the i-th time period.

[0048] According to an embodiment of the present invention, the expected charging amount of a new energy vehicle can characterize the amount of electricity that the new energy vehicle needs to store for subsequent trips. The charging duration can characterize the duration required to charge the expected charging amount for the new energy vehicle at the charging station. The historical charging information can characterize the relevant operation information for charging the new energy vehicle at the charging station. For example, the historical charging information can include information such as the charging start time, charging end time, gun unplugging time, charging duration, and overtime occupancy duration for charging the new energy vehicle during a previous period of time.

[0049] According to an embodiment of the present invention, the dynamic resource trading parameter can characterize dividing 24 hours of a day into more time periods and the respective initial resource trading parameters for each time period on the basis of the preset time-sharing resource trading parameters.

[0050] According to an embodiment of the present invention, based on the expected charging amount and the charging duration corresponding to the expected charging amount, by adjusting the M time periods and the respective resource trading parameters of the M time periods, the N time periods of the dynamic resource trading parameter and the respective initial resource trading parameters of the N time periods can be obtained. For example, in the case where the preset comprehensive resource trading parameter divides 24 hours of a day into 4 time periods of peak, normal, valley, and deep valley and the respective resource trading parameters of the 4 time periods, by adjusting the 4 time periods of peak, normal, valley, and deep valley and the respective resource trading parameters of the 4 time periods, the dynamic resource trading parameter can be to divide 24 hours of a day into 96 time periods and the respective initial resource trading parameters of the 96 time periods.

[0051] According to an embodiment of the present invention, the target object can characterize the vehicle owner who charges the new energy vehicle at the charging station.

[0052] According to an embodiment of the present invention, a fuzzy set may be obtained by fuzzifying historical charging information. The fuzzy set may include a first fuzzy set, a second fuzzy set, a third fuzzy set and a fourth fuzzy set. The first fuzzy set may be obtained by fuzzifying the timeout occupancy duration in the historical charging information. The second fuzzy set may be obtained by fuzzifying the net discharge compensation consumption. The third fuzzy set may be obtained by fuzzifying the discharge margin. The fourth fuzzy set may be obtained by fuzzifying the initial discharge intention.

[0053] According to an embodiment of the present invention, the first fuzzy set includes a first subset, a second subset and a third subset corresponding to the timeout occupation duration, and the first subset, the second subset and the third subset corresponding to the timeout occupation duration respectively represent the long, medium and short timeout occupation duration.

[0054] According to an embodiment of the present invention, by statistically analyzing the charging start time, charging end time, gun removal time, overtime occupancy time and other information in the historical charging information, the net discharge compensation consumption and the discharge margin can be obtained. The net discharge compensation consumption can represent the consumption caused by compensating the new energy vehicle for discharging the charging station. The net discharge compensation consumption may include discharge compensation, discharge loss, parking fees and occupancy penalty fees. The discharge margin can represent the state of charge (SOC) that can be discharged after the new energy vehicle completes charging without affecting future travel needs. The charge state is the ratio of the current remaining power of the battery to its total capacity.

[0055] According to an embodiment of the present invention, the second fuzzy set includes a first subset, a second subset and a third subset corresponding to the net discharge compensation consumption, and the first subset, the second subset and the third subset corresponding to the net discharge compensation consumption respectively represent the high, medium and low of the net discharge compensation consumption. The third fuzzy set includes a first subset, a second subset and a third subset corresponding to the dischargeable margin, and the first subset, the second subset and the third subset corresponding to the dischargeable margin respectively represent the high, medium and low of the amount of electricity available for discharge. The fourth fuzzy set includes a first subset, a second subset and a third subset corresponding to the initial discharge intention. The first subset corresponding to the initial discharge intention can represent the discharge intention "negative", the second subset corresponding to the initial discharge intention can represent the discharge intention "middle", and the third subset corresponding to the initial discharge intention can represent the discharge intention "negative", and the first subset, the second subset and the third subset corresponding to the initial discharge intention respectively correspond to the high, medium and low of the discharge intention.

[0056] According to an embodiment of the present invention, the preset membership function may include multiple types, for example, a trapezoidal membership function, a Gaussian membership function, a sigmoid membership function, and the like.

[0057] According to an embodiment of the present invention, based on historical charging information, a preset membership function, and a fuzzy set, the overtime occupancy duration, net discharge compensation consumption, and discharge margin can be mapped to one or more subsets. The membership values of the overtime occupancy duration, net discharge compensation consumption, and discharge margin can be determined according to the preset membership function, that is, the degree to which the overtime occupancy duration, net discharge compensation consumption, and discharge margin belong to a certain subset, so as to obtain the initial discharge willingness of the target object in each of the N time periods.

[0058] Figure 3A The membership diagram of the net discharge compensation consumption according to an embodiment of the present invention is shown. Figure 3B The membership diagram of the discharge margin according to an embodiment of the present invention is shown. Figure 3C The membership diagram of the overtime occupancy duration according to an embodiment of the present invention is shown.

[0059] As Figure 3A shown, the membership values of the first subset, second subset, and third subset corresponding to the net discharge compensation consumption based on the trapezoidal membership function are shown. The first subset corresponding to the net discharge compensation consumption represents "high" net discharge compensation consumption, the second subset corresponding to the net discharge compensation consumption represents "medium" net discharge compensation consumption, and the third subset corresponding to the net discharge compensation consumption represents "low" net discharge compensation consumption.

[0060] As Figure 3B shown, the membership values of the first subset, second subset, and third subset corresponding to the discharge margin based on the trapezoidal membership function are shown. The first subset corresponding to the discharge margin represents "high" discharge margin, the second subset corresponding to the discharge margin represents "medium" discharge margin, and the third subset corresponding to the discharge margin represents "low" discharge margin.

[0061] As Figure 3C shown, the membership values of the first subset, second subset, and third subset corresponding to the overtime occupancy duration based on the trapezoidal membership function and the Gaussian membership function are shown. The first subset corresponding to the overtime occupancy duration represents "long" overtime occupancy duration, the second subset corresponding to the overtime occupancy duration represents "medium" overtime occupancy duration, and the third subset corresponding to the overtime occupancy duration represents "short" overtime occupancy duration.

[0062] According to an embodiment of the present invention, a two-layer decision model can be used to update and iterate the initial discharge willingness and the initial resource trading parameters. A two-layer decision model can be constructed according to the initial discharge willingness of the target object in each of the N time periods, the N time periods of the dynamic resource trading parameters, and the initial resource trading parameters in each of the N time periods.

[0063] According to an embodiment of the present invention, the operation consumption of the charging station can characterize the cost required for the operation of the charging station. The discharge exchange value can characterize the compensation obtained by the new energy vehicle for charging the charging pile.

[0064] According to an embodiment of the present invention, the target constraint condition can characterize the constraints on the power of the charging station, the power and capacity of the energy storage device, and the output of the photovoltaic device.

[0065] According to an embodiment of the present invention, based on the target constraint condition and the two-layer decision-making model, the initial resource trading parameters for each of the N time periods and the initial discharge willingness of the target object for each of the N time periods can be adjusted, so that the operation consumption of the charging station is minimized while the discharge exchange value of the new energy vehicle is maximized, thereby obtaining the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods.

[0066] According to an embodiment of the present invention, by adjusting the M time periods of the preset comprehensive resource trading parameters and the resource trading parameters for each of the M time periods, the N time periods of the dynamic resource trading parameters and the initial resource trading parameters for each of the N time periods are obtained, and with the minimum operation consumption of the charging station and the maximum discharge exchange value of the new energy vehicle as the objective function, based on the target constraint condition and the two-layer decision-making model, the initial resource trading parameters for each of the N time periods and the initial discharge willingness of the target object for each of the N time periods are adjusted to obtain the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods. Based on the two-layer decision-making model, the discharge exchange value of the target object and the operation consumption of the charging station can be considered simultaneously, achieving the balance of the load of the charging station and the power grid, avoiding the formation of new load peaks, improving the schedulability of flexible resources and the operation robustness of the charging station, and further achieving the purpose of peak shaving and valley filling, optimizing the load, and maximizing the discharge exchange value of the target object.

