Method, device and equipment for determining inertia compensation coefficient of inertia providing equipment

Abstract

The present application discloses a method, device and equipment for determining the inertia compensation coefficient of an inertia providing device, belonging to the field of power technology. The method includes: obtaining the inertia constraint condition of the power system based on the set uncompensated resource model and non-consumable resource model of the inertia providing device; constructing a master-slave game model according to the parameters of the uncompensated resource model, the parameters of the non-consumable resource model, the inertia constraint condition and the inertia compensation coefficient of the inertia providing device. The master-slave game model includes an upper-layer leader model and a lower-layer follower model. The upper-layer leader model is used to represent the minimum compensation resource amount provided for the inertia providing device based on the inertia compensation coefficient, and the lower-layer follower model is used to represent the minimum resource consumption amount after compensation based on the inertia compensation coefficient; solving the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model. According to the embodiments of the present application, the effect of resource compensation of the power system can be improved.

Description

Technical Field

[0001] This application belongs to the technical field of electric power, and particularly relates to a method, device, and equipment for determining the inertia compensation coefficient of an inertia providing device. Background Technique

[0002] With the continuous development of new energy technologies, the proportion of new energy devices in the power system is increasing continuously, and the randomness faced by the power system is also increasing continuously, challenging the stability of the power system. The inertia providing device in the power system can provide inertia for the power system to improve the stability of the power system.

[0003] To encourage the inertia providing device to provide inertia, resource compensation can be carried out for the inertia providing device. However, the current resource compensation often adopts a fixed compensation method, that is, a fixed amount of resource compensation is carried out for each inertia providing device. However, this resource compensation method does not match the actual situation of the inertia providing device and cannot reflect the contribution value of the inertia providing device in the inertia providing service, resulting in a poor resource compensation effect for the inertia providing device. Summary of the Invention

[0004] The embodiments of this application provide a method, device, and equipment for determining the inertia compensation coefficient of an inertia providing device, which can improve the resource compensation effect of the power system.

[0005] In a first aspect, the embodiments of this application provide a method for determining the inertia compensation coefficient of an inertia providing device, including: obtaining the inertia constraint condition of the power system including the inertia providing device based on the set uncompensated resource model and non-consumable resource model of the inertia providing device; constructing a master-slave game model according to the parameters of the uncompensated resource model, the parameters of the non-consumable resource model, the inertia constraint condition, and the inertia compensation coefficient of the inertia providing device. The master-slave game model includes an upper-layer leader model and a lower-layer follower model. The upper-layer leader model is used to represent the minimum compensation resource amount provided for the inertia providing device based on the inertia compensation coefficient, and the lower-layer follower model is used to represent the minimum resource consumption amount after compensation based on the inertia compensation coefficient; solving the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model.

[0006] In some possible embodiments, based on the set uncompensated resource model and non-consumptive resource model of the inertia providing device, the inertia constraint conditions of the power system including the inertia providing device are obtained, including: respectively solving the uncompensated resource model and the non-consumptive resource model to obtain the first output power and the second output power of the inertia providing device; according to the first output power, the second output power of the inertia providing device and the inertia time constant of the inertia providing device, obtaining the first inertia demand boundary value and the second inertia demand boundary value of the power system, where the second inertia demand boundary value is less than the first inertia demand boundary value; based on the first inertia demand boundary value and the second inertia demand boundary value, using the inertia demand factor of the power system for weighted operation to obtain the inertia constraint conditions.

[0007] In some possible embodiments, the parameters of the uncompensated resource model include the load time period, the consumption coefficient of the inertia providing device, and the output power of the inertia providing device during the load time period; the parameters of the non-consumptive resource model include the load time period, the inertia time constant of the inertia providing device, and the output power of the inertia providing device during the load time period; the constraint conditions of the upper-layer leader model include the constraint conditions of the inertia compensation coefficient of the inertia providing device and the inertia constraint conditions; the constraint conditions of the lower-layer follower model include the constraint conditions of the output power of the inertia providing device and the power of the power system during the load time period being equal to the power demand during the load time period.

[0008] In some possible embodiments, solving the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model includes: converting the master-slave game model into a single-layer mixed-integer linear programming model; solving the single-layer mixed-integer linear programming model, and determining the inertia compensation coefficient that satisfies the single-layer mixed-integer linear programming model as the inertia compensation coefficient that satisfies the master-slave game model.

[0009] In some possible embodiments, converting the master-slave game model into a single-layer mixed-integer linear programming model includes: using the KKT condition algorithm to convert the master-slave game model into a single-layer equilibrium-constrained mathematical programming model; using the linear optimization programming algorithm to convert the single-layer equilibrium-constrained mathematical programming model into a mixed-integer linear programming model; using the strong duality principle to convert the mixed-integer linear programming model into a single-layer mixed-integer linear programming model.

[0010] In some possible embodiments, using the KKT condition algorithm to convert the master-slave game model into a single-layer equilibrium-constrained mathematical programming model includes: using the KKT condition algorithm to optimize the lower-layer follower model to obtain the KKT conditions corresponding to the lower-layer follower model; combining the KKT conditions and the upper-layer leader model to obtain a single-layer equilibrium-constrained mathematical programming model.

[0011] In some possible embodiments, a linear optimization programming algorithm is used to convert a single-layer equilibrium constraint mathematical programming model into a mixed-integer linear programming model, including: using the linear optimization programming algorithm to process the non-linear conditions in the KKT conditions of the single-layer equilibrium constraint mathematical programming model to obtain linear conditions; and obtaining a mixed-integer linear programming model according to the linear conditions, the KKT conditions, and the upper-layer leader model.

[0012] In some possible embodiments, the strong duality principle is used to convert the mixed-integer linear programming model into a single-layer mixed-integer linear programming model, including: using the strong duality principle to obtain the dual function of the lower-layer follower model; substituting the dual function into the upper-layer leader model to obtain the target upper-layer leader model; and updating the upper-layer leader model in the mixed-integer linear programming model to the target upper-layer leader model to obtain a single-layer mixed-integer linear programming model.

