A distribution network resource aggregation method, device, equipment and storage medium

Abstract

The present invention discloses a distribution network resource aggregation method, device, equipment and storage medium, which relates to the technical field of power user demand response modeling and includes the following steps: step 1, collecting data information such as the grid structure, node safety constraints, and line safety constraints of the distribution network; step 2, collecting data information such as power, climbing, and energy constraints of different flexible resources in the distribution network; step 3, based on the basic theory of Chino polyhedron, constructing a group of corresponding generators for each constraint to generate a hyperplane of the corresponding constraint in the solution space, and simplifying and deleting the generators corresponding to all constraints; step 4, based on the final generator set, characterizing the safe feasible domain after the aggregation of different flexible resources; at the same time, when the present invention is used, based on the basic theory of Chino polyhedron, constructing a group of corresponding generators for each constraint in the aggregation problem to generate a hyperplane of the corresponding constraint in the solution space.

Description

Technical Field

[0001] The present invention relates to the technical field of power user demand response modeling, and in particular to a distribution network resource aggregation method, device, equipment and storage medium. Background Art

[0002] Small controllable loads distributed on the demand side have the potential to supplement system flexibility, including temperature control loads, electric vehicles and distributed energy storage, which can all participate in regulating system balance through certain control methods, collectively referred to as demand-side flexibility resources. However, it is very difficult to quantitatively analyze and coordinate the large number of demand-side flexibility resources. In order to make full use of potential flexibility and reduce the difficulty of calling and computational complexity, the demand-side flexibility must be aggregated and formed into a concise form that can be uniformly called. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above-mentioned background technology, the purpose of the present invention is to provide a distribution network resource aggregation method, device, equipment and storage medium. The present invention can provide power dispatching personnel with accurate modeling of the demand response and regulation capabilities of massive flexible resources within the distribution network, and support distributed resources to participate in the safe and economic regulation of the power grid.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A distribution network resource aggregation method comprises the following steps:

[0006] Step 1: Collect data information such as the grid structure, node safety constraints, and line safety constraints of the distribution network;

[0007] Step 2: Collect data information such as power, ramp rate, and energy constraints of different flexible resources in the distribution network;

[0008] Step 3: Based on the basic theory of Qino polyhedron, a set of corresponding generators is constructed for each constraint to generate the hyperplane of the corresponding constraint in the solution space, and the generators corresponding to all constraints are simplified and deleted;

[0009] Step 4: Based on the final generator set, characterize the safe feasible domain after aggregating different flexible resources.

[0010] Furthermore, the specific contents of step 1 are:

[0011] (1) Grid structure, node safety constraints, and line safety constraints

[0012] The network structure information of the distribution network includes the nodes and topology information of the distribution network. For a distribution network with N nodes, it can be represented by an N*N adjacency matrix A. For the element a in A ij is defined as follows:

[0013]

[0014] The safety constraint of the distribution network node refers to the voltage v i The upper and lower limit constraints need to be met, which can be expressed as:

[0015] v i ∈[v min ,v max ]

[0016] Among them, v min Represents the lower limit of node voltage, v max Indicates the upper limit of node voltage.

[0017] The line safety constraint of the distribution network refers to the branch power p of any branch k in the distribution network. i The upper and lower limit constraints need to be met, which can be expressed as:

[0018] p i ∈[p min ,p max ]

[0019] Among them, p min Indicates the lower limit of line power, p max Indicates the upper limit of line power.

[0020] (2) Voltage load sensitivity matrix

[0021] The voltage load sensitivity matrix includes the active voltage load sensitivity matrix and the reactive voltage load sensitivity matrix, which represents the impact of active / reactive power injection at a certain node on the voltage of each node in the distribution network. p For example, the element p ij It represents the change of voltage at node j after injecting 1 unit of active power into node i. For reactive node load sensitivity matrix VLSM q For example, the element q ij It represents the voltage change of node j after injecting 1 unit of reactive power into node i. The total voltage change of the node is caused by the change of active power and reactive power of each node, which is expressed as:

[0022] |δV|=|VLSM P ||δP|+|VLSM P ||δQ|

[0023] ie,

[0024]

[0025] Where δV(n) represents the voltage change at node n, δP n represents the active power change of node N, δQ n Represents the reactive power change of node N.

