Subsidy control method and device for maximizing primary frequency modulation capacity

Abstract

This application provides a subsidy control method and apparatus for maximizing primary frequency regulation capacity. The method includes: acquiring historical frequency regulation response data; constructing a target numerical model based on the historical frequency regulation response data, the target numerical model simulating the changing trends of target revenue and target frequency regulation capacity with the ratios corresponding to a first target subsidy and a second target subsidy; constructing an objective function based on the target numerical model, the objective function being to maximize both the target revenue and the target frequency regulation capacity; solving the objective function using a genetic algorithm and a particle swarm optimization algorithm to obtain a first target ratio and a second target ratio; calculating a third target ratio based on the first target ratio and the second target ratio; and determining the third target ratio as the ratio of the first target subsidy and the second target subsidy. This method solves the problem in the prior art where the chaotic subsidy allocation between aggregators and users leads to poor incentive effects for power grid companies on primary frequency regulation.

Description

Technical Field

[0001] This invention relates to the field of data communication technology, and more specifically, to a subsidy control method, apparatus, computer-readable storage medium, and power grid system for maximizing primary frequency regulation capacity. Background Technology

[0002] With the construction of interconnected vehicle-to-everything (V2X) charging infrastructure and corresponding operation and maintenance service network platforms, as well as the investment and services of electric vehicle operators, electric vehicles are entering a period of steady growth. This means that more and more electric vehicles will be connected to the power grid and interact with it in the future. On the one hand, electric vehicles are a flexible power load that will obtain power from the grid at different times and locations. On the other hand, they are also a distributed and mobile energy storage resource. Since most electric vehicles spend a lot of time at their parking spots throughout the day, they have great potential to participate in the primary frequency regulation of the power system through vehicle-to-grid interaction technology.

[0003] In existing technologies, power grid companies typically incentivize charging station aggregators and electric vehicle users to participate in primary frequency regulation by issuing subsidies. However, due to the different subsidy allocation ratios for different aggregators and users, the incentive effect on the participation of aggregators and users varies, making it difficult for power grid companies to predict the correlation between subsidies and grid frequency regulation capacity and adjust their subsidy strategies accordingly. Summary of the Invention

[0004] The main objective of this application is to provide a subsidy regulation method, apparatus, computer-readable storage medium, and power grid system for maximizing primary frequency regulation capacity, so as to at least solve the problem in the prior art where the grid company's incentive effect on primary frequency regulation is poor due to the chaotic subsidy allocation between aggregators and users.

[0005] To achieve the above objectives, according to one aspect of this application, a subsidy regulation method for maximizing primary frequency regulation capacity is provided, comprising: acquiring historical frequency regulation response data, wherein the historical frequency regulation response data includes first cost data, second cost data, and a target frequency regulation capacity corresponding to different ratios of a first target subsidy and a second target subsidy, wherein the first target subsidy is the subsidy from the power grid company to aggregators participating in primary frequency regulation, the second target subsidy is the subsidy from the aggregator to users participating in primary frequency regulation, the first cost data includes the relevant costs of users participating in the primary frequency regulation, the second cost data includes the relevant costs of aggregators participating in primary frequency regulation, and the target frequency regulation capacity is the total frequency regulation capacity increased by users participating in primary frequency regulation; constructing a target numerical model based on the historical frequency regulation response data, wherein the target numerical model... The model is used to simulate the changing trends of target revenue and target frequency modulation capacity with the ratios corresponding to the first target subsidy and the second target subsidy, where the target revenue is the sum of the revenue of the aggregator and the user; an objective function is constructed based on the target numerical model, where the objective function is to maximize both the target revenue and the target frequency modulation capacity; the objective function is solved using a genetic algorithm to obtain a first target ratio, and the objective function is solved using a particle swarm optimization algorithm to obtain a second target ratio; a third target ratio is calculated based on the first target ratio and the second target ratio, and the third target ratio is determined as the ratio of the first target subsidy and the second target subsidy. The first target ratio, the second target ratio, and the third target ratio are all the same as the ratio of the first target subsidy to the second target subsidy.

[0006] Optionally, constructing a target numerical model based on the historical frequency modulation response data includes: determining the difference between the first target subsidy and the second target subsidy as a third target subsidy; determining the ratio of the third target subsidy to the second target subsidy as an allocation ratio; fitting a first objective function to the allocation ratio and the first cost data to obtain a first objective function, the first objective function being used to characterize the changing trend of the first cost data with the allocation ratio; fitting a second objective function to the allocation ratio and the second cost data to obtain a second objective function, the second objective function being used to characterize the changing trend of the second cost data with the allocation ratio; fitting a third objective function to the allocation ratio and the first target subsidy to obtain a third objective function, the third objective function being used to characterize the changing trend of the first target subsidy with the allocation ratio; and fitting the allocation ratio and the target frequency modulation capacity... A fourth objective function is obtained by fitting the quantities, which characterizes the trend of the target frequency modulation capacity changing with the allocation ratio. A first objective formula, a second objective formula, and a third objective formula are constructed. The first objective formula is used to calculate a first revenue based on the third target subsidy and the second cost data. The second objective formula is used to calculate a second revenue based on the second target subsidy and the first cost data. The third objective formula is used to calculate the target revenue based on the first revenue and the second revenue. The first revenue is the net revenue of the aggregator participating in a primary frequency modulation, and the second revenue is the net revenue of the user participating in a primary frequency modulation. The first objective function, the second objective function, the third objective function, the fourth objective function, the first objective formula, the second objective formula, and the third objective formula are combined to obtain the target numerical model.

[0007] Optionally, fitting a first objective function based on the allocation ratio and the first cost data includes: determining a target number based on the historical frequency modulation response data, where the target number is the total number of users participating in a frequency modulation; fitting a fifth objective function based on the allocation ratio and the target number, where the fifth objective function characterizes the trend of the target number changing with the allocation ratio; determining user travel cost, travel impact cost, and user charging cost based on the first cost data; and fitting a sixth objective function based on the target number and the user travel cost, the target number and the travel impact cost, and the target number and the user charging cost, respectively. The system comprises a sixth objective function, a seventh objective function, and an eighth objective function. The sixth objective function characterizes the trend of the user's travel cost with the target quantity; the seventh objective function characterizes the trend of the travel impact cost with the target quantity; and the eighth objective function characterizes the trend of the user's charging cost with the target quantity. A fourth objective formula is constructed to calculate the first cost data based on the user's travel cost, the travel impact cost, and the user's charging cost. The first objective function is obtained by fitting the system with the fifth objective function, the sixth objective function, the seventh objective function, the eighth objective function, and the fourth objective formula.

[0008] Optionally, a second objective function is obtained by fitting the allocation ratio and the second cost data, including: determining the charging service cost and the charging electricity price cost based on the second cost data; obtaining a ninth objective function and a tenth objective function by fitting the target quantity and the charging service cost, and the target quantity and the charging electricity price cost, respectively, wherein the ninth objective function characterizes the trend of the charging service cost with the target quantity, and the tenth objective function characterizes the trend of the charging electricity price cost with the target quantity; constructing a fifth objective formula, which is used to calculate the second cost data based on the charging service cost and the charging electricity price cost; and obtaining a first objective function by fitting the fifth objective function, the ninth objective function, the tenth objective function, and the fifth objective formula.

[0009] Optionally, a third objective function is obtained by fitting the allocation ratio and the first target subsidy, including: obtaining an eleventh objective function by fitting the target quantity and the first target subsidy, the eleventh objective function being used to characterize the changing trend of the first target subsidy with the target quantity; and obtaining the third objective function by fitting the fifth objective function and the eleventh objective function. Optionally, a fourth objective function is obtained by fitting the allocation ratio and the target frequency modulation capacity, including: obtaining a twelfth objective function by fitting the target quantity and the target frequency modulation capacity, the twelfth objective function being used to characterize the changing trend of the target frequency modulation capacity with the target quantity; and obtaining the fourth objective function by fitting the fifth objective function and the twelfth objective function.

[0010] Optionally, after constructing the objective function based on the target numerical model, the method further includes: determining that both the second target subsidy and the third target subsidy are not zero as a first constraint condition of the objective function; determining that the target frequency modulation capacity is greater than or equal to a first threshold as a second constraint condition of the objective function; and determining that the first target subsidy is greater than or equal to the second threshold as a third constraint condition of the objective function.

