Distribution network reconstruction method, system, device, storage medium and program product

Abstract

The present disclosure relates to the field of smart grid technology and provides a distribution network reconstruction method, system, device, storage medium and program product. The method includes: establishing a distribution network flow model and determining constraint conditions; obtaining a distribution network grid-connected power flexibility aggregation model based on the distribution network flow model; obtaining a distribution network grid-connected flexibility calculation model based on the distribution network grid-connected power flexibility aggregation model and a two-stage robust optimization model; constructing a distribution network network reconstruction model; obtaining a network reconstruction robust optimization model that maximizes grid flexibility based on the distribution network grid-connected flexibility calculation model and the distribution network network reconstruction model; solving the network reconstruction robust optimization model that maximizes grid flexibility to obtain an optimal solution. Utilizing the present disclosure to formulate a distribution network network reconstruction strategy can provide the main network with the maximum power flexibility regulation resources and improve the power support capacity of the common coupling node of the distribution network.

Description

Technical Field

[0001] The present disclosure belongs to the field of smart grid technology, and in particular relates to a distribution network reconstruction method, system, device, storage medium and program product. Background Art

[0002] As a system where multiple distributed energy sources of "source-grid-load-storage" coexist, the distribution network is a key hub between the main grid and the distribution system. It can provide flexibility to the main grid through the common coupling point, solve the power balance and peak regulation of the main grid, and is of great significance to improving the economy and safety of power system operation.

[0003] Given the constraints within distribution networks, grid-connected power flexibility is not simply the sum of the power flexibility of all distributed resources. In many scenarios, distributed resource flexibility cannot be effectively reflected at the common coupling node. Limited power flexibility at the common coupling node remains a pressing issue, driven by internal security and voltage constraints within the distribution network.

[0004] Although it has been found that the network topology of the distribution network can be optimized through network reconstruction and the power flexibility of distributed resources in the distribution network can be fully released, it is also extremely challenging to combine the distribution network reconstruction problem optimization model with the distribution network common coupling node power flexibility evaluation model. Summary of the Invention

[0005] To solve the above problems, the present disclosure provides a distribution network reconstruction method for improving the grid-connected power flexibility. Based on a two-stage adaptive robust method, the common coupling node of the distribution network is used as the aggregation point, and the power flexibility of all distributed resources in the distribution network is aggregated to the common coupling node, thereby improving the power support capability of the common coupling node of the distribution network to the main network.

[0006] The technical contribution of this disclosure can be reflected in the following solutions:

[0007] In a first aspect, the present disclosure provides a distribution network reconstruction method, comprising:

[0008] Establish distribution network power flow model and determine constraints;

[0009] According to the distribution network power flow model, the power flexibility aggregation model of the common coupling point is obtained;

[0010] Based on the distribution network grid-connected power flexibility aggregation model and the two-stage robust optimization model, a distribution network grid-connected flexibility calculation model is obtained;

[0011] Construct a distribution network reconstruction model;

[0012] Based on the distribution network connection flexibility calculation model and the distribution network reconstruction model, a network reconstruction robust optimization model that maximizes grid connection flexibility is obtained;

[0013] Solve the network reconfiguration robust optimization model to maximize grid-connected flexibility and obtain the optimal solution.

[0014] Further,

[0015] Establish a distribution network power flow model and determine the constraints, including:

[0016] Establish distribution network power flow model;

[0017] Determine the network constraints for each distributed resource.

[0018] Further,

[0019] According to the distribution network power flow model, the distribution network grid-connected power flexibility aggregation model is obtained, including:

[0020] All state variables on the total optimization time scale in the distribution network power flow model are defined as vectors ;

[0021] According to the vector defined , establish a comprehensive system network model of the distribution network;

[0022] Based on the comprehensive system network model of the distribution network, a distribution network grid-connected power flexibility aggregation model is created.