[0067] According to an embodiment of the present invention, the initial discharge willingness of the target object for each of the N time periods is obtained according to the historical charging information, the preset membership function, and the fuzzy set, including: determining the membership value of the historical charging information according to the preset membership function and the fuzzy set; determining the fuzzy control rule according to the membership value of the historical charging information; and obtaining the initial discharge willingness of the target object for each of the N time periods according to the historical charging information and the fuzzy control rule.

[0068] According to an embodiment of the present invention, by statistically analyzing the charging start time, charging end time, gun removal time, overtime occupancy duration, etc. in the historical charging information, the net discharge compensation consumption and the dischargeable margin can be obtained, and the historical charging information can also include the net discharge compensation consumption and the dischargeable margin.

[0069] According to an embodiment of the present invention, a fuzzy set of the overtime occupancy duration, a fuzzy set of the net discharge compensation consumption, and a fuzzy set of the dischargeable margin can be constructed. Based on a preset membership function, the overtime occupancy duration can be mapped onto one or more fuzzy sets, so that the membership values of the historical charging information can be determined, that is, the membership function values of the overtime occupancy duration, the membership function values of the net discharge compensation consumption, and the membership function values of the dischargeable margin.

[0070] According to an embodiment of the present invention, the selection of the preset membership function needs to fully consider the preferences of the target object for the overtime occupancy duration, the net discharge compensation consumption, and the dischargeable margin. The preset membership function usually adopts a trapezoidal membership function, a Gaussian membership function, a sigmoid membership function, and combinations between a trapezoidal membership function, a Gaussian membership function, and a sigmoid membership function, etc.

[0071] According to an embodiment of the present invention, the trapezoidal membership function can effectively simulate the requirements of piecewise changes. The trapezoidal membership function usually defines multiple trapezoidal functions to cover different intervals, as shown in formula (2) below.

[0072] (2)

[0073] where a represents the first lower boundary of the trapezoid, d represents the second lower boundary of the trapezoid, b represents the first upper boundary of the trapezoid, c represents the second upper boundary of the trapezoid, and x represents the input of the membership function.

[0074] According to an embodiment of the present invention, by adjusting the four boundaries of the trapezoid, different fuzzy intervals can be flexibly defined to express the membership of the input.

[0075] According to an embodiment of the present invention, the Gaussian membership function usually defines a fuzzy set in the form of a normal distribution, as shown in formula (3) below.

[0076] (3)

[0077] where represents the center value, represents the width parameter.

[0078] According to an embodiment of the present invention, by adjusting the center value and the width parameter, the center position and the expansion range of the Gaussian membership function can be controlled.

[0079] According to an embodiment of the present invention, the Sigmoid membership function can be used to process non-linear data in fuzzy logic control, as shown in formula (4) below.

[0080] (4)

[0081] Among them, k represents the steepness of the Sigmoid membership function, and c represents the central position of the Sigmoid membership function.

[0082] According to the embodiments of the present invention, fuzzy control rules can be determined according to the membership values of historical charging information. For example, fuzzy control rules are formulated according to the magnitude of the membership function value of the overtime occupancy duration, the magnitude of the membership function value of the net discharge compensation consumption, and the magnitude of the membership function value of the available discharge margin.

[0083] According to the embodiments of the present invention, the nth fuzzy control rule is as shown in the following formula (5).

[0084] (5)

[0085] Among them, represents the available discharge margin, represents the overtime occupancy duration, represents the net discharge compensation consumption, represents the initial discharge willingness of the target object, represents the fuzzy set of the available discharge margin under the nth fuzzy control rule, represents the fuzzy set of the overtime occupancy duration under the nth fuzzy control rule, represents the fuzzy set of the net discharge compensation consumption under the nth fuzzy control rule, represents the fuzzy set of the initial discharge willingness under the nth fuzzy control rule, and N is the number of fuzzy control rules.

[0086] According to the embodiments of the present invention, the initial discharge willingness of the target object in each of the N time periods can be obtained according to the historical charging information and the fuzzy control rules. For example, the initial discharge willingness of the target object in each of 96 time periods can be obtained.

[0087] According to the embodiments of the present invention, the initial discharge willingness , is as shown in the following formula (6).

[0088] (6)

[0089] Among them, represents the membership function of the available discharge margin, represents the membership function of the overtime occupancy duration, represents the membership function of the net discharge compensation consumption, represents the membership function of the initial discharge willingness, represents the membership function of the initial discharge willingness corresponding to the point when taking the maximum value.

[0090] According to an embodiment of the present invention, the fuzzy basis function in the nth fuzzy control rule , as shown in the following formula (7).

[0091] (7)

[0092] According to an embodiment of the present invention, by setting the , in the above formula (7), the above formula (7) can be written as the following formula (8).

[0093] (8)

[0094] According to an embodiment of the present invention, the first subset corresponding to the initial discharge willingness can represent the discharge willingness as "negative", the second subset corresponding to the initial discharge willingness can represent the discharge willingness as "medium", and the third subset corresponding to the initial discharge willingness can represent the discharge willingness as "positive", where the range of the initial discharge willingness is [0, 1]. Gaussian membership and Sigmoid membership functions can be used to describe the response of the target object to the fluctuations of resource trading parameters and the change of the initial discharge willingness. The Gaussian membership function is applicable to describe the concentration of the "neutral" initial discharge willingness of the target object when the resource trading parameters are stable, and the Sigmoid membership function is applicable to reflect the smooth transition of the initial discharge willingness of the target object from "negative" to "positive" when the resource trading parameters fluctuate frequently. When the Sigmoid membership function is at the central position (x = c), the initial discharge willingness is 0.5, and the initial discharge willingness fluctuates between 0 and 1 with the change of the central position x. By calculating the membership value through the membership function and using the membership value as the input quantity of the fuzzy inference rule, the initial discharge willingness can be obtained. By integrating all fuzzy control rules, the overall initial discharge willingness of the target object under specific resource trading parameters can be obtained.

[0095] According to an embodiment of the present invention, by according to the preset membership function and the fuzzy set, the membership value of the historical charging information can be determined, and then the fuzzy control rule can be determined. Based on the fuzzy control rule and the historical charging information, the initial discharge willingness of the target object in each of the N time periods can be obtained. Different preset membership functions can be used to describe in detail different levels of the initial discharge willingness of the target object and simulate the behavioral responses of the target object in a complex market environment, so as to formulate a more reasonable discharge strategy for new energy vehicles, thereby improving the operation efficiency of the charging station and the participation degree of the target object in V2G.

[0096] According to an embodiment of the present invention, the multi-objective optimization method based on the flexibility resources of the charging station further includes: creating an affine number according to historical charging information and a preset interval; determining the membership value of the affine number according to the affine number, a preset membership function, and a fuzzy set; determining a first fuzzy control rule according to the membership value of the affine number; and obtaining the first discharge willingness of the target object in each of the N time periods according to the historical charging information and the first fuzzy control rule.

[0097] According to an embodiment of the present invention, the affine number can represent that the historical charging information has a central value and an error range. The preset interval can include multiple ones, and the preset interval can be determined according to the historical charging information, and then the affine number can be created, as shown in the following formula (9). , as shown in the following formula (9).

[0098] (9)

[0099] Wherein, represents the central value, represents the error term, represents the coefficient.

[0100] According to an embodiment of the present invention, a random sampling method can be used to simulate the preset interval of the affine number, and the membership value of the affine number can be calculated for the fuzzy set and the preset membership function, as shown in the following formula (10). , as shown in the following formula (10).