[0026] (3) Flow load sensitivity matrix

[0027] The load flow sensitivity matrix includes the active load flow sensitivity matrix and the reactive load flow sensitivity matrix, which represents the impact of active / reactive power injection at a certain node on the flow of each line in the distribution network. Assuming that the distribution network has N nodes and M branches, for the active load flow sensitivity matrix PLSM p For example, the element p mn It represents the change of active power on line m after injecting 1 unit of active power into node n. For the reactive node load sensitivity matrix PLSM q For example, the element q mn It indicates the change of reactive power flow on line m after injecting 1 unit reactive power into node n, which is expressed as:

[0028] |δP|=|PLSM P ||δP|

[0029] |δQ|=|PLSM q ||δQ|

[0030] ie,

[0031]

[0032] In the formula, δP(n) represents the active power flow change of line n, δQ(n) represents the reactive power flow change of line n, and δP n represents the active power change of node n, δQ n Represents the reactive power change of node n.

[0033] Furthermore, the specific contents of step 2 are as follows:

[0034] The power constraint of the flexible resources in the distribution network means that the power output of the flexible resources needs to meet the upper and lower limit constraints. The ramp constraint means that the power difference of the flexible resources in two adjacent time intervals needs to meet the upper and lower limit constraints. The energy constraint means that the total output of the flexible resources in a certain continuous time period (including multiple time intervals) needs to meet the upper and lower limit constraints.

[0035] Furthermore, the specific contents of step 3 include:

[0036] For a constraint of the form i represents the i-th variable, λ iRepresents the parameter corresponding to the i-th variable, and c is a constant.

[0037]

[0038] Constructing the following set of generators can ensure that the resulting Chino polyhedron has a hyperplane parallel to the constraint. The specific form is as follows:

[0039]

[0040] In the formula, g i is the i-th generator. After constructing a set of generators based on each constraint, the generators in the same direction or opposite direction are deleted. Specifically, it is expressed as: if g i =g j , then g i With g j In the same direction, if g i =-g j , then g i With g j Reverse direction.

[0041] Furthermore, the specific contents of step 4 are:

[0042] For the problem of obtaining the maximum inscribed Chino polyhedron Z in the original irregular feasible domain P, the feasible domain P satisfies the constraint

[0043] Ax≤b

[0044] Among them, A and b are coefficient matrices, and x is a variable matrix. Arbitrarily construct S normal vectors By solving the linear programming problem to find s The diameters of the feasible regions Z and P in the direction define similarity based on their position and length relationship:

[0045]

[0046] Where: Two feasible regions are in α s The diameter in the direction. s ∈[0,1], the closer it is to 1, the higher the similarity.

[0047] Known α s , the linear programming problem of finding the tangent point of a convex polyhedron and calculating the diameter can be written as:

[0048]

[0049] In the formula, ε is a sufficiently large constant.

[0050] Tangent points and diameter of Chino polyhedron and α s The relationship can be written as:

[0051]

[0052] In the formula, G is the generator group, β max is the maximum width of the generator.

[0053] By solving 2S linear programming problems, we can get Then, the Qino polyhedron approximation problem can be written as:

[0054]

[0055] Where c is the center point of the Chino polyhedron, Ac+|AG|β max ≤b can be guaranteed

[0056] Beneficial effects of the present invention:

[0057] The present invention is based on the basic theory of Chino polyhedron. For each constraint in the aggregation problem, a set of corresponding generators is constructed to generate hyperplanes of the corresponding constraints in the solution space, and the generators corresponding to all constraints are simplified and deleted. Based on the final set of generators, the safe feasible domain after the aggregation of different flexible resources is characterized, and finally the effective aggregation of massive distributed resources is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 is a flow chart of the method of the present invention;

[0060] Figure 2 This is a schematic diagram of a 5-node distribution network;

[0061] Figure 3 A schematic diagram of a device for determining secure aggregation of flexible resources;

[0062] Figure 4 This is a schematic diagram of the flexible resource security aggregation device structure. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0065] A distribution network resource aggregation method, device, equipment and storage medium, as shown in the figure, includes the following steps:

[0066] Step 1: Collect data information such as the grid structure, node safety constraints, and line safety constraints of the distribution network;

[0067] Step 2: Collect data information such as power, ramp rate, and energy constraints of different flexible resources in the distribution network;

[0068] Step 3: Based on the basic theory of Qino polyhedron, a set of corresponding generators is constructed for each constraint to generate the hyperplane of the corresponding constraint in the solution space, and the generators corresponding to all constraints are simplified and deleted;

[0069] Step 4: Based on the final generator set, characterize the safe feasible domain after aggregating different flexible resources.