[0023] Further,

[0024] Construct a distribution network reconstruction model, including:

[0025] According to the constraints, determine the voltage drop equation of the distribution network branch;

[0026] According to the distribution network grid-connected power flexibility aggregation model and the distribution network branch voltage drop equation, a distribution network reconstruction model is established.

[0027] Further,

[0028] Solve the network reconfiguration robust optimization model to maximize grid connection flexibility and obtain the optimal solution, including:

[0029] Decompose the first-stage max problem of the two-stage adaptive robust optimization problem and use the Gurobi solver to solve it;

[0030] Based on the strong duality theorem, the inner max duality of the second-stage min-max problem is merged with the outer min. Based on the solution of the first-stage max problem, the second-stage min-max problem is solved and new uncertain parameters are generated.

[0031] New constraints are established based on the new uncertain parameters, and the C&CG algorithm is used to iteratively solve the first-stage max problem and the second-stage min-max problem until the convergence conditions are met.

[0032] In a second aspect, based on the same inventive concept, the present disclosure further provides a distribution network reconstruction system for implementing any of the aforementioned distribution network reconstruction methods, comprising: a distribution network flow module, a flexibility aggregation module, a calculation model generation module, a network reconstruction module, a model optimization module, and a solution module;

[0033] Distribution network flow module, used to establish distribution network flow model and determine constraint conditions;

[0034] A flexibility aggregation module is used to obtain a distribution network grid-connected power flexibility aggregation model based on a distribution network power flow model;

[0035] A calculation model generation module is used to obtain a distribution network grid-connected flexibility calculation model based on a distribution network grid-connected power flexibility aggregation model and a two-stage robust optimization model;

[0036] Network reconstruction module, used to build a distribution network reconstruction model;

[0037] A model optimization module is used to obtain a network reconfiguration robust optimization model that maximizes grid connection flexibility based on the distribution network connection flexibility calculation model and the distribution network reconfiguration model;

[0038] The solution module is used to solve the network reconfiguration robust optimization model that maximizes grid-connected flexibility and obtain the optimal solution.

[0039] In a third aspect, based on the same inventive concept, the present disclosure further provides an electronic device, comprising at least one processor and at least one memory electrically connected;

[0040] The memory is electrically connected to the processor, wherein the memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to perform any of the aforementioned distribution network reconstruction methods.

[0041] In a fourth aspect, based on the same inventive concept, the present disclosure further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program;

[0042] When the computer program is executed by a processor, any of the aforementioned distribution network reconstruction methods is implemented.

[0043] In a fifth aspect, based on the same inventive concept, the present disclosure further provides a computer program product, wherein the computer program product is stored in at least one storage medium;

[0044] The computer program product includes several instructions for causing at least one electronic device to execute any of the aforementioned distribution network reconstruction methods.

[0045] Compared with the prior art, the present disclosure has the following advantages:

[0046] The technical solution disclosed in this paper optimizes the distribution network topology and constructs a two-stage adaptive robust optimization model to maximize the flexibility of the distribution network's grid-connected power. This model is used to formulate a distribution network reconfiguration strategy and fully exploit the flexibility of distributed resources. This solution can provide the main grid with maximum power flexibility regulation resources and improve the power support capability of the distribution network's common coupling nodes.

[0047] Other features and advantages of the present disclosure will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present disclosure. The purposes and other advantages of the present disclosure can be realized and obtained by the structures indicated in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A schematic diagram of power flexibility aggregation for a distribution network connection according to an embodiment of the present disclosure is shown;

[0050] Figure 2 A schematic diagram of a flow chart of a method for reconfiguring a power distribution network according to an embodiment of the present disclosure is shown;

[0051] Figure 3 A schematic structural diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0053] In the embodiment of the present disclosure, the adjustable range of power absorbed or emitted by the distribution network from the main grid through the common coupling point over the entire time scale under a determined network topology is referred to as the distribution network grid-connected power flexibility.

[0054] The distribution network power flexibility provides the maximum grid flexibility range of the distribution network that meets safety constraints, which facilitates the evaluation and optimization of the power flexibility of the common coupling point.

