A method and device for evaluating the carrying capacity of distributed new energy in distribution network

Abstract

The present invention provides a method and device for evaluating the carrying capacity of distributed renewable energy in a distribution network, and relates to the technical field of distribution network optimization. The present invention establishes a distribution network photovoltaic carrying capacity evaluation model that takes into account the static safety constraints of the distribution network operation, and then solves the distribution network photovoltaic carrying capacity evaluation model based on a successive second-order cone algorithm, thereby successively iterating the second-order cone programming model to continuously tighten the relaxation error, effectively reducing the error introduced by relaxation, and solving the problem that the existing method directly uses second-order cone programming to solve the carrying capacity problem, the relaxation error is too large, and the solution obtained does not meet the feasible domain of the original problem, resulting in the second-order cone relaxation being inaccurate and the solution being meaningless. Therefore, by solving the distribution network photovoltaic carrying capacity evaluation model based on the successive second-order cone algorithm of the present invention, the solution accuracy and efficiency can be improved, and the distribution network carrying capacity evaluation optimization can be accurately realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network optimization, and in particular to a method and device for evaluating the distributed new energy carrying capacity of a power distribution network. Background Art

[0002] Under the background of "dual carbon", coal-fired power plants are being retired on a large scale, and new energy sources are being connected to the grid in large quantities. Photovoltaic power generation (especially distributed photovoltaic power generation) is the main form of new energy utilization in the power grid, and has the characteristics of large peak-to-valley difference in output and strong volatility. The large-scale distributed photovoltaic access to the distribution network will change the passive and single power flow characteristics of the traditional distribution network, and transform the passive distribution network into an active distribution network. The operation mode of the distribution network will become more complicated, and the number of uncertain factors affecting the consumption of new energy will increase, which will bring great challenges to the safe and stable operation of the distribution network. Therefore, how to combine the operating status of the distribution network with the inherent voltage regulation equipment of the system to reasonably evaluate the distributed new energy carrying capacity of the distribution network has become a research focus in the current power system field.

[0003] In the case of distributed photovoltaic access to the distribution network, the distribution network carrying capacity assessment requires maximizing the photovoltaic access capacity. Therefore, the distribution network distributed new energy carrying capacity assessment is essentially an optimization problem. In the prior art, the distribution network optimization problem usually uses the branch flow model to perform flow calculations, and the linearization of the constraint and the accurate solution of the model are achieved through the second-order cone relaxation technology. However, when the objective function of the distribution network optimization problem is not a branch current increasing function (such as carrying capacity as the objective function), the relaxation error is too large when the second-order cone programming is directly used to solve the carrying capacity problem. The solution obtained does not satisfy the feasible domain of the original problem, and the second-order cone relaxation is inaccurate, resulting in the problem that the solution is meaningless. Therefore, the existing conventional second-order cone programming model can only handle the distribution network optimization problem when the objective function is a branch current increasing function (such as network loss as the objective function).

[0004] In view of this, in order to solve the problem that conventional second-order cone programming cannot be applied to distribution network optimization problems with non-branch current increasing functions as objective functions such as carrying capacity evaluation, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for evaluating the carrying capacity of distributed renewable energy in a distribution network, which can reduce the relaxation error by successive iterations, improve the solution accuracy, and accurately realize the optimization of the distribution network carrying capacity evaluation.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for evaluating the carrying capacity of distributed renewable energy in a distribution network, comprising:

[0008] S1. Establish a distribution network photovoltaic carrying capacity assessment model that takes into account the static safety constraints of distribution network operation, including: S11. Establish an objective function with the goal of maximizing photovoltaic access capacity; S12. Set static safety constraints for the objective function; S13. Construct a distribution network photovoltaic carrying capacity second-order cone programming model based on the objective function and static safety constraints as a distribution network photovoltaic carrying capacity assessment model;

[0009] S2. Solving the photovoltaic carrying capacity assessment model of the distribution network based on the successive second-order cone algorithm includes: S21. Solving the photovoltaic carrying capacity assessment model of the distribution network to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error of each branch; S22. Constructing a linear constraint condition for the convergence of the relaxation error and adding a penalty term to the objective function; S23. Updating the penalty term of the objective function and successively solving the iterative distribution network photovoltaic carrying capacity assessment model to calculate and update the power flow change of the mth iteration, calculating the second-order cone relaxation error of the mth iteration of each branch, until the iteration stops when the linear constraint condition for the convergence of the relaxation error is met, and outputting the optimization result.

