Power distribution network reconstruction method, system and device, storage medium and program product

Abstract

The invention relates to the technical field of smart power grids, and provides a power distribution network reconstruction method, system and device, a storage medium and a program product, and the method comprises the steps: building a power flow model of a power distribution network, and determining a constraint condition; obtaining a grid-connected power flexibility aggregation model of the power distribution network according to the power flow model of the power distribution network; obtaining a grid-connected flexibility calculation model of the power distribution network according to the grid-connected power flexibility aggregation model of the power distribution network and the two-stage robust optimization model; constructing a network reconstruction model of the power distribution network; obtaining a network reconstruction robust optimization model with maximized grid-connected flexibility based on the grid-connected flexibility calculation model of the power distribution network and the network reconstruction model of the power distribution network; and solving the network reconstruction robust optimization model with maximized grid-connected flexibility to obtain an optimal solution. The power distribution network reconstruction strategy is formulated by using the method, the maximum power flexibility adjustment resource can be provided for the main network, and the power support capability of the common coupling node of the power distribution network is improved.

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] Considering the internal network constraints of the distribution network, the grid-connected power flexibility is not a simple sum of the power flexibility of all distributed resources. In many scenarios, the flexibility of distributed resources cannot be effectively reflected in the common coupling nodes. Under the internal security constraints and voltage constraints of the distribution network, the problem of limited power flexibility of the common coupling nodes of the distribution network is still an urgent problem to be solved.

[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 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 capacity 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: In a first aspect, the present disclosure provides a distribution network reconstruction method, comprising: Establish distribution network power flow model and determine constraints; According to the power flow model of the distribution network, the power flexibility aggregation model of the common coupling point 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; Construct a distribution network reconstruction model; Based on the distribution network connection flexibility calculation model and the distribution network reconstruction model, a network reconstruction robust optimization model for maximizing the grid connection flexibility is obtained. The robust optimization model of network reconfiguration that maximizes grid-connected flexibility is solved to obtain the optimal solution.

[0007] Furthermore, Build a power flow model for the distribution network and determine the constraint conditions, including: Build a power flow model for the distribution network; Determine the network constraint conditions for each distributed resource.

[0008] Furthermore, According to the power flow model of the distribution network, obtain the aggregated model of the grid-connected power flexibility of the distribution network, including: Define all state variables on the total optimization time scale in the power flow model of the distribution network as a vector ; According to the defined vector , build an integrated system network model for the distribution network; Based on the integrated system network model of the distribution network, create an aggregated model of the grid-connected power flexibility of the distribution network.

[0009] Furthermore, Construct a network reconfiguration model for the distribution network, including: According to the constraint conditions, determine the branch voltage drop equation of the distribution network; According to the aggregated model of the grid-connected power flexibility of the distribution network and the branch voltage drop equation of the distribution network, build a network reconfiguration model for the distribution network.

[0010] Furthermore, Solve the robust optimization model of network reconfiguration for maximizing grid-connected flexibility to obtain the optimal solution, including: Decompose the first-stage max problem of the two-stage adaptive robust optimization problem and call the Gurobi solver to solve it; Based on the strong duality theorem, combine the inner-layer max dual of the second-stage min-max problem with the outer-layer min, and according to the solution result of the first-stage max problem, solve the second-stage min-max problem and generate new uncertain parameters; Based on the new uncertain parameters, establish new constraint conditions, and use the C&CG algorithm to iteratively solve the first-stage max problem and the second-stage min-max problem until the convergence condition is met.

