Multi-objective optimization method, device and equipment based on flexible resources of charging station

Abstract

The invention provides a multi-objective optimization method, device and equipment based on flexible resources of a charging station, and can be applied to the technical field of new energy automobiles. The method comprises the following steps: in response to a scheduling request for flexible resources of a charging station, obtaining a preset comprehensive resource transaction parameter, a predicted charging amount, a charging duration and historical charging information; obtaining a dynamic resource transaction parameter according to the predicted charging amount and the charging duration; according to the historical charging information, a preset membership function and the fuzzy set, obtaining respective initial discharging willingness of the target object in the N time periods; taking the lowest operation consumption of the charging station and the highest discharge exchange value of the new energy vehicle as target functions, and adjusting the initial resource transaction parameters and the initial discharge intention based on a target constraint condition and a double-layer decision model constructed according to the initial discharge intention and the dynamic resource transaction parameters; and obtaining respective target resource transaction parameters of the N time periods and respective target discharge willingness of the target object in the N time periods.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle technology, and more specifically to a multi-objective optimization method, device and equipment based on charging station flexibility resources. Background Art

[0002] Batteries are the core components of new energy vehicles. As battery technology continues to mature, the number of new energy vehicles is gradually increasing. Advances in battery technology have significantly improved the performance and reliability of new energy vehicles. For example, the battery charging speed has been increased, reducing the charging time, providing convenience for new energy vehicle users.

[0003] In the process of realizing the present invention, the inventor discovered through investigation that since frequent charging and discharging operations will accelerate the degradation of the battery, new energy vehicle users have an aversion to the vehicle-to-grid (V2G) mode, which makes it common to ignore the dynamic changes in the willingness of new energy vehicle users when quantifying the flexibility resources of new energy vehicles, resulting in low dispatchability of flexibility 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 charging station flexibility resources.

[0005] According to a first aspect of the present invention, a multi-objective optimization method based on flexible resources of a charging station is provided, comprising: in response to a scheduling request for flexible resources of a charging station, obtaining preset comprehensive resource trading parameters, an estimated charging amount of a new energy vehicle, a charging time corresponding to the estimated charging amount, and historical charging information, wherein the preset comprehensive resource trading parameters include M time periods and resource trading parameters of each of the M time periods, where M is an integer greater than 1; according to the estimated charging amount and the charging time, adjusting the resource trading parameters of the M time periods and the M time periods 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; according to the historical charging information, the preset resource trading parameters The attribute function and fuzzy set are used to obtain the initial discharge intention of the target object in N time periods, where the fuzzy set is obtained by fuzzifying the historical charging information; a two-level decision model is constructed according to the initial discharge intention of the target object in N time periods, the N time periods of dynamic resource trading parameters and the initial resource trading parameters of the N time periods; 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 target constraints and the two-level decision model, the initial resource trading parameters of the N time periods and the initial discharge intention of the target object in the N time periods are adjusted to obtain the target resource trading parameters of the N time periods and the target discharge intention of the target object in the N time periods.

[0006] According to the multi-objective optimization method, device and equipment based on flexible resources of charging stations provided by the present invention, by adjusting the M time periods of preset comprehensive resource trading parameters and the resource trading parameters of each of the M time periods, N time periods of dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods are obtained, and the lowest operating consumption of the charging station and the highest discharge exchange value of new energy vehicles are taken as the objective function. Based on the target constraints 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. Based on the two-layer decision model, the discharge exchange value of the target object and the operating consumption of the charging station can be considered at the same time, so as to achieve the balance of the load of the charging station and the power grid, avoid the formation of new load peaks, improve the dispatchability of flexible resources and the operational robustness of the charging station, and thus achieve the purpose of peak shaving and valley filling, load optimization and maximization of the discharge exchange value of the target object. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0008] Figure 1 An application scenario diagram of a multi-objective optimization method based on charging station flexibility resources according to an embodiment of the present invention is shown.

[0009] Figure 2 A flow chart of a multi-objective optimization method based on charging station flexibility resources according to an embodiment of the present invention is shown.

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

[0011] Figure 3B FIG. 4 is a schematic diagram showing a membership degree of a discharge margin according to an embodiment of the present invention.

[0012] Figure 3C A schematic diagram of membership of a timeout occupancy duration according to an embodiment of the present invention is shown.

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

[0014] Figure 5 A schematic diagram of dynamic resource transaction parameters according to an embodiment of the present invention is shown.

[0015] Figure 6 A schematic diagram showing load peak shaving of a charging station using dynamic resource trading parameters according to an embodiment of the present invention is shown.

[0016] Fig. 7A A schematic diagram of the supply and demand relationship of flexibility without considering V2G according to an embodiment of the present invention is shown.

[0017] Figure 7B A schematic diagram of the supply and demand relationship of flexibility considering V2G according to an embodiment of the present invention is shown.

[0018] Figure 8 A schematic diagram of a discharge flexibility region under different discharge intention thresholds according to an embodiment of the present invention is shown.

[0019] Fig. 9 A structural block diagram of a multi-objective optimization device based on charging station flexibility resources according to an embodiment of the present invention is shown.