[0013] In a second aspect, an inertia compensation coefficient determination device for an inertia providing device provided by an embodiment of the present application includes: an inertia demand determination module, configured to obtain the inertia constraint conditions of a power system including the inertia providing device based on the uncompensated resource model and the non-consumed resource model set for the inertia providing device; a model establishment module, configured to construct a master-slave game model according to the parameters of the uncompensated resource model, the parameters of the non-consumed resource model, the inertia constraint conditions, and the inertia compensation coefficient of the inertia providing device, where the master-slave game model includes an upper-layer leader model and a lower-layer follower model, the upper-layer leader model is used to represent the minimum amount of compensated resources provided for the inertia providing device based on the inertia compensation coefficient, and the lower-layer follower model is used to represent the minimum resource consumption amount after compensation based on the inertia compensation coefficient; and a model solving module, configured to solve the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model.

[0014] In a third aspect, an inertia compensation coefficient determination device for an inertia providing device provided by an embodiment of the present application includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the inertia compensation coefficient determination method for the inertia providing device in the first aspect is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the inertia compensation coefficient determination method for the inertia providing device in the first aspect is implemented.

[0016] An embodiment of the present application provides a method, device, and equipment for determining the inertia compensation coefficient of an inertia providing device. In the embodiment of the present application, according to the uncompensated resource model that can represent the minimum resource consumption of the power system without considering resource compensation and the non-consumable resource model that can represent the minimum inertia of the power system without considering resource consumption, the inertia constraint condition of the power system is determined, and this inertia constraint condition can represent the demand of the power system for inertia. According to the parameters of the uncompensated resource model, the parameters of the non-consumable resource model, the inertia constraint condition, and the unknown inertia compensation coefficient, a master-slave game model is constructed. The master-slave game model includes an upper-layer leader model that can represent the minimum compensation resources provided for the inertia providing device based on the inertia compensation coefficient and a lower-layer follower model that can represent the minimum resource consumption after compensation based on the inertia compensation coefficient. The obtained inertia compensation coefficient that satisfies the master-slave game model is used to compensate the resources of the inertia providing device, which can coordinate the incentive of the resources obtained by providing inertia and the resources consumed by providing inertia, and the inertia compensation coefficients of different inertia providing devices can be different, which can reflect the contribution value of the inertia providing device in providing inertia, thereby improving the effect of resource compensation in the power system. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of a method for determining the inertia compensation coefficient of an inertia providing device provided by an embodiment of the present application;

[0019] Figure 2 It is a flowchart of a method for determining the inertia compensation coefficient of an inertia providing device provided by another embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of an example of the inertia compensation coefficients of each inertia providing device corresponding to different inertia demand factors provided by an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of an example of the compensation resources and consumption resources of the power system under different inertia demand factors provided by an embodiment of the present application;

[0022] Figure 5 It is a flowchart of a method for determining the inertia compensation coefficient of an inertia providing device provided by another embodiment of the present application;

[0023] Figure 6 It is a schematic diagram of the structure of an inertia compensation coefficient determination device for an inertia providing device provided by an embodiment of the present application;

[0024] Figure 7 This is a schematic structural diagram of an inertia compensation coefficient determination device for an inertia providing device provided by an embodiment of the present application. Detailed implementation manners

[0025] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than limiting the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present application by showing examples of the present application. It should be noted that the acquisition, storage, use, processing, etc. of information and data in the embodiments of the present application have obtained the authorization of users or relevant institutions, and comply with the relevant regulations of national laws and regulations.

[0026] With the continuous development of new energy technologies, the proportion of new energy devices in the power system is increasing continuously, and the randomness faced by the power system is also increasing continuously. The stability of the power system is challenged. For example, in the case of voltage and frequency oscillations in the power grid, the stability of the power system will drop significantly. The new energy in the power system can be used as an inertia providing device to provide inertia for the power system, resist load disturbances, adjust the frequency deviation of the power system, and ensure the stability and safety of the power system. In order to encourage the inertia providing device to provide inertia, resource compensation can be provided for the inertia providing device. In the development stage of inertia providing services, a fixed compensation method of providing a fixed amount of resource compensation for each inertia providing device is adopted. This fixed compensation method does not match the actual situation of the inertia provided by the inertia providing device, cannot distinguish the differences in the inertia provided by different inertia providing devices, and is also difficult to reflect the contribution value of the inertia providing device in inertia providing services, resulting in a poor resource compensation effect for the inertia providing device.

[0027] The present application provides a method, device, equipment and medium for determining the inertia compensation coefficient of an inertia providing device, which can first determine that the power system including the inertia providing device meets the inertia constraint conditions of production scheduling, and then establish a master-slave game model in which the incentive strategy for resource compensation is coordinated with the inertia providing strategy of the inertia providing device, and solve the master-slave game model to obtain the inertia compensation coefficient of the inertia providing device that can satisfy the master-slave game model. The inertia compensation coefficients of different inertia providing devices can reflect the differences in the inertia provided by different inertia providing devices, and can also reflect the contribution value of the inertia device in the inertia service. Using this inertia compensation coefficient for resource compensation of the inertia providing device has a better effect, and can also improve the enthusiasm of the inertia providing device to provide inertia, and better promote the inertia compensation service of the power system.

[0028] The method, device, equipment and medium for determining the inertia compensation coefficient of the inertia providing device provided by the present application will be described separately below.

[0029] In the first aspect of the present application, a method for determining the inertia compensation coefficient of an inertia providing device is provided, which can be applied to a power system including an inertia providing device. The inertia providing device may include an energy device capable of providing inertia for the power system. For example, the inertia providing device may include, but is not limited to, thermal power units, wind power units, photovoltaic power units, hydroelectric power units, etc. The method for determining the inertia compensation coefficient of the inertia providing device may be executed by an inertia compensation coefficient determination device, equipment, etc. of the inertia providing device, which is not limited herein. Figure 1 The flowchart of the method for determining the inertia compensation coefficient of the inertia providing device provided in an embodiment of the present application is as Figure 1 shown. The method for determining the inertia compensation coefficient of the inertia providing device may include steps S101 to S103.

