Power distribution network dynamic safety space solving method and system for virtual power plant aggregation

Abstract

The invention discloses a power distribution network dynamic safety space solving method and system for virtual power plant aggregation, and relates to the technical field of power distribution network safety operation, and the method comprises the steps: obtaining the network topology information of a power distribution network, and carrying out the calculation based on the network topology information of the power distribution network to obtain a hyperplane representation power distribution network safety domain; obtaining a resource fuzzy boundary uploaded by the virtual power plant, performing optimization decision calculation based on the resource fuzzy boundary uploaded by the virtual power plant, and generating a power distribution network resource fuzzy model by using an optimization decision value; inputting the fuzzy model of the power distribution network resource into a hyperplane representation power distribution network security domain, outputting to obtain a fuzzy power distribution network dynamic security space, and performing reduction and defuzzification on the fuzzy power distribution network dynamic security space by using an improved enhanced opposite search algorithm to obtain a power distribution network dynamic security space; inputting the dynamic safety space of the power distribution network into a pre-established aggregation model of a virtual power plant, and outputting to obtain aggregation capacity of the virtual power plant; and calculating the virtual power plant blocking capacity caused by the dynamic safety space of the power distribution network in combination with the original virtual power plant aggregation capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of safe operation of distribution networks, and specifically to a method and system for solving the dynamic safety space of a distribution network for virtual power plant aggregation. Background Art

[0002] More and more renewable energy sources are connected to the power system, which poses a great challenge to the power balance of the power system. Various types of distributed resources on the user side (such as distributed energy storage, electric vehicles, and air conditioners) provide adjustable flexibility to solve this problem. However, due to the large variety and quantity of distributed resources, their geographical locations are scattered, and their adjustable scales are small. Under such circumstances, virtual power plants have become an effective way to manage distributed resources and serve the power grid.

[0003] Since distributed resources are located at different nodes in the distribution network, all distributed resources must comply with the power flow safety constraints of the power grid, such as voltage stability limits and thermal stability limits. For a virtual power plant that manages numerous distributed resources, it must also comply with the power flow safety constraints during the process of aggregating and controlling distributed resources. This further strengthens the operating relationship between the virtual power plant and the distribution network. However, the operating safety constraints of the distribution network are the exclusive privacy data of distribution network operators. It is necessary to solve the problem of the safe operation of the distribution network during the process of virtual power plant managing distributed resources. Summary of the Invention

[0004] To solve the deficiencies mentioned in the above background art, the purpose of the present invention is to provide a method and system for solving the dynamic safety space of a distribution network for virtual power plant aggregation.

[0005] In a first aspect, the purpose of the present invention can be achieved through the following technical solutions: A method for solving the dynamic safety space of a distribution network for virtual power plant aggregation, the method comprising the following steps:

[0006] Obtain the distribution network topology information, and calculate based on the distribution network topology information to obtain a hyperplane representing the distribution network safety domain; obtain the resource fuzzy boundary uploaded by the virtual power plant, perform optimization decision calculation based on the resource fuzzy boundary uploaded by the virtual power plant, and establish a fuzzy model of the distribution network resources using the optimization decision value;

[0007] Input the fuzzy model of the distribution network resources into the hyperplane representing the distribution network safety domain, and output to obtain a fuzzy distribution network dynamic safety space. Use the improved enhanced opposite search algorithm to reduce the type and defuzzify the fuzzy distribution network dynamic safety space to obtain the distribution network dynamic safety space;

[0008] Input the distribution network dynamic safety space into the pre-established aggregation model of the virtual power plant, and output to obtain the virtual power plant aggregation capacity; combine the original virtual power plant aggregation capacity, and calculate the virtual power plant blocking capacity caused by the distribution network dynamic safety space.

[0009] Combined with the first aspect, in some implementations of the first aspect, the method further includes: the power distribution network security domain describes the power flow security constraints from the perspective of the domain. The power flow security constraints include the voltage constraint limits of each node and the current constraint limits of each branch. The power distribution network security domain with the node injection power as the decision space can be expressed by the following formula:

[0010]

[0011] In the formula: represents the voltage security domain of node j, and represents the hyperplane coefficient of node h in the voltage upper limit security domain of node j; and represents the hyperplane coefficient of node h in the voltage upper limit security domain of node j, P h and Q h represent the active and reactive power injected by node h; represents the thermal stability security domain of line i, and represent the hyperplane coefficients of node h in the thermal stability security domain of line i; Ω DSR represents the hyperplane security domain.

[0012] Based on the actual power flow operation results of DistFlow, the relevant hyperplane coefficients are corrected, and the compact form of the power distribution network security domain is given as:

[0013]

[0014] In the formula, γ and χ represent the compact form of the hyperplane coefficients, P j and Q j represent the compact form of the node injection power.

[0015] Combined with the first aspect, in some implementations of the first aspect, the method further includes: the set optimization objectives of the power distribution network include the lowest global network loss and the smallest voltage deviation of the power distribution network. The multi-objective optimization problem M1 is set as follows, where the objective function is:

[0016]

[0017] S n = F(P, Q, U, I) (13)

[0018] In the formula, λ represents the multi-objective coefficient, represents the network loss of line l at time t, represents the voltage deviation of node i at time t; and represent Ndso The active and reactive powers of the virtual generator in and represent N dso the upper and lower limits of the active power of the virtual generator in and represent N dso the upper and lower limits of the power factor angle of the virtual generator in and represent N dso the active and reactive powers of the virtual energy storage in represent N dso the active power limit of the virtual energy storage in and represent N dso the energy limit of the virtual energy storage in represent N dso the energy of the virtual energy storage in represent N dso the apparent power limit of the virtual energy storage in and represent N for each node provided by the virtual power plant vpp fuzzy output, and represent the triangular fuzzy membership function of the output upper and lower limits, where a, b, and c represent the parameters of the triangular fuzzy membership function. and represent N nor the active and reactive powers of. S n represents the power flow constraint.

[0019] Among them, (6)-(7) represent the output constraint of the virtual generator in the distribution network operator's control node N dso in, (8)-(9) represent N dso the output constraint of the virtual energy storage in, (11) represents the N for each node provided by the virtual power plant vpp fuzzy output boundary constraint, (12) represents N nor output constraint, (13) represents the DistFlow power flow constraint, and the optimization problem contains random variables and fuzzy variables and

[0020] Combined with the first aspect, in some implementations of the first aspect, the method further includes: performing optimization decision calculation based on the fuzzy boundary of the resources uploaded by the virtual power plant:

[0021] The optimization problem M1 is solved according to a specific operation mode to obtain N dso the operation result of, and a fuzzy model is used for N dsoModel the resource output of the nodes;

[0022] For the N obtained by solving the optimization problem M1 dso Decision value As the maximum possible operation result under a specific distribution network operation state, separately perform individual fuzzy modeling on the active power and reactive power in the N dso nodes:

[0023] First, use the type-I triangular fuzzy membership function to model the active power of the virtual generator in :

[0024]

[0025] In the formula, and represent the three parameters of the triangular fuzzy membership function.