[0020] Fig.10 A block diagram of an electronic device suitable for implementing a multi-objective optimization method based on flexibility resources of charging stations according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0021] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

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

[0023] All terms (including technical and scientific terms) used herein 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] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression 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 A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0025] In the technical solution of the present invention, the user information (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 used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

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

[0027] In the process of implementing the present invention, in the related technology, since frequent charging and discharging operations will accelerate the attenuation of the battery, new energy vehicle users have an aversion to the V2G mode, which makes the dynamic changes in the willingness of new energy vehicle users often ignored when quantifying the flexibility resources of new energy vehicles, resulting in low dispatchability of flexibility resources and low operational robustness of charging stations.

[0028] In view of this, an embodiment of the present invention provides a multi-objective optimization method based on the flexibility resources of a charging station, in response to a scheduling request for the flexibility resources of a charging station, obtaining preset comprehensive resource trading parameters, an estimated charging amount of a new energy vehicle, a charging time corresponding to the estimated charging amount, and historical charging information, wherein the preset comprehensive resource trading parameters include M time periods and resource trading parameters of each of the M time periods, where M is an integer greater than 1; according to the estimated charging amount and the charging time, the resource trading parameters of the M time periods and 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; according to the historical charging information, the preset subordinate The initial discharge intention of the target object in N time periods is obtained by using a degree function and a fuzzy set, wherein the fuzzy set is obtained by fuzzifying the historical charging information; a two-layer decision model is constructed according to the initial discharge intention of the target object in N time periods, the N time periods of the dynamic resource trading parameters and the initial resource trading 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 constraints and the two-layer decision model, the initial resource trading parameters of the N time periods and the initial discharge intention of the target object in N time periods are adjusted to obtain the target resource trading parameters of the N time periods and the target discharge intention of the target object in N time periods.

[0029] Figure 1 An application scenario diagram of a multi-objective optimization method based on charging station flexibility resources according to an embodiment of the present invention is shown.

[0030] like Figure 1 As 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 a communication link 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 optical fiber cables, etc.

[0031] The user can 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 can 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 for example).

[0032] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0033] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for 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 may analyze and process the received data such as user requests, and feed back 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 charging station flexibility resources provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the multi-objective optimization device based on charging station flexibility resources provided in the embodiment of the present invention can generally be set in the server 105. The multi-objective optimization method based on charging station flexibility resources provided in the embodiment of the present invention can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the multi-objective optimization device based on charging station flexibility resources provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate 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 number of the first terminal device, the second terminal device, the third terminal device, the network and the server in the embodiment is only for illustration. According to the implementation requirements, there may be any number of the first terminal device, the second terminal device, the third terminal device, the network and the server.

[0036] Figure 2 A flow chart of a multi-objective optimization method based on charging station flexibility resources according to an embodiment of the present invention is shown.

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

[0038] In operation S210, in response to a scheduling request for flexibility resources of a charging station, 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 are acquired.

[0039] In operation S220, the M time periods and the resource trading parameters of each of the M time periods are adjusted 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, where N is an integer greater than M.

[0040] In operation S230 , the initial discharge intentions of the target objects in N time periods are obtained according to the historical charging information, the preset membership function and the fuzzy set.

[0041] In operation S240 , 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 trading parameters, and the initial resource trading parameters of the N time periods.

[0042] In operation S250, 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 target constraints and the two-layer decision model, the initial resource trading parameters of each N time period and the initial discharge intention of the target object in each N time period are adjusted to obtain the target resource trading parameters of each N time period and the target discharge intention of the target object in each N time period.

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

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

[0045] For example, the 24 hours in a day can be divided into four periods: peak, flat, valley, and deep valley. That is, the preset comprehensive resource trading parameters include four periods and resource trading parameters for each of the four periods. The charging process of new energy vehicles may involve one or more of the peak, flat, valley, and deep valley periods. Near the supply and 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] in, represents the charging demand in the i-th period, represents the resource transaction parameters of the i-th period, Indicates peak time period, Indicates the normal period, Indicates the valley period, Indicates the deep valley period, , represents the slope of the ith period, represents the intercept of the ith period.

[0048] According to an embodiment of the present invention, the estimated charge amount of a new energy vehicle may represent the amount of electricity that the new energy vehicle needs to store for subsequent travel. The charging time may represent the time required to charge the new energy vehicle to the estimated charge amount at a charging station. The historical charging information may represent the relevant operation information for charging the new energy vehicle at the charging station. For example, the historical charging information may include information such as the charging start time, charging end time, gun removal time, charging time, and overtime occupancy time of the new energy vehicle in the previous period of time.

[0049] According to an embodiment of the present invention, the dynamic resource trading parameters may represent dividing 24 hours a day into more time periods based on preset time-sharing resource trading parameters and the initial resource trading parameters of each time period.

[0050] According to an embodiment of the present invention, N time periods of dynamic resource trading parameters and initial resource trading parameters of each N time period can be obtained by adjusting the resource trading parameters of each M time period according to the expected charging amount and the charging time corresponding to the expected charging amount. For example, in the case where the preset comprehensive resource trading parameters divide 24 hours a day into four time periods of peak, flat, valley and deep valley and the resource trading parameters of each of the four time periods, by adjusting the four time periods of peak, flat, valley and deep valley and the resource trading parameters of each of the four time periods, the dynamic resource trading parameters can be obtained, which can be divided into 96 time periods and the initial resource trading parameters of each of the 96 time periods.

[0051] According to an embodiment of the present invention, the target object may represent a car owner who charges a new energy vehicle at a 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, preset membership functions and fuzzy sets, the overtime occupancy duration, net discharge compensation consumption and discharge margin can be mapped to one or more subsets, and 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, thereby obtaining the initial discharge intention of the target object in N time periods.

