Flexible resource partitioning method and system based on power flow distribution

Abstract

Embodiments of the present application provide a flexible resource partitioning method and system based on power flow distribution. The method comprises: using a flexible resource aggregation model partitioning method to perform topological aggregation on flexible resources to obtain aggregation units; using the aggregation units as the basic unit for aggregating the adjustable capacity of the flexible resources, using an adaptive robust optimization algorithm to construct an aggregated resource adjustable capacity calculation model considering network constraints, and performing aggregation calculation on a power adjustment range that can be provided by all the flexible resources within an aggregation area for power dispatch; and solving the aggregated resource adjustable capacity calculation model considering the network constraints, so that feasible upper and lower power bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed among the flexible resources, thereby implementing flexible resource partition aggregation in continuous time periods.

Description

A flexible resource partitioning method and system based on power flow distribution

[0001] Cross-reference to Related Applications

[0002] The present application is based on the Chinese patent application No. 202411279038.7, filed on September 12, 2024, entitled "A flexible resource partitioning method and system based on power flow distribution", and claims priority to the Chinese patent application No. 202411279038.7, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present application relates to, but is not limited to, the field of power system automation technology, and in particular to a flexible resource partitioning method and system based on power flow distribution. BACKGROUND

[0004] Under the "double carbon" target, the proportion of new energy installed capacity has increased significantly, and the traditional power supply side regulation capacity mainly based on conventional units is increasingly scarce. The potential of flexible resources represented by electric vehicles, distributed energy storage and distributed photovoltaic in participating in grid regulation is huge, but a large number of dispersedly connected flexible resources have not yet formed available regulation capacity, making it difficult to effectively participate in system regulation.

[0005] Currently, flexible resource aggregation is mostly carried out in the unit of power grid, which cannot meet the fast and accurate matching of multi-level, multi-scenario and multi-border power dispatching requirements. SUMMARY

[0006] The technical problem to be solved by the present application is to provide a flexible resource partitioning method and system based on power flow distribution, which aggregates flexible resources based on topology, to solve the technical problem that the traditional flexible resources are aggregated by region in the main grid level, without considering network topology and safety constraints, and that the traditional aggregation method cannot meet the peak shaving and equipment overload disposal requirements within the dispatching section of large power grids in the case of wide-area access of large-scale flexible resources.

[0007] The present application adopts the following technical solutions:

[0008] A flexible resource partitioning method based on power flow distribution, comprising the following steps:

[0009] A flexible resource aggregation model partitioning method is used to aggregate flexible resources based on topology to obtain an aggregated unit;

[0010] The aggregated unit is used as the basis for aggregating the adjustable capacity of flexible resources, and an adaptive robust optimization algorithm is used to construct an aggregated resource adjustable capacity calculation model considering network constraints to aggregate and calculate the power regulation range provided by all flexible resources in the aggregated area for power dispatching.

[0011] Solving the aggregated resource adjustable capacity calculation model considering network constraints, so that the power upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, realizing flexible resource partitioning aggregation in continuous time periods.

[0012] In some embodiments, the flexible resource is topologically aggregated by using the flexible resource aggregation model division method, which specifically comprises:

[0013] Building a bus regulation model;

[0014] Taking the generator-like flexible resource and the energy storage-like flexible resource starting from the 10kV voltage level as the aggregation objects, searching for the power supply path based on the network topology, realizing the partitioning aggregation of the flexible resource from the physical connection level, and forming an aggregation unit;

[0015] Aggregating the adjustable capacity of the flexible resource in units of the aggregation unit. If the aggregation unit is equivalent to a generator-like flexible resource, the aggregation unit is a generator-like aggregation unit. If the aggregation unit is equivalent to an energy storage-like flexible resource, the aggregation unit is an energy storage-like aggregation unit. If the aggregation unit contains both types of flexible resources, the aggregation unit is a mixed-type aggregation unit.

[0016] The network supplying power to the flexible resource in the aggregation unit is processed by the Ward static equivalence method to construct a flexible resource aggregation scheduling model.

[0017] In some embodiments, the flexible resource aggregation model division method comprises the following steps:

[0018] S1031, inputting the 10kV bus where the flexible resource is located;

[0019] S1032, determining the computing node where the 10kV bus is located;

[0020] S1033, judging whether the voltage level required for partitioning aggregation is reached. If not, step S1034 is executed, and if yes, step S1037 is executed.

[0021] S1034, if not reached, searching for the transformer winding connected to the corresponding bus according to the power flow direction, judging whether the transformer where the winding is located has a higher voltage level, and if yes, finding the winding of the higher voltage level, otherwise, going to step S1035;

[0022] S1035, judging whether the winding is connected to the line supplying power thereto. If yes, step S1036 is executed, and if no, step S1038 is executed.

[0023] S1036. Determine its power supply line and locate the busbar that supplies power to it, and execute step S1032.

[0024] S1037. Determine whether the substation in question has a power supply relationship with other substations, i.e., whether there is a reverse power flow. If yes, the substations that supply power to each other are the same aggregation unit as this substation. Otherwise, this substation is an aggregation unit. Proceed to step S1038.

[0025] S1038. Record all transformer windings and lines passed through during the process, then end.

[0026] In some embodiments, the flexible resource aggregation scheduling model is as follows:

[0027] in, S represents the equivalent injected power of external network nodes at boundary nodes. B Y represents the injected power at the equivalent front boundary node. BE Let represent the mutual admittance matrix from node B to node E. S represents the inverse of the self-admittance matrix of node E. E This represents the injected power of the external network node before the equivalent value.

[0028] In some embodiments, the power of the generator-like aggregation unit at the equivalent boundary node is:

[0029] Power of the energy storage aggregation unit at the boundary node after equivalent conversion:

[0030] Equivalent standard load power at boundary nodes:

[0031] in, The generator-like power column vector for the equivalent front boundary nodes; This is a column vector of generator-like power for external network nodes before the equivalent value is achieved; This is the energy storage power column vector of the equivalent front boundary nodes; This is the energy storage power column vector of the external network nodes before the equivalent value; This is the column vector of the conventional load power of the equivalent front boundary nodes; This is the column vector of the conventional load power of the external network nodes before the equivalent value.

[0032] In some embodiments, the calculation model for the aggregated resource adjustability considering network constraints is specifically as follows: E G x(ξ G )≤g G E B x(ξ B)≤g B

[0033] wherein P G,flex , P B,flex , is a constant vector composed of P t G,flex , P t B,flex , is a constant vector composed of P G , P B , is a constant vector composed of P G , B is a vector composed of other decision variables except P t G,flex and P t B,flex ; g G , g B , E G , E B are system parameter matrices; represents the Hadamard product of the corresponding elements of the matrices.

[0034] In some embodiments, the constraint conditions of the aggregated resource adjustable capacity calculation model are as follows:

[0035] wherein N, G, D, L, T, B, G', B' represent the set of grid nodes, the set of units, the set of conventional loads, the set of branches, the set of times, the set of independent energy storages, the set of generator-like flexible resources, and the set of energy storage-like flexible resources, respectively; p g,i,t represents the active power of the unit g of the node i∈N; b,j,t represents the active power of the energy storage b of the node j∈N; represents the active power of the generator-like flexible resource g' of the node j∈N; represents the active power of the energy storage-like flexible resource b' of the node j∈N; p loss,t represents the system network loss; d,i,t represents the active power of the load d of the node i∈N; ij,t represents the active power flow of the branch (i, j); represents the upper limit of the line flow.

[0036] In some embodiments, the aggregated resource adjustable capacity calculation model considering network constraints is solved, so that the power feasible upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, specifically:

[0037] At the kth iteration, the main problem is solved, and the main problem will add a variable vector and related constraints every time the iteration is performed, and the optimization result P of the main problem is obtained by solving * G,flex , P * B,flex , and is passed to the sub-problem, and the sub-problem is solved based on the main problem solving result, and the scene ξ obtained by optimizing the sub-problem after the kth main problem solving result P * G,flex , P * B,flex , is given, the scene ξ obtained by optimizing the sub-problem * as the known parameter of the k+1th iteration of the main problem, if there is a vector ξ such that the sub-problem has no feasible solution, the objective function value of the sub-problem is -∞, otherwise the objective function value is 0; by iteratively solving the main problem and the sub-problem, the error is gradually reduced until the convergence condition is met, and finally the flexible resource aggregation optimization result is obtained.