[0010] The present invention provides a preferred solution in the first aspect, and the static safety constraints include: power flow constraints, which calculate branch power flows through branch power flow models, requiring that after photovoltaic access, the power flows of each branch in the distribution network meet safe operation conditions; node voltage constraints, which require that after photovoltaic access, the node voltages of each node in the distribution network do not exceed the limit; line thermal stability constraints, which require that after photovoltaic access, the reverse load rate indicators of each line in the distribution network do not exceed the limit; short-circuit current constraints, which require that after photovoltaic access, the short-circuit current of the distribution network system does not exceed the limit.

[0011] In a first aspect, the present invention provides a preferred solution, wherein the relaxation error convergence linear constraint condition includes: a first constraint condition: in the initialization state or during the mth iteration, the maximum second-order cone relaxation error among the second-order cone relaxation errors of each branch is within a preset threshold value. Inside.

[0012] In the first aspect, the present invention provides a more preferred scheme, and the relaxation error convergence linear constraint condition also includes: a second constraint condition: in the initialization state or during the mth iteration, the second-order cone relaxation error of each branch is less than or equal to the maximum second-order cone relaxation error of the m-1th iteration multiplied by the auxiliary variable of the mth iteration, the auxiliary variable is solved and updated in each iteration, and the auxiliary variable of the mth iteration is between [0,1).

[0013] Furthermore, during the m-th iteration, the penalty term is: the penalty factor during the m-th iteration multiplied by the maximum second-order cone relaxation error of the m-1-th iteration multiplied by the auxiliary variable of the m-th iteration.

[0014] In a first aspect, the present invention provides a preferred solution, which linearizes the second constraint in the relaxed error convergence linear constraint using a first-order Taylor expansion.

[0015] The present invention provides a preferred solution in a first aspect, wherein the branch flow model adopts the Distflow branch flow model, which simplifies the original branch flow model by ignoring the phase angle of voltage and current, and is also applicable to branch flow calculation of radial distribution networks.

[0016] In the first aspect, the present invention provides a preferred solution, wherein the node voltage constraint requires that the voltage amplitude of each node in the distribution network is between an upper limit and a lower limit; the short-circuit current constraint requires that the short-circuit current injected by distributed photovoltaics into the distribution network system does not exceed 1.5 times the rated current of the distribution network system.

[0017] In a second aspect, the present invention provides a distribution network distributed renewable energy carrying capacity assessment device, comprising: an objective function construction module, used to establish an objective function with the goal of maximizing photovoltaic access capacity; a constraint setting module, used to set static safety constraints for the objective function; a second-order cone programming model construction module, used to construct a distribution network photovoltaic carrying capacity second-order cone programming model according to the objective function and the static safety constraints, as a distribution network photovoltaic carrying capacity assessment model; an initialization solution module, used to solve the distribution network photovoltaic carrying capacity assessment model to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error of each branch; a convergence condition and penalty setting module, used to construct a relaxation error convergence linear constraint condition and add a penalty term to the objective function; an iterative calculation and update module, used to update the objective function penalty term and successively solve the iterative distribution network photovoltaic carrying capacity assessment model to calculate and update the power flow change of the mth iteration, calculate the second-order cone relaxation error of the mth iteration of each branch, until the iteration stops when the relaxation error convergence linear constraint condition is met, and output the optimization result.

[0018] Compared with the prior art, the above technical solution has the following advantages:

[0019] The present invention realizes the solution of the photovoltaic carrying capacity assessment model of the distribution network based on the successive second-order cone algorithm, and continuously tightens the relaxation error of the second-order cone programming model by successive iterations, effectively reducing the error introduced by relaxation, and solving the problem that the relaxation error of the existing method for the carrying capacity problem is too large when directly using the second-order cone programming to solve the problem, and the obtained solution does not meet the feasible domain of the original problem, resulting in the second-order cone relaxation being inaccurate and the solution being meaningless. Therefore, by realizing the solution of the photovoltaic carrying capacity assessment model of the distribution network based on the successive second-order cone algorithm of the present invention, the solution accuracy and efficiency can be improved, and the distribution network carrying capacity assessment optimization can be accurately realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0021] Figure 1 A flow chart of a method for evaluating the carrying capacity of distributed renewable energy in a distribution network provided by a specific embodiment of the present invention;

