Aggregation method and device for adjustable power domain of power grid demand side resources

Abstract

The invention belongs to the technical field of power grid regulation and control, and particularly relates to a power grid demand side resource adjustable power domain aggregation method and device. The method comprises the following steps: S1, constructing adjustable power domains of various demand side resources containing time sequence coupling characteristic constraints, and representing the adjustable power domains of the demand side resources in adjacent time periods by using time-segmented linear boundary constraints; s2, establishing a demand side resource comprehensive probability model containing various correlations according to historical data of various demand side resources; and S3, constructing an adjustable power domain aggregation model of the demand side resources in combination with the linear boundary constraint and the comprehensive probability model, and obtaining a flexibility decision space of the demand side resources through model optimization. According to the scheme, the linear constraint of each demand side resource and the correlation constraint between the demand side resources are considered at the same time, the obtained feasible region is finer, the supply and demand regulation and control effect of the new energy transformer area is improved, and the stability and safety of a power grid are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid regulation and control, and in particular relates to a method for aggregating adjustable power domains of power grid demand-side resources and a corresponding device. Background Art

[0002] On the supply side of the power grid, with the continuous growth of wind power and photovoltaic installed capacity and grid-connected power, the gradual increase in the penetration rate of new energy has brought new challenges to the operation of the power system. On the one hand, the intermittent output of new energy units such as wind power and photovoltaics and the limited power control capabilities increase the uncertainty of the source end of the power grid. On the other hand, the reduction in the proportion of thermal power makes the low inertia characteristics of the system prominent, and the system regulation ability and stability level are greatly reduced. Therefore, in order to ensure the smooth operation of the power grid, it is necessary to introduce more flexible resources on the demand side to participate in the dispatch, so as to fully absorb the grid-connected power of unstable new energy on the supply side, and then ensure the safety and reliability of the new energy station area.

[0003] At present, the demand-side resources used to achieve power regulation of the power grid are mainly various small controllable loads, including temperature-controlled loads, electric vehicles, and distributed energy storage. These controllable loads can participate in regulating the system balance through certain control methods and have the potential to supplement the system flexibility. They are collectively referred to as demand-side flexibility resources. Users holding these controllable loads report their dispatchable capacity based on their willingness to participate in demand-side response.

[0004] Under existing technical conditions, it is very difficult to directly conduct quantitative analysis and coordinated control of demand-side flexibility resources due to the large number of them. In addition, in order to make full use of the potential flexibility of demand-side resources and reduce the computational complexity of regulation decisions, the regulation means provided by technicians must aggregate demand-side flexibility resources to form a concise and unified form of regulation. On this basis, in order to achieve unified regulation of different types of demand-side resources, it is first necessary to determine the feasible domain of power regulation for each demand-side resource when it participates in regulation. However, the existing technology only designs the adjustable power domain of different types of demand-side resources from the perspective of their respective linear constraints, lacks consideration of the related influences between different demand-side resources, and therefore cannot achieve fine management of the adjustable power of various demand-side resources; this makes it difficult to achieve the expected regulation effect of power grid supply and demand balance, posing risks to the stability and security of power grid operation. Summary of the invention

[0005] In order to solve the problem that the prior art cannot finely manage the adjustable power of various resources on the demand side of the power grid, thereby affecting the smooth operation of the power grid, the present invention provides a method for aggregating the adjustable power domain of resources on the demand side of the power grid and a corresponding device.

[0006] The technical solution provided by the present invention is: A method for aggregating adjustable power domains of power grid demand-side resources is used to optimize the feasible domains of different types of demand-side resources performing power regulation during power grid regulation. The method comprises the following steps: S1: Construct an adjustable power domain of various demand-side resources with timing coupling characteristic constraints, and use the linear boundary constraints of time periods to characterize the adjustable power domain of demand-side resources in adjacent time periods.

[0007] S2: Based on the daily power characteristics of the historical data of various demand-side resources, a comprehensive probability model of demand-side resources containing various correlations is established. The process includes: S21: Get any two demand-side resources i and j The daily power sample observation sequence in the historical data.

