Method and device for determining new energy bearing capacity of power distribution network, medium and electronic equipment

Abstract

The invention discloses a power distribution network new energy bearing capacity determination method and device, a medium and electronic equipment, and relates to the technical field of new energy access, and the method comprises the steps: obtaining a plurality of transformer area topology networks of a target power distribution network and operation parameters of multilevel new energy bearing capacity evaluation indexes corresponding to the transformer area topology networks; performing constraint condition construction by adopting the operation parameters corresponding to the topological networks to obtain constraint conditions corresponding to the topological networks; based on each constraint condition and a target function taking maximization of the photovoltaic access capacity of the target power distribution network as a target, a mixed integer second-order cone programming model is adopted to carry out model construction, and an optimization model of the target power distribution network is obtained; and solving the optimization model by adopting a branch and bound algorithm to obtain the new energy bearing capacity of the target power distribution network. The new energy bearing capacity of the power distribution network determined by the method is more accurate.

Description

Technical Field

[0001] The present invention relates to the field of new energy access technology, and in particular to a method, device, medium and electronic equipment for determining the new energy carrying capacity of a distribution network. Background Art

[0002] Distribution networks are undergoing innovation toward efficiency, flexibility, intelligence, and sustainability. A rational assessment of distribution network carrying capacity is a crucial prerequisite for guiding the orderly integration of renewable energy into the grid and reducing wind and solar curtailment. It is also a key foundation for further improving the integration of distributed renewable energy. While existing assessments of distribution network renewable energy carrying capacity take distributed renewable energy into account, with the increasing diversity of distribution network resources and equipment, existing evaluation index systems and methods are unable to comprehensively and accurately measure the distributed renewable energy carrying capacity of distribution networks. Summary of the Invention

[0003] In view of this, the present invention provides a method, device, medium and electronic equipment for determining the new energy carrying capacity of a distribution network, the main purpose of which is to solve the current problem of inaccurate assessment of the distributed new energy carrying capacity of the distribution network.

[0004] To solve the above problems, the present application provides a method for determining the new energy carrying capacity of a distribution network, comprising:

[0005] Obtaining multiple substation topology networks of the target distribution network and operating parameters of multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks;

[0006] respectively constructing constraint conditions using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective topological networks;

[0007] Based on the constraints and the objective function of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct a model to obtain an optimization model of the target distribution network;

[0008] The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network.

[0009] Optionally, respectively using the operating parameters corresponding to each of the topological networks to construct constraint conditions to obtain constraint conditions corresponding to each of the topological networks specifically includes:

[0010] For each of the topological networks, a branch power flow model is constructed based on the active power parameter, reactive power parameter, resistance parameter, reactance parameter, effective voltage parameter, and effective current parameter in the operating parameters, wherein the branch power flow model includes an active power linear equation, a reactive power linear equation, and a voltage linear equation;

[0011] Based on the active power parameter, reactive power parameter, effective voltage parameter and effective current parameter in the operating parameters, the constraint conditions of the branch flow model are constructed to obtain the network flow constraint conditions corresponding to the topological network of each substation.

[0012] Optionally, the constructing of constraint conditions by respectively using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective topological networks further includes:

[0013] For each of the substation topology networks, constraints are constructed based on the first active power of the substation transformer, the first reactive power of the substation transformer, the second active power of the target distribution network main transformer, the second reactive power of the target distribution network main transformer, the substation transformer rated capacity, the main transformer rated capacity, and a predetermined overload factor in each time period, to obtain the substation transformer and main transformer load rate constraints corresponding to each of the substation topology networks;

[0014] Based on the effective current parameter, the line safe current carrying capacity and the predetermined overload factor in the operating parameters of each time period, the constraint conditions are constructed to obtain the line current carrying rate constraint conditions of each line in each substation topology network;

[0015] Constraints are constructed based on the voltage amplitude, voltage amplitude upper limit and voltage amplitude lower limit of each node in the operating parameters of each time period to obtain node voltage constraints corresponding to each node in the substation topology network.

[0016] Optionally, the constructing of constraint conditions by respectively using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective substation topological networks further includes:

[0017] Building an energy storage charge and discharge operation model based on the energy storage charge and discharge power, energy storage charging power, energy storage discharging power and predetermined energy storage charge and discharge efficiency in the operation parameters of each time period;

[0018] Based on the charge and discharge control variables in the operating parameters of each time period, the proportional coefficient of the configuration capacity to the maximum charge and discharge power, the upper limit of the energy storage system configuration capacity, and the lower limit of the energy storage system configuration capacity, the constraints of the energy storage charge and discharge operation model are constructed to obtain the energy storage equipment operation constraints corresponding to each substation topology network.

[0019] Optionally, the constructing of constraint conditions by respectively using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective topological networks further includes:

[0020] Constraints are constructed based on the loads corresponding to each of the substation topology networks in different time periods, the photovoltaic output per unit value of the target distribution network, predetermined uncertainty parameters, and the photovoltaic access capacity allocated to each substation to obtain the substation power balance constraints corresponding to each of the substation topology networks.