[0055] The grid-connected power flexibility boundary of the distribution network is defined as the set of critical operating points that meet the distribution network operation constraints.

[0056] Figure 1 This is a schematic diagram of power flexibility aggregation in a distribution network. In the disclosed embodiments, the common coupling point of the distribution network is selected as the power flexibility aggregation point. Under the premise of ensuring voltage safety within the distribution network and no overload of branches, the flexibility of all distributed resources in the distribution network is aggregated to the common coupling point.

[0057] Flexible distributed resources include distributed photovoltaics, distributed energy storage, distributed wind power, adjustable loads, electric vehicles, generators, etc. The nodes described in this disclosure are grid-connected feed-in points for distributed resources.

[0058] A method for reconfiguring a power distribution network according to an embodiment of the present disclosure includes the following steps:

[0059] S1, establish the distribution network flow model and determine the constraints.

[0060] S11, establish distribution network flow model

[0061] Considering that voltage is the main operating limitation of a high proportion of distributed generation feeding into the distribution network, this paper adopts the LinDistFlow power flow model that takes voltage distribution into account. This model combines the advantages of linearization processing, high-precision voltage estimation, flexibility and scalability, and has important application value in power system analysis and optimization. The distribution network power flow model is expressed as:

[0062] ,

[0063] The formulas represent in sequence: the active power balance equation of any node; the reactive power balance equation of any node; the voltage drop equation of any branch; and the capacity constraint of any branch.

[0064] Where: for The photovoltaic generator injects the node at the moment Active power; for The photovoltaic generator injects the node at the moment Reactive power; for Distributed energy storage system injection node at any time Active power; For branch resistance; For branch reactance; m For nodes connected nodes; for Time Node Active power of the load; for Time Node Reactive power of the load; for Time Node Inject into node Active power; for Time Node Inject into node Reactive power; for Time outflow node Active power; for Time outflow node Reactive power; For branch The first node The voltage amplitude; For branch The end node of The voltage amplitude; For branch The upper capacity limit of .

[0065] S12, determining network constraints of each distributed resource.

[0066] (1) Constraints on the flexibility of distributed photovoltaics

[0067] ,

[0068] The formulas represent in sequence: the upper and lower limits of the active power of the photovoltaic motor; the upper and lower limits of the reactive power of the photovoltaic motor; Time Node The capacity constraints of the photovoltaic generator;

[0069] in: for The photovoltaic generator injects the node at the moment Active power upper limit; for The photovoltaic generator injects the node at the moment The lower limit of active power; for The photovoltaic generator injects the node at the moment The upper limit of reactive power; for The photovoltaic generator injects the node at the moment The lower limit of reactive power; For nodes The apparent power capacity of the photovoltaic generator.

[0070] (2) Constraints on the flexibility of distributed energy storage systems

[0071] ,

[0072] The formulas are expressed in turn: Energy conservation constraints at all times; Energy conservation constraints at all times; Time Node Upper and lower limit constraints on the charge and discharge power of the upper energy storage device; Time Node Upper and lower limit constraints on the power of the energy storage device;

[0073] in: for Time Node The amount of electricity in the energy storage device; for Time Node The charging and discharging power of the upper energy storage device. T represents the total optimization time; Representation node Energy loss coefficient of the upper energy storage device under charging and discharging changes with time; ∆t Indicates the time change; Representation node The initial state of charge of the upper energy storage device; Representation node Upper energy storage device The amount of electricity at the moment; express Energy storage system injection node at all times Active power upper limit; express Energy storage system injection node at all times The lower limit of active power; express Energy storage system injection node at all times The upper limit of reactive power; express Energy storage system injection node at all times The upper and lower limits of reactive power.

[0074] S2, based on the distribution network power flow model, obtains the distribution network grid-connected power flexibility aggregation model.

