Fine adjustment resource power plant dynamic aggregation packaging method based on high-dimensional model expression

Abstract

The invention relates to the technical field of power systems, in particular to a fine adjustment resource power plant dynamic aggregation packaging method based on high-dimensional model expression, and the method comprises the steps: carrying out the aggregation of feasible regions of different power generation factors, and obtaining a feasible region aggregation model of the power generation factors; the distributed load resources are aggregated into virtual units, aggregation and scheduling of power plant dynamic aggregation packaging scheduling objects are dynamically optimized based on power generation characteristics and power generation economy, and dynamic construction and aggregation of the power plant dynamic aggregation packaging scheduling objects are achieved; the mapping relation between the physical parameters and the aggregation model parameters is established through the high-dimensional model expression technology, and real-time online identification of the aggregation unit external characteristic parameters based on the high-dimensional model expression technology is achieved. According to the method, resource aggregation and optimization can be dynamically carried out in real time on the premise of ensuring the calculation efficiency and precision, unified regulation and optimal utilization of flexible resources are ensured, and quick response can be realized under different scheduling requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power systems, and in particular to a method for dynamically aggregating and packaging micro-regulation resources in a power plant based on high-dimensional model expression. Background Art

[0002] With the rapid development of renewable energy, resources such as wind and solar energy have gradually become an important part of the power grid, which has also made the power grid face more and more volatility and uncertainty. In order to meet these challenges, micro-regulation resource power plant has emerged as a new type of power dispatching mode. The dynamic aggregation technology of flexible resource power plant has effectively improved the regulation ability and stability of the power grid. The core technology of power plant dynamic aggregation encapsulation dispatching objects lies in how to efficiently and accurately dynamically aggregate distributed energy and flexible resources to meet the dispatching needs of the power grid and maximize its economic benefits.

[0003] The existing aggregation methods have achieved certain results to a certain extent, but with the continuous increase of many distributed energy resources, the existing methods still have limitations in high-dimensional data processing, computational efficiency and accuracy, and are difficult to cope with complex and diverse resource requirements. They still face some challenges in practical applications. First, the existing technology is still insufficient in processing large-scale, high-dimensional data. Especially when facing dynamic and complex distributed resources, the computational efficiency and accuracy of the existing model cannot fully meet the dispatching needs of the power grid. Secondly, the current dynamic construction technology of power plant dynamic aggregation encapsulation dispatching objects has not yet fully realized the online dynamic identification of resources, resulting in limited dispatching accuracy and flexibility after resource aggregation. Especially in the face of power dispatching of different types and demands, the existing methods cannot accurately and efficiently identify and match the external characteristic parameters of the power plant state aggregation encapsulation dispatching objects in real time, which affects the dispatching response capability of the power plant dynamic aggregation encapsulation dispatching objects.

[0004] Therefore, a new technical method is urgently needed that can aggregate and optimize resources in real time and dynamically while ensuring computing efficiency and accuracy, especially in high-dimensional and large-scale data processing, to ensure that the power plant dynamic aggregation and encapsulation scheduling objects can respond quickly to different scheduling requirements. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a method for dynamically aggregating and packaging micro-adjusting resources in power plants based on high-dimensional model expression. The technical solutions adopted are as follows: In a first aspect, the present invention provides a method for dynamically aggregating and packaging micro-adjusting resources into power plants based on high-dimensional model expression, comprising the following steps: Aggregate the feasible domains of different power generation factors to obtain a feasible domain aggregation model of power generation factors; Aggregate distributed load resources into virtual machine groups, dynamically optimize the aggregation and scheduling of power plant-based dynamic aggregation and encapsulation scheduling objects based on power generation characteristics and power generation economy, and realize dynamic construction and aggregation of power plant-based dynamic aggregation and encapsulation scheduling objects; Match the power generation operation mode of the power plant dynamic aggregation package scheduling object with the equipment operation mode corresponding to each response characteristic curve in the building potential characteristic library to achieve online mode matching of the external characteristic parameters of the aggregation unit; The mapping relationship between physical parameters and aggregation model parameters is established through high-dimensional model expression technology, and the real-time online identification of the external characteristic parameters of the aggregation unit based on high-dimensional model expression technology is realized.

[0006] In combination with the first aspect above, in some possible implementations, the feasible domains of different power generation factors are aggregated to obtain a feasible domain aggregation model of the power generation factors, including: The Zeno polyhedron is used to approximate the feasible domain of power generation factors, and the feasible domains of different power generation factors are aggregated through the Minkowski sum operation of the Zeno polyhedron, so as to obtain the feasible domain aggregation model of power generation factors.

[0007] In combination with the first aspect above, in some possible implementations, a Zeno polyhedron is used to approximate the feasible domain of power generation factors, and the feasible domains of different power generation factors are aggregated through the Minkowski sum operation of the Zeno polyhedron, so as to obtain a feasible domain aggregation model of power generation factors, including: The feasible domain of a single power generation factor is represented by a convex polyhedron: (1) In the formula, Convex polyhedron representing a single power generation factor; The power set representing the power generation factor; Represents the dimension of the power generation factor; is the constraint matrix; represents the constraint space; Indicates the number of constraints; is the constraint vector; The aggregation of the feasible domains of multiple power generation factors is expressed as the Minkowski sum of all power generation factors. The feasible domain after aggregation is The formula is as follows: (2) In the formula, represents the feasible domain of the aggregate; represents the feasible domain of the jth power generation factor; Represents the total number of power generation factors; Zeno polyhedrons are used to approximate the feasible region of each power generation factor. The feasible region of a single power generation factor is Zeno polyhedron The approximate formula is as follows: (3) In the formula, The power vector representing the power generation factor; is the center of the Zeno polyhedron; represents the dimension of the generator matrix G; is the generator matrix; Represents the generator coefficient Dimensions; are the coefficients of the generators; is the maximum scaling factor of the generator; Determine the optimization objective and constraints of the Zeno polyhedron approximation problem, solve the optimization objective based on the constraints, determine the Zeno polyhedron approximate feasible domain that meets feasibility, and merge the Zeno polyhedron approximate feasible domain by Minkowski sum operation to obtain the Zeno polyhedron representation of the aggregate, and determine the Zeno polyhedron representation of the aggregate as the feasible domain aggregation model of the power generation factor.