[0021] Optionally, the objective function based on the constraints and aiming to maximize the photovoltaic access capacity of the target distribution network is constructed using a mixed integer second-order cone programming model to obtain an optimization model of the target distribution network, specifically including:

[0022] A model is constructed based on the constraints and an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, thereby obtaining a probability assessment model for the new energy carrying capacity of the distribution network;

[0023] A mixed integer second-order cone programming model is used to reconstruct the probability assessment model of the distribution network's new energy carrying capacity to obtain an optimization model of the target distribution network.

[0024] Optionally, the adopting a branch and bound algorithm to solve the optimization model to obtain the renewable energy carrying capacity of the target distribution network specifically includes:

[0025] Solving the initial relaxation problem of the optimization model to obtain an upper bound value of the initial new energy carrying capacity, and adding each root node of the initial relaxation problem to a list of active nodes;

[0026] Select a root node from the active node list for branching, and obtain a branch sub-problem corresponding to the root node;

[0027] Performing a delimitation process on each of the branch subproblems to obtain a function value of the objective function corresponding to each of the branch subproblems, and updating a global optimal value based on a maximum function value among the function values ​​that satisfies each of the constraint conditions;

[0028] Pruning each of the branch subproblems using an optimality pruning method and an integer solution pruning method based on each of the function values;

[0029] A root node is selected from the active node list in an iterative manner to perform branching, delimiting, and pruning processes until the active node list is empty, and a current global optimal value is determined as the new energy carrying capacity of the target distribution network.

[0030] To solve the above problems, the present application provides a device for determining the new energy carrying capacity of a distribution network, comprising:

[0031] An acquisition module, configured to acquire operating parameters of multiple area topology networks of a target distribution network and multi-level new energy carrying capacity evaluation indicators corresponding to each of the area topology networks;

[0032] A constraint condition construction module, configured to respectively use the operating parameters corresponding to each of the topological networks to construct constraint conditions, thereby obtaining constraint conditions corresponding to each of the topological networks;

[0033] A model construction module is used to construct a model using a mixed integer second-order cone programming model based on the constraints and an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, so as to obtain an optimization model of the target distribution network;

[0034] A solution module is used to solve the optimization model using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network.

[0035] In order to solve the above problems, the present application provides a storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the above-mentioned method for determining the new energy carrying capacity of the distribution network are implemented.

[0036] To solve the above problems, the present application provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored on the memory, and the processor implements the steps of the above-mentioned method for determining the new energy carrying capacity of the distribution network when executing the computer program on the memory.

[0037] The beneficial effects of this application are as follows: This application obtains the operating parameters of multiple substation topology networks of the target distribution network and the multi-level new energy carrying capacity evaluation index corresponding to each of the substation topology networks; the multi-level new energy carrying capacity evaluation index system not only embodies systematicity and scientificity in methodology, but also fully demonstrates an in-depth understanding of the complexity and dynamics of the new power system, and provides a multi-dimensional and precise evaluation tool for the orderly and safe access of new energy. The operating parameters corresponding to each of the topological networks are respectively used to construct constraints to obtain the constraints corresponding to each of the topological networks; based on the constraints and the objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct a model to obtain an optimization model of the target distribution network; the solution efficiency and calculation accuracy are high, the model has strong applicability and optimization effect, and a more economical scheduling result can be obtained. The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network. The branch and bound algorithm can adapt to complex constraints to improve the new energy carrying capacity, while ensuring the efficiency of the optimization process and the reliability of the optimization results.

[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0040] Figure 1 A flow chart of a method for determining the new energy carrying capacity of a distribution network provided in an embodiment of the present application is shown;

[0041] Figure 2 A flow chart of a method for determining the new energy carrying capacity of a distribution network provided in an embodiment of the present application is shown;

[0042] Figure 3 A structural block diagram of a device for determining the new energy carrying capacity of a distribution network provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] Various aspects and features of the present application are described herein with reference to the accompanying drawings.

[0044] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.

[0045] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0046] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0047] It should also be understood that although the present application has been described with reference to certain specific examples, those skilled in the art will readily be able to implement many other equivalent forms of the present application.

[0048] The above and other aspects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0049] Specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments described are merely examples of the present application and may be implemented in a variety of ways. Familiar and / or repetitive functions and structures are not described in detail to avoid obscuring the present application with unnecessary or redundant details. Therefore, the specific structural and functional details described herein are not intended to be limiting, but rather serve merely as a basis and representative basis for the claims to teach those skilled in the art to variously utilize the present application with substantially any suitable detailed structure.

[0050] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may all refer to one or more of the same or different embodiments according to the present application.