[0030] In step S101, based on the set uncompensated resource model and non-consumable resource model of the inertia providing device, the inertia constraint conditions of the power system including the inertia providing device are obtained.

[0031] The inertia providing device has inertia support capabilities. The relationship between the rated capacity, inertia time constant and adaptive active power supply of the inertia providing device can be shown as the following formula (1):

[0032]

[0033] where H is the inertia time constant of the inertia providing device; E k is the adaptive active power supply of the inertia providing device. The adaptive active power supply is the maximum active power supply that the inertia providing device can guarantee by automatically adjusting the output power according to the frequency change of the power system in the face of disturbances; S B is the rated capacity of the inertia providing device.

[0034] A power system may include multiple inertia providing devices. The inertia with support value provided by each inertia providing device is related to the load rate of the inertia providing device. For example, the inertia provided by each inertia providing device to meet the production scheduling of the power system can be shown as the following formula (2):

[0035]

[0036] where, E Ki is the active energy supply provided by the i-th inertia providing device connected to the grid to automatically adjust the output power according to the frequency change of the power system under the condition of meeting the production scheduling of the power system, which can characterize the contribution of the i-th inertia providing device to provide inertia support; H i is the inertia time constant of the i-th inertia providing device; P i is the actual output power of the i-th inertia providing device, that is, the actual operating power; E K is the adaptive active energy supply of the inertia providing device; S B is the rated capacity of the inertia providing device; a is the load rate of the inertia providing device.

[0037] The inertia demand of the power system under the condition of meeting the production scheduling can be the sum of the active energy supplies provided by each inertia providing device to meet the demand in each load time period. For example, the inertia demand of the power system under the condition of meeting the production scheduling can be shown as the following formula (3):

[0038] E p = ∑ i E Ki = ∑ i H i P i (3)

[0039] where, E p is the inertia demand to maintain the safe and stable operation of the inertia providing devices in the power system, which can be regarded as the total active support required by the power system; for the definitions of other parameters, reference can be made to the relevant content of the above embodiments, which will not be elaborated here.

[0040] The inertia constraint condition of the power system is set based on the inertia demand for maintaining the safe and stable operation of the inertia-providing equipment in the power system. The actual inertia demand in the actual operation of the power system is less than or equal to the inertia demand for maintaining the safe and stable operation of the inertia-providing equipment in the power system. To determine the inertia demand for maintaining the safe and stable operation of the inertia-providing equipment in the power system and obtain the inertia constraint condition, a non-compensated resource model and a non-consumed resource model can be preset in advance. The non-compensated resource model can represent the minimum resource consumption of the power system in the absence of resource compensation, that is, the non-compensated resource model can obtain the minimum resource consumption required for the power system to complete production scheduling without considering resource compensation. The non-consumed resource model can represent the minimum inertia of the power system in the absence of consumption costs, that is, the non-consumed resource model can obtain the minimum inertia required for the power system to complete production scheduling without considering resource consumption. By solving the non-compensated resource model and the non-consumed resource model, the maximum and minimum values of the inertia demand of the power system can be obtained, and then, based on the maximum and minimum values of the inertia demand of the power system and the coefficient representing the demand of the power system, the evaluated inertia demand for maintaining the safe and stable operation of the inertia-providing equipment in the power system can be obtained. For the convenience of description, the evaluated inertia demand for maintaining the safe and stable operation of the inertia-providing equipment in the power system is hereinafter simply referred to as the constrained inertia demand.

[0041] In step S102, a principal-agent game model is constructed according to the parameters of the non-compensated resource model, the parameters of the non-consumed resource model, the inertia constraint condition, and the inertia compensation coefficient of the inertia-providing equipment.

[0042] The parameters of the non-compensated resource model are used to construct the objective function and the corresponding constraint conditions for the minimum resource consumption of the power system in the absence of resource compensation. The parameters of the non-consumed resource model are used to construct the objective function and the corresponding constraint conditions for the minimum inertia of the power system in the absence of consumption costs. The value of the inertia compensation coefficient in the process of constructing the principal-agent game model is unknown. The principal-agent game model includes the inertia compensation coefficient, and the inertia compensation coefficient can be obtained by solving the principal-agent game model later, that is, the value of the inertia compensation coefficient can be obtained.

[0043] The principal-agent game model includes an upper-layer leader model and a lower-layer follower model. The lower-layer follower model determines its own strategy based on the actions of the upper-layer leader model. The upper-layer leader model determines its own actions based on the response of the strategy adopted by the lower-layer follower model. The upper-layer leader model and the lower-layer follower model are interconnected and mutually referential. The upper-layer leader model can be used to represent the minimum compensation resources provided for the inertia-providing equipment based on the inertia compensation coefficient. The lower-layer follower model can be used to represent the minimum resource consumption after compensation based on the inertia compensation coefficient. By constructing the principal-agent game model, the incentive for resource compensation obtained by providing inertia and the resources consumed by providing inertia can be coordinated with each other.

[0044] In step S103, the master-slave game model is solved to obtain the inertia compensation coefficient that satisfies the master-slave game model.

[0045] The inertia compensation coefficient that satisfies the master-slave game model obtained by solving the master-slave game model can coordinate the incentives for resource compensation and resource consumption, and can reflect the contribution value of the corresponding inertia-providing device in providing inertia. The inertia compensation coefficients of different inertia-providing devices can be different. The inertia compensation coefficient can be used to perform resource compensation on the corresponding inertia-providing device.