[0101] (10)

[0102] Wherein, represents the membership value at the central value , represents the derivative of the membership function at the central value .

[0103] According to an embodiment of the present invention, the first fuzzy control rule can be determined according to the membership value of the affine number, that is, according to the magnitude of the membership value of the affine number, so that the first discharge willingness of the target object in each of the N time periods can be obtained according to the historical charging information and the first fuzzy control rule. Compared with the initial discharge willingness, the first discharge willingness can be closer to the true discharge willingness of the target object due to the introduction of the affine number.

[0104] According to an embodiment of the present invention, by introducing the affine number, the central value and the error range of the historical charging information are considered, that is, the randomness of the historical charging information is considered, so that the obtained first discharge willingness is more accurate.

[0105] According to an embodiment of the present invention, according to the predicted charging amount and charging duration, the resource trading parameters of M time periods and the respective resource trading parameters of the M time periods are adjusted to obtain N time periods of dynamic resource trading parameters and the respective initial resource trading parameters of the N time periods, including: determining the self-elasticity coefficient and the cross-elasticity coefficient according to the predicted charging amount, charging duration, M time periods and the respective resource trading parameters of the M time periods; establishing a relationship between charging demand and resource trading parameters according to the self-elasticity coefficient and the cross-elasticity coefficient; and adjusting the resource trading parameters of the M time periods and the respective resource trading parameters of the M time periods according to the relationship between charging demand and resource trading parameters to obtain N time periods of dynamic resource trading parameters and the respective initial resource trading parameters of the N time periods.

[0106] According to an embodiment of the present invention, it can be considered that the charging time and charging amount are continuous. Therefore, the charging demand can be related to the resource trading parameters of multiple time periods. The self-elasticity coefficient can characterize the sensitivity of the charging demand in the current time period to the resource trading parameters. By taking the partial derivative of the above formula (1), the following formula (11) can be obtained. The cross-elasticity coefficient can characterize the influence of the resource trading parameters in the j-th time period on the charging demand in the i-th time period. The larger the cross-elasticity coefficient, the greater the influence of the resource trading parameters on the charging demand. Since the charging demand remains unchanged, that is, the demand transfer between time periods satisfies , the cross-elasticity coefficient is as follows formula (12), that is, the self-elasticity coefficient can be determined according to the predicted charging amount, charging duration, M time periods and the respective resource trading parameters of the M time periods and the cross-elasticity coefficient .

[0107] (11)

[0108] (12)

[0109] Wherein, represents the charging demand in the j-th time period, represents the slope in the j-th time period, .

[0110] According to an embodiment of the present invention, the total resource trading parameter for new energy vehicle charging can characterize the sum of the resource trading parameters of each time period. The total resource trading parameter S is as follows formula (13).

[0111] (13)

[0112] According to an embodiment of the present invention, substituting the above formula (1) into the above formula (13), the following formula (14) can be obtained.

[0113] (14)

[0114] According to an embodiment of the present invention, by transposing and simplifying the above formula, the following formula (15) can be obtained, and thus the charging demand for the k-th period can be obtained, as shown in the following formula (16).

[0115] (15)

[0116] (16)

[0117] According to an embodiment of the present invention, by taking the partial derivative of the above formula, the following formula (17) can be obtained.

[0118] (17)

[0119] According to an embodiment of the present invention, for the k-th period and the f-th period, the cross-elasticity coefficient is as shown in the following formula (18), and thus the cross-elasticity coefficient for any two different periods, for example, the i-th period and the j-th period, can be obtained, as shown in the following formula (19).

[0120] (18)

[0121] (19)

[0122] According to an embodiment of the present invention, based on the own-elasticity coefficient of the above formula (11) and the cross-elasticity coefficient of the above formula (19), the relationship between the charging demand fluctuation and the resource trading parameter fluctuation can be established, that is, the relationship between the charging demand and the resource trading parameter, as shown in the following formula (20). By further simplifying the above formula (20), the following formula (21) can be obtained.

[0123] (20)

[0124] (21)

[0125] Wherein, represents the charging demand at time t when the resource trading parameters of each of the M periods are executed, represents the charging demand at time t when the resource trading parameters of each of the N periods are executed, represents the resource trading parameters of each of the M periods, represents the resource trading parameters of each of the N periods, wherein, The general expression form of is

[0126] According to an embodiment of the present invention, the resource trading parameters for M time periods and each of the M time periods can be adjusted according to the relationship between the charging demand and the resource trading parameters. However, during the adjustment of the resource trading parameters for the M time periods and each of the M time periods, the predicted charging amount of the target object and the total resource trading parameters satisfy the power consistency constraint and the consumption optimization constraint, so as to obtain the N time periods of the dynamic resource trading parameters and the initial resource trading parameters for each of the N time periods.

[0127] According to an embodiment of the present invention, the power consistency constraint can characterize that the charging amount of the new energy vehicle can reach the predicted charging amount during the charging duration, and the charging demand fluctuation amount before and after the adjustment of the resource trading parameters is as shown in the following formula (22).

[0128] (22)

[0129] Wherein, represents the charging demand fluctuation amount of the e-th new energy vehicle before and after the adjustment of the resource trading parameters, represents the set of time periods for the e-th new energy vehicle to charge, represents the charging start time, represents the charging end time, represents the change amount of the charging power of the new energy vehicle at time t before and after the adjustment of the resource trading parameters.

[0130] According to an embodiment of the present invention, in order to make the power consistency constraint satisfied before and after the adjustment of the resource trading parameters, it is necessary to make .

[0131] According to an embodiment of the present invention, the consumption optimization constraint can characterize that the charging consumption after the adjustment of the resource trading parameters should not be higher than the charging consumption before the adjustment of the resource trading parameters. Because the new energy vehicle is the key to peak shaving and valley filling, under the condition of the power consistency constraint, the overtime occupancy consumption should also be reduced.

[0132] According to an embodiment of the present invention, the relationship between the charging demand and the resource trading parameters can be established through the self-elasticity coefficient and the cross-elasticity coefficient. Based on the relationship between the charging demand and the resource trading parameters, the resource trading parameters for M time periods and each of the M time periods can be adjusted, so as to obtain the N time periods of the dynamic resource trading parameters and the initial resource trading parameters for each of the N time periods. By adjusting the time periods and the resource trading parameters, the interval of the time periods and the trading resource parameters are refined, the load of the charging station and the power grid is balanced, new load peaks are avoided, and the power consistency constraint and the consumption optimization constraint should also be satisfied during the adjustment process to ensure the charging demand and economic benefits of the target object.

[0133] According to an embodiment of the present invention, a two-layer decision-making model is constructed based on the initial discharge willingness of the target object in each of the N time periods, the N time periods of dynamic resource trading parameters, and the initial resource trading parameters of each of the N time periods, including: constructing a charging station decision-making model according to the N time periods of dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods; constructing a target object model according to the initial discharge willingness of each of the N time periods and the charging station decision-making model; constructing a two-layer decision-making model according to the charging station decision-making model and the target object decision-making model.

[0134] According to an embodiment of the present invention, the two-layer decision-making model may include a charging station decision-making model and a target object decision-making model. The charging station decision-making model corresponds to the charging station layer, and the target object model corresponds to the target object layer.

[0135] According to an embodiment of the present invention, a charging station decision-making model can be constructed according to the N time periods of dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods. The charging station decision-making model can be composed of a charging station objective function and charging station constraint conditions.

[0136] According to an embodiment of the present invention, the goal of the charging station is to minimize the operation consumption of the charging station by adjusting the resource trading parameters and the discharge exchange value, which can be achieved by reducing the power purchase consumption, compensation consumption, operation and maintenance consumption, and the discharge consumption of the energy storage device. The charging station objective function is as follows in formula (23).