[0011] In a second aspect, based on the same inventive concept, the present disclosure also provides a distribution network network reconfiguration system for implementing any one of the foregoing distribution network network reconfiguration methods, including: a distribution network power flow module, a flexibility aggregation module, a calculation model generation module, a network reconfiguration module, a model optimization module, and a solution module; The distribution network power flow module is used to build a power flow model for the distribution network and determine the constraint conditions; The flexibility aggregation module is used to obtain the aggregated model of the grid-connected power flexibility of the distribution network according to the power flow model of the distribution network; A calculation model generation module, configured to obtain a grid connection flexibility calculation model of a distribution network according to a grid connection power flexibility aggregation model of the distribution network and a two-stage robust optimization model; A network reconstruction module, configured to construct a network reconstruction model of the distribution network; A model optimization module, configured to obtain a network reconstruction robust optimization model that maximizes grid connection flexibility based on the grid connection flexibility calculation model of the distribution network and the network reconstruction model of the distribution network; A solution module, configured to solve the network reconstruction robust optimization model that maximizes grid connection flexibility to obtain an optimal solution.

[0012] In a third aspect, based on the same inventive concept, the present disclosure further provides an electronic device, including at least one processor and at least one memory that are electrically connected; The memory is electrically connected to the processor, wherein the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute any of the foregoing distribution network network reconstruction methods.

[0013] In a fourth aspect, based on the same inventive concept, the present disclosure further provides a computer-readable storage medium, in which a computer program is stored; When the computer program is executed by a processor, it implements any of the foregoing distribution network network reconstruction methods.

[0014] In a fifth aspect, based on the same inventive concept, the present disclosure further provides a computer program product, and 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 any of the foregoing distribution network network reconstruction methods.

[0015] Compared with the prior art, the present disclosure has the following advantages: The technical solution of the present disclosure optimizes the network topology structure of the distribution network, constructs a two-stage adaptive robust optimization model that maximizes the grid connection power flexibility of the distribution network, formulates a distribution network network reconstruction strategy, and fully exploits the flexibility of distributed resources. The present disclosure can provide the main grid with the maximum power flexibility regulation resources and improve the power support ability of the point of common coupling of the distribution network.

[0016] Other features and advantages of the present disclosure will be described in the following specification, and part of them will become obvious from the specification or will be understood by implementing the present disclosure. The objectives and other advantages of the present disclosure can be achieved and obtained through the structures pointed out in the specification, the claims, and the drawings. Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It shows a schematic diagram of the aggregation of the grid-connected power flexibility of the distribution network according to an embodiment of the present disclosure; Figure 2 It shows a schematic flow chart of a distribution network network reconstruction method according to an embodiment of the present disclosure; Figure 3 It shows a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present disclosure.

[0020] In the embodiments of the present disclosure, the adjustable interval of the power absorbed or injected by the distribution network from the main grid through the point of common coupling over the entire time scale under the determined network topology structure is defined as the grid-connected power flexibility of the distribution network.

[0021] The grid-connected power flexibility of the distribution network gives the maximum grid-connected flexibility range that the distribution network satisfies the security constraints, which provides convenience for the evaluation and optimization of the power flexibility at the point of common coupling.

[0022] The boundary of the grid-connected power flexibility of the distribution network is defined as the set of critical operating points that satisfy the operating constraint conditions of the distribution network.

[0023] Figure 1 It is a schematic diagram of the aggregation of the power flexibility of the distribution network. In the embodiments of the present disclosure, the point of common coupling of the distribution network is selected as the power flexibility aggregation point. On the premise of ensuring the internal voltage safety of the distribution network and no overload of the branches, the flexibility of all distributed resources in the distribution network is aggregated to the point of common coupling.

[0024] The distributed resources with flexibility include distributed photovoltaic, distributed energy storage, distributed wind power, adjustable load, electric vehicle, generator, etc. The nodes described in the present disclosure are the grid-connected feeding connection points of the distributed resources.

[0025] A distribution network reconfiguration method according to an embodiment of the present disclosure includes the following steps: S1. Establish a distribution network power flow model and determine the constraint conditions.

[0026] S11. Establish a distribution network power flow model Considering that the voltage problem is the main operation limit for high-proportion distributed power sources feeding into the distribution network, the present disclosure adopts a LinDistFlow power flow model that takes into account voltage distribution. This model has advantages such as 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: , Each formula represents in turn: the active power balance equation of any node; the reactive power balance equation of any node; the voltage drop equation of any branch; the capacity constraint of any branch.