[0008] S22: Using non-parametric kernel density estimation method to estimate demand-side resources i and j The marginal distribution of the two-dimensional kernel density estimation model is estimated, and then the power distribution characteristics of the two are obtained. and .

[0009] S23: Construct demand-side resources based on the state distribution of the previous step i and j Joint two-dimensional kernel density estimation model .

[0010] S24: Repeat the method of steps S21-S23 to obtain the correlation between all demand-side resources, and then expand the comprehensive probability model to a multidimensional model that includes all demand-side resource types.

[0011] S3: Combining linear boundary constraints and comprehensive probability models, we build an adjustable power domain aggregation model for demand-side resources and iterate and optimize it to obtain the following flexibility decision space for demand-side resources: ; ; In the above formula, N is the quantity of demand-side resources; is the reference polyhedron corresponding to the basic decision space; and Respectively represent n The scaling factors and displacement factors of the demand-side resource decision space are calculated.

[0012] Will The feasible domain of any demand-side resource is obtained by mapping it to the preset mathematical model that characterizes the regulation strategy.

[0013] As a further improvement of the present invention, the types of demand-side resources include electric vehicles, energy storage equipment, temperature control load equipment, etc.; the adjustable power domains correspond one-to-one to the types of demand-side resources with flexibility.

[0014] As a further improvement of the present invention, in step S1, the method for constructing the adjustable power domain of each demand-side resource that satisfies the linear boundary constraint is as follows: S11: Divide a flexible resource scheduling cycle into m time period, then express k The power of the time period, where k =1,2,…, m .

[0015] S12: The constraint space of demand-side resource operation is represented by the following set of inequality constraints, including: (1) Each demand-side resource k The power of the time period meets the following power constraints: ; In the above formula, and They are k The upper and lower limits of power for a time period.

[0016] (2) Each demand-side resource k The power variation during the time period satisfies the following ramp constraints: ; In the above formula, and They are k The upper and lower limits of the ramp rate for the time period.

[0017] (3) Each demand-side resource meets the following state constraints during energy storage or energy use: ; In the above formula, express k Status of the time slot; and They are k The upper and lower bounds of the state of the time period; , and They represent the flexible demand side resources in the 0th period, the 1st period and the k -1 period of disturbance; , and Respectively represent the weights of state, disturbance and power in the state equation; , , They represent the coefficients of the parameters at different times.

[0018] S13: Combining the above three types of constraints, we can describe any demand-side resource through a convex polyhedron represented by inequality constraints. i The feasible domain of operation : ; In the above formula, Indicates i The power vector of each demand-side resource in the decision cycle of each time period; express m dimensional real number space; The coefficients of the inequality constraint matrix that characterizes the feasible domain; Indicates i The disturbance of demand-side resources in different time periods; Indicates i The constraints on the state variables of the demand-side resources in each time period.

[0019] As a further improvement of the present invention, in step S22, any demand-side resource i Power distribution characteristics Satisfy the following formula: ; In the above formula, Expressed as t Demand-side resources at sampling time i The actual power; T Indicates the sampling duration; Demand-side resources i The kernel density estimation smoothing parameter of ; Indicates t Demand-side resources at sampling time i The predicted power of Represents the standard normal distribution function.

[0020] As a further improvement of the present invention, in step S23, the demand side resource i and j Joint two-dimensional kernel density estimation model as follows: ; In the above formula, is the general Archimedean Copula function; is the estimated parameter of the Archimedean Copula function; Demand-side resources j The power distribution characteristics.

[0021] As a further improvement of the present invention, in step S24, assuming that the demand side resources have Y , then the expanded Y Dimensional comprehensive probability model The expression is as follows: ; In the above formula, They are demand-side resources 1~ Y Power distribution characteristics; , , , , They are , , , , The inverse function of , , , , is the generator of the Archimedean Copulas function.

[0022] As a further improvement of the present invention, after the expansion Y In the dimensional comprehensive probability model, any generator Satisfy the following formula: ; in, Represents a generator The value of the unknown parameter in is estimated by the maximum likelihood estimation method.