[0075] S21, the distribution network flow model All state variables at the moment are defined as vectors ,

[0076] ,

[0077] Here, the superscript T on the vector is in traditional Chinese, indicating the transpose operation of the matrix.

[0078] S22, according to the vector defined , establish the distribution network comprehensive system network model:

[0079] ,

[0080] The formulas represent in sequence: the active power balance equation at the common coupling point of the distribution network; the reactive power balance equation at the common coupling point of the distribution network; all second-order cone constraints; and other network constraints.

[0081] in: is the active power at the common coupling point of the distribution network; is the reactive power at the common coupling point of the distribution network; All are vectors The coefficient matrix of (state variables); These are all given parameters, such as node load power, branch resistance , branch reactance wait; is the total number of second-order cone constraints.

[0082] Since the constraints in the battery energy storage system model have time coupling characteristics, in order to simplify the expression of the coefficient matrix and facilitate problem solving, the following energy conservation constraints need to be introduced:

[0083] ,

[0084] Where: are coefficient matrices; All are given parameters.

[0085] S23, based on the distribution network integrated system network model, creates a distribution network grid-connected power flexibility aggregation model.

[0086] In the present disclosure, the essence of flexibility aggregation is to project a high-dimensional space determined by the flexibility resource state variables in the Euclidean space onto the distribution network common coupling point state variable injection space.

[0087] Assume that the grid-connected power flexibility of the distribution network aggregated to the common coupling point is , represented as a high-dimensional polyhedron:

[0088] ,

[0089] in: express The upper limit of the distribution network's grid-connected power flexibility at any given moment; express The lower bound of the distribution network's grid-connected power flexibility at any given moment; , T Expressed as the total optimization time.

[0090] The objective function of the distribution network grid-connected power flexibility aggregation model is expressed as:

[0091] ,

[0092] Distribution network grid-connected power flexibility has the maximum interior approximation to the true solution. For any operating point in the system, a corresponding operating point can be found after deaggregation (vector ), to regulate and manage flexible resources through scheduling instructions, so it should meet the following requirements:

[0093] .

[0094] Therefore, the constraints of the distribution network grid-connected power flexibility aggregation model are:

[0095] ,

[0096] Among them, the constraints include all safety constraints, power flow constraints and disaggregation feasibility constraints.

[0097] S3, based on the distribution network grid-connected power flexibility aggregation model and the two-stage robust optimization model, the distribution network grid-connected flexibility calculation model is obtained.

[0098] The essence of the distribution network grid-connected power flexibility aggregation is to project a high-dimensional space determined by the flexibility resource state variables onto the power injection space of the distribution network common coupling point in the Euclidean space. Any operating point in the power injection space of the distribution network common coupling point can be mapped to the high-dimensional space determined by the flexibility resource state variables, which satisfies the feasibility of disaggregation.

[0099] Distribution network grid-connected power flexibility In essence, it is also the power feasible domain of the common coupling point. For dispatching, it is also the dispatchable range of the distribution network. For the distribution network, it is also the operation safety domain of its system.

[0100] It can be seen that the distribution network grid-connected power flexibility aggregation model is only a physical property model, which needs to be rewritten into a mathematical model.

[0101] Therefore, a two-stage adaptive robust optimization model is used to quantify the power flexibility of the distribution network common coupling point. The distribution network grid-connected power flexibility aggregation model is transformed in two stages: aggregation in the first stage and feasibility verification of deaggregation in the second stage. Finally, the distribution network grid-connected power flexibility calculation model is obtained, that is, the power flexibility of all distributed resources is aggregated to obtain the maximum power flexibility of the distribution network common coupling point. For any operating point in the aggregated distribution network common coupling point power flexibility space, there is a vector after deaggregation. Deal with it.

[0102] In order to verify whether the maximum power flexibility obtained by aggregation meets the feasibility of deaggregation, some operating points are randomly selected for verification without conservativeness. It is obviously infeasible to verify each operating point in the maximum power flexibility space of the common coupling point of the distribution network.