[0027] In the formula: is the active power injected by the photovoltaic generator into node at time is the reactive power injected by the photovoltaic generator into node at time is the active power injected by the distributed energy storage system into node at time is the resistance of branch ; is the reactance of branch ; m is the node connected to node ; is the active power of the load at node at time is the reactive power of the load at node at time is the active power injected into node from node at time is the reactive power injected into node from node at time is the active power flowing out of node at time is the reactive power flowing out of node at time is the branch The head node The voltage amplitude; Is the branch The end node The voltage amplitude; Is the branch The upper capacity limit.

[0028] S12, determine the network constraint conditions of each distributed resource.

[0029] (1) Constraint conditions for the flexibility of distributed photovoltaics , Each formula represents in turn: 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; The node at time The capacity constraint of the photovoltaic generator on; Among them: Is The upper limit of the active power injected by the photovoltaic generator into node at time; Is The lower limit of the active power injected by the photovoltaic generator into node at time; Is The upper limit of the reactive power injected by the photovoltaic generator into node at time; Is The lower limit of the reactive power injected by the photovoltaic generator into node at time; Is the node The apparent power capacity of the photovoltaic generator on.

[0030] (2) Constraint conditions for the flexibility of distributed energy storage systems , Each formula represents in turn: The energy conservation constraint at time; The energy conservation constraint at time; The node at time The upper and lower limits of the charge and discharge power of the energy storage device on; The node at time The upper and lower limits of the electricity quantity of the energy storage device on; Among them: Is The electricity quantity of the energy storage device on the node at time; Is The charge and discharge power of the energy storage device on the node at time. TIndicates the total optimization time; Indicates the node The energy loss coefficient of the charge and discharge of the energy storage device on the node over time; ∆t Indicates the change in time; Indicates the node The power of the initial state of the energy storage device on the node; Indicates the node The energy storage device on the node The power at time Indicates The upper limit of the active power injected by the energy storage system into the node at time ; Indicates The lower limit of the active power injected by the energy storage system into the node at time ; Indicates The upper limit of the reactive power injected by the energy storage system into the node at time ; Indicates The upper and lower limits of the reactive power injected by the energy storage system into the node at time .

[0031] S2. According to the power flow model of the distribution network, the aggregated model of the grid-connected power flexibility of the distribution network is obtained.

[0032] S21. Define all state variables in the power flow model of the distribution network at time as a vector , , Here, the superscript T of the vector is in upright type, indicating the transpose operation of the matrix.

[0033] S22. According to the defined vector , establish the integrated system network model of the distribution network: , Each formula represents in turn: the active power balance equation of the point of common coupling of the distribution network; the reactive power balance equation of the point of common coupling of the distribution network; all second-order cone constraints; other network constraints.

[0034] Among them: Is the active power of the point of common coupling of the distribution network; Is the reactive power of the point of common coupling of the distribution network; Are all coefficient matrices of the vector (state variables); Are all given parameters, such as node load power, branch resistance , branch reactance etc.; Is the total number of second-order cone constraints.

[0035] Due to the time-coupled characteristics of the constraints in the battery energy storage system model, in order to simplify the expression of the coefficient matrix and facilitate the solution of the problem, the following energy conservation constraints need to be introduced: , where: are all coefficient matrices; are all given parameters.

[0036] S23. Based on the integrated system network model of the distribution network, a flexible aggregation model of the distribution network grid-connected power is created.

[0037] In this disclosure, the essence of flexible aggregation is to project a high-dimensional space determined by the state variables of flexible resources onto the injection space of the state variables at the point of common coupling of the distribution network in Euclidean space.

[0038] Let the flexible grid-connected power of the distribution network aggregated to the point of common coupling be , expressed as a high-dimensional polyhedron: , where: represents the upper boundary of the flexible grid-connected power of the distribution network at time represents the lower boundary of the flexible grid-connected power of the distribution network at time , T represents the total optimization time.