[0023] As a further improvement of the present invention, in step S3, the flexibility decision space The aggregation process is as follows: S31: Combined with known linear boundary constraints, define a reference polyhedron that represents the basic decision space of demand-side resources as follows: ; In the above formula, express Y dimensional real number space; is the basic polyhedral space; is the corresponding benchmark decision variable, is the corresponding reference disturbance variable, The coefficients of the inequality constraint matrix that characterizes the base polyhedron, Constraints that characterize the state variables of the basis polyhedron.

[0024] S32: Based on the comprehensive probability model, the scaling and displacement transformation method is used to maximize the approximation of the decision space of each flexibility resource individual, and then generate the first n Flexible resource operation space as follows: ; In the above formula, Indicates n Demand-side resources k The power of the time period.

[0025] S33: Finding the biggest and its corresponding ,right Minkowski summation is used as the flexibility decision space of demand-side resources : ; In the above formula, represents the Minkowski addition operation.

[0026] As a further improvement of the present invention, the decision space calculated in step S33 is middle, and By solving the following linear optimization model: ; In the above formula, , ; is the auxiliary matrix introduced, Indicates n The inequality constraint matrix coefficients of the feasible region of demand-side resources, Indicates n The state variable constraints of the demand-side resources.

[0027] The present invention also includes a device for aggregating adjustable power domains of power grid demand side resources, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the aforementioned method for aggregating adjustable power domains of power grid demand side resources is adopted to optimize the feasible domains when different types of demand side resources perform power regulation during the power grid regulation process.

[0028] The technical solution provided by the present invention has the following beneficial effects: The adjustable power domain of each resource aggregated by the present invention takes into account the temporal state correlation between the power variables of demand-side resources, realizes the accurate characterization of the dynamic adjustment characteristics of demand-side resource power, and can provide a modeling method for the adjustable power of demand-side resources at different time scales. At the same time, the solution uses a set of standard virtual energy storage models to characterize the adjustable power of demand-side resources, reducing the risk of information exposure.

[0029] The solution of the present invention fully considers the probabilistic correlation between the output characteristics of different demand-side resources, establishes a high-dimensional comprehensive probability model of demand-side resources, overcomes the problem of overly loose aggregation results caused by traditional methods, greatly simplifies the modeling of demand-side resources participating in power grid dispatching, and facilitates the dispatch of demand-side resources by the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flowchart of the steps of the method for aggregating adjustable power domains of grid demand-side resources provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0032] Example 1 This embodiment provides a method for aggregating adjustable power domains of grid demand-side resources, which is used to optimize the feasible domains when different types of demand-side resources perform power regulation during grid regulation. On the one hand, the solution of the present invention analyzes the temporal state correlation between the power variables of demand-side resources, and thus accurately characterizes the dynamic regulation characteristics of demand-side resources. On the other hand, the present invention fully considers the probabilistic correlation between the output characteristics of different demand-side resources, and thus provides more refined constraint features between resources, so as to overcome the problem of overly loose adjustable power domains in traditional solutions.

[0033] Specifically, Figure 1 As shown, the method for aggregating adjustable power domains of grid demand-side resources provided in this embodiment includes the following steps: S1: Construct an adjustable power domain of various demand-side resources with timing coupling characteristic constraints, and use the linear boundary constraints of time periods to characterize the adjustable power domain of demand-side resources in adjacent time periods.

[0034] In this embodiment, considering the time series correlation between demand-side power, the linear boundary constraint of time periods is adopted to propose the adjustable power domain of adjacent time periods of demand-side resources based on the improved general virtual energy storage model. Specifically, the method for constructing the adjustable power domain of each demand-side resource that meets the linear boundary constraint is as follows: S11: Divide a flexible resource scheduling cycle into m time period, then express k The power of the time period, express k -1 period of power; where, k =1,2,…, m .

[0035] S12: In this embodiment, the constraint space of the demand-side resource operation is represented by a set of inequality constraints, including: (1) Each demand-side resource k The power of the time period meets the following power constraints: ; In the above formula, and They are k The upper and lower limits of power for a time period.