[0008] In combination with the first aspect above, in some possible implementations, the optimization objective and constraint conditions of the Zeno polyhedron approximation problem are determined, and the corresponding calculation formula is: (4) st (5) In the formula, Represents a Zeno polyhedron and the convex polyhedron of the feasible region In the The diameter ratio in each direction, , and Zeno polyhedron and convex polyhedrons In the The diameter in each direction; Indicates the total number of directions.

[0009] In combination with the first aspect above, in some possible implementations, distributed load resources are aggregated into virtual machine groups, and the aggregation and scheduling of power plant-based dynamic aggregation and encapsulation scheduling objects are dynamically optimized based on power generation characteristics and power generation economy, so as to realize dynamic construction and aggregation of power plant-based dynamic aggregation and encapsulation scheduling objects, including: 1) Aggregate distributed load resources into virtual machine groups. The virtual machine group model is shown in the following formula: (6) In the formula, A model representing a group of virtual machines; and They represent the upper and lower limits of the output of the virtual machine group respectively; and They represent the maximum ramp-up and ramp-down rates of the virtual machine group respectively; and They represent the minimum running time and minimum downtime of the virtual machine group respectively; represents the cost function of the virtual machine group; 2) Collect the real-time regulation data of each central air-conditioning load in the power plant dynamic aggregation package scheduling object, including the regulation potential and manageable time; 3) Preprocess the collected load data, remove noise and perform normalization to ensure data consistency and comparability; 4) The regulation potential of each central air conditioner It is the minimum value of the adjustment amount in each control cycle within the specified control time. The calculation can be expressed as: (7) In the formula, Indicates the maximum adjustment amount of the central air conditioner; is the regulated load; is the total number of control cycles; 5) Calculate the regulation potential of each central air conditioner, obtain its regulation characteristic curve, and fit the relationship curve between the adjustable potential and control time of a single air conditioner. The formula is as follows: (8) In the formula, Indicates control time; represents the fitting function; 6) Compare the adjustment characteristic curves of different central air conditioners to determine their consistency and consistency The calculation formula is: (9) In the formula, and Respectively represent Central air conditioner and Central air conditioner in adjustment cycle The regulatory potential of Indicates the total number of adjustment cycles; 7) The central air conditioners are aggregated using the agglomerative hierarchical clustering method. The goal is to form a power plant-like dynamic aggregation package scheduling object that meets the scheduling requirements. The optimization objective function is shown in the following formula: (10) In the formula, It represents the number of all combinations of any two numbers among M numbers; Represents the number of central air conditioners in the group after aggregation; the constraint conditions are as shown in the following formula: (11) In the formula, represents the load adjustment of the i-th central air conditioner; It represents the total regulation amount of the power plant dynamic aggregation package scheduling object; 8) Calculate the adjustment quotation of each central air conditioner, which is adjusted according to the adjustment potential, rated power and comfort of the central air conditioner. The formula is as follows: (12) In the formula, Indicates Central air conditioning adjustment quotation; Indicates The quotation decision coefficient of a central air conditioner; Indicates Rated power of central air conditioner; Indicates The comfort level of central air conditioning; 9) Minimize the total cost of the power plant dynamic aggregation package scheduling object, and define the total cost function of the power plant dynamic aggregation package scheduling object. The formula is as follows: (13) In the formula, represents the total cost function; Indicates the control duration; Indicates The regulation amount of a central air conditioner; N represents the total number of central air conditioners; 10) The dispatch of the power plant dynamic aggregation and encapsulation dispatch object needs to meet the total regulation requirements of the power grid dispatch department. The constraint formula is as follows: (14) In the formula, Indicates the total adjustment amount of the scheduling plan; Indicates The load regulation potential of a central air conditioner.

[0010] In combination with the first aspect above, in some possible implementations, the power generation operation mode of the power plant dynamic aggregation package scheduling object is matched with the equipment operation mode corresponding to each response characteristic curve in the building potential characteristic library to achieve online mode matching of the external characteristic parameters of the aggregation unit, including: 1) Taking the maximum input power of intermittent energy such as wind and solar power as input, obtain the external characteristic parameters of the computer group and the virtual power plant as a whole; 2) Establish a normalized information model for obtaining online parameters of power plant dynamic aggregation and encapsulation scheduling objects. The formula is as follows: (15) In the formula, Indicates The maximum output of each unit represents the output uncertainty of the distributed generator set; Indicates the time when the control signal is released; Indicates Unit in The winning bid quantity for the time period; Indicates Unit in The number of spare winning bids of various types in the time period; Respectively represent Dynamic climbing and descending rate of each unit (MW / s); 3) Model the output sequence of the power plant-based dynamic aggregation and packaging scheduling objects and internal power generation resources. The formula is as follows: (16) In the formula, Indicates that the power plant dynamic aggregation package scheduling object is Each time during the period Response sequence to system output regulation signal; Indicates that the power plant dynamic aggregation package scheduling object is The basic output of the time period is also the bidding strategy of the virtual power plant in the h period; Indicates that the power plant dynamic aggregation package scheduling object is Each time during the period output sequence; 4) Model the maximum output of the power plant dynamic aggregation package scheduling object. The formula is as follows: (17) In the formula, Indicates that the power plant dynamic aggregation package scheduling object is At each time during the period The maximum output sequence of Indicates the first Intermittent energy units At each moment in the time period The maximum output sequence depends on the current maximum input and control interval of the unit. The product of climbing / descending rate; Indicates the first Maximum output power of conventional units; 5) Carry out real-time standby modeling of power plant dynamic aggregation and encapsulation scheduling objects. The formula is as follows: (18) (19) In the formula, Indicates remaining spare; Indicates that each intermittent energy unit Each time during the period The load shedding ratio depends on the standby control strategy of each unit and the load shedding ratio constraint; Indicates the first Conventional units in Timely backup; Indicates that the virtual power plant is in the period At each moment The reserve power; 6) In the performance parameters of the power plant dynamic aggregation package scheduling object, the subsequent climbing / declining rate calculation formula is as follows: (20) In the formula, Indicates that under different scheduling or control instructions, The climbing / descending rate during the time period; in the subsequent climbing / descending rate calculation, Dynamically aggregate and encapsulate the output data of dispatch objects for actual power plants; 7) In the performance parameters of the power plant dynamic aggregation package scheduling object, the calculation formula of the pre-climbing / downhill rate is consistent with the post-calculation expression. From the above power plant dynamic aggregation package dispatch object output sequence calculation model, we can get: It is an arbitrary parameter; 8) The frequency regulation performance score of the power plant dynamic aggregation and packaging scheduling object in each time period is calculated as follows: (twenty one) (twenty two) (twenty three) (twenty four) (25) In the formula, represents the frequency modulation performance score, which is the weighted average of the three scores; DS represents the response delay score; CS represents the correlation score; PS represents the accuracy score; A, B, C represent the weights, all of which are 1 / 3; It indicates the response delay at which CS and DS achieve their maximum values; Indicates the predetermined adjustment power; Indicates the actual regulated power; represents the correlation function; Indicates the average value of the scheduled regulated power per hour.