[0051] The present application provides a method for determining the new energy carrying capacity of a distribution network. Figure 1 Shown, including:

[0052] Step S101: obtaining multiple substation topology networks of a target distribution network and operating parameters of a multi-level new energy carrying capacity evaluation index corresponding to each of the substation topology networks;

[0053] During the specific implementation of this step, first, a multi-dimensional evaluation index system for the new energy carrying capacity of the target distribution network is constructed; for each substation, the substation topology network corresponding to each substation can be obtained, and the distribution network new energy carrying capacity evaluation system is established by considering multiple dimensions such as "station-line-transformer" multiple levels, each node voltage, distribution transformer capacity, and each line current rate. At the station level, the new energy carrying capacity evaluation index is mainly the reverse load rate; at the line level, the new energy carrying capacity evaluation index is constructed from two aspects: voltage and current; the new energy carrying capacity evaluation index at the voltage level includes node voltage; the new energy carrying capacity evaluation index at the current level includes each line current; at the bottom substation transformer level, through indicators such as the forward and reverse load rates of the distribution transformer, the impact of distributed new energy access on the operating status of the substation equipment is deeply analyzed, revealing the changing mechanism of the distribution network infrastructure carrying capacity under the background of high penetration of new energy.

[0054] Step S102: constructing constraint conditions using the operating parameters corresponding to each of the topological networks to obtain constraint conditions corresponding to each of the topological networks;

[0055] During the specific implementation of this step, the operating parameters corresponding to each of the topological networks are used to construct constraints to obtain constraints corresponding to each of the topological networks; each of the constraints includes: network flow constraints, substation transformer and main transformer load rate constraints, line current rate constraints, node voltage constraints, energy storage equipment operation constraints, and substation power balance constraints.

[0056] Step S103: Based on the constraints and the objective function of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct a model to obtain an optimization model of the target distribution network;

[0057] During the specific implementation of this step, a model is constructed based on the various constraints and an objective function aimed at maximizing the photovoltaic access capacity of the target distribution network to obtain a probabilistic assessment model of the distribution network's new energy carrying capacity; a mixed integer second-order cone programming model is used to reconstruct the probabilistic assessment model of the distribution network's new energy carrying capacity to obtain an optimized model of the target distribution network.

[0058] Step S104: using a branch and bound algorithm to solve the optimization model to obtain the new energy carrying capacity of the target distribution network.

[0059] This step uses a mixed-integer second-order cone programming model, which can be efficiently solved using common solvers such as Gurobi and Cplex. By adjusting the uncertainty in the model and considering the impact of varying load and photovoltaic fluctuations on the renewable energy carrying capacity, the solver can be used to determine the renewable energy carrying capacity of the entire distribution network and the corresponding capacity allocated to each substation. This yields the renewable energy carrying capacity of the entire target distribution network for each time period, as well as the photovoltaic capacity allocated to each substation in each time period.

[0060] This application obtains the operating parameters of multiple substation topology networks of the target distribution network and the multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks; the multi-level new energy carrying capacity evaluation indicator system not only embodies systematicity and scientificity in methodology, but also fully demonstrates an in-depth understanding of the complexity and dynamics of the new power system, and provides a multi-dimensional and precise evaluation tool for the orderly and safe access of new energy. The operating parameters corresponding to each of the topological networks are respectively used to construct constraints to obtain the constraints corresponding to each of the topological networks; based on the constraints and the objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct the model to obtain the optimization model of the target distribution network; the solution efficiency and calculation accuracy are high, the model has strong applicability and optimization effect, and a more economical scheduling result can be obtained. The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network. The branch and bound algorithm can adapt to complex constraints to improve the new energy carrying capacity, while ensuring the efficiency of the optimization process and the reliability of the optimization results.

[0061] Another embodiment of the present application provides another method for determining the new energy carrying capacity of a distribution network, such as Figure 2 Shown, including:

[0062] Step S201: obtaining multiple substation topology networks of a target distribution network and operating parameters of a multi-level new energy carrying capacity evaluation index corresponding to each of the substation topology networks;

[0063] During the specific implementation of this step, first, a multi-dimensional evaluation index system for the target distribution network's new energy carrying capacity is constructed; the distribution network's new energy carrying capacity evaluation system can be established from the station level, line level, and transformer level dimensions using multi-dimensional evaluation indicators such as the voltage of each node, the distribution transformer capacity, and the current carrying rate of each line. For each substation, the substation topology network corresponding to each substation can be obtained, and the distribution network's new energy carrying capacity evaluation system can be established by considering multiple levels of "station-line-transformer", the voltage of each node, the distribution transformer capacity, the current carrying rate of each line, and other dimensions. At the station level, the new energy carrying capacity evaluation index is mainly the reverse load rate; at the line level, the new energy carrying capacity evaluation index is constructed from two aspects: voltage and current; the new energy carrying capacity evaluation index at the voltage level includes node voltage; the new energy carrying capacity evaluation index at the current level includes the current of each line; at the bottom substation transformer level, through indicators such as the forward and reverse load rates of the distribution transformer, the impact of distributed new energy access on the operating status of the substation equipment is deeply analyzed, revealing the changing mechanism of the distribution network infrastructure carrying capacity under the background of high penetration of new energy.