[0070] The specific contents of step 1 are:

[0071] (1) Grid structure, node safety constraints, and line safety constraints

[0072] The network structure information of the distribution network includes the nodes and topology information of the distribution network. For a distribution network with N nodes, it can be represented by an N*N adjacency matrix A. The definition of the element aij in A is as follows:

[0073]

[0074] The safety constraint of the distribution network node refers to the voltage v i The upper and lower limit constraints need to be met, which can be expressed as:

[0075] v i ∈[v min ,v max ]

[0076] Among them, v min Represents the lower limit of node voltage, v max Indicates the upper limit of node voltage.

[0077] The line safety constraint of the distribution network refers to the branch power p of any branch k in the distribution network. i The upper and lower limit constraints need to be met, which can be expressed as:

[0078] p i ∈[p min ,p max ]

[0079] Among them, p min Indicates the lower limit of line power, p max Indicates the upper limit of line power.

[0080] (2) Voltage load sensitivity matrix

[0081] The voltage load sensitivity matrix includes the active voltage load sensitivity matrix and the reactive voltage load sensitivity matrix, which represents the impact of active / reactive power injection at a certain node on the voltage of each node in the distribution network. p For example, the element p ij It represents the change of voltage at node j after injecting 1 unit of active power into node i. For reactive node load sensitivity matrix VLSM q For example, the element q ij It represents the voltage change of node j after injecting 1 unit of reactive power into node i. The total voltage change of the node is caused by the change of active power and reactive power of each node, which is expressed as:

[0082] |δV|=|VLSM P ||δP|+|VLSM P ||δQ|

[0083] ie,

[0084]

[0085] Where δV(n) represents the voltage change at node n, δP n represents the active power change of node N, δQ n Represents the reactive power change of node N.

[0086] (3) Flow load sensitivity matrix

[0087] The load flow sensitivity matrix includes the active load flow sensitivity matrix and the reactive load flow sensitivity matrix, which represents the impact of active / reactive power injection at a certain node on the flow of each line in the distribution network. Assuming that the distribution network has N nodes and M branches, for the active load flow sensitivity matrix PLSM p For example, the element p mn It represents the change of active power on line m after injecting 1 unit of active power into node n. For the reactive node load sensitivity matrix PLSM q For example, the element q mnIt indicates the change of reactive power flow on line m after injecting 1 unit reactive power into node n, which is expressed as:

[0088] |δP|=|PLSM P ||δP|

[0089] |δQ|=|PLSM q ||δQ|

[0090] ie,

[0091]

[0092] In the formula, δP(n) represents the active power flow change of line n, δQ(n) represents the reactive power flow change of line n, and δP n represents the active power change of node n, δQ n Represents the reactive power change of node n.

[0093] The specific contents of step 2 are as follows:

[0094] The power constraint of the flexible resources in the distribution network means that the power output of the flexible resources needs to meet the upper and lower limit constraints. The ramp constraint means that the power difference of the flexible resources in two adjacent time intervals needs to meet the upper and lower limit constraints. The energy constraint means that the total output of the flexible resources in a certain continuous time period (including multiple time intervals) needs to meet the upper and lower limit constraints.

[0095] The specific contents of step 3 are:

[0096] For a constraint of the form i represents the i-th variable, λ i Represents the parameter corresponding to the i-th variable, and c is a constant.

[0097]

[0098] Constructing the following set of generators can ensure that the resulting Chino polyhedron has a hyperplane parallel to the constraint. The specific form is as follows:

[0099]

[0100] In the formula, g i is the i-th generator. After constructing a set of generators based on each constraint, the generators in the same direction or opposite direction are deleted. Specifically, it is expressed as: if g i =g j , then g i With g j In the same direction, if g i =-g j , then g i With g j Reverse direction.

[0101] The specific contents of step 4 are:

[0102] For the problem of obtaining the maximum inscribed Chino polyhedron Z in the original irregular feasible domain P, the feasible domain P satisfies the constraint

[0103] Ax≤b

[0104] Among them, A and b are coefficient matrices, and x is a variable matrix. Arbitrarily construct S normal vectors By solving the linear programming problem to find s The diameters of the feasible regions Z and P in the direction define similarity based on their position and length relationship:

[0105]

[0106] Where: Two feasible regions are in α s The diameter in the direction. s ∈[0,1], the closer it is to 1, the higher the similarity.

[0107] Known α s , the linear programming problem of finding the tangent point of a convex polyhedron and calculating the diameter can be written as:

[0108]

[0109] In the formula, ε is a sufficiently large constant.

[0110] Tangent points and diameter of Chino polyhedron and α s The relationship can be written as:

[0111]

[0112] In the formula, G is the generator group, β max is the maximum width of the generator.

[0113] By solving 2S linear programming problems, we can get Then, the Qino polyhedron approximation problem can be written as:

[0114]

[0115] Where c is the center point of the Chino polyhedron, Ac+|AG|β max ≤b can be guaranteed

[0116] Example 1

[0117] Figure 2 According to the IEEE standard 14-node network, a 5-node topology is separated and an example analysis is performed on this topology.