[0039] The objective function of the flexible aggregation model of the distribution network grid-connected power is expressed as: , The flexible grid-connected power of the distribution network has the largest internal approximation with a true solution. For any operating point within the aggregated , after disaggregation, a corresponding operating point (vector ) can be found, and the flexible resources can be regulated and managed through scheduling instructions, so it should satisfy: .

[0040] Therefore, the constraint conditions of the flexible aggregation model of the distribution network grid-connected power are: , where the constraint conditions include all safety constraints, power flow constraints, and disaggregation feasibility constraints.

[0041] S3. According to the flexible aggregation model of the distribution network grid-connected power and the two-stage robust optimization model, a calculation model of the distribution network grid-connected flexibility is obtained.

[0042] The essence of aggregating the grid-connected power flexibility of the distribution network is as follows: in Euclidean space, project a high-dimensional space determined by the state variables of flexibility resources onto the power injection space at the point of common coupling (PCC) of the distribution network. Any operating point in the power injection space at the PCC of the distribution network can be mapped into the high-dimensional space determined by the state variables of flexibility resources, that is, it satisfies the feasibility of disaggregation.

[0043] Grid-connected power flexibility of the distribution network Essentially, it is also the power feasible region at the PCC, which is also the schedulable interval of the distribution network for dispatch and the operating safety region of its system for the internal part of the distribution network.

[0044] It can be seen that the aggregation model of the grid-connected power flexibility of the distribution network is only a physical property model and needs to be rewritten into a mathematical model.

[0045] Therefore, using a two-stage adaptive robust optimization model, quantify the power flexibility at the PCC of the distribution network and transform the aggregation model of the grid-connected power flexibility of the distribution network in two stages: the first stage is aggregation, and the second stage is to verify the feasibility of disaggregation. Finally, obtain the calculation model of the grid-connected power flexibility of the distribution network, that is, aggregate the power flexibility of all distributed resources to obtain the maximum power flexibility at the PCC of the distribution network. For any operating point in the aggregated power flexibility space at the PCC of the distribution network, there exists a vector after disaggregation to cope with it.

[0046] To verify whether the aggregated maximum power flexibility satisfies the feasibility of disaggregation, randomly selecting some operating points for verification is not conservative, and it is obviously infeasible to verify each operating point in the maximum power flexibility space at the PCC of the distribution network.

[0047] Therefore, first introduce uncertain parameters to represent the active power at the PCC of the distribution network , according to the continuity of the feasible region, take as a decimal between 0 and 1, which is equivalent to taking an interpolation between the upper and lower bounds and of the maximum power flexibility at the PCC, ensuring that each operating point in the maximum power flexibility space at the PCC of the distribution network can be obtained: , Define the uncertain parameter belonging to the uncertainty set : .

[0048] The calculation model of the grid-connected power flexibility of the distribution network optimized by the two-stage adaptive robust peer model is expressed as: , where the optimization quantities in the first stage are the upper and lower limits of the maximum power flexibility, and the purpose is to find the maximum power flexibility of the point of common coupling of the distribution network; in the second stage, through two-layer optimization to ensure that when the uncertainty parameter takes the worst-case scenario of the uncertainty set , there exists a flexibility resource scheduling scheme to achieve and ensure the feasibility of disaggregation.

[0049] The constraint conditions are: , The constraint conditions include all network topology constraints and aggregation and disaggregation feasibility constraints.

[0050] S4. Construct a distribution network reconfiguration model.

[0051] S41. Determine the branch voltage drop equation of the distribution network according to the constraint conditions.

[0052] According to the characteristics of the distribution network operating in an open-loop manner with a closed-loop design, two integer variables and are introduced to ensure that the network remains a tree structure after reconfiguration.

[0053] , where: represents the switch state of branch , when is 1, it means that branch is in the conducting state, and when is 0, it means that branch is in the disconnected state; represents the node incidence matrix of node and the parent node of node . When node is the parent node of node , is 1, otherwise it is 0.

