A scheduling method and device for an aggregation model of demand-side resources in the electricity market

Abstract

The present invention discloses a scheduling method, device and storage medium for a precise aggregation model of demand-side resources under the background of the electricity market. The method includes the steps of: (1) data preparation, including electricity market price data, basic constraints of different demand-side load devices, relevant information on operating parameters, etc.; (2) depicting the operation feasible region of a single device based on the Chino polyhedron; (3) aggregating the feasible regions of single devices through Minkowski Sum operation to obtain a precise aggregation response model of demand-side resources; (4) handing over the aggregation model to a demand response aggregator agent to directly participate in the wholesale market competition, and establishing an optimization decision model for bidding strategies with the maximization of the aggregator's profit as the goal. The present invention realizes the aggregation of high-dimensional and large-scale feasible regions of demand-side resources, reduces the complexity of aggregation calculation based on the representation form of the Chino polyhedron, and conducts bidding decision scheduling based on the demand-side resource aggregation response model under the background of the electricity market.

Description

Technical Field

[0001] The present invention relates to power technology, and particularly to a scheduling method, device and storage medium for a precise aggregation model of demand-side resources under the background of the power market. Background Art

[0002] In recent years, numerous demand-side flexibility resources (flexible loads, energy storage, electric vehicles, etc.) have developed rapidly. The participation of demand-side resources in the market has the advantages of low carbon, high flexibility, fast response speed, etc. Compared with traditional response resources, it can better maintain the stable operation of the power grid and bring certain economic benefits at the same time.

[0003] However, most demand-side resources have the characteristics of being dispersed, diverse in types, and different in characteristics, making it difficult to directly regulate and control. In order to enable demand-side resources to give full play to their roles, improve the consumption of distributed resources, and fully explore the response characteristics of response resources, the resource aggregation technology has emerged. Aggregation refers to integrating multiple demand-side resources with single characteristics and small power into a single or several aggregates with certain maneuvering capabilities through a certain aggregation principle based on the evaluation of characteristics, so as to better coordinate the system scheduling.

[0004] The demand-side resource aggregation response entity can serve as an intermediate agent for the interaction between demand-side users and the power market. On the one hand, the demand-side resource aggregation response entity and the supply-side power operation entity jointly participate in power market transactions, providing energy, capacity and ancillary services in various types of power markets such as day-ahead, reserve and real-time, while ensuring their own economic interests and improving the overall social benefits. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention aims to propose a scheduling method for a precise aggregation model of demand-side resources under the background of the power market. First, the operation feasible region of a single flexibility device is depicted based on the Chino polyhedron to represent its single-period flexibility constraint and multi-period coupling constraint. Then, the precise aggregation response model of demand-side resources is obtained through Minkowski Sum operation. Finally, the demand response aggregator participates in the wholesale market competition and makes decision scheduling with the goal of maximizing the aggregator's profit.

[0006] Technical Solution: The scheduling method for a precise aggregation model of demand-side resources under the background of the power market according to the present invention includes:

[0007] Step S1, data preparation, including power market electricity price data, basic constraints and operation parameter-related information of different demand-side load devices;

[0008] Step S2, for the single-period flexibility constraint and multi-period coupling constraint of dispersed flexibility resources, depict the operation feasible region of a single device based on the Chino polyhedron;

[0009] Step S3: Aggregate the feasible regions of individual devices in S2 through Minkowski Sum operation to obtain an accurate aggregation response model for demand-side resources;

[0010] Step S4: Hand over the aggregation model in Step S3 to the demand response aggregator agent to directly participate in the wholesale market competition, and establish an optimized scheduling model for bidding strategies with the goal of maximizing the aggregator's profit.

[0011] Optionally, the specific analysis of the operation constraints of the demand-side load devices in S2 is as follows:

[0012] Considering the limited and discrete operation cycle Nt of the demand-side devices, there are N operating time periods in total, and the time interval of each period is t. Let p(t) be the operating power during the operating period t ∈ [(k - 1)t, kt, k = 1,..., N]. Then the flexibility feasible region of device i is described by the following single-period flexibility constraint and multi-period coupling constraint:

[0013] (1) Power constraint

[0014] Assuming that the power of the device is constant within the interval t, the power constraint can be expressed as:

[0015] p i，min ≤p i,k ≤p i，max , k = 1, 2,..., N (1)

[0016] Where p k is the constant power of the demand-side device i in the k-th interval; p min and p max are the lower and upper limits of the operating power of the demand-side device, respectively;

[0017] (2) Energy constraint

[0018]

[0019] Where e i,min and e i,max are the lower and upper limits of the energy constraint of the demand-side device i, respectively;

[0020] (3) Ramp constraint

[0021] r i,min ≤p i,k -p i,k-1 ≤r i,max , k = 2, 3,..., N (3)

[0022] Where r i,min and r i,maxThey are the lower and upper limits of the power change amount of the demand-side device i in adjacent periods, respectively.