[0011] In combination with the first aspect, in some possible implementations, a mapping relationship between physical parameters and aggregation model parameters is established through high-dimensional model expression technology to achieve real-time online identification of external characteristic parameters of aggregation units based on high-dimensional model expression technology, including: 1) The influence of uncertainty in physical parameters of air conditioning load on aggregation model parameters is analyzed through high-dimensional model expression. The calculation formula is as follows: (26) In the formula, represents a constant term; represents the first-order component; represents the second-order component; represents the order of the component; the system input is , the output is , and so on; 2) Generate A set of input samples, the input includes multiple physical parameters of air conditioning load; 3) Each group of input samples is simulated to obtain the aggregate model parameters, thus obtaining Output samples, as shown in the following formula: (27) In the formula, Indicates Group input sample The corresponding aggregation model output; Indicates Group input samples; Indicates The value of the output quantity; 4) Standardize each input quantity, the formula is as follows: (28) In the formula, and Represent the actual value and standard value of the input variable respectively; and Respectively represent the minimum and maximum values ​​of the input; 5) Use the input and output values ​​of N0 to establish a high-dimensional model expression model for the air conditioning group and calculate the correlation coefficient , And the global sensitivity of the physical parameters of the air conditioning load is as follows: (29) (30) In the formula, , , , , and All represent the physical parameters of the air conditioning load. By analogy, we can obtain higher-order coefficients of the first-order component function of the input quantity and the second-order component function of the input quantity. represents the first-order component function; 6) Calculate the global sensitivity of the independent and coupled effects of the input quantities; 7) Establish a high-dimensional model expression model database for air conditioning aggregation model parameters at different time scales.

[0012] In combination with the first aspect above, in some possible implementations, the Quasi Monte calor method is used to generate The input sample of the group air conditioning system can be expressed as: (31) In the formula, , , , , , and Both represent the physical parameters of air conditioning load.

[0013] In a second aspect, the present invention further provides a micro-adjustment resource power plant dynamic aggregation packaging system based on high-dimensional model expression, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the above-mentioned first aspect or any possible implementation of the first aspect.

[0014] In a third aspect, the present invention further provides a computer program product, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium storing a computer program code. When the computer program code runs on a computer, the computer executes the method in the first aspect or any possible implementation of the first aspect.

[0016] The present invention has the following beneficial effects: the present invention obtains a feasible domain aggregation model of power generation factors by aggregating the feasible domains of different power generation factors; aggregates distributed load resources into virtual machine groups, dynamically optimizes the aggregation and scheduling of power plant-based dynamic aggregation and packaging scheduling objects based on power generation characteristics and power generation economy, and realizes the dynamic construction and aggregation of power plant-based dynamic aggregation and packaging scheduling objects; establishes a mapping relationship between physical parameters and aggregation model parameters through high-dimensional model expression technology, and realizes real-time online identification of external characteristic parameters of aggregation units based on high-dimensional model expression technology. The present invention can aggregate and optimize resources in real time and dynamically while ensuring computing efficiency and accuracy, especially in high-dimensional and large-scale data processing, ensuring unified regulation and optimal utilization of flexible resources, and being able to respond quickly to different scheduling requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flowchart of the steps of the method for dynamic aggregation and packaging of micro-adjustment resources power plants based on high-dimensional model expression according to an embodiment of the present invention; Figure 2 A flowchart for dynamically constructing a power plant-based dynamic aggregation and packaging scheduling object according to an embodiment of the present invention; Figure 3 A schematic diagram of a high-dimensional model expression modeling process according to an embodiment of the present invention; Figure 4 This is a flow chart of a real-time identification framework of air conditioning load aggregation model parameters according to an embodiment of the present invention; Figure 5 A comparison diagram of the power pulse capacity obtained by independent simulation and high-dimensional model expression according to an embodiment of the present invention; Figure 6 Schematic diagram of relative error comparison of parameter identification results expressed based on high-dimensional model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to clearly illustrate the technical features of the present invention, the present invention is described in detail below through specific implementation methods in conjunction with the accompanying drawings.

[0020] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0021] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0022] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0023] It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0024] Although operations or steps are described in a specific order in the drawings in the embodiments of the present invention, it should not be understood that it is required to perform these operations or steps in the specific order shown or in a serial order, or that all the operations or steps shown must be performed to obtain the desired results. In the embodiments of the present invention, these operations or steps may be performed in series; these operations or steps may also be performed in parallel; or some of these operations or steps may be performed.

[0025] At the same time, it is understood that the data involved in the technical solution of the present invention (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of the relevant laws, regulations and relevant provisions. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those generally understood by technicians in the technical field of the present invention, and all parameters or indicators in the formulas involved in the present invention are normalized values ​​that eliminate the influence of dimensions.

[0026] In order to overcome the shortcomings of the prior art, an embodiment of the present invention provides a method for dynamic aggregation and packaging of fine-tuning resources in a power plant based on high-dimensional model expression, aiming to solve the problems of complex existing resource characteristics, large principle differences, and difficult external characteristics characterization. It is a new development in the management of flexible resources and provides basic support for the unified regulation and optimal utilization of flexible resources.

[0027] A method for dynamic aggregation and packaging of fine-tuning resources in a power plant based on high-dimensional model expression provided by an embodiment of the present invention will be introduced in detail below in conjunction with the accompanying drawings.