[0022] Figure 2 A schematic diagram of a power flow model of a distribution network branch provided by a specific implementation mode of the present invention;

[0023] Figure 3 A module diagram of a distribution network distributed renewable energy carrying capacity assessment device provided by a specific implementation method of the present invention;

[0024] Figure 4 A flow chart of a method for evaluating the carrying capacity of distributed renewable energy in a distribution network provided by another specific embodiment of the present invention;

[0025] Figure 5 It is an improved IEEE33 node topology diagram of the power distribution network in the present invention;

[0026] Figure 6 It is a graph of light intensity and load level at each time period of a typical day in the present invention;

[0027] Figure 7 A diagram of the convergence of iterative errors for solving the distribution network carrying capacity based on the successive second-order cone algorithm provided by a specific implementation method of the present invention.

[0028] The figure numbers are as follows: model building unit 1, objective function construction module 11, constraint setting module 12, second-order cone programming model construction module 13, model solving unit 2, initialization solution module 21, convergence condition and penalty setting module 22, iterative calculation and update module 23. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1: Please refer to Figure 1This embodiment provides a method for evaluating the carrying capacity of distributed renewable energy in a distribution network, which is mainly implemented through the following steps:

[0031] First, S1. Establish a distribution network photovoltaic carrying capacity assessment model considering the static safety constraints of distribution network operation:

[0032] S11. Establish an objective function with the goal of maximizing the photovoltaic access capacity.

[0033] S12. Set static safety constraints for the objective function.

[0034] S13. A second-order cone programming model for the photovoltaic carrying capacity of the distribution network is constructed based on the objective function and static safety constraints as a photovoltaic carrying capacity evaluation model for the distribution network.

[0035] In step S11, the distribution network carrying capacity assessment requires maximizing the photovoltaic access capacity, and the objective function is as follows:

[0036] (1)

[0037] In the formula A set of node numbers that allow photovoltaic grid connection; and Represent the existing and newly added photovoltaic installed capacity of node i, is the total photovoltaic capacity of node i, satisfying: .

[0038] As a preferred implementation, the static safety constraints of the distribution network operation in step S12 mainly introduce the following types of constraints:

[0039] 1) Flow constraint: The branch flow is calculated through the branch flow model. It is required that after the photovoltaic power is connected, the flow of each branch in the distribution network meets the safe operation conditions. The details are as follows: The distribution network is based on "closed-loop design, open-loop operation" to carry out grid planning, which is generally a radial network form. The Distflow branch flow model simplifies the original branch flow model by ignoring the phase angle of voltage and current, and is also applicable to radial branch flow calculations. All branches of the radial distribution network can be simplified to Figure 2 The form of branch ij is shown, where i is the branch head node and j is the terminal node. , are the complex voltages of nodes i and j respectively, is the injected complex power of node i, represents the injected active power of node i, represents the injected reactive power of node i, , , Similarly, is the injected complex power of node j, represents the injected active power of node j, represents the injected reactive power of node j; is the complex impedance of branch ij, is the complex current flowing from branch i to branch j, is the complex power emitted from node i on branch ij, and They represent the active power and reactive power at the head end of branch ij respectively. In power flow calculation, the head end power of branch power flow is usually expressed in the form of complex power. The Dist-flow branch power flow model is derived below.

[0040] According to the power flow analysis, it can be known that the branch power flow related variables satisfy the following power flow equation:

[0041] Ohm's Law: (2)

[0042] Power at the head end of branch power flow: (3)

[0043] Node power balance: (4)

[0044] in, For current conjugate, other notations The same applies to superscript parameters. represents the total outflow power from node j to the lower-level node k, represents the head-end power of branch jk, It represents the total incoming power received by node j from the upper node i. is the transmission power loss in branch ij. Equation (3) and Equation (4) have nonlinear terms. and , which is a non-convex constraint.

