Flexible resource partition division method and system based on power flow distribution

Abstract

The invention discloses a flexible resource partition division method and system based on power flow distribution, and the method comprises the steps: carrying out the topological aggregation of flexible resources through employing a flexible resource aggregation model division method, and obtaining an aggregation unit; taking an aggregation unit as a unit as a basis of flexible resource adjustable capability aggregation, constructing an aggregation resource adjustable capability calculation model considering network constraints by adopting an adaptive robust optimization algorithm, and performing aggregation calculation on a power adjustment range, which can be provided for power dispatching, of all flexible resources in an aggregation region; and solving the aggregation resource adjustable capability calculation model considering the network constraint, so that the power feasible upper and lower bounds of the aggregation resource adjustable capability calculation model can be decomposed and executed in each flexible resource, and flexible resource partition aggregation in a continuous time period is realized. According to the invention, efficient scheduling and management of flexible resources are realized, the efficiency and safety of power grid operation are improved, efficient utilization of new energy is promoted, and the operation cost of a power system is reduced.

Description

Background Art

[0002] Under the "dual carbon" goal, the proportion of new energy installed capacity has increased significantly, and the regulation capacity of the traditional power supply side dominated by conventional units is becoming increasingly scarce. Flexible resources represented by electric vehicles, distributed energy storage, and distributed photovoltaics have great potential to participate in grid regulation, but a large number of dispersed flexible resources have not yet formed available regulation capabilities and it is difficult for them to effectively participate in system regulation.

[0003] At present, flexible resource aggregation is mostly carried out on a power grid basis, and dynamic aggregation has not yet been performed taking into account resource response characteristics and electrical network topology, which cannot meet the needs of fast and accurate matching of multi-level, multi-scenario, and multi-boundary power dispatching. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a flexible resource zoning method and system based on flow distribution to address the deficiencies in the above-mentioned prior art, and to perform topological aggregation of flexible resources to solve the technical problem that traditional flexible resources at the main network level are aggregated according to regional totals without considering network topology and security constraints. In the case of wide-area access of large-scale flexible resources, traditional aggregation methods are difficult to meet the peak-shaving and equipment overload disposal needs within the dispatching section of the large power grid.

[0005] The present invention adopts the following technical solutions: A flexible resource partitioning method based on power flow distribution includes the following steps: The flexible resource aggregation model partitioning method is used to perform topological aggregation on the flexible resources to obtain the aggregation unit. Taking the aggregation unit as the basis for the aggregation of the adjustable capacity of flexible resources, an adaptive robust optimization algorithm is used to build an aggregation resource adjustable capacity calculation model that considers network constraints, and the power adjustment range that all flexible resources in the aggregation area can provide for power dispatching is aggregated and calculated; The calculation model of adjustable capacity of aggregated resources considering network constraints is solved so that the feasible upper and lower bounds of power of the calculation model of adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource, realizing the partition aggregation of flexible resources in continuous time periods.

[0006] Preferably, the flexible resource aggregation model partitioning method is used to perform topological aggregation on the flexible resources as follows: Construct busbar regulation model; Generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level are used as aggregation objects, and the power supply path search is performed based on the network topology. The flexible resources are partitioned and aggregated at the physical connection level to form an aggregation unit. Aggregate the adjustable capacity of flexible resources in aggregation units. If the aggregation unit contains flexible resources similar to equivalent generators, the aggregation unit is a similar equivalent generator aggregation unit; if the aggregation unit contains flexible resources similar to equivalent energy storage, the aggregation unit is a similar energy storage aggregation unit; if there are two types of flexible resources in the aggregation unit, the aggregation unit is a hybrid aggregation unit; The power system network is equivalently treated through Ward static equivalent method, the network that supplies power to flexible resources under the aggregation unit is equivalently treated, and a flexible resource aggregation scheduling model is constructed.

[0007] Preferably, the steps of the flexible resource aggregation model division method are as follows: S1031, 10kV bus where the input flexible resources are located; S1032. Determine the calculation node where the 10 kV bus is located; S1033, determine whether the voltage level required for partition aggregation is reached, if not, execute step S1034, if so, execute step S1037; S1034. If the voltage level is not reached, search for the transformer winding connected to the corresponding bus according to the power flow direction of the corresponding bus, and determine whether the transformer where the corresponding winding is located has a higher voltage level. If so, find the winding with a higher voltage level, otherwise go to step S1035; S1035, determining whether the winding is connected to a power supply line, if yes, executing step S1036, if not, executing step S1038; S1036, determine its power supply line, locate the busbar that supplies power to it, and execute step S1032; S1037, determine whether the substation where the substation is located has a situation where it supplies power to other substations, that is, whether there is a reverse line flow, if so, the substations that supply power to each other and the substation are the same aggregation unit, otherwise, the substations are an aggregation unit, and go to step S1038; S1038. Record all transformer windings and lines passed during the process, and end.

[0008] Preferably, the flexible resource aggregation scheduling model is as follows:

[0009] in, represents the equivalent injected power of the external network node on the boundary node, Expressed as the injected power of the boundary node before the equalization, Representation Node To Node The mutual admittance matrix of Representation Node The inverse of the self-admittance matrix of Indicates the injected power of the external network node before equalization.

[0010] Preferably, the generator-like aggregate unit power of the boundary nodes after equalization is:

[0011] The energy storage aggregation unit power of the boundary node after equalization:

[0012] Normal load power of boundary nodes after equalization:

[0013] in, is the generator-like power column vector of the boundary nodes before isovalue; is the generator-like power column vector of the external network node before equivalence; is the energy storage power column vector of the equal value front boundary node; is the energy storage power column vector of the external network node before equalization; is the conventional load power column vector of the boundary node before equalization; is the conventional load power column vector of the external network node before equalization.

[0014] Preferably, the calculation model of the adjustable capacity of aggregated resources considering network constraints is specifically:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] in, , , , The variables at each moment are , , , The vector composed of , , , A constant vector composed of; , For and A vector of other decision variables; , , , , , is the system parameter matrix; Represents the Hadamard product of corresponding matrix elements.

[0026] Preferably, the constraints of the aggregate resource adjustable capacity calculation model are as follows:

[0027]

[0028]

[0029] in, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; Representation Node Energy storage Active power; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow.