[0137] (23)

[0138] Wherein, represents the exchange value of the charging station, represents the charging exchange value of the charging station at time t, represents the parking exchange value of the charging station at time t, represents the power purchase consumption of the charging station at time t, represents the discharge compensation consumption of the new energy vehicle at time t, represents the operation and maintenance consumption of the charging station at time t, represents the charge and discharge consumption of the energy storage device at time t.

[0139] According to an embodiment of the present invention, the charging exchange value is as follows in formula (24), the parking exchange value is as follows in formula (25), the power purchase consumption is as follows in formula (26), the discharge compensation consumption is as follows in formula (27), the operation and maintenance consumption is as follows in formula (28), and the charge and discharge consumption of the energy storage device is as follows in formula (29).

[0140] (24)

[0141] (25)

[0142] (26)

[0143] (27)

[0144] (28)

[0145] (29)

[0146] Among them, represents the set of new energy vehicles, represents the resource trading parameters at time period t, represents the charging power of new energy vehicle n at time period t, represents the scheduling time interval, represents the parking exchange value rate, represents the in-station status of new energy vehicle n at time period t, represents the grid power purchase resource trading parameters, represents the discharging power of new energy vehicle n at time period t, represents the charging power of the energy storage device at time period t, represents the discharging power of the energy storage device at time period t, represents the output of the photovoltaic device at time period t, represents the discharging willingness, represents the discharging resource trading parameters at time period t, represents the operation and maintenance consumption coefficient, represents the unit usage consumption of the energy storage device.

[0147] According to the embodiments of the present invention, the in-station status of the new energy vehicle at time period t characterizes whether the new energy vehicle is in the charging station at the t-th time period, is a 0-1 variable, which is 1 if the new energy vehicle is in the station and 0 otherwise. The operation and maintenance consumption includes equipment maintenance, labor and other consumptions. The unit usage consumption of the energy storage device may include charge and discharge losses, equipment depreciation, etc.

[0148] According to the embodiments of the present invention, the charging station constraint conditions include: resource trading parameter constraint conditions, charging station power balance constraint conditions, energy storage power and energy constraint conditions, and photovoltaic operation constraint conditions.

[0149] According to the embodiments of the present invention, the resource trading parameter constraint conditions can characterize that the target resource trading parameters should be within a reasonable range. The resource trading parameter constraint conditions are as follows in formula (30).

[0150] (30)

[0151] Among them, Represents the upper limit of the resource trading parameter Represents the lower limit of the resource trading parameter Represents the target resource trading parameter obtained through the two - layer decision - making model

[0152] According to the embodiments of the present invention, the charging station power balance constraint condition is as follows in formula (31).

[0153] (31)

[0154] Wherein Represents the purchased power of the charging station at time t Represents the total power demand of the charging station at time t

[0155] According to the embodiments of the present invention, the energy storage power and energy constraint conditions are as follows in formulas (32) - (34).

[0156] (32)

[0157] (33)

[0158] (34)

[0159] Wherein Represents the maximum charge - discharge power of the energy storage device Represents the initial capacity of the energy storage device Represents the minimum capacity of the energy storage device Represents the maximum capacity of the energy storage device And Is a 0 - 1 variable And Respectively represent the charging state and discharging state of the energy storage device at time t Represents the charging efficiency of the energy storage device Represents the discharging efficiency of the energy storage device

[0160] According to the embodiments of the present invention, the photovoltaic operation constraint condition can characterize the constraint condition of the photovoltaic device operation. The photovoltaic operation constraint condition is as follows in formula (35).

[0161] (35)

[0162] Wherein Represents the predicted output of the photovoltaic device at time t Represents the maximum output of the photovoltaic device Represents the fluctuation deviation of the photovoltaic device

[0163] According to an embodiment of the present invention, an object model can be constructed based on the initial discharge willingness of each of the N time periods and a charging station decision model. The object model can be composed of an object charging function and object constraint conditions.

[0164] According to an embodiment of the present invention, the goal of the object is to minimize charging consumption, battery loss, and overtime occupancy penalty consumption. The object charging function is as shown in the following formula (36).

[0165] (36)

[0166] Wherein, represents the total exchange value of the object, represents the discharge exchange value of new energy vehicle n at time period t, represents the charging consumption of new energy vehicle n at time period t, represents the battery loss consumption of new energy vehicle n at time period t, represents the overtime occupancy penalty consumption of new energy vehicle n at time period t.

[0167] According to an embodiment of the present invention, the discharge exchange value is as shown in the following formula (37), the charging consumption is as shown in the following formula (38), the battery loss is as shown in the following formula (39), and the overtime occupancy penalty consumption is as shown in the following formula (40).

[0168] (37)

[0169] (38)

[0170] (39)

[0171] (40)

[0172] Wherein, represents the unit battery loss consumption, represents the overtime occupancy penalty coefficient, represents the expected departure time of new energy vehicle n from the charging station.

[0173] According to an embodiment of the present invention, the object constraint conditions can include: new energy vehicle charge and discharge constraint conditions, new energy vehicle power constraint conditions, and new energy vehicle charging time window constraint conditions.

[0174] According to an embodiment of the present invention, the new energy vehicle charge and discharge constraint conditions can characterize the constraint conditions of the discharge power of the new energy vehicle, as shown in the following formulas (41) and (42).

[0175] (41)

[0176] (42)

[0177] Among them, represents the maximum charge and discharge power of new energy vehicle n.

[0178] According to an embodiment of the present invention, the above formula (42) indicates that a new energy vehicle cannot charge and discharge simultaneously in the same time period.

[0179] According to an embodiment of the present invention, the power quantity constraint conditions of a new energy vehicle are as follows in formulas (43) and (44).

[0180] (43)

[0181] (44)

[0182] Among them, represents the initial battery power of the new energy vehicle, represents the minimum battery capacity of the new energy vehicle, represents the maximum battery capacity of the new energy vehicle, represents the expected final power quantity of the new energy vehicle.

[0183] According to an embodiment of the present invention, the charging time window constraint condition of a new energy vehicle is as follows in formula (45).

[0184] (45)

[0185] Among them, represents the arrival time of new energy vehicle n, represents the departure time of new energy vehicle n.

[0186] According to an embodiment of the present invention, a two - layer decision - making model can be constructed based on the charging station decision - making model and the target object decision - making model. A two - layer game model, that is, a two - layer decision - making model, can be constructed based on the Stackelberg game theory. The strategy of the charging station directly affects the decision of the target object, and the response of the target object in turn acts on the exchange value and load balance of the charging station.

[0187] According to an embodiment of the present invention, the two - layer decision - making model based on the Stackelberg game can effectively coordinate the optimal strategies of the charging station layer and the target object layer, enabling the charging station to guide the target object to make reasonable discharge decisions through dynamic resource trading parameters and different discharge exchange values, thereby improving the operation flexibility of the charging station.

[0188] Figure 4 Shows a framework diagram of the two - layer decision - making model according to an embodiment of the present invention.

[0189] AsFigure 4 As shown, according to the resource trading parameters 411 of each of the obtained M time periods and N time periods at the charging station layer, the self-elasticity coefficient and cross-elasticity can be calculated. Based on the self-elasticity coefficient and cross-elasticity coefficient, a resource trading parameter elasticity matrix 412 is constructed. Through resource trading parameter elastic adjustment 413 based on the number of months of power consistency and consumption optimization constraint 414, the initial resource trading parameters 415 of each of the N time periods and N time periods are obtained, and the initial resource trading parameters 415 are sent to the target object layer. The target object layer performs fuzzy processing on the timeout occupancy duration 421 to obtain the first fuzzy set 424, performs fuzzy processing on the net discharge compensation consumption 422 to obtain the second fuzzy set 425, performs fuzzy processing on the discharge margin 423 to obtain the third fuzzy set 426, calculates the membership degree value of the timeout occupancy duration 421 based on the first preset membership function 427, calculates the membership degree value of the net discharge compensation consumption 422 based on the second preset membership function 428, calculates the membership degree value of the discharge margin 423 based on the third preset membership function 429, so that the initial discharge willingness 430 of the target object can be obtained, and the initial discharge willingness 430 is transmitted to the charging station layer.