[0058] Figure 3A A schematic diagram of membership of net discharge compensation consumption according to an embodiment of the present invention is shown. Figure 3B FIG. 4 is a schematic diagram showing a membership degree of a discharge margin according to an embodiment of the present invention. Figure 3C A schematic diagram of membership of a timeout occupancy duration according to an embodiment of the present invention is shown.

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

[0061] like Figure 3C As shown, the membership values ​​of the first subset, the second subset and the third subset corresponding to the timeout occupation duration based on the trapezoidal membership function and the Gaussian membership function, the first subset corresponding to the timeout occupation duration represents the "long" timeout occupation duration, the second subset corresponding to the timeout occupation duration represents the "medium" timeout occupation duration, and the third subset corresponding to the timeout occupation duration represents the "short" timeout occupation 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 based on the initial discharge willingness of the target object in N time periods, the N time periods of the dynamic resource trading parameters, and the initial resource trading parameters of the N time periods.

[0063] According to an embodiment of the present invention, the operating consumption of the charging station may represent the cost required for operating the charging station. The discharge exchange value may represent 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 may represent 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, 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 can be adjusted based on the target constraints and the two-layer decision model, so as to minimize the operating consumption of the charging station and maximize the discharge exchange value of the new energy vehicle, thereby obtaining 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.

[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 of each of the M time periods, 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 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 target constraints 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. Based on the two-layer decision model, the discharge exchange value of the target object and the operating consumption of the charging station can be considered at the same time, thereby balancing the load of the charging station and the power grid, avoiding the formation of new load peaks, improving the dispatchability of flexible resources and the operational robustness of the charging station, and thus achieving the purpose of peak shaving and valley filling, optimizing load and maximizing the discharge exchange value of the target object.

[0067] According to an embodiment of the present invention, based on historical charging information, a preset membership function and a fuzzy set, the initial discharge intention of the target object in N time periods is obtained, 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 intention of the target object in N time periods according to the historical charging information and the fuzzy control rule.

[0068] According to an embodiment of the present invention, since the net discharge compensation consumption and the discharge margin can be obtained by performing statistical analysis on the charging start time, charging end time, gun drawing time, overtime occupancy time, etc. in the historical charging information, the historical charging information may also include the net discharge compensation consumption and the discharge margin.

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

[0070] According to an embodiment of the present invention, the selection of the preset membership function needs to fully consider the target object's preference for the overtime occupancy time, net discharge compensation consumption and discharge margin. The preset membership function usually adopts a trapezoidal membership function, a Gaussian membership function, a sigmoid membership function, and a combination of a trapezoidal membership function, a Gaussian membership function, and a sigmoid membership function.

[0071] According to an embodiment of the present invention, the trapezoidal membership function can effectively simulate the demand for segmented changes. Usually, multiple trapezoidal functions are defined to cover different intervals, as shown in formula (2).

[0072] (2)

[0073] Among them, 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 the 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 Normal distribution is usually used to define fuzzy sets, as shown in formula (3).

[0076] (3)

[0077] in, represents the center value, Represents the width parameter.

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

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

[0080] (4)

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

[0082] According to an embodiment of the present invention, a fuzzy control rule can be determined based on the membership value of historical charging information. For example, a fuzzy control rule is formulated based on the size of the membership function value of the overtime occupancy time, the size of the membership function value of the net discharge compensation consumption, and the size of the membership function value of the discharge margin.

[0083] According to an embodiment of the present invention, the nth fuzzy control rule is as follows: Formula (5).

[0084] (5)

[0085] in, Indicates the discharge margin, Indicates the timeout duration. represents the net discharge compensation consumption, Indicates the target object's initial discharge intention, represents the fuzzy set of discharge margin under the nth fuzzy control rule, represents the fuzzy set of the timeout duration under the nth fuzzy control rule, represents the fuzzy set of net discharge compensation consumption under the nth fuzzy control rule, represents the fuzzy set of initial discharge intention under the nth fuzzy control rule, and N is the number of fuzzy control rules.

[0086] According to an embodiment of the present invention, the initial discharge intention of the target object in N time periods can be obtained based on historical charging information and fuzzy control rules. For example, the initial discharge intention of the target object in 96 time periods can be obtained.

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

[0088] (6)

[0089] 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 membership function representing the initial discharge willingness, Membership function representing initial discharge willingness The point corresponding to 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 formula (7).

[0091] (7)

[0092] According to an embodiment of the present invention, the above formula (7) is set to , , 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 intention can characterize the discharge intention as "negative", the second subset corresponding to the initial discharge intention can characterize the discharge intention as "neutral", and the third subset corresponding to the initial discharge intention can characterize the discharge intention as "negative", wherein the range of the initial discharge intention is [0,1]. Gaussian membership and Sigmoid membership functions can be used to describe the response of the target object to the fluctuation of the resource trading parameters and the change of the initial discharge intention. The Gaussian membership function is suitable for describing the centrality of the "neutral" initial discharge intention of the target object when the resource trading parameters are stable, and the Sigmoid membership function is suitable for reflecting the smooth transition of the initial discharge intention of the target object from "negative" to "positive" when the resource trading parameters fluctuate frequently. When the Sigmoid membership function is at the center position (x=c), the initial discharge intention is 0.5, and the initial discharge intention fluctuates between 0 and 1 with the change of the center position x. The initial discharge intention can be obtained by calculating the membership value through the membership function and taking the membership value as the input of the fuzzy inference rule. By integrating all fuzzy control rules, the overall initial discharge intention of the target object under specific resource transaction parameters can be obtained.