[0046] In the embodiment of the present application, according to the uncompensated resource model that can characterize the minimum resource consumption of the power system without considering resource compensation and the unconsumed resource model that can characterize the minimum inertia of the power system without considering resource consumption, the inertia constraint condition of the power system is determined. This inertia constraint condition can characterize the demand of the power system for inertia. According to the parameters of the uncompensated resource model, the parameters of the unconsumed resource model, the inertia constraint condition, and the unknown inertia compensation coefficient, a master-slave game model is constructed. The master-slave game model includes an upper-layer leader model that can characterize the minimum compensation resource amount provided for the inertia-providing device based on the inertia compensation coefficient and a lower-layer follower model that can characterize the minimum resource consumption after compensation based on the inertia compensation coefficient. The inertia compensation coefficient that satisfies the master-slave game model obtained by solving is used to perform resource compensation on the inertia-providing device, which can coordinate the incentives for resource compensation obtained by providing inertia and the resources consumed by providing inertia, and the inertia compensation coefficients of different inertia-providing devices can be different, which can reflect the contribution value of the inertia-providing device in providing inertia, thereby improving the effect of resource compensation of the power system.

[0047] In some embodiments, the parameters of the uncompensated resource model include the load time period, the consumption coefficient of the inertia-providing device, and the output power of the inertia-providing device during the load time period. The load time period can be set in advance. For example, the duration of the load time period can be 1 hour, 12 hours, one day, etc., which is not limited here. The consumption coefficient of the inertia-providing device can characterize the resource consumption rate of the inertia-providing device under operating conditions. For example, the unit of the consumption coefficient can be resource amount / MWh. In some examples, the uncompensated resource model can be shown as the following formula (4):

[0048]

[0049] where, ΔT j is the jth load time period; b i is the consumption coefficient; P ij is the output power of the ith inertia-providing device during the jth load time period; is the minimum output power of the ith inertia-providing device; Provide the maximum output power of the device for the i-th inertia; D j Be the power demand for the j-th load time period. min∑ i ∑ j ΔT j b i P ij Is the objective function of the uncompensated resource model, representing the minimum resource consumption of the power system in the absence of resource compensation; Is a constraint condition for constraining the output power of the inertia-providing device; ∑ i P ij = D j Is a constraint condition, representing the power balance condition of the load time period.

[0050] The parameters of the non-consumptive resource model include the load time period, the inertia time constant of the inertia-providing device, and the output power of the inertia-providing device during the load time period. In some examples, the non-consumptive resource model can be shown as the following formula (5):

[0051]

[0052] Wherein, H i Is the inertia time constant of the i-th inertia-providing device, and the definitions of other parameters can be referred to the relevant descriptions in the above embodiments, which will not be elaborated here. min∑ i ∑ j ΔT j H i P ij Is the objective function of the non-consumptive resource model, representing the minimum inertia of the power system in the absence of consumption cost; Is a constraint condition for constraining the output power of the inertia-providing device; ∑ i P ij = D j Is a constraint condition, representing the power balance condition of the load time period.

[0053] Correspondingly, Figure 2 Is the flowchart of the inertia compensation coefficient determination method for the inertia-providing device provided by another embodiment of the present application, Figure 2 Differ from Figure 1 In that Figure 1 Step S101 in Figure 2 Can be specifically refined into

[0054] In step S1011, solve the uncompensated resource model and the non-consumptive resource model respectively to obtain the first output power and the second output power of the inertia-providing device.

[0055] The first output power is the output power of the inertia providing device obtained by solving the uncompensated resource model. The second output power is the output power of the inertia providing device obtained by solving the non-consumptive resource model.

[0056] In step S1012, according to the first output power of the inertia providing device, the second output power, and the inertia time constant of the inertia providing device, the first inertia demand boundary value and the second inertia demand boundary value of the power system are obtained.

[0057] Based on the output power and the inertia time constant of the inertia providing device, the inertia demand of the power system can be obtained. Correspondingly, according to the first output power and the inertia time constant, the first inertia demand boundary value can be obtained; according to the second output power and the inertia time constant, the second inertia demand boundary value can be obtained. The second inertia demand boundary value is less than the first inertia demand boundary value. For example, the first output power and the inertia time constant of each inertia providing device can be substituted into the above formula (3), and the obtained E p is determined as the first inertia demand boundary value, and the first inertia demand boundary value can be regarded as the maximum value of the inertia demand; the second output power and the inertia time constant of each inertia providing device can be substituted into the above formula (3), and the obtained E p is determined as the second inertia demand boundary value, and the second inertia demand boundary value can be regarded as the minimum value of the inertia demand.

[0058] In step S1013, based on the first inertia demand boundary value and the second inertia demand boundary value, a weighted operation is performed using the inertia demand factor of the power system to obtain the inertia constraint condition.

[0059] The inertia demand factor of the power system can characterize the demand of the power system, can be set by the main controller of the power system, and this inertia demand factor can be adjusted according to specific situations. In some examples, the value range of the inertia demand factor can be [0, 1]. The larger the inertia demand factor, the greater the constrained inertia demand in the inertia constraint condition. For example, the constrained inertia demand in the inertia constraint condition can be according to the following formula (6):

[0060] E p = αE pmin +(1 - α)E pmax (6)

[0061] where, E p is the constrained inertia demand; E pmin is the second inertia demand boundary value; E pmax is the first inertia demand boundary value; α is the inertia demand factor.

[0062] Differences in inertia-providing devices and inertia demand factors can result in different inertia compensation coefficients that satisfy the master-slave game model. For example, if the load time periods are as shown in Table 1 below and the inertia-providing devices are as shown in Table 2 below, the definitions of the parameters in Table 2 can be referred to the relevant descriptions in the above embodiments and will not be elaborated here; corresponding to Table 1 and Table 2, the first inertia demand boundary E pmax and the second inertia demand boundary of the power system are as shown in Table 3 below; the value of the inertia demand factor can be 0, 0.2, 0.4, 0.6, 0.8, 1; by using the inertia compensation coefficient determination method of the inertia-providing device in the embodiment of the present application, the inertia compensation coefficients of each inertia-providing device corresponding to different inertia demand factors can be obtained.