[0036] (2) The power that the demand-side resource can generate in the current period is not only affected by the parameters of its own equipment and the scheduling time scale, but also by the power constraints of the previous period; this is reflected in the fact that the power change of the demand-side resource in adjacent periods will be subject to the ramp constraint. k The ramp constraints for power changes during the time period are as follows: ; In the above formula, and They are k The upper and lower limits of the ramp rate for the time period.

[0037] (3) The operating state of demand-side resources is a continuous cumulative quantity that changes with the power changes in each period, such as the remaining capacity of energy storage, the temperature of the temperature control system, etc. Taking energy storage loads as an example, each demand-side resource meets the following state constraints during energy storage or energy use: ; In the above formula, express k Status of the time slot; and They are k The state upper and lower bounds of the time period; , and They represent the flexible demand side resources in the 0th period, the 1st period and the k -1 period of disturbance; , and Respectively represent the weights of state, disturbance and power in the state equation; , , They represent the coefficients of the parameters at different times.

[0038] S13: Combining the above three types of constraints, we can describe any demand-side resource through a convex polyhedron represented by inequality constraints. i The feasible domain of operation : ; In the above formula, express m dimensional real number space; Indicates i The power vector of a demand-side resource in each time period decision cycle satisfies: ,in, Demand-side resources i In the k The power in a period of time. The coefficients of the inequality constraint matrix that characterizes the feasible domain; Indicates i The disturbance of demand-side resources in each period satisfies: , Representation flexibility resources i In the k Disturbance within a period of time; Indicates i The constraints on the state variables of the demand-side resources in each period satisfy: ; Representation flexibility resources i In the k The constraints on the state variables within a period.

[0039] S2: Based on the daily power characteristics of the historical data of various demand-side resources, a comprehensive probability model of demand-side resources including various correlations is established. The process includes: S21: Get any two demand-side resources i and j The daily power sample observation sequence in the historical data :in, and Respectively expressed as t Demand-side resources at sampling time i and j The actual power; T Indicates the sampling duration, .

[0040] S22: Using non-parametric kernel density estimation method to estimate demand-side resourcesi and j The marginal distribution of the two-dimensional kernel density estimation model is estimated, and then the power distribution characteristics of the two are obtained. and : ; In the above formula, and Represents demand-side resources i and j The kernel density estimation smoothing parameter of ; and Respectively represent t Demand-side resources at sampling time i and j The predicted power of represents the standard normal distribution function, which is expressed as: .

[0041] S23: Construct demand-side resources based on the state distribution of the previous step i and j Joint two-dimensional kernel density estimation model : ; In the above formula, is the general Archimedean Copula function; is the parameter to be estimated of the Archimedean Copula function.

[0042] S24: Based on the above ideas, the method of steps S21-S23 is repeatedly adopted to obtain the correlation between all demand-side resources, and then the comprehensive probability model is expanded to a multidimensional model that includes all demand-side resource types.

[0043] For example, suppose the demand-side resource is Y , then the expanded Y Dimensional comprehensive probability model The expression is as follows: ; In the above formula, They are demand-side resources 1~ Y Power distribution characteristics; , , , , They are , , , , The inverse function of , , , , is the generator of the Archimedean Copulas function. Satisfy the following formula: ; in, Represents a generator The value of the unknown parameter in is estimated by the maximum likelihood estimation method.

[0044] S3: Combining linear boundary constraints and comprehensive probability models, we build an adjustable power domain aggregation model for demand-side resources and iterate and optimize it to obtain the following flexibility decision space for demand-side resources: ; ; In the above formula, N is the quantity of demand-side resources; is the reference polyhedron corresponding to the basic decision space; and Respectively represent n The scaling factors and displacement factors of the demand-side resource decision space are calculated.

[0045] Will The feasible domain of any demand-side resource is obtained by mapping it to the preset mathematical model that characterizes the regulation strategy.

[0046] Specifically, in this embodiment, the flexibility decision space The aggregation process is as follows: S31: Combined with known linear boundary constraints, define a reference polyhedron that represents the basic decision space of demand-side resources as follows: ; In the above formula, express Y dimensional real number space; is the basic polyhedral space; is the corresponding benchmark decision variable, is the corresponding reference disturbance variable, The coefficients of the inequality constraint matrix that characterizes the base polyhedron, Constraints that characterize the state variables of the basis polyhedron.