[0095] According to an embodiment of the present invention, the membership value of the historical charging information can be determined according to a preset membership function and a fuzzy set, and then the fuzzy control rule can be determined. Based on the fuzzy control rule and the historical charging information, the initial discharge intention of the target object in N time periods can be obtained. Different preset membership functions can be used to describe in detail the different levels of the initial discharge intention of the target object, and simulate the behavioral response 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 operating efficiency of the charging station and the target object's participation in V2G.

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

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

[0098] (9)

[0099] in, represents the center 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 a preset interval of an affine number, and the membership value of the affine number is calculated for a fuzzy set and a preset membership function. , as shown in formula (10).

[0101] (10)

[0102] in, Indicates the center value The membership value at Indicates that the membership function is at the center value The derivative at .

[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 size of the membership value of the affine number, so as to obtain the first discharge intention of the target object in each of the N time periods according to the historical charging information and the first fuzzy control rule. Compared with the initial discharge intention, the first discharge intention can be closer to the real discharge intention of the target object due to the introduction of the affine number.

[0104] According to the embodiment of the present invention, by introducing affine numbers, the central value and error range of the historical charging information are taken into consideration, that is, the randomness of the historical charging information is taken into consideration, so that the obtained first discharge intention is more accurate.

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

[0106] According to the embodiment of the present invention, the charging time and the charging amount can be considered to be 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 of 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 of the jth time period on the charging demand of the ith 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 (12), that is, the self elasticity coefficient can be determined according to the expected charging amount, charging time, M time periods and the resource trading parameters of each M time period: and the cross elastic coefficient .

[0107] (11)

[0108] (12)

[0109] in, represents the charging demand in the jth period, represents the slope of the jth period, .

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

[0111] (13)

[0112] According to an embodiment of the present invention, by 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 moving terms and simplifying the above formula, the following formula (15) can be obtained, so that the charging demand in the kth time 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 partial derivatives of the above formula, the following formula (17) can be obtained.

[0118] (17)

[0119] According to an embodiment of the present invention, for the kth time period and the fth time period, the cross elasticity coefficient is as follows: formula (18), so that the cross elasticity coefficient for any two different time periods, for example, the ith time period and the jth time period, can be obtained as follows: formula (19).

[0120] (18)

[0121] (19)

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

[0123] (20)

[0124] (twenty one)

[0125] in, It represents the charging demand in period t when executing the resource trading parameters of each period in M ​​periods. It represents the charging demand in period t when executing the resource trading parameters of each period N. Represents the resource transaction parameters of each of the M time periods, Represents the resource transaction parameters of each of the N time periods, where: The general expression of .

[0126] According to an embodiment of the present invention, the M time periods and the resource trading parameters of each of the M time periods can be adjusted according to the relationship between the charging demand and the resource trading parameters. However, in the process of adjusting the M time periods and the resource trading parameters of each of the M time periods, the expected charging amount and the total resource trading parameters of the target object meet the power consistency constraint and the optimal consumption constraint, thereby obtaining N time periods of dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods.

[0127] According to an embodiment of the present invention, the power consistency constraint can represent that the charging amount of the new energy vehicle can reach the expected charging amount within the charging time, and the charging demand fluctuation amount before and after the resource trading parameters are adjusted, as shown in the following formula (22).

[0128] (twenty two)

[0129] in, It represents the fluctuation of charging demand of the e-th new energy vehicle before and after the resource trading parameters are adjusted. represents the set of time periods for charging of the e-th new energy vehicle, Indicates the time when charging starts. Indicates the end time of charging. It represents the change in charging power of new energy vehicles in period t before and after the resource trading parameters are adjusted.

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

[0131] According to an embodiment of the present invention, the optimal consumption constraint can indicate that the charging consumption after the resource trading parameters are adjusted should not be higher than the charging consumption before the resource trading parameters are adjusted. Because new energy vehicles are the key to peak load reduction, under the condition of power consistency constraint, overtime occupancy consumption must also be reduced.

[0132] According to an embodiment of the present invention, the relationship between charging demand and resource trading parameters can be established through the self-elasticity coefficient and the cross-elasticity coefficient. Based on the relationship between charging demand and resource trading parameters, M time periods and the resource trading parameters of each of the M time periods can be adjusted, thereby obtaining N time periods and the initial resource trading parameters of each of the N time periods. By adjusting the time periods and resource trading parameters, the time period intervals and trading resource parameters are refined, the loads of the charging stations and the power grid are balanced, and new load peaks are avoided. In addition, during the adjustment process, the power consistency constraints and the optimal consumption constraints must be met, thereby ensuring the charging needs and economic benefits of the target objects.

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

[0134] According to an embodiment of the present invention, the two-layer decision model may include a charging station decision model and a target object decision model. The charging station decision 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 model may be constructed based on 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 model may be composed of a charging station objective function and charging station constraints.

[0136] According to an embodiment of the present invention, the objective of the charging station is to minimize the operating 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 objective function of the charging station is as follows: Formula (23).

[0137] (twenty three)

[0138] in, represents the redemption value of the charging station, represents the charging exchange value of the charging station during period t, represents the parking redemption value of the charging station during period t, represents the electricity consumption of the charging station during period t, represents the discharge compensation consumption of new energy vehicles during period t, represents the operation and maintenance consumption of the charging station during period t, Represents the charging and discharging consumption of the energy storage device during period t.