[0064] Step S202: For each of the topological networks, construct a branch power flow model based on the active power parameter, reactive power parameter, resistance parameter, reactance parameter, effective voltage parameter, and effective current parameter in the operating parameters, wherein the branch power flow model includes an active power linear equation, a reactive power linear equation, and a voltage linear equation;

[0065] During the specific implementation of this step, the mathematical expression of the active power linear equation can be expressed as follows:

[0066]

[0067] The mathematical expression of the reactive power linear equation can be expressed as follows:

[0068]

[0069] The mathematical expression of the voltage linear equation can be expressed as follows:

[0070]

[0071] Among them, P ij ,Q ij are the active power and reactive power flowing from node i to node j, respectively, p j ,q j are the active power and reactive power flowing out of node j, r ij ,x ij are the resistance and reactance parameters of line ij, V i is the square of the effective value of the voltage at node i, i.e., the effective voltage parameter, l ij is the square of the effective value of the current in line ij, that is, the effective current parameter, and k represents the number of nodes.

[0072] Step S203: constructing the constraint conditions of the branch power flow model based on the active power parameter, reactive power parameter, effective voltage parameter, and effective current parameter in the operating parameters to obtain the network power flow constraint conditions corresponding to each substation topology network;

[0073] During the specific implementation of this step, the mathematical expression of the network power flow constraint condition can be expressed as the following formula (4):

[0074]

[0075] in, Represents a collection of distribution network branches.

[0076] Step S204: constructing load rate constraints of the substation transformer and main transformer corresponding to each substation topology network;

[0077] During the specific implementation of this step, for each of the substation topology networks, constraint conditions are constructed based on the first active power of the substation transformer in each time period, the first reactive power of the substation transformer, the second active power of the target distribution network main transformer, the second reactive power of the target distribution network main transformer, the rated capacity of the substation transformer, the rated capacity of the main transformer, and the predetermined overload factor, to obtain the substation transformer and main transformer load rate constraint conditions corresponding to each of the substation topology networks; the mathematical expression of the substation transformer and main transformer load rate constraint conditions can be expressed as follows:

[0078]

[0079] Among them, P i,t ,Q i,t are the first active power and the first reactive power transmitted by the transformer in the area i during period t, P a,t ,Q a,t are the second active power and the second reactive power transmitted by the main transformer in period t, and S a,N are the rated capacity of transformer in substation i and the rated capacity of main transformer respectively, and β is the overload factor.

[0080] Step S205: constructing line current carrying rate constraint conditions for each line in each of the substation topology networks;

[0081] During the specific implementation of this step, constraints are constructed based on the effective current parameters, line safety current carrying capacity, and predetermined overload coefficients in the operating parameters of each time period to obtain the line current carrying rate constraint conditions of each line in each substation topology network. The mathematical expression of the line current carrying rate constraint conditions can be expressed as follows:

[0082]

[0083] Among them, I ij,t is the effective current parameter flowing through line ij during period t, is the safe current carrying capacity of line ij.

[0084] Step S206: constructing node voltage constraint conditions corresponding to each node in each of the substation topology networks;

[0085] During the specific implementation of this step, constraints are constructed based on the voltage amplitude, voltage amplitude upper limit, and voltage amplitude lower limit of each node in the operating parameters of each time period to obtain the node voltage constraint corresponding to each node in the substation topology network. The mathematical expression of the node voltage constraint can be expressed as the following formula (7):

[0086]

[0087] Among them, V j,k,t is the voltage amplitude of node j during period t, V j and are the upper and lower limits of the voltage amplitude respectively.

[0088] Step S207: Constructing energy storage device operation constraints corresponding to each of the substation topology networks;

[0089] During the specific implementation of this step, an energy storage charge and discharge operation model is constructed based on the energy storage charge and discharge power, energy storage charging power, energy storage discharging power and predetermined energy storage charge and discharge efficiency in the operation parameters of each time period; the mathematical expression of the energy storage charge and discharge operation model can be expressed as the following formula (8):

[0090] P ess，t =P ch，t ·η ess -P dch，t / η ess (8)

[0091] Among them, η ess Indicates the energy storage charging and discharging efficiency, P ess，t Indicates the charging and discharging power of energy storage; P ch，t Indicates the energy storage charging power; P dch，t Represents the energy storage discharge power. Based on the charge and discharge control variables in the operating parameters of each time period, the ratio coefficient between the configuration capacity and the maximum charge and discharge power, the upper limit of the energy storage system configuration capacity, and the lower limit of the energy storage system configuration capacity, the constraint conditions of the energy storage charge and discharge operation model are constructed to obtain the energy storage device operation constraint conditions corresponding to each of the substation topology networks. The mathematical expression of the energy storage device operation constraint conditions can be expressed as the following formula (9):

[0092] 0≤P ch，t ≤u t P ess

[0093] 0≤P dch，t ≤(1-u t )P ess

[0094] P ess =r·E ess

[0095]

[0096] Among them, u t is the charge and discharge control variable, 1 represents charging, 0 represents discharging, r is the proportional coefficient of the configuration capacity and the maximum charge and discharge power, Respectively represent the upper and lower limits of the configuration capacity.