[0038] In some embodiments, the main problem is represented as: E G x(ξ G )≤g G E B x(ξ B )≤g B

[0039] The sub-problem is represented as:

[0040] wherein, is the worst scenario vector obtained by solving the k-1th sub-problem; and are the variable vectors added by the main problem in the kth iteration, N, G, D, L, T, B, G', B' represent the set of grid nodes, the set of units, the set of regular loads, the set of branches, the set of time, the set of independent energy storage, the set of generator-like flexible resources, and the set of energy storage-like flexible resources; p g,i,t represents the active power of the unit g of the node i∈N; if the node i has no unit, then p g,i,t =0; p b,j,t represents the active power of the energy storage b of the node j∈N; if the node j has no energy storage, then p b,j,t =0; represents the active power of the generator-like flexible resource g' of the node j∈N; denotes the active power of the class energy storage flexible resource b' of the node j∈N; P loss,t denotes the system network loss; p d,i,t denotes the active power of the load d of the node i∈N; p ij,t denotes the active power flow of the branch (i,j) respectively; denotes the line flow upper limit; f M is the main problem objective function; is the feasible upper bound of the output of the class generator aggregation unit after the flexible resource partition is aggregated; P G,flex is the feasible lower bound of the output of the class generator aggregation unit after the flexible resource partition is aggregated; is the feasible upper bound of the output of the class energy storage aggregation unit after the flexible resource partition is aggregated; P B,flex is the feasible lower bound of the output of the class energy storage aggregation unit after the flexible resource partition is aggregated; ξ G is the output uncertainty of the class generator aggregation unit; x(ξ G ), x(ξ B ) are adaptive variables after the uncertainty occurs; g B is a parameter matrix; f s is the sub-problem objective function.

[0041] In a second aspect, the embodiments of the present application provide a flexible resource partition division system based on power flow distribution, characterized in that the system comprises:

[0042] An aggregation module that adopts a flexible resource aggregation model division method to topologically aggregate flexible resources;

[0043] A calculation module that adopts an adaptive robust optimization algorithm to construct an aggregated resource adjustable capacity calculation model considering network constraints, and aggregates and calculates the power adjustment range provided by all flexible resources in the aggregated area for power dispatching;

[0044] A division module that solves the aggregated resource adjustable capacity calculation model considering network constraints, so that the power feasible upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, thereby realizing flexible resource partition aggregation in a continuous time period.

[0045] In some embodiments, topologically aggregating flexible resources by adopting the flexible resource aggregation model division method specifically comprises:

[0046] Constructing a bus regulation model;

[0047] Taking class generator flexible resources and class energy storage flexible resources starting from the 10kV voltage level as the aggregation objects, searching for power supply paths based on network topology, and realizing flexible resource partition aggregation from the physical connection level to form aggregation units;

[0048] The flexible resource adjustable capacity is aggregated in units of aggregation units, the flexible resources in the aggregation unit are equivalent to a generator, the aggregation unit is an equivalent generator aggregation unit; the flexible resources in the aggregation unit are equivalent to a storage, the aggregation unit is an equivalent storage aggregation unit; if there are two kinds of flexible resources in the aggregation unit, the aggregation unit is a mixed aggregation unit;

[0049] The network supplied by the flexible resources in the aggregation unit is equivalent, and a flexible resource aggregation and scheduling model is constructed.

[0050] In some embodiments, the flexible resource partitioning step is as follows:

[0051] S1031, input the 10kV bus where the flexible resource is located;

[0052] S1032, determine the computing node where the 10kV bus is located;

[0053] S1033, determine whether the voltage level required for partition aggregation is reached, if not, execute step S1034, if yes, execute step S1037;

[0054] S1034, if not reached, search for the transformer winding connected according to the power flow direction of the corresponding bus, determine whether the transformer winding has a higher voltage level, if yes, find the higher voltage level winding, otherwise go to step S1035;

[0055] S1035, determine whether the winding is connected to the line that supplies power to it, if yes, execute step S1036, if no, execute step S1038;

[0056] S1036, determine the power supply line and locate the bus that supplies power to it, execute step S1032;

[0057] S1037, determine whether the substation exists with other substations to supply power to each other, that is, whether there is a reverse line power flow, if yes, the mutually powered substations and the present substation are in the same aggregation unit, otherwise the substation is an aggregation unit, go to step S1038;

[0058] S1038, record all the transformer windings and lines passed in the process, end.

[0059] In some embodiments, the flexible resource aggregation and scheduling model is as follows:

[0060] wherein, S represents the equivalent injection power of the external network node on the boundary node, S B Y represents the injection power of the boundary node before equivalence, YBE Let represent the mutual admittance matrix from node B to node E. S represents the inverse of the self-admittance matrix of node E. E This represents the injected power of the external network node before the equivalent value;

[0061] Power of generator-like aggregation unit at the boundary node after equivalence:

[0062] Power of the energy storage aggregation unit at the boundary node after equivalent conversion:

[0063] Equivalent standard load power at boundary nodes:

[0064] in, The generator-like power column vector for the equivalent front boundary nodes; This is a column vector of generator-like power for external network nodes before the equivalent value is achieved; This is the energy storage power column vector of the equivalent front boundary nodes; This is the energy storage power column vector of the external network nodes before the equivalent value; This is the column vector of the conventional load power of the equivalent front boundary nodes; This is the column vector of the conventional load power of the external network nodes before the equivalent value.

[0065] In some embodiments, solving the aggregated resource adjustability calculation model considering network constraints, such that the power feasible upper and lower bounds of the aggregated resource adjustability calculation model can be decomposed and executed in each flexible resource, specifically involves:

[0066] In the k-th iteration, the main problem is solved, and a new variable vector is added to the main problem in each iteration. and Given the relevant constraints, the optimization result P of the main problem is obtained by solving the problem. * G,flex , P * B,flex , And pass it on to the subproblems, solve the subproblems based on the solution to the main problem, and the result of the k-th main problem solution P * G,flex , P * B,flex , Given the scenario ξ obtained by optimizing the subproblem, *If there is a vector ξ such that the sub-problem has no feasible solution, the objective function value of the sub-problem is -∞, otherwise the objective function value is 0, as the known parameters of the k+1th main problem iteration; by iteratively solving the main problem and the sub-problem, gradually reducing the error until the convergence condition is met, the flexible resource aggregation optimization result is finally obtained.

[0067] In some embodiments, the main problem is represented as: G x(ξ G )≤g G B x(ξ B )≤g B

[0068] The sub-problem is represented as:

[0069] wherein, is the worst scenario vector obtained by solving the k-1th sub-problem; and are variable vectors newly added in the kth iteration of the main problem, N, G, D, L, T, B, G', B' represent the set of grid nodes, the set of units, the set of regular loads, the set of branches, the set of time, the set of independent energy storage, the set of generator-like flexible resources, and the set of energy storage-like flexible resources, respectively. g,i,t p g,i,t represents the active power of unit g at node i∈N; if node i has no unit, p b,j,t =0; p b,j,t represents the active power of energy storage b at node j∈N; if node j has no energy storage, p loss,t =0; represents the active power of generator-like flexible resource g' at node j∈N; represents the active power of energy storage-like flexible resource b' at node j∈N; p d,i,t represents the active power of load d at node i∈N; p ij,t represents the active power flow of branch (i,j); represents the upper limit of line flow; f M is the objective function of the main problem; is the feasible upper bound of the output of the generator-like aggregation unit after flexible resource partitioning and aggregation; p G,flex is the feasible lower bound of the output of the generator-like aggregation unit after flexible resource partitioning and aggregation; is the feasible upper bound of the output of the energy storage-like aggregation unit after flexible resource partitioning and aggregation; p B,flex is the feasible lower bound of the output of the energy storage-like aggregation unit after flexible resource partitioning and aggregation; ξ G is the output uncertainty of the generator-like aggregation unit; x(ξ​​G ), x (ξ B ) is an adaptive variable after uncertainty occurs; g B is a parameter matrix; f s is a sub-problem objective function.

[0070] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the power flow distribution-based flexible resource partitioning method when executing the computer program.

[0071] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium including a computer program, and the computer program implements the steps of the power flow distribution-based flexible resource partitioning method when executed by a processor.