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

[0140] (twenty four)

[0141] (25)

[0142] (26)

[0143] (27)

[0144] (28)

[0145] (29)

[0146] in, Represents the collection of new energy vehicles, represents the resource transaction parameters in period t, represents the charging power of new energy vehicle n during period t, Indicates the scheduling time interval, Indicates the parking redemption value rate, represents the station status of new energy vehicle n in period t, Indicates the transaction parameters of power grid purchasing resources, represents the discharge power of new energy vehicle n in period t, represents the charging power of the energy storage device in period t, represents the discharge power of the energy storage device in period t, represents the output of the photovoltaic equipment in period t, Indicates willingness to discharge. represents the discharge resource trading parameters in period t, represents the operation and maintenance consumption coefficient, Indicates the unit usage consumption of energy storage equipment.

[0147] According to an embodiment of the present invention, the station status of the new energy vehicle in the t period represents whether the new energy vehicle is in the charging station in the t period. It is a 0-1 variable, which is 1 if the new energy vehicle is at the station, otherwise it is 0. Operation and maintenance consumption Including equipment maintenance, labor and other consumption. The unit usage consumption of energy storage equipment can include charging and discharging losses, equipment depreciation and so on.

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

[0149] According to an embodiment of the present invention, the resource transaction parameter constraint condition can indicate that the target resource transaction parameter must be within a reasonable range. The resource transaction parameter constraint condition is as follows: Formula (30).

[0150] (30)

[0151] in, Indicates the upper limit of resource transaction parameters. Indicates the lower limit of resource transaction parameters, Represents the target resource transaction parameters obtained through the two-level decision model.

[0152] According to an embodiment of the present invention, the power balance constraint condition of the charging station is as follows: Formula (31).

[0153] (31)

[0154] in, represents the power purchased by the charging station during period t, represents the total power demand of the charging station during period t.

[0155] According to an embodiment of the present invention, the energy storage power constraint conditions are as follows: formulas (32) to (34).

[0156] (32)

[0157] (33)

[0158] (34)

[0159] in, Indicates the maximum charge and discharge power of the energy storage device. represents the initial capacity of the energy storage device, Indicates the minimum capacity of the energy storage device, Indicates the maximum capacity of the energy storage device, and is a 0-1 variable, and They represent the charging state and discharging state of the energy storage device at time t respectively. Represents the charging efficiency of the energy storage device, Indicates the discharge efficiency of the energy storage device.

[0160] According to an embodiment of the present invention, the photovoltaic operation constraint condition may represent the constraint condition for the operation of the photovoltaic device, and the photovoltaic operation constraint condition is as follows: Formula (35).

[0161] (35)

[0162] in, represents the predicted output of the photovoltaic equipment at time t, Indicates the maximum output of the photovoltaic equipment, Indicates the fluctuation deviation of photovoltaic equipment.

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

[0164] According to an embodiment of the present invention, the goal of the target object is to minimize charging consumption, battery loss, and overtime occupancy penalty consumption. The target object charging function is as follows: Formula (36).

[0165] (36)

[0166] in, Indicates the total redemption value of the target object, represents the discharge exchange value of new energy vehicle n in period t, represents the charging consumption of new energy vehicle n in period t, It represents the battery consumption of new energy vehicle n in period t, It represents the penalty consumption of overtime occupation of new energy vehicle n in time period t.

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

[0168] (37)

[0169] (38)

[0170] (39)

[0171] (40)

[0172] in, Indicates the unit battery loss consumption, Indicates the penalty coefficient for overtime occupation. It represents the expected time for new energy vehicle n to leave the charging station.

[0173] According to an embodiment of the present invention, the target object constraint conditions may include: new energy vehicle charging and discharging 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 charging and discharging constraint condition may represent the constraint condition of the discharge power of the new energy vehicle, as shown in the following formulas (41) and (42).

[0175] (41)

[0176] (42)

[0177] in, Indicates the maximum charging and discharging power of new energy vehicle n.

[0178] According to an embodiment of the present invention, the above formula (42) indicates that the new energy vehicle cannot be charged and discharged at the same time in the same period.

[0179] According to an embodiment of the present invention, the power constraint conditions of new energy vehicles are as follows: Formulas (43) and (44).

[0180] (43)

[0181] (44)

[0182] in, Indicates the initial battery capacity of new energy vehicles. Indicates the minimum battery capacity of new energy vehicles, Indicates the maximum battery capacity of new energy vehicles, Indicates the expected final power of new energy vehicles.

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

[0184] (45)

[0185] in, represents the arrival time of new energy vehicle n, Indicates the departure time of new energy vehicle n.

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

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

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

[0189] like Figure 4 As shown, at the charging station layer, the self-elasticity coefficient and the cross-elasticity can be calculated according to the resource transaction parameters 411 of the M time periods and the N time periods respectively obtained, and the resource transaction parameter elasticity matrix 412 is constructed according to the self-elasticity coefficient and the cross-elasticity coefficient. The resource transaction parameter elastic adjustment 413 is realized by the number of months of power consistency and the optimal consumption constraint 414, so as to obtain the initial resource transaction parameters 415 of the N time periods and the N time periods respectively, and the initial resource transaction parameters 415 are sent to the target object layer, and the target object layer performs fuzzy processing on the timeout occupation duration 421 to obtain the first model The fuzzy set 424 is used to fuzzify the net discharge compensation consumption 422 to obtain the second fuzzy set 425, and the discharge margin 423 is fuzzified to obtain the third fuzzy set 426. The membership value of the timeout occupancy duration 421 is calculated based on the first preset membership function 427, the membership value of the net discharge compensation consumption 422 is calculated based on the second preset membership function 428, and the membership value of the discharge margin 423 is calculated based on the third preset membership function 429, so that the initial discharge intention 430 of the target object can be obtained, and the initial discharge intention 430 is transmitted to the charging station layer.