[0063] Table 1

[0064]

[0065] Table 2

[0066]

[0067] Table 3

[0068] <![CDATA[E pmax > <![CDATA[E pmin > <![CDATA[7.413×10 7 > <![CDATA[8.283×10 7 >

[0069] Figure 3 is a schematic diagram of an example of the inertia compensation coefficients of each inertia-providing device corresponding to different inertia demand factors provided by the embodiment of the present application, Figure 3 the involved load time periods and inertia-providing devices are as shown in Table 1 and Table 2, the abscissa is the label of the inertia-providing device, and the ordinate is the inertia compensation coefficient. It can be seen that Figure 3 by using the inertia compensation coefficient determination method of the inertia-providing device in the embodiment of the present application, under the condition of the same inertia demand factor, the inertia compensation coefficients of different inertia-providing devices can be different; under the condition of different inertia demand factors, the inertia compensation coefficients of the same inertia-providing device can be different; the different inertia compensation coefficients of different inertia-providing devices can reflect the contribution value of different inertia-providing devices in providing inertia. In some cases, the greater the inertia demand of the power system, the greater the total power generation of the inertia-providing device, the more prominent the role of the inertia-providing device in providing inertia, the higher the corresponding inertia compensation coefficient, and the stronger the incentive effect.

[0070] By using the inertia compensation coefficient determination method of the inertia-providing device in the embodiment of the present application, under different inertia demand factor conditions, the inertia compensation coefficients of different inertia-providing devices are different, so that the compensation resources and consumption resources of the power system are also different. For example, Figure 4 is a schematic diagram of an example of the compensation resources and consumption resources of the power system provided by the embodiment of the present application under different inertia demand factors. The abscissa is the inertia demand factor, and the ordinate is the resource quantity. It can be seen fromFigure 4 It can be seen that under the conditions where the inertia demand factors are 0, 0.2, 0.4, 0.6, 0.8, and 1 respectively, the compensation resources and consumption resources of the power system are not fixed, but change dynamically with the change of each inertia compensation coefficient. Compared with the method of fixed resource compensation without considering the performance of inertia-providing equipment and the inertia demand of the power system, the method in the embodiments of the present application can highlight the contribution value of the inertia actually provided by each inertia-providing equipment.

[0071] In some embodiments, the upper-layer leader model in the master-slave game model may include an objective function and constraint conditions. The constraint conditions of the upper-layer leader model may include the constraint conditions of the inertia compensation coefficient of the inertia-providing equipment and the inertia constraint conditions. The objective function of the upper-layer leader model represents the minimum compensation resource amount provided for the inertia-providing equipment based on the inertia compensation coefficient, which can be obtained according to the load time period, the inertia compensation coefficient, the inertia time constant, and the output power of the inertia-providing equipment in the load time period. For example, the upper-layer leader model may be as shown in the following formula (7):

[0072]

[0073] Wherein, is the objective function of the upper-layer leader model; is the constraint condition of the inertia compensation coefficient; d i is the inertia compensation coefficient of the i-th inertia-providing equipment. For example, the unit of the inertia compensation coefficient can be resource amount / megawatt-second 2 ; is the maximum value of the inertia compensation coefficient of the i-th inertia-providing equipment, which can be set according to specific scenarios, requirements, experience, etc.; ∑ j ∑ i ΔT j H i P ij ≤E p is the inertia constraint condition; E p is the constrained inertia demand determined in the above embodiments. The definitions of other parameters can be referred to the relevant descriptions in the above embodiments and will not be elaborated here.

[0074] In some embodiments, the lower-layer follower model in the master-slave game model may include an objective function and constraint conditions. The constraint conditions of the lower-layer follower model include the constraint condition of the output power of the inertia providing device, and the power of the power system during the load period is equal to the power demand during the load period. The objective function of the lower-layer follower model represents the minimum resource consumption after being compensated based on the inertia compensation coefficient, which can be obtained according to the consumption coefficient, the inertia compensation coefficient, the inertia time constant, and the output power of the inertia providing device during the load period. For example, the lower-layer follower model may be as shown in the following formula (8):

[0075]

[0076] where, min∑ i (b i -d i H i )P ij is the objective function; is the constraint condition; ∑ i P ij =D j is the constraint condition; for the definitions of the parameters in formula (8), reference can be made to the relevant descriptions in the above embodiments, which will not be elaborated here.

[0077] The inertia compensation coefficient and the output power of the inertia providing device during the load period are bridge parameters that connect the upper-layer leader model and the lower-layer follower model, linking the upper-layer leader model and the lower-layer follower model together to form the master-slave game model.

[0078] In some examples, it is relatively difficult to directly solve the master-slave game model, and the solving efficiency is low, which will lead to a low efficiency of obtaining the inertia compensation coefficient. The master-slave game model can be simplified first, and then the simplified model can be solved to improve the efficiency of obtaining the inertia compensation coefficient. Figure 5 This is a flowchart of the method for determining the inertia compensation coefficient of the inertia providing device provided by another embodiment of the present application, Figure 5 which is different from Figure 1 in that Figure 1 step S103 in Figure 5 can be specifically refined into step S1031 and step S1032 in

[0079] In step S1031, the master-slave game model is converted into a single-layer mixed-integer linear programming model.

[0080] The master-slave game model is a non-linear model, which is difficult to solve and has low solution efficiency. The single-layer mixed-integer linear programming model is a new model, with relatively lower solution difficulty and relatively higher solution efficiency. The master-slave game model can be gradually converted into a single-layer mixed-integer linear programming model. Specifically, the Karush-Kuhn-Tucker (KKT) condition algorithm can be used to convert the master-slave game model into a single-layer mathematical program with equilibrium constraints model; the linear optimization programming algorithm can be used to convert the single-layer mathematical program with equilibrium constraints model into a mixed-integer linear programming model; and the strong duality principle can be used to convert the mixed-integer linear programming model into a single-layer mixed-integer linear programming model.