[0190] Figure 5 Shows a schematic diagram of dynamic resource trading parameters according to an embodiment of the present invention.

[0191] As Figure 5 shown, it can be seen that the dynamic resource trading parameters are dynamically adjusted on the basis of the preset resource trading parameters. The preset resource trading parameters have 4 platforms with different heights, that is, the preset resource trading parameters can include 4 time periods. The dynamic resource trading parameters have 96 time periods, and the division granularity of the dynamic resource trading parameters can be smaller than that of the preset resource trading parameters.

[0192] According to an embodiment of the present invention, with the lowest operating consumption of the charging station and the highest discharge exchange value of new energy vehicles as the objective function, based on the objective constraint conditions and the two-layer decision-making model, the initial resource trading parameters of each of the N time periods and the initial discharge willingness of the target object in each of the N time periods are adjusted to obtain the target resource trading parameters of each of the N time periods and the target discharge willingness of the target object in each of the N time periods, including: adjusting the initial discharge willingness of the target object in each of the N time periods according to the target object charging function and the target object constraint conditions to obtain the intermediate discharge willingness of the target object in each of the N time periods; adjusting the initial resource trading parameters of each of the N time periods and the intermediate discharge willingness according to the charging station objective function, the charging station constraint conditions and the intermediate discharge willingness to obtain the target resource trading parameters of each of the N time periods and the target discharge willingness of the target object in each of the N time periods.

[0193] According to an embodiment of the present invention, the objective function may include a charging station objective function and a target object charging function, and the objective constraints may include charging station constraints and target object constraints.

[0194] According to an embodiment of the present invention, as a follower, the target object aims to reduce the overtime occupancy penalty consumption of new energy vehicles, increase the discharge compensation exchange value, reduce the charging consumption and battery loss consumption based on the objective constraints.

[0195] According to an embodiment of the present invention, the initial discharge willingness of the target object in each of the N time periods can be adjusted according to the target object charging function and the target object constraints, that is, solving for the target object layer. First, a Lagrangian function is constructed as shown in the following formula (46).

[0196] (46)

[0197] Among them, represents an equality constraint, represents an inequality constraint, represents the first Lagrange multiplier, represents the second Lagrange multiplier, represents the third Lagrange multiplier, represents the fourth Lagrange multiplier.

[0198] According to an embodiment of the present invention, the Karush-Kuhn-Tucker conditions (referred to as KKT conditions) are used to determine the optimal discharge behavior of the target object under resource trading parameters and different discharge exchange values. The KKT conditions mainly include first-order necessary conditions, complementary slack conditions, and feasibility conditions. Among them, by taking the derivative of each variable in the Lagrangian function L and setting it to 0, the first-order necessary conditions can be obtained as shown in the following formulas (47) and (48).

[0199] (47)

[0200] (48)

[0201] According to an embodiment of the present invention, all inequality constraints in the Lagrangian function L and the corresponding Lagrange multiplier

[0202] (49)

[0203] According to an embodiment of the present invention, the complementary slackness condition ensures that when the constraint reaches the boundary, the Lagrange multiplier takes a non-zero value. All decision variables in the Lagrangian function L must satisfy the objective constraint conditions, thus satisfying the feasibility condition.

[0204] According to an embodiment of the present invention, after solving the KKT conditions, a discharge behavior decision of the target object can be obtained.

[0205] According to an embodiment of the present invention, according to the charging function of the target object and the constraint conditions of the target object, the initial discharge willingness of the target object in each of the N time periods is adjusted to obtain the intermediate discharge willingness of the target object in each of the N time periods, including: constructing a discharge exchange value function according to the comfort coefficient, the expected charging amount, and the discharge amount; obtaining the perceived exchange value according to the subjective probability weight function, the discharge exchange value function, and the discharge amount; based on the charging function of the target object and the constraint conditions of the target object, adjusting the initial discharge willingness of the target object in each of the N time periods according to the perceived exchange value to obtain the intermediate discharge willingness of the target object in each of the N time periods.

[0206] According to an embodiment of the present invention, the discharge amount can represent the power supply of a new energy vehicle to a charging station during the target time period.

[0207] According to an embodiment of the present invention, the comfort coefficient can be determined according to the sensitivity of the target object to the resource trading parameters. The larger the comfort coefficient, the more sensitive the target object is to comfort. The usual value range of the comfort coefficient is 0.5 to 2. For a conservative target object, the value range of the comfort coefficient can be 1.5 to 2. For an aggressive target object, the value range of the comfort coefficient can be 0.5 to 1. Different sensitivities of the target object to the resource trading parameters result in different comfort coefficients of the target object. A discharge exchange value function can be constructed according to the comfort coefficient, the expected charging amount, and the discharge amount, as shown in the following formula (50).

[0208] (50)

[0209] Wherein, represents the discharge exchange value function of the target object at time t, represents the comfort coefficient, represents the initial power of the new energy vehicle, represents the discharge amount of the new energy vehicle at time t, represents the charging resource trading parameter at time t.

[0210] According to an embodiment of the present invention, the subjective probability weight function is constructed based on the rationality coefficient of the target object and a preset objective probability. The rationality coefficient can represent the rational degree of the target object towards the exchange value. The smaller the rationality coefficient, the less rational the target object is. The value range of the rationality coefficient is 0 to 1. When the rationality coefficient is 1, the subjective probability is equal to the objective probability, indicating that the target object is completely rational, and the perception of the target object towards the resource exchange value and the consumption corresponding to the resource exchange value is symmetric. When the rationality coefficient is less than 1, the target object exhibits bounded rationality, is more sensitive to the consumption corresponding to the resource exchange value, and usually shows a higher risk aversion tendency. In the embodiment of the present invention, the value of the rationality coefficient can be between 0.5 and 0.8.

[0211] According to an embodiment of the present invention, the subjective probability weight function is as follows in formula (51).

[0212] (52)

[0213] Wherein, represents the objective probability, represents the subjective probability of the target object for different discharge exchange values, represents the rationality coefficient of the target object.

[0214] According to an embodiment of the present invention, as can be seen from the above formula (50), the discharge exchange value function is a concave function. The perceived exchange value function can be constructed based on the subjective probability weight function, the discharge exchange value function, and the discharge power, and then the perceived exchange value at different discharge powers. The perceived exchange value function R is as follows in formula (53).

[0215] (53)

[0216] According to an embodiment of the present invention, based on the target object charging function and the target object constraint conditions, the initial discharge willingness of the target object in each of the N time periods can be adjusted according to the perceived exchange value to obtain the intermediate discharge willingness of the target object in each of the N time periods.

[0217] According to an embodiment of the present invention, when the intermediate discharge willingness of the target object in each of the N time periods is determined, it is introduced into the charging station layer as a constraint condition for the charging station layer decision-making. That is, according to the charging station objective function, the charging station constraint conditions, and the intermediate discharge willingness, the initial resource trading parameters and the intermediate discharge willingness in each of the N time periods can be adjusted to obtain the target resource trading parameters in each of the N time periods and the target discharge willingness of the target object in each of the N time periods.

[0218] According to an embodiment of the present invention, for the charging station layer, a genetic algorithm, for example, a heuristic algorithm, can be used to solve the problem, so as to obtain the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods.

[0219] According to an embodiment of the present invention, the process of obtaining the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods may include generating an initial population with the resource trading parameters and the discharge resource trading parameters, evaluating the fitness of each candidate solution by calculating the objective function of the charging station layer, and during the calculation, based on the intermediate discharge willingness for each of the N time periods, and then updating the population through genetic operations, such as selection, crossover, mutation, etc., to gradually approach the optimal solution. When the resource exchange value of the charging station layer converges to a preset threshold or the number of iterations reaches a preset number of iterations, the optimal solution is obtained, that is, the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods are obtained.