[0097] Step S208: constructing a power balance constraint condition corresponding to each of the substation topology networks;

[0098] During the specific implementation of this step, constraints are constructed based on the loads corresponding to each of the substation topology networks in different time periods, the photovoltaic output per unit value of the target distribution network, predetermined uncertainty parameters, and the photovoltaic access capacity allocated to each substation, thereby obtaining the substation power balance constraints corresponding to each of the substation topology networks. The mathematical expression of the substation power balance constraints can be expressed as follows:

[0099]

[0100] Among them, P i,L,t is the load of the station i in the typical day t period, p i,pv,t is the per-unit value of photovoltaic output of substation i during period t on a typical day, ranging from 0 to 1, and ε is the uncertainty, which reflects the maximum fluctuation of renewable energy output and load.

[0101] Step S209: constructing a model based on the constraints and an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network to obtain a probabilistic assessment model of the new energy carrying capacity of the distribution network;

[0102] In the specific implementation process of this step, the mathematical expression of the objective function can be expressed as the following formula (11):

[0103]

[0104] Among them, σ i is the distributed photovoltaic capacity connected to the substation i.

[0105] Step S210: reconstructing the distribution network new energy carrying capacity probability assessment model using a mixed integer second-order cone programming model to obtain an optimization model of the target distribution network;

[0106] During the specific implementation of this step, first, each of the constraints is converted into a second-order cone constraint. Converting each of the constraints into a second-order cone constraint can transform the nonlinear problem into a convex optimization problem, making it easier to solve. Discrete variables are introduced into the distribution network's new energy carrying capacity probabilistic assessment model, for example: switch state variables (0 indicates disconnection, 1 indicates closing), access point selection variables for distributed power sources, etc. These discrete variables and continuous variables (such as power and voltage) together constitute a mixed integer second-order cone programming model to obtain the optimization model of the target distribution network.

[0107] Step S211: using a branch and bound algorithm to solve the optimization model to obtain the new energy carrying capacity of the target distribution network.

[0108] During the specific implementation of this step, the optimization model is initially relaxed to obtain an upper bound on the initial renewable energy carrying capacity, and each root node of the initial relaxed problem is added to the active node list. Specifically, the model's continuous relaxed problem is first solved, ignoring the integer constraints on integer variables and treating all variables as continuous. The purpose of this step is to obtain an initial upper bound. After solving the relaxed problem, the solver will obtain an optimal solution and the corresponding objective function value. This value becomes the initial bound value and is used in the subsequent branch and bound process. The root node of the initial relaxed problem is added to the active node list. The active node list is used to store all pending subproblems. A root node is selected from the active node list for branching, obtaining a branching subproblem corresponding to the root node. Specifically, a node is selected from the active node list for branching. The node selection strategy can be best bound first, depth first, or other strategies. The best bound first strategy selects the node whose current bound value is closest to the global optimal value for branching, while the depth first strategy prioritizes exploring deeper nodes. Within the selected node, an undetermined integer variable is selected for branching. Variables that take non-integer values ​​in the relaxed problem are typically selected because they are most likely to affect the feasibility of integer solutions. Two new subproblems are generated based on the branching operation and added to the active node list. Each branched subproblem is bounded to obtain the function value of the objective function corresponding to each branched subproblem. The global optimal value is updated based on the maximum function value among the function values ​​that satisfies the constraints. For each newly generated subproblem, the solver solves the relaxed problem again to obtain a new bound value. The optimal value of the relaxed problem is used as the upper bound to obtain a new bound value. If the solution to the relaxed problem of a subproblem satisfies the integer constraints of all integer variables (i.e., a feasible integer solution is found) and its objective function value is better than the currently known global optimal value, the global optimal value is updated and recorded as the current optimal solution. Based on the function values, each branched subproblem is pruned using the optimality pruning method and the integer solution pruning method. If the bound value of a subproblem is already greater than (for minimization problems) or less than (for maximization problems) the currently known global optimal value, the subproblem can be pruned because it cannot contain a better solution. If the relaxation of a subproblem has no feasible solution (for example, it violates certain constraints), the subproblem can be pruned. If the solution to the relaxation of a subproblem already satisfies the integer constraints of all integer variables and its objective function value is not better than the currently known global optimal value, the subproblem can be pruned. A branching, delimiting, and pruning process is performed on the root node selected from the active node list in a loop until the active node list is empty, and the current global optimal value is determined as the new energy carrying capacity of the target distribution network.Repeat the above-mentioned branching, delimiting and pruning steps, continuously select nodes from the active node list for branching operations, solve subproblems, update the global optimal value, and prune subproblems that cannot contain better solutions. When the active node list is empty, the search ends. At this time, the recorded optimal solution is the global optimal solution. In practical applications, the search can also be terminated in advance according to the preset number of iterations or time limit, and the current global optimal value is determined as the new energy carrying capacity of the target distribution network, and the carrying capacity of the entire target distribution network for new energy corresponding to each time period and the photovoltaic capacity allocated to each substation distribution network corresponding to each time period are obtained.