[0081] In some examples, the KKT condition algorithm can be used to optimize the lower-layer follower model to obtain the KKT conditions corresponding to the lower-layer follower model; the KKT conditions and the upper-layer leader model are combined to obtain a single-layer mathematical program with equilibrium constraints (MPEC) model. The KKT conditions corresponding to the lower-layer follower model can be obtained according to the consumption system, the inertia compensation coefficient, the dual variables of the output power constraint conditions of the inertia-providing device, the constraint condition that the power of the power system during the load period is equal to the power demand during the load period, and the dual variables of this constraint condition. For example, the KKT conditions corresponding to the lower-layer follower model are shown in the following formula (9):

[0082]

[0083] Among them, and are the dual variables of the output power constraint conditions of the inertia-providing device; u j is the dual variable of the constraint condition that the power of the power system during the load period is equal to the power demand during the load period; means and at most only one of them is positive, that is, and one of them is greater than 0 and the other is equal to 0; means and at most only one of them is positive, that is, and one of them is greater than 0 and the other is equal to 0.

[0084] Through the KKT conditions, the master-slave game model can be equivalently transformed into a single-layer equilibrium-constrained mathematical programming model. For example, if the master-slave game model is as shown in the above equations (7) and (8), and the KKT conditions are as shown in the above equation (9), then the obtained single-layer equilibrium-constrained mathematical programming model is as shown in the following equation (10):

[0085]

[0086] The definitions of the parameters in the above equation (10) can be found in the relevant descriptions in the above embodiments and will not be elaborated here.

[0087] In some examples, after obtaining the single-layer equilibrium-constrained mathematical programming model, a linear optimization algorithm can be used to process the non-linear conditions in the KKT conditions of the single-layer equilibrium-constrained mathematical programming model to obtain linear conditions; according to the linear conditions, the KKT conditions, and the upper-layer leader model, a mixed-integer linear programming (MILP) model can be obtained. The linear optimization algorithm is used to convert non-linear conditions into linear conditions. For example, the linear optimization algorithm may include, but is not limited to, the Big-M algorithm. The Big-M algorithm can be used to process the and in the above equation (10), and the obtained linear conditions can be as shown in the following equation (11). Correspondingly, the and in the above equation (10) are replaced with the following equation (11) to obtain the corresponding mixed-integer linear programming model.

[0088]

[0089] where M is a sufficiently large positive number introduced by the Big-M algorithm; and are binary auxiliary variables of the Big-M algorithm; the definitions of other parameters can be found in the relevant descriptions in the above embodiments and will not be elaborated here.

[0090] In some examples, the strong duality principle can be utilized to obtain the dual function of the lower-layer follower model; substituting the dual function into the upper-layer leader model to obtain the target upper-layer leader model; updating the upper-layer leader model in the mixed-integer linear programming model to the target upper-layer leader model to obtain a single-layer mixed-integer linear programming model. When the inertia compensation coefficient remains unchanged, the objective function of the lower-layer follower model is a convex function and satisfies the strong duality principle. The objective function of the lower-layer follower model can be converted into its dual function, and the dual function is substituted into the upper-layer leader model, thereby converting to obtain a single-layer mixed-integer linear programming model. For example, if the lower-layer follower model is as shown in the above formula (8), then the dual function of the above formula (8) is as shown in the following formula (12). The following formula (13) can be obtained according to the following formula (12). Substituting the following formula (13) into the upper-layer leader model, the objective function of the obtained target upper-layer leader model is the following formula (14). The single-layer mixed-integer linear programming model obtained by synthesizing the above content is as shown in the following formula (15):

[0091]

[0092]

[0093] The definitions of the parameters in the above formula (12) to the above formula (15) can be referred to the relevant descriptions in the above embodiments and will not be elaborated here.

[0094] In step S1032, solve the single-layer mixed-integer linear programming model, and determine the inertia compensation coefficient that satisfies the single-layer mixed-integer linear programming model as the inertia compensation coefficient that satisfies the master-slave game model.

[0095] The single-layer mixed-integer linear programming model is obtained by converting the master-slave game model. The inertia compensation coefficient that satisfies the single-layer mixed-integer linear programming model is the inertia compensation coefficient that satisfies the master-slave game model.

[0096] In the embodiment of the present application, the method of simplifying and then solving the master-slave game model according to the strong duality theory and KKT conditions has a faster iteration speed and a more accurate convergence result compared with the intelligent algorithm.

[0097] The second aspect of the present application provides a device for determining the inertia compensation coefficient of an inertia providing device. Figure 6 For the structural schematic diagram of the device for determining the inertia compensation coefficient of the inertia providing device provided by an embodiment of the present application, as Figure 6 shown, the device 200 for determining the inertia compensation coefficient of the inertia providing device may include an inertia demand determination module 201, a model establishment module 202, and a model solution module 203.

[0098] The inertia demand determination module 201 can be used to obtain the inertia constraint conditions of the power system including the inertia providing device based on the uncompensated resource model and the non-consumptive resource model of the set inertia providing device.

[0099] The model establishment module 202 can be used to construct a master-slave game model according to the parameters of the uncompensated resource model, the parameters of the non-consumptive resource model, the inertia constraint conditions, and the inertia compensation coefficient of the inertia providing device.

[0100] The master-slave game model includes an upper-layer leader model and a lower-layer follower model. The upper-layer leader model is used to represent the minimum compensation resource amount provided to the inertia providing device based on the inertia compensation coefficient. The lower-layer follower model is used to represent the minimum resource consumption amount after compensation based on the inertia compensation coefficient.

[0101] The model solving module 203 can be used to solve the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model.