[0011] According to another aspect of this application, a subsidy-based frequency regulation control device for maximizing primary frequency regulation capacity is provided. The device includes: an acquisition unit for acquiring historical frequency regulation response data, the historical frequency regulation response data including first cost data, second cost data, and a target frequency regulation capacity corresponding to different ratios of a first target subsidy and a second target subsidy, wherein the first target subsidy is the subsidy from the power grid company to aggregators participating in primary frequency regulation, the second target subsidy is the subsidy from the aggregator to users participating in primary frequency regulation, the first cost data includes the relevant costs of users participating in primary frequency regulation, the second cost data includes the relevant costs of aggregator parameters for primary frequency regulation, and the target frequency regulation capacity is the total frequency regulation capacity increased by users participating in primary frequency regulation; and a first construction unit for constructing a target numerical model based on the historical frequency regulation response data, the target numerical model being used... The simulation target revenue and the target frequency modulation capacity change with the ratios corresponding to the first target subsidy and the second target subsidy, wherein the target revenue is the sum of the revenue of the aggregator and the user; the second construction unit is used to construct an objective function based on the target numerical model, wherein the objective function is to maximize the target revenue and the target frequency modulation capacity; the calculation unit is used to solve the objective function using a genetic algorithm to obtain a first target ratio, solve the objective function using a particle swarm optimization algorithm to obtain a second target ratio, calculate a third target ratio based on the first target ratio and the second target ratio, and determine the third target ratio as the ratio of the first target subsidy and the second target subsidy, wherein the first target ratio, the second target ratio, and the third target ratio are the ratio of the first target subsidy and the second target subsidy.

[0012] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0013] According to another aspect of this application, a power grid system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0014] Applying the technical solution of this application, in the above-mentioned subsidy control method for maximizing primary frequency regulation capacity, firstly, historical frequency regulation response data is acquired. This historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, and the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in primary frequency regulation, and the second cost data includes the relevant costs of the aggregator's primary frequency regulation parameters. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. Then, a target numerical model is constructed based on the historical frequency regulation response data. This target numerical model is used to simulate the target... The revenue and the target frequency modulation capacity are varied according to the ratios of the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user. Then, an objective function is constructed based on the target numerical model, whereby the objective function aims to maximize both the target revenue and the target frequency modulation capacity. Finally, the objective function is solved using a genetic algorithm to obtain the first target ratio, and then solved using a particle swarm optimization algorithm to obtain the second target ratio. A third target ratio is calculated based on the first and second target ratios, and this third target ratio is determined as the ratio of the first target subsidy to the second target subsidy. The first, second, and third target ratios are all considered as the ratio of the first target subsidy to the second target subsidy. This application constructs a numerical model based on historical data of users and aggregators participating in primary frequency regulation. This model simulates the impact of different proportions of grid company subsidies used by users and aggregators in primary frequency regulation on primary frequency regulation capacity and total revenue. Based on the above numerical model, and considering the impact of revenue on the enthusiasm of users and aggregators, as well as the effect of primary frequency regulation, the objective functions are determined to maximize total revenue and maximize target frequency regulation capacity. The numerical model is then solved to obtain the subsidy ratio between aggregators and users. This method solves the problem in the prior art where the chaotic allocation of subsidies between aggregators and users leads to poor incentive effects for primary frequency regulation by the grid company. Attached Figure Description

[0015] Figure 1 A hardware block diagram of a mobile terminal is shown, illustrating a subsidy control method for maximizing primary frequency modulation capacity according to an embodiment of this application.

[0016] Figure 2 A flowchart illustrating a subsidy control method for maximizing primary frequency modulation capacity according to an embodiment of this application is shown.

[0017] Figure 3A structural block diagram of a subsidy control device for maximizing primary frequency modulation capacity, according to an embodiment of this application, is shown.

[0018] The above figures include the following reference numerals:

[0019] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] As described in the background section, the different subsidy allocation ratios between aggregators and users in the prior art result in different incentive effects on the participation of aggregators and users. This makes it difficult for power grid companies to predict the correlation between subsidies and grid frequency regulation capacity and adjust their subsidy strategies accordingly. To address the problem of poor incentive effect for primary frequency regulation by power grid companies due to the chaotic subsidy allocation between aggregators and users in the prior art, embodiments of this application provide a subsidy regulation method, apparatus, computer-readable storage medium, and power grid system for maximizing primary frequency regulation capacity.

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0025] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a subsidy control method that maximizes primary frequency modulation capacity according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0027] This embodiment provides a subsidy control method for maximizing primary frequency modulation capacity, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0028] Figure 2 This is a flowchart of a subsidy control method for maximizing primary frequency modulation capacity according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0029] Step S201: Obtain historical frequency regulation response data. The historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation. The second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in the primary frequency regulation. The second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation.

[0030] Specifically, to investigate the impact of the allocation ratio of subsidies issued by the power grid company to aggregators and users on frequency regulation, this application sets up a system that extracts data based on the historical records of aggregators and users participating in primary frequency regulation, obtains the subsidies issued by the power grid company to obtain the aforementioned first target subsidy, obtains the subsidies allocated by aggregators to users to obtain the aforementioned second target subsidy, then determines the allocation ratio k based on the first target subsidy and the second target subsidy, and then extracts the cost of aggregators participating in primary frequency regulation under different k values ​​to obtain the aforementioned second cost data, the cost of users participating in primary frequency regulation to obtain the aforementioned first cost data, and the corresponding target frequency regulation capacity.

[0031] Step S202: Construct a target numerical model based on the aforementioned historical frequency modulation response data. The target numerical model is used to simulate the changing trends of the target revenue and the target frequency modulation capacity with respect to the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user.

[0032] Specifically, based on the aforementioned allocation ratio k, the aggregator's revenue is calculated according to the first target subsidy and the second cost data corresponding to the aggregator, and the user's revenue is calculated according to the second target subsidy and the first cost data corresponding to the user. The target revenue is determined based on the aggregator's revenue and the user's revenue. An expression representing the trend of change is obtained by fitting the target revenue with the value of k in historical data. Furthermore, it can be understood that the higher the total revenue, the higher the enthusiasm of aggregators and users to participate in primary frequency regulation. The more users participate, the more electric vehicles there are, and the greater the capacity available for primary frequency regulation of the power grid. Then, the relationship between the target revenue and the target frequency regulation capacity is determined by fitting historical data to obtain the corresponding expression. The aforementioned target numerical model can be obtained immediately.

[0033] Step S203: Construct an objective function based on the above objective numerical model. The objective function is to maximize the above objective benefit and the above objective frequency regulation capacity.

[0034] Specifically, it is easy to understand that the relationship between the target frequency regulation capacity and the target revenue is monotonically positive. Therefore, when the target revenue reaches its maximum value, the target frequency regulation capacity also reaches its maximum value. Thus, in order to maximize the incentive effect of the power grid company's subsidy, this application sets the objective function of the target numerical model to maximize the target revenue and the target frequency regulation capacity. Let the target revenue be C1 and the target frequency regulation capacity be C2, that is, the objective function is maxC1, maxC2.

[0035] Step S204: Solve the objective function using a genetic algorithm to obtain a first objective ratio; solve the objective function using a particle swarm optimization algorithm to obtain a second objective ratio; calculate a third objective ratio based on the first and second objective ratios; and determine the third objective ratio as the ratio of the first objective subsidy to the second objective subsidy. The first objective ratio, the second objective ratio, and the third objective ratio are the ratio of the first objective subsidy to the second objective subsidy.

[0036] Specifically, based on the above objective function being determined as an optimization problem, the corresponding optimal solutions are obtained by solving the problem using genetic algorithm and particle swarm optimization algorithm, respectively. These are the first objective ratio and the second objective ratio. Then, the first objective ratio and the second objective ratio are weighted according to preset weights to obtain the final optimal solution, which is the third objective ratio.

[0037] The steps for solving the problem using a genetic algorithm include: initializing the population based on each k value to obtain an initial population; calculating the fitness of individuals in the population according to the objective function; determining a certain number of individuals with the highest fitness as parent individuals for crossover and mutation operations to generate a new population; selecting optimized individuals based on fitness to replace the population; and further iterating until the termination condition is met (the objective function converges or the maximum number of iterations is reached); and finally, determining the k value corresponding to the maximum fitness value as the first objective ratio mentioned above.

[0038] The steps for solving the problem using the particle swarm optimization algorithm include: randomly generating a certain number of examples based on the value of k; initializing the position and velocity; calculating the fitness of each particle based on its position, i.e., the value of k; taking the current position as the individual optimal position for each particle; updating the position based on the fitness value; selecting the example with the best fitness from the particle swarm as the global optimal position; updating the particle velocity and position based on the individual optimal position and the global optimal position; and iterating further until the termination condition is met (the objective function converges or the maximum number of iterations is reached), finally obtaining the optimal solution and the aforementioned second objective ratio.