[0103] To this end, we first introduce the uncertainty parameter To express the active power at the common coupling point of the distribution network , according to the continuity of the feasible region, take A decimal between 0 and 1, equivalent to the upper and lower bounds of the maximum power flexibility at the point of common coupling and An interpolation is taken between them to ensure that every operating point in the maximum power flexibility space of the distribution network common coupling point can be obtained:

[0104] ,

[0105] Defining uncertain parameters Belongs to the uncertainty set :

[0106] .

[0107] The distribution network power flexibility calculation model optimized by the two-stage adaptive robust peer model is expressed as:

[0108] ,

[0109] The optimization quantity in the first stage is the upper and lower limits of the maximum power flexibility, the purpose of which is to find the maximum power flexibility of the common coupling point of the distribution network; Two-level optimization ensures that the uncertainty parameters Get the uncertainty set In the worst-case scenario, there is a flexible resource scheduling solution To achieve and ensure the feasibility of depolymerization.

[0110] The constraints are:

[0111] ,

[0112] Constraints include all network topology constraints and aggregation and disaggregation feasibility constraints.

[0113] S4, construct a distribution network reconstruction model.

[0114] S41, determining the voltage drop equation of the distribution network branch according to the constraint conditions.

[0115] According to the characteristics of open-loop operation in closed-loop design of distribution network, two integer variables are introduced: and , ensuring that the network remains in a tree structure after reconstruction.

[0116] ,

[0117] in: Indicates a branch The switch state, When it is 1, it indicates a branch In the on state, When it is 0, it indicates a branch It is in disconnected state; Representation node With node The node association matrix of the parent node, when the node For nodes When the parent node is 1 if the value is set, otherwise it is 0.

[0118] According to graph theory and spanning tree theory, each node in the distribution network topology, except for the common coupling point, has only one parent node, and the root node has no parent node. It can be expressed as:

[0119] ,

[0120] Where: If there is a branch , then the node is a node The parent node is At the same time, it is necessary to ensure that the node Not a node The parent node is , which can be constrained as:

[0121] .

[0122] When the branch After disconnection, ensure that the branch Active power on , reactive power is 0, so the aforementioned branch The constraints should be expressed as:

[0123] ,

[0124] Where: If the branch In disconnected state, due to branch Constraints will and The branch voltage drop equation mentioned above will become equal to 0, that is, the branch is forced to be disconnected. The voltage amplitudes at both ends are equal, which is obviously incorrect, so we introduce The branch voltage drop equation is transformed into:

[0125] ,

[0126] S42, establishing a distribution network reconstruction model.

[0127] The objective function formula of the distribution network reconstruction model is:

[0128] ,

[0129] Constraints:

[0130] ,

[0131] Among them: the constraints include all safety constraints of distribution network operation, power flow constraints and radial constraints of network reconstruction.

[0132] S5, based on the distribution network grid-connected power flexibility calculation model and the distribution network reconstruction model, a network reconstruction robust optimization model for maximizing grid-connected flexibility is obtained.

[0133] The flexibility of distribution network connection can be improved through network reconstruction. Therefore, based on the network reconstruction model and power flexibility calculation model for maximizing operation flexibility, the power flexibility aggregation problem considering network reconstruction can be written as a two-stage adaptive robust optimization model. That is, the network topology is optimized and the power flexibility of distributed resources is aggregated to obtain the maximum power flexibility of the common coupling point of the distribution network. For any operating point in the aggregated feasible domain, there is a Deal with it.