[0030] Preferably, the aggregate resource adjustable capacity calculation model considering network constraints is solved so that the feasible upper and lower bounds of the power of the aggregate resource adjustable capacity calculation model can be decomposed and executed in each flexible resource as follows: In the When solving the main problem, the main problem will add a variable vector at each iteration. and Related constraints are solved to obtain the optimization result of the main problem , , , , and pass it to the sub-problem, solve the sub-problem based on the solution of the main problem, and in the Secondary problem solution results , , , Given, the scenario obtained by sub-problem optimization As the The known parameters of the sub-master problem iteration are given by a vector The subproblem has no feasible solution, and the subproblem objective function value is , otherwise the objective function value is 0; by iteratively solving the main problem and sub-problems, the error is gradually reduced until the convergence adjustment is met, and finally the flexible resource aggregation optimization result is obtained.

[0031] Preferably, the main problem is expressed as:

[0032] The sub-problem is expressed as:

[0033]

[0034] in, For the The worst-case scenario vector obtained by solving the subproblem; and For the The variable vector added to the main problem at the iteration, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; if the node If there is no unit ; Representation Node Energy storage Active power; if the node No energy storage ; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow; is the objective function of the main problem; The feasible upper bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible upper bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; Output uncertainty for aggregated units of similar generators; , It is the adaptive variable after the uncertainty occurs; is the parameter matrix; is the objective function of the subproblem.

[0035] In a second aspect, an embodiment of the present invention provides a flexible resource partitioning system based on power flow distribution, characterized in that it includes: Aggregation module, which uses the flexibility resource aggregation model partitioning method to perform topological aggregation on flexible resources; The calculation module uses an adaptive robust optimization algorithm to build an aggregate resource adjustable capacity calculation model that takes network constraints into account, and performs aggregate calculations on the power adjustment range that all flexible resources in the aggregate area can provide for power dispatching; The partitioning module solves the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource, realizing the partition aggregation of flexible resources in continuous time periods.

[0036] Preferably, the flexible resource aggregation model partitioning method is used to perform topological aggregation on the flexible resources as follows: Construct busbar regulation model; Generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level are used as aggregation objects, and the power supply path search is performed based on the network topology. The flexible resources are partitioned and aggregated at the physical connection level to form an aggregation unit. Aggregate the adjustable capacity of flexible resources in aggregation units. If the aggregation unit contains flexible resources similar to equivalent generators, the aggregation unit is a similar equivalent generator aggregation unit; if the aggregation unit contains flexible resources similar to equivalent energy storage, the aggregation unit is a similar energy storage aggregation unit; if there are two types of flexible resources in the aggregation unit, the aggregation unit is a hybrid aggregation unit; The power system network is equivalently treated through Ward static equivalent method, the network that supplies power to flexible resources under the aggregation unit is equivalently treated, and a flexible resource aggregation scheduling model is constructed.

[0037] Preferably, the steps of flexible resource partitioning are as follows: S1031, 10kV bus where the input flexible resources are located; S1032. Determine the calculation node where the 10 kV bus is located; S1033, determine whether the voltage level required for partition aggregation is reached, if not, execute step S1034, if so, execute step S1037; S1034. If the voltage level is not reached, search for the transformer winding connected to the corresponding bus according to the power flow direction of the corresponding bus, and determine whether the transformer where the corresponding winding is located has a higher voltage level. If so, find the winding with a higher voltage level, otherwise go to step S1035; S1035, determining whether the winding is connected to a power supply line, if yes, executing step S1036, if not, executing step S1038; S1036, determine its power supply line, locate the busbar that supplies power to it, and execute step S1032; S1037, determine whether the substation where the substation is located has a situation where it supplies power to other substations, that is, whether there is a reverse line flow, if so, the substations that supply power to each other and the substation are the same aggregation unit, otherwise, the substations are an aggregation unit, and go to step S1038; S1038. Record all transformer windings and lines passed during the process, and end.

[0038] Preferably, the flexible resource aggregation scheduling model is as follows:

[0039] in, represents the equivalent injected power of the external network node on the boundary node, Expressed as the injected power of the boundary node before the equalization, Representation Node To Node The mutual admittance matrix of Representation Node The inverse of the self-admittance matrix of represents the injected power of the external network node before equalization; Generator-like aggregate unit power at boundary nodes after equalization:

[0040] The energy storage aggregation unit power of the boundary node after equalization:

[0041] Normal load power of boundary nodes after equalization:

[0042] in, is the generator-like power column vector of the boundary nodes before isovalue; is the generator-like power column vector of the external network node before equivalence; is the energy storage power column vector of the equal value front boundary node; is the energy storage power column vector of the external network node before equalization; is the conventional load power column vector of the boundary node before equalization; is the conventional load power column vector of the external network node before equalization.

[0043] Preferably, the aggregate resource adjustable capacity calculation model considering network constraints is solved so that the feasible upper and lower bounds of the power of the aggregate resource adjustable capacity calculation model can be decomposed and executed in each flexible resource as follows: In the When solving the main problem, the main problem will add a variable vector at each iteration. and Related constraints are solved to obtain the optimization result of the main problem , , , , and pass it to the sub-problem, solve the sub-problem based on the solution of the main problem, and in the Secondary problem solution results , , , Given, the scenario obtained by sub-problem optimization As the The known parameters of the sub-master problem iteration are given by a vector The subproblem has no feasible solution, and the subproblem objective function value is , otherwise the objective function value is 0; by iteratively solving the main problem and sub-problems, the error is gradually reduced until the convergence adjustment is met, and finally the flexible resource aggregation optimization result is obtained.

[0044] Preferably, the main problem is expressed as:

[0045] The sub-problem is expressed as:

[0046]

[0047] in, For the The worst-case scenario vector obtained by solving the subproblem; and For the The variable vector added to the main problem at the iteration, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; if the node If there is no unit ; Representation Node Energy storage Active power; if the node No energy storage ; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow; is the objective function of the main problem; The feasible upper bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible upper bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; Output uncertainty for aggregated units of similar generators; , It is the adaptive variable after the uncertainty occurs; is the parameter matrix; is the objective function of the subproblem.

[0048] 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, wherein the processor implements the steps of the above-mentioned flexible resource partitioning method based on flow distribution when executing the computer program.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned flexible resource partitioning method based on power flow distribution.