[0039] In this embodiment, firstly, historical frequency regulation response data is acquired. This historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, and the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in primary frequency regulation, and the second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. Then, a target numerical model is constructed based on the historical frequency regulation response data. This target numerical model is used to simulate the target revenue and the target frequency regulation capacity as... The trend of the ratio between the first target subsidy and the second target subsidy is described, and the target revenue is the sum of the revenue of the aggregator and the user. Then, an objective function is constructed based on the target numerical model, where the objective function aims to maximize both the target revenue and the target frequency modulation capacity. Finally, the objective function is solved using a genetic algorithm to obtain the first target ratio, and then solved using a particle swarm optimization algorithm to obtain the second target ratio. A third target ratio is calculated based on the first and second target ratios, and this third target ratio is determined as the ratio of the first target subsidy to the second target subsidy. The first target ratio, the second target ratio, and the third target ratio are all considered as the ratio of the first target subsidy to the second target subsidy. This application constructs a numerical model based on historical data of users and aggregators participating in primary frequency regulation. This model simulates the impact of different proportions of grid company subsidies used by users and aggregators in primary frequency regulation on primary frequency regulation capacity and total revenue. Based on the above numerical model, and considering the impact of revenue on the enthusiasm of users and aggregators, as well as the effect of primary frequency regulation, the objective functions are determined to maximize total revenue and maximize target frequency regulation capacity. The numerical model is then solved to obtain the subsidy ratio between aggregators and users. This method solves the problem in the prior art where the chaotic allocation of subsidies between aggregators and users leads to poor incentive effects for primary frequency regulation by the grid company.

[0040] To construct the aforementioned target numerical model, in one optional implementation, step S202 includes:

[0041] Step S2021: The difference between the first target subsidy and the second target subsidy is determined as the third target subsidy, and the ratio between the third target subsidy and the second target subsidy is determined as the allocation ratio.

[0042] Specifically, the difference between the first target subsidy and the second target subsidy is calculated to obtain the third target subsidy, which is the portion of the power grid company's subsidy that is allocated to the aggregator. The ratio of the third target subsidy to the second target subsidy is the k value.

[0043] Step S2022: A first objective function is obtained by fitting the above allocation ratio and the above first cost data. The first objective function is used to characterize the changing trend of the above first cost data with the above allocation ratio.

[0044] In practice, based on the above target numerical model, the independent variable in the historical data is determined to be the above-mentioned k value, and the dependent variables are the target revenue and the target frequency regulation capacity. However, directly fitting the above data based on the historical data will result in the model's modeling accuracy not meeting the usage requirements due to the large number of variables involved and the complex relationships.

[0045] Specifically, the fitting relationship is decomposed according to the above-mentioned calculation method of target revenue. First, the allocation ratio k is fitted with the cost of users participating in one frequency modulation based on historical data to obtain the corresponding expression, and thus the above-mentioned first objective function is obtained.

[0046] It is understandable that the cost for a user to participate in a frequency adjustment is mainly the cost of the impact on the user's travel caused by parking electric vehicles at charging stations.

[0047] Step S2023: A second objective function is obtained by fitting the above allocation ratio and the above second cost data. The second objective function is used to characterize the trend of the above second cost data with the above allocation ratio.

[0048] Specifically, the fitting relationship is decomposed according to the above-mentioned method of calculating the target return, and the allocation ratio k is fitted with the cost of the aggregator participating in the first frequency modulation based on historical data to obtain the corresponding expression, thus obtaining the above-mentioned second objective function.

[0049] It is understandable that the cost for aggregators to participate in frequency regulation is mainly due to the discrepancy between the electricity price adjustment and the grid's electricity sales price, which leads to an increase in the service cost per unit of electricity.

[0050] Step S2024: A third objective function is obtained by fitting the above allocation ratio and the above first target subsidy. The third objective function is used to characterize the changing trend of the above first target subsidy with the above allocation ratio.

[0051] Specifically, the fitting relationship is decomposed according to the above-mentioned method of calculating the target revenue. Based on historical data, the allocation ratio k is fitted with the total subsidies issued by aggregators and users participating in the primary frequency regulation grid to obtain the corresponding expression, thus obtaining the above-mentioned third objective function.

[0052] Step S2025: A fourth objective function is obtained by fitting the above allocation ratio and the above target frequency modulation capacity. The fourth objective function is used to characterize the changing trend of the above target frequency modulation capacity with the above allocation ratio.

[0053] Specifically, the fitting relationship is decomposed according to the above-mentioned method of calculating the target benefit, and the allocation ratio k is fitted with the target frequency modulation capacity based on historical data to obtain the above-mentioned fourth objective function.

[0054] Step S2026: Construct a first target formula, a second target formula, and a third target formula. The first target formula is used to calculate a first revenue based on the third target subsidy and the second cost data. The second target formula is used to calculate a second revenue based on the second target subsidy and the first cost data. The third target formula is used to calculate the target revenue based on the first revenue and the second revenue. The first revenue is the net revenue of the aggregator participating in a frequency modulation, and the second revenue is the net revenue of the user participating in a frequency modulation.

[0055] Specifically, the first objective formula is to calculate the difference between the third objective subsidy and the second cost data, the second objective formula is to calculate the difference between the second objective subsidy and the first cost data, and the third objective formula is to calculate the sum of the first revenue and the second revenue.

[0056] Step S2027: Combine the above-mentioned first objective function, second objective function, third objective function, fourth objective function, first objective formula, second objective formula and third objective formula to obtain the above-mentioned objective numerical model.

[0057] Specifically, the above-mentioned first objective function, second objective function, third objective function, fourth objective function, first objective formula, second objective formula, and third objective formula are combined to obtain the above-mentioned objective numerical model.

[0058] To obtain the first objective function mentioned above, in an optional implementation, step S2022 includes:

[0059] Step S20221: Determine the target number based on the aforementioned historical frequency modulation response data. The target number is the total number of the aforementioned users participating in a frequency modulation.

[0060] In practice, based on the first objective function, the independent variable is determined to be the k value and the dependent variable is the first cost data. However, directly fitting the above data based on historical data will result in the model's modeling accuracy not meeting the requirements due to the large number of variables involved and the complex relationships.

[0061] It is easy to understand that the above target ratio will affect the user's enthusiasm for participating in a frequency modulation, and thus affect the cost of the user participating in a frequency modulation. In this application, the number of users participating in a frequency modulation corresponding to different k values ​​is first determined based on the above historical frequency modulation response data.

[0062] Step S20222: A fifth objective function is obtained by fitting the above allocation ratio and the above target quantity. The fifth objective function is used to characterize the changing trend of the above target quantity with the above allocation ratio.

[0063] Specifically, based on the aforementioned historical frequency modulation response data, the relationship between the k value and the aforementioned target quantity is fitted. It is easy to understand that the aforementioned target quantity is negatively correlated with the aforementioned k value. The larger the proportion, the smaller the allocation proportion of users and the fewer users participate in a frequency modulation. In addition, the proportion may be different for different time periods when users participate in a frequency modulation. Therefore, the influence of time period needs to be introduced when performing the fitting.

[0064] Step S20223: Determine the user's travel cost, travel impact cost, and user charging cost based on the aforementioned first cost data;

[0065] Specifically, based on the aforementioned historical frequency regulation response data, the user's cost data is broken down. This application determines that the cost for a user to participate in a frequency regulation is mainly the cost calculated by the impact of parking the electric vehicle at the charging station on the user's travel, i.e., the aforementioned user travel cost. In addition, since the electric vehicle is parked at the charging station, the user needs to use other modes of travel, which in turn generates the aforementioned travel impact cost. Furthermore, the charging amount of the user's travel during the parking period decreases, and the cost saved is set as the aforementioned user charging cost, which is taken as a negative value.

[0066] Step S20224: Based on the target quantity and user travel cost, the target quantity and travel impact cost, and the target quantity and user charging cost, respectively, fit the sixth objective function, the seventh objective function, and the eighth objective function to obtain them. The sixth objective function is used to characterize the trend of user travel cost with the target quantity, the seventh objective function is used to characterize the trend of travel impact cost with the target quantity, and the eighth objective function is used to characterize the trend of user charging cost with the target quantity.

[0067] Specifically, based on the aforementioned historical frequency response data, the relationship between the target number and user travel costs is fitted. It can be understood that user travel costs are positively correlated with the number of users. As the number of users increases, the fitting coefficient approaches different values. Therefore, the aforementioned correlation is fitted as a piecewise function. Similarly, the relationship between the aforementioned travel impact costs and user charging costs and the target number is fitted to obtain the aforementioned sixth objective function, seventh objective function, and eighth objective function.