[0045] To facilitate the solution of the GUROBI solver, this embodiment preferably relaxes the above non-convex constraints into convex constraints. Now the constraints are transformed by phase angle relaxation, record , , represents the square of the current amplitude of branch ij, Represents the square of the voltage amplitude at node i. The specific process is as follows:

[0046] ① Substituting formula (3) into formula (2), we can get , multiplying both ends of the equation by their own conjugates yields:

[0047] (5)

[0048] In the formula, represents the square of the voltage amplitude at node j; and Represent the active power and reactive power at the head end of branch ij respectively; and are the resistance and reactance of branch ij respectively.

[0049] ②Multiply both sides of equation (3) by their own conjugate to obtain

[0050] (6)

[0051] ③ Separate the active and reactive balances in the node power balance equation (4) to obtain:

[0052] (7)

[0053] In the formula, and They represent the active and reactive power at the head end of branch jk respectively.

[0054] Therefore, (2) to (4) can be transformed into constraint formula (8) which is independent of the phase angle of voltage and current, thus achieving phase angle relaxation:

[0055] (8)

[0056] The quadratic equality constraint The existence of will still lead to a non-convex model, and it is necessary to further expand the feasible domain and relax the equality constraint to a second-order cone constraint. The specific transformation process is as follows:

[0057] (9)

[0058] in express The second norm of, quadratic equality constraint After convex relaxation, it is transformed into

[0059] (10)

[0060] Formula (10) is a second-order cone relaxation constraint, and its feasible domain is a convex set.

[0061] At this point, the original non-convex branch power flow model is successfully transformed into a branch power flow model based on Dist-flow, as described by a series of convex constraints in formula (11).

[0062] (11)

[0063] 2) Node voltage constraint: after photovoltaic access, the node voltage of each node in the distribution network should not exceed the limit. The details are as follows:

[0064] In the process of evaluating the distributed photovoltaic carrying capacity of the distribution network, it is required that the voltage of each node of the distribution network does not exceed the limit, so it is necessary to meet the following requirements:

[0065] (12)

[0066] In the formula and The upper and lower limits of the node voltage amplitude.

[0067] 3) Line thermal stability constraint, requiring that after photovoltaic access, the reverse load rate index of each line of the distribution network does not exceed the limit. The details are as follows: This embodiment uses the reverse load rate index To measure the thermal stability of the line during the load-bearing capacity calculation process:

[0068] (13)

[0069] In the formula is the predicted value of total photovoltaic active output within the power supply range of the line at time t; It is the predicted value of equivalent power load within the same power supply range; Represents the transmission line operating limit.

[0070] Therefore, during the operation of the distribution network, the following conditions must be met:

[0071] (14)

[0072] In the formula, is the maximum reverse load rate of the line.

[0073] 4) Short-circuit current constraint: after photovoltaic power is connected, the short-circuit current of the distribution network system does not exceed the limit. The details are as follows: the short-circuit current injected into the system by distributed photovoltaic power generation usually does not exceed 1.5 times its rated current. Therefore, after the addition of photovoltaic power generation, the system short-circuit current should meet the following requirements:

[0074] (15)

[0075] In the formula is the rated voltage level of the distribution network to be evaluated; is the current value of the short-circuit current of the distribution network under the maximum operation mode; It is the short-circuit current limit of the distribution network of corresponding voltage level.

[0076] Then, S2. solves the photovoltaic carrying capacity evaluation model of the distribution network based on the successive second-order cone algorithm:

[0077] S21. Solve the photovoltaic carrying capacity assessment model of the distribution network to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error of each branch.

[0078] S22. Construct relaxed error convergence linear constraints and add penalty terms to the objective function.

[0079] S23. Update the penalty term of the objective function and solve the iterative distribution network photovoltaic carrying capacity assessment model successively to calculate and update the power flow change of the mth iteration, calculate the second-order cone relaxation error of each branch at the mth iteration, and stop the iteration when the linear constraint condition of relaxation error convergence is met, and output the optimization result.