[0026] Combined with the geometric relationship in Equation (6) and Figure 1 in, the parameters of can be successively expressed as It can be seen from Equation (7) that the reactive power range of the virtual generator is directly related to the active power of the virtual generator; since the active power of the virtual generator is characterized by a type-I triangular fuzzy membership function, use a type-II triangular fuzzy membership function to perform fuzzy modeling on the reactive power of the virtual generator, integrating the reactive power value range characterized by Equation (7) and the fuzzy modeling of the active power of the virtual generator characterized by Equation (14);

[0027] Obtain the main membership function of the type-II triangular fuzzy membership function of the reactive power of the virtual generator. After substituting the parameters, there is:

[0028]

[0029] In the formula, represents the reactive power output value based on the optimization decision.

[0030] Due to the parameter magnitude relationship of the type-II triangular fuzzy membership function, the corrected parameters are given as:

[0031] and

[0032] Its corresponding upper membership function and lower membership function are respectively:

[0033]

[0034] The corresponding left endpoint l is a type-I triangular fuzzy membership function with parameters as follows:

[0035]

[0036] Similarly, the corresponding right endpoint r also has similar parameter characteristics, which are:

[0037]

[0038] Through the setting rules of the fuzzy system, a type-II triangular fuzzy membership function of the reactive power of the virtual generator is obtained;

[0039] For As can be seen from Equation (10), Unlike When modeling , by first establishing a type-I triangular fuzzy membership function for , and then establishing a type-II triangular fuzzy membership function for , a fuzzy model of the virtual energy storage is established by a method with the completely opposite order. Since is directly determined by , and in order to unify the modeling format with , is determined as the basic model of the type-I triangular fuzzy membership function;

[0040] The parameter values of

[0041] Using the same method as the virtual generator to perform fuzzy modeling on , the type-II triangular fuzzy membership function is Substituting Equation (10) and N dso Decision into the main membership function:

[0042]

[0043] The corresponding upper membership function and the lower membership function are respectively:

[0044]

[0045] The membership functions of l and r are respectively:

[0046]

[0047] In the formula, Obtain N dso Complete fuzzy modeling, combined with N norModeling; the fuzzy form of the dynamic security space of the distribution network can be obtained as follows:

[0048]

[0049] Equation (22) containing fuzzy and random factors can be expressed in the form of a credibility chance constraint, and Equation (22) is expressed as:

[0050]

[0051] Where Ch represents the chance measure and α is the credibility.

[0052] For the dso and nor random factors in, the randomness is removed by means of probability confidence, and Equation (23) is equivalent to:

[0053]

[0054] Where Cr represents the credibility measure, Pr represents the probability measure, and α * represents the confidence.

[0055] It is transformed into:

[0056]

[0057] Where represents the inverse function of the cumulative distribution function.

[0058] By the deterministic transformation of the random variables representing the output and parameters in the model, a fuzzy chance-constrained model of the dynamic security space of the distribution network containing type-I triangular fuzzy membership functions and type-II triangular fuzzy membership functions will be given.

[0059] Combined with the first aspect, in some implementation manners of the first aspect, the method further includes: in the process of reducing the fuzzy distribution network dynamic security space by using the improved enhanced opposite-direction search algorithm, for the type-II triangular fuzzy membership function in the distribution network dynamic security space, the left endpoint of the centroid set of its α-plane can be defined Then there must exist a sampling point of the α-plane at l α ∈{k|1≤k≤N} such that Further combining Equations (16) and (20), the following equation is obtained:

[0060]

[0061] Set the function as:

[0062]

[0063] The upper membership function and the lower membership function The longitudinal difference in the α-plane is defined as At the same time, it is defined that:

[0064]

[0065] Equation (27) is expressed as

[0066]

[0067] Define The iterative formula can be obtained

[0068]

[0069] The termination condition is to obtain the conversion point. From the critical size relationship of the conversion point, it can be obtained that the termination condition is satisfied:

[0070]

[0071] At this time, k = l α , and the left endpoint can be obtained as:

[0072]

[0073] Using the same idea, when k = r α The right endpoint is expressed as:

[0074]

[0075] Due to the unique correspondence characteristics of the and of the type-II triangular fuzzy membership function with q i When α: 0 → 1, Based on the characteristics of the type-II triangular fuzzy membership function, the improved enhanced opposite-direction search algorithm is used to realize the conversion of the type-II triangular fuzzy membership function to the type-I triangular fuzzy membership function.

[0076] Combined with the first aspect, in some implementation manners of the first aspect, the method further includes: the defuzzification process of the fuzzy distribution network dynamic security space:

[0077] After converting the type-II triangular fuzzy membership function of the distribution network dynamic security space into a type-I triangular fuzzy membership function through the improved enhanced opposite-direction search algorithm, the unified form of all power fuzzy membership functions of the virtual generator and the virtual energy storage is given as follows:

[0078]

[0079] LL,R Represents a piecewise fuzzy membership function composed of and . The piecewise fuzzy membership functions represented by and respectively represent the piecewise fuzzy membership functions composed of the left and right endpoints and the corresponding α under each α. The fuzzy membership function of each segment is abbreviated as L(C(α)). Further, the dynamic security space of the distribution network characterized by Equation (23) is defuzzified and equivalent;

[0080] Separate the N in the dynamic security space of the distribution network dso , and Equation (23) can be transformed into:

[0081]

[0082] In the formula, and represent the compact form of the N dso control resources, and represent the compact form of the N nor control resources; and represent the compact form of the N vpp control resources; represents the credibility.

[0083] Characterize the least common multiple interval of the multi-segment linear membership function of Equation (34) as Within this interval, the credibility opportunity constraint can be expressed as:

[0084]

[0085] where represents the node power of N dso , represents the hyperplane coefficient γ or χ corresponding to node j or line ij. Equation (37) is transformed into:

[0086]

[0087] The following equivalent transformation can be achieved:

[0088]

[0089] For there is

[0090] Further combined with Equation (37), there is:

[0091]

[0092] Let For each i, there is:

[0093]

[0094] Combined with Equation (40), we get: Wherein, is the solution of Equation (41);

[0095] When Equation (37) is transformed into:

[0096]

[0097] Similarly, when there is:

[0098]

[0099] Thus far, the fuzzy power distribution network dynamic safety space is defuzzified.