[0054] According to the graph theory - spanning tree theory, each node of the distribution network topology except the point of common coupling has only one parent node, and the root node has no parent node, which can be expressed as: , where: if there exists a branch , then node is the parent node of node , that is 。Meanwhile, it is necessary to ensure that the node will not be the parent node of the node , that is . It can be constrained as: .

[0055] When the branch is disconnected, it should be ensured that the active power and reactive power on the branch are 0. Therefore, the aforementioned branch constraint should be expressed as: , where: if the branch is in the disconnected state, due to the branch constraint, and will be restricted to 0. The aforementioned branch voltage drop equation will become identically equal to 0, that is, forcing the voltage magnitudes at both ends of the unconnected branch to be equal, which is obviously incorrect. Therefore, the method is introduced to transform the branch voltage drop equation into: , S42, establish the distribution network reconfiguration model.

[0056] Objective function formula of the distribution network reconfiguration model: , Constraint conditions: , where: the constraint conditions include all safety constraints, power flow constraints, and radial constraints of the distribution network operation.

[0057] S5, based on the distribution network grid-connected power flexibility calculation model and the distribution network reconfiguration model, obtain the robust optimization model of network reconfiguration with maximized grid-connected flexibility.

[0058] Network reconfiguration can improve the grid-connected flexibility of the distribution network. Therefore, according to the network reconfiguration model and power flexibility calculation model for maximizing operation flexibility, the power flexibility aggregation problem considering network reconfiguration can be written as a two-stage adaptive robust optimization model. That is, optimize the network topology structure and aggregate the power flexibility of distributed resources to obtain the maximum power flexibility at the point of common coupling of the distribution network. For any operating point in the aggregated feasible region, there exists a to cope with it after disaggregation.

[0059] Based on the above, the outer layer of the distribution network grid-connected power flexibility calculation model and the distribution network reconfiguration model Combined (the calculation model is obtained from the aggregated model of the grid-connected power flexibility of the distribution network ), so the two essentially have the same objective function expression), optimize the power flexibility of the point of common coupling of the distribution network, and form a robust optimization model for network reconfiguration to maximize the grid-connected flexibility: Optimization objective function: , Among them: The optimization quantities in the first stage include the line switch state, the upper and lower limits of the maximum power feasible region, and the purpose is to find the maximum power flexibility of the point of common coupling of the distribution network under the optimal topological structure; in the second stage, through two-layer optimization to ensure that when the uncertainty parameter takes the worst-case scenario of the uncertainty set , there exists a flexibility resource scheduling scheme to achieve, ensuring the feasibility of the disaggregation.

[0060] Constraint conditions: , Among them: The constraint conditions include all network topology constraints and aggregation and disaggregation feasibility constraints, and include the tree topology constraints that need to be satisfied for network reconfiguration.

[0061] S6. Solve the robust optimization model for network reconfiguration to maximize the grid-connected flexibility.

[0062] For the two-stage adaptive robust optimization problem, the column and constraint generation algorithm (C&CG) is used to solve it. First, the two-stage adaptive robust optimization problem is decomposed into a master problem and a sub-problem, and then the master and sub-problems are iteratively solved to obtain the optimal solution. The specific decomposition process of the master and sub-problems and the solution process of the C&CG algorithm are as follows: S61. Solve the master problem.

[0063] 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: Objective function of the master problem: , Constraint conditions of the master problem: , Among them: represents the scenario generated by the sub-problem. At the first iteration, is the given initial value; is the current iteration number; is the total number of iterations; is the solution adapted to the scenario , so at will be adaptively generated under a scenario one .

[0064] Call the Gurobi solver to solve the master problem.

[0065] S62, Solve the sub-problem.

[0066] After solving the master problem, the switching variables that satisfy all the constraints of the master problem , the upper and lower bounds of the maximum power flexibility at the point of common coupling and as well as all state variables .