[0068] Step S20225: Construct a fourth objective formula, which is used to calculate the first cost data based on the user travel cost, the travel impact cost, and the user charging cost.

[0069] Specifically, the fourth objective formula is the sum of the user's travel costs, the travel impact costs, and the user's charging costs.

[0070] Step S20226: The first objective function is obtained by fitting the above-mentioned fifth objective function, sixth objective function, seventh objective function, eighth objective function and fourth objective formula.

[0071] Specifically, the first objective function is obtained by simultaneously solving the above-mentioned fifth objective function, sixth objective function, seventh objective function, eighth objective function and fourth objective formula, and then substituting the above-mentioned historical frequency modulation response data into the form of a fitting solution.

[0072] To obtain the second objective function described above, in one optional implementation, step S2023 includes:

[0073] Step S20231: Determine the charging service cost and charging electricity price cost based on the second cost data mentioned above;

[0074] Specifically, based on the aforementioned historical frequency regulation response data, the cost data of aggregators is broken down. This application determines that the cost of aggregators participating in primary frequency regulation mainly comes from the deviation between the electricity price at which aggregators reduce electricity prices to guide users to charge for longer periods and the electricity price before the guidance. The aforementioned charging service cost is the price at which aggregators provide charging services, and the aforementioned charging electricity price cost is the electricity price of the power grid company.

[0075] Step S20232: Based on the above target quantity and the above charging service cost, and the above target quantity and the above charging electricity cost, respectively, a ninth objective function and a tenth objective function are obtained by fitting. The ninth objective function is used to characterize the changing trend of the above charging service cost with the above target quantity, and the tenth objective function is used to characterize the changing trend of the above charging electricity cost with the above target quantity.

[0076] Specifically, based on the aforementioned historical frequency regulation response data, the relationship between the target quantity and the aforementioned charging service cost is fitted. It can be understood that the user's travel cost is negatively correlated with the number of users. To ensure accuracy, the fitting is performed based on different electricity prices at different times. Similarly, the relationship between the aforementioned charging electricity price cost and the target quantity is fitted to obtain the aforementioned ninth objective function and tenth objective function.

[0077] In one embodiment of this application, during the above-mentioned fitting segmentation process, when fitting is performed based on different electricity prices for different time periods, the corresponding electricity price will also affect the above-mentioned target quantity, and a correction coefficient will be set to further correct the fitting result.

[0078] Step S20233: Construct a fifth objective formula, which is used to calculate the second cost data based on the charging service cost and the charging electricity price cost.

[0079] Specifically, the fifth objective formula mentioned above is the difference between the charging electricity price cost and the charging service cost.

[0080] Step S20234: The first objective function is obtained by fitting the above-mentioned fifth objective function, the above-mentioned ninth objective function, the above-mentioned tenth objective function and the above-mentioned fifth objective formula.

[0081] Specifically, the second objective function is obtained by simultaneously solving the above-mentioned fifth objective function, ninth objective function, tenth objective function and fifth objective formula, and then substituting the above-mentioned historical frequency modulation response data into the system for fitting.

[0082] To obtain the aforementioned third objective function, in an optional implementation, step S2024 includes:

[0083] Step S20241: Based on the above target quantity and the above first target subsidy, an eleventh objective function is obtained by fitting. The eleventh objective function is used to characterize the changing trend of the above first target subsidy with the above target quantity.

[0084] Specifically, the eleventh objective function is obtained by fitting the target quantity with the first target subsidy based on the aforementioned historical frequency regulation response data. It can be understood that, in order to encourage users to participate in primary frequency regulation, the first target subsidy is positively correlated with the number of users, and its specific fitting coefficient depends on the incentive strategy of the power grid company.

[0085] Step S20242: Fit the above-mentioned fifth objective function and the above-mentioned eleventh objective function to obtain the above-mentioned third objective function.

[0086] Specifically, the third objective function is obtained by combining the fifth and eleventh objective functions and substituting the historical frequency modulation response data into the system.

[0087] To obtain the aforementioned fourth objective function, in an optional implementation, step S2025 includes:

[0088] Step S20251: Based on the above target quantity and the above target frequency modulation capacity, a twelfth objective function is obtained by fitting. The twelfth objective function is used to characterize the changing trend of the above target frequency modulation capacity with the above target quantity.

[0089] Specifically, the twelfth objective function is obtained by fitting the target number and the target frequency modulation capacity based on the aforementioned historical frequency modulation response data. It can be understood that the target frequency modulation capacity is positively correlated with the number of users, and the specific fitting coefficient depends on the vehicle type and performance.

[0090] Step S20252: The fourth objective function is obtained by fitting the fifth objective function and the twelfth objective function.

[0091] Specifically, the fourth objective function is obtained by combining the fifth and twelfth objective functions and substituting the historical frequency modulation response data into the system.

[0092] To ensure the effectiveness of the optimal solution, in one optional implementation, after constructing the objective function based on the aforementioned objective numerical model, the method further includes:

[0093] Step S301: Determine that both the second target subsidy and the third target subsidy are not zero as the first constraint condition of the objective function.

[0094] Specifically, this application sets the first constraint condition for the above-mentioned target numerical model to ensure that the second target subsidy and the third target subsidy are not zero, that is, the subsidies received by users and aggregators are not zero. If one of them is zero, it can be understood that the variable parameter relationship of the above-mentioned target numerical model is meaningless.

[0095] Step S302: The target frequency modulation capacity being greater than or equal to the first threshold is determined as the second constraint condition of the objective function.

[0096] Specifically, this application sets the second constraint condition for the above-mentioned target numerical model to ensure that the above-mentioned target frequency modulation capacity after excitation is greater than a certain value. If the above-mentioned target frequency modulation capacity is less than the above-mentioned second threshold, the excitation strategy is considered to be useless.

[0097] Step S303: The first target subsidy being greater than or equal to the second threshold is determined as the third constraint condition of the objective function.

[0098] Specifically, similarly, the third constraint condition for the objective function is that the first target subsidy issued by the power grid company is greater than or equal to the second threshold. When the first target subsidy is small, it is considered to have a small effect on promoting the enthusiasm of aggregators and users.

[0099] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0100] This application also provides a subsidy control device for maximizing primary frequency modulation capacity. It should be noted that this subsidy control device can be used to execute the subsidy control method for maximizing primary frequency modulation capacity provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0101] The following describes the subsidy control device for maximizing primary frequency modulation capacity provided in the embodiments of this application.

[0102] Figure 3 This is a structural block diagram of a subsidy control device for maximizing primary frequency modulation capacity according to an embodiment of this application. Figure 3 As shown, the device includes:

[0103] The acquisition unit 10 is used to acquire historical frequency regulation response data. The historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity when the first target subsidy and the second target subsidy correspond to different ratios. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation. The second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in the primary frequency regulation. The second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation.

[0104] Specifically, to investigate the impact of the allocation ratio of subsidies issued by the power grid company to aggregators and users on frequency regulation, this application sets up a system that extracts data based on the historical records of aggregators and users participating in primary frequency regulation, obtains the subsidies issued by the power grid company to obtain the aforementioned first target subsidy, obtains the subsidies allocated by aggregators to users to obtain the aforementioned second target subsidy, then determines the allocation ratio k based on the first target subsidy and the second target subsidy, and then extracts the cost of aggregators participating in primary frequency regulation under different k values ​​to obtain the aforementioned second cost data, the cost of users participating in primary frequency regulation to obtain the aforementioned first cost data, and the corresponding target frequency regulation capacity.

[0105] The first construction unit 20 is used to construct a target numerical model based on the aforementioned historical frequency modulation response data. The target numerical model is used to simulate the changing trend of the target revenue and the target frequency modulation capacity with the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user.

[0106] Specifically, based on the aforementioned allocation ratio k, the aggregator's revenue is calculated according to the first target subsidy and the second cost data corresponding to the aggregator, and the user's revenue is calculated according to the second target subsidy and the first cost data corresponding to the user. The target revenue is determined based on the aggregator's revenue and the user's revenue. An expression representing the trend of change is obtained by fitting the target revenue with the value of k in historical data. Furthermore, it can be understood that the higher the total revenue, the higher the enthusiasm of aggregators and users to participate in primary frequency regulation. The more users participate, the more electric vehicles there are, and the greater the capacity available for primary frequency regulation of the power grid. Then, the relationship between the target revenue and the target frequency regulation capacity is determined by fitting historical data to obtain the corresponding expression. The aforementioned target numerical model can be obtained immediately.