[0028] Figure 1 A basic flow chart of a method for dynamically aggregating and packaging micro-adjusting resources in a power plant based on high-dimensional model expression provided by an embodiment of the present invention is shown. Figure 1 As shown, the method specifically comprises the following steps: Step S100: using Zeno polyhedron to approximate the feasible domain of power generation factors, and performing Minkowski sum operation of Zeno polyhedron to aggregate the feasible domain of power generation factors.

[0029] Specifically, based on the Minkowski and merged power generation factor constraint space, the Zeno polyhedron is used to approximate the power generation factor feasible domain, and the Minkowski and operation of the Zeno polyhedron is used for aggregation, so as to realize the standardized power generation factor aggregation. The specific implementation steps include: 1) The power feasible domain of a single power generation factor can be represented by a convex polyhedron The specific formula is as follows: (1) In the formula, Convex polyhedron representing a single power generation factor; The power set representing the power generation factor; Represents the dimension of the power generation factor; is the constraint matrix; represents the constraint space; Indicates the number of constraints; is the constraint vector; 2) The aggregation of the feasible domains of multiple power generation factors can be expressed as the Minkowski sum of these power generation factors. The feasible domain after aggregation is The formula is as follows: (2) In the formula, represents the power feasible region of the aggregate; represents the power feasible region of the jth power generation factor; Represents the total number of power generation factors.

[0030] 3) Use Zeno polyhedron to approximate the feasible domain of each power generation factor. The feasible domain of a single power generation factor Zeno polyhedron The approximate formula is as follows: (3) In the formula, The power vector representing the power generation factor; is the center of the Zeno polyhedron; represents the dimension of the generator matrix G; is the generator matrix; Represents the generator coefficient Dimensions; are the coefficients of the generators; is the maximum scaling factor of the generator; 4) Design an optimization problem, the optimization goal is to maximize the number of Zeno polyhedra and original convex polyhedra The similarity between them, with the constraint that the Zeno polyhedron Must be on a convex polyhedron Internally, the formula is as follows: (4) The similarity is defined as the diameter ratio of two feasible regions in a certain direction: (5) In the formula, and Zeno polyhedron and convex polyhedrons In the Directions The diameter on.

[0031] 5) The optimization objective formula for the Zeno polyhedron approximation problem is defined as follows: (6) In the formula, Indicates the total number of directions.

[0032] The constraint formula is as follows: (7) The solution is to obtain the approximate feasible region of the Zeno polyhedron that satisfies feasibility.

[0033] 6) Combine the feasible regions of multiple Zeno polyhedra through Minkowski sum operation to obtain the Zeno polyhedron representation of the aggregate, as shown in the following formula: (8) (9) (10) in, represents the cluster of power generation factors; Represents a uniqueness operation, used to remove duplicate generators; Indicates The generator matrix of Zeno polyhedra; represents the center of the merged Zeno polyhedron; Indicates The center of a Zeno polyhedron; Step S200: Aggregate distributed load resources into virtual machine groups, dynamically optimize the aggregation and scheduling of power plant-based dynamic aggregation and packaging scheduling objects based on power generation characteristics and power generation economy, and realize dynamic construction and aggregation of power plant-based dynamic aggregation and packaging scheduling objects.

[0034] Specifically, Figure 2 The flowchart of the dynamic construction of the power plant dynamic aggregation package scheduling object is shown. By aggregating distributed load resources into virtual machine groups, defining performance parameters, and dynamically optimizing the aggregation and scheduling of the power plant dynamic aggregation package scheduling objects based on power generation characteristics and economic efficiency, the dynamic construction and aggregation of the power plant dynamic aggregation package scheduling objects are realized. The specific implementation steps include: 1) Aggregate distributed load resources into virtual machine groups. The virtual machine group model is shown in the following formula: (11) In the formula, A model representing a group of virtual machines; and They represent the upper and lower limits of the output of the virtual machine group respectively; and They represent the maximum ramp-up and ramp-down rates of the virtual machine group respectively; and They represent the minimum running time and minimum downtime of the virtual machine group respectively; Represents the cost function for a group of virtual machines.

[0035] 2) Collect the real-time regulation data of each central air-conditioning load in the power plant dynamic aggregation package scheduling object, including the regulation potential and controllable time.

[0036] 3) Preprocess the collected load data, remove noise and perform normalization to ensure the consistency and comparability of the data.

[0037] 4) The regulation potential of each central air conditioner It is the minimum value of the adjustment amount in each control cycle within the specified control time. The calculation can be expressed as: (12) In the formula, Indicates the maximum adjustment amount of the central air conditioner; is the regulated load; is the total number of control cycles.

[0038] 5) Calculate the regulation potential of each central air conditioner, obtain its regulation characteristic curve, and fit the relationship curve between the adjustable potential and control time of a single air conditioner. The formula is as follows: (13) In the formula, Indicates control time; represents the fitting function.

[0039] 6) Compare the adjustment characteristic curves of different central air conditioners to determine their consistency and consistency The calculation formula is: (14) In the formula, and Respectively represent Central air conditioner and Central air conditioner in adjustment cycle The regulatory potential of Indicates the total number of adjustment cycles.

[0040] 7) Agglomerative Hierarchical Clustering is used to aggregate central air conditioners. The goal is to form a power plant-like dynamic aggregation package scheduling object that meets the scheduling requirements. The optimization objective function is shown in the following formula: (15) In the formula, It represents the number of all combinations of any two numbers among M numbers; Represents the number of central air conditioners in the group after aggregation. The constraint condition is as follows: (16) In the formula, represents the load adjustment of the i-th central air conditioner; It represents the total regulation amount of the power plant dynamic aggregation package scheduling objects.

[0041] 8) Calculate the adjustment quotation of each central air conditioner, which is adjusted according to the adjustment potential, rated power and comfort of the central air conditioner. The formula is as follows: (17) In the formula, Indicates Central air conditioning adjustment quotation; Indicates The quotation decision coefficient of a central air conditioner; Indicates Rated power of central air conditioner; Indicates Central air conditioning comfort.

[0042] 9) Minimize the total cost of the power plant dynamic aggregation package scheduling object, and define the total cost function of the power plant dynamic aggregation package scheduling object. The formula is as follows: (18) In the formula, represents the total cost function; Indicates the control duration; Indicates The regulation amount of a central air conditioner; N represents the total number of central air conditioners.