[0072] Compared with the prior art, the present application has at least the following beneficial effects:

[0073] A flexible resource partitioning method based on power flow distribution, which divides and aggregates flexible resources (such as distributed energy, energy storage systems, demand response resources, etc.) based on power flow distribution. This process not only considers the physical characteristics of the power grid, but also integrates various risks and uncertainties in the operation of the power grid, such as load fluctuations, equipment failures, changes in renewable energy output, etc., thereby ensuring the robustness and adaptability of the dispatch strategy. Specifically, the partitioning and aggregation strategy first divides the flexible resources into several independent and interconnected aggregation units based on factors such as power flow direction, voltage level, geographical distribution, etc. The resources within these units can work together to respond to dispatch instructions from the power system. When partitioning, the power exchange capacity between regions must also be considered to ensure that the partitioning scheme is beneficial for local optimization and easy for global coordination. Next, the system needs to comprehensively evaluate the impact of various factors on the operation of the power grid under various risk and uncertainty scenarios. This includes but is not limited to: load forecasting errors, intermittency and volatility of renewable energy generation, equipment failure probabilities, network security threats, etc. By constructing a corresponding risk assessment model, the system can quantify the impact of these uncertainty factors on the operation of the power grid, providing strong support for subsequent dispatch decisions. Under the premise of considering the power grid topology and network security constraints, the system further calculates the feasible upper and lower bounds of the active power output interval for each aggregation unit. This process fully considers the physical limitations of the power grid (such as line transmission capacity, transformer capacity, etc.) and network security requirements (such as preventing malicious attacks, protecting user privacy, etc.), ensuring the feasibility and safety of the dispatch strategy in actual implementation. By accurately calculating the active power output interval, the system can provide clear operational guidance for dispatch personnel, helping them make precise decisions under multi-level and multi-scenario power dispatch requirements. Ultimately, this flexible resource partitioning and aggregation-based dispatch method can significantly improve the flexibility and response speed of the power system, effectively dealing with various uncertainty factors, and ensuring the safe, stable, and economic operation of the power grid in complex and changing operating environments. At the same time, this method provides important technical support and theoretical reference for the construction and development of future smart grids.

[0074] Further, since the massive flexible resources can be equivalent to class generator flexible resources and class energy storage flexible resources from the perspective of the main grid, flexible resource aggregation is considered from these two types.

[0075] Further, due to the characteristics of flexible regulation resources, such as multiple types, large quantity, small size, and large total amount, if the class generator flexible resources and class energy storage flexible resources under the 10kV bus of the main grid dispatch personnel are taken as the dispatch object, the regulation amount is too small, and the dispatch personnel cannot quickly determine the specific regulation object according to the disposal demand, therefore, it is necessary to search for the power supply path based on the network topology, and to realize the partitioning and aggregation of flexible resources from the physical connection level.

[0076] Further, the adaptive robust algorithm is used to adjust the flexible resource aggregation requirement, and there is a feasible solution corresponding to any variable in the uncertainty set, which can ensure the feasibility of the decomposition of the aggregation result.

[0077] Further, in order to make any variable in the uncertainty set have a feasible solution corresponding to the aggregated capacity, two-stage optimization is required to solve the aggregation model, which is divided into main problem solving and sub-problem solving.

[0078] It can be understood that the beneficial effects of the above-mentioned second aspect can be referred to the related description in the above-mentioned first aspect, which will not be repeated here.

[0079] In summary, the present application considers the flexible resource to be divided according to the power flow distribution and the adaptive robust model to be used to solve the aggregated capacity, which solves the problem that the traditional flexible resource is aggregated according to the total amount of the region, and the network topology and safety constraints have not been considered, and it is difficult to meet the peak shaving and equipment overload disposal requirements in the dispatch section of the large power grid.

[0080] The technical solutions of the present application will be further described in detail below with the help of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0081] Fig. 1 is a schematic diagram of flexible resource aggregation;

[0082] Fig. 2 is a flow of flexible resource partitioning based on power flow distribution;

[0083] Fig. 3 is a schematic diagram of the static equivalence principle of the aggregation unit;

[0084] Fig. 4 is a schematic diagram of a computer device provided by an embodiment of the present application;

[0085] Fig. 5 is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0086] The technical solutions in the embodiments of the present application will be described clearly and completely below with the help of the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0087] In the description of the present application, it should be understood that the terms "include" and "contain" indicate the existence of described features, whole, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0088] It should also be understood that the terms used in the specification and the appended claims are intended to be interpreted broadly and liberally, and are intended to include more than what is specifically enumerated. The use of the term "including" or "having" in the specification and the appended claims has the same meaning as the term "comprising." The use of the term "or" in the specification and the appended claims has the same meaning as "and / or." The use of the term "based on" in the specification and the appended claims is not intended to foreclose implementations from using a combination of data values.

[0089] It should also be further understood that the term "and / or" used in the specification and the appended claims, means and includes any and all combinations of one or more of the associated listed items and all possible combinations thereof, for example, A and / or B, can mean: existence of A alone, existence of A and B together, existence of B alone. In addition, the character " / " in the present application generally represents an "or" relationship between the front and rear associated objects.

[0090] It should be understood that, although the terms first, second, third, etc. can be used in the embodiments of the present application to describe a certain range, etc., these ranges should not be limited to these terms. These terms are only used to distinguish the ranges from each other. For example, the first range can also be referred to as the second range, and similarly, the second range can also be referred to as the first range, without departing from the scope of the embodiments of the present application.

[0091] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if a stated condition or event occurs" can be interpreted to mean "when it is determined" or "in response to determining" or "when a stated condition or event occurs" or "in response to detecting a stated condition or event."

[0092] Various structural diagrams according to the embodiments of the present disclosure are shown in the accompanying drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for the purpose of clarity and understanding, and certain details can be omitted. The shapes of various regions, layers, and their relative sizes and positional relationships shown in the drawings are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be additionally designed by those skilled in the art according to actual needs.

[0093] The application provides a flexible resource partitioning method based on power flow distribution. Since the power adjustment range provided by all flexible resources in the aggregation area needs to be aggregated and calculated for power dispatching, the application proposes an aggregation resource adjustable capacity calculation model considering network constraints, and uses an adaptive robust optimization algorithm for modeling. Finally, a solution method for the aggregation model is proposed, which divides the main problem into two stages for solving, ensuring that the upper and lower bounds of the power of the obtained aggregation model can be decomposed and executed in each flexible resource. The application considers the network constraints and the adjustment characteristics of flexible resources to realize flexible resource partitioning aggregation in continuous periods.

[0094] Referring to FIG. 2, the flexible resource partitioning method based on power flow distribution includes the following steps:

[0095] S1, using a flexible resource aggregation model division method to aggregate the topology of flexible resources;

[0096] S101, 10kV bus regulation model

[0097] Most types of flexible resources are small and scattered load objects, which are connected to the main grid from the distribution network and are supplied by the 10kV bus. From the perspective of the main grid, a large number of flexible resources are equivalent to generator-like flexible resources and energy storage-like flexible resources supplied by the 10kV bus. The former is a resource considering power output limitation, and the latter is a resource considering input-output total energy limitation. The application focuses on the 10kV bus regulation model as the aggregation object to consider the network topology constraints of flexible resource partitioning aggregation technology. Here, only the generator-like flexible resource regulation model and the energy storage-like flexible resource regulation model are modeled, and the Minowski and method is used for the aggregation process of flexible resources to the 10kV bus regulation model.

[0098] Generator-like flexible resource regulation model

[0099] Energy storage-like flexible resource regulation model

[0100] Among them, Pgi(t) is the active power output of the generator-like flexible resource regulation model; Pgi(t) is the active power output of the i-th generator-like flexible resource regulation model at time t; Pgi,min is the minimum value of the generator-like active power output descending rate, Pgi,max is the maximum value of the generator-like active power output ascending rate; Pgi,min is the minimum value of the generator-like active power output; Pgi,max is the maximum value of the generator-like active power output. The meanings of the variables in the energy storage-like flexible resource regulation model are similar.