[0050] Compared with the prior art, the present invention has at least the following beneficial effects: A flexible resource partitioning method based on power flow distribution is proposed. Flexible resources (such as distributed energy, energy storage systems, demand response resources, etc.) are finely partitioned and aggregated based on power flow distribution. This process not only takes into account the physical characteristics of the power grid, but also incorporates various risks and uncertainties in the operation of the power grid, such as load fluctuations, equipment failures, and changes in renewable energy output, thereby ensuring the robustness and adaptability of the dispatching strategy. Specifically, the partitioning aggregation strategy first divides the flexible resources into a number of independent and interconnected aggregation units based on factors such as the power flow direction, voltage level, and geographical distribution of the power grid. The resources in these units can work together to respond to the dispatching instructions of the power system. When partitioning, the power exchange capacity between regions must also be considered to ensure that the partitioning scheme is conducive to both local optimization and global coordination. Next, taking into account various risk uncertainty scenarios, the system needs to comprehensively evaluate the impact of various factors on the operation of the power grid. This includes but is not limited to: load forecasting errors, intermittent and volatile renewable energy generation, equipment failure probability, network security threats, etc. By constructing a corresponding risk assessment model, the system can quantify the impact of these uncertain factors on the operation of the power grid and provide strong support for subsequent dispatching decisions. Taking into account the grid topology and network security constraints, the system further calculates the feasible upper and lower bounds of the active output range of 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 security of the dispatching strategy in actual implementation. By accurately calculating the active output range, the system can provide clear operational guidance for dispatchers to help them make accurate decisions under multi-level and multi-scenario power dispatching requirements. Ultimately, this dispatching method based on flexible resource partitioning and aggregation can significantly improve the flexibility and response speed of the power system, effectively deal with various uncertain factors, and ensure that the power grid maintains a safe, stable and economical operating state in a complex and changeable operating environment. At the same time, this method also provides important technical support and theoretical reference for the construction and development of future smart grids.

[0051] Furthermore, since massive flexible resources can be equivalently viewed as generator-like flexible resources and energy storage-like flexible resources from the perspective of the main grid, flexible resource aggregation is considered from these two types.

[0052] Furthermore, since flexible adjustment resources are characterized by complete types, large numbers, small volumes and large total amounts, if the main grid dispatchers use generator-like flexible resources and energy storage-like flexible resources under the 10kV bus as the dispatch objects, the adjustment amount is too small, and the dispatchers cannot quickly determine the specific objects to be adjusted based on the handling needs. Therefore, it is necessary to search for power supply paths based on the network topology and realize the partitioning and aggregation of flexible resources from the physical connection level.

[0053] Furthermore, the adaptive robust algorithm for the aggregation of adjustable capabilities of flexible resources requires that there is a corresponding feasible solution for any variable in the uncertainty set, which can ensure the feasibility of decomposing the capability aggregation results.

[0054] Furthermore, in order to ensure that any variable in the uncertainty set has a feasible solution corresponding to the aggregation capability, it is necessary to adopt a two-stage optimization solution for the aggregation model, which is divided into two parts: solving the main problem and solving the sub-problem.

[0055] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0056] To sum up, the present invention considers dividing flexible resources according to flow distribution and adopts an adaptive robust model to solve the aggregation capability, which solves the problem that the traditional flexible resource method aggregates according to regional total amount, has not considered the network topology and safety constraints, and is difficult to meet the peak regulation and equipment overload disposal needs within the large power grid dispatching section.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of flexible resource aggregation; Figure 2 Partitioning processes for flexible resource partitioning based on power flow distribution; Figure 3 It is the static equivalent principle block diagram of the aggregation unit; Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 5 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0061] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0062] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0063] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0064] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0065] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0066] The present invention provides a flexible resource zoning method based on power flow distribution. Since it is necessary to aggregate and calculate the power adjustment range that all flexible resources in the aggregation area can provide for power dispatching, the present invention proposes an aggregate resource adjustable capacity calculation model that considers network constraints, and adopts an adaptive robust optimization algorithm for modeling; finally, a solution method for the aggregation model is proposed, and the main problem is divided into two stages for solution, ensuring that the feasible upper and lower bounds of the power of the obtained aggregation model can be decomposed and executed in each flexible resource. The present invention takes into account the power grid network constraints and the flexible resource adjustment characteristics, and realizes the flexible resource zoning aggregation under continuous time periods.

[0067] See also Figure 2 The present invention provides a flexible resource partitioning method based on power flow distribution, comprising the following steps: S1, topologically aggregate flexible resources using a flexible resource aggregation model partitioning method; S101, 10kV busbar regulation model Various types of flexible resources are mostly small and dispersed load objects, which are connected to the distribution network and gathered upward to be powered by the 10kV bus of the main grid. From the perspective of the main grid, the massive flexible resources are equivalent to generator-like flexible resources and energy storage-like flexible resources powered by 10kV bus. The former is a resource that considers power output limitations, and the latter is a resource that considers total input and output energy limitations. The present invention focuses on the flexible resource partitioning aggregation technology that considers network topology constraints based on the 10kV bus regulation model as the aggregation object. Here, only the generator-like flexible resource regulation model and the energy storage-like flexible resource regulation model are modeled, and the Minkowski and method are used in the aggregation process of flexible resources to the 10kV bus regulation model.

[0068] Generator-like flexible resource regulation model (11) Flexible resource regulation model similar to energy storage (12) in, Active output of the generator-like flexible resource regulation model; for Moment Active output of each type of generator flexible resource regulation model; is the minimum value of the active output decrease rate of the generator, The maximum value of the rate of increase of the active output of the generator; is the minimum active output of the generator; It is the maximum value of the active output of the generator. The meaning of the variables in the energy storage flexible resource regulation model is similar.

[0069] S102. Define flexible resource partition aggregation See also Figure 1 Flexible adjustment resources are characterized by complete types, large numbers, small volumes and large total amounts. If the main grid dispatcher uses the generator-like flexible resources and energy storage-like flexible resources under the 10kV bus as the dispatch objects, the adjustment amount is too small, and the dispatcher cannot quickly determine the specific adjustment object according to the disposal needs. Therefore, it is necessary to perform partition aggregation according to the dispatch needs. The flexible resources after partition aggregation are called aggregation units, and each aggregation unit includes two types of objects, namely generator-like aggregation units and energy storage-like aggregation units.