[0220] According to an embodiment of the present invention, based on the charging function of the target object and the constraint conditions of the target object, the initial discharge willingness can be adjusted to obtain the intermediate discharge willingness. Furthermore, based on the intermediate discharge willingness, the objective function of the charging station, and the constraint conditions of the charging station, the initial resource trading parameters and the intermediate discharge willingness for each of the N time periods are adjusted to obtain the target resource trading parameters for each of the N time periods and the target discharge willingness of the target object for each of the N time periods, improving the accuracy of the target resource trading parameters and the target discharge willingness.

[0221] According to an embodiment of the present invention, a Sigmoid function can be introduced to describe the change of the initial discharge willingness, reflecting the gradually increasing or decreasing non-linear response of the target object under different discharge exchange values. The influence of the Sigmoid function on the initial discharge willingness is as shown in the following formula (54).

[0222] (54)

[0223] Where, w represents the intermediate discharge willingness, k represents the steepness, and c is the probability value representing the midpoint of the Sigmoid function.

[0224] According to an embodiment of the present invention, the intermediate discharge willingness is a further adjustment and correction of the initial discharge willingness under different discharge exchange values. The steepness k can be taken as 10, and the probability value c of the midpoint of the Sigmoid function can be taken as 0.5.

[0225] According to an embodiment of the present invention, the Sigmoid function mainly smooths the relationship between the discharging willingness of the target object and the resource trading parameter, avoiding excessive influence of the drastic fluctuation of the resource trading parameter on the behavior of the target object, so as to make the load scheduling of the charging station more stable.

[0226] According to an embodiment of the present invention, in order to maintain the resource trading parameter within a reasonable range, the adjustment amplitude of the resource trading parameter should be related to the resource trading parameters of each of the M time periods. For example, when the M time periods are respectively the peak time period, the normal time period, the valley time period, and the deep valley time period, the conditional amplitude of the resource trading parameter should be within the range of the difference between the resource trading parameters between the valley time period and the deep valley time period, and the adjustment of the resource trading parameter satisfies the following formula (55).

[0227] (55)

[0228] Wherein, represents the average discharging willingness value of all new energy vehicles in the t time period, represents the resource trading parameter in the valley time period, represents the resource trading parameter in the deep valley time period, represents the steepness parameter, represents the skewness parameter, represents the target resource trading parameter.

[0229] According to an embodiment of the present invention, the value of the steepness parameter can be 10, and the value of the skewness parameter can be the resource trading parameter in the deep valley time period.

[0230] Figure 6 Shows a schematic diagram of peak shaving of the load of the charging station by the dynamic resource trading parameter according to an embodiment of the present invention.

[0231] As Figure 6 shown, the typical daily load has been in a relatively stable state. Due to the large number of new energy vehicles, a short-term charging load peak may occur in a certain time period, that is, the peak time period is around the 50th time period. Based on the dynamic resource trading parameter, peak shaving can be achieved. It can be seen that the load after peak shaving at the charging station in the peak time period has decreased by 21.93%.

[0232] Figure 7A Shows a schematic diagram of the flexibility supply-demand relationship without considering V2G according to an embodiment of the present invention. Figure 7B Shows a schematic diagram of the flexibility supply-demand relationship considering V2G according to an embodiment of the present invention.

[0233] As Figure 7A and 7BAs shown, the flexible supply includes the upper flexible supply and the lower flexible supply. The upper flexible supply can represent the upward flexible range of flexible resources, and the lower flexible range can represent the downward flexible range of flexible resources. The flexible demand includes the upper flexible demand and the lower flexible demand. The upper flexible demand can represent the upward flexible range demand of new energy vehicles, and the lower flexible demand can represent the downward flexible range demand of new energy vehicles. It can be seen that without considering V2G, there will be periods when the upward flexible supply is less than the upward flexible demand and the downward flexible supply is less than the downward flexible demand. That is, during the charging peak period without considering V2G, the flexible demand is greater than the flexible supply. When considering V2G, the upward flexible supply meets the upward flexible demand during all periods, and the downward flexible supply meets the downward flexible demand during all periods. Moreover, during the charging peak period, the flexible supply is also much greater than the flexible demand.

[0234] Figure 8 Fig. shows a schematic diagram of the discharge flexible range under different discharge willingness thresholds according to an embodiment of the present invention.

[0235] As Figure 8 shown, the discharge willingness threshold (abbreviated as DW) increases from 0 to 0.6 at intervals of 0.1. It can be seen that as the discharge willingness threshold increases, the discharge flexible range has a gradually shrinking trend.

[0236] Based on the above multi-objective optimization method based on the flexible resources of the charging station, the present invention also provides a multi-objective optimization device based on the flexible resources of the charging station. The following will be combined with Figure 9 to describe this device in detail.

[0237] Figure 9 Fig. shows a structural block diagram of a multi-objective optimization device based on the flexible resources of the charging station according to an embodiment of the present invention.

[0238] As Figure 9 shown, the multi-objective optimization device 900 based on the flexible resources of the charging station in this embodiment includes an acquisition module 910, a first adjustment module 920, a first obtaining module 930, a construction module 940, and a second adjustment module 950.

[0239] The acquisition module 910 is configured to obtain preset comprehensive resource trading parameters, the estimated charging amount of new energy vehicles, the charging duration corresponding to the estimated charging amount, and historical charging information in response to a scheduling request for the flexible resources of the charging station. Among them, the preset comprehensive resource trading parameters include M time periods and the resource trading parameters of each of the M time periods, and M is an integer greater than 1. In one embodiment, the acquisition module 910 can be used to perform the operation S210 described above, which will not be elaborated here.

[0240] The first adjustment module 920 is configured to adjust the M time periods and the respective resource trading parameters of the M time periods according to the predicted charging amount and charging duration, so as to obtain the N time periods of the dynamic resource trading parameters and the respective initial resource trading parameters of the N time periods, where N is an integer greater than M. In one embodiment, the first adjustment module 920 may be configured to perform the operation S220 described above, which will not be elaborated herein.

[0241] The first obtaining module 930 is configured to obtain the respective initial discharging willingness of the target object in the N time periods according to the historical charging information, the preset membership function, and the fuzzy set, where the fuzzy set is obtained by performing fuzzy processing on the historical charging information. In one embodiment, the first obtaining module 930 may be configured to perform the operation S230 described above, which will not be elaborated herein.

[0242] The construction module 940 is configured to construct a two-layer decision model according to the respective initial discharging willingness of the target object in the N time periods, the N time periods of the dynamic resource trading parameters, and the respective initial resource trading parameters of the N time periods. In one embodiment, the construction module 930 may be configured to perform the operation S240 described above, which will not be elaborated herein.

[0243] The second adjustment module 950 is configured to take the minimum operating consumption of the charging station and the highest discharging exchange value of the new energy vehicle as the objective function, and based on the objective constraint conditions and the two-layer decision model, adjust the respective initial resource trading parameters of the N time periods and the respective initial discharging willingness of the target object in the N time periods, so as to obtain the respective target resource trading parameters of the N time periods and the respective target discharging willingness of the target object in the N time periods. In one embodiment, the second adjustment module 930 may be configured to perform the operation S250 described above, which will not be elaborated herein.

[0244] According to an embodiment of the present invention, the first obtaining module 930 includes: a first obtaining sub-module, a second obtaining sub-module, and a third obtaining sub-module.

[0245] The first obtaining sub-module is configured to determine the membership degree value of the historical charging information according to the preset membership function and the fuzzy set.

[0246] The second obtaining sub-module is configured to determine the fuzzy control rule according to the membership degree value of the historical charging information.