[0101] S102, define flexible resource partitioning aggregation

[0102] Please refer to FIG. 1, the flexible regulation resource presents the characteristics of full type, large quantity, small size and large total amount. If the dispatch personnel takes the generator-like flexible resource and the energy storage-like flexible resource under the 10kV bus as the dispatch object, the regulation amount is too small, and the dispatch personnel cannot quickly determine the specific object according to the treatment demand, so it is necessary to aggregate according to the dispatch demand. The flexible resource after the partition aggregation is called an aggregated unit, and each aggregated unit includes two types of objects, namely the generator-like aggregated unit and the energy storage-like aggregated unit.

[0103] In actual application, the generator-like flexible resource and the energy storage-like flexible resource starting from the 10kV voltage level are taken as the aggregated object, the power supply path is searched based on the network topology, and the partition aggregation of the flexible resource is realized from the physical connection level. The partition here is a dynamic partition according to the dispatch demand. The adjustable range of the aggregated unit after the aggregation is the active power regulation range provided for the entire power system dispatch, which takes into account the regulation characteristics of the flexible resource itself, the active power flow constraint of the grid branch, and the uncertainty of the uncertain flexible resource output, so as to realize the decomposition of the dispatch regulation instruction of the flexible resource layer by layer under the premise of ensuring the safe operation of the grid. As shown in FIG. 1, each aggregated unit is hung on the node of the grid calculation bus. From the regulation characteristics, it is mainly divided into two types, namely the generator-like aggregated unit and the energy storage-like aggregated unit.

[0104] S103, partition division of flexible resource based on power flow distribution

[0105] The 10kV bus regulation model is partitioned and aggregated in the application to form an aggregated unit. First, the partition division of the flexible resource is realized from the physical connection level. In actual application, the aggregated unit is often composed of the bus of a substation or the bus of several substations connected together. Here, the bus of the substation is taken as the main body of the aggregated unit, so it is necessary to divide the flexible resource into different situations. This method supports the aggregation demand of the flexible resource of different voltage levels.

[0106] Please refer to FIG. 2, the steps of the partition division of the flexible resource are as follows:

[0107] S1031, input the 10kV bus where the flexible resource is located;

[0108] S1032, determine the calculation node where the 10kV bus is located;

[0109] S1033, judge whether the required partition aggregation voltage level is reached, that is, whether the partition aggregation voltage level is equal to the 10kV bus voltage level. If not, step S1034 is executed, and if yes, step S1037 is executed.

[0110] S1034, if not, search the transformer winding connected to the busbar according to the power flow direction, determine whether the transformer with the corresponding winding has a higher voltage level, if yes, find the winding with a higher voltage level, otherwise go to step S1035;

[0111] S1035, determine whether the winding is connected to the line supplying power thereto, if yes, go to step S1036, otherwise go to step S1038;

[0112] S1036, determine the power supply line and locate the busbar supplying power thereto, and go to step S1032;

[0113] S1037, determine whether the substation exists in the power supply condition with other substations, that is, whether there is a reverse line power flow, if yes, the mutually powered substations and the substation are the same aggregation unit, otherwise the substation is an aggregation unit, and go to step S1038;

[0114] S1038, record all the transformer windings and lines passed in the process, and end.

[0115] S104, flexible resource aggregation network model.

[0116] Referring to FIG. 3, based on the flexible resource partitioning result of the power flow distribution, the adjustable capacity of the flexible resource is aggregated in units of aggregation units, if the aggregation unit is all flexible resource equivalent to a generator, the aggregation unit is a flexible resource equivalent to a generator aggregation unit, if the aggregation unit is all flexible resource equivalent to a storage, the aggregation unit is a flexible resource equivalent to a storage aggregation unit, and if the aggregation unit contains two kinds of flexible resources, the aggregation unit is a mixed type aggregation unit; the Ward static equivalence method is used to equivalently process the network supplied by the flexible resource in the aggregation unit, and a flexible resource aggregation and dispatching model is constructed, and the model calculation and solving time is greatly reduced through the power flow calculation and solving of the simplified network.

[0117] The linear equation of the unsimplified network is described as:

[0118] Wherein, Y is the busbar admittance matrix; I is the busbar injection current vector; V is the busbar voltage vector.

[0119] According to the Ward equivalence principle, the network nodes are divided into inner network nodes I, boundary nodes B and outer network nodes E, the first two types are reserved nodes, and the latter is a node to be eliminated, and the state parameters and impedance parameters after equivalence are solved through admittance matrix [Y] decomposition and change.

[0120] [Y BE ], [Y EE ], [YEB ]、[Y BB ] four sub-matrices. The original bus admittance matrix is divided as follows:

[0121] Where, the network node number from E, B, I from small to number.

[0122] From the above formula, the following formula is obtained:

[0123] By eliminating the external system network, that is, equivalent elimination variable [V E ], the following formula is obtained by matrix operation:

[0124] The equivalent network of the aggregation unit [Y EQ ] = - [Y BE ] [Y EE ] -1 [Y EB ], Each node where the aggregation unit is located is hung with a class equivalent generator unit, a class equivalent energy storage unit, and a conventional load. Multiply the above formula by the voltage to get the equivalent injection power of the external network, that is,

[0125] The equivalent state parameters of the equivalent network of the aggregation unit are as follows:

[0126] Where, S E and V E represent the injection power and voltage vector of the equivalent external network node before equivalence, respectively, and the meanings of other variables are similar.

[0127] Simplify the above formula to obtain:

[0128] The power of the class generator aggregation unit of the boundary node after equivalence:

[0129] The power of the class energy storage aggregation unit of the boundary node after equivalence:

[0130] The power of the ordinary load of the boundary node after equivalence:

[0131] Where, is the class generator power column vector of the boundary node before equivalence; is the class generator power column vector of the external network node before equivalence; is the class energy storage power column vector of the boundary node before equivalence; The class energy storage power column vector of the equivalent front external network node; The regular load power column vector of the equivalent front boundary node; The regular load power column vector of the equivalent front external network node.

[0132] S2, an adaptive robust optimization algorithm is used to construct an aggregated resource adjustable capacity calculation model considering network constraints, and the power adjustment range provided by all flexible resources in the aggregated area for power dispatch is aggregated and calculated;

[0133] 1. The pre-aggregated power grid flow model satisfies the following constraints:

[0134] Wherein, N, G, D, L, T, B, G', B' represent the node set, the unit set, the regular load set, the branch set, the time set, the independent energy storage set, the class generator flexible resource set, and the class energy storage flexible resource set; p g,i,t represents the active power of the unit g of the node i∈N; if the node i has no unit, then p g,i,t =0; p b,j,t represents the active power of the energy storage b of the node j∈N; if the node j has no energy storage, then p b,j,t =0; represents the active power of the class generator flexible resource g' of the node j∈N; represents the active power of the class energy storage flexible resource b' of the node j∈N; p loss,t represents the system loss; p d,i,t represents the active power of the load d of the node i∈N; p ij,t represents the active power flow of the branch (i,j); p represents the upper limit of the line flow.

[0135] 2. The post-aggregated flexible resource power grid flow model

[0136] In the aggregation process, the feasible upper and lower bounds of the aggregation unit are not simply the sum of the feasible upper and lower bounds of the active power of each 10kV bus regulation model. The branch flow safety constraint under the uncertainty set needs to be considered. First, the branch set L' passed through the flexible resource 10kV bus model of the network equivalent aggregation unit bus node is calculated, second, the sensitivity of the 10kV bus regulation model and the balance unit to each branch of the set L' is calculated, the branch flow situation after the active regulation of the flexible resource is calculated by the distribution coefficient method, and the post-aggregated power grid flow model is as follows:

[0137] Wherein, Φ k,ij is the flow transfer distribution factor of the node k∈N to the ij end branch; The active power regulation range of the node k∈N class generator g′; The active power regulation range of the node k∈N class energy storage b′. t G The output of the aggregated class generator unit, P t B The output of the aggregated class energy storage unit, M is the set of aggregated nodes in the unit before aggregation.

[0138] 3. Network constraint considering aggregated resource adjustable capacity calculation model

[0139] Class generator unit output interval is expressed in the following mathematical form:

[0140] wherein, P t G,flex and respectively represent the feasible lower bound and upper bound of the output of the aggregated class generator unit after flexible resource partition aggregation at time t.

[0141] Class energy storage unit output interval is expressed in the following mathematical form:

[0142] wherein, P t B,flex and respectively represent the feasible lower bound and upper bound of the output of the aggregated class energy storage unit after flexible resource partition aggregation at time t.