[0134] Based on the above, the outer layer of the distribution network grid-connected power flexibility calculation model and distribution network reconstruction model Merger (the calculation model is composed of the distribution network grid power flexibility aggregation model Therefore, the two have essentially the same objective function expression), optimizing the power flexibility of the distribution network common coupling point and forming a network reconfiguration robust optimization model that maximizes grid connection flexibility:

[0135] Optimization objective function:

[0136] ,

[0137] The first stage optimization includes the state of the line switch, the upper and lower limits of the maximum power feasible region, and the purpose is to find the maximum power flexibility of the common coupling point of the distribution network under the optimal topology; the second stage is to find the maximum power flexibility of the distribution network under the optimal topology; Two-level optimization ensures that the uncertainty parameters Get the uncertainty set In the worst-case scenario, there is a flexible resource scheduling solution To achieve and ensure the feasibility of depolymerization.

[0138] Constraints:

[0139] ,

[0140] Among them: the constraints include all network topology constraints and aggregation and disaggregation feasibility constraints, and include tree topology constraints that the network reconstruction must meet.

[0141] S6, solves the network reconfiguration robust optimization model to maximize grid-connected flexibility.

[0142] The column and constraint generation (C&CG) algorithm is used to solve the two-stage adaptive robust optimization problem. First, the two-stage adaptive robust optimization problem is decomposed into a main problem and subproblems. Then, the main and subproblems are solved iteratively to obtain the optimal solution. The specific decomposition process of the main problem and subproblems and the solution process of the C&CG algorithm are as follows:

[0143] S61, solve the main problem.

[0144] According to the solution mechanism of the C&CG algorithm, the first-stage max problem of the two-stage adaptive robust optimization problem is written as follows:

[0145] The objective function of the main problem is:

[0146] ,

[0147] The constraints of the main problem are:

[0148] ,

[0149] in: Represents the scenario of sub-problem generation, at the first iteration is the given initial value; is the current iteration number; is the total number of iterations; For the scene Adaptive solution, so in In each scenario, adaptive generation indivual .

[0150] Call the Gurobi solver to solve the main problem.

[0151] S62, solve the subproblem.

[0152] After solving the main problem, the switch variables that satisfy all the constraints of the main problem have been obtained , upper and lower bounds of maximum power flexibility at the point of common coupling and and all state variables .

[0153] Based on the strong duality theorem, the inner max duality of the second-stage min-max problem is merged with the outer min, and the sub-problem can be expressed as an optimization problem:

[0154] Objective function:

[0155] ,

[0156] Constraints:

[0157] ,

[0158] Among them, the constraints include all network constraints.

[0159] S621, the dual problem of the subproblem.

[0160] Since the objective function of the subproblem is a min-max two-level optimization problem, in order to transform the inner max problem into a min problem and merge it with the outer min problem, it is necessary to find the dual problem of the inner max problem.

[0161] Assume that the Lagrange multipliers of the dual variables in the constraints are , based on the strong duality theory, the objective function of the subproblem is transformed into:

[0162] ,

[0163] When verifying the feasibility of disaggregation in the sub-problem, due to the convexity of the power flexibility injection space, as long as the worst scenario in the power flexibility injection space aggregated by the main problem can meet the feasibility of disaggregation, that is, the boundary points of the power flexibility injection space can meet the feasibility of disaggregation, all operating points in the power flexibility injection space will also meet the feasibility of disaggregation. Therefore, the random variable The value of can be simplified to a binary variable:

[0164] ,

[0165] The constraints of the subproblem can be reformulated as:

[0166] .

[0167] S622, Linearization of subproblems.

[0168] There are nonlinear terms in the objective function of the transformed sub-problem , which makes it difficult to solve, so we use The objective function of the sub-problem is transformed into:

[0169] ,

[0170] in: They are all intermediate variables generated during the linearization process.

[0171] when , , ;when hour, , , subject to the following constraints:

[0172] .

[0173] According to the above description, the subproblem can be reformulated as follows:

[0174] ,

[0175] ,

[0176] ,

[0177] ,

[0178] The solution of the subproblem will therefore be transformed into the solution of the mixed integer second-order cone problem, which is also solved directly using the Gurobi solver based on the solution results of the first-stage max problem.

[0179] S63, the C&CG algorithm iterates until the main problem meets the convergence conditions.