[0070] In practical applications, the present invention uses generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level as aggregation objects, searches for power supply paths based on the network topology, and implements partitioned aggregation of flexible resources from the physical connection level. The partitions here are dynamic partitions based on scheduling requirements. The adjustable range of the aggregated unit after aggregation is the active power adjustment range provided by the unit for the entire power system scheduling. This range takes into account the self-regulation characteristics of the aggregated flexible resources, the active power flow constraints of the power grid branches, and the uncertainty of the output of uncertain flexible resources, so as to achieve layer-by-layer decomposition of the flexible resource adjustment instructions by scheduling under the premise of ensuring the safe operation of the power grid. Figure 1 Each aggregation unit is connected to the node of the power grid computing bus. From the perspective of regulation characteristics, it can be divided into two categories: generator-like aggregation units and energy storage-like aggregation units.

[0071] S103, Flexible resource partitioning based on power flow distribution The present invention performs partition aggregation on the 10kV busbar regulation model to form an aggregation unit. First, the flexible resource partitioning is realized from the physical connection level. In practical applications, the aggregation unit is often composed of a busbar of a substation or a section formed by connecting several substation busbars. Here, the substation busbar is used as the main body of the aggregation unit. Therefore, it is necessary to partition the flexible resources according to different situations. This method supports the aggregation requirements of flexible resources of different voltage levels.

[0072] See also Figure 2 ,The steps of flexible resource partitioning are as follows: S1031, 10kV bus where the input flexible resources are located; S1032. Determine the calculation node where the 10 kV bus is located; S1033, determine whether the voltage level required for partition aggregation is reached, that is, whether the partition aggregation voltage level is equal to the 10kV bus voltage level, if not, execute step S1034, if yes, execute step S1037; S1034. If not, search the transformer winding connected to the corresponding bus according to the power flow direction of the corresponding bus, and determine whether the transformer where the corresponding winding is located has a higher voltage level. If so, find the winding with a higher voltage level, otherwise go to step S1035; S1035, determine whether the winding is connected to a power supply line, if yes, go to step S1036, if not, go to step S1038; S1036, determine its power supply line, and locate the busbar that supplies power to it, and go to step S1032; S1037, determine whether the substation where the substation is located has a situation where it supplies power to other substations, that is, whether there is a reverse line flow, if so, the substations that supply power to each other and the substation are the same aggregation unit, otherwise, the substations are an aggregation unit, and go to step S1038; S1038. Record all transformer windings and lines passed during the process, and end.

[0073] S104, flexible resource aggregation network model.

[0074] See also Figure 3 Based on the results of flexible resource zoning division of power flow distribution, the adjustable capacity of flexible resources is first aggregated in aggregation units. If all the flexible resources in the aggregation unit are equivalent generators, the aggregation unit is an equivalent generator aggregation unit. If all the flexible resources in the aggregation unit are equivalent energy storage, the aggregation unit is an energy storage aggregation unit. If there are two flexible resources in the aggregation unit, the aggregation unit is a hybrid aggregation unit. The power system network is equivalent to the Ward static equivalent method, and the network powered by flexible resources under the aggregation unit is equivalent to construct a flexible resource aggregation scheduling model. The simplified network is used to perform power flow calculation and solution, which will greatly reduce the model calculation and solution time.

[0075] The unsimplified network linear equation is described as: (15) in, is the busbar admittance matrix; Inject current vector into busbar; is the bus voltage vector.

[0076] According to the Ward equivalence principle, network nodes are divided into intranet nodes , boundary nodes and external network nodes , the first two categories are the nodes to be retained, and the latter are the nodes to be eliminated, through the admittance matrix Decompose the changes to solve the state parameters and impedance parameters after the equivalent values.

[0077] from Extract , , , Four sub-matrices. The original busbar admittance matrix is ​​divided as follows: (16) The network node number is from , , From small to numbered.

[0078] The following formula is obtained from the above formula: (17) By eliminating the network of the external system, that is, equivalently eliminating the variables , obtained through matrix operations: (18) Get the equivalent network of aggregation units , ,Each node where the aggregation unit is located is connected with a quasi-equivalent generator unit, a quasi-equivalent energy storage unit and a conventional load. Multiplying the above formula by the voltage gives the equivalent injection power of the external network, that is, (19) The equivalent state parameters of the aggregation unit equivalent network are as follows: (20) in, It represents the equivalent injected power of the external network node on the boundary node; and They respectively represent the injected power and voltage vector of the external network node before equalization, and the meanings of other variables are similar.

[0079] Simplifying the above formula, we get: (twenty one) Generator-like aggregate unit power at boundary nodes after equalization:

[0080] The energy storage aggregation unit power of the boundary node after equalization:

[0081] Normal load power of boundary nodes after equalization:

[0082] in, is the generator-like power column vector of the boundary nodes before isovalue; is the generator-like power column vector of the external network node before equivalence; is the energy storage power column vector of the equal value front boundary node; is the energy storage power column vector of the external network node before equalization; is the conventional load power column vector of the boundary node before equalization; is the conventional load power column vector of the external network node before equalization.

[0083] S2. Adopting an adaptive robust optimization algorithm to construct an aggregate resource adjustable capacity calculation model that considers network constraints, and aggregate calculation of the power adjustment range that all flexible resources in the aggregate area can provide for power dispatching; 1. The power flow model before aggregation satisfies the following constraints: (twenty two) (25) (26) in, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; if the node If there is no unit ; Representation Node Energy storage Active power; if the node No energy storage ; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow.

[0084] 2. Grid flow model after flexible resource aggregation In the aggregation process, the feasible upper and lower bounds of the aggregation unit are not the simple sum of the feasible upper and lower bounds of the active power of each 10kV bus regulation model, but the branch power flow safety constraints under the uncertainty set need to be considered. First, calculate the set of branches from the bus node of the network equivalent aggregation unit to the flexible resource 10kV bus model. , Secondly, calculate the 10kV busbar regulation model and the set of balanced units The sensitivity of each branch is calculated by the distribution coefficient method, and the branch power flow after the active power regulation of flexible resources is calculated. The aggregated power flow model is as follows: (29) (30) (31) (32) (33) (34) (35) (36) in, For Node right The power flow transfer distribution factor of the end branch; For Node Generator Active power adjustment range; For Node Energy storage Active power adjustment range. The aggregated unit output of the aggregated generator type, Output for the aggregated energy storage aggregate unit. It is the set of aggregated nodes in the unit before the aggregation.

[0085] 3. Calculation model of adjustable capacity of aggregated resources considering network constraints Generator-like aggregate unit output range It is expressed in the following mathematical form: (37) in, and Respectively expressed as The feasible lower and upper bounds of the output of generator-like aggregation units after the time-flexible resource partition aggregation.