[0109] This application takes into account the network flow constraints, the load rate constraints of the substation transformer and the main transformer, the line current rate constraints, the node voltage constraints, the energy storage equipment operation constraints and the substation power balance constraints. Taking multiple constraints into consideration is conducive to obtaining a more accurate distribution network's new energy carrying capacity; adopting an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network can improve the utilization of new energy and reduce the amount of abandoned light; using a mixed integer second-order cone programming model for model construction to obtain an optimization model of the target distribution network; the solution efficiency and calculation accuracy are high, the model has strong applicability and optimization effect, and a more economical scheduling result can be obtained. The branch and bound algorithm is used to solve the optimization model to obtain the new energy carrying capacity of the target distribution network. The branch and bound algorithm can adapt to complex constraints, thereby improving the new energy carrying capacity, while ensuring the efficiency of the optimization process and the reliability of the optimization results.

[0110] Another embodiment of the present application provides a device for determining the new energy carrying capacity of a distribution network, such as Figure 3 Shown, including:

[0111] Acquisition module 1, used to obtain multiple substation topology networks of the target distribution network and operating parameters of the multi-level new energy carrying capacity evaluation index corresponding to each of the substation topology networks;

[0112] A constraint condition construction module 2 is used to respectively use the operating parameters corresponding to each of the topological networks to construct constraint conditions, thereby obtaining constraint conditions corresponding to each of the topological networks;

[0113] A model construction module 3 is configured to construct a model using a mixed integer second-order cone programming model based on the constraints and an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, thereby obtaining an optimization model of the target distribution network;

[0114] The solving module 4 is used to solve the optimization model using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network.

[0115] During the specific implementation process, the constraint condition construction module 2 is specifically used to: for each of the topological networks, construct a branch flow model based on the active power parameters, reactive power parameters, resistance parameters, reactance parameters, effective voltage parameters and effective current parameters in the operating parameters, and the branch flow model includes an active power linear equation, a reactive power linear equation and a voltage linear equation; construct the constraints of the branch flow model based on the active power parameters, reactive power parameters, effective voltage parameters and effective current parameters in the operating parameters to obtain the network flow constraints corresponding to each substation topological network.

[0116] During the specific implementation process, the constraint condition construction module 2 is also used to: for each of the substation topology networks, based on the first active power of the substation transformer in each time period, the first reactive power of the substation transformer, the second active power of the target distribution network main transformer, the second reactive power of the target distribution network main transformer, the rated capacity of the substation transformer, the rated capacity of the main transformer and the predetermined overload factor, construct constraints to obtain the load rate constraints of the substation transformer and the main transformer corresponding to each of the substation topology networks; based on the effective current parameters, the line safe current carrying capacity and the predetermined overload factor in the operating parameters in each time period, construct constraints to obtain the line current carrying rate constraints of each line in each of the substation topology networks; based on the voltage amplitude, the voltage amplitude upper limit and the voltage amplitude lower limit of each node in the operating parameters in each time period, construct constraints to obtain the node voltage constraints corresponding to each node in each of the substation topology networks.

[0117] During the specific implementation process, the constraint condition construction module 2 is also used to: construct an energy storage charge and discharge operation model based on the energy storage charge and discharge power, energy storage charging power, energy storage discharging power and predetermined energy storage charge and discharge efficiency in the operating parameters of each time period; construct the constraint conditions of the energy storage charge and discharge operation model based on the charge and discharge control variables, the proportional coefficient of the configuration capacity and the maximum charge and discharge power, the upper limit of the energy storage system configuration capacity and the lower limit of the energy storage system configuration capacity in the operating parameters of each time period, and obtain the energy storage equipment operation constraint conditions corresponding to each substation topology network.

[0118] During the specific implementation process, the constraint condition construction module 2 is also used to: construct constraints based on the loads corresponding to each substation topology network in different time periods, the photovoltaic output per unit value of the target distribution network, predetermined uncertainty parameters and the photovoltaic access capacity allocated to each substation, and obtain the substation power balance constraints corresponding to each substation topology network.

[0119] During the specific implementation process, the model construction module 3 is specifically used to: construct a model based on the various constraints and the objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network to obtain a probability evaluation model of the new energy carrying capacity of the distribution network; use a mixed integer second-order cone programming model to reconstruct the probability evaluation model of the new energy carrying capacity of the distribution network to obtain an optimization model of the target distribution network.

[0120] During the specific implementation process, the solution module 4 is specifically used to: solve the initial relaxation problem of the optimization model to obtain the upper limit value of the initial new energy carrying capacity, and add each root node of the initial relaxation problem to the active node list; select the root node from the active node list for branching processing to obtain the branch sub-problem corresponding to the root node; delimit each branch sub-problem to obtain the function value of the objective function corresponding to each branch sub-problem, and update the global optimal value based on the maximum function value of each function value that meets each constraint condition; prune each branch sub-problem based on each function value using the optimality pruning method and the integer solution pruning method; iteratively select the root node from the active node list for branching processing, delimiting processing and pruning processing until when the active node list is empty, the current global optimal value is determined as the new energy carrying capacity of the target distribution network.