[0023] The operation constraints of a series of demand-side devices, such as temperature control loads, storage loads, electric vehicles, etc., can be described using formulas (1), (2), and (3). And since all the above constraints are linear constraints, the feasible region range of the device can be summarized in the form of a convex polyhedron, that is:

[0024] P = {p ∈ R N : Ap ≤ b} (4)

[0025] Among them, P is the convex polyhedron representing the feasible region range of the device; N is the dimension of the convex polyhedron; (A, b) integrates all the linear operation constraint coefficients of the device and is represented in matrix form.

[0026] Optionally, the method for characterizing the feasible region of a single device based on the zonotope in S2 is as follows:

[0027] The zonotope is also called the completely symmetric polytope. A polytope is a geometric object composed of flat boundaries. Polytopes can exist in any dimension and extend to spaces higher than three dimensions, such as polyhedra. The zonotope Z is a special form of the convex polyhedron and has the property of central symmetry. It can be defined by a center point, a generator matrix, and the corresponding scaling coefficients:

[0028]

[0029] Among them, Z is the zonotope characterizing the feasible region range of the device; c is the center of the polyhedron representing the geometric position; β is the scaling coefficient corresponding to the direction of the generator, which determines the extension distance of the zonotope in this direction; is the upper limit of the scaling coefficient; G is the generator matrix representing the geometric shape, which is composed of multiple generators:

[0030] G = {g (1) , g (2) ,..., g (M)} ∈ R N×M (6)

[0031] Among them, M is the number of generators, g (j) ∈ R N , j = 1, 2,..., M represents one of the generators and satisfies: ||g (j) || = 1, that is, the generator g (j) is a normal vector, which determines the extension direction of the zonotope.

[0032] In the problem of approximately characterizing the feasible region of demand-side resources by the Chinohedron, we consider the generator matrix \(G\) as a known quantity. For the power constraints, electricity quantity constraints, and ramping constraints of the devices described in \(S1\), the corresponding generator matrix is designed as follows:

[0033]

[0034]

[0035]

[0036] Among them, formula (7) represents the first \(n\) generators, corresponding to the \(n\) power constraints of the load devices; formula (8) represents the middle \(n - 1\) generators, corresponding to the \(n - 1\) electricity quantity constraints of the load devices; formula (9) represents the last \(n - 1\) generators, corresponding to the \(n - 1\) ramping constraints of the load devices. Thus, the obtained generator matrix is \(G\in R\) N×(3N-2) ;

[0037] The Chinohedron has the characteristic of being convenient for calculating the Minkowski Sum, which can greatly simplify the computational complexity in realizing the aggregation problem of the feasible regions of high-dimensional and large-scale devices. Therefore, we use the expression of the Chinohedron to approximately characterize the feasible region of the devices, that is, the form is transformed from formula (5) to formula (6). This problem can be modeled as an optimization problem, and the modeling process is as follows:

[0038] (1) Objective function

[0039] Intuitively, on the premise of ensuring \(Z\in P\), the larger the "volume" of the Chinohedron, the higher the approximation degree to the original feasible region of the device. However, calculating the volume of a convex polyhedron requires finding its vertex expression, and converting the feasible region from the half-space expression to the vertex expression in a high-dimensional space has a high computational complexity. Therefore, this paper uses the objective transformation method to arbitrarily construct \(n\) f normal vectors \(f\) (1) , \(f\) (2) ,..., \(f\) (nf) , and find the diameters of the feasible regions \(Z\) and \(P\) in the direction of \(f\) by solving a linear programming problem. Define the similarity according to the ratio relationship of their positions and lengths:

[0040]

[0041] where: \(\Delta\) Z,l and \(\Delta\) P,l are the diameters of the two feasible regions in the direction of the normal vector \(f\) (l) respectively; the closer \(\Lambda\) f is to 1, the higher the similarity between the Chinohedron and the original feasible region. And through mathematical derivation, it can be obtained that:

[0042]

[0043] where F is a matrix composed of n f normal vectors:

[0044] Substituting formula (10) into formula (11), the objective function is obtained as:

[0045]

[0046] (2) Constraint conditions

[0047] The approximation process adopts the inner approximation method. Therefore, the constraint condition is that the obtained Chinohedron is inside the feasible region of the original equipment, that is:

[0048]

[0049] Through mathematical derivation, the formula can be transformed into inequality constraints That is:

[0050]

[0051] In summary, the Chinohedron solution model corresponding to the equipment feasible region is:

[0052]

[0053] Optionally, the precise aggregation response model of the demand-side resources obtained based on the Minkowski Sum operation in S3 is as follows:

[0054] To reduce the decision-making complexity at the system operator level, the load aggregator needs to aggregate the feasible regions of all users to form the aggregated feasible region of the user cluster. The aggregated feasible region represents the adjustable range of flexibility when all devices j are simultaneously controlled. The aggregated feasible region Z agg expressed based on the Chinohedron can be achieved through the Minkowski Sum and can be expressed as:

[0055]

[0056] where Z1, Z2,..., Z J is the Chinohedron corresponding to the flexibility feasible region of a single demand-side device under the load aggregator, and Z agg is the aggregated feasible region.

[0057] The Minkowski Sum is the sum of the point sets of two Euclidean spaces A and B, also known as the dilation set of these two spaces. Vectors are drawn from the origin to each point inside the figure A, and the figure B is moved along each vector. The union of all the final positions is the Minkowski Sum (which has the commutative law). The Minkowski Sum calculation of the feasible region expressed based on the Chinohedron is very efficient and can easily aggregate the flexible feasible regions of large-scale load devices and devices with high time dimensions. The aggregation model is as follows:

[0058]

[0059]

[0060] Among them, c agg and are respectively the center point and the scaling coefficient of the aggregated Chinohedron. That is, only by performing addition operations on the center point and the scaling technology of the feasible region corresponding to a single load device's feasible region can the aggregation model be obtained.

[0061] Optionally, the optimal scheduling model of the bidding strategy based on the demand-side aggregation model in the electricity market in S4 is as follows:

[0062] By aggregating the aggregated response models of demand-side resources and handing them over to demand response aggregators to participate in the electricity wholesale market competition. Each demand response aggregator maximizes its own profit by choosing to increase or cut down the load volume during each time period. Its decision variables are and Then the demand response aggregator p has a quadratic electricity benefit function u p,t (DR p,t ) at time t, which can be expressed as:

[0063]

[0064] Among them, p = 1, 2,..., P; t = 1, 2,..., T, v p,t and w p,t are the parameters of the utility function and are both non-negative real numbers; L p,t is the basic load of the load aggregator p, that is, the normal electricity consumption load that does not participate in demand response.

[0065] The profit of the demand response aggregator in the electricity wholesale market is equal to the benefit obtained from using electric energy at all times minus the corresponding electricity purchase cost. Then the optimization problem of each demand response aggregator in the wholesale market during T time periods can be expressed as:

[0066]

[0067] Among them, r t is the price in the wholesale market at time t; L p,max represents the maximum load of the demand response aggregator p; λ i is the load shedding coefficient and 0 ≤ λ i ≤ 1.

[0068] It can be seen from formula (20) that the role of the demand-side response aggregator is different in different situations:

[0069] (1) When : At this time, the aggregator is equivalent to selling electric energy in the wholesale market at the electric energy price at that moment, and its role is similar to that of a power generator.

[0070] (2) When : At this time, its role is an electric energy demander. For every unit of electric energy consumed, it needs to pay the corresponding electricity purchase cost according to the wholesale market price at that moment.

[0071] The present invention also provides a device for the scheduling strategy of the demand-side resource precise aggregation model under the background of the power market, including:

[0072] One or more processors;

[0073] A memory for storing one or more programs;

[0074] The one or more programs are executed by the one or more processors, so that the one or more processors implement the above method.

[0075] In addition, the present invention also provides a storage medium containing computer-executable instructions. At least one instruction, at least one program, a code set or an instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the above method.