[0043] 10) The dispatch of the power plant dynamic aggregation and encapsulation dispatch object needs to meet the total regulation requirements of the power grid dispatch department. The constraint formula is as follows: (19) In the formula, Indicates the total adjustment amount of the scheduling plan; Indicates The load regulation potential of a central air conditioner; N represents the total number of central air conditioners.

[0044] Step S300: Match the power generation operation mode of the power plant dynamic aggregation package scheduling object with the equipment operation mode corresponding to each response characteristic curve in the building potential characteristic library to achieve online mode matching of the external characteristic parameters of the aggregation unit.

[0045] Specifically, the power generation operation mode of the power plant dynamic aggregation package scheduling object is matched with the equipment operation mode corresponding to each response characteristic curve in the building potential characteristic library to achieve online mode matching of the external characteristic parameters of the aggregation unit. The specific implementation steps include: 1) Taking the maximum input power of intermittent energy such as wind and solar power as input, the external characteristic parameters of the computer group and the virtual power plant as a whole are obtained. The external characteristic parameters of the virtual power plant include: rated power, upper and lower limits of economic power, dead zone of unit control error, upward and downward climbing rate, speed regulation droop of governor, upper and lower limits of secondary frequency regulation (AGC), minimum time interval between unit shutdown and start-up, and cost-capacity function.

[0046] 2) Establish a normalized information model for obtaining online parameters of power plant dynamic aggregation and encapsulation scheduling objects. The formula is as follows: (20) In the formula, Indicates The maximum output of each unit represents the output uncertainty of the distributed generator set; Indicates the time when the control signal is released; Indicates Unit in The winning bid quantity for the time period; Indicates Unit in The number of spare winning bids of various types in the time period; Respectively represent Dynamic climbing and descending rate of each unit (MW / s).

[0047] 3) Model the output sequence of the power plant-based dynamic aggregation and packaging scheduling objects and internal power generation resources. The formula is as follows: (twenty one) In the formula, Indicates that the power plant dynamic aggregation package scheduling object is Each time during the period Response sequence to system output regulation signal; Indicates that the power plant dynamic aggregation package scheduling object is Basic output during the time period; Indicates that the power plant dynamic aggregation package scheduling object is Each time during the period output sequence; 4) Model the maximum output of the power plant dynamic aggregation package scheduling object. The formula is as follows: (twenty two) In the formula, Indicates that the power plant dynamic aggregation package scheduling object is Each time during the period The maximum output sequence of Indicates the first Intermittent energy units At each moment in the time period The maximum output sequence depends on the current maximum input and control interval of the unit. The product of climbing / descending rate; Indicates the first The maximum output power of a conventional unit.

[0048] 5) Carry out real-time standby modeling of power plant dynamic aggregation and encapsulation scheduling objects. The formula is as follows: (twenty three) (twenty four) In the formula, Indicates remaining spare; Indicates that each intermittent new energy unit is At each time during the period The load shedding ratio depends on the standby control strategy of each unit and the load shedding ratio constraint; Indicates the first Conventional units in Spare time slots; Indicates that the virtual power plant is in the period At each moment of standby power.

[0049] 6) In the performance parameters of the power plant dynamic aggregation package scheduling object, the subsequent climbing / declining rate calculation formula is as follows: (25) In the formula, Indicates that under different scheduling or control instructions, The climbing / descending rate during the time period; in the subsequent climbing / descending rate calculation, Dynamically aggregate and encapsulate the output data of scheduling objects for actual power plants.

[0050] 7) In the performance parameters of the power plant dynamic aggregation package scheduling object, the calculation formula of the pre-climbing / downhill rate is consistent with the post-calculation expression. From the above power plant dynamic aggregation package dispatch object output sequence calculation model, we can get: This is an arbitrary parameter.

[0051] 8) The frequency regulation performance score of the power plant dynamic aggregation and packaging scheduling object in each time period is calculated as follows: (26) (27) (28) (29) (30) In the formula, represents the frequency modulation performance score, which is the weighted average of the three scores; DS represents the response delay score; CS represents the correlation score; PS represents the accuracy score; A, B, C represent the weights, all of which are 1 / 3; It indicates the response delay at which CS and DS achieve their maximum values; Indicates the predetermined adjustment power; Indicates the actual regulated power; represents the correlation function; Indicates the average value of the scheduled regulated power per hour.

[0052] Step S400: A mapping relationship between physical parameters and aggregation model parameters is established through high-dimensional model expression technology, so as to realize real-time online identification of external characteristic parameters of aggregation units based on high-dimensional model expression technology.

[0053] Specifically, Figure 3 The figure shows a schematic diagram of the high-dimensional model expression modeling process. Figure 4 The flowchart of the real-time identification framework of air conditioning load aggregation model parameters is shown. The mapping relationship between physical parameters and aggregation model parameters is established through high-dimensional model expression technology to realize the real-time online identification of the external characteristic parameters of the aggregation unit based on high-dimensional model expression technology. The specific implementation steps include: 1) The influence of uncertainty in physical parameters of air conditioning load on aggregation model parameters is analyzed through high-dimensional model expression. The calculation formula is as follows: (31) In the formula, represents a constant term; represents the first-order component; represents the second-order component; represents the order of the component; the system input is , the output is , and so on.

[0054] 2) Generate The input samples include multiple physical parameters of air conditioning load. In specific implementation, the Quasi Monte Carlo method can be used to generate Group input samples.

[0055] 3) Each group of input samples is simulated to obtain the aggregate model parameters, thus obtaining Output samples, as shown in the following formula: (32) In the formula, Indicates Group input sample The corresponding aggregation model output; Indicates Group input samples; Indicates The value of the output quantity; 4) Standardize each input quantity, the formula is as follows: (33) In the formula, and Represent the actual value and standard value of the input variable respectively; and Represents the minimum and maximum values ​​of the input quantity respectively.

[0056] 5) Use the input and output values ​​of N0 to establish a high-dimensional model expression model for the air conditioning group and calculate the correlation coefficient , And the global sensitivity of the physical parameters of the air conditioning load is as follows: (34) (35) In the formula, , , , , and etc. all represent the physical parameters of the air conditioning load. By analogy, we can obtain higher-order coefficients of the first-order component function of the input quantity and the second-order component function of the input quantity. represents a first-order component function.