[0121] This application obtains the operating parameters of multiple substation topology networks of the target distribution network and the multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks; the multi-level new energy carrying capacity evaluation indicator system not only embodies systematicity and scientificity in methodology, but also fully demonstrates an in-depth understanding of the complexity and dynamics of the new power system, and provides a multi-dimensional and precise evaluation tool for the orderly and safe access of new energy. The operating parameters corresponding to each of the topological networks are respectively used to construct constraints to obtain the constraints corresponding to each of the topological networks; based on the constraints and the objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct the model to obtain the optimization model of the target distribution network; the solution efficiency and calculation accuracy are high, the model has strong applicability and optimization effect, and a more economical scheduling result can be obtained. The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network. The branch and bound algorithm can adapt to complex constraints to improve the new energy carrying capacity, while ensuring the efficiency of the optimization process and the reliability of the optimization results.

[0122] Another embodiment of the present application provides a storage medium storing a computer program. When the computer program is executed by a processor, the following method steps are implemented:

[0123] Step 1: Obtain multiple substation topology networks of the target distribution network and operating parameters of the multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks;

[0124] Step 2: constructing constraint conditions using the operating parameters corresponding to each of the topological networks to obtain constraint conditions corresponding to each of the topological networks;

[0125] Step 3: Based on the constraints and the objective function of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct a model to obtain an optimization model of the target distribution network;

[0126] Step 4: Use a branch and bound algorithm to solve the optimization model to obtain the new energy carrying capacity of the target distribution network.

[0127] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0128] Those skilled in the art will clearly understand that for the convenience and brevity of description, the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and 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.

[0129] The specific implementation process of the above method steps can be found in the embodiments of any of the above-mentioned methods for determining the new energy carrying capacity of the distribution network, and this embodiment will not be repeated here.

[0130] This application obtains the operating parameters of multiple substation topology networks of the target distribution network and the multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks; the multi-level new energy carrying capacity evaluation indicator system not only embodies systematicity and scientificity in methodology, but also fully demonstrates an in-depth understanding of the complexity and dynamics of the new power system, and provides a multi-dimensional and precise evaluation tool for the orderly and safe access of new energy. The operating parameters corresponding to each of the topological networks are respectively used to construct constraints to obtain the constraints corresponding to each of the topological networks; based on the constraints and the objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct the model to obtain the optimization model of the target distribution network; the solution efficiency and calculation accuracy are high, the model has strong applicability and optimization effect, and a more economical scheduling result can be obtained. The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network. The branch and bound algorithm can adapt to complex constraints to improve the new energy carrying capacity, while ensuring the efficiency of the optimization process and the reliability of the optimization results.

[0131] Another embodiment of the present application provides an electronic device, which may be a server, and the electronic device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external client via a network connection. When the electronic device program is executed by the processor, it implements the functions or steps on the service side of a method for determining the new energy carrying capacity of a distribution network.

[0132] In one embodiment, an electronic device is provided, which may be a client. The electronic device includes a processor, a memory, a network interface, a display screen, and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external server via a network connection. When the electronic device program is executed by the processor, it implements the functions or steps on the client side of a method for determining the new energy carrying capacity of a distribution network.

[0133] Another embodiment of the present application provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the following method steps when executing the computer program in the memory:

[0134] Step 1: Obtain multiple substation topology networks of the target distribution network and operating parameters of the multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks;

[0135] Step 2: constructing constraint conditions using the operating parameters corresponding to each of the topological networks to obtain constraint conditions corresponding to each of the topological networks;

[0136] Step 3: Based on the constraints and the objective function of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct a model to obtain an optimization model of the target distribution network;

[0137] Step 4: Use a branch and bound algorithm to solve the optimization model to obtain the new energy carrying capacity of the target distribution network.

[0138] The specific implementation process of the above method steps can be found in the embodiments of any of the above-mentioned methods for determining the new energy carrying capacity of the distribution network, and this embodiment will not be repeated here.

[0139] This application obtains the operating parameters of multiple substation topology networks of the target distribution network and the multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks; the multi-level new energy carrying capacity evaluation indicator system not only embodies systematicity and scientificity in methodology, but also fully demonstrates an in-depth understanding of the complexity and dynamics of the new power system, and provides a multi-dimensional and precise evaluation tool for the orderly and safe access of new energy. The operating parameters corresponding to each of the topological networks are respectively used to construct constraints to obtain the constraints corresponding to each of the topological networks; based on the constraints and the objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct the model to obtain the optimization model of the target distribution network; the solution efficiency and calculation accuracy are high, the model has strong applicability and optimization effect, and a more economical scheduling result can be obtained. The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network. The branch and bound algorithm can adapt to complex constraints to improve the new energy carrying capacity, while ensuring the efficiency of the optimization process and the reliability of the optimization results.

[0140] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.

Claims

1. A method for determining the new energy carrying capacity of a distribution network, characterized in that: include: Obtaining multiple substation topology networks of the target distribution network and operating parameters of multi-level new energy carrying capacity evaluation indicators corresponding to each of the substation topology networks; respectively constructing constraint conditions using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective topological networks; Based on the constraints and the objective function of maximizing the photovoltaic access capacity of the target distribution network, a mixed integer second-order cone programming model is used to construct a model to obtain an optimization model of the target distribution network; The optimization model is solved by using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network.