[0047] S32: Based on the comprehensive probability model, the scaling and displacement transformation method is used to maximize the approximation of the decision space of each flexibility resource individual, and then generate the first n Flexible resource operation space as follows: ; In the above formula, Indicates n Demand-side resources k The power of the time period.

[0048] S33: Finding the biggest and its corresponding ,right Minkowski summation is used as the flexibility decision space of demand-side resources : ; In the above formula, represents the Minkowski addition operation.

[0049] In the final decision space middle, and By solving the following linear optimization model: ; In the above formula, , ; is the auxiliary matrix introduced, Indicates n The inequality constraint matrix coefficients of the feasible region of demand-side resources, Indicates n The state variable constraints of the demand-side resources.

[0050] Example 2 Based on the solution of Example 1, this embodiment further provides an aggregation device for adjustable power domains of power grid demand side resources, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the aggregation method for adjustable power domains of power grid demand side resources as described above is adopted to optimize the feasible domains when different types of demand side resources perform power regulation during the power grid regulation process.

[0051] The aggregation device of the adjustable power domain of the grid demand side resources provided in this embodiment is essentially a computer device for implementing the solution in Embodiment 1. The computer device can be an intelligent terminal capable of executing a program, a tablet computer, a laptop computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including an independent server, or a server cluster composed of multiple servers), etc.

[0052] The computer device indicated in this embodiment includes at least but is not limited to: a memory and a processor that can be connected to each other through a system bus. Among them, the memory (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0053] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in a memory or process data.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for aggregating adjustable power domains of power grid demand-side resources, which is used to optimize the feasible domains when different types of demand-side resources perform power regulation during power grid regulation; characterized in that: It includes the following steps: S1: Construct the adjustable power domain of various demand-side resources with timing coupling characteristic constraints, and use the linear boundary constraints of time periods to characterize the adjustable power domain of demand-side resources in adjacent time periods; S2: Based on the daily power characteristics of the historical data of various demand-side resources, a comprehensive probability model of demand-side resources including various correlations is established; include: S21: Get any two demand-side resources i and j Daily power sample observation sequence in historical data; S22: Using nonparametric kernel density estimation method to estimate demand-side resources i and j The marginal distribution of the two-dimensional kernel density estimation model is estimated, and then the power distribution characteristics of the two are obtained. and ; S23: Construct demand-side resources based on the state distribution of the previous step i and j Joint two-dimensional kernel density estimation model ; S24: Repeat the method of steps S21-S23 to obtain the correlation between all demand-side resources, and then expand the comprehensive probability model to a multidimensional model that includes all demand-side resource types; S3: Combining the linear boundary constraint and the comprehensive probability model, an adjustable power domain aggregation model of demand-side resources is constructed, and iterative optimization is performed to obtain the following flexibility decision space of demand-side resources: ; ； In the above formula, N is the quantity of demand-side resources; is the reference polyhedron corresponding to the basic decision space; and Respectively represent n Scaling factors and displacement factors of the demand-side resource decision space; Will The feasible domain of any demand-side resource is obtained by mapping it to the preset mathematical model that characterizes the regulation strategy.

2. The method for aggregating adjustable power domains of power grid demand side resources according to claim 1, characterized in that: The types of demand-side resources include electric vehicles, energy storage equipment, and temperature-controlled load equipment; the adjustable power domains correspond one-to-one to the types of flexible demand-side resources.