[0080] Please refer to Figure 3 This embodiment provides a distributed renewable energy carrying capacity assessment device for a distribution network, which mainly includes the following modules: an objective function construction module 11, which is used to establish an objective function with the goal of maximizing photovoltaic access capacity; a constraint setting module 12, which is used to set static safety constraints for the objective function; a second-order cone programming model construction module 13, which is used to construct a second-order cone programming model of the photovoltaic carrying capacity of the distribution network according to the objective function and the static safety constraints, as a distribution network photovoltaic carrying capacity assessment model. The above-mentioned objective function construction module 11, constraint setting module 12, and second-order cone programming model construction module 13 constitute a model building unit 1, which is used to establish a distribution network photovoltaic carrying capacity assessment model that takes into account the static safety constraints of the distribution network operation. The initialization solution module 21 is used to solve the distribution network photovoltaic carrying capacity assessment model to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error of each branch; the convergence condition and penalty setting module 22 is used to construct the relaxation error convergence linear constraint and add a penalty term to the objective function; the iterative calculation and update module 23 is used to update the objective function penalty term and successively solve the iterative distribution network photovoltaic carrying capacity assessment model to calculate and update the power flow change of the mth iteration, calculate the second-order cone relaxation error of each branch at the mth iteration, and stop the iteration when the relaxation error convergence linear constraint is met, and output the optimization result. The above-mentioned initialization solution module 21, convergence condition and penalty setting module 22, iterative calculation and update module 23 constitute the model solution unit 2, which is used to solve the distribution network photovoltaic carrying capacity assessment model based on the successive second-order cone algorithm.

[0081] Embodiment 2: Based on Embodiment 1, a more preferred method for evaluating the distributed renewable energy carrying capacity of a distribution network is provided, especially for S2. Based on the successive second-order cone algorithm, a more detailed and complete process is provided, and the preferred relaxed error convergence linear constraint conditions and penalty terms are specifically provided. The specific implementation methods are as follows:

[0082] Let the relaxation error of the mth iteration branch ij be for:

[0083] (16)

[0084] The superscript m in the formula represents the corresponding variable value in the mth iteration process.

[0085] Preferably, in this embodiment, the relaxed error convergence linear constraint condition includes:

[0086] The first constraint condition: In the initial state or during the mth iteration, the maximum second-order cone relaxation error among the second-order cone relaxation errors of each branch is within the preset threshold Inside. Right now: , .

[0087] To gradually reduce the second-order cone relaxation error to a preset threshold The second constraint condition is constructed: in the initial state or during the mth iteration, the second-order cone relaxation error of each branch is less than or equal to the maximum second-order cone relaxation error of the m-1th iteration multiplied by the auxiliary variable of the mth iteration, and the auxiliary variable of the mth iteration is between [0,1). The details are as follows:

[0088] (17)

[0089] In the formula represents the maximum second-order cone relaxation error at the m-1th iteration; is an auxiliary variable. To ensure that the relaxation error is iteratively reduced, In this embodiment, an auxiliary variable is added to the model , to be solved and updated in each iteration, the variable can be solved by the solver.

[0090] In order to improve the efficiency of iterative solution, the penalty term is added to the objective function in the mth iteration process. In the mth iteration process, the penalty term is: the penalty factor in the mth iteration process multiplied by the maximum second-order cone relaxation error of the m-1th iteration multiplied by the auxiliary variable of the mth iteration, then the iterative solution model becomes:

[0091] (18)

[0092] In the formula is the penalty factor for the mth iteration, , is the penalty factor growth rate, is the upper limit of the penalty factor.

[0093] Active variable For example, the power flow variables of the system after the mth iteration are It can be updated according to formula (19), and the other parameters of the power flow variables are similar:

[0094] (19)

[0095] The relaxation error can be continuously tightened by successively solving the second-order cone programming model (18). , the iteration stops. In this embodiment, preferably, the second constraint in the relaxed error convergence linear constraint is linearized using a first-order Taylor expansion. Considering that equation (17) is a nonlinear constraint, this embodiment further linearizes it using a first-order Taylor expansion:

[0096] (20)

[0097] In summary, the calculation model of photovoltaic carrying capacity of distribution network based on the successive second-order cone algorithm can be finally expressed as:

[0098] (twenty one)

[0099] The static safety constraints in formulas (17) and (21) refer to 1) power flow constraint, 2) node voltage constraint, 3) line thermal stability constraint and 4) short-circuit current constraint in the first embodiment.