[0190] Figure 5 A schematic diagram of dynamic resource transaction parameters according to an embodiment of the present invention is shown.

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

[0192] According to an embodiment of the present invention, 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 constraints 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, including: adjusting the initial discharge intentions of the target objects in the N time periods according to the target object charging function and the target object constraints to obtain the intermediate discharge intentions of the target objects in the N time periods; adjusting the initial resource trading parameters and the intermediate discharge intentions of the N time periods according to the charging station objective function, the charging station constraints and the intermediate discharge intentions 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.

[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 target constraint condition may include a charging station constraint condition and a target object constraint condition.

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

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

[0196] (46)

[0197] in, 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 condition (KKT condition for short) is used to determine the optimal discharge behavior of the target object under resource trading parameters and different discharge exchange values. The KKT condition mainly includes a first-order necessary condition, a complementary relaxation condition and a feasibility condition. The first-order necessary condition can be obtained by taking the derivative of each variable in the Lagrangian function L and setting it to 0, 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 , then the complementary relaxation condition is satisfied, as shown in formula (49).

[0202] (49)

[0203] According to an embodiment of the present invention, the complementary relaxation condition ensures that the Lagrangian multiplier takes a non-zero value when the constraint reaches the boundary. All decision variables in the Lagrangian function L must satisfy the target constraint condition, and then the feasibility condition is met.

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

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

[0206] According to an embodiment of the present invention, the discharged power may represent the power supply provided by the new energy vehicle to the charging station during the target 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 comfort coefficient usually ranges from 0.5 to 2. For conservative target objects, the comfort coefficient can range from 1.5 to 2. For aggressive target objects, the comfort coefficient can range from 0.5 to 1. The target objects have different sensitivities to the resource trading parameters, resulting in different comfort coefficients for the target objects. A discharge exchange value function can be constructed based on the comfort coefficient, the expected charge amount, and the discharge amount, as shown in the following formula (50).

[0208] (50)

[0209] in, represents the discharge redemption value function of the target object in period t, represents the comfort coefficient, Indicates the initial power of new energy vehicles, represents the discharge power of new energy vehicles in period t, Represents the charging resource trading parameters during period t.

[0210] According to an embodiment of the present invention, a subjective probability weight function is constructed based on a rational coefficient of a target object and a preset objective probability. The rational coefficient can characterize the rationality of the target object to the exchange value. The smaller the rational coefficient is, the less rational the target object is. The value range of the rational coefficient is 0 to 1. When the rational coefficient is 1, the subjective probability is equal to the objective probability, indicating that the target object is completely rational. The target object's perception of the resource exchange value and the consumption corresponding to the resource exchange value is symmetrical. When the rational coefficient is less than 1, the target object exhibits limited rationality, is more sensitive to the consumption corresponding to the resource exchange value, and generally exhibits a higher risk aversion tendency. In an embodiment of the present invention, the value of the rational 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: Formula (51).

[0212] (52)

[0213] in, 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 redemption value function is a concave function. The perceived redemption value function can be constructed based on the subjective probability weight function, the discharge redemption value function and the discharge power, and then the perceived redemption value under different discharge powers, the perceived redemption value function R, is as follows 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 intention of the target object in N time periods can be adjusted according to the perceived exchange value to obtain the intermediate discharge intention of the target object in N time periods.

[0217] According to an embodiment of the present invention, when the intermediate discharge intention of the target object in N time periods is determined, it is introduced into the charging station layer as a constraint condition for the charging station layer decision. That is, according to the charging station objective function, the charging station constraint condition and the intermediate discharge intention, the initial resource trading parameters and the intermediate discharge intention of each of the N time periods can be 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 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 may be used for solving the problem, thereby obtaining the target resource transaction parameters of each of the N time periods and the target discharge intentions of the target objects of each of the N time periods.

[0219] According to an embodiment of the present invention, the process of obtaining 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 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 in the calculation process, it is also based on the intermediate discharge intentions of each of the N time periods, and then updating the population through genetic operations, such as selection, crossover, mutation, etc., so that it gradually approaches the optimal solution, and 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 of each of the N time periods and the target discharge intentions of the target objects in the N time periods are obtained.

[0220] According to an embodiment of the present invention, based on the target object charging function and the target object constraints, the initial discharge intention can be adjusted to obtain the intermediate discharge intention, and then based on the intermediate discharge intention, the charging station target function and the charging station constraints, 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, thereby improving the accuracy of the target resource trading parameters and the target discharge intention.

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

[0222] (54)

[0223] Among them, w represents the intermediate discharge intention, 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 10, and the probability value c of the midpoint of the Sigmoid function can be 0.5.