[0076] Beneficial effects: Compared with the prior art, the present invention provides a scheduling method for the demand-side resource precise aggregation model under the background of the power market, which has the following advantages: The flexibility feasible region of the demand-side resources is more accurately characterized based on the expression form of the Chino polyhedron. Through the Minkowski Sum operation, high-dimensional and large-scale feasible region aggregation is realized, and an aggregation response model of the demand-side resources is obtained and decision-making scheduling is carried out under the background of the power market, effectively encouraging flexible users to perform demand response while improving the economic benefits of the load aggregator. Description of the Drawings

[0077] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0078] Figure 1 is a schematic flow chart of an embodiment of the present invention;

[0079] Figure 2 is a schematic diagram of a demand-side resource precise aggregation model scheduling and control device provided in the second embodiment of the present invention;

[0080] Figure 3 is a schematic structural diagram of a device provided in the third embodiment of the present invention; Detailed implementation manners

[0081] The inventive method will be further elaborated below in conjunction with embodiments.

[0082] The method for scheduling the precise aggregation model of demand-side resources in the context of the electricity market according to the present invention includes:

[0083] Step 1: Data preparation, including electricity market price data, basic constraints of different demand-side load devices, and information related to operating parameters, etc.;

[0084] Step 2: For the single-period flexibility constraint and multi-period coupling constraint of distributed flexibility resources, the feasible operating region of a single device is characterized based on the Chino polyhedron;

[0085] Step 3: Based on the bottom-up aggregation idea, the feasible regions of single devices are aggregated through Minkowski Sum operation to obtain a precise aggregation response model of demand-side resources;

[0086] Step 4: The aggregation model is handed over to the demand response aggregator agent to directly participate in the wholesale market competition, and a bidding strategy optimization scheduling model is established with the maximization of the aggregator's profit as the goal.

[0087] Embodiment 1

[0088] In Step 1, consider that the power of a certain air conditioner in two periods is x1 and x2, and its power consumption constraint and power constraint are as follows:

[0089]

[0090] First, characterize the feasible region using the expression of the convex polyhedron P(A, b):

[0091]

[0092] Thus, it is obtained that:

[0093]

[0094] That is:

[0095]

[0096] By using the model in Step 2 to solve the Chinohedron corresponding to the feasible region, the generator matrix can be obtained from the constraint conditions as follows:

[0097]

[0098] Construct the normal vector matrix as follows:

[0099]

[0100] Substitute the data into the model to obtain:

[0101]

[0102]

[0103] Finally, the Chinohedron obtained by solving is:

[0104]

[0105] where c = (1.5, 1.5) and β = [0.25, 0.25, 0.354]

[0106] Embodiment 2

[0107] Figure 2 FIG. is a schematic diagram of a scheduling control device for a precise aggregation model of demand-side resources under the background of the power market provided by Embodiment 2 of the present invention. This embodiment is applicable to the case of day-ahead scheduling simulation of target resources. The device can be implemented in a software and / or hardware manner and can be configured in a terminal device. The determination device includes: a measured flexibility resource parameter acquisition module 410 and a flexibility resource scheduling amount output module 420.

[0108] Among them, the measured flexibility resource parameter acquisition module 410 is used to acquire the measured state parameters and measured resource parameters of the target resources.

[0109] The measured flexibility resource scheduling amount output module 420 is used to input the measured parameters of the target flexibility resources into the target decision model to obtain the output of the scheduling amount of the measured flexibility resources.

[0110] The device for determining the scheduling strategy of the demand-side resource precise aggregation model under the power market background provided by the embodiments of the present invention can be used to execute the method for determining the scheduling strategy of the demand-side resource precise aggregation model under the power market background provided by the embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the method.

[0111] It should be noted that in the embodiments of the above-mentioned determining device, the included various units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0112] Embodiment III

[0113] Figure 3 FIG. 10 is a schematic structural diagram of a device provided in Embodiment III of the present invention. The embodiments of the present invention provide services for implementing the method for determining the scheduling strategy of the demand-side resource precise aggregation model under the power market background, and can configure the device for determining the scheduling strategy of the demand-side resource precise aggregation model in the above embodiments. Figure 3 FIG. 12 shows a block diagram of an exemplary device 12 suitable for implementing the embodiments of the present invention. Figure 3 The shown device 12 is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present invention.

[0114] As Figure 3 shown, the device 12 is presented in the form of a general-purpose computing device. The components of the device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components including the system memory 28 and the processing unit 16.

[0115] The bus 18 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus structures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0116] The device 12 typically includes a variety of computer system-readable media. These media can be any available media accessible by the device 12, including volatile and non-volatile media, removable and non-removable media.