[0100] Please refer to Figure 4 Based on S13 of the first embodiment, a second-order cone programming model of the photovoltaic carrying capacity of the distribution network is constructed, and combined with the above-mentioned relaxed error convergence linear constraints and penalty terms of this embodiment, this embodiment provides a method for evaluating the distributed new energy carrying capacity of a distribution network, and a process for solving the photovoltaic carrying capacity evaluation model of the distribution network by a successive second-order cone algorithm, that is, a process for calculating the photovoltaic carrying capacity of the distribution network, as follows:

[0101] S211. Initialize m=0, solve the second-order cone programming model of the original distribution network photovoltaic carrying capacity to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error (convex relaxation error) of each branch. ;

[0102] S212. Determine the initial maximum second-order cone relaxation error Is it within the preset threshold? within, that is , otherwise, execute step S221; if yes, execute step S236;

[0103] S221. The number of iterations increases by 1, that is, m=m+1;

[0104] S222. Construct relaxed error convergence linear constraints;

[0105] S231. Update objective function penalty factor ;

[0106] S232. Solve the iterative model to obtain the desired flow change , , , ;

[0107] S233. Update the power flow variables calculated for the mth time ;

[0108] S234. Calculate the second-order cone relaxation error of each branch at the mth iteration ;

[0109] S235. Determine whether the maximum second-order cone relaxation error of the mth iteration is within a preset threshold within, that is , otherwise repeat steps S221 to S235, if yes, execute step S236;

[0110] S236. Output optimization results.

[0111] It should be noted that, in actual application, the algorithm based on the successive second-order cone proposed in the present invention can be modeled using the YALMIP platform of Matlab, and the Gurobi solver can be called for solution. This embodiment provides a test example to verify the effect of the present invention: Taking an improved IEEE33 node distribution system as an example, the system grid topology is as follows Figure 5 As shown, Figure 5 In the figure, PV stands for distributed photovoltaic, CB is a reactive power compensation capacitor bank, ESS is an energy storage system, and SVC is a static reactive power compensation device. The light intensity and load level at each time period of a typical day are as follows: Figure 6 As shown, the vertical axis parameters are the per-unit values ​​of photovoltaic and load output coefficients, and the initial new energy installed capacity of the system is shown in Table 1.

[0112] Table 1. New energy installed capacity

[0113]

[0114] For the current test system, considering the static security constraints of the distribution network, the proposed successive second-order cone algorithm is used to calculate the new energy carrying capacity of the distribution network. The results are as follows: Figure 7 As shown, Figure 7 The error convergence diagram of each iteration of the distribution network carrying capacity assessment using the improved second-order cone programming algorithm is given. It can be seen from the figure that the initial maximum relaxation error of the distribution network carrying capacity assessment using the second-order cone programming algorithm is 0.4999, but after 6 iterative calculations, the relaxation error is reduced to , meeting the accuracy requirements of engineering applications.

[0115] In a more preferred embodiment, in addition to considering the steady-state operation constraints of the distribution network, the inherent voltage regulating equipment of the distribution network, such as reactive capacitor banks, energy storage devices and static reactive compensation devices, can also be comprehensively considered to improve the carrying capacity assessment model. That is, the second-order cone programming model of the photovoltaic carrying capacity of the distribution network constructed in step S13 is further improved.

[0116] The inherent operating constraints of reactive voltage regulation equipment in distribution network can be expressed as:

[0117] a. Reactive capacitor bank constraints:

[0118] (twenty two)

[0119] The big M method (penalty factor method) is used to further process equation (22) as follows:

[0120] (twenty three)

[0121] In the formula, is the reactive power generated by the capacitor bank; is the rated reactive power of a capacitor bank; and They are respectively the number of reactive power compensation capacitor groups put into operation and the upper limit of the number of capacitor groups that can be put into operation in the current period; and are the number of reactive power compensation capacitor groups put into operation in period t and period t+1 respectively; It is a 0-1 variable used to determine whether the number of capacitor banks put into operation at this node changes in adjacent time periods; It is the maximum switching times of reactive compensation capacitor group in one day.

[0122] b. Static VAR compensation device constraints:

[0123] (twenty four)

[0124] In the formula, It is the reactive power generated by the static VAR compensation device; It is the rated capacity of the static VAR compensation device.