[0086] Output range of energy storage aggregation unit It is expressed in the following mathematical form: (38) in, and Respectively expressed as The feasible lower and upper bounds of the output of energy storage-like aggregation units after the time-flexible resource partition aggregation.

[0087] Using Box Uncertain Sets An adaptive robust optimization model is constructed to transform the output interval of the aggregated flexible resource partition aggregation unit into determinism. The expression is as follows: (39) (40) in, Represents the uncertainty of the output of the aggregated unit of the generator class; Represents the output uncertainty of energy storage aggregation units; for The vector composed of for Vector composed of.

[0088] Based on this adaptive robust optimization model, the compact form of the flexible resource aggregation model is expressed as: (40) (41) (42) (43) (44) (45) (46) (47) (48) (49) (50) in, , , , The variables at each moment are , , , The vector composed of , , , A constant vector composed of; , For and A vector of other decision variables; , , , is the system parameter matrix; represents the Hadamard product of 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), and (34), and equation (50) contains equations (29), (31), (33), and (34).

[0089] S3. Solve the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource, and realize the partition aggregation of flexible resources in continuous time periods.

[0090] This problem is an adaptive robust optimization model. , , , is the decision variable before the uncertainty arrives. This stage optimizes the aggregation power of flexible resources in the whole network. , It is an adaptive variable after uncertainty occurs. Through the optimization in the third stage, it ensures that the worst scenario There is still a corresponding feasible solution , , that is, to ensure the feasibility of decomposing the results of flexible resource aggregation.

[0091] S301, Solving the main problem In the When solving the problem for the second time, the main problem is expressed as: (51) in, For the The worst-case scenario vector obtained by solving the subproblem; and For the The vector of variables added to the main problem at iteration .

[0092] At each iteration, the main problem will add a new variable vector and Related constraints are solved to obtain the optimization result of the main problem , , , , and passed to the sub-problem.

[0093] S302, sub-problem solving Based on the solution of the main problem, the sub-problem is expressed as:

[0094] (52) In the Secondary problem solution results , , , Given, the scenario obtained by sub-problem optimization As the The known parameters of the sub-master problem iteration are given by a vector The subproblem has no feasible solution, and the subproblem objective function value will be , otherwise the objective function value is 0. By iteratively solving the main problem and sub-problems, the error is gradually reduced until the convergence adjustment is met, and finally the flexible resource aggregation optimization result is obtained.

[0095] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0096] In another embodiment of the present invention, a flexible resource partitioning system based on flow distribution is provided, which can be used to implement the above-mentioned flexible resource partitioning method based on flow distribution. Specifically, the flexible resource partitioning system based on flow distribution includes an aggregation module, a calculation module and a partitioning module.

[0097] Among them, the aggregation module uses the flexibility resource aggregation model partitioning method to perform topological aggregation on flexible resources; The flexible resource aggregation model partitioning method is used to perform topological aggregation of flexible resources as follows: Construct busbar regulation model; Generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level are used as aggregation objects, and the power supply path search is performed based on the network topology. The flexible resources are partitioned and aggregated at the physical connection level to form an aggregation unit. Aggregate the adjustable capacity of flexible resources in aggregation units. If the aggregation unit contains flexible resources similar to equivalent generators, the aggregation unit is a similar equivalent generator aggregation unit; if the aggregation unit contains flexible resources similar to equivalent energy storage, the aggregation unit is a similar energy storage aggregation unit; if there are two types of flexible resources in the aggregation unit, the aggregation unit is a hybrid aggregation unit; The power system network is equivalently treated through Ward static equivalent method, the network that supplies power to flexible resources under the aggregation unit is equivalently treated, and a flexible resource aggregation scheduling model is constructed.

[0098] The steps of using the flexible resource aggregation model partitioning method are as follows: S1031, 10kV bus where the input flexible resources are located; S1032. Determine the calculation node where the 10 kV bus is located; S1033, determine whether the voltage level required for partition aggregation is reached, if not, execute step S1034, if so, execute step S1037; S1034. If the voltage level is not reached, search for the transformer winding connected to the corresponding bus according to the power flow direction of the corresponding bus, and determine whether the transformer where the corresponding winding is located has a higher voltage level. If so, find the winding with a higher voltage level, otherwise go to step S1035; S1035, determining whether the winding is connected to a power supply line, if yes, executing step S1036, if not, executing step S1038; S1036, determine its power supply line, locate the busbar that supplies power to it, and execute step S1032; S1037, determine whether the substation where the substation is located has a situation where it supplies power to other substations, that is, whether there is a reverse line flow, if so, the substations that supply power to each other and the substation are the same aggregation unit, otherwise, the substations are an aggregation unit, and go to step S1038; S1038. Record all transformer windings and lines passed during the process, and end.

[0099] The flexible resource aggregation scheduling model is as follows:

[0100] in, represents the equivalent injected power of the external network node on the boundary node, Expressed as the injected power of the boundary node before the equalization, Representation Node To Node The mutual admittance matrix of Representation Node The inverse of the self-admittance matrix =, represents the injected power of the external network node before equalization; Generator-like aggregate unit power at boundary nodes after equalization:

[0101] The energy storage aggregation unit power of the boundary node after equalization:

[0102] Normal load power of boundary nodes after equalization:

[0103] in, is the generator-like power column vector of the boundary nodes before isovalue; is the generator-like power column vector of the external network node before equivalence; is the energy storage power column vector of the equal value front boundary node; is the energy storage power column vector of the external network node before equalization; is the conventional load power column vector of the boundary node before equalization; is the conventional load power column vector of the external network node before equalization.

[0104] The calculation module uses an adaptive robust optimization algorithm to build an aggregate resource adjustable capacity calculation model that takes network constraints into account, and performs aggregate calculations on the power adjustment range that all flexible resources in the aggregate area can provide for power dispatching; The partitioning module solves the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource, realizing the partition aggregation of flexible resources in continuous time periods.

[0105] Solve the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource as follows: In the When solving the main problem, the main problem will add a variable vector at each iteration. and Related constraints are solved to obtain the optimization result of the main problem , , , , and pass it to the sub-problem, solve the sub-problem based on the solution of the main problem, and in the Secondary problem solution results , , , Given, the scenario obtained by sub-problem optimization As the The known parameters of the sub-master problem iteration are given by a vector The subproblem has no feasible solution, and the subproblem objective function value is , otherwise the objective function value is 0; by iteratively solving the main problem and sub-problems, the error is gradually reduced until the convergence adjustment is met, and finally the flexible resource aggregation optimization result is obtained.