[0225] According to an embodiment of the present invention, the Sigmoid function mainly smoothes the relationship between the target object's discharge intention and the resource trading parameters to avoid excessive impact of drastic fluctuations in the resource trading parameters on the behavior of the target object, thereby making 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 parameters within a reasonable range, the adjustment range of the resource trading parameters should be related to the resource trading parameters of each of the M time periods. For example, when the M time periods are peak period, normal period, valley period and deep valley period, the conditional range of the resource trading parameters should be within the difference range between the resource trading parameters between the valley period and the deep valley period, and the adjustment of the resource trading parameters satisfies the following formula (55).

[0227] (55)

[0228] in, represents the average discharge willingness value of all new energy vehicles in period t, Indicates the resource trading parameters during the valley period. Indicates the resource trading parameters during the valley period. represents the steepness parameter, represents the skewness parameter, Indicates the target resource transaction parameters.

[0229] According to an embodiment of the present invention, the value of the steepness parameter may be 10, and the value of the skewness parameter may be a resource trading parameter during a valley period.

[0230] Figure 6 A schematic diagram showing load peak shaving of a charging station using dynamic resource trading parameters according to an embodiment of the present invention is shown.

[0231] like Figure 6 As shown in the figure, 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 period of time, that is, the peak period is around the 50th period. Based on the dynamic resource trading parameters, peak reduction can be achieved. It can be seen that the load of the charging station after peak shaving during the peak period is reduced by 21.93%.

[0232] Fig. 7A A schematic diagram of the supply and demand relationship of flexibility without considering V2G according to an embodiment of the present invention is shown. Figure 7B A schematic diagram of the supply and demand relationship of flexibility considering V2G according to an embodiment of the present invention is shown.

[0233] like Fig. 7A and 7BAs shown, flexibility supply includes upper flexibility supply and lower flexibility supply. Upper flexibility supply can represent the upward flexibility domain of flexibility resources, and lower flexibility domain can represent the downward flexibility domain of flexibility resources. Flexibility demand includes upper flexibility demand and lower flexibility demand. Upper flexibility demand can represent the upward flexibility domain demand of new energy vehicles, and lower flexibility demand can represent the downward flexibility domain demand of new energy vehicles. It can be seen that without considering V2G, there will be periods when the upward flexibility supply is less than the upward flexibility demand and when the downward flexibility supply is less than the downward flexibility demand. That is, without considering V2G, during the peak charging period, the flexibility demand is greater than the flexibility supply. When considering V2G, the upward flexibility supply meets the upward flexibility demand in all periods and the downward flexibility supply meets the downward flexibility demand in all periods. In addition, during the peak charging period, the flexibility supply is also much greater than the flexibility demand.

[0234] Figure 8 A schematic diagram of a discharge flexibility region under different discharge intention thresholds according to an embodiment of the present invention is shown.

[0235] like Figure 8 As shown in FIG. 1 , the discharge willingness threshold (DW for short) increases from 0 to 0.6 with an interval of 0.1. It can be seen that as the discharge willingness threshold increases, the discharge flexibility region tends to gradually shrink.

[0236] Based on the above multi-objective optimization method based on charging station flexibility resources, the present invention also provides a multi-objective optimization device based on charging station flexibility resources. Fig. 9 The device is described in detail.

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

[0238] like Fig. 9 As shown, the multi-objective optimization device 900 based on charging station flexibility resources of 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 used to obtain preset comprehensive resource transaction parameters, the expected charging amount of the new energy vehicle, the charging time corresponding to the expected charging amount, and historical charging information in response to the scheduling request for the flexible resources of the charging station, wherein the preset comprehensive resource transaction parameters include M time periods and resource transaction 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 repeated here.

[0240] The first adjustment module 920 is used 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, and obtain the N time periods of dynamic resource trading parameters and the initial resource trading parameters of each of the N time periods, where N is an integer greater than M. In one embodiment, the first adjustment module 920 can be used to perform the operation S220 described above, which will not be repeated here.

[0241] The first obtaining module 930 is used to obtain the initial discharge intention of the target object in N time periods according to the historical charging information, the preset membership function and the fuzzy set, wherein the fuzzy set is obtained by fuzzifying the historical charging information. In one embodiment, the first obtaining module 930 can be used to perform the operation S230 described above, which will not be repeated here.

[0242] The construction module 940 is used to construct a two-layer decision model according to the initial discharge intention of the target object in each of the N time periods, the N time periods of the dynamic resource transaction parameters, and the initial resource transaction parameters of each of the N time periods. In one embodiment, the construction module 930 can be used to perform the operation S240 described above, which will not be repeated here.

[0243] The second adjustment module 950 is used to adjust the initial resource trading parameters of each of the N time periods and the initial discharge willingness of the target object in the N time periods based on the target constraint conditions and the two-layer decision model, with the lowest operating consumption of the charging station and the highest discharge exchange value of the new energy vehicle as the objective function, to obtain the target resource trading parameters of each of the N time periods and the target discharge willingness of the target object in the N time periods. In one embodiment, the second adjustment module 930 can be used to perform the operation S250 described above, which will not be repeated here.

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

[0245] The first obtaining submodule is used to determine the membership value of the historical charging information according to a preset membership function and a fuzzy set.

[0246] The second obtaining submodule is used to determine the fuzzy control rules according to the membership value of the historical charging information.

[0247] The third obtaining submodule is used to obtain the initial discharge intention of the target object in N time periods according to the historical charging information and the fuzzy control rules.

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

[0249] A creation module is used to create affine numbers based on historical charging information and preset intervals.