[0100] Combined with the first aspect, in some implementation manners of the first aspect, the method further includes: The aggregation model of the pre-established virtual power plant is as follows:

[0101] [eq(6)-eq(10)] vpp (35)

[0102]

[0103] In the formula, [eq(6)-eq(10)] vpp means changing the subscripts in Formulas (6)-(10) to vpp, which is the resource model of VPP; and represent the active and reactive powers of the resources in the virtual power plant; Ω dsor represents the power distribution network dynamic safety space; P t vpp and represent the active and reactive powers of the virtual power plant.

[0104] All constraint conditions in the aggregation model of the virtual power plant are linear constraint conditions. The faces of the polyhedron in the high-dimensional space represented by the aggregation model of the virtual power plant are all planes. By solving the optimization problem, the vertices of the projection of the virtual power plant operation domain are obtained. The optimization problem M2 is:

[0105]

[0106] In the formula, the direction vector μ h is and represent the active and reactive powers in the projection plane.

[0107] The calculation formula for the blocking capacity of the virtual power plant is as follows:

[0108]

[0109] In the formula, ω 1 represents the regulation coefficient, and represent the power operating points without considering the dynamic security space of the distribution network, and represent the projected areas considering and not considering the dynamic security space of the distribution network.

[0110] The day-ahead virtual power plant blocking capacity is comprehensively evaluated by the maximum blocking Υ max and the average blocking Υ ave

[0111]

[0112] In a second aspect, to achieve the above object, the present invention discloses a distribution network dynamic security space solving system for virtual power plant aggregation, including:

[0113] A basic modeling module, configured to obtain the distribution network topology information, calculate based on the distribution network topology information to obtain a hyperplane representing the distribution network security domain; obtain the resource fuzzy boundary uploaded by the virtual power plant, perform optimization decision calculation based on the resource fuzzy boundary uploaded by the virtual power plant, and establish a fuzzy model of the distribution network resources using the optimization decision value;

[0114] A space solving module, configured to input the distribution network resource fuzzy model into the hyperplane representing the distribution network security domain, output to obtain the fuzzy distribution network dynamic security space, and perform type reduction and defuzzification on the fuzzy distribution network dynamic security space using an improved enhanced opposite search algorithm to obtain the distribution network dynamic security space;

[0115] A space analysis module, configured to input the distribution network dynamic security space into a pre-established aggregation model of the virtual power plant, output to obtain the virtual power plant aggregation capacity; combine with the original virtual power plant aggregation capacity to calculate the virtual power plant blocking capacity caused by the distribution network dynamic security space.

[0116] In another aspect of the present invention, to achieve the above object, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The computer program stored in the memory can run on the processor. When the processor loads and executes the computer program, the distribution network dynamic security space solving method for virtual power plant aggregation as described above is adopted.

[0117] ​In another aspect of the present invention, to achieve the above object, a computer-readable storage medium is disclosed. A computer program is stored in the computer-readable storage medium. When the computer program is loaded and executed by a processor, the method and system for solving the dynamic security space of a distribution network for virtual power plant aggregation as described above are adopted.

[0118] Advantages of the present invention:

[0119] The present invention proposes a new coordinated operation framework for virtual power plants and distribution networks. Based on the dynamic security space of the distribution network calculated by the distribution network, the virtual power plant realizes data privacy protection between the virtual power plant and the distribution network with minimal data sharing, reducing the risk of power flow security over-limit in the distribution system;

[0120] The hybrid fuzzy modeling method of the dynamic security space of the distribution network based on the type-I triangular fuzzy membership function and the type-II triangular fuzzy membership function characterizes the safety boundaries of distributed resources within the virtual power plant. The dynamic security space of the distribution network only constrains the output power of distributed resources within the virtual power plant, making the dynamic security space of the distribution network more applicable than the traditional distribution network security domain. In addition, the dynamic security space of the distribution network considers the power flow coupling relationship between geographically dispersed distributed resources. Therefore, compared with the operation envelope, the dynamic security space of the distribution network can more conservatively describe the aggregation ability of the virtual power plant.

[0121] To clarify the impact of distribution operation security risks on the aggregation ability of the virtual power plant, an improved enhanced reverse search algorithm is used for the fuzzy defuzzification of the dynamic security space of the distribution network. In addition, a method for clarifying the piecewise membership function based on the generalized credibility theory is proposed to clarify the dynamic security space of the distribution network, which helps to manage the grid operation risks when the virtual power plant has no direct restrictions on power flow operation constraints.

[0122] The concept of virtual power plant blocking capacity proposed by the present invention quantifies the aggregation capacity reduced by the virtual power plant due to power system power flow constraints. This helps to coordinate the safe and economic operation between the virtual power plant and the distribution network. Brief Description of the Drawings

[0123] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings;

[0124] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0125] Figure 2 It is a schematic diagram of the parameters of the virtual generator;

[0126] Figure 3 Schematic diagram of parameters for virtual energy storage;

[0127] Figure 4 Schematic diagram of parameterization of the blocking capacity of a virtual power plant;

[0128] Figure 5 Schematic diagram of the projection of the operating region of a virtual power plant under different confidence levels;

[0129] Figure 6 Schematic diagram of the projection of the operating region of a virtual power plant under different network constraints;

[0130] Figure 7 Schematic diagram of the blocking capacity of a virtual power plant under different model parameters;

[0131] Figure 8 Schematic diagram of the system structure of the present invention. Specific implementation manners

[0132] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0133] Embodiment 1:

[0134] As shown in Figure 1 , a method and system for solving the dynamic security space of a distribution network for virtual power plant aggregation, the method comprising the following steps:

[0135] S101: Obtain the network topology information of the distribution network, calculate based on the network topology information of the distribution network to obtain a hyperplane representing the security domain of the distribution network; obtain the fuzzy boundary of the resources uploaded by the virtual power plant, perform optimization decision calculation based on the fuzzy boundary of the resources uploaded by the virtual power plant, and establish a fuzzy model of the distribution network resources using the optimization decision value;

[0136] The security domain of the distribution network describes the power flow security constraints from the perspective of the domain. The power flow security constraints include the voltage constraint limits of each node and the current constraint limits of each branch. The security domain of the distribution network with the node injection power as the decision space can be expressed by the following formula:

[0137]

[0138] In the formula: represents the voltage security domain of node j, and represent the hyperplane coefficients of node h in the voltage upper limit security domain of node j; and Denote the hyperplane coefficient of node h in the safety region of the upper voltage limit of node j, P h and Q h Denote the active and reactive power injected by node h; Denote the thermal stability safety region of line i, and Denote the hyperplane coefficient of node h in the thermal stability safety region of line i; Ω DSR Denote the hyperplane safety region.