[0067] Based on the strong duality theorem, the inner max of the second-stage min-max problem is dualized and merged with the outer min. The sub-problem can be formulated as an optimization problem:[[]] Objective function:[[]] , Constraint conditions:[[]] , where the constraint conditions include all network constraints.

[0068] S621, The dual problem of the sub-problem.

[0069] Since the objective function of the sub-problem is a min-max two-layer optimization problem, in order to transform the inner max problem into a min problem and merge it with the outer min problem, the dual problem of the inner max problem needs to be solved.

[0070] Let the Lagrange multipliers of the dual variables in the constraint conditions be respectively. Based on the strong duality theory, the objective function of the sub-problem is transformed into:[[]] , When verifying the feasibility of decomposition in the sub-problem, due to the convex hull property of the power flexibility injection space, as long as the worst-case scenario in the power flexibility injection space aggregated by the master problem can satisfy the feasibility of decomposition, that is, the boundary points of the power flexibility injection space can satisfy the feasibility of decomposition, all operating points in the power flexibility injection space will also satisfy the feasibility of decomposition. Therefore, the value of the random variable can be simplified to a binary variable:[[]] , The constraint conditions of the sub-problem will be reformulated as:[[]] .

[0071] S622, Linearization of the sub-problem.

[0072] There are non - linear terms in the transformed sub - problem objective function , which brings difficulties to the solution. Therefore, the method is used to transform the objective function of the sub - problem again into: , where: are all intermediate variables generated during the linearization process.

[0073] When , , ; when , , , the constraints are: .

[0074] According to the above description, the sub - problem will be reformulated as the following expression: , , , , The solution of the sub - problem will thus be transformed into the solution of a mixed - integer second - order cone problem. According to the solution result of the first - stage max problem, the Gurobi solver is also used to directly solve it.

[0075] S63, the C&CG algorithm iterates until the master problem meets the convergence condition.

[0076] The solution strategies for the two - stage robust optimization problem are the Benders - dual cutting - plane algorithm and the Column - and - Constraint Generation algorithm (C&CG). Considering the excellent computational performance of C&CG and its advantage of being insensitive to problems, the C&CG algorithm is used to solve in this embodiment.

[0077] After the sub - problem is solved, when the objective function value of the sub - problem still does not meet the convergence condition, new variables will be generated. Then, new constraint expressions are added to the master problem for continued iterative operation. The new constraint expressions added are: .

[0078] Use the C&CG algorithm to iteratively solve the first - stage max problem and the second - stage min - max problem until the convergence condition is met, and obtain the optimal solution of the master problem and .

[0079] In summary, the embodiments of the present disclosure first quantitatively evaluate the flexibility of all distributed resources in the distribution network, establish an evaluation model based on power flexibility aggregation with the point of common coupling as the aggregation point; then, establish a grid-connected power flexibility optimization model considering the network reconfiguration of the distribution network, and propose a two-stage adaptive robust optimization model with the maximum grid-connected power flexibility as the goal. In the first stage of the model, by optimizing the topological structure of the distribution network and aggregating the maximum flexibility of all distributed resources at the point of common coupling, and in the second stage, verify the disaggregation feasibility of the flexible operating range of the point of common coupling aggregated in the first stage.

[0080] Based on the same inventive concept, the embodiments of the present disclosure also provide a distribution network network reconfiguration system corresponding to the foregoing method, including a distribution network power flow module, a flexibility aggregation module, a calculation model generation module, a network reconfiguration module, a model optimization module, and a solution module.

[0081] The distribution network power flow module is used to establish a distribution network power flow model and determine the constraint conditions; The flexibility aggregation module is used to obtain a power flexibility aggregation model of the point of common coupling according to the distribution network power flow model; The calculation model generation module is used to optimize the power flexibility aggregation model of the point of common coupling to obtain a two-stage adaptive robust equivalent model; The network reconfiguration module is used to construct a distribution network network reconfiguration model; The model optimization module is used to obtain a network reconfiguration robust optimization model for maximizing grid-connected flexibility based on the two-stage adaptive robust equivalent model and the distribution network network reconfiguration model; The solution module is used to solve the network reconfiguration robust optimization model for maximizing grid-connected flexibility to obtain an optimal solution.