[0180] Two-stage robust optimization problems can be solved using the Benders-dual tangent plane algorithm and the Column-and-Constraint Generation (C&CG) algorithm. Considering C&CG's superior computational performance and insensitivity to the problem, the C&CG algorithm is used in this example.

[0181] After the subproblem is solved, if the subproblem objective function value still does not meet the convergence condition, a new variable will be generated Then add new constraints to the main problem and continue iterative calculations. The new constraints added are:

[0182] .

[0183] The C&CG algorithm is used to iteratively solve the first-stage max problem and the second-stage min-max problem until the convergence conditions are met, and the optimal solution of the main problem is obtained. and .

[0184] In summary, the embodiments of the present disclosure first quantitatively evaluate the flexibility of all distributed resources in the distribution network, and establish an evaluation model based on power flexibility aggregation with the common coupling point as the aggregation point; then, a grid-connected power flexibility optimization model considering the network reconstruction of the distribution network is established, and a two-stage adaptive robust optimization model with the maximum grid-connected power flexibility as the goal is proposed. In the first stage of the model, the topology of the distribution network is optimized and the maximum flexibility of all distributed resources at the common coupling point is aggregated. In the second stage, the feasibility of deaggregating the flexible operating range of the common coupling point obtained by aggregation in the first stage is verified.

[0185] Based on the same inventive concept, an embodiment of the present disclosure also provides a distribution network reconstruction system corresponding to the aforementioned method, including a distribution network flow module, a flexibility aggregation module, a calculation model generation module, a network reconstruction module, a model optimization module and a solution module.

[0186] Distribution network flow module, used to establish distribution network flow model and determine constraint conditions;

[0187] A flexibility aggregation module is used to obtain a power flexibility aggregation model of a common coupling point based on a distribution network power flow model;

[0188] A computational model generation module is used to optimize the common coupling point power flexibility aggregation model to obtain a two-stage adaptive robust peer-to-peer model;

[0189] Network reconstruction module, used to build a distribution network reconstruction model;

[0190] A model optimization module is used to obtain a network reconfiguration robust optimization model that maximizes grid connection flexibility based on a two-stage adaptive robust peer-to-peer model and a distribution network reconfiguration model;

[0191] The solution module is used to solve the network reconfiguration robust optimization model that maximizes grid-connected flexibility and obtain the optimal solution.

[0192] Based on the same inventive concept as the above disclosure, the present disclosure also provides an electronic device. Figure 3 As shown, the electronic device of an embodiment of the present disclosure includes at least one processor and at least one memory electrically connected to each other, wherein the memory is electrically connected to the processor, wherein the memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the method as described above.

[0193] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0194] Based on the same inventive concept, the present disclosure further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0195] Based on the same inventive concept, the present disclosure further provides a computer program product, which is stored in at least one storage medium; the computer program product includes several instructions for enabling at least one computer device to execute the above method.

[0196] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A distribution network reconstruction method, characterized in that: The method comprises: Establish distribution network power flow model and determine constraints; According to the distribution network power flow model, the distribution network grid-connected power flexibility aggregation model is obtained; According to the distribution network grid-connected power flexibility aggregation model and the two-stage robust optimization model, the distribution network grid-connected flexibility calculation model is obtained: ， ， in, represents the upper bound of the maximum power flexibility at the point of common coupling; represents the lower bound of the maximum power flexibility at the point of common coupling; is an uncertain parameter; I is the uncertainty set, ; Indicates a flexible resource scheduling scheme; is a vector; All are vectors The coefficient matrix of All are coefficient matrices of decision variables with time-coupled related constraints; , All are given parameters; is the total number of second-order cone constraints; Constructing a distribution network reconstruction model: ， ， in, is the active power at the common coupling point of the distribution network; is the reactive power at the common coupling point of the distribution network; Indicates a branch The switch state, When it is 1, it indicates a branch In the on state, When it is 0, it indicates a branch It is in disconnected state; Representation node With node The node association matrix of the parent node, when the node For nodes When the parent node is 1, otherwise 0; To aggregate the power flexibility of the distribution network connected to the point of common coupling, ; T Expressed as the total optimization time, ; Based on the distribution network connection flexibility calculation model and the distribution network reconstruction model, a network reconstruction robust optimization model for maximizing grid connection flexibility is obtained: The outer layer of the distribution network power flexibility calculation model is merged with the distribution network network reconstruction model. The network reconstruction robust optimization model for maximizing grid flexibility has the same objective function expression as the distribution network power flexibility calculation model: ， ； Solve the network reconfiguration robust optimization model to maximize grid-connected flexibility and obtain the optimal solution.