[0139] Since the hyperplane coefficients such as γ and χ are the analytical results based on some assumptions (such as ignoring network losses, etc.), there is a deviation between them and the true safety boundary. In this paper, the relevant hyperplane coefficients are corrected based on the actual power flow operation results of DistFlow. The compact form of the safety region of the distribution network is given as:

[0140]

[0141] In the formula, γ and χ denote the compact form of the hyperplane coefficient, P j and Q j Denote the compact form of the power injected by the node.

[0142] The dynamic safety space of the distribution network can be understood as a hyperplane space obtained by the evolution and dimensionality reduction of the safety region of the distribution network. Its most significant feature is that only N vpp (Virtual power plant control node) is used as the decision variable. Therefore, in the process of evolving and reducing the dimensionality of the safety region of the distribution network, the main difficulty lies in real-numbering all variables except N vpp In the distribution network, different grid operation modes result in different power flow distributions. In order to maximize the regulation ability of all nodes in the grid, this paper takes the optimal power flow under a specific operation mode of the distribution network as the starting point and conducts real-numbering modeling for relevant nodes.

[0143] The set optimization objectives of the distribution network include the lowest global network loss and the smallest voltage deviation of the distribution network. The multi-objective optimization problem M1 is set as follows, where the objective function is:

[0144]

[0145] S n = F(P, Q, U, I) (13)

[0146] In the formula, λ denotes the multi-objective coefficient, Denotes the network loss of line l at time t, Denotes the voltage deviation of node i at time t; and Denote the active and reactive power of the virtual generator in N dso And the reactive power, and represent the active upper and lower limit values of the virtual generator in N dso ; and represent the upper and lower limit values of the power factor angle of the virtual generator in N dso ; and represent the active and reactive powers of the virtual energy storage in N dso ; represent the active power limit of the virtual energy storage in N dso ; and represent the energy limit of the virtual energy storage in N dso ; represent the energy of the virtual energy storage in N dso ; represent the apparent power limit of the virtual energy storage in N dso ; and represent the fuzzy output of each node provided by the virtual power plant, N vpp ; and represent the triangular fuzzy membership function of the output upper and lower limits, where a, b, and c represent the parameters of the triangular fuzzy membership function. and represent the active and reactive powers of N nor . S n represents the power flow constraint.

[0147] Among them, (6)-(7) represent the output constraint of the virtual generator in the distribution network operator's control node N dso ; (8)-(9) represent the output constraint of the virtual energy storage in N dso ; (11) represents the fuzzy output boundary constraint of each node provided by the virtual power plant, N vpp ; (12) represents the output constraint of N nor ; (13) represents the DistFlow power flow constraint. The optimization problem includes random variables and fuzzy variables and

[0148] S102: Input the distribution network resource fuzzy model into the hyperplane to represent the distribution network safety domain, and output the fuzzy distribution network dynamic safety space. Use the improved enhanced opposite search algorithm to reduce the type and defuzzify the fuzzy distribution network dynamic safety space to obtain the distribution network dynamic safety space;

[0149] Perform optimization decision calculation based on the fuzzy boundary of the resources uploaded by the virtual power plant:

[0150] The optimization problem M1 is solved according to a specific operation mode to obtain N dso of the operation result. A fuzzy model is used to model the resource output of the N dso nodes;

[0151] For the N dso decision value obtained by solving the optimization problem M1, as the maximum possible operation result under a specific distribution network operation state, separate fuzzy modeling is carried out for the active power and reactive power in the N dso nodes respectively:

[0152] First, the I-type triangular fuzzy membership function is used to model the active power of the virtual generator in:

[0153]

[0154] In the formula, and represent the three parameters of the triangular fuzzy membership function.

[0155] Combined with the geometric relationship in formula (6) and Figure 1 in, the parameters of can be successively expressed as It can be seen from formula (7) that the reactive power range of the virtual generator is directly related to the active power of the virtual generator; since the active power of the virtual generator is characterized by the I-type triangular fuzzy membership function, the II-type triangular fuzzy membership function is used to perform fuzzy modeling on the reactive power of the virtual generator, and the reactive power value range characterized by formula (7) and the fuzzy modeling of the active power of the virtual generator characterized by formula (14) are integrated;

[0156] to obtain the main membership function of the II-type triangular fuzzy membership function of the reactive power of the virtual generator in the form of, and after substituting the parameters, there is:

[0157]

[0158] In the formula, represents the reactive power output value based on the optimization decision.

[0159] Due to the parameter size relationship of the II-type triangular fuzzy membership function, the corrected parameters are given as:

[0160] and

[0161] Its corresponding upper membership function and lower membership function are respectively:

[0162]

[0163] The corresponding left endpoint l is a type-I triangular fuzzy membership function with parameters as follows:

[0164]

[0165] Similarly, the corresponding right endpoint r also has similar parameter characteristics, which are:

[0166]

[0167] Through the setting rules of the fuzzy system, a type-II triangular fuzzy membership function of the reactive power of the virtual generator is obtained;

[0168] For As can be seen from Equation (10), Different from When modeling , by first establishing a type-I triangular fuzzy membership function for , and then establishing a type-II triangular fuzzy membership function for , a fuzzy model of the virtual energy storage is established by a method with the completely opposite order. Since Is directly determined by , and in order to unify the modeling format with , Is determined as the basic model of the type-I triangular fuzzy membership function;

[0169] The parameter values of

[0170] Adopt the same method as the virtual generator to perform fuzzy modeling on , and the type-II triangular fuzzy membership function is Substitute Equation (10) and N dso Decision Into the main membership function as:

[0171]

[0172] The corresponding upper membership function And the lower membership function Are respectively:

[0173]

[0174] The membership functions of l and r are respectively:

[0175]

[0176] Wherein, Obtain N dso Complete fuzzy modeling, combined with N nor Modeling; the fuzzy form of the dynamic security space of the distribution network can be obtained as:

[0177]

[0178] Equation (22) containing fuzzy and random factors can be expressed in the form of a credibility opportunity constraint, and Equation (22) is expressed as:

[0179]

[0180] In the formula, Ch represents the opportunity measure, and α is the credibility level.