[0107] The second construction unit 30 is used to construct an objective function based on the above-mentioned objective numerical model, wherein the objective function is to maximize the above-mentioned objective benefit and maximize the above-mentioned objective frequency regulation capacity.

[0108] Specifically, it is easy to understand that the relationship between the target frequency regulation capacity and the target revenue is monotonically positive. Therefore, when the target revenue reaches its maximum value, the target frequency regulation capacity also reaches its maximum value. Thus, in order to maximize the incentive effect of the power grid company's subsidy, this application sets the objective function of the target numerical model to maximize the target revenue and the target frequency regulation capacity. Let the target revenue be C1 and the target frequency regulation capacity be C2, that is, the objective function is maxC1, maxC2.

[0109] The calculation unit 40 is used to solve the objective function using a genetic algorithm to obtain a first objective ratio, solve the objective function using a particle swarm optimization algorithm to obtain a second objective ratio, calculate a third objective ratio based on the first objective ratio and the second objective ratio, and determine the third objective ratio as the ratio of the first objective subsidy and the second objective subsidy. The first objective ratio, the second objective ratio, and the third objective ratio are the ratio of the first objective subsidy and the second objective subsidy.

[0110] Specifically, based on the above objective function being determined as an optimization problem, the corresponding optimal solutions are obtained by solving the problem using genetic algorithm and particle swarm optimization algorithm, respectively. These are the first objective ratio and the second objective ratio. Then, the first objective ratio and the second objective ratio are weighted according to preset weights to obtain the final optimal solution, which is the third objective ratio.

[0111] The steps for solving the problem using a genetic algorithm include: initializing the population based on each k value to obtain an initial population; calculating the fitness of individuals in the population according to the objective function; determining a certain number of individuals with the highest fitness as parent individuals for crossover and mutation operations to generate a new population; selecting optimized individuals based on fitness to replace the population; and further iterating until the termination condition is met (the objective function converges or the maximum number of iterations is reached); and finally, determining the k value corresponding to the maximum fitness value as the first objective ratio mentioned above.

[0112] The steps for solving the problem using the particle swarm optimization algorithm include: randomly generating a certain number of examples based on the value of k; initializing the position and velocity; calculating the fitness of each particle based on its position, i.e., the value of k; taking the current position as the individual optimal position for each particle; updating the position based on the fitness value; selecting the example with the best fitness from the particle swarm as the global optimal position; updating the particle velocity and position based on the individual optimal position and the global optimal position; and iterating further until the termination condition is met (the objective function converges or the maximum number of iterations is reached), finally obtaining the optimal solution and the aforementioned second objective ratio.

[0113] In this embodiment, the acquisition unit acquires historical frequency regulation response data. This historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, and the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in primary frequency regulation, and the second cost data includes the relevant costs of the aggregator's primary frequency regulation parameters. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. The first construction unit constructs a target numerical model based on the historical frequency regulation response data. This target numerical model is used to simulate the target revenue and the target frequency regulation capacity as... The trend of the ratio between the first target subsidy and the second target subsidy; the target revenue is the sum of the revenue of the aggregator and the user; the second construction unit constructs an objective function based on the target numerical model, the objective function being to maximize the target revenue and the target frequency modulation capacity; the calculation unit solves the objective function using a genetic algorithm to obtain the first target ratio, solves the objective function using a particle swarm optimization algorithm to obtain the second target ratio, calculates the third target ratio based on the first target ratio and the second target ratio, and determines the third target ratio as the ratio between the first target subsidy and the second target subsidy; the first target ratio, the second target ratio, and the third target ratio are the ratio between the first target subsidy and the second target subsidy. This application constructs a numerical model based on historical data of users and aggregators participating in primary frequency regulation. This model simulates the impact of different proportions of grid company subsidies used by users and aggregators in primary frequency regulation on primary frequency regulation capacity and total revenue. Based on the above numerical model, and considering the impact of revenue on the enthusiasm of users and aggregators, as well as the effect of primary frequency regulation, the objective functions are determined to maximize total revenue and maximize target frequency regulation capacity. The numerical model is then solved to obtain the subsidy ratio between aggregators and users. This method solves the problem in the prior art where the chaotic allocation of subsidies between aggregators and users leads to poor incentive effects for primary frequency regulation by the grid company.

[0114] To construct the aforementioned target numerical model, in one optional implementation, the first construction unit includes:

[0115] The first determining module is used to determine the difference between the first target subsidy and the second target subsidy as the third target subsidy, and to determine the ratio between the third target subsidy and the second target subsidy as the allocation ratio.

[0116] Specifically, the difference between the first target subsidy and the second target subsidy is calculated to obtain the third target subsidy, which is the portion of the power grid company's subsidy that is allocated to the aggregator. The ratio of the third target subsidy to the second target subsidy is the k value.

[0117] The first fitting module is used to fit a first objective function based on the above allocation ratio and the above first cost data. The first objective function is used to characterize the changing trend of the above first cost data with the above allocation ratio.

[0118] In practice, based on the above target numerical model, the independent variable in the historical data is determined to be the above-mentioned k value, and the dependent variables are the target revenue and the target frequency regulation capacity. However, directly fitting the above data based on the historical data will result in the model's modeling accuracy not meeting the usage requirements due to the large number of variables involved and the complex relationships.

[0119] Specifically, the fitting relationship is decomposed according to the above-mentioned calculation method of target revenue. First, the allocation ratio k is fitted with the cost of users participating in one frequency modulation based on historical data to obtain the corresponding expression, and thus the above-mentioned first objective function is obtained.

[0120] It is understandable that the cost for a user to participate in a frequency adjustment is mainly the cost of the impact on the user's travel caused by parking electric vehicles at charging stations.

[0121] The second fitting module is used to fit a second objective function based on the above allocation ratio and the above second cost data. The second objective function is used to characterize the trend of the above second cost data with the above allocation ratio.

[0122] Specifically, the fitting relationship is decomposed according to the above-mentioned method of calculating the target return, and the allocation ratio k is fitted with the cost of the aggregator participating in the first frequency modulation based on historical data to obtain the corresponding expression, thus obtaining the above-mentioned second objective function.

[0123] It is understandable that the cost for aggregators to participate in frequency regulation is mainly due to the discrepancy between the electricity price adjustment and the grid's electricity sales price, which leads to an increase in the service cost per unit of electricity.

[0124] The third fitting module is used to fit a third objective function based on the above allocation ratio and the above first target subsidy. The third objective function is used to characterize the changing trend of the above first target subsidy with the above allocation ratio.

[0125] Specifically, the fitting relationship is decomposed according to the above-mentioned method of calculating the target revenue. Based on historical data, the allocation ratio k is fitted with the total subsidies issued by aggregators and users participating in the primary frequency regulation grid to obtain the corresponding expression, thus obtaining the above-mentioned third objective function.

[0126] The fourth fitting module is used to fit the above allocation ratio and the above target frequency modulation capacity to obtain a fourth objective function. The fourth objective function is used to characterize the changing trend of the above target frequency modulation capacity with the above allocation ratio.

[0127] Specifically, the fitting relationship is decomposed according to the above-mentioned method of calculating the target benefit, and the allocation ratio k is fitted with the target frequency modulation capacity based on historical data to obtain the above-mentioned fourth objective function.

[0128] The module is used to construct a first target formula, a second target formula, and a third target formula. The first target formula is used to calculate a first revenue based on the third target subsidy and the second cost data. The second target formula is used to calculate a second revenue based on the second target subsidy and the first cost data. The third target formula is used to calculate the target revenue based on the first revenue and the second revenue. The first revenue is the net revenue of the aggregator participating in a frequency modulation, and the second revenue is the net revenue of the user participating in a frequency modulation.

[0129] Specifically, the first objective formula is to calculate the difference between the third objective subsidy and the second cost data, the second objective formula is to calculate the difference between the second objective subsidy and the first cost data, and the third objective formula is to calculate the sum of the first revenue and the second revenue.

[0130] The fifth fitting module is used to simultaneously solve the first objective function, the second objective function, the third objective function, the fourth objective function, the first objective formula, the second objective formula, and the third objective formula to obtain the above-mentioned objective numerical model.

[0131] Specifically, the above-mentioned first objective function, second objective function, third objective function, fourth objective function, first objective formula, second objective formula, and third objective formula are combined to obtain the above-mentioned objective numerical model.

[0132] To obtain the aforementioned first objective function, in one optional implementation, the first fitting module includes:

[0133] The first determining submodule is used to determine the target quantity based on the aforementioned historical frequency modulation response data, wherein the target quantity is the total number of the aforementioned users participating in a frequency modulation.