2. The method according to claim 1, wherein The operation parameters corresponding to each of the topological networks are respectively used to construct constraint conditions to obtain constraint conditions corresponding to each of the topological networks, specifically including: For each of the topological networks, a branch power flow model is constructed based on the active power parameter, reactive power parameter, resistance parameter, reactance parameter, effective voltage parameter, and effective current parameter in the operating parameters, wherein the branch power flow model includes an active power linear equation, a reactive power linear equation, and a voltage linear equation; Based on the active power parameter, reactive power parameter, effective voltage parameter and effective current parameter in the operating parameters, the constraint conditions of the branch flow model are constructed to obtain the network flow constraint conditions corresponding to the topological network of each substation.

3. The method according to claim 1, wherein The step of constructing constraint conditions by respectively using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective topological networks further includes: For each of the substation topology networks, constraints are constructed based on the first active power of the substation transformer, the first reactive power of the substation transformer, the second active power of the target distribution network main transformer, the second reactive power of the target distribution network main transformer, the substation transformer rated capacity, the main transformer rated capacity, and a predetermined overload factor in each time period, to obtain the substation transformer and main transformer load rate constraints corresponding to each of the substation topology networks; Based on the effective current parameter, the line safe current carrying capacity and the predetermined overload factor in the operating parameters of each time period, the constraint conditions are constructed to obtain the line current carrying rate constraint conditions of each line in each substation topology network; Constraints are constructed based on the voltage amplitude, voltage amplitude upper limit and voltage amplitude lower limit of each node in the operating parameters of each time period to obtain node voltage constraints corresponding to each node in the substation topology network.

4. The method according to claim 1, wherein The step of constructing constraint conditions by respectively using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective substation topological networks further includes: Building an energy storage charge and discharge operation model based on the energy storage charge and discharge power, energy storage charging power, energy storage discharging power and predetermined energy storage charge and discharge efficiency in the operation parameters of each time period; Based on the charge and discharge control variables in the operating parameters of each time period, the proportional coefficient of the configuration capacity to the maximum charge and discharge power, the upper limit of the energy storage system configuration capacity, and the lower limit of the energy storage system configuration capacity, the constraints of the energy storage charge and discharge operation model are constructed to obtain the energy storage equipment operation constraints corresponding to each substation topology network.

5. The method according to claim 1, wherein The step of constructing constraint conditions by respectively using the operating parameters corresponding to the respective topological networks to obtain constraint conditions corresponding to the respective topological networks further includes: Constraints are constructed based on the loads corresponding to each of the substation topology networks in different time periods, the photovoltaic output per unit value of the target distribution network, predetermined uncertainty parameters, and the photovoltaic access capacity allocated to each substation to obtain the substation power balance constraints corresponding to each of the substation topology networks.

6. The method according to claim 1, wherein The objective function based on the constraints and maximizing the photovoltaic access capacity of the target distribution network is constructed using a mixed integer second-order cone programming model to obtain an optimization model of the target distribution network, specifically including: A model is constructed based on the constraints and an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, thereby obtaining a probability assessment model for the new energy carrying capacity of the distribution network; A mixed integer second-order cone programming model is used to reconstruct the probability assessment model of the distribution network's new energy carrying capacity to obtain an optimization model of the target distribution network.

7. The method according to claim 1, wherein The branch and bound algorithm is used to solve the optimization model to obtain the new energy carrying capacity of the target distribution network, specifically including: Solving the initial relaxation problem of the optimization model to obtain an upper bound value of the initial new energy carrying capacity, and adding each root node of the initial relaxation problem to a list of active nodes; Select a root node from the active node list for branching, and obtain a branch sub-problem corresponding to the root node; Performing a delimitation process on each of the branch subproblems to obtain a function value of the objective function corresponding to each of the branch subproblems, and updating a global optimal value based on a maximum function value among the function values ​​that satisfies each of the constraint conditions; Pruning each of the branch subproblems using an optimality pruning method and an integer solution pruning method based on each of the function values; A root node is selected from the active node list in an iterative manner to perform branching, delimiting, and pruning processes until the active node list is empty, and a current global optimal value is determined as the new energy carrying capacity of the target distribution network.

8. A device for determining the new energy carrying capacity of a distribution network, characterized in that: include: An acquisition module, configured to acquire operating parameters of multiple area topology networks of a target distribution network and multi-level new energy carrying capacity evaluation indicators corresponding to each of the area topology networks; A constraint condition construction module, configured to respectively use the operating parameters corresponding to each of the topological networks to construct constraint conditions, thereby obtaining constraint conditions corresponding to each of the topological networks; A model construction module is used to construct a model using a mixed integer second-order cone programming model based on the constraints and an objective function with the goal of maximizing the photovoltaic access capacity of the target distribution network, so as to obtain an optimization model of the target distribution network; A solution module is used to solve the optimization model using a branch and bound algorithm to obtain the new energy carrying capacity of the target distribution network.

9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for determining the new energy carrying capacity of the distribution network according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The method comprises at least a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program in the memory, the method implements the steps of the method for determining the new energy carrying capacity of the distribution network as described in any one of claims 1 to 7.

Images