[0082] Based on the same inventive concept as the above-disclosed content, correspondingly, the present disclosure also provides an electronic device. As Figure 3 shown, the electronic device of the embodiments of the present disclosure includes at least one processor and at least one memory that are electrically connected, the memory is electrically connected to the processor, wherein the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the method as described above.

[0083] It should be noted that the electrical connections between the above-mentioned various units do not necessarily mean direct connections between the lines. Indirect connection methods, as long as the purpose of the present disclosure is achieved, are applicable to the embodiments of the present disclosure.

[0084] Based on the same inventive concept, the present disclosure also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the above-described method is implemented.

[0085] Based on the same inventive concept, the present disclosure also provides a computer program product, which is stored in at least one storage medium; the computer program product includes several instructions for causing at least one computer device to execute the above-described method.

[0086] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; 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 method for distribution network reconfiguration, characterized in that, The method includes: Establish a distribution network power flow model and determine the constraint conditions; According to the distribution network power flow model, obtain the aggregated model of the grid-connected power flexibility of the distribution network; According to the aggregated model of the grid-connected power flexibility of the distribution network and the two-stage robust optimization model, obtain the calculation model of the grid-connected flexibility of the distribution network; Construct a distribution network reconfiguration model; Based on the calculation model of the grid-connected flexibility of the distribution network and the distribution network reconfiguration model, obtain the robust optimization model of the network reconfiguration that maximizes the grid-connected flexibility; Solve the robust optimization model of the network reconfiguration that maximizes the grid-connected flexibility to obtain the optimal solution.

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

3. The method according to claim 1 or 2, characterized in that, According to the distribution network power flow model, obtain the aggregated model of the grid-connected power flexibility of the distribution network, including: Define all state variables on the total optimization time scale in the distribution network power flow model as a vector ; According to the defined vectors , a comprehensive system network model of the distribution network is established; Based on the comprehensive system network model of the distribution network, create the aggregated model of the grid-connected power flexibility of the distribution network.

4. The method according to claim 1, characterized in that, Construct a distribution network reconfiguration model, including: According to the constraint conditions, determine the distribution network branch voltage drop equation; According to the aggregated model of the grid-connected power flexibility of the distribution network and the distribution network branch voltage drop equation, establish a distribution network reconfiguration model.

5. The method according to claim 1, characterized in that, Solve the robust optimization model of the network reconfiguration that maximizes the grid-connected flexibility to obtain the optimal solution, including: Decompose the first-stage max problem of the two-stage adaptive robust optimization problem and call the Gurobi solver to solve it; Based on the strong duality theorem, combine the inner-layer max dual of the second-stage min-max problem with the outer-layer min, and according to the solution result of the first-stage max problem, solve the second-stage min-max problem and generate new uncertain parameters; Based on the new uncertain parameters, establish new constraint conditions, and use the C&CG algorithm to iteratively solve the first-stage max problem and the second-stage min-max problem until the convergence condition is met.

6. The method according to claim 5, characterized in that, According to the solution result of the first-stage max problem, solve the second-stage min-max problem of the two-stage adaptive robust optimization problem, including: Based on the strong duality theorem, re-express and linearize the sub-problem into a mixed-integer second-order cone problem; Use the Gurobi solver to directly solve the mixed-integer second-order cone problem.

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

8. An electronic device, characterized in that it includes at least one processor and at least one memory that are electrically connected; the memory is electrically connected to the processor, wherein the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the distribution network network reconfiguration method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium; when the computer program is executed by a processor, it implements the distribution network network reconfiguration method according to any one of claims 1-6.

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 a number of instructions for causing at least one electronic device to execute the distribution network network reconfiguration method according to any one of claims 1-6.

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