[0102] In some embodiments, the inertia demand determination module 201 can be specifically used to: solve the uncompensated resource model and the non-consumptive resource model respectively to obtain the first output power and the second output power of the inertia providing device; obtain the first inertia demand boundary value and the second inertia demand boundary value of the power system according to the first output power, the second output power of the inertia providing device, and the inertia time constant of the inertia providing device, and the second inertia demand boundary value is less than the first inertia demand boundary value; based on the first inertia demand boundary value and the second inertia demand boundary value, perform weighted operation using the inertia demand factor of the power system to obtain the inertia constraint conditions.

[0103] In some embodiments, the parameters of the uncompensated resource model include the load time period, the consumption coefficient of the inertia providing device, and the output power of the inertia providing device during the load time period. The parameters of the non-consumptive resource model include the load time period, the inertia time constant of the inertia providing device, and the output power of the inertia providing device during the load time period. The constraint conditions of the upper-layer leader model include the constraint conditions of the inertia compensation coefficient of the inertia providing device, and the inertia constraint conditions. The constraint conditions of the lower-layer follower model include the constraint conditions of the output power of the inertia providing device, and the power of the power system during the load time period is equal to the power demand during the load time period.

[0104] In some embodiments, the model solving module 203 can be used to: convert the master-slave game model into a single-layer mixed integer linear programming model; solve the single-layer mixed integer linear programming model, and determine the inertia compensation coefficient that satisfies the single-layer mixed integer linear programming model as the inertia compensation coefficient that satisfies the master-slave game model.

[0105] In some embodiments, the model solving module 203 may specifically be configured to: use the KKT condition algorithm to convert the master-slave game model into a single-layer equilibrium-constrained mathematical programming model; use the linear optimization programming algorithm to convert the single-layer equilibrium-constrained mathematical programming model into a mixed-integer linear programming model; use the strong duality principle to convert the mixed-integer linear programming model into a single-layer mixed-integer linear programming model.

[0106] In some examples, the model solving module 203 may specifically be configured to: use the KKT condition algorithm to optimize the lower-layer follower model to obtain the KKT conditions corresponding to the lower-layer follower model; combine the KKT conditions and the upper-layer leader model to obtain a single-layer equilibrium-constrained mathematical programming model.

[0107] In some examples, the model solving module 203 may specifically be configured to: use the linear optimization programming algorithm to process the non-linear conditions in the KKT conditions in the single-layer equilibrium-constrained mathematical programming model to obtain linear conditions; obtain a mixed-integer linear programming model according to the linear conditions, the KKT conditions, and the upper-layer leader model.

[0108] In some examples, the model solving module 203 may specifically be configured to: use the strong duality principle to obtain the dual function of the lower-layer follower model; substitute the dual function into the upper-layer leader model to obtain the target upper-layer leader model; update the upper-layer leader model in the mixed-integer linear programming model to the target upper-layer leader model to obtain a single-layer mixed-integer linear programming model.

[0109] It should be noted that the inertia compensation coefficient determination device 200 of the inertia providing device corresponds to the inertia compensation coefficient determination method of the above-mentioned inertia providing device. All implementation manners in the above method embodiments are applicable to the embodiments of this device and can achieve the same technical effects.

[0110] The third aspect of this application provides an inertia compensation coefficient determination device for an inertia providing device. Figure 7 FIG. is a schematic structural diagram of an inertia compensation coefficient determination device for an inertia providing device provided by an embodiment of this application. As Figure 7 shown, the inertia compensation coefficient determination device 300 of the inertia providing device includes a memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.

[0111] In some examples, the above-mentioned processor 302 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0112] The memory 301 may include a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method for determining the inertia compensation coefficient of the inertia providing device according to the embodiments of the present application.

[0113] The processor 302 runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory 301, so as to implement the method for determining the inertia compensation coefficient of the inertia providing device in the above embodiments.

[0114] In some examples, the inertia compensation coefficient determination device 300 of the inertia providing device may further include a communication interface 303 and a bus 304. Among them, as Figure 7 shown, the memory 301, the processor 302, and the communication interface 303 are connected through the bus 304 and complete communication with each other.

[0115] The communication interface 303 is mainly used to implement communication between various modules, devices, units, and / or devices in the embodiments of the present application. The input device and / or output device may also be accessed through the communication interface 303.

[0116] The bus 304 includes hardware, software, or both, and couples the components of the inertia compensation coefficient determination device 300 of the inertia providing device to each other. By way of example and not limitation, the bus 304 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low pin count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 304 may include one or more buses. Although embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0117] A fourth aspect of the present application provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the inertia compensation coefficient determination method of the inertia providing device in the above embodiments can be implemented and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here. Among them, the above computer-readable storage medium may include a non-transitory computer-readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc., which is not limited herein.

[0118] An embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the inertia compensation coefficient determination method of the inertia providing device in the above embodiments can be implemented and the same technical effects can be achieved. To avoid repetition, it will not be elaborated here.

[0119] It should be clear that the various embodiments in this specification are all described in a progressive manner. For the parts that are the same or similar among the various embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. For the apparatus embodiments, device embodiments, computer-readable storage medium embodiments, and computer program product embodiments, reference can be made to the description part of the method embodiments for the relevant parts. The present application is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application. And, for the sake of brevity, the detailed description of known method technologies is omitted here.