[0106] The main problem is expressed as:

[0107] The sub-problem is expressed as:

[0108]

[0109] in, For the The worst-case scenario vector obtained by solving the subproblem; and For the The variable vector added to the main problem at the iteration, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; if the node If there is no unit ; Representation Node Energy storage Active power; if the node No energy storage ; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow; is the objective function of the main problem; The feasible upper bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible upper bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; Output uncertainty for aggregated units of similar generators; , It is the adaptive variable after the uncertainty occurs; is the parameter matrix; is the objective function of the subproblem.

[0110] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the flexible resource partitioning method based on the flow distribution, including: The flexible resources are topologically aggregated by the flexible resource aggregation model partitioning method to obtain the aggregation unit. The aggregation unit is used as the basis for the aggregation of the adjustable capacity of flexible resources, and the adaptive robust optimization algorithm is used to construct the aggregation resource adjustable capacity calculation model considering network constraints, and the power adjustment range that all flexible resources in the aggregation area can provide for power dispatching is aggregated and calculated. The aggregation resource adjustable capacity calculation model considering network constraints is solved so that the feasible upper and lower bounds of the power of the aggregation resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, thereby realizing the partitioned aggregation of flexible resources in continuous time periods.

[0111] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions 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: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0112] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0113] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0114] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the flexible resource partitioning method based on power flow distribution in the above embodiment; the processor may load and execute the following steps: The flexible resources are topologically aggregated by the flexible resource aggregation model partitioning method to obtain the aggregation unit. The aggregation unit is used as the basis for the aggregation of the adjustable capacity of flexible resources, and the adaptive robust optimization algorithm is used to construct the aggregation resource adjustable capacity calculation model considering network constraints, and the power adjustment range that all flexible resources in the aggregation area can provide for power dispatching is aggregated and calculated. The aggregation resource adjustable capacity calculation model considering network constraints is solved so that the feasible upper and lower bounds of the power of the aggregation resource adjustable capacity calculation model can be decomposed and executed in each flexible resource, thereby realizing the partitioned aggregation of flexible resources in continuous time periods.

[0115] See also Figure 4 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the flexible resource partitioning method based on power flow distribution in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the flexible resource partitioning system based on power flow distribution in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0116] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0117] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics 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, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0118] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may 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., equipped on the computer device 60.

[0119] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0120] Any reference to a memory, database or other medium used in the embodiments provided in the present application may include at least one of a non-volatile and a volatile memory. Non-volatile memory may include a read-only memory (ROM), a magnetic tape, a floppy disk, a flash memory, an optical memory, a high-density embedded non-volatile memory, a resistive random access memory (ReRAM), a magnetic random access memory (MRAM), a ferroelectric random access memory (FRAM), a phase change memory (PCM), a graphene memory, etc. Volatile memory may include a random access memory (RAM) or an external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), etc.

[0121] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0122] See also Figure 5 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may 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, etc.

[0123] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above method part of this specification.

[0124] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0125] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0126] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0127] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the 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, etc.

[0128] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0129] This embodiment aims to demonstrate how to use the flexible resource aggregation model partitioning method to perform topological aggregation of flexible resources, and use an adaptive robust optimization algorithm to build an aggregated resource adjustable capacity calculation model that considers network constraints. By solving this calculation model, we can obtain the feasible upper and lower bounds of the power of aggregated resources in continuous time periods, thereby realizing partitioned aggregation of flexible resources.

[0130] Implementation steps Real-time data collection: First, the grid operation information is collected in real time from the grid monitoring system, including key information such as the regulation information of the 10kV bus where the flexible resources are located, the active power flow of the grid branches, the adjustable capacity of the units, and the aggregation demand of flexible resources.

[0131] Aggregation unit division: By using the flexibility resource aggregation model partitioning method, the flexible resources are divided into several aggregation units according to factors such as the power grid topology, network constraints, and performance limitations of flexible resources.

[0132] Network Equivalence and Modeling: The network structure in each aggregation unit is processed equally to simplify the network model. Based on the network model after equalization, a calculation model for the adjustable capacity of aggregation resources considering network constraints is constructed.

[0133] Adaptive robust optimization algorithm application: An adaptive robust optimization algorithm is used to aggregate the power regulation range that all flexible resources in the aggregation area can provide for power dispatching. The algorithm can handle uncertain factors in the operation of the power grid, such as the randomness of renewable energy output and the volatility of power demand.

[0134] The feasible upper and lower bounds of power are solved: The computational model of the adjustable capacity of aggregated resources considering network constraints is solved to obtain the feasible upper and lower bounds of the power of aggregated resources in continuous time periods. These bounds represent the maximum and minimum power adjustment that the aggregated resources can provide under different operating conditions.

[0135] Scheduling Decision Making: According to the feasible upper and lower bounds of power obtained, combined with the operation status of the power grid and the dispatching objectives (such as minimizing operating costs, maximizing revenue, or meeting specific power grid service requirements, etc.), the corresponding dispatching decisions are made. Dispatching decisions can include operation instructions such as starting and stopping flexible resources and adjusting power.

[0136] Scheduling execution and monitoring: Send the dispatch decision to the corresponding flexible resources and execute the dispatch operation. At the same time, continuously monitor the grid operation status and the actual performance of flexible resources to ensure the realization of dispatch goals and the safe and stable operation of the grid.

[0137] Results and Evaluation By implementing this method, the partition aggregation of flexible resources is achieved, and the operation efficiency and flexibility of the power grid are improved.

[0138] Compared with traditional dispatching methods, this method effectively copes with the uncertainty in power grid operation through an adaptive robust optimization algorithm, and enhances the robustness and adaptability of the power grid.

[0139] The implementation results show that this method can provide more scientific and reasonable dispatching decisions for the power dispatching automation system, reduce operating costs and improve the overall performance of the power grid.

[0140] This embodiment shows in detail how to use the flexible resource aggregation model partitioning method and adaptive robust optimization algorithm to achieve partition aggregation of flexible resources and calculation of power regulation range. Through real-time data collection, network equivalence, modeling, solution, and the formulation and execution of dispatching decisions, this method provides strong support for the power dispatching automation system and helps to improve the economy and safety of power grid operation.