[0125] c. Energy storage device constraints:

[0126] (25)

[0127] (26)

[0128] (27)

[0129] In the formula, and are 0-1 variables, representing the charging and discharging status of the energy storage device in time period t; , and , They are the upper and lower limits of the charging and discharging power of the energy storage device respectively; and They represent the charging and discharging power of the energy storage device in time period t respectively; , and are the storage capacity of the energy storage device in period t, period t+1 and the initial state respectively; It is the upper limit of the storage capacity of the energy storage device.

[0130] (28)

[0131] In the formula, and They represent the charging and discharging efficiency of the energy storage device respectively. Formula (28) ensures that in the subsequent scheduling process, the capacity of the energy storage is equal at the beginning and end of the scheduling cycle, which is conducive to the cyclic scheduling of energy storage.

[0132] In this implementation, a method that can comprehensively consider the steady-state operation constraints of the distribution network and the operation constraints of the inherent reactive voltage regulating equipment of the distribution network is adopted to reasonably evaluate the new energy carrying capacity of the distribution network, so as to ensure that the resulting carrying capacity evaluation results are more in line with the actual power grid situation and further improve the accuracy of the evaluation. The proposed improved second-order cone algorithm can solve the problem of excessive relaxation error when the traditional second-order cone programming is applied to the carrying capacity evaluation problem, thereby achieving efficient solution of the carrying capacity evaluation model. This algorithm can serve as an effective reference for other distribution network optimization problems that do not meet the second-order cone precise relaxation conditions.

[0133] Through the above embodiments, the present invention can achieve the following beneficial technical effects:

[0134] 1. Improved accuracy of photovoltaic capacity assessment: The present invention solves the photovoltaic capacity assessment model of the distribution network based on the successive second-order cone algorithm, and continuously tightens the relaxation error of the second-order cone programming model through successive iterations, effectively reducing the error introduced by relaxation, improving the accuracy and efficiency of the solution, and accurately realizing the optimization of the distribution network capacity assessment. Moreover, the present invention converts the originally difficult-to-solve non-convex constraints into convex constraints through the phase angle relaxation and second-order cone relaxation technology of the DistFlow branch flow model, making the model solution more efficient and retaining the main features of the original problem, thereby further improving the accuracy of the photovoltaic capacity assessment.

[0135] 2. Enhanced computational efficiency of the model: This invention uses solvers such as GUROBI to efficiently solve convex constraint models, which significantly reduces the computational time and improves the computational efficiency of the model compared to directly solving non-convex models. In addition, the first-order Taylor expansion linearization of nonlinear constraints simplifies the model structure and further improves the solution speed.

[0136] 3. Comprehensive consideration of multiple grid operation constraints: The model of the present invention comprehensively considers multiple grid operation constraints such as DistFlow flow constraints, node voltage constraints, line thermal stability constraints and short-circuit current constraints during the evaluation process, ensuring that the evaluation results are closer to the actual grid operation. This comprehensive constraint consideration enables the model to flexibly respond to the grid operation challenges brought about by high-proportion distributed photovoltaic access, ensuring the safe and stable operation of the grid.

[0137] 4. Improve the capacity to absorb new energy: This invention takes maximizing photovoltaic access capacity as the objective function, and obtains the best photovoltaic access solution through optimization algorithm, which helps to improve the distribution network's ability to absorb new energy. This model can provide strong photovoltaic carrying capacity assessment support for subsequent grid dispatching, which helps to achieve low-carbon emission reduction goals while ensuring safe and stable operation of the grid.

[0138] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the above-mentioned embodiments of the methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0139] The technical features of the above-described embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above-described embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above-described embodiments only express several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several variations and improvements can be made, which all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for evaluating the carrying capacity of distributed renewable energy in a distribution network, characterized in that: include: S11. Establish an objective function with the goal of maximizing photovoltaic access capacity; S12. Setting static safety constraints for the objective function; The static safety constraints include: power flow constraints, which calculate the branch power flow through the branch power flow model, requiring that after photovoltaic access, the power flow of each branch in the distribution network meets the safe operation conditions; Node voltage constraint: after photovoltaic connection, the node voltage of each node of the distribution network shall not exceed the limit; line thermal stability constraint: after photovoltaic connection, the reverse load rate index of each line of the distribution network shall not exceed the limit; short-circuit current constraint: after photovoltaic connection, the short-circuit current of the distribution network system shall not exceed the limit; S13. Construct a second-order cone programming model of the photovoltaic carrying capacity of the distribution network according to the objective function and the static safety constraint as a photovoltaic carrying capacity evaluation model of the distribution network; S21. Solve the photovoltaic carrying capacity assessment model of the distribution network to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error of each branch; S22. Construct relaxed error convergence linear constraints and add penalty terms to the objective function; S23. Update the penalty term of the objective function and solve the iterative distribution network photovoltaic carrying capacity assessment model successively to calculate and update the power flow change of the mth iteration, calculate the second-order cone relaxation error of each branch at the mth iteration, and stop the iteration when the linear constraint condition of relaxation error convergence is met, and output the optimization result.