[0250] The first determination module is used to determine the membership value of the affine number according to the affine number, the preset membership function and the fuzzy set.

[0251] The second determination module is used to determine the first fuzzy control rule according to the membership value of the affine number.

[0252] The second obtaining module is used to obtain the first discharge intention of the target object in 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 submodule, a second adjustment submodule and a third adjustment submodule.

[0254] The first adjustment submodule is used to determine the self-elasticity coefficient and the cross-elasticity coefficient according to the expected charging amount, the charging time, the M time periods and the resource trading parameters of each of the M time periods.

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

[0256] The third adjustment submodule is used 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 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 model includes a charging station decision model and a target object decision model, and the construction module 940 includes: a first construction submodule, a second construction submodule and a third construction submodule.

[0258] The first construction submodule is used to construct a charging station decision model according to the N time periods of the dynamic resource transaction parameters and the initial resource transaction parameters of each of the N time periods.

[0259] The second construction submodule is used to construct a target object model according to the initial discharge intentions of the N time periods and the charging station decision model;

[0260] The third construction submodule is used to construct a two-layer decision model according to the charging station decision model and the target object decision 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 target constraint condition includes a charging station constraint condition and a target object constraint condition; wherein the second adjustment module 950 includes: a fourth adjustment submodule and a fifth adjustment submodule.

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

[0263] The fifth adjustment submodule is used to adjust the initial resource trading parameters and intermediate discharge intentions of each of the N time periods according to the charging station objective function, the charging station constraints and the intermediate discharge intention, so as 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.

[0264] According to an embodiment of the present invention, the fourth adjustment submodule 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 discharged amount, wherein the comfort coefficient is determined according to the sensitivity of the target object to the resource trading parameters, and the discharged amount represents the power supply of the new energy vehicle to the charging station during the target 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 power, 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 rationality of the target object to the exchange value.

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

[0268] According to an embodiment of the present invention, any multiple modules among the acquisition module 910, the first adjustment module 920, the first acquisition module 930, the construction module 940, and the second adjustment module 950 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can 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 acquisition module 930, the construction module 940, and the second adjustment module 950 can 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 a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any 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, which may perform corresponding functions when executed.

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

[0270] like Fig.10 As shown, the electronic device 1000 according to an embodiment of the present invention includes a processor 1001, which can 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 part 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard 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 RAM 1003, various programs and data required for the operation of electronic device 1000 are stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program 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, which 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 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 installed on the drive 1010 as needed, so that a computer program read therefrom is 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 embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment 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: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

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

[0276] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 1001. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0277] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1009, and / or installed from the removable medium 1011. The program code contained in the computer program may be transmitted using any appropriate 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 installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0279] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment 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 process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0280] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0281] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present invention may 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 may be combined and / or combined in various ways. All of these combinations and / or combinations fall within the scope of the present invention.

[0282] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A multi-objective optimization method based on charging station flexibility resources, characterized in that: The method comprises: In response to a scheduling request for the flexibility resources of the charging station, obtaining preset comprehensive resource transaction parameters, an estimated charging amount of the new energy vehicle, a charging time corresponding to the estimated charging 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, and M is an integer greater than 1; 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; According to the historical charging information, a preset membership function and a fuzzy set, the initial discharge intention of the target object in each of the N time periods is obtained, wherein the fuzzy set is obtained by fuzzifying the historical charging information; 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 target 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.

2. The method according to claim 1, characterized in that The obtaining of the initial discharge intentions of the target objects in the N time periods according to the historical charging information, the preset membership function and the fuzzy set includes: Determining a membership value of the historical charging information according to the preset membership function and the fuzzy set; Determining a fuzzy control rule according to the membership value of the historical charging information; According to the historical charging information and the fuzzy control rule, the initial discharge intentions of the target object in the N time periods are obtained.

3. The method according to claim 2, 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.

4. 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.

5. 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.

6. The method according to claim 5, 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.

7. The method according to claim 6, characterized in that The step of adjusting the initial discharge intentions of the target objects in the N time periods according to the target object charging function and the target object constraint condition to obtain the intermediate discharge intentions of the target objects in the N time periods includes: Constructing a discharge exchange value function according to the comfort coefficient, the estimated charging amount and the discharged amount, wherein the comfort coefficient is determined according to the sensitivity of the target object to the resource trading parameter, and the discharged amount represents the amount of power supplied by the new energy vehicle to the charging station during the target period; According to the subjective probability weight function, the discharge exchange value function and the discharge quantity, a perceived exchange value is obtained, wherein the subjective probability weight function is constructed according to the rationality coefficient of the target object and a preset objective probability, and the rationality coefficient represents the rationality degree of the target object to the exchange value; Based on the target object charging function and the target object constraint condition, and according to the perceived exchange value, the initial discharge intentions of the target object in the N time periods are adjusted to obtain the intermediate discharge intentions of the target object in the N time periods.

8. 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.

9. A multi-objective optimization device based on charging station flexibility resources, characterized in that: The device comprises: an acquisition module, configured to obtain, in response to a scheduling request for flexible resources of a charging station, preset comprehensive resource transaction parameters, an estimated charge amount of a new energy vehicle, a charging time 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, and M is an integer greater than 1; 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; A first obtaining module is used to obtain the initial discharge intention 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 fuzzifying the historical charging information; 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, based on the target constraint conditions and the two-layer decision-making model, to 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, so as 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.

10. 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 8.

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