[0181] For N dso And N nor For the random factors in, de-randomization is carried out by means of probability confidence, and Equation (23) is equivalent to:

[0182]

[0183] In the formula, Cr represents the credibility measure, Pr represents the probability measure, and α * Represents the confidence level.

[0184] Is transformed into:

[0185]

[0186] In the formula, Represents the inverse function of the cumulative distribution function.

[0187] By the deterministic transformation of the random variables representing the output and parameters inside the model, a fuzzy opportunity constraint model of the distribution network dynamic security space containing type-I triangular fuzzy membership function and type-II triangular fuzzy membership function will be given.

[0188] By the deterministic transformation of the random variables representing the output and parameters inside the model, a fuzzy opportunity constraint model of the distribution network dynamic security space containing type-I triangular fuzzy membership function and type-II triangular fuzzy membership function will be given. Although the distribution network dynamic security space has been represented by Equation (25), it cannot be directly applied to the calculation of the aggregation regulation ability of the virtual power plant. This is caused by its inclusion of the type-II triangular fuzzy membership function, which makes it impossible to directly clarify and equivalent the opportunity constraint containing Q. The fuzzy model of Q needs to be reduced in type. The credibility opportunity constraint is transformed into a clear equivalent form under a specific confidence level through model reduction, so that the distribution network dynamic security space given by the distribution network operator to the virtual power plant is directly available.

[0189] Based on the centroid defuzzification of α - cut, this paper uses an advanced improved enhanced opposite - direction search algorithm to defuzzify the type - II triangular fuzzy membership function. Further, combined with the theory of generalized credibility measure, an equivalent method for chance - constrained dynamic security space of distribution network based on piece - wise membership function representation is proposed.

[0190] For the type - II triangular fuzzy membership function in the dynamic security space of the distribution network, the left - hand endpoint of the centroid set on its α - plane can be defined. Then there must exist a sampling point on the α - plane at l α ∈{k|1≤k≤N} such that Further combining equations (16) and (20), the following equation can be obtained:

[0191]

[0192] Set the function as:

[0193]

[0194] The upper membership function and the lower membership function The longitudinal difference on the α - plane is defined as At the same time, define:

[0195]

[0196] Equation (27) can be expressed as

[0197]

[0198] Define The iterative formula can be obtained

[0199]

[0200] The termination condition is to obtain the conversion point. From the critical size relationship of the conversion point, the termination condition should be satisfied

[0201]

[0202] At this time, k = l α , and the left - hand endpoint can be obtained as:

[0203]

[0204] Using the same idea, when k = r α The right - hand endpoint can be expressed as:

[0205]

[0206] Due to the and unique correspondence with q i of the type-II triangular fuzzy membership function, it can be known that when α: 0 → 1, Based on this characteristic of the type-II triangular fuzzy membership function, the improved enhanced opposite-direction search algorithm can be used to realize the transformation of the type-II triangular fuzzy membership function into the type-I triangular fuzzy membership function.

[0207] After transforming the type-II triangular fuzzy membership function of the distribution network dynamic security space into the type-I triangular fuzzy membership function through the improved enhanced opposite-direction search algorithm, the unified form of all power fuzzy membership functions of the virtual generator and virtual energy storage can be given as follows:

[0208]

[0209] L L,R Denotes the piecewise fuzzy membership function composed of and , and respectively denote the piecewise fuzzy membership functions composed of the left and right endpoints and the corresponding α under each α. The fuzzy membership function of each segment can be abbreviated as L(C(α)). Further, the distribution network dynamic security space characterized by equation (23) is defuzzified equivalently.

[0210] Separate N dso in the distribution network dynamic security space, and equation (23) can be transformed into:

[0211]

[0212] In the formula, and denote the compact form of N dso control resources, and denote the compact form of N nor control resources; and denote the compact form of N vpp control resources; denotes the credibility.

[0213] Characterize the least common multiple interval of the multi-segment linear membership function in equation (34) as Within this interval, the credibility opportunity constraint can be expressed as:

[0214]

[0215] Where denotes Ndso The node power, represents the hyperplane coefficient γ (or χ) corresponding to node j (or line ij), and further, with a confidence level as an example, a piecewise clarity equivalence method based on the generalized credibility theory is proposed. Equation (37) can be transformed into:

[0216]

[0217] So the following equivalent transformation can be achieved:

[0218]

[0219] For there is

[0220] Further combined with Equation (37), we have:

[0221]

[0222] Let For each i, there is:

[0223]

[0224] Combined with Equation (40), we can get: where is the solution of Equation (41).

[0225] When Equation (37) can be transformed into:

[0226]

[0227] Similarly, when there is:

[0228]

[0229] So far, the clear characterization of the distribution network dynamic security space based on the credibility measure has been realized. This series of transformation operations enables the virtual power plant to directly apply the distribution network dynamic security space to calculate the aggregation regulation ability.

[0230] S103: Input the distribution network dynamic security space into the pre-established aggregation model of the virtual power plant, and output the blocking capacity of the virtual power plant as the solution result of the distribution network dynamic security space.

[0231] Since the dynamic security space of the distribution network evolves from the security domain of the distribution network, the number of its constraint conditions is consistent with the number of network nodes. The above work has achieved the reduction of the number of distribution network nodes. In order to more thoroughly conceal the network topology information, the redundant constraints of the dynamic security space of the distribution network can be identified and deleted through the umbrella-shaped constraint identification method. The dynamic security space of the distribution network can achieve security guarantee within the scope of resources covered by the virtual power plant. Next, the aggregation ability of the virtual power plant is calculated based on the dynamic security space of the distribution network.

[0232] The virtual power plant also includes virtual generators and virtual energy storage. The network topology and power flow constraints use the dynamic security space model of the distribution network based on the credibility level established in this paper. The virtual generator and virtual energy storage models are the same as N dso . The virtual power plant aggregation model can be expressed as:

[0233] [eq(6)-eq(10)] vpp (74)

[0234]

[0235] In the formula, [eq(6)-eq(10)] vpp means changing the subscripts in formulas (6)-(10) to vpp, which is the resource model of VPP; and represent the active and reactive powers of the resources in the virtual power plant; Ω dsor represents the dynamic security space of the distribution network; P t vpp and represent the active and reactive powers of the virtual power plant.