2. The method for evaluating the carrying capacity of distributed renewable energy in distribution network according to claim 1, characterized in that: The linear constraint conditions for the convergence of the relaxation error include: the first constraint condition: in the initialization state or during the mth iteration, the maximum second-order cone relaxation error among the second-order cone relaxation errors of each branch is within a preset threshold. Inside.

3. The method for evaluating the carrying capacity of distributed renewable energy in distribution network according to claim 2, characterized in that: The relaxation error convergence linear constraint condition also includes: a second constraint condition: in the initialization state or during the mth iteration, the second-order cone relaxation error of each branch is less than or equal to the maximum second-order cone relaxation error of the m-1th iteration multiplied by the auxiliary variable of the mth iteration, the auxiliary variable is solved and updated in each iteration, and the auxiliary variable of the mth iteration is between [0,1).

4. The method for evaluating the carrying capacity of distributed renewable energy in a distribution network according to claim 1, characterized in that: During the mth iteration, the penalty term is: the penalty factor during the mth iteration multiplied by the maximum second-order cone relaxation error of the m-1th iteration multiplied by the auxiliary variable of the mth iteration.

5. The method for evaluating the carrying capacity of distributed renewable energy in distribution network according to claim 3, characterized in that: The second constraint in the relaxed error convergence linear constraint is linearized using the first-order Taylor expansion.

6. The method for evaluating the carrying capacity of distributed renewable energy in distribution network according to claim 1, characterized in that: The objective function is as follows: （1） In the formula A set of node numbers that allow photovoltaic grid connection; and Represent the existing and newly added photovoltaic installed capacity of node i, is the total photovoltaic capacity of node i, satisfying: .

7. The method for evaluating the carrying capacity of distributed renewable energy in distribution network according to claim 1, characterized in that: The branch power flow model adopts the Distflow branch power flow model, which simplifies the original branch power flow model by ignoring the phase angle of voltage and current, and is also applicable to the branch power flow calculation of the radial distribution network.

8. The method for evaluating the carrying capacity of distributed renewable energy in distribution network according to claim 1, characterized in that: The node voltage constraint requires that the voltage amplitude of each node in the distribution network is between an upper limit and a lower limit; the short-circuit current constraint requires that the short-circuit current injected by distributed photovoltaics into the distribution network system does not exceed 1.5 times the rated current of the distribution network system.

9. A device for evaluating the carrying capacity of distributed renewable energy in a distribution network, used to execute the method for evaluating the carrying capacity of distributed renewable energy in a distribution network as described in any one of claims 1 to 8, characterized in that: include: An objective function building module is used to establish an objective function with the goal of maximizing the photovoltaic access capacity; A constraint setting module is used to set static safety constraints for the objective function; A second-order cone programming model construction module is used to construct a second-order cone programming model of the photovoltaic carrying capacity of the distribution network according to the objective function and static safety constraints as a distribution network photovoltaic carrying capacity evaluation model; Initialization solution module, used to solve the distribution network photovoltaic carrying capacity assessment model to obtain the initial value of the power flow variable and use it to calculate the initial second-order cone relaxation error of each branch; Convergence condition and penalty setting module, which is used to construct relaxed error convergence linear constraints and add penalty terms to the objective function; The iterative calculation and update module is used to update the penalty term of the objective function and successively solve the iterative distribution network photovoltaic carrying capacity assessment model to calculate and update the power flow change of the mth iteration, calculate the second-order cone relaxation error of each branch at the mth iteration, and stop the iteration when the linear constraint condition of relaxation error convergence is met, and output the optimization result.

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