[0143] An adaptive robust optimization model is constructed using the box uncertainty set U, and the output interval of the aggregated flexible resource partition aggregation unit is converted into certainty, and the expression is as follows:

[0144] wherein, ξ t G represents the output uncertainty of the class generator unit; ξ t B represents the output uncertainty of the class energy storage unit; ξ G is the vector composed of ξ t G is the vector composed of ξ B t B is the vector composed of ξ

[0145] Based on the adaptive robust optimization model, the compact form of the flexible resource aggregation model is expressed as: E G x(ξ G )≤g G ​(49) E B x(ξ B )≤g B (50)

[0146] where P G,flex 、 P B,flex 、 is the vector of variable P t G,flex 、 P t B,flex 、 at each time; P G 、 P B 、 is a constant vector; x(ξ G ), x(ξ B ) is the vector of other decision variables except P t G,flex and P t B,flex ; g G , g B is the system parameter matrix; represents the Hadamard product of the corresponding elements of the matrices. Equation (47) contains equations (35) and (39), equation (48) contains equations (36) and (40), equation (49) contains equations (29), (30), (32), (34), and equation (50) contains equations (29), (31), (33), (34).

[0147] S3, solve the aggregated resource adjustable capacity calculation model considering network constraints, so that the power upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, realizing flexible resource partitioning aggregation in continuous time periods.

[0148] The problem is an adaptive robust optimization model, P G,flex 、 P B,flex 、 of the first stage is a decision variable before the uncertainty arrives, and the flexible resource aggregation power of the whole network is optimized in this stage; x(ξ G ), x(ξ B ) of the second stage is an adaptive variable after the uncertainty occurs, and through the optimization of the third stage, it is ensured that there is a corresponding feasible solution x(ξ G ), x(ξ B ) under the worst scenario ξ, that is, the feasibility of the flexible resource aggregation result decomposition is ensured.

[0149] S301, main problem solving

[0150] In the kth solving, the main problem is expressed as: E G x(ξ G )≤g G E B x(ξ B )≤g B

[0151] Wherein, is the worst scenario vector obtained by the k-1th sub-problem solving; and are the variable vectors newly added to the main problem in the kth iteration.

[0152] In each iteration, the main problem will add the constraint conditions related to the variable vectors and , and solve the optimization result P of the main problem * G,flex 、 P * B,flex 、 and pass to the sub-problem.

[0153] S302, sub-problem solving

[0154] Based on the main problem solving result, the sub-problem is expressed as:

[0155] After the kth main problem solving result P * G,flex 、 P * B,flex 、 is given, the scenario ξ * optimized by the sub-problem is taken as the known parameter of the k+1th main problem iteration. If there is a vector ξ that makes the sub-problem have no feasible solution, the sub-problem objective function value will be -∞, otherwise the objective function value is 0. By iteratively solving the main problem and the sub-problem, the error is gradually reduced until the convergence condition is met, and finally the flexible resource aggregation optimization result is obtained.

[0156] Those skilled in the art belonging to the technical field can understand that various aspects of the present application can be implemented as a system, a method or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module" or "platform" here.

[0157] In another embodiment of the present application, a flexible resource partitioning system based on power flow distribution is provided, which can be used to implement the above-mentioned flexible resource partitioning method based on power flow distribution. Specifically, the flexible resource partitioning system based on power flow distribution includes an aggregation module, a calculation module, and a partitioning module.

[0158] The aggregation module aggregates the flexible resources using a flexible resource aggregation model partitioning method.

[0159] The flexible resource aggregation model partitioning method specifically includes:

[0160] Building a bus regulation model.

[0161] Taking the 10kV voltage level as the starting point, the generator-like flexible resources and the energy storage-like flexible resources are aggregated, the power supply path is searched based on the network topology, the flexible resources are aggregated from the physical connection level, and the aggregated units are formed.

[0162] The adjustable capacity of the flexible resources is aggregated in the aggregated units. If the aggregated unit contains equivalent generator-like flexible resources, it is an equivalent generator aggregated unit. If the aggregated unit contains equivalent energy storage-like flexible resources, it is an energy storage aggregated unit. If the aggregated unit contains both types of flexible resources, it is a mixed aggregated unit.

[0163] The Ward static equivalence method is used to equivalently value the power system network, and the network supplying power to the flexible resources in the aggregated unit is equivalently processed to build a flexible resource aggregation scheduling model.

[0164] The steps of the flexible resource aggregation model partitioning method are as follows:

[0165] S1031, inputting the 10kV bus where the flexible resource is located.

[0166] S1032, determining the calculation node where the 10kV bus is located.

[0167] S1033, judging whether the voltage level required for partitioning and aggregation is reached. If not, step S1034 is executed, and if yes, step S1037 is executed.

[0168] S1034, if not reached, searching for the transformer winding connected to the corresponding bus according to the power flow direction, judging whether the transformer of the corresponding winding has a higher voltage level, and if yes, finding the higher voltage level winding, otherwise, going to step S1035.

[0169] S1035, judging whether the winding is connected to the line supplying power to it, if yes, executing step S1036, and if no, executing step S1038.

[0170] S1036, determine its power supply line, and locate the busbar that supplies power to it, execute step S1032;

[0171] S1037, determine whether there is a mutual power supply between the substation and other substations, that is, whether there is a reverse line flow, if yes, the mutually powered substations and the substation are the same aggregation unit, otherwise, the substation is an aggregation unit, go to step S1038;

[0172] S1038, record all the transformer windings and lines passed in the process, end.

[0173] The flexible resource aggregation scheduling model is as follows:

[0174] wherein, S represents the equivalent injection power of the external network node on the boundary node, S B Y represents the injection power of the equivalent pre-boundary node, Y BE Y represents the mutual admittance matrix from node B to node E, S represents the inverse of the self-admittance matrix of node E =, S E Y represents the injection power of the equivalent pre-external network node;

[0175] The generator-like aggregation unit power of the equivalent post-boundary node is:

[0176] The energy storage-like aggregation unit power of the equivalent post-boundary node is:

[0177] The normal load power of the equivalent post-boundary node is:

[0178] wherein, Y is the generator-like power column vector of the equivalent pre-boundary node; Y is the generator-like power column vector of the equivalent pre-external network node; Y is the energy storage-like power column vector of the equivalent pre-boundary node; Y is the energy storage-like power column vector of the equivalent pre-external network node; Y is the normal load power column vector of the equivalent pre-boundary node; Y is the normal load power column vector of the equivalent pre-external network node.

[0179] The calculation module adopts an adaptive robust optimization algorithm to construct an aggregation resource adjustable capacity calculation model considering network constraints, and aggregates and calculates the power adjustment range provided by all flexible resources in the aggregation area for power dispatching;

[0180] The partition module solves the aggregated resource adjustable capacity calculation model considering network constraints, so that the power feasible upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, and the flexible resource partition aggregation in a continuous period is realized.

[0181] The aggregated resource adjustable capacity calculation model considering network constraints is solved, so that the power feasible upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, and the flexible resource partition aggregation in a continuous period is realized.

[0182] In the kth solving, the main problem is solved, and the main problem will add a variable vector and related constraint conditions every iteration, and the optimization result P of the main problem is obtained by solving. * G,flex , P * B,flex , and is passed to the sub-problem, and the sub-problem is solved based on the main problem solving result P * G,flex , P * B,flex , is given, and the scene ξ obtained by optimizing the sub-problem is given. * As the known parameter of the k+1th main problem iteration, if there is a vector ξ such that the sub-problem has no feasible solution, the objective function value of the sub-problem is -∞, otherwise the objective function value is 0; by iteratively solving the main problem and the sub-problem, the error is gradually reduced until the convergence condition is met, and finally the flexible resource aggregation optimization result is obtained.