[0236] Since the dynamic security space of the distribution network is represented by a hyperplane, it can be known that all constraint conditions in the virtual power plant model are linear constraint conditions. The faces of the polyhedron in the high-dimensional space represented by the virtual power plant model are all planes. Therefore, the projection of the virtual power plant operation domain is a polygon with a finite number of sides. The basic idea of the vertex enumeration method is to solve the vertices of the projected polygon point by point through a series of optimization problems with different direction functions, and then approximately give the projection of the feasible region polygon by solving the convex hull. Solving the vertices of the projection of the virtual power plant operation domain through the optimization problem, the optimization problem M2 is:

[0237]

[0238] In the formula, the direction vector μ h is and represent the active and reactive powers in the projection plane.

[0239] Due to the intervention of the dynamic security space of the distribution network, there is a high possibility of affecting the aggregation regulation ability boundary of the virtual power plant. In the P-Q two-dimensional plane, it is mainly manifested as the reduction of the aggregated active power and reactive power of the virtual power plant. At the same time, the area of the projection of the virtual power plant operation domain will also be reduced. This paper proposes the concept of virtual power plant blocking capacity to quantify the impact of the distribution network dynamic security space on the aggregation regulation ability of the virtual power plant.

[0240] Figure 3 Shows the blocking schematic diagram under a certain direction vector μ l Among them, the blue is the projection of the original virtual power plant operation domain, and the purple is the projection of the virtual power plant operation domain considering the distribution network dynamic security space. This paper first quantifies the relative reduction of active power and reactive power after adding the distribution network dynamic security space under different power factors, and further quantifies the relative reduction of the area after adding the distribution network dynamic security space. The calculation formula for the virtual power plant blocking capacity is given as:

[0241]

[0242] In the formula, ω 1 represents the regulation coefficient, and represent the power operation points without considering the distribution network dynamic security space, and represent the projected areas considering and not considering the distribution network dynamic security space.

[0243] The day-ahead virtual power plant blocking capacity is comprehensively evaluated by the maximum blocking Υ max and the average blocking Υ ave

[0244]

[0245] Example 2: To verify the effectiveness of the method and system proposed in this patent, the following actual verification case is set as follows: The internal topological structure of the virtual power plant case is selected as the IEEE-33 node system, the current limit is selected as 0.6 kA, the upper and lower limits of the voltage are 12.66 × 1.05 kV and 12.66 × 0.95 kV respectively, the confidence level of the random variable of the optimization problem M1 is selected as 0.95, and the credibility of the fuzzy model is selected as 0.85. When performing the downscaling of the TT2FS of the virtual generator and virtual energy storage, the α-cut is selected as N α = 1000. When aggregating and solving the virtual power plant, the number of direction vectors is selected as 100 × 2π / 8 ≈ 79. The following control groups are set:

[0246] C1: Calculate the projection of the virtual power plant feasible region through the method and system proposed in this patent;

[0247] C2: The complete power flow network constraint limit of the distribution network for the virtual power plant;

[0248] C3-1: The operating envelope considering the decoupling of all nodes in the distribution network; C3-2: Only decouple the power relationship between aggregated distributed resources and calculate their operating envelopes;

[0249] C4: Completely disregard the power flow network constraint limit;

[0250] C5: Change the operating mode of the distribution system by changing the objective function (5) and the grid switch combination. C5-1 to C5-3 only change the grid topology by changing the connection status of the distribution network tie lines and the switches in the basic network topology; C5-4 and C5-5 only change the objective function.

[0251] As Figure 6 shown, as the confidence level increases, it is manifested as the tightening of the limit of the distribution network dynamic security space, which further leads to a smaller aggregation capacity of the virtual power plant. In other words, the high reliability of the distribution system means an improvement in security, which needs to be achieved by reducing the aggregation capacity of the virtual power plant. On the contrary, as the confidence level decreases, the limit of the distribution network dynamic security space becomes looser. The projection of the virtual power plant operation domain restricted by the distribution network dynamic security space gradually becomes larger. A higher virtual power plant aggregation capacity means a higher distribution system security risk. This means that the boundary of the virtual power plant aggregation capacity becomes more radical. The confidence level reflects the current operating risk of the distribution system. This risk mainly comes from the output power uncertainty of all distributed resources in the distribution system. The distribution network needs to control the grid security risk through the confidence level.

[0252] As Figure 7 can be seen, as the confidence level increases, the boundary of the virtual power plant operation domain projection becomes smaller and smaller, and at the same time, the overall position of the virtual power plant operation domain projection also changes. This is because as the confidence level increases, the operating envelopes of the distributed resources in the virtual power plant become smaller and smaller, and there is a situation where the adjustable boundary of the virtual generator in N vpp has no intersection with the operating envelope. When the confidence level reaches 0.7, the virtual generator in N vpp has a very small or even zero power output in order to meet the operating envelope. At this time, the virtual energy storage mainly provides power support within the projection of the virtual power plant operation domain.

[0253] As Figure 8It can be seen that by changing the operation mode of the distribution network, it has a significant impact on the projection of the operation domain of the virtual power plant. This also reflects that the operation modes of some nodes in the distribution system have a huge impact on other nodes. The operation envelope that realizes power decoupling between nodes sacrifices the power adjustable ability of distributed resources and has strong conservatism. Different grid switch combinations and distribution network operation strategies will affect the dynamic security space of the distribution network and the projection of the operation domain of the virtual power plant. Therefore, the virtual power plant blocking capacity can be used for the benefit coordination between the virtual power plant and the distribution network. The distribution network can calculate the dynamic security space of the distribution network under various feasible operation modes for the day-ahead, and provide all the dynamic security spaces of the distribution network to the virtual power plant. The virtual power plant calculates the projection of the operation domain of the virtual power plant under all the dynamic security spaces of the distribution network, and selects the most reasonable dynamic security space of the distribution network by balancing the adjustment cost of the virtual power plant blocking capacity and the expected profit to achieve the maximum profit. The distribution network restricts the aggregation of the virtual power plant within the safety boundary through the dynamic security space of the distribution network to ensure the safety of the distribution system. This is a win-win situation for the virtual power plant and the distribution network.

[0254] Embodiment 3: Second aspect, as Figure 8 shown, to achieve the above object, the present invention discloses a distribution network dynamic security space solving system for virtual power plant aggregation, including:

[0255] A basic modeling module 11, configured to obtain the distribution network topology information, calculate based on the distribution network topology information to obtain a hyperplane representing the distribution network security domain; obtain the resource fuzzy boundary uploaded by the virtual power plant, perform optimization decision calculation based on the resource fuzzy boundary uploaded by the virtual power plant, and establish a fuzzy model of the distribution network resources using the optimization decision value;

[0256] A space solving module 12, configured to input the distribution network resource fuzzy model into the hyperplane representing the distribution network security domain, output to obtain a fuzzy distribution network dynamic security space, and perform type reduction and defuzzification on the fuzzy distribution network dynamic security space using an improved enhanced opposite search algorithm to obtain the distribution network dynamic security space;

[0257] A space analysis module 13, configured to input the distribution network dynamic security space into the pre-established aggregation model of the virtual power plant, output to obtain the virtual power plant aggregation capacity; combine the original virtual power plant aggregation capacity to calculate the virtual power plant blocking capacity caused by the distribution network dynamic security space.