[0057] 6) Based on the values ​​of higher-order coefficients α and β and the HDMR model of the conditioning group, the global sensitivity of the independent and coupled effects of the input quantities is calculated.

[0058] 7) Establish a high-dimensional model expression model database for air conditioning aggregation model parameters at different time scales, see Figure 4 .

[0059] 8) The above is generated using the Quasi Monte Calcor method The input sample of the group air conditioning system can be expressed as: (36) In the formula, , , , , , , , , , , and All represent the physical parameters of air conditioning load. In order to verify the effect of the dynamic aggregation encapsulation method of micro-regulation resource power plant based on high-dimensional model expression, the above method is put into practical application and a modified real example is selected.

[0060] This example mainly verifies the identification results of the air conditioning load aggregation model parameters based on the high-dimensional model. The number of fixed-frequency and variable-frequency air conditioners is 10,000 respectively. Assuming that the air conditioning load parameters are normally distributed, the distribution of the physical parameters of a certain air conditioning group is shown in Table 1 below. In Table 1, R μ , C a , P, T o , δ, η μ , P min , P max , k1, l1, k2, l2 respectively represent the response characteristics of air conditioning load, heat capacity or load, power or load, set temperature, temperature variation range or control error, air conditioning efficiency, minimum power, maximum power, adjustment coefficient, nonlinear control coefficient, adjustment coefficient and control error correlation coefficient; μ represents the mean; χ represents the standard deviation; before the air conditioning load participates in the demand response, the entire air conditioning group operates in a stable state, and each air conditioning load operates at the set temperature. For the fixed frequency air conditioner, its indoor temperature is [ ] are evenly distributed.

[0061] Table 1 Parameter distribution of air conditioning load parameter μ χ <![CDATA[R μ ]]> 2 0.2 <![CDATA[C a ]]> 2 0.2 P 5.6 0.56 <![CDATA[T set ]]> 22.5 2.25 δ 0.3 0.03 <![CDATA[η μ ]]> 2.5 0.25 <![CDATA[P min ]]> 0.5 0.05 <![CDATA[P max ]]> 5 0.5 <![CDATA[k1]]> 0.03 0.003 <![CDATA[l1]]> -0.4 0.04 <![CDATA[k2]]> 0.06 0.006 <![CDATA[l2]]> -0.3 0.03 First, we take the minute-level power pulse generated by the fixed-frequency air-conditioning load as an example to analyze the high-dimensional model expression modeling results of its power capacity. In this example, only three physical parameters of the air-conditioning load have a significant impact on the capacity of the power pulse, which are , and According to the high-dimensional model expression modeling results, the capacity of the power pulse The relationship between the physical parameters of the air conditioning load is: (37) In the formula, , and Respectively represent the standardized values; , Represent the first and second order component functions respectively.

[0062] The distribution of physical parameters of an air conditioning group is shown in Table 2. When the distribution of physical parameters remains unchanged, 100 groups of parameter samples of the air conditioning group are generated according to the distribution of the physical parameters of the air conditioning group, and the actual capacity of 100 power pulses is obtained by independent simulation.

[0063] Table 2 Global sensitivity of physical parameters of air conditioning load parameter #timg# #timg# #timg# Global Sensitivity 0.5164 0.3902 0.0445 Figure 5The results of the air conditioning load power pulse capacity obtained by independent simulation and calculation are compared. As can be seen from the figure, the power capacity calculated by the high-dimensional model expression and the independent simulation method is very different. The average relative error calculated is only 4.38%, indicating that the high-dimensional model expression can accurately establish the mapping relationship between power capacity and the physical parameters of the air conditioning load.

[0064] In addition, the multivariate linear regression method is used to establish the relationship between the power pulse capacity of the air-conditioning load and its physical parameters, and the modeling results are compared with the modeling results expressed by the high-dimensional model. When the scheduling period is 20 minutes, the modeling results of the multivariate linear regression are: (38) The modeling results expressed by the high-dimensional model are: (39) In the formula, , , Represent the functions of each order component respectively.

[0065] Verify the modeling results expressed by the high-dimensional model by Figure 6 According to the simulation comparison, the average relative errors of the two methods are 15.03% and 5.52% respectively. In terms of parameter identification of air-conditioning load aggregation model, the high-dimensional model expression has higher modeling accuracy.

[0066] In the above-mentioned power plant dynamic aggregation and packaging method of fine-tuning resources based on high-dimensional model expression, the mathematical model and properties of Minkowski sum and Zeno polyhedron are used to express the feasible domain of power generation factors in the form of convex polyhedron, and then the constraint space of power generation factor aggregation power is expressed as the Minkowski sum of the constraint space of each power generation factor, and a standardized power generation factor aggregation method is proposed; according to the constructed power plant dynamic aggregation and packaging scheduling object unit model, from the two perspectives of power generation characteristics and power generation economy, a power plant dynamic aggregation and packaging scheduling object dynamic construction technology is proposed, which greatly improves the aggregation efficiency and accuracy; an aggregation unit external characteristic parameter index system is proposed, and the power generation operation mode of the aggregation unit is matched with the equipment operation mode corresponding to each response characteristic curve in the resource potential characteristic library, so as to realize the online dynamic identification of the external characteristic parameters of the aggregation unit. This method can aggregate and optimize resources in real time and dynamically under the premise of ensuring calculation efficiency and accuracy, especially in high-dimensional and large-scale data processing, which can ensure the unified regulation and optimal utilization of flexible resources and can respond quickly under different scheduling requirements.

[0067] Based on the same inventive concept, an embodiment of the present invention also provides a system for dynamically aggregating and packaging fine-tuning resources for power plants based on high-dimensional model expression, the system comprising: a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the system can execute any one of the methods for dynamically aggregating and packaging fine-tuning resources for power plants based on high-dimensional model expression described above.

[0068] The embodiment of the present invention can divide the system into functional modules according to the above method example. For example, each functional module can be corresponded to, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0069] Based on the same inventive concept, an embodiment of the present invention also provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, enables the computer to execute any one of the aforementioned methods for dynamic aggregation and packaging of fine-tuning resource power plants based on high-dimensional model expression.

[0070] Based on the same inventive concept, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes any one of the aforementioned methods for dynamic aggregation and packaging of micro-regulation resources for power plants based on high-dimensional model expression.