[0183] The main problem is represented as: E G x(ξ G )≤g G E B x(ξ B )≤g B

[0184] The sub-problem is represented as:

[0185] Among them, is the worst scene vector obtained by solving the k-1th sub-problem; and are the variable vectors newly added by the main problem in the kth iteration, N, G, D, L, T, B, G', B' represent the set of grid nodes, the set of units, the set of conventional loads, the set of branches, the set of time, the set of independent energy storage, the set of generator-like flexible resources, and the set of energy storage-like flexible resources; p g,i,tdenotes the g active power of the unit at node i ∈ N; if node i has no unit then p g,i,t = 0; p b,j,t denotes the b active power of the storage at node j ∈ N; if node j has no storage then p b,j,t = 0; denotes the g' active power of the g-like flexible resource at node j ∈ N; denotes the b' active power of the b-like flexible resource at node j ∈ N; p loss,t denotes the system network loss; p d,i,t denotes the d active power of the load at node i ∈ N; p ij,t denotes the active power flow of branch (i, j) respectively; p denotes the line flow upper limit; f M is the main problem objective function; is the feasible upper bound of the g-like aggregated unit after the flexible resource partition is aggregated; p G,flex is the feasible lower bound of the g-like aggregated unit after the flexible resource partition is aggregated; is the feasible upper bound of the b-like aggregated unit after the flexible resource partition is aggregated; p B,flex is the feasible lower bound of the b-like aggregated unit after the flexible resource partition is aggregated; ξ G is the g-like aggregated unit output uncertainty; x(ξ G ), x(ξ B ) are the adaptive variables after the uncertainty occurs; g B is the parameter matrix; f s is the sub-problem objective function.

[0186] In another embodiment of the present application, a terminal device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function. The processor in the embodiments of the present application can be used for the operation of the flexible resource partitioning method based on the power flow distribution, which comprises:

[0187] The flexible resource is topologically aggregated by using the flexible resource aggregation model partitioning method to obtain an aggregated unit. The aggregated unit is used as the basis of the aggregation of the adjustable capacity of the flexible resource. An adaptive robust optimization algorithm is used to construct an aggregated resource adjustable capacity calculation model considering network constraints to aggregate and calculate the power adjustment range provided by all flexible resources in the aggregated area for power dispatching. The aggregated resource adjustable capacity calculation model considering network constraints is solved, so that the upper and lower bounds of the power of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, and the flexible resource partitioning aggregation in the continuous period is realized.

[0188] In another embodiment of the present application, a computer readable storage medium is also provided, specifically a computer readable storage medium (Memory) which is a memory device in the terminal equipment, used for storing programs and data. It can be understood that the computer readable storage medium here can include the built-in storage medium in the terminal equipment, of course, can also include the expansion storage medium supported by the terminal equipment, can be any tangible medium containing or storing programs, which can be used by or in combination with the instruction execution system, device or instrument. The computer readable storage medium provides a storage space which stores the operating system of the terminal. And in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, which can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer readable storage medium here include: electrical connection with one or more conductive lines, portable disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0189] The computer readable storage medium also includes a data signal carried in the baseband or as a part of a carrier wave, in which readable program codes are borne. Such a propagated data signal can take various forms, including but not limited to electro-magnetic signal, optical signal or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in combination with the instruction execution system, device or instrument. The program codes contained on the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0190] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet service provider.

[0191] The one or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the method for flexible resource partitioning based on power flow distribution in the above embodiments; the one or more instructions stored in the computer-readable storage medium are loaded and executed by the processor to implement the following steps:

[0192] The flexible resources are topologically aggregated by using the flexible resource aggregation model partitioning method to obtain aggregated units; the aggregated units are used as a basis for aggregation of the adjustable capacity of the flexible resources, and an adaptive robust optimization algorithm is used to construct an aggregated resource adjustable capacity calculation model considering network constraints to aggregate and calculate the power adjustment range provided by all flexible resources in the aggregated area for power dispatch; the aggregated resource adjustable capacity calculation model considering network constraints is solved, so that the upper and lower bounds of the power of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, and the flexible resource partitioning aggregation in a continuous period is realized.

[0193] Referring to FIG. 4, the terminal device is a computer device, and the computer device 60 of the embodiment includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. The computer program 63, when executed by the processor 61, implements the method for flexible resource partitioning based on power flow distribution in the embodiment. To avoid repetition, details are not described herein. Alternatively, the computer program 63, when executed by the processor 61, implements the functions of each model / unit in the system for flexible resource partitioning based on power flow distribution in the embodiment. To avoid repetition, details are not described herein.

[0194] The computer device 60 can be a desktop computer, a notebook, a palm computer, a cloud server, and the like. The computer device 60 can include, but is not limited to, the processor 61 and the memory 62. Those skilled in the art can understand that FIG. 4 is only an example of the computer device 60, and does not limit the computer device 60, which can include more or fewer components than those shown, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, and the like.

[0195] The processor 61 can be a central processing unit (CPU), and can also be other general-purpose processors, central processing units, graphics processing units, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, quantum computing-based data processing logic components, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0196] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0197] Further, the memory 62 can include both an internal storage unit and an external storage device of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0198] Any reference to storage, databases or other media used to store data in the embodiments provided herein is intended to include at least one of volatile and non-volatile storage. Non-volatile storage can include, for example, optical, floppy disks, hard disks, or solid state drives. Volatile storage can include, for example, random access memory (RAM) or external cache memory. The RAM can be a variety of types, including, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or a hybrid of static and dynamic random access memory (SRAM / DRAM). The embodiments provided herein are not limited to any particular type of storage.

[0199] The databases involved in the embodiments provided herein can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, and the like, but is not limited thereto. The processor involved in the embodiments provided herein can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, and the like, but is not limited thereto.

[0200] Referring to FIG. 5, the terminal device 600 is an electronic device in the form of a general computing device. The components of the electronic device can include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components including the storage unit 620 and the processing unit 610, a display unit 640, and the like.

[0201] The storage unit stores program codes that can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present application described in the method part of the present specification.

[0202] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202, and can further include a read-only memory (ROM) 6203.

[0203] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0204] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0205] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0206] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0207] This example aims to demonstrate how to use the flexible resource aggregation model partitioning method to topologically aggregate flexible resources and construct a network-constrained aggregated resource adjustable capacity calculation model using an adaptive robust optimization algorithm. By solving this calculation model, we can obtain the power feasible upper and lower bounds of the aggregated resources in continuous time periods, and thus realize the partitioning aggregation of flexible resources.

[0208] Implementation steps

[0209] Real-time data collection:

[0210] First, real-time collection of power grid operation information from the grid monitoring system, including key information such as the adjustment information of the 10kV bus where the flexible resource is located, the active power flow of the grid branch, the adjustable capacity of the unit, and the aggregation demand of the flexible resource.

[0211] Aggregation unit partitioning:

[0212] Using the flexible resource aggregation model partitioning method, the flexible resources are divided into several aggregation units according to the grid topology structure, network constraints, and performance limitations of flexible resources, etc.

[0213] Network equivalence and modeling:

[0214] The network structure within each aggregation unit is processed for equivalence, simplifying the network model. Based on the equivalent network model, a network-constrained aggregated resource adjustable capacity calculation model is constructed.

[0215] Application of adaptive robust optimization algorithm:

[0216] The adaptive robust optimization algorithm is used to aggregate and calculate the power adjustment range provided by all flexible resources in the aggregation area for power dispatching. This algorithm can handle uncertainty factors in the process of grid operation, such as the randomness of renewable energy output and the volatility of power demand.

[0217] Power feasible upper and lower bound solution:

[0218] Solve the network-constrained aggregated resource adjustable capacity calculation model to obtain the power feasible upper and lower bounds of the aggregated resources in continuous time periods. These boundaries represent the maximum and minimum power adjustment amounts that the aggregated resources can provide under different operating conditions.

[0219] Dispatching decision making:

[0220] Based on the power feasible upper and lower bounds obtained, combined with the operating state of the grid and the dispatching objectives (such as minimizing operating cost, maximizing benefit, or meeting specific grid service requirements, etc.), the corresponding dispatching decisions are made. Dispatching decisions can include start-stop, power adjustment, and other operation instructions for flexible resources.

[0221] Dispatch execution and monitoring:

[0222] The dispatch decision is sent to the corresponding flexible resource to execute the dispatch operation. At the same time, the state of the power grid operation and the actual performance of the flexible resource are continuously monitored to ensure the realization of the dispatch target and the safe and stable operation of the power grid.

[0223] Results and evaluation

[0224] By implementing the method, the partition aggregation of flexible resources is realized, and the operation efficiency and flexibility of the power grid are improved.