[0141] The method of the present invention can be used in the power dispatching automation system for the dispatching of flexible resources to participate in the peak load regulation of the power grid, the disposal of equipment overload and other scenarios. First, the power grid operation information is obtained in real time, including the 10kV busbar regulation information where the flexible resources are located, the active power flow of the power grid branch, the adjustable capacity of the unit, and the aggregation demand of the flexible resources; then, a flexible resource aggregation model considering network constraints is constructed, and the flexible resources are formed into aggregation units according to the aggregation demand through the branch power flow distribution, and the topological network in the unit is network-equalized; then, an aggregation unit power adjustable range model is constructed, and the feasible upper and lower bounds of the active output of the aggregation unit are calculated, and then the upper and lower bounds of the active adjustable amount of the flexible resource aggregation unit are obtained.

[0142] In summary, the flexible resource zoning method and system based on power flow distribution of the present invention solves the problem that traditional flexible resources are aggregated by regional total amount at the main network level without considering network topology and security constraints. By partitioning and aggregating flexible resources according to power flow distribution, considering various risk uncertainty scenarios, taking into account power grid topology and network security constraints, and calculating the feasible upper and lower bounds of the active output range of the aggregated unit, the result can meet the flexible resource precise matching multi-level and multi-scenario power dispatching needs. .

[0143] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0144] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0146] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0147] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0149] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained 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, computer-readable media do not include electric carrier signals and telecommunication signals.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0153] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A flexible resource partitioning method based on power flow distribution, It is characterized in that The following steps are involved: The flexible resource aggregation model partitioning method is used to perform topological aggregation on the flexible resources to obtain the aggregation unit. Taking the aggregation unit as the basis for the aggregation of the adjustable capacity of flexible resources, an adaptive robust optimization algorithm is used to build an aggregation resource adjustable capacity calculation model that considers network constraints, and the power adjustment range that all flexible resources in the aggregation area can provide for power dispatching is aggregated and calculated; The calculation model of adjustable capacity of aggregated resources considering network constraints is solved so that the feasible upper and lower bounds of power of the calculation model of adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource, realizing the partition aggregation of flexible resources in continuous time periods.

2. According to the flexible resource partitioning method based on power flow distribution according to claim 1, It is characterized in that The flexible resource aggregation model partitioning method is used to perform topological aggregation of flexible resources as follows: Construct busbar regulation model; Generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level are used as aggregation objects, and the power supply path search is performed based on the network topology. The flexible resources are partitioned and aggregated at the physical connection level to form an aggregation unit. Aggregate the adjustable capacity of flexible resources in aggregation units. If the aggregation unit contains flexible resources similar to equivalent generators, the aggregation unit is a similar equivalent generator aggregation unit; if the aggregation unit contains flexible resources similar to equivalent energy storage, the aggregation unit is a similar energy storage aggregation unit; if there are two types of flexible resources in the aggregation unit, the aggregation unit is a hybrid aggregation unit; The power system network is equivalently treated through Ward static equivalent method, the network that supplies power to flexible resources under the aggregation unit is equivalently treated, and a flexible resource aggregation scheduling model is constructed.

3. According to the flexible resource partitioning method based on power flow distribution according to claim 2, It is characterized in that The steps of using the flexible resource aggregation model division method are as follows: S1031, 10kV bus where the input flexible resources are located; S1032. Determine the calculation node where the 10 kV bus is located; S1033, determine whether the voltage level required for partition aggregation is reached, if not, execute step S1034, if so, execute step S1037; S1034. If the voltage level is not reached, search for the transformer winding connected to the corresponding bus according to the power flow direction of the corresponding bus, and determine whether the transformer where the corresponding winding is located has a higher voltage level. If so, find the winding with a higher voltage level, otherwise go to step S1035; S1035, determining whether the winding is connected to a power supply line, if yes, executing step S1036, if not, executing step S1038; S1036, determine its power supply line, locate the busbar that supplies power to it, and execute step S1032; S1037, determine whether the substation where the substation is located has a situation where the substation supplies power to each other with other substations, that is, whether there is a reverse line flow, if yes, the substations that supply power to each other and the substation are the same aggregation unit, otherwise the substations are an aggregation unit, and go to step S1038; S1038. Record all transformer windings and lines passed during the process, and end.

4. The flexible resource partitioning method based on power flow distribution according to claim 2, It is characterized in that The flexible resource aggregation scheduling model is as follows: in, represents the equivalent injected power of the external network node on the boundary node, Expressed as the injected power of the boundary node before the equalization, Representation Node To Node The mutual admittance matrix of Representation Node The inverse of the self-admittance matrix of Indicates the injected power of the external network node before equalization.

5. According to the flexible resource partitioning method based on power flow distribution according to claim 4, It is characterized in that Generator-like aggregate unit power at boundary nodes after equalization: The energy storage aggregation unit power of the boundary node after equalization: Normal load power of boundary nodes after equalization: in, is the generator-like power column vector of the boundary nodes before isovalue; is the generator-like power column vector of the external network node before equivalence; is the energy storage power column vector of the equal value front boundary node; is the energy storage power column vector of the external network node before equalization; is the conventional load power column vector of the boundary node before equalization; is the conventional load power column vector of the external network node before equalization.

6. The flexible resource partitioning method based on power flow distribution according to claim 1, It is characterized in that The calculation model of the adjustable capacity of aggregated resources considering network constraints is as follows: in, , , , The variables at each moment are , , , The vector composed of , , , A constant vector composed of; , For and A vector of other decision variables; , , , , , is the system parameter matrix; Represents the Hadamard product of corresponding matrix elements.

7. The flexible resource partitioning method based on power flow distribution according to claim 6, It is characterized in that The constraints of the aggregate resource adjustable capacity calculation model are as follows: in, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; Representation Node Energy storage Active power; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow.

8. The flexible resource partitioning method based on power flow distribution according to claim 1, It is characterized in that Solve the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource as follows: In the When solving the main problem, the main problem will add a variable vector at each iteration. and Related constraints are solved to obtain the optimization result of the main problem , , , , and pass it to the sub-problem, solve the sub-problem based on the solution of the main problem, and in the Secondary main problem solution , , , Given, the scenario obtained by sub-problem optimization As the The known parameters of the sub-master problem iteration are given by a vector The subproblem has no feasible solution, and the subproblem objective function value is , otherwise the objective function value is 0; by iteratively solving the main problem and sub-problems, the error is gradually reduced until the convergence adjustment is met, and finally the flexible resource aggregation optimization result is obtained.