[0134] In practice, based on the first objective function, the independent variable is determined to be the k value and the dependent variable is the first cost data. However, directly fitting the above data based on historical data will result in the model's modeling accuracy not meeting the requirements due to the large number of variables involved and the complex relationships.

[0135] It is easy to understand that the above target ratio will affect the user's enthusiasm for participating in a frequency modulation, and thus affect the cost of the user participating in a frequency modulation. In this application, the number of users participating in a frequency modulation corresponding to different k values ​​is first determined based on the above historical frequency modulation response data.

[0136] The first fitting submodule is used to fit a fifth objective function based on the above allocation ratio and the above target quantity. The fifth objective function is used to characterize the changing trend of the above target quantity with the above allocation ratio.

[0137] Specifically, based on the aforementioned historical frequency modulation response data, the relationship between the k value and the aforementioned target quantity is fitted. It is easy to understand that the aforementioned target quantity is negatively correlated with the aforementioned k value. The larger the proportion, the smaller the allocation proportion of users and the fewer users participate in a frequency modulation. In addition, the proportion may be different for different time periods when users participate in a frequency modulation. Therefore, the influence of time period needs to be introduced when performing the fitting.

[0138] The second determining submodule is used to determine the user's travel cost, travel impact cost, and user charging cost based on the aforementioned first cost data.

[0139] Specifically, based on the aforementioned historical frequency regulation response data, the user's cost data is broken down. This application determines that the cost for a user to participate in a frequency regulation is mainly the cost calculated by the impact of parking the electric vehicle at the charging station on the user's travel, i.e., the aforementioned user travel cost. In addition, since the electric vehicle is parked at the charging station, the user needs to use other modes of travel, which in turn generates the aforementioned travel impact cost. Furthermore, the charging amount of the user's travel during the parking period decreases, and the cost saved is set as the aforementioned user charging cost, which is taken as a negative value.

[0140] The second fitting submodule is used to fit a sixth objective function, a seventh objective function, and an eighth objective function based on the target number and the user travel cost, the target number and the travel impact cost, and the target number and the user charging cost, respectively. The sixth objective function is used to characterize the trend of the user travel cost with the target number, the seventh objective function is used to characterize the trend of the travel impact cost with the target number, and the eighth objective function is used to characterize the trend of the user charging cost with the target number.

[0141] Specifically, based on the aforementioned historical frequency response data, the relationship between the target number and user travel costs is fitted. It can be understood that user travel costs are positively correlated with the number of users. As the number of users increases, the fitting coefficient approaches different values. Therefore, the aforementioned correlation is fitted as a piecewise function. Similarly, the relationship between the aforementioned travel impact costs and user charging costs and the target number is fitted to obtain the aforementioned sixth objective function, seventh objective function, and eighth objective function.

[0142] The first construction submodule is used to construct the fourth objective formula, which is used to calculate the first cost data based on the user travel cost, the travel impact cost and the user charging cost.

[0143] Specifically, the fourth objective formula is the sum of the user's travel costs, the travel impact costs, and the user's charging costs.

[0144] The third fitting submodule is used to fit the first objective function based on the fifth objective function, the sixth objective function, the seventh objective function, the eighth objective function and the fourth objective formula.

[0145] Specifically, the first objective function is obtained by simultaneously solving the above-mentioned fifth objective function, sixth objective function, seventh objective function, eighth objective function and fourth objective formula, and then substituting the above-mentioned historical frequency modulation response data into the form of a fitting solution.

[0146] To obtain the second objective function, in one optional implementation, the second fitting module includes:

[0147] The third determining submodule is used to determine the charging service cost and the charging electricity price cost based on the aforementioned second cost data.

[0148] Specifically, based on the aforementioned historical frequency regulation response data, the cost data of aggregators is broken down. This application determines that the cost of aggregators participating in primary frequency regulation mainly comes from the deviation between the electricity price at which aggregators reduce electricity prices to guide users to charge for longer periods and the electricity price before the guidance. The aforementioned charging service cost is the price at which aggregators provide charging services, and the aforementioned charging electricity price cost is the electricity price of the power grid company.

[0149] The fourth fitting submodule is used to fit the ninth objective function and the tenth objective function based on the target quantity and the charging service cost, and the target quantity and the charging electricity cost, respectively. The ninth objective function is used to characterize the trend of the charging service cost with the target quantity, and the tenth objective function is used to characterize the trend of the charging electricity cost with the target quantity.

[0150] Specifically, based on the aforementioned historical frequency regulation response data, the relationship between the target quantity and the aforementioned charging service cost is fitted. It can be understood that the user's travel cost is negatively correlated with the number of users. To ensure accuracy, the fitting is performed based on different electricity prices at different times. Similarly, the relationship between the aforementioned charging electricity price cost and the target quantity is fitted to obtain the aforementioned ninth objective function and tenth objective function.

[0151] In one embodiment of this application, during the above-mentioned fitting segmentation process, when fitting is performed based on different electricity prices for different time periods, the corresponding electricity price will also affect the above-mentioned target quantity, and a correction coefficient will be set to further correct the fitting result.

[0152] The second construction submodule is used to construct the fifth objective formula, which is used to calculate the second cost data based on the charging service cost and the charging electricity price cost.

[0153] Specifically, the fifth objective formula mentioned above is the difference between the charging electricity price cost and the charging service cost.

[0154] The fifth fitting submodule is used to fit the first objective function based on the fifth objective function, the ninth objective function, the tenth objective function and the fifth objective formula.

[0155] Specifically, the second objective function is obtained by simultaneously solving the above-mentioned fifth objective function, ninth objective function, tenth objective function and fifth objective formula, and then substituting the above-mentioned historical frequency modulation response data into the system for fitting.

[0156] To obtain the aforementioned third objective function, in one optional implementation, the third fitting module includes:

[0157] The sixth fitting submodule is used to fit the above target quantity and the above first target subsidy to obtain the eleventh objective function. The eleventh objective function is used to characterize the changing trend of the above first target subsidy with the above target quantity.

[0158] Specifically, the eleventh objective function is obtained by fitting the target quantity with the first target subsidy based on the aforementioned historical frequency regulation response data. It can be understood that, in order to encourage users to participate in primary frequency regulation, the first target subsidy is positively correlated with the number of users, and its specific fitting coefficient depends on the incentive strategy of the power grid company.

[0159] The seventh fitting submodule is used to fit the above-mentioned fifth objective function and the above-mentioned eleventh objective function to obtain the above-mentioned third objective function.

[0160] Specifically, the third objective function is obtained by combining the fifth and eleventh objective functions and substituting the historical frequency modulation response data into the system.

[0161] To obtain the aforementioned fourth objective function, in one optional implementation, the fourth fitting module includes:

[0162] The eighth fitting submodule is used to fit the above target quantity and the above target frequency modulation capacity to obtain the twelfth objective function. The twelfth objective function is used to characterize the changing trend of the above target frequency modulation capacity with the above target quantity.

[0163] Specifically, the twelfth objective function is obtained by fitting the target number and the target frequency modulation capacity based on the aforementioned historical frequency modulation response data. It can be understood that the target frequency modulation capacity is positively correlated with the number of users, and the specific fitting coefficient depends on the vehicle type and performance.

[0164] The ninth fitting submodule is used to fit the above-mentioned fifth objective function and the above-mentioned twelfth objective function to obtain the above-mentioned fourth objective function.

[0165] Specifically, the fourth objective function is obtained by combining the fifth and twelfth objective functions and substituting the historical frequency modulation response data into the system.

[0166] To ensure the validity of the optimal solution, in one optional embodiment, the above-mentioned apparatus further includes:

[0167] The first determining unit is used to determine that the second target subsidy and the third target subsidy are both non-zero as the first constraint condition of the objective function after constructing the objective function according to the above target numerical model.

[0168] Specifically, this application sets the first constraint condition for the above-mentioned target numerical model to ensure that the second target subsidy and the third target subsidy are not zero, that is, the subsidies received by users and aggregators are not zero. If one of them is zero, it can be understood that the variable parameter relationship of the above-mentioned target numerical model is meaningless.

[0169] The second determining unit is used to determine the target frequency modulation capacity being greater than or equal to the first threshold as the second constraint condition of the objective function.

[0170] Specifically, this application sets the second constraint condition for the above-mentioned target numerical model to ensure that the above-mentioned target frequency modulation capacity after excitation is greater than a certain value. If the above-mentioned target frequency modulation capacity is less than the above-mentioned second threshold, the excitation strategy is considered to be useless.