[0117] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 can be used for reading and writing on non-removable, non-volatile magnetic media ( Figure 3 not shown, typically referred to as a "hard disk drive"). Although Figure 3 not shown in the figure, a disk drive for reading and writing on removable non-volatile disks (such as "floppy disks") and an optical disk drive for reading and writing on removable non-volatile optical disks (such as CD-ROM, DVD-ROM or other optical media) can be provided. In these cases, each drive can be connected to bus 18 through one or more data media interfaces. Memory 28 may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the various embodiments of the present invention.

[0118] A program / utilities 40 having a set (at least one) of program modules 42 can be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include the implementation of a network environment. Program modules 42 generally perform the functions and / or methods in the embodiments described in the present invention.

[0119] Device 12 can also communicate with one or more external devices 14 (such as a keyboard, a pointing device, a display 24, etc.), and can also communicate with one or more devices that enable a user to interact with the device 12, and / or communicate with any device that enables the device 12 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through an input / output (I / O) interface 22. Also, device 12 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. As Figure 3 shown, network adapter 20 communicates with other modules of device 12 through bus 18. It should be understood that although not shown in the figure, other hardware and / or software modules can be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0120] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, for example, implementing the method for determining the scheduling strategy of the precise aggregation model of demand-side resources under the background of the power market provided by the embodiments of the present invention.

[0121] Through the above-mentioned device, while fully considering the feasible regions of various types of demand-side flexibility resources, high-dimensional and large-scale aggregation of demand-side resources is achieved, and decision-making scheduling is carried out under the background of the power market, effectively encouraging flexible users to perform demand response while improving the economic benefits of load aggregators.

[0122] Embodiment 4

[0123] Embodiment 4 of the present invention further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the method for determining the scheduling strategy of the precise aggregation model of demand-side resources under the background of the power market when executed by a computer processor. The method includes:

[0124] Obtain the measured parameters of the target resources;

[0125] Input the measured parameters into the preset scheduling model of the precise aggregation entity of the target demand-side resources to obtain the scheduling amounts of different flexibility resources.

[0126] The computer storage medium of the embodiments of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, apparatus, or device.

[0127] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0128] The program code contained on a computer-readable medium can be transmitted by any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0129] The computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0130] Of course, a storage medium containing computer-executable instructions provided by an embodiment of the present invention, the computer-executable instructions are not limited to the above method operations, and can also perform related operations in the method for determining the scheduling strategy of the demand-side resource precise aggregation model in the context of the power market provided by any embodiment of the present invention.

[0131] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A scheduling method for a precise aggregation model of demand-side resources under the background of the electricity market, characterized in that, It includes the following steps: Step S1, data preparation, including electricity market price data, basic constraints of different demand-side load devices, and information related to operation parameters; Step S2, for the single-period flexibility constraint and multi-period coupling constraint of distributed flexibility resources, based on the Chino polyhedron, depict the operation feasible region of a single device; Step S3, aggregate the feasible region of a single device in S2 through Minkowski Sum operation to obtain an accurate aggregation response model of demand-side resources; Step S4, hand over the aggregation model in Step S3 to a demand response aggregator agent to directly participate in the wholesale market competition, and establish a bidding strategy optimization scheduling model with the maximization of aggregator profit as the goal; Among them, the method of depicting the operation feasible region of a single device based on the Chino polyhedron in Step S2 includes the following steps: A Chinu polyhedron is defined by a center point, a generator matrix, and corresponding dilation coefficients Z : （5） Among them, Z is the Chino polyhedron that depicts the feasible region range of the device; c is the center of the polyhedron that represents the geometric position; β is the scaling coefficient corresponding to the direction of the generator, which determines the extension distance of the Chino polyhedron in this direction; is the upper limit of the scaling coefficient; G is the generator matrix that represents the geometric shape and consists of multiple generators: （6） where M is the number of generators, denotes one of the generators and satisfies: , that is, the generator is a normal vector, which determines the extension direction of the Chinu polyhedron; For the power constraint, energy constraint, and ramping constraint of the device in S1, design the corresponding generator matrix as follows: （7） （8） （9） Among them, formula (7) is the first n generators, corresponding to the n power constraints of the load equipment; formula (8) is the middle n n - 1 generators, corresponding to the n n - 1 power quantity constraints of the load equipment; formula (9) is the last n n - 1 generators, corresponding to the n n - 1 ramp constraints, and the generator matrix is ; Construct an optimization model, and the modeling process is as follows: (1) Objective function Arbitrarily construct using the objective transformation method n f normal vectors , and find the diameter of the feasible region in the f direction by solving a linear programming problem. Define the similarity according to the ratio relationship between its position and length: Z , P ​ （10） Wherein: and are respectively the diameters of the two feasible regions in the direction of the normal vector ; The closer it is to 1, the higher the similarity between the Chino polyhedron and the original feasible region; and through mathematical derivation, it can be obtained that: （11） Among them F is n f a matrix composed of normal vectors: Substitute formula (10) into formula (11) to obtain the objective function as: （12） (2) Constraint conditions The constraint condition is that the obtained Chino polyhedron is inside the original device feasible region, that is: （13） The formula can be transformed into an inequality constraint through mathematical derivation , that is: （14） The Chino polyhedron solution model corresponding to the device feasible region is: （15）。 2. The scheduling method for the precise aggregation model of demand-side resources in the context of the electricity market according to claim 1, characterized in that The method for constraining the operation feasible region of the device in Step S2 includes the following steps: Considering the limited and discrete operation cycles of demand-side equipment N t , there are N operation time periods, and the time interval for each period is t ; p (t) is the operating power during the operating period , then the flexibility feasible region of the equipment i is described by the following single-period flexibility constraints and multi-period coupling constraints: (1) Power constraint Assume that the power of the device is constant in the interval t , then the power constraint can be expressed as: （1） Among them, pk is the demand-side device i at the k constant power within the pmin and pmax are respectively the lower limit and upper limit of the operating power of the demand-side device; (2) Energy constraint （2） Among them, ei,min and ei,max are the lower and upper limits of the power consumption constraint of the demand-side device i respectively; (3) Ramping constraint （3） Among them, ri,min and ri,max are respectively the lower limit and the upper limit of the power change amount in adjacent time periods of the demand-side device i ; The feasible region range of the device can be summarized as a convex polyhedron representation form, that is: （4） Among them, P is a convex polyhedron representing the feasible region of the device; N is the dimension of the convex polyhedron; ( A, b ) Integrate all the linear operation constraint coefficients of the device and represent them in matrix form.