3. The method for aggregating adjustable power domains of power grid demand side resources according to claim 2, characterized in that: In step S1, the method for constructing the adjustable power domain of each demand-side resource that meets the linear boundary constraint is as follows: S11: Divide a flexible resource scheduling cycle into m time period, then express k The power of the time period, where k =1,2,…, m ; S12: The constraint space of demand-side resource operation is represented by the following set of inequality constraints, including: (1) Each demand-side resource k The power of the time period meets the following power constraints: ； In the above formula, and They are k The upper and lower limits of power for a time period; (2) Each demand-side resource k The power variation during the time period satisfies the following ramp constraints: ； In the above formula, and They are k The upper and lower limits of the ramp rate for the time period; (3) Each demand-side resource meets the following state constraints during energy storage or energy use: ； In the above formula, express k Status of the time slot; and They are k The state upper and lower bounds of the time period; , and They represent the flexible demand side resources in the 0th period, the 1st period and the k -1 period of disturbance; , and Respectively represent the weights of state, disturbance and power in the state equation; , , Respectively represent the coefficients of parameters at different times; S13: Combining the above three types of constraints, we can describe any demand-side resource through a convex polyhedron represented by inequality constraints. i The feasible domain of operation : ； In the above formula, Indicates i The power vector of each demand-side resource in the decision cycle of each time period; express m dimensional real number space; The coefficients of the inequality constraint matrix that characterizes the feasible domain; Indicates i The disturbance of demand-side resources in different time periods; Indicates i The constraints on the state variables of the demand-side resources in each time period.

4. The method for aggregating adjustable power domains of power grid demand side resources according to claim 3, characterized in that: In step S22, any demand-side resource i Power distribution characteristics Satisfy the following formula: ； In the above formula, Expressed as t Demand-side resources at sampling time i The actual power; T Indicates the sampling duration; Demand-side resources i The kernel density estimation smoothing parameter of ; Indicates t Demand-side resources at sampling time i The predicted power of Represents the standard normal distribution function.

5. The method for aggregating adjustable power domains of power grid demand side resources according to claim 4, characterized in that: In step S23, the demand side resources i and j Joint two-dimensional kernel density estimation model as follows: ； In the above formula, is the general Archimedean Copula function; is the estimated parameter of the Archimedean Copula function; Demand-side resources j The power distribution characteristics.

6. The method for aggregating adjustable power domains of grid demand side resources according to claim 5, characterized in that: In step S24, assuming that there are Y types of demand-side resources, the expanded Y-dimensional comprehensive probability model The expression is as follows: ； In the above formula, They are demand-side resources 1~ Y Power distribution characteristics; , , , , They are , , , , The inverse function of , , , , is the generator of the Archimedean Copulas function.

7. The method for aggregating adjustable power domains of grid demand side resources according to claim 6, characterized in that: After the expansion Y In the dimensional comprehensive probability model, any generator Satisfy the following formula: ； in, Represents a generator The value of the unknown parameter in is estimated by the maximum likelihood estimation method.

8. The method for aggregating adjustable power domains of power grid demand side resources according to claim 6, characterized in that: In step S3, the flexibility decision space The aggregation process is as follows: S31: Combined with known linear boundary constraints, define a reference polyhedron that represents the basic decision space of demand-side resources as follows: ； In the above formula, express Y dimensional real number space; is the basic polyhedral space; is the corresponding benchmark decision variable, is the corresponding reference disturbance variable, The coefficients of the inequality constraint matrix that characterizes the base polyhedron, Constraints that characterize state variables of a reference polyhedron; S32: Based on the comprehensive probability model, the decision space of each flexibility resource individual is maximized and approximated by using the scaling and displacement transformation method, thereby generating the first n Flexible resource operation space as follows: ； In the above formula, Indicates n Demand-side resources k Power of the time period; S33: Finding the biggest and its corresponding ,right Minkowski summation is used as the flexibility decision space of demand-side resources : ； In the above formula, represents the Minkowski addition operation.

9. The method for aggregating adjustable power domains of power grid demand side resources according to claim 8, characterized in that: The decision space calculated in step S33 middle, and By solving the following linear optimization model: ； In the above formula, , ; is the auxiliary matrix introduced, Indicates n The inequality constraint matrix coefficients of the feasible region of demand-side resources, Indicates n The state variable constraints of the demand-side resources.

10. A device for aggregating power domains of power grid demand-side resources with adjustable power domains, comprising a memory, a processor, and a computer program stored in the memory and running in the processor, characterized in that: When the processor executes the computer program, it adopts the method for aggregating the adjustable power domain of the power grid demand side resources as described in any one of claims 1-9 to optimize the feasible domain when different types of demand side resources perform power regulation during the power grid regulation process.

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