[0120] The above has described aspects of the present application with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each block in the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices to generate a machine, such that these instructions executed by the processor of the computer or other programmable data processing devices enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0121] Those skilled in the art should be able to understand that the above embodiments are all exemplary rather than restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Those skilled in the art should be able to understand and implement other variant embodiments of the disclosed embodiments based on the study of the drawings, the specification, and the claims. In the claims, the term "comprising" does not exclude other devices or steps; the quantifier "one" does not exclude a plurality; the terms "first" and "second" are used to label names rather than to indicate any specific order. Any reference signs in the claims should not be construed as limiting the scope of protection. The functions of multiple parts appearing in the claims can be implemented by a single hardware or software module. The fact that certain technical features appear in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A method for determining the inertia compensation coefficient of an inertia providing device, characterized in that, Including: Based on the uncompensated resource model and the non-consumptive resource model of the inertia providing device, obtaining the inertia constraint conditions of the power system including the inertia providing device, where the uncompensated resource model represents the minimum resource consumption of the power system in the absence of resource compensation, and the non-consumptive resource model represents the minimum inertia of the power system in the absence of consumption costs; According to the parameters of the uncompensated resource model, the parameters of the non-consumptive resource model, the inertia constraint conditions, and the inertia compensation coefficient of the inertia providing device, constructing a master-slave game model, where the master-slave game model includes an upper-layer leader model and a lower-layer follower model. The upper-layer leader model is used to represent the minimum compensation resource quantity provided for the inertia providing device based on the inertia compensation coefficient, and the lower-layer follower model is used to represent the minimum resource consumption after compensation based on the inertia compensation coefficient. The parameters of the uncompensated resource model include the load time period, the consumption coefficient of the inertia providing device, and the output power of the inertia providing device during the load time period. The parameters of the non-consumptive resource model include the load time period, the inertia time constant of the inertia providing device, and the output power of the inertia providing device during the load time period; Solving the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model.

2. The method according to claim 1, characterized in that, The obtaining of the inertia constraint conditions of the power system including the inertia providing device based on the uncompensated resource model and the non-consumptive resource model of the set inertia providing device includes: Respectively solving the uncompensated resource model and the non-consumptive resource model to obtain the first output power and the second output power of the inertia providing device; According to the first output power, the second output power of the inertia providing device, and the inertia time constant of the inertia providing device, obtaining the first inertia demand boundary value and the second inertia demand boundary value of the power system, where the second inertia demand boundary value is less than the first inertia demand boundary value; Based on the first inertia demand boundary value and the second inertia demand boundary value, using the inertia demand factor of the power system for weighted operation to obtain the inertia constraint conditions.

3. The method according to claim 1, wherein The constraint conditions of the upper-layer leader model include the constraint conditions of the inertia compensation coefficient of the inertia providing device and the inertia constraint conditions; The constraint conditions of the lower-layer follower model include the constraint conditions of the output power of the inertia providing device and that the power of the power system during the load time period is equal to the power demand during the load time period.

4. The method according to claim 1, wherein The solving of the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model includes: Converting the master-slave game model into a single-layer mixed-integer linear programming model; Solving the single-layer mixed-integer linear programming model, and determining the inertia compensation coefficient that satisfies the single-layer mixed-integer linear programming model as the inertia compensation coefficient that satisfies the master-slave game model.

5. The method according to claim 4, characterized in that, The converting of the master-slave game model into a single-layer mixed-integer linear programming model includes: Using the KKT condition algorithm, convert the master-slave game model into a single-layer equilibrium-constrained mathematical programming model; Using the linear optimization programming algorithm, convert the single-layer equilibrium-constrained mathematical programming model into a mixed-integer linear programming model; Using the strong duality principle, convert the mixed-integer linear programming model into the single-layer mixed-integer linear programming model.

6. The method according to claim 5, wherein The use of the KKT condition algorithm to convert the master-slave game model into a single-layer equilibrium-constrained mathematical programming model includes: Using the KKT condition algorithm, optimize the lower-layer follower model to obtain the KKT conditions corresponding to the lower-layer follower model; Combine the KKT conditions and the upper-layer leader model to obtain the single-layer equilibrium-constrained mathematical programming model.

7. The method according to claim 5, characterized in that, The use of the linear optimization programming algorithm to convert the single-layer equilibrium-constrained mathematical programming model into a mixed-integer linear programming model includes: Using the linear optimization programming algorithm, process the non-linear conditions in the KKT conditions in the single-layer equilibrium-constrained mathematical programming model to obtain linear conditions; According to the linear conditions, the KKT conditions, and the upper-layer leader model, obtain the mixed-integer linear programming model.

8. The method according to claim 5, characterized in that The use of the strong duality principle to convert the mixed-integer linear programming model into the single-layer mixed-integer linear programming model includes: Using the strong duality principle, obtain the dual function of the lower-layer follower model; Substitute the dual function into the upper-layer leader model to obtain the target upper-layer leader model; Update the upper-layer leader model in the mixed-integer linear programming model to the target upper-layer leader model to obtain the single-layer mixed-integer linear programming model.

9. An inertia compensation coefficient determination device for an inertia providing device, characterized in that, Includes: An inertia demand determination module, configured to obtain the inertia constraint conditions of a power system including the inertia-providing device based on the uncompensated resource model and the non-consumable resource model of the set inertia-providing device, where the uncompensated resource model represents the minimum resource consumption of the power system in the absence of resource compensation, and the non-consumable resource model represents the minimum inertia of the power system in the absence of consumption costs; A model establishment module, configured to construct a master-slave game model according to the parameters of the uncompensated resource model, the parameters of the non-consumable resource model, the inertia constraint conditions, and the inertia compensation coefficient of the inertia-providing device. The master-slave game model includes an upper-layer leader model and a lower-layer follower model. The upper-layer leader model is used to represent the minimum compensation resource amount provided for the inertia-providing device based on the inertia compensation coefficient, and the lower-layer follower model is used to represent the minimum resource consumption after compensation based on the inertia compensation coefficient. The parameters of the uncompensated resource model include the load time period, the consumption coefficient of the inertia-providing device, and the output power of the inertia-providing device during the load time period. The parameters of the non-consumable resource model include the load time period, the inertia time constant of the inertia-providing device, and the output power of the inertia-providing device during the load time period; A model solving module, configured to solve the master-slave game model to obtain the inertia compensation coefficient that satisfies the master-slave game model.

10. An inertia compensation coefficient determination device for an inertia providing device, characterized in that, Comprising: A processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for determining the inertia compensation coefficient of the inertia providing device according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by the processor, the method for determining the inertia compensation coefficient of the inertia providing device according to any one of claims 1 to 8 is implemented.

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