[0247] The third obtaining sub-module is configured to obtain the respective initial discharging willingness of the target object in the N time periods according to the historical charging information and the fuzzy control rule.

[0248] According to an embodiment of the present invention, the above multi-objective optimization device based on the flexibility resources of the charging station further includes: a creation module, a first determination module, a second determination module, and a second obtaining module.

[0249] A creation module, configured to create an affine number according to historical charging information and a preset interval.

[0250] A first determination module, configured to determine a membership value of the affine number according to the affine number, a preset membership function, and a fuzzy set.

[0251] A second determination module, configured to determine a first fuzzy control rule according to the membership value of the affine number.

[0252] A second obtaining module, configured to obtain a first discharge willingness of the target object in each of N time periods according to the historical charging information and the first fuzzy control rule.

[0253] According to an embodiment of the present invention, the first adjustment module 920 includes: a first adjustment sub-module, a second adjustment sub-module, and a third adjustment sub-module.

[0254] The first adjustment sub-module is configured to determine a self-elasticity coefficient and a cross-elasticity coefficient according to the predicted charging amount, the charging duration, M time periods, and the resource trading parameters of each of the M time periods.

[0255] The second adjustment sub-module is configured to establish a relationship between the charging demand and the resource trading parameters according to the self-elasticity coefficient and the cross-elasticity coefficient.

[0256] The third adjustment sub-module is configured to adjust the M time periods and the resource trading parameters of each of the M time periods according to the relationship between the charging demand and the resource trading parameters, so as to obtain N time periods of the dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods.

[0257] According to an embodiment of the present invention, the two-layer decision-making model includes a charging station decision-making model and a target object decision-making model, and the construction module 940 includes: a first construction sub-module, a second construction sub-module, and a third construction sub-module.

[0258] The first construction sub-module is configured to construct a charging station decision-making model according to N time periods of the dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods.

[0259] The second construction sub-module is configured to construct a target object model according to the initial discharge willingness of each of the N time periods and the charging station decision-making model;

[0260] The third construction sub-module is configured to construct a two-layer decision-making model according to the charging station decision-making model and the target object decision-making model.

[0261] According to an embodiment of the present invention, the objective function includes a charging station objective function and a target object charging function, and the objective constraint conditions include charging station constraint conditions and target object constraint conditions; wherein, the second adjustment module 950 includes: a fourth adjustment sub-module and a fifth adjustment sub-module.

[0262] The fourth adjustment sub-module is used to adjust the initial discharge willingness of the target object in each of the N time periods according to the target object charging function and the target object constraint conditions, so as to obtain the intermediate discharge willingness of the target object in each of the N time periods.

[0263] The fifth adjustment sub-module is used to adjust the initial resource trading parameters and the intermediate discharge willingness in each of the N time periods according to the charging station target function, the charging station constraint conditions and the intermediate discharge willingness, so as to obtain the target resource trading parameters in each of the N time periods and the target discharge willingness of the target object in each of the N time periods.

[0264] According to an embodiment of the present invention, the fourth adjustment sub-module includes: a first adjustment unit, a second adjustment unit and a third adjustment unit.

[0265] The first adjustment unit is used to construct a discharge exchange value function according to the comfort coefficient, the expected charging amount and the discharge amount, wherein the comfort coefficient is determined according to the sensitivity of the target object to the resource trading parameters, and the discharge amount represents the power supply of the new energy vehicle to the charging station in the target time period.

[0266] The second adjustment unit is used to obtain the perceived exchange value according to the subjective probability weight function, the discharge exchange value function and the discharge amount, wherein the subjective probability weight function is constructed according to the rationality coefficient of the target object and the preset objective probability, and the rationality coefficient represents the rational degree of the target object to the exchange value.

[0267] The third adjustment unit is used to adjust the initial discharge willingness of the target object in each of the N time periods based on the target object charging function and the target object constraint conditions according to the perceived exchange value, so as to obtain the intermediate discharge willingness of the target object in each of the N time periods.

[0268] According to an embodiment of the present invention, any plurality of modules among the acquisition module 910, the first adjustment module 920, the first obtaining module 930, the construction module 940, and the second adjustment module 950 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the acquisition module 910, the first adjustment module 920, the first obtaining module 930, the construction module 940, and the second adjustment module 950 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on a substrate, a system in a package, an application specific integrated circuit (ASIC), or may be implemented by any other reasonable means such as hardware or firmware for integrating or packaging circuits, or may be implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the acquisition module 910, the first adjustment module 920, the first obtaining module 930, the construction module 940, and the second adjustment module 950 may be at least partially implemented as a computer program module, and when the computer program module is run, it may execute the corresponding functions.

[0269] Figure 10 The block diagram of an electronic device suitable for implementing a multi-objective optimization method based on flexibility resources of a charging station according to an embodiment of the present invention is shown.

[0270] As Figure 10 shown, the electronic device 1000 according to an embodiment of the present invention includes a processor 1001, which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), and so on. The processor 1001 may also include on-board memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0271] In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other via the bus 1004. The processor 1001 performs various operations of the method flow according to the embodiments of the present invention by executing the programs in the ROM 1002 and / or the RAM 1003. It should be noted that the programs may also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to the embodiments of the present invention by executing the programs stored in the one or more memories.

[0272] According to an embodiment of the present invention, the electronic device 1000 may further include an input / output (I / O) interface 1005, and the input / output (I / O) interface 1005 is also connected to the bus 1004. The electronic device 1000 may further include one or more of the following components connected to the input / output (I / O) interface 1005: an input portion 1006 including a keyboard, a mouse, etc.; an output portion 1007 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk, etc.; and a communication portion 1009 including a network interface card such as a LAN card, a modem, etc. The communication portion 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output (I / O) interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 1010 as needed so that a computer program read from it can be installed into the storage portion 1008 as needed.

[0273] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present invention is implemented.

[0274] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the above-described ROM 1002 and / or RAM 1003 and / or one or more memories other than ROM 1002 and RAM 1003.

[0275] An embodiment of the present invention further includes a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program code is used to enable the computer system to implement the multi-objective optimization method based on the flexibility resources of the charging station provided by the embodiment of the present invention.

[0276] When the computer program is executed by the processor 1001, it executes the above functions defined in the system / apparatus of the embodiment of the present invention. According to an embodiment of the present invention, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0277] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and be downloaded and installed through the communication part 1009, and / or be installed from the removable medium 1011. The program code included in the computer program can be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0278] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or be installed from the removable medium 1011. When the computer program is executed by the processor 1001, it executes the above functions defined in the system of the embodiment of the present invention. According to an embodiment of the present invention, the above-described systems, devices, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0279] According to embodiments of the present invention, program code for executing the computer programs provided by the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0280] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the 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 marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0281] Those skilled in the art can understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0282] The above describes the embodiments of the present invention. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present invention. Although the embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present invention.