[0118] Assuming that the demand response event lasts for 2 hours, the DR response power of the aggregator at nodes 3 and 4 is and The fixed load remained constant during the three periods.

[0119] 1. Constraints

[0120] (1) Power Constraint

[0121]

[0122] (2) Energy Constraint

[0123]

[0124] (3) Climbing constraints

[0125]

[0126] (4) Line flow constraints

[0127]

[0128] Among them, line 1-2 is prone to power flow exceeding the limit.

[0129]

[0130] Simplifying, we get: (5) Node voltage constraints

[0131]

[0132] Node 4 is prone to voltage exceeding the limit.

[0133]

[0134] Simplifying, we get:

[0135]

[0136] Combining all the above constraints, the constructed A matrix, b matrix and G matrix are:

[0137] Normal vector:

[0138]

[0139] The center coordinates of the Chino polyhedron are: [0.875, 1.125, 1.125, 0.875] and the scaling coefficients β of each generator are: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.88388348, 0.35355339, 0.88388348, 0.35355339, 0.35355339, 0.0, 0.0].

[0140] Example 2

[0141] Figure 3 Schematic diagram of a flexible resource security aggregation determination device provided by Embodiment 2 of the present invention. This embodiment is applicable to the case of performing security aggregation on target flexible resources. The device can be implemented in software and / or hardware, and the device can be configured in a terminal device. The flexible resource security aggregation determination device includes: a distribution network parameter acquisition module 410 and a flexible resource parameter acquisition module 420.

[0142] Among them, the distribution network parameter acquisition module 410 is used to collect data information such as the grid structure, node safety constraints, and line safety constraints of the distribution network; the flexible resource parameter acquisition module 420 is used to collect data information such as power, climbing, and energy constraints of different flexible resources in the distribution network.

[0143] The technical solution of this embodiment is based on the basic theory of Chino polyhedron. For each constraint, a set of corresponding generators is constructed to generate the hyperplane of the corresponding constraint in the solution space, and the generators corresponding to all constraints are simplified and deleted; based on the final set of generators, the safe feasible domain after the aggregation of different flexible resources is characterized. It can accurately model the demand response regulation capability of the power dispatching personnel for the massive flexible resources within the distribution network, and support the participation of distributed resources in the safe and economic regulation of the power grid.

[0144] Example 3

[0145] Figure 4 It is a structural diagram of a device provided by Example 3 of the present invention. The embodiment of the present invention provides services for the implementation of the flexible resource security aggregation of the above embodiment of the present invention, and can configure the flexible resource security aggregation determination device in the above embodiment. Figure 3 A block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 3 The device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0146] like Figure 3As shown, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and bus 18 that connects various system components (including system memory 28 and processing unit 16).

[0147] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0148] Device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by device 12, including volatile and non-volatile media, removable and non-removable media.

[0149] 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 may be used to read and write non-removable, non-volatile magnetic media ( Figure 3 not shown, usually called a "hard drive"). Although Figure 3 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0150] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0151] The device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the device 12, and / or any device that enables the device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the device 12 via the 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 the 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.

[0152] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the demand response capability quantification method provided in the embodiment of the present invention.

[0153] The above-mentioned equipment solves the problem of safe aggregation of massive flexible resources in the distribution network.

[0154] Example 4

[0155] Embodiment 4 of the present invention further provides a storage medium containing computer executable instructions, which, when executed by a computer processor, is used to execute a distribution network flexible resource security aggregation method based on Chino polyhedron, the method comprising:

[0156] Get the distribution network parameter acquisition module;

[0157] The measured parameters are input into the pre-trained flexible resource security aggregation model to obtain the aggregation feasible domain of the current resources under the security constraints of the distribution network.