[0225] Compared with the traditional dispatch method, the method effectively deals with the uncertainty in the operation of the power grid through the adaptive robust optimization algorithm, and enhances the robustness and adaptability of the power grid.

[0226] The implementation results show that the method can provide more scientific and reasonable dispatch decisions for the power dispatch automation system, reduce the operation cost, and improve the overall performance of the power grid.

[0227] This embodiment details how to use the flexible resource aggregation model division method and the adaptive robust optimization algorithm to realize the partition aggregation of flexible resources and the calculation of the power regulation range. Through real-time data collection, network equivalence, modeling, solving, and the formulation and execution of dispatch decisions, the method provides strong support for the power dispatch automation system, which helps to improve the economy and safety of the power grid operation.

[0228] The method can be used in the dispatch of the power dispatch automation system for scenarios such as flexible resource participation in power grid peak shaving and equipment overload handling. First, real-time power grid operation information is obtained, including flexible resource 10kV bus regulation information, power grid branch active power flow, unit adjustable capacity, and flexible resource aggregation demand; then a flexible resource aggregation model considering network constraints is constructed, the flexible resource is formed into an aggregation unit according to the aggregation demand through branch power flow distribution, and the topology network within the unit is network equivalent; then an aggregated unit power adjustable range model is constructed to calculate the feasible upper and lower bounds of the active power output of the aggregated unit, and then the upper and lower bounds of the active power adjustable amount of the flexible resource aggregation unit are obtained.

[0229] In summary, the flexible resource partition division method and system based on power flow distribution solve the problem of traditional flexible resource aggregation by total amount in the main grid level, which does not consider network topology and safety constraints. By partitioning and aggregating the flexible resource according to the power flow distribution, considering various risk uncertainty scenarios, taking into account the power grid topology and network safety constraints, the feasible upper and lower bounds of the active power output interval of the aggregated unit are calculated, which can meet the precise matching of the flexible resource to the multi-level and multi-scenario power dispatch demand.

[0230] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0231] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0232] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0233] In the embodiments provided in the present application, it should be understood that the disclosed apparatus / terminal and method can be implemented in other ways. For example, the apparatus / terminal embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, apparatus or unit, and can be electrical, mechanical or other forms.

[0234] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0235] In addition, each of the function units in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software function unit.

[0236] The integrated module / unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such an understanding, all or part of the flow of the method in the above-mentioned embodiments can also be implemented by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can be executed by a processor to implement the steps of each method embodiment. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0237] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device that implements the functions specified in one or more flows in the flowcharts and / or one or more blocks in the block diagrams.

[0238] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction devices that implement the functions specified in one or more flows in the flowcharts and / or one or more blocks in the block diagrams.

[0239] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate a computer implemented process, so that the instructions executed on the computer or other programmable data processing devices provide steps for implementing the functions specified in one or more flows of the flowchart and / or one or more blocks of the block diagram.

[0240] The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, and any modification made on the basis of the technical idea of the present application falls within the protection scope of the claims of the present application. Industrial applicability

[0241] The embodiment of the present application provides a flexible resource partitioning method and system based on power flow distribution, adopts a flexible resource aggregation model partitioning method to perform topology aggregation on flexible resources, to obtain an aggregation unit; taking the aggregation unit as a basis for flexible resource adjustable capacity aggregation, an adaptive robust optimization algorithm is adopted to construct an aggregation resource adjustable capacity calculation model considering network constraints, and the power regulation range provided by all flexible resources under the aggregation area for power dispatch is aggregated and calculated; the aggregation resource adjustable capacity calculation model considering network constraints is solved, so that the power feasible upper and lower bounds of the aggregation resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, and flexible resource partitioning aggregation in a continuous period is realized. Efficient scheduling and management of flexible resources are realized, the efficiency and safety of power grid operation are improved, efficient use of new energy is promoted, and the operation cost of the power system is reduced.

Claims

1. A flexible resource partitioning method based on power flow distribution, comprising the following steps: topologically aggregating flexible resources using a flexible resource aggregation model partitioning method to obtain aggregation units; using the aggregation units as the basis for aggregating the adjustable capacity of flexible resources, using an adaptive robust optimization algorithm to construct an aggregated resource adjustable capacity calculation model considering network constraints, and aggregating and calculating the power adjustment range provided by all flexible resources in the aggregation area for power dispatch; solving the aggregated resource adjustable capacity calculation model considering network constraints, so that the upper and lower bounds of the power of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, and the flexible resource partitioning aggregation in the continuous period is realized. 2.The flexible resource partitioning method based on power flow distribution according to claim 1, wherein the topologically aggregating flexible resources using a flexible resource aggregation model partitioning method is specifically: constructing a bus regulation model; taking generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level as the aggregation objects, searching for power supply paths based on network topology, and aggregating the flexible resources from the physical connection level to form aggregation units; aggregating the adjustable capacity of flexible resources in units of aggregation units, and if the aggregation unit is equivalent to a generator-like flexible resource, the aggregation unit is a generator-like aggregation unit; if the aggregation unit is equivalent to an energy storage-like flexible resource, the aggregation unit is an energy storage-like aggregation unit; and if the aggregation unit contains both types of flexible resources, the aggregation unit is a mixed-type aggregation unit; equivalent processing the network supplying power to the flexible resources in the aggregation unit by the Ward static equivalent method, and constructing a flexible resource aggregation dispatching model. 3.The flexible resource partitioning method based on power flow distribution according to claim 2, wherein the steps of using a flexible resource aggregation model partitioning method are as follows: S1031, inputting the 10kV bus where the flexible resource is located; S1032, determining the calculation node where the 10kV bus is located; S1033, judging whether the voltage level required for partitioning aggregation is reached, if not, executing step S1034, and if yes, executing step S1037; S1034, if not reached, searching for the transformer winding connected to it according to the power flow direction supplying power to the corresponding bus, judging whether the transformer winding has a higher voltage level, if yes, finding the higher voltage level winding, otherwise, going to step S1035; S1035, judging whether the winding is connected to the line supplying power to it, if yes, executing step S1036, and if no, executing step S1038; S1036, determining the power supply line and locating the bus supplying power to it, and executing step S1032; S1037, judging whether the substation exists in the power supply relationship with other substations, i.e., whether there is a reverse line power flow, if yes, the mutually power-supplying substations and the substation are in the same aggregation unit, otherwise, the substation is an aggregation unit, and going to step S1038; S1038, recording all the transformer windings and lines passed in the process, and ending.

4. The flexible resource partitioning method based on tidal current distribution according to claim 2, the flexible resource aggregation scheduling model is as follows: wherein denotes the equivalent injection power of the external node at the border node, S B denotes the injection power of the equivalent pre-border node, Y BE denotes the mutual admittance matrix from node B to node E, S-1 represents the inverse of the self-admittance matrix of node E E P inj represents the injection power of the equivalent pre-outside network node.

5. The flexible resource partitioning method based on power flow distribution according to claim 4, the generator-like aggregation unit power of the boundary nodes after equalization: Class energy storage aggregation unit power of equivalent back boundary node: Normal load power of the equivalent back boundary node: wherein a class of generator-like power column vectors for equivalent frontiers nodes; a class generator power column vector for the equivalent front outer network node; a class of energy storage power column vectors for equivalent front boundary nodes; a class of energy storage power column vectors for equivalent front outer network nodes; a regular load power column vector for the equivalent front boundary node; The conventional load power column vector of the equivalent pre-outer network node.

6. The method of claim 1, wherein the aggregated resource adjustable capacity calculation model considering network constraints is specifically: wherein P G,flex 、 P B,flex 、 P is the variable at each time t G,flex , P t B,flex 、 vector of components; P G , P B 、 constant vector of the composition; x(ξ G ), x(ξ B ) is a vector of other decision variables than P t G,flex and P t B,flex ; g G 、g B 、E G 、E B is a system parameter matrix; Hadamard product representing multiplication of corresponding elements of matrices.

7. The method of claim 6, wherein the constraints of the aggregated resource adjustable capability computation model are as follows: wherein N, G, D, L, T, B, G', B' represent grid node set, unit set, regular load set, branch set, time set, independent energy storage set, generator-like flexible resource set, energy storage-like flexible resource set, respectively; p g,i,t represents the active power of unit g of node i∈N; p b,j,t represents the active power of energy storage b of node j∈N; representing the class generator flexible resource g' active power of the node j e N; Pb'j represents the active power of the class energy storage flexible resource b' of node j e N; loss,t p represents the system network loss; d,i,t Pi represents the active power of the load d of node i e N; ij,t Pij and Pji represent the active power flow of branch (i, j) respectively; Line flow upper limit.