[0258] Based on the same inventive concept, the present invention further provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions. Specifically, it is used to load and execute one or more instructions in the computer storage medium to implement the above method.

[0259] It should be further noted that, based on the same inventive concept, the present invention further provides a computer storage medium, on which a computer program is stored, and the computer program, when run by a processor, executes the above method. The storage medium may be any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may, for example, but not be limited to, be an electrical, magnetic, optical, electrical, magnetic, 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 having 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 the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in combination with an instruction execution system, apparatus, or device.

[0260] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0261] The foregoing has shown and described the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure will have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure claimed.

Claims

1. A method for solving the dynamic security space of a distribution network for virtual power plant aggregation, characterized in that: The method comprises the following steps: Obtain the network topology information of the distribution network, and calculate the hyperplane to represent the security domain of the distribution network based on the network topology information of the distribution network; obtain the fuzzy boundary of the resources uploaded by the virtual power plant, perform optimization decision calculation based on the fuzzy boundary of the resources uploaded by the virtual power plant, and use the optimized decision value to generate a fuzzy model of the distribution network resources; The fuzzy model of distribution network resources is input into the hyperplane representation distribution network security domain, and the fuzzy distribution network dynamic security space is output. The fuzzy distribution network dynamic security space is reduced and defuzzified using the improved enhanced opposite search algorithm to obtain the distribution network dynamic security space. The dynamic security space of the distribution network is input into the aggregation model of the pre-established virtual power plant, and the aggregate capacity of the virtual power plant is output; Combined with the original virtual power plant aggregation capacity, the virtual power plant blocking capacity caused by the dynamic safety space of the distribution network is calculated.

2. The method for solving the dynamic security space of distribution network for virtual power plant aggregation according to claim 1 is characterized in that: The distribution network security domain describes the power flow safety constraints from the perspective of the domain. The power flow safety constraints include the voltage constraint limit of each node and the current constraint limit of each branch. The distribution network security domain with node injection power as the decision space can be expressed as follows: Where: represents the voltage safety domain of node j, and Represents the hyperplane coefficient of node h in the safety region of the upper voltage limit of node j; and represents the hyperplane coefficient of node h in the safety region of the upper voltage limit of node j, P h With Q h represents the active and reactive power injected into node h; represents the thermal stability safety region of line i, and represents the hyperplane coefficient of node h in the thermal stability safety region of line i; Ω DSR represents the hyperplane safety domain; Based on the actual power flow operation results of DistFlow, the relevant hyperplane coefficients are corrected, and the compact form of the distribution network security region is given as: Where γ and χ represent the compact form of the hyperplane coefficients, P j and Q j A compact form of expressing the injected power at a node.

3. The method for solving the dynamic security space of distribution network for virtual power plant aggregation according to claim 2 is characterized in that: The set optimization objectives of the distribution network include the lowest global network loss and the smallest voltage deviation of the distribution network. The multi-objective optimization problem M1 is set as follows, where the objective function is: S n =F(P,Q,U,I) (13) In the formula, λ represents the multi-objective coefficient, represents the network loss of line l at time t, represents the voltage deviation of node i at time t; and N dso The active and reactive power of the virtual generator, and N dso The upper and lower limits of the active power of the virtual generator, and N dso The upper and lower limits of the power factor angle of the virtual generator; and N dso The active and reactive power of virtual energy storage, N dso Active power limitation of virtual energy storage, and N dso Energy limit of virtual energy storage in N dso The energy of virtual energy storage, N dso The apparent power limit of the virtual energy storage in and N represents the number of nodes provided by the virtual power plant vpp Blurred output, and The triangular fuzzy membership function represents the upper and lower limits of the output, and a, b and c represent the parameters of the triangular fuzzy membership function; and N nor Active and reactive power, S n represents the power flow constraint; Among them, (6)-(7) represent the control node N of the distribution network operator dso The output constraints of the virtual generators in (8)-(9) represent N dso The output constraint of the virtual energy storage in (11) represents the N of each node provided by the virtual power plant. vpp Fuzzy output boundary constraint, (12) represents N nor Output constraint, (13) represents DistFlow power flow constraint, and the optimization problem contains random variables and Fuzzy variables and 4. The method for solving the dynamic security space of distribution network for virtual power plant aggregation according to claim 1 is characterized in that: The optimization decision calculation is performed based on the fuzzy boundary of resources uploaded by the virtual power plant: The optimization problem M1 is solved according to a specific operation mode to obtain N dso The running results of N dso Modeling the resource output of nodes; For the optimization problem M1, we get N dso Decision Value As the maximum possible operation result under the specific distribution network operation state, for N dso The active power and reactive power in the node are modeled separately by fuzzy: Firstly, the I-type triangular fuzzy membership function is used to center the active power of the virtual generator. To model: In the formula, and Represent the three parameters of the triangular fuzzy membership function; N dso Active power of virtual generator The parameters can be expressed as It can be seen from formula (7) that the reactive power range of the virtual generator is directly related to the active power of the virtual generator; since the active power of the virtual generator is represented by a type I triangular fuzzy membership function, the type II triangular fuzzy membership function is used to perform fuzzy modeling on the reactive power of the virtual generator, and the fuzzy modeling of the reactive power value range represented by formula (7) and the active power of the virtual generator represented by formula (14) is integrated; The main membership function of the virtual generator reactive power type II triangular fuzzy membership function is obtained. After substituting the parameters, we get: In the formula, Represents the reactive power output value based on the optimization decision; Due to the parameter size relationship of type II triangular fuzzy membership function, the corrected parameters are given as: and Its corresponding upper membership function With the lower membership function They are: The corresponding left endpoint l is the I-type triangular fuzzy membership function with the following parameters: Similarly, the corresponding right endpoint r also has similar parameter characteristics, which is: Through the setting rules of fuzzy system, the type II triangular fuzzy membership function of virtual generator reactive power is obtained; for From formula (10), we can see that and Different, in When modeling, we first Establish I-type triangular fuzzy membership function, and then The method of establishing type II triangular fuzzy membership function is to establish the fuzzy model of virtual energy storage by a completely opposite order method. Depend on Direct decision, and in order to The modeling format is unified. The basic model is determined to be type I triangular fuzzy membership function; The parameter values ​​are The same method as the virtual generator is used to To perform fuzzy modeling, we use equation (10) and N dso decision making Substituting into the main membership function: The corresponding upper membership function With the lower membership function They are: The membership functions of l and r are: In the formula, Get N dso Complete fuzzy modeling, combined with N nor Modeling; the fuzzy form of the dynamic safety space of the distribution network can be obtained as follows: In the formula, and Fuzzy compact form of injecting power into nodes; Formula (22) containing fuzzy and random factors can be expressed in the form of credibility chance constraints. Formula (22) is expressed as: In the formula, Ch represents the chance measure, and α is the credibility; For N dso With N nor The random factors in are de-randomized by means of probability confidence, and formula (23) is equivalent to: In the formula, Cr represents the credibility measure, Pr represents the probability measure, and α * Indicates confidence; Translates to: In the formula, Represents the inverse of the cumulative distribution function; By deterministically transforming the random variables that characterize the output and parameters in the model, a distribution network dynamic security space fuzzy chance constraint model including type I triangular fuzzy membership function and type II triangular fuzzy membership function is given.