3. The scheduling method for the precise aggregation model of demand-side resources in the context of the electricity market according to claim 1, wherein The accurate aggregation response model of demand-side resources obtained based on Minkowski Sum operation in Step S3 includes the following steps: The load aggregator needs to aggregate the feasible regions of all users to form the aggregated feasible region of the user cluster, which is based on the aggregated feasible region expressed by the Chino polyhedron Implemented by Minkowski Sum: （16） Among them is the Chino polyhedron corresponding to the flexibility feasible region of a single demand-side device under the load aggregator, is the aggregated feasible region; The aggregation model is as follows: （17） （18） Among them, and are respectively the center point and the scaling factor of the aggregated Chino polyhedron, that is, the aggregated model can be obtained by simply performing an addition operation on the center point of the feasible region corresponding to the feasible region of a single load device and the scaling technology.

4. The method for scheduling the precise aggregation model of demand-side resources according to claim 1, characterized in that The method for establishing a bidding strategy optimization scheduling model in Step S4 includes the following steps: An aggregated response model of demand-side resources is obtained through aggregation and handed over to demand response aggregators to participate in the competition in the electricity wholesale market. Each demand response aggregator pursues its own profit maximization by choosing to increase or reduce the load volume in each time period. Its decision variables are and ; then the demand response aggregator p at t has a quadratic electricity consumption benefit function which can be expressed as: （19） wherein, p = 1, 2, …, P ; t = 1, 2, …, T , and are utility function parameters, both being non - negative real numbers; is the basic load of the load aggregator p , that is, the normal electricity consumption load that does not participate in demand response; Then T The optimization problem of each demand response aggregator in the wholesale market during the time period can be expressed as: （20） Among them, r t is t the price of the wholesale market at a certain moment; L p,max represents the maximum load of the demand response aggregator p ; is the load reduction coefficient and .

5. A device for the scheduling strategy of a precise aggregation model of demand-side resources under the background of the electricity market, characterized in that, It includes: One or more processors; A memory for storing one or more programs; The one or more programs are executed by the one or more processors, so that the one or more processors implement the scheduling method of the accurate aggregation model of demand-side resources under the background of the electricity market as described in any one of claims 1-4.

6. A storage medium containing computer-executable instructions, characterized in that, At least one instruction, at least one segment of program, code set or instruction set is stored in the storage medium, and the at least one instruction, the at least one segment of program, the code set or instruction set is loaded and executed by the processor to implement the scheduling method of the accurate aggregation model of demand-side resources under the background of the electricity market as described in any one of claims 1-4.

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