[0171] The third determining unit is used to determine that the first target subsidy being greater than or equal to the second threshold is the third constraint condition of the objective function.

[0172] Specifically, similarly, the third constraint condition for the objective function is that the first target subsidy issued by the power grid company is greater than or equal to the second threshold. When the first target subsidy is small, it is considered to have a small effect on promoting the enthusiasm of aggregators and users.

[0173] The aforementioned subsidy control device for maximizing primary frequency modulation capacity includes a processor and a memory. The aforementioned acquisition unit, first unit, etc., are all stored as program units in the memory, and the processor executes these program units stored in the memory to achieve the corresponding functions. All of the aforementioned modules are located in the same processor; alternatively, the aforementioned modules may be located in different processors in any combination.

[0174] The processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured, and adjusting core parameters can enhance the effect of subsidies on increasing primary frequency modulation capacity.

[0175] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0176] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the subsidy control method for maximizing primary frequency modulation capacity.

[0177] Specifically, subsidy control methods that maximize primary frequency regulation capacity include:

[0178] Step S201: Obtain historical frequency regulation response data. The historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation. The second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in the primary frequency regulation. The second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation.

[0179] Step S202: Construct a target numerical model based on the aforementioned historical frequency modulation response data. The target numerical model is used to simulate the changing trends of the target revenue and the target frequency modulation capacity with respect to the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user.

[0180] Step S203: Construct an objective function based on the above objective numerical model. The objective function is to maximize the above objective benefit and the above objective frequency regulation capacity.

[0181] Step S204: Solve the objective function using a genetic algorithm to obtain a first objective ratio; solve the objective function using a particle swarm optimization algorithm to obtain a second objective ratio; calculate a third objective ratio based on the first and second objective ratios; and determine the third objective ratio as the ratio of the first objective subsidy to the second objective subsidy. The first objective ratio, the second objective ratio, and the third objective ratio are the ratio of the first objective subsidy to the second objective subsidy.

[0182] This invention provides a processor for running a program, wherein the program executes the above-described subsidy control method for maximizing primary frequency modulation capacity.

[0183] Specifically, subsidy control methods that maximize primary frequency regulation capacity include:

[0184] Step S201: Obtain historical frequency regulation response data. The historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation. The second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in the primary frequency regulation. The second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation.

[0185] Step S202: Construct a target numerical model based on the aforementioned historical frequency modulation response data. The target numerical model is used to simulate the changing trends of the target revenue and the target frequency modulation capacity with respect to the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user.

[0186] Step S203: Construct an objective function based on the above objective numerical model. The objective function is to maximize the above objective benefit and the above objective frequency regulation capacity.

[0187] Step S204: Solve the objective function using a genetic algorithm to obtain a first objective ratio; solve the objective function using a particle swarm optimization algorithm to obtain a second objective ratio; calculate a third objective ratio based on the first and second objective ratios; and determine the third objective ratio as the ratio of the first objective subsidy to the second objective subsidy. The first objective ratio, the second objective ratio, and the third objective ratio are the ratio of the first objective subsidy to the second objective subsidy.

[0188] This invention provides a power grid system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0189] Step S201: Obtain historical frequency regulation response data. The historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation. The second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in the primary frequency regulation. The second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation.

[0190] Step S202: Construct a target numerical model based on the aforementioned historical frequency modulation response data. The target numerical model is used to simulate the changing trends of the target revenue and the target frequency modulation capacity with respect to the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user.

[0191] Step S203: Construct an objective function based on the above objective numerical model. The objective function is to maximize the above objective benefit and the above objective frequency regulation capacity.

[0192] Step S204: Solve the objective function using a genetic algorithm to obtain a first objective ratio; solve the objective function using a particle swarm optimization algorithm to obtain a second objective ratio; calculate a third objective ratio based on the first and second objective ratios; and determine the third objective ratio as the ratio of the first objective subsidy to the second objective subsidy. The first objective ratio, the second objective ratio, and the third objective ratio are the ratio of the first objective subsidy to the second objective subsidy.

[0193] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0194] Step S201: Obtain historical frequency regulation response data. The historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation. The second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in the primary frequency regulation. The second cost data includes the relevant costs of the aggregator parameters for primary frequency regulation. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation.

[0195] Step S202: Construct a target numerical model based on the aforementioned historical frequency modulation response data. The target numerical model is used to simulate the changing trends of the target revenue and the target frequency modulation capacity with respect to the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user.

[0196] Step S203: Construct an objective function based on the above objective numerical model. The objective function is to maximize the above objective benefit and the above objective frequency regulation capacity.

[0197] Step S204: Solve the objective function using a genetic algorithm to obtain a first objective ratio; solve the objective function using a particle swarm optimization algorithm to obtain a second objective ratio; calculate a third objective ratio based on the first and second objective ratios; and determine the third objective ratio as the ratio of the first objective subsidy to the second objective subsidy. The first objective ratio, the second objective ratio, and the third objective ratio are the ratio of the first objective subsidy to the second objective subsidy.

[0198] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0199] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0201] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0202] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0203] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0204] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0205] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0206] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0207] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0208] 1) The subsidy control method for maximizing primary frequency regulation capacity of this application firstly acquires historical frequency regulation response data. This historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, and the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in primary frequency regulation, and the second cost data includes the relevant costs of the aggregator's primary frequency regulation parameters. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. Then, a target numerical model is constructed based on the historical frequency regulation response data. This target numerical model is used to simulate the target revenue and... The target frequency modulation capacity is described as changing with the ratio of the first target subsidy and the second target subsidy, and the target revenue is the sum of the revenue of the aggregator and the user. Then, an objective function is constructed based on the target numerical model, whereby the objective function aims to maximize both the target revenue and the target frequency modulation capacity. Finally, the objective function is solved using a genetic algorithm to obtain the first target ratio, and then solved using a particle swarm optimization algorithm to obtain the second target ratio. A third target ratio is calculated based on the first and second target ratios, and this third target ratio is determined as the ratio of the first target subsidy to the second target subsidy. The first, second, and third target ratios are all considered as the ratio of the first target subsidy to the second target subsidy. This application constructs a numerical model based on historical data of users and aggregators participating in primary frequency regulation. This model simulates the impact of different proportions of grid company subsidies used by users and aggregators in primary frequency regulation on primary frequency regulation capacity and total revenue. Based on the above numerical model, and considering the impact of revenue on the enthusiasm of users and aggregators, as well as the effect of primary frequency regulation, the objective functions are determined to maximize total revenue and maximize target frequency regulation capacity. The numerical model is then solved to obtain the subsidy ratio between aggregators and users. This method solves the problem in the prior art where the chaotic allocation of subsidies between aggregators and users leads to poor incentive effects for primary frequency regulation by the grid company.

[0209] 2) The subsidy control device for maximizing primary frequency regulation capacity of this application acquires historical frequency regulation response data. This historical frequency regulation response data includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, and the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation. The first cost data includes the relevant costs of the user participating in primary frequency regulation, and the second cost data includes the relevant costs of the aggregator's primary frequency regulation parameters. The target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. The first construction unit constructs a target numerical model based on the historical frequency regulation response data. This target numerical model is used to simulate the target revenue and... The target frequency modulation capacity is described as changing with the ratio corresponding to the first target subsidy and the second target subsidy, and the target revenue is the sum of the revenue of the aggregator and the user. The second construction unit constructs an objective function based on the target numerical model, and the objective function is to achieve the maximum value of the target revenue and the maximum value of the target frequency modulation capacity. The calculation unit solves the objective function using a genetic algorithm to obtain the first target ratio, solves the objective function using a particle swarm optimization algorithm to obtain the second target ratio, calculates the third target ratio based on the first target ratio and the second target ratio, and determines the third target ratio as the ratio of the first target subsidy and the second target subsidy. The first target ratio, the second target ratio, and the third target ratio are the ratio of the first target subsidy and the second target subsidy. This application constructs a numerical model based on historical data of users and aggregators participating in primary frequency regulation. This model simulates the impact of different proportions of grid company subsidies used by users and aggregators in primary frequency regulation on primary frequency regulation capacity and total revenue. Based on the above numerical model, and considering the impact of revenue on the enthusiasm of users and aggregators, as well as the effect of primary frequency regulation, the objective functions are determined to maximize total revenue and maximize target frequency regulation capacity. The numerical model is then solved to obtain the subsidy ratio between aggregators and users. This method solves the problem in the prior art where the chaotic allocation of subsidies between aggregators and users leads to poor incentive effects for primary frequency regulation by the grid company.