[0071] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A dynamic aggregation and packaging method for power plants based on micro-adjustment resources expressed in high-dimensional models, characterized in that: The following steps are involved: Aggregate the feasible domains of different power generation factors to obtain a feasible domain aggregation model of power generation factors; Aggregate distributed load resources into virtual machine groups, dynamically optimize the aggregation and scheduling of power plant-based dynamic aggregation and encapsulation scheduling objects based on power generation characteristics and power generation economy, and realize dynamic construction and aggregation of power plant-based dynamic aggregation and encapsulation scheduling objects; Match the power generation operation mode of the power plant dynamic aggregation package scheduling object with the equipment operation mode corresponding to each response characteristic curve in the building potential characteristic library to achieve online mode matching of the external characteristic parameters of the aggregation unit; The mapping relationship between physical parameters and aggregation model parameters is established through high-dimensional model expression technology, and real-time online identification of the external characteristic parameters of the aggregation unit based on high-dimensional model expression technology is realized.

2. According to the method for dynamic aggregation and packaging of fine-tuning resources for power plants based on high-dimensional model expression according to claim 1, it is characterized in that: The feasible domains of different power generation factors are aggregated to obtain the feasible domain aggregation model of power generation factors, including: The Zeno polyhedron is used to approximate the feasible domain of power generation factors, and the feasible domains of different power generation factors are aggregated through the Minkowski sum operation of the Zeno polyhedron, so as to obtain the feasible domain aggregation model of power generation factors.

3. The method for dynamic aggregation and packaging of power plants based on micro-adjustment resource expression based on high-dimensional model expression according to claim 2 is characterized in that: The feasible domain of power generation factors is approximated by Zeno polyhedron, and the feasible domains of different power generation factors are aggregated through the Minkowski sum operation of Zeno polyhedron, so as to obtain the feasible domain aggregation model of power generation factors, including: The feasible domain of a single power generation factor is represented by a convex polyhedron: (1) In the formula, Convex polyhedron representing a single power generation factor; The power set representing the power generation factor; Represents the dimension of the power generation factor; is the constraint matrix; represents the constraint space; Indicates the number of constraints; is the constraint vector; The aggregation of the feasible domains of multiple power generation factors is expressed as the Minkowski sum of all power generation factors. The feasible domain after aggregation is The formula is as follows: (2) In the formula, represents the feasible domain of the aggregate; represents the feasible domain of the jth power generation factor; Represents the total number of power generation factors; Zeno polyhedrons are used to approximate the feasible region of each power generation factor. The feasible region of a single power generation factor is Zeno polyhedron The approximate formula is as follows: (3) In the formula, The power vector representing the power generation factor; is the center of the Zeno polyhedron; represents the dimension of the generator matrix G; is the generator matrix; Represents the generator coefficient Dimensions; are the coefficients of the generators; is the maximum scaling factor of the generator; Determine the optimization objective and constraints of the Zeno polyhedron approximation problem, solve the optimization objective based on the constraints, determine the Zeno polyhedron approximate feasible domain that meets feasibility, and merge the Zeno polyhedron approximate feasible domain by Minkowski sum operation to obtain the Zeno polyhedron representation of the aggregate, and determine the Zeno polyhedron representation of the aggregate as the feasible domain aggregation model of the power generation factor.

4. The method for dynamic aggregation and packaging of power plants based on micro-adjustment resources expressed in high-dimensional models according to claim 3 is characterized in that: Determine the optimization objective and constraints of the Zeno polyhedron approximation problem. The corresponding calculation formula is: (4) s.t. (5) In the formula, Represents a Zeno polyhedron and the convex polyhedron of the feasible region In the The diameter ratio in each direction, , and Zeno polyhedron and convex polyhedrons In the The diameter in each direction; Indicates the total number of directions.

5. The method for dynamic aggregation and packaging of micro-adjustment resources for power plants based on high-dimensional model expression according to claim 1 is characterized in that: Aggregate distributed load resources into virtual machine groups, dynamically optimize the aggregation and scheduling of power plant dynamic aggregation and encapsulation scheduling objects based on power generation characteristics and power generation economy, and realize dynamic construction and aggregation of power plant dynamic aggregation and encapsulation scheduling objects, including: 1) Aggregate distributed load resources into virtual machine groups. The virtual machine group model is shown in the following formula: (6) In the formula, A model representing a group of virtual machines; and They represent the upper and lower limits of the output of the virtual machine group respectively; and They represent the maximum ramp-up and ramp-down rates of the virtual machine group respectively; and They represent the minimum running time and minimum downtime of the virtual machine group respectively; represents the cost function of the virtual machine group; 2) Collect the real-time regulation data of each central air-conditioning load in the power plant dynamic aggregation package scheduling object, including the regulation potential and manageable time; 3) Preprocess the collected load data, remove noise and perform normalization to ensure data consistency and comparability; 4) The regulation potential of each central air conditioner It is the minimum value of the adjustment amount in each control cycle within the specified control time. The calculation can be expressed as: (7) In the formula, Indicates the maximum adjustment amount of the central air conditioner; is the regulated load; is the total number of control cycles; 5) Calculate the regulation potential of each central air conditioner, obtain its regulation characteristic curve, and fit the relationship curve between the adjustable potential and control time of a single air conditioner. The formula is as follows: (8) In the formula, Indicates control time; represents the fitting function; 6) Compare the adjustment characteristic curves of different central air conditioners to determine their consistency and consistency The calculation formula is: (9) In the formula, and Respectively represent Central air conditioner and Central air conditioner in adjustment cycle The regulatory potential of Indicates the total number of adjustment cycles; 7) The central air conditioners are aggregated using the agglomerative hierarchical clustering method. The goal is to form a power plant-like dynamic aggregation package scheduling object that meets the scheduling requirements. The optimization objective function is shown in the following formula: (10) In the formula, It represents the number of all combinations of any two numbers among M numbers; Represents the number of central air conditioners in the group after aggregation; the constraint conditions are as shown in the following formula: (11) In the formula, represents the load adjustment of the i-th central air conditioner; It represents the total regulation amount of the power plant dynamic aggregation package scheduling object; 8) Calculate the adjustment quotation of each central air conditioner, which is adjusted according to the adjustment potential, rated power and comfort of the central air conditioner. The formula is as follows: (12) In the formula, Indicates Central air conditioning adjustment quotation; Indicates The quotation decision coefficient of a central air conditioner; Indicates Rated power of central air conditioner; Indicates The comfort level of central air conditioning; 9) Minimize the total cost of the power plant dynamic aggregation package scheduling object, and define the total cost function of the power plant dynamic aggregation package scheduling object. The formula is as follows: (13) In the formula, represents the total cost function; Indicates the control duration; Indicates The regulation amount of a central air conditioner; N represents the total number of central air conditioners; 10) The dispatch of the power plant dynamic aggregation and encapsulation dispatch object needs to meet the total regulation requirements of the power grid dispatch department. The constraint formula is as follows: (14) In the formula, Indicates the total adjustment amount of the scheduling plan; Indicates The load regulation potential of a central air conditioner.