9. The flexible resource partitioning method based on power flow distribution according to claim 8, It is characterized in that The main problem is expressed as: The sub-problem is expressed as: in, For the The worst-case scenario vector obtained by solving the subproblem; and For the The variable vector added to the main problem at the iteration, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; if the node If there is no unit ; Representation Node Energy storage Active power; if the node No energy storage ; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow; is the objective function of the main problem; The feasible upper bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible upper bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; Output uncertainty for aggregated units of similar generators; , It is the adaptive variable after the uncertainty occurs; is the parameter matrix; is the objective function of the subproblem.

10. A flexible resource partitioning system based on power flow distribution, It is characterized in that include: Aggregation module, which uses the flexibility resource aggregation model partitioning method to perform topological aggregation on flexible resources; The calculation module uses an adaptive robust optimization algorithm to build an aggregate resource adjustable capacity calculation model that takes network constraints into account, and performs aggregate calculations on the power adjustment range that all flexible resources in the aggregate area can provide for power dispatching; The partitioning module solves the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource, realizing the partition aggregation of flexible resources in continuous time periods.

11. The flexible resource partitioning system based on power flow distribution according to claim 10, It is characterized in that The flexible resource aggregation model partitioning method is used to perform topological aggregation of flexible resources as follows: Construct busbar regulation model; Generator-like flexible resources and energy storage-like flexible resources starting from the 10kV voltage level are used as aggregation objects, and the power supply path search is performed based on the network topology. The flexible resources are partitioned and aggregated at the physical connection level to form an aggregation unit. Aggregate the adjustable capacity of flexible resources in aggregation units. If the aggregation unit contains flexible resources similar to equivalent generators, the aggregation unit is a similar equivalent generator aggregation unit; if the aggregation unit contains flexible resources similar to equivalent energy storage, the aggregation unit is a similar energy storage aggregation unit; if there are two types of flexible resources in the aggregation unit, the aggregation unit is a hybrid aggregation unit; The power system network is equivalently treated through Ward static equivalent method, the network that supplies power to flexible resources under the aggregation unit is equivalently treated, and a flexible resource aggregation scheduling model is constructed.

12. The flexible resource partitioning method based on power flow distribution according to claim 11, It is characterized in that The steps for flexible resource partitioning are as follows: S1031, 10kV bus where the input flexible resources are located; S1032. Determine the calculation node where the 10 kV bus is located; S1033, determine whether the voltage level required for partition aggregation is reached, if not, execute step S1034, if so, execute step S1037; S1034. If the voltage level is not reached, search for the transformer winding connected to the corresponding bus according to the power flow direction of the corresponding bus, and determine whether the transformer where the corresponding winding is located has a higher voltage level. If so, find the winding with a higher voltage level, otherwise go to step S1035; S1035, determining whether the winding is connected to a power supply line, if yes, executing step S1036, if not, executing step S1038; S1036, determine its power supply line, locate the busbar that supplies power to it, and execute step S1032; S1037, determine whether the substation where the substation is located has a situation where it supplies power to other substations, that is, whether there is a reverse line flow, if so, the substations that supply power to each other and the substation are the same aggregation unit, otherwise, the substations are an aggregation unit, and go to step S1038; S1038. Record all transformer windings and lines passed during the process, and end.

13. The flexible resource partitioning method based on power flow distribution according to claim 11, It is characterized in that The flexible resource aggregation scheduling model is as follows: in, represents the equivalent injected power of the external network node on the boundary node, Expressed as the injected power of the boundary node before the equalization, Representation Node To Node The mutual admittance matrix of Representation Node The inverse of the self-admittance matrix of represents the injected power of the external network node before equalization; Generator-like aggregate unit power at boundary nodes after equalization: The energy storage aggregation unit power of the boundary node after equalization: Normal load power of boundary nodes after equalization: in, is the generator-like power column vector of the boundary nodes before isovalue; is the generator-like power column vector of the external network node before equivalence; is the energy storage power column vector of the equal value front boundary node; is the energy storage power column vector of the external network node before equalization; is the conventional load power column vector of the boundary node before equalization; is the conventional load power column vector of the external network node before equalization.

14. The flexible resource partitioning system based on power flow distribution according to claim 10, It is characterized in that Solve the calculation model of the adjustable capacity of aggregated resources considering network constraints, so that the feasible upper and lower bounds of the power of the calculation model of the adjustable capacity of aggregated resources can be decomposed and executed in each flexible resource as follows: In the When solving the main problem, the main problem will add a variable vector at each iteration. and Related constraints are solved to obtain the optimization result of the main problem , , , , and pass it to the sub-problem, solve the sub-problem based on the solution of the main problem, and in the Secondary main problem solution , , , Given, the scenario obtained by sub-problem optimization As the The known parameters of the sub-master problem iteration are given by a vector The subproblem has no feasible solution, and the subproblem objective function value is , otherwise the objective function value is 0; by iteratively solving the main problem and sub-problems, the error is gradually reduced until the convergence adjustment is met, and finally the flexible resource aggregation optimization result is obtained.

15. The flexible resource partitioning system based on power flow distribution according to claim 14, It is characterized in that The main problem is expressed as: The sub-problem is expressed as: in, For the The worst-case scenario vector obtained by solving the subproblem; and For the The variable vector added to the main problem at the iteration, They represent the grid node set, the unit set, the conventional load set, the branch set, the time set, the independent energy storage set, the generator-like flexible resource set, and the energy storage-like flexible resource set respectively; Representation Node The crew Active power; if the node If there is no unit ; Representation Node Energy storage Active power; if the node No energy storage ; Representation Node Flexible resources for class generators Active power; Representation Node Flexible resources like energy storage Active power; Indicates system network loss; Representation Node Load Active power; Respectively represent branches Active power flow power; Indicates the upper limit of the line power flow; is the objective function of the main problem; The feasible upper bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the generator-like aggregation unit after the flexible resource partition aggregation; The feasible upper bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; The feasible lower bound of the output of the energy storage-like aggregation unit after the flexible resource partition aggregation; Output uncertainty for aggregated units of similar generators; , It is the adaptive variable after the uncertainty occurs; is the parameter matrix; is the objective function of the subproblem.

16. A computer-readable storage medium storing one or more programs, It is characterized in that The one or more programs include 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, It is characterized in that include: 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 include steps for executing the method according to any one of claims 1 to 9.

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