5. The method for solving the dynamic security space of distribution network for virtual power plant aggregation according to claim 1 is characterized in that: The process of reducing the dynamic safety space of the fuzzy distribution network using the improved enhanced opposite search algorithm is as follows: For the type II triangular fuzzy membership function in the dynamic safety space of the distribution network, the left endpoint of the centroid set of its α plane can be defined as Then there must be a plane α in l α ∈{k|1≤k≤N} sampling points Make Further combining equations (16) and (20), we obtain the following equation: Setting up the function for: The membership function With the lower membership function The longitudinal difference in the α plane Defined as Also define: Formula (27) is expressed as definition The iterative formula is The termination condition is to obtain the conversion point. From the critical size relationship of the conversion point, it can be obtained that the termination condition is satisfied: At this time k = l α , we can get the left endpoint for: Using the same idea, when k = r α Right endpoint It is expressed as: Since the type II triangular fuzzy membership function and With q i The only corresponding characteristic of is when α:0→1, Based on the characteristics of type II triangular fuzzy membership function, the improved enhanced opposite search algorithm is used to realize the transformation of type II triangular fuzzy membership function into type I triangular fuzzy membership function.

6. The method for solving the dynamic security space of distribution network for virtual power plant aggregation according to claim 5 is characterized in that: The defuzzification process of the fuzzy distribution network dynamic safety space is as follows: After the type II triangular fuzzy membership function of the dynamic safety space of the distribution network is transformed into a type I triangular fuzzy membership function by improving the enhanced opposite search algorithm, the unified form of all power fuzzy membership functions of virtual generators and virtual energy storage is given as follows: L L,R Indicated by and The piecewise fuzzy membership function composed of and Respectively represent the piecewise fuzzy membership function composed of the left and right endpoints and the corresponding α under each α, and the fuzzy membership function of each segment is abbreviated as L(C(α)), which further clarifies the dynamic safety space of the distribution network represented by formula (23); The N in the dynamic safety space of the distribution network dso Separation, formula (23) can be transformed into: In the formula, and N dso A compact form of control resources, and N nor Compact form of control resources; and N vpp A compact form of control resources; Indicates credibility; The minimum common factor interval of the multi-segment linear membership function of formula (34) is represented as In this interval, the credibility opportunity constraint can be expressed as: in N dso The node power, Represents the hyperplane coefficient γ or χ corresponding to node j or line ij, and formula (37) is transformed into: The following equivalent conversions can be achieved: for have Further combined with formula (37), we have: make For each i, we have: Combining formula (40), we get: in, is the solution of equation (41); when Formula (37) is transformed into: Similarly, when have: At this point, the dynamic safety space of the fuzzy distribution network is defuzzified.

7. The method for solving the dynamic security space of distribution network for virtual power plant aggregation according to claim 1 is characterized in that: The aggregation model of the pre-built virtual power plant is as follows: [eq(6)-eq(10)] vpp (35) In the formula, [eq(6)-eq(10)] vpp It means that changing the subscript in formula (6)-(10) to vpp is the resource model of VPP; and Represents the active and reactive power of resources in the virtual power plant; Ω dsor represents the dynamic safety space of the distribution network; P t vpp and Indicates the active and reactive power of the virtual power plant; All constraints in the aggregation model of the virtual power plant are linear constraints. The faces of the high-dimensional space polyhedron represented by the aggregation model of the virtual power plant are all planes. The vertices of the projection of the virtual power plant operation domain are solved by optimization problems. The optimization problem M2 is: In the formula, the direction vector μ h for and Indicates the active and reactive power within the projection surface; Virtual power plant blocking capacity t The calculation formula is: In the formula, ω1 represents the adjustment coefficient, and represents the power operation point without considering the dynamic safety space of the distribution network. and It represents the projected area with or without considering the dynamic safety space of the distribution network; By the maximum blocking max With average blocking ave Comprehensively evaluate the blocking capacity of virtual power plants on the day before; 8. A distribution network dynamic security space solution system for virtual power plant aggregation, characterized in that: include: A basic modeling module is used to obtain the network topology information of the distribution network, and calculate the hyperplane based on the network topology information to represent the safety domain of the distribution network; Obtain the fuzzy boundary of resources uploaded by the virtual power plant, perform optimization decision calculation based on the fuzzy boundary of resources uploaded by the virtual power plant, and use the optimized decision value to establish a fuzzy model of distribution network resources; The space solving module is used to input the fuzzy model of distribution network resources into the distribution network security domain represented by the hyperplane, and output the fuzzy distribution network dynamic security space. The fuzzy distribution network dynamic security space is reduced and defuzzified using the improved enhanced opposite search algorithm to obtain the distribution network dynamic security space; The spatial analysis module is used to input the dynamic safety space of the distribution network into the pre-established aggregation model of the virtual power plant, and output the aggregate capacity of the virtual power plant; Combined with the original virtual power plant aggregation capacity, the virtual power plant blocking capacity caused by the dynamic safety space of the distribution network is calculated.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: The memory stores a computer program that can be run on the processor. When the processor loads and executes the computer program, the method for solving the dynamic safety space of the distribution network for virtual power plant aggregation described in any one of claims 1 to 7 is adopted.

10. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by the processor, the method for solving the dynamic safety space of the distribution network for virtual power plant aggregation described in any one of claims 1 to 7 is adopted.

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