[0158] The computer storage medium of the embodiment 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, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media 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, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0159] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0160] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0161] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or combinations thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0162] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0163] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A distribution network resource aggregation method, characterized in that: The following steps are involved: Step 1: Collect data information such as the grid structure, node safety constraints, and line safety constraints of the distribution network; Step 2: Collect data information such as power, ramp rate, and energy constraints of different flexible resources in the distribution network; Step 3: Based on the basic theory of Qino polyhedron, a set of corresponding generators is constructed for each constraint to generate the hyperplane of the corresponding constraint in the solution space, and the generators corresponding to all constraints are simplified and deleted; Step 4: Based on the final generator set, describe the safe feasible domain after aggregation of different flexible resources; The specific contents of step 4 are: For the problem of obtaining the maximum inscribed Chino polyhedron Z in the original irregular feasible domain P, the feasible domain P satisfies the constraints: Ax≤b Among them, A and b are coefficient matrices, and x is a variable matrix; arbitrarily construct S normal vectors By solving the linear programming problem to find s The diameters of the feasible regions Z and P in the direction define similarity based on their position and length relationship: Where: Two feasible regions are in α s The diameter in the direction, Λ s ∈[0,1], the closer to 1, the higher the similarity; Known α s , the linear programming problem of finding the tangent point of a convex polyhedron and calculating the diameter is written as: In the formula, ε is a sufficiently large constant; Tangent points and diameter of Chino polyhedron and α s Relationship writing: In the formula, G is the generator group, β max is the maximum width of the generator; By solving 2S linear programming problems, we can get Then, the Qino polyhedron approximation problem can be written as: Where c is the center point of the Chino polyhedron, Ac+|AG|β max ≤b can be guaranteed 2. A distribution network resource aggregation method according to claim 1, characterized in that: The specific contents of step 1 are: (1) Grid structure, node safety constraints, and line safety constraints The network structure information of the distribution network includes the nodes and topology information of the distribution network. For a distribution network with N nodes, it is represented by an N*N adjacency matrix A. For the element a in A ij is defined as follows: The safety constraint of the distribution network node refers to the voltage v i The upper and lower limit constraints need to be met, which can be expressed as: v i ∈[v min ,v max ] Among them, v min Represents the lower limit of node voltage, v max Indicates the upper limit of node voltage; The line safety constraint of the distribution network refers to the branch power p of any branch k in the distribution network. i The upper and lower limit constraints need to be met, which can be expressed as: p i ∈[p min ,p max ] Among them, p min Indicates the lower limit of line power, p max Indicates the upper limit of line power; (2) Voltage load sensitivity matrix The voltage load sensitivity matrix includes the active voltage load sensitivity matrix and the reactive voltage load sensitivity matrix, which represents the impact of active / reactive power injection at a certain node on the voltage of each node in the distribution network; for the active node load sensitivity matrix VLSM p For example, the element p ij It represents the voltage change of node j after injecting 1 unit of active power into node i; for the reactive node load sensitivity matrix VLSM q For example, the element q ij It represents the voltage change of node j after injecting 1 unit of reactive power into node i. The total voltage change of the node is caused by the change of active power and reactive power of each node, which is expressed as: |δV|=|VLSM P ||δP|+|VLSM P ||δQ| (1) ie, Where δV(n) represents the voltage change at node n, δP n represents the active power change of node N, δQ n Represents the reactive power change of node N; (3) Flow load sensitivity matrix The power flow load sensitivity matrix includes the active power flow load sensitivity matrix and the reactive power flow load sensitivity matrix, which represents the impact of active / reactive power injection at a certain node on the power flow of each line in the distribution network. Assuming that the distribution network has N nodes and M branches, for the active power flow load sensitivity matrix PLSM p For example, the element p mn It represents the change of active power on line m after injecting 1 unit of active power into node n; for the reactive node load sensitivity matrix PLSM q For example, the element q mn It indicates the change of reactive power flow on line m after injecting 1 unit reactive power into node n, which is expressed as: |δP|=|PLSM P ||δP| |δQ|=|PLSM q ||δQ| ie, In the formula, δP(n) represents the active power flow change of line n, δQ(n) represents the reactive power flow change of line n, and δP n represents the active power change of node n, δQ n Represents the reactive power change of node n.

3. A distribution network resource aggregation method according to claim 1, characterized in that: The specific contents of step 2 are: The power constraint of the flexible resources in the distribution network means that the power output of the flexible resources needs to meet the upper and lower limit constraints. The ramp constraint means that the power difference of the flexible resources in two adjacent time intervals needs to meet the upper and lower limit constraints. The energy constraint means that the total output of the flexible resources in a certain continuous time period (including multiple time intervals) needs to meet the upper and lower limit constraints.

4. A distribution network resource aggregation method according to claim 1, characterized in that: The specific contents of step 3 are: For a constraint of the form i represents the i-th variable, λ i represents the parameter corresponding to the i-th variable, and c is a constant; Constructing the following set of generators can ensure that the resulting Chino polyhedron has a hyperplane parallel to the constraint. The specific form is as follows: In the formula, g i For the i-th generator, after constructing a set of generators based on each constraint, the generators in the same direction or opposite direction are deleted. Specifically, it is expressed as: if g i =g j , then g i With g j In the same direction, if g i =-g j , then g i With g j Reverse direction.

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