Claims

1. A scheduling method based on charging station flexibility resources, characterized in that: The method comprises: In response to a scheduling request for flexible resources of a charging station, a preset comprehensive resource transaction parameter, an estimated charge amount of a new energy vehicle, a charging time corresponding to the estimated charge amount, and historical charging information are obtained, wherein the preset comprehensive resource transaction parameter includes M time periods and resource transaction parameters of each of the M time periods, M is an integer greater than 1, and the historical charging information includes a charging start time, a charging end time, a gun removal time, a charging time, and an overtime occupation time for charging the new energy vehicle in a period of time; According to the estimated charging amount and the charging duration, the M time periods and the resource trading parameters of each of the M time periods are adjusted to obtain N time periods of dynamic resource trading parameters and initial resource trading parameters of each of the N time periods, where N is an integer greater than M; Statistical analysis is performed on the charging start time, charging end time, gun removal time, and overtime occupation time to obtain the net discharge compensation consumption and discharge margin; Determine the membership value of the historical charging information according to the preset membership function, the fuzzy set of the overtime occupation time, the fuzzy set of the net discharge compensation consumption and the fuzzy set of the discharge margin, wherein the membership value of the historical charging information includes the membership value of the overtime occupation time, the net discharge compensation consumption and the discharge margin, wherein the fuzzy set of the overtime occupation time, the fuzzy set of the net discharge compensation consumption and the discharge margin are obtained by fuzzifying the overtime occupation time, the net discharge compensation consumption and the discharge margin respectively; According to the membership value of historical charging information, the fuzzy control rules are determined; According to the historical charging information and fuzzy control rules, the initial discharge intention of the target object in N time periods is obtained. The initial discharge intention is expressed by the following formula: in, Represents the membership function of the discharge margin, The membership function that represents the timeout period for occupation. represents the membership function of the net discharge compensation consumption, The point corresponding to the maximum value of the membership function representing the initial discharge willingness; A two-layer decision model is constructed according to the initial discharge intentions of the target objects in the N time periods, the N time periods of the dynamic resource transaction parameters, and the initial resource transaction parameters of the N time periods; Taking the lowest operating consumption of the charging station and the highest discharge exchange value of the new energy vehicle as the objective function, based on the objective constraint conditions and the two-layer decision model, the initial resource trading parameters of each of the N time periods and the initial discharge intentions of the target objects in the N time periods are adjusted to obtain the target resource trading parameters of each of the N time periods and the target discharge intentions of the target objects in the N time periods. Specifically, the resource trading parameters and the discharge resource trading parameters are used to generate an initial population, and the fitness of each candidate solution is evaluated by calculating the objective function of the charging station layer. In the calculation process, the intermediate discharge intentions of each of the N time periods are also used, and then genetic operations are performed to obtain the target resource trading parameters of each of the N time periods and the target discharge intentions of the target objects in the N time periods. The intermediate discharge intentions are further adjusted and corrected to the initial discharge intentions under different discharge exchange values.

2. The method according to claim 1, characterized in that The method further comprises: Creating an affine number according to the historical charging information and a preset interval; Determining a membership value of the affine number according to the affine number, the preset membership function and the fuzzy set; Determining a first fuzzy control rule according to the membership value of the affine number; According to the historical charging information and the first fuzzy control rule, the first discharge intentions of the target object in the N time periods are obtained.

3. The method according to claim 1, characterized in that: The adjusting the M time periods and the resource trading parameters of each of the M time periods according to the expected charging amount and the charging duration to obtain N time periods of dynamic resource trading parameters and initial resource trading parameters of each of the N time periods includes: Determine a self-elasticity coefficient and a cross-elasticity coefficient according to the expected charging amount, the charging duration, the M time periods, and resource trading parameters of each of the M time periods; Establishing a relationship between charging demand and resource trading parameters according to the self-elasticity coefficient and the cross-elasticity coefficient; According to the relationship between the charging demand and the resource trading parameters, the M time periods and the resource trading parameters of each of the M time periods are adjusted to obtain N time periods of dynamic resource trading parameters and initial resource trading parameters of each of the N time periods.

4. The method according to claim 1, characterized in that: The two-layer decision model includes a charging station decision model and a target object decision model. The two-layer decision model is constructed according to the initial discharge intentions of the target objects in the N time periods, the N time periods of the dynamic resource trading parameters, and the initial resource trading parameters of the N time periods, including: Constructing the charging station decision model according to the N time periods of the dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods; Constructing the target object model according to the initial discharge intentions of each of the N time periods and the charging station decision model; A two-layer decision model is constructed according to the charging station decision model and the target object decision model.

5. The method according to claim 4, characterized in that The objective function includes a charging station objective function and a target object charging function, and the target constraint condition includes a charging station constraint condition and a target object constraint condition; wherein, The objective function is to minimize the operation consumption of the charging station and maximize the discharge exchange value of the new energy vehicle, and based on the target constraint condition and the two-layer decision model, adjust the initial resource trading parameters of the N time periods and the initial discharge intentions of the target objects in the N time periods to obtain the target resource trading parameters of the N time periods and the target discharge intentions of the target objects in the N time periods, including: According to the charging function of the target object and the constraint condition of the target object, adjusting the initial discharge intentions of the target object in the N time periods to obtain the intermediate discharge intentions of the target object in the N time periods; According to the charging station objective function, the charging station constraint conditions and the intermediate discharge intention, the initial resource trading parameters and the intermediate discharge intention of each of the N time periods are adjusted to obtain the target resource trading parameters of each of the N time periods and the target discharge intention of the target object in each of the N time periods.

6. The method according to claim 1, characterized in that The charging station constraints include: resource trading parameter constraints, charging station power balance constraints, energy storage power constraints and photovoltaic operation constraints; the target object constraints include: new energy vehicle charging and discharging constraints, new energy vehicle power constraints and new energy vehicle charging time window constraints.

7. A scheduling device based on charging station flexibility resources, characterized in that: The device comprises: An acquisition module, for responding to a scheduling request for flexible resources of a charging station, to acquire preset comprehensive resource transaction parameters, an estimated charge amount of a new energy vehicle, a charging duration corresponding to the estimated charge amount, and historical charging information, wherein the preset comprehensive resource transaction parameters include M time periods and resource transaction parameters of each of the M time periods, M being an integer greater than 1, and the historical charging information includes a charging start time, a charging end time, a gun removal time, a charging duration, and an overtime occupation duration for charging a new energy vehicle in a period of time; A first adjustment module, configured to adjust the M time periods and the resource trading parameters of each of the M time periods according to the expected charging amount and the charging duration, to obtain N time periods of dynamic resource trading parameters and initial resource trading parameters of each of the N time periods, wherein N is an integer greater than M; The first obtaining module performs statistical analysis on the charging start time, charging end time, gun drawing time, and overtime occupation time to obtain the net discharge compensation consumption and the discharge margin; determines the membership value of the historical charging information according to the preset membership function, the fuzzy set of the overtime occupation time, the fuzzy set of the net discharge compensation consumption, and the fuzzy set of the discharge margin, wherein the membership value of the historical charging information includes the membership value of the overtime occupation time, the net discharge compensation consumption, and the discharge margin, wherein the fuzzy set of the overtime occupation time, the fuzzy set of the net discharge compensation consumption, and the fuzzy set of the discharge margin are obtained by fuzzifying the overtime occupation time, the net discharge compensation consumption, and the discharge margin; determines the fuzzy control rule according to the membership value of the historical charging information; obtains the initial discharge intention of the target object in N time periods according to the historical charging information and the fuzzy control rule, and the initial discharge intention is expressed by the following formula: in, Represents the membership function of the discharge margin, The membership function that represents the timeout period for occupation. represents the membership function of the net discharge compensation consumption, The point corresponding to the maximum value of the membership function representing the initial discharge willingness; A construction module, used to construct a two-layer decision model according to the initial discharge intentions of the target objects in the N time periods, the N time periods of the dynamic resource transaction parameters, and the initial resource transaction parameters of the N time periods; The second adjustment module is used to take the lowest operating consumption of the charging station and the highest discharge exchange value of the new energy vehicle as the objective function, and based on the target constraint conditions and the two-layer decision-making model, adjust the initial resource trading parameters of each of the N time periods and the initial discharge intentions of the target objects in the N time periods to obtain the target resource trading parameters of each of the N time periods and the target discharge intentions of the target objects in the N time periods. Specifically, the resource trading parameters and the discharge resource trading parameters are used to generate an initial population, and the fitness of each candidate solution is evaluated by calculating the objective function of the charging station layer. In the calculation process, the intermediate discharge intentions of each of the N time periods are also used, and then genetic operations are performed to obtain the target resource trading parameters of each of the N time periods and the target discharge intentions of the target objects in the N time periods. The intermediate discharge intentions are further adjusted and corrected to the initial discharge intentions under different discharge exchange values.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

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