8. The flexible resource partitioning method based on power flow distribution according to claim 1, wherein the aggregated resource adjustable capacity calculation model considering network constraints is solved, so that the power feasible upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, specifically as follows: At the kth solution, the main problem is solved, and the main problem will add a new variable vector each time iteration and The associated constraints, the optimization result P of the main problem is obtained by solving * G,flex 、 P * B,flex 、 and passed to the sub-problems, the sub-problems are solved based on the results of the main problem, and the results of the kth main problem solution P * G,flex 、 P * B,flex 、 Given the posterior, the sub-problem optimization obtains the scenario ξ * As the known parameter of the k+1th main problem iteration, if there is a vector ξ such that the sub-problem has no feasible solution, the objective function value of the sub-problem is -∞, otherwise the objective function value is 0; by iteratively solving the main problem and the sub-problem, the error is gradually reduced until the convergence condition is met, and finally the flexible resource aggregation optimization result is obtained.

9. The method of claim 8, the master problem is represented as: The sub-problem is expressed as: wherein the worst-case scenario vector obtained for the k-1th sub-problem; and N, G, D, L, T, B, G', B' represent the set of grid nodes, the set of units, the set of regular loads, the set of branches, the set of times, the set of independent storages, the set of generator-like flexible resources, and the set of storage-like flexible resources, respectively, for the main problem in the kth iteration; p g,i,t Pgi represents the active power of the unit g of the node i ∈ N; If node i has no generation, then p g,i,t = 0; p b,j,t represents the energy storage b active power of node j e N. If node j has no energy storage then p b,j,t = 0; representing the class generator flexible resource g' active power of the node j e N; Pb'j represents the active power of the class energy storage flexible resource b' of node j e N; loss,t Ploss represents the system network loss; d,i,t Pi represents the active power of the load d of node i e N; ij,t Pij and Pji represent the active power flow of branch (i, j) respectively; Line flow upper limit; f M for the main problem objective function; P G,flex P P B,flex P G P G P B P B P s P 10. A flexible resource partitioning system based on power flow distribution, comprising: an aggregation module that aggregates flexible resources using a flexible resource aggregation model partitioning method; a calculation module that constructs an aggregated resource adjustable capacity calculation model considering network constraints using an adaptive robust optimization algorithm, and aggregates and calculates the power adjustment range provided by all flexible resources in the aggregated area for power dispatch; a partitioning module that solves the aggregated resource adjustable capacity calculation model considering network constraints, so that the power feasible upper and lower bounds of the aggregated resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, realizing flexible resource partitioning aggregation in continuous time periods.

11. The flexible resource partitioning system based on power flow distribution according to claim 10, wherein the flexible resource is topologically aggregated using the flexible resource aggregation model partitioning method, specifically as follows: a bus regulation model is constructed; generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level are taken as the aggregation objects, power supply path search is performed based on network topology, flexible resource partitioning aggregation is realized from the physical connection level, and an aggregated unit is formed; the adjustable capacity of the flexible resource is aggregated in units of the aggregated unit, and if the aggregated unit is equivalent to a generator-like flexible resource, the aggregated unit is a generator-like aggregated unit; if the aggregated unit is equivalent to an energy storage-like flexible resource, the aggregated unit is an energy storage-like aggregated unit; and if the aggregated unit contains both types of flexible resources, the aggregated unit is a mixed-type aggregated unit; the network supplying power to the flexible resource in the aggregated unit is equivalently processed by the Ward static equivalence method, and a flexible resource aggregation dispatching model is constructed.

12. The flexible resource partitioning method based on power flow distribution according to claim 11, wherein the flexible resource partitioning steps are as follows: S1031, input the 10kV bus where the flexible resource is located; S1032, determine the calculation node where the 10kV bus is located; S1033, determine whether the voltage level required for partitioning aggregation is reached, if not, execute step S1034, and if yes, execute step S1037; S1034, if not reached, search for the transformer winding connected to the corresponding bus according to the power flow direction, and determine whether the transformer of the corresponding winding has a higher voltage level, if yes, find the higher voltage level winding, otherwise, go to step S1035; S1035, determine whether the winding is connected to the line supplying power thereto, if yes, execute step S1036, and if no, execute step S1038; S1036, determine the power supply line and locate the bus supplying power thereto, and execute step S1032; S1037, judging whether there is a power supply relationship between the substation and other substations, i.e. whether there is a reverse line power flow, if yes, the substation and the substation are in the same aggregation unit, otherwise, the substation is an aggregation unit, to step S1038; S1038, recording all the transformer windings and lines passed in the process, and ending.

13. The flexible resource partitioning method based on tidal current distribution according to claim 11, the flexible resource aggregation scheduling model is as follows: wherein, S represents the equivalent injection power of the external node at the boundary node B Y represents the injection power of the equivalent pre-boundary node BE Y represents the injection power of the equivalent pre-boundary node S-1 represents the inverse of the self-admittance matrix of node E E P inj represents the injection power of the equivalent pre-outside network node Generator-like aggregated unit power of equivalent back border nodes: Class energy storage aggregation unit power of equivalent back boundary node: Normal load power of the equivalent back boundary node: wherein a class of generator-like power column vectors for equivalent frontiers nodes; a class generator power column vector for the equivalent front outer network node; a class of energy storage power column vectors for equivalent front boundary nodes; a class of energy storage power column vectors for equivalent front outer network nodes; a regular load power column vector for the equivalent frontier node; a conventional load power column vector of the equivalent pre-external network node. 14.The flexible resource partitioning system based on power flow distribution of claim 10, solving an aggregated resource adjustable capability calculation model considering network constraints, so that the power feasible upper and lower bounds of the aggregated resource adjustable capability calculation model can be decomposed and executed in each flexible resource, specifically: At the kth solution, the main problem is solved, and the main problem adds a variable vector each time the iteration is performed and The associated constraints, the optimization result P of the main problem is obtained by solving * G,flex 、 P * B,flex 、 and passed to the sub-problems, the sub-problems are solved based on the results of the main problem, and the results of the kth main problem solution P * G,flex 、 P * B,flex 、 Given the posterior, the sub-problem optimization obtains the scenario ξ * As the known parameter of the k+1th main problem iteration, if there is a vector ξ such that the sub-problem has no feasible solution, the objective function value of the sub-problem is -∞, otherwise the objective function value is 0; by iteratively solving the main problem and the sub-problem, the error is gradually reduced until the convergence condition is met, and finally the flexible resource aggregation optimization result is obtained.

15. The flexible resource partitioning system based on tidal current distribution of claim 14, the master problem is represented as: The sub-problem is expressed as: wherein the worst-case scenario vector obtained for the k-1th sub-problem; and N, G, D, L, T, B, G', B' represent the set of grid nodes, the set of units, the set of regular loads, the set of branches, the set of times, the set of independent storages, the set of generator-like flexible resources, and the set of storage-like flexible resources, respectively, for the main problem in the kth iteration; p g,i,t Pgi represents the active power of the unit g of the node i ∈ N; If node i has no generation, then p g,i,t = 0; p b,j,t represents the energy storage b active power of node j e N. If node j has no energy storage then representing the class generator flexible resource g' active power of the node j e N; Pb'j represents the active power of the class energy storage flexible resource b' of node j e N; loss,t p represents the system network loss; d,i,t Pi represents the active power of the load d of node i e N; ij,t Pij and Pji represent the active power flow of branch (i, j) respectively; represents the line flow upper limit; f M is the main problem objective function; P G,flex P The upper bound of the output of the energy storage-like aggregation unit after flexible resource partitioning and aggregation; P B,flex The feasible lower bound for the output of the energy storage-like aggregation unit after flexible resource partitioning and aggregation; ξ G Uncertainty in the output of the generator-like aggregation unit; x(ξ) G ), x(ξ) B ) represents the adaptive variable after uncertainty occurs; g B f is a parameter matrix; s Let be the objective function of the subproblem. 16.A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 9.

17. A computing device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise steps for performing the method of any one of claims 1 to 9.

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