[0210] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A subsidy control method for maximizing primary frequency modulation capacity, characterized in that, include: Historical frequency regulation response data is obtained, which includes first cost data, second cost data, and target frequency regulation capacity under different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation, the first cost data includes the relevant costs of the user participating in the primary frequency regulation, the second cost data includes the relevant costs of the aggregator's primary frequency regulation parameters, and the target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. A target numerical model is constructed based on the historical frequency regulation response data. The target numerical model is used to simulate the changing trends of the target revenue and the target frequency regulation capacity with the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user. An objective function is constructed based on the target numerical model, wherein the objective function is to maximize the target revenue and the target frequency regulation capacity. The objective function is solved using a genetic algorithm to obtain a first objective ratio. The objective function is then solved using a particle swarm optimization algorithm to obtain a second objective ratio. A third objective ratio is calculated based on the first and second objective ratios. This third objective ratio is determined as the ratio of the first objective subsidy to the second objective subsidy. In essence, the first objective ratio, the second objective ratio, and the third objective ratio represent the ratio of the first objective subsidy to the second objective subsidy. Constructing a target numerical model based on the historical frequency modulation response data includes: The difference between the first target subsidy and the second target subsidy is determined as the third target subsidy, and the ratio between the third target subsidy and the second target subsidy is determined as the allocation ratio. A first objective function is obtained by fitting the allocation ratio and the first cost data. The first objective function is used to characterize the trend of the first cost data with the allocation ratio. A second objective function is obtained by fitting the allocation ratio and the second cost data. The second objective function is used to characterize the trend of the second cost data with the allocation ratio. A third objective function is obtained by fitting the allocation ratio and the first target subsidy, and the third objective function is used to characterize the changing trend of the first target subsidy with the allocation ratio. A fourth objective function is obtained by fitting the allocation ratio and the target frequency modulation capacity. The fourth objective function is used to characterize the changing trend of the target frequency modulation capacity with the allocation ratio. Construct a first target formula, a second target formula, and a third target formula. The first target formula is used to calculate a first revenue based on the third target subsidy and the second cost data. The second target formula is used to calculate a second revenue based on the second target subsidy and the first cost data. The third target formula is used to calculate the target revenue based on the first revenue and the second revenue. The first revenue is the net revenue of the aggregator participating in one frequency modulation, and the second revenue is the net revenue of the user participating in one frequency modulation. The objective numerical model is obtained by simultaneously solving the first objective function, the second objective function, the third objective function, the fourth objective function, the first objective formula, the second objective formula, and the third objective formula.

2. The method according to claim 1, characterized in that, A first objective function is obtained by fitting the allocation ratio and the first cost data, including: The target number is determined based on the historical frequency modulation response data, where the target number is the total number of users participating in a frequency modulation. A fifth objective function is obtained by fitting the allocation ratio and the target quantity, and the fifth objective function is used to characterize the changing trend of the target quantity with the allocation ratio. Based on the first cost data, determine the user's travel cost, travel impact cost, and user charging cost; A sixth objective function, a seventh objective function, and an eighth objective function are obtained by fitting the target quantity and the user travel cost, the target quantity and the travel impact cost, and the target quantity and the user charging cost, respectively. The sixth objective function is used to characterize the trend of the user travel cost with the target quantity, the seventh objective function is used to characterize the trend of the travel impact cost with the target quantity, and the eighth objective function is used to characterize the trend of the user charging cost with the target quantity. A fourth objective formula is constructed, which is used to calculate the first cost data based on the user travel cost, the travel impact cost, and the user charging cost; The first objective function is obtained by fitting the fifth objective function, the sixth objective function, the seventh objective function, the eighth objective function, and the fourth objective formula.

3. The method according to claim 2, characterized in that, A second objective function is obtained by fitting the allocation ratio and the second cost data, including: The charging service cost and the charging electricity price cost are determined based on the second cost data. A ninth objective function and a tenth objective function are obtained by fitting the target quantity and the charging service cost, and the target quantity and the charging electricity cost, respectively. The ninth objective function is used to characterize the changing trend of the charging service cost with the target quantity, and the tenth objective function is used to characterize the changing trend of the charging electricity cost with the target quantity. A fifth objective formula is constructed, which is used to calculate the second cost data based on the charging service cost and the charging electricity price cost. The first objective function is obtained by fitting the fifth objective function, the ninth objective function, the tenth objective function, and the fifth objective formula.

4. The method according to claim 2, characterized in that, A third objective function is obtained by fitting the allocation ratio and the first target subsidy, including: An eleventh objective function is obtained by fitting the target quantity and the first target subsidy together. The eleventh objective function is used to characterize the changing trend of the first target subsidy with the target quantity. The third objective function is obtained by fitting the fifth objective function and the eleventh objective function.

5. The method according to claim 2, characterized in that, A fourth objective function is obtained by fitting the allocation ratio and the target frequency modulation capacity, including: A twelfth objective function is obtained by fitting the target quantity and the target frequency modulation capacity. The twelfth objective function is used to characterize the changing trend of the target frequency modulation capacity with the target quantity. The fourth objective function is obtained by fitting the fifth objective function and the twelfth objective function.

6. The method according to claim 1, characterized in that, After constructing the objective function based on the target numerical model, the method further includes: The first constraint condition of the objective function is that both the second target subsidy and the third target subsidy are not zero. The target frequency modulation capacity being greater than or equal to the first threshold is determined as the second constraint condition of the objective function; The first target subsidy being greater than or equal to the second threshold is determined as the third constraint condition of the objective function.

7. A subsidized frequency modulation control device for maximizing primary frequency modulation capacity, characterized in that, The device includes: The acquisition unit is used to acquire historical frequency regulation response data, which includes first cost data, second cost data, and target frequency regulation capacity corresponding to different ratios of the first target subsidy and the second target subsidy. The first target subsidy is the subsidy from the power grid company to the aggregator participating in primary frequency regulation, the second target subsidy is the subsidy from the aggregator to the user participating in primary frequency regulation, the first cost data includes the relevant costs of the user participating in the primary frequency regulation, the second cost data includes the relevant costs of the aggregator's primary frequency regulation parameters, and the target frequency regulation capacity is the total frequency regulation capacity increased by the user participating in primary frequency regulation. The first construction unit is used to construct a target numerical model based on the historical frequency modulation response data. The target numerical model is used to simulate the changing trend of the target revenue and the target frequency modulation capacity with the ratios corresponding to the first target subsidy and the second target subsidy. The target revenue is the sum of the revenue of the aggregator and the user. The second construction unit is used to construct an objective function based on the target numerical model, wherein the objective function is to maximize the target benefit and the target frequency regulation capacity. The computing unit is used to solve the objective function using a genetic algorithm to obtain a first target ratio, and to solve the objective function using a particle swarm optimization algorithm to obtain a second target ratio. It then calculates a third target ratio based on the first and second target ratios, and determines the third target ratio as the ratio of the first target subsidy to the second target subsidy. The first target ratio, the second target ratio, and the third target ratio together represent the ratio of the first target subsidy to the second target subsidy. The first building unit includes: The first determining module is used to determine the difference between the first target subsidy and the second target subsidy as the third target subsidy, and to determine the ratio between the third target subsidy and the second target subsidy as the allocation ratio. The first fitting module is used to fit a first objective function based on the allocation ratio and the first cost data, and the first objective function is used to characterize the changing trend of the first cost data with the allocation ratio. The second fitting module is used to fit a second objective function based on the allocation ratio and the second cost data. The second objective function is used to characterize the changing trend of the second cost data with the allocation ratio. The third fitting module is used to fit a third objective function based on the allocation ratio and the first target subsidy, and the third objective function is used to characterize the changing trend of the first target subsidy with the allocation ratio. The fourth fitting module is used to fit a fourth objective function based on the allocation ratio and the target frequency modulation capacity. The fourth objective function is used to characterize the changing trend of the target frequency modulation capacity with the allocation ratio. A construction module is used to construct a first target formula, a second target formula, and a third target formula. The first target formula is used to calculate a first revenue based on the third target subsidy and the second cost data. The second target formula is used to calculate a second revenue based on the second target subsidy and the first cost data. The third target formula is used to calculate the target revenue based on the first revenue and the second revenue. The first revenue is the net revenue of the aggregator participating in one frequency modulation, and the second revenue is the net revenue of the user participating in one frequency modulation. The fifth fitting module is used to simultaneously solve the first objective function, the second objective function, the third objective function, the fourth objective function, the first objective formula, the second objective formula, and the third objective formula to obtain the target numerical model.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

9. A power grid system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 6.

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