2. The method according to claim 1, characterized in that Establish a distribution network power flow model and determine the constraints, including: Establish distribution network power flow model; Determine the network constraints for each distributed resource.

3. The method according to claim 1 or 2, characterized in that According to the distribution network power flow model, the distribution network grid-connected power flexibility aggregation model is obtained, including: All state variables on the total optimization time scale in the distribution network power flow model are defined as vectors ; According to the vector defined , establish a comprehensive system network model of the distribution network; Based on the comprehensive system network model of the distribution network, a distribution network grid-connected power flexibility aggregation model is created.

4. The method according to claim 1, wherein Construct a distribution network reconstruction model, including: According to the constraints, determine the voltage drop equation of the distribution network branch; According to the distribution network grid-connected power flexibility aggregation model and the distribution network branch voltage drop equation, a distribution network reconstruction model is established.

5. The method according to claim 1, wherein Solve the network reconfiguration robust optimization model to maximize grid connection flexibility and obtain the optimal solution, including: Decompose the first-stage max problem of the two-stage adaptive robust optimization problem and use the Gurobi solver to solve it; Based on the strong duality theorem, the inner max duality of the second-stage min-max problem is merged with the outer min. Based on the solution of the first-stage max problem, the second-stage min-max problem is solved and new uncertain parameters are generated. New constraints are established based on the new uncertain parameters, and the C&CG algorithm is used to iteratively solve the first-stage max problem and the second-stage min-max problem until the convergence conditions are met.

6. The method according to claim 5, characterized in that Based on the solution of the first-stage max problem, the second-stage min-max problem of the two-stage adaptive robust optimization problem is solved, including: Based on the strong duality theorem, the subproblem is reformulated and linearized into a mixed integer second-order cone problem; Directly solve the mixed-integer second-order cone problem using the Gurobi solver.

7. A distribution network reconstruction system, used to implement the distribution network reconstruction method according to any one of claims 1 to 6, characterized in that: The system includes a distribution network flow module, a flexibility aggregation module, a calculation model generation module, a network reconstruction module, a model optimization module and a solution module; Distribution network flow module, used to establish distribution network flow model and determine constraint conditions; A flexibility aggregation module is used to obtain a distribution network grid-connected power flexibility aggregation model based on a distribution network power flow model; A calculation model generation module is used to obtain a distribution network grid-connected flexibility calculation model based on a distribution network grid-connected power flexibility aggregation model and a two-stage robust optimization model; Network reconstruction module, used to build a distribution network reconstruction model; A model optimization module is used to obtain a network reconfiguration robust optimization model that maximizes grid connection flexibility based on the distribution network connection flexibility calculation model and the distribution network reconfiguration model; The solution module is used to solve the network reconfiguration robust optimization model that maximizes grid-connected flexibility and obtain the optimal solution.

8. An electronic device, characterized in that: comprising at least one processor and at least one memory electrically connected; The memory is electrically connected to the processor, wherein the memory stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors to enable at least one of the processors to execute the distribution network reconstruction method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer readable storage medium stores a computer program; When the computer program is executed by a processor, the distribution network reconstruction method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product is stored in at least one storage medium; The computer program product includes several instructions for causing at least one electronic device to execute the distribution network reconstruction method according to any one of claims 1 to 6.

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