6. The method for dynamic aggregation and packaging of power plants based on micro-adjustment resources expressed in high-dimensional models according to claim 1 is characterized in that: Match the power generation operation mode of the power plant dynamic aggregation package scheduling object with the equipment operation mode corresponding to each response characteristic curve in the building potential characteristic library to achieve online mode matching of the external characteristic parameters of the aggregation unit, including: 1) Taking the maximum input power of intermittent energy such as wind and solar power as input, obtain the external characteristic parameters of the computer group and the virtual power plant as a whole; 2) Establish a normalized information model for obtaining online parameters of power plant dynamic aggregation and encapsulation scheduling objects. The formula is as follows: (15) In the formula, Indicates The maximum output of each unit represents the output uncertainty of the distributed generator set; Indicates the time when the control signal is released; Indicates Unit in The winning bid quantity for the time period; Indicates Unit in The number of spare winning bids of various types in the time period; Respectively represent Dynamic climbing and descending rate of each unit (MW / s); 3) Model the output sequence of the power plant-based dynamic aggregation and packaging scheduling objects and internal power generation resources. The formula is as follows: (16) In the formula, Indicates that the power plant dynamic aggregation package scheduling object is At each time during the period Response sequence to system output regulation signal; Indicates that the power plant dynamic aggregation package scheduling object is The basic output of the time period is also the bidding strategy of the virtual power plant in the h period; Indicates that the power plant dynamic aggregation package scheduling object is At each time during the period output sequence; 4) Model the maximum output of the power plant dynamic aggregation package scheduling object. The formula is as follows: (17) In the formula, Indicates that the power plant dynamic aggregation package scheduling object is At each time during the period The maximum output sequence of Indicates the first Intermittent energy units At each moment in the time period The maximum output sequence depends on the current maximum input and control interval of the unit. The product of climbing / descending rate; Indicates the first Maximum output power of conventional units; 5) Carry out real-time standby modeling of power plant dynamic aggregation and encapsulation scheduling objects. The formula is as follows: (18) (19) In the formula, Indicates remaining spare; Indicates that each intermittent energy unit At each time during the period The load reduction ratio depends on the standby control strategy of each unit and the load reduction ratio constraints; Indicates the first Conventional units in Time slot backup; Indicates that the virtual power plant is in the period At each moment The reserve power; 6) In the performance parameters of the power plant dynamic aggregation package scheduling object, the subsequent climbing / declining rate calculation formula is as follows: (20) In the formula, Indicates that under different scheduling or control instructions, The climbing / descending rate during the time period; in the subsequent climbing / descending rate calculation, Dynamically aggregate and encapsulate the output data of dispatch objects for actual power plants; 7) In the performance parameters of the power plant dynamic aggregation package scheduling object, the calculation formula of the pre-climbing / downhill rate is consistent with the post-calculation expression. From the above power plant dynamic aggregation package dispatch object output sequence calculation model, we can get: It is an arbitrary parameter; 8) The frequency regulation performance score of the power plant dynamic aggregation and packaging scheduling object in each time period is calculated as follows: (21) (22) (23) (24) (25) In the formula, represents the frequency modulation performance score, which is the weighted average of the three scores; DS represents the response delay score; CS represents the correlation score; PS represents the accuracy score; A, B, C represent the weights, all of which are 1 / 3; represents the response delay at which CS and DS achieve their maximum values; Indicates the predetermined adjustment power; Indicates the actual regulated power; represents the correlation function; Indicates the average value of the scheduled regulated power per hour.

7. The method for dynamic aggregation and packaging of power plants based on micro-adjustment resources expressed in high-dimensional models according to claim 1 is characterized in that: The mapping relationship between physical parameters and aggregation model parameters is established through high-dimensional model expression technology, and the real-time online identification of the external characteristic parameters of the aggregation unit based on high-dimensional model expression technology is realized, including: 1) The influence of uncertainty in physical parameters of air conditioning load on aggregation model parameters is analyzed through high-dimensional model expression. The calculation formula is as follows: (26) In the formula, represents a constant term; represents the first-order component; represents the second-order component; represents the order of the component; the system input is , the output is , and so on; 2) Generate A set of input samples, the input includes multiple physical parameters of air conditioning load; 3) Each group of input samples is simulated to obtain the aggregate model parameters, thus obtaining Output samples, as shown in the following formula: (27) In the formula, Indicates Group input sample The corresponding aggregation model output; Indicates Group input samples; Indicates The value of the output quantity; 4) Standardize each input quantity, the formula is as follows: (28) In the formula, and Represent the actual value and standard value of the input variable respectively; and Respectively represent the minimum and maximum values ​​of the input; 5) Use the input and output values ​​of N0 to establish a high-dimensional model expression model for the air conditioning group and calculate the correlation coefficient , And the global sensitivity of the physical parameters of the air conditioning load is as follows: (29) (30) In the formula, , , , , and All represent the physical parameters of the air conditioning load. By analogy, we can obtain higher-order coefficients of the first-order component function of the input quantity and the second-order component function of the input quantity. represents the first-order component function; 6) Calculate the global sensitivity of the independent and coupled effects of the input quantities; 7) Establish a high-dimensional model expression model database for air conditioning aggregation model parameters at different time scales.

8. The method for dynamic aggregation and packaging of micro-adjustment resources for power plants based on high-dimensional model expression according to claim 7 is characterized in that: Generated using the Quasi Monte Calcor method The input sample of the group air conditioning system can be expressed as: (31) In the formula, , , , , , and Both represent the physical parameters of air conditioning load.

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