Power distribution network reconstruction method and system for maximization of absorption rate of wind-light aggregation virtual power plant, medium and processor

Abstract

The invention provides a power distribution network reconstruction method for maximizing the absorption rate of a wind-light aggregation virtual power plant, and the method comprises the steps: carrying out the coding of each switch in a power distribution network through employing an integer type coding strategy based on an ordered ring matrix mode, so as to remove repeated branches in each loop, and reduce the calculation amount of an algorithm; establishing a power distribution network reconstruction model for maximizing the absorption rate of the wind-light aggregation virtual power plant; the power distribution network reconstruction model comprises an objective function and constraint conditions; the target function is that the wind and light abandoning quantity is minimum; constructing an integer sparrow search algorithm suitable for an integer type coding strategy; and adjusting various configurations of the power distribution network by adopting an integer sparrow search algorithm, optimizing the target function under constraint conditions to obtain an optimal solution, and realizing reconstruction of the power distribution network. According to the scheme, the clean energy consumption rate of the wind and light aggregated virtual power plant is improved; a large number of infeasible solutions are eliminated, and the calculation amount of the algorithm is reduced; the method has a strong global search capability, can avoid local optimum, and improves the quality of a solution.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network reconstruction, and in particular to a distribution network reconstruction method, system, medium and processor for maximizing the absorption rate of a wind-solar aggregate virtual power plant. Background Art

[0002] With the influence of factors such as urban expansion and population explosion, the distribution breadth and distribution range of distribution networks are expanding. In actual distribution networks, the number of switches is huge, which makes the distribution network reconstruction problem become a complex nonlinear mixed integer programming problem. The control variable of this problem is the combination of the on and off states of the branch tie switches and the section switches in the distribution network. When solving the solution vector composed of the on and off states of the switches, if traditional mathematical methods are used, the problem will evolve into a combinatorial explosion problem that is difficult to solve. The amount of calculation is huge, the memory occupied is also very large, and the calculation time is difficult to meet the actual requirements. The final result may not be able to converge reliably. In addition, under the background of the "dual carbon" goal, how to promote the consumption of clean energy has become an issue that must be considered in the operation of the power system and the reconstruction of the distribution network. Therefore, it is necessary to find a distribution network reconstruction solution algorithm that is feasible and has good performance with the goal of improving the clean energy consumption rate.

[0003] In view of this, a distribution network reconstruction method, system, medium and processor are needed to maximize the absorption rate of wind-solar aggregate virtual power plants. Summary of the invention

[0004] In view of the problem that the calculation time of the distribution network reconstruction in the prior art is difficult to meet the actual requirements, the present invention provides a distribution network reconstruction method, system, medium and processor for maximizing the absorption rate of wind-solar aggregate virtual power plants, which can improve the absorption rate of clean energy, eliminate a large number of infeasible solutions, reduce the amount of algorithm calculation, and have feasibility and good performance. The specific technical solution is as follows:

[0005] A distribution network reconstruction method for maximizing the consumption rate of wind-solar aggregate virtual power plants, including:

[0006] Based on the ordered ring matrix, an integer coding strategy is used to encode each switch in the distribution network to remove repeated branches in each loop, eliminate a large number of infeasible solutions, and reduce the computational complexity of the algorithm.

[0007] Establish a distribution network reconstruction model for maximizing the absorption rate of wind-solar aggregate virtual power plants; the distribution network reconstruction model includes an objective function and constraints; the objective function is to minimize the amount of wind and solar abandonment;

[0008] According to the characteristics of distribution network reconstruction problem, an integer sparrow search algorithm suitable for integer coding strategy is constructed;

[0009] The integer sparrow search algorithm is used to adjust the configurations of the distribution network, and the objective function is optimized under constraints to obtain the optimal solution and realize the reconstruction of the distribution network.

[0010] Furthermore, the strategy of using integer coding to encode each switch in the distribution network means that all switches in the loop are encoded in the order of natural numbers and used as the value range of the variable parameter. When the variable takes a natural number within the range, it means that the corresponding distribution switch is turned on.

[0011] Furthermore, the expression of the objective function is as follows:

[0012]

[0013] In the above formula, f is the amount of wind and solar power abandoned; Φ wind , Φ pv They represent the collection of wind turbine and photovoltaic access nodes in the system respectively; They represent the output power of wind turbine and photovoltaic connected to node i respectively; They represent the actual power of wind turbine and PV injected into node i respectively.

[0014] Furthermore, the constraint condition includes a power flow constraint, and the formula is as follows:

[0015]

[0016] In the above formula, P j and Q j are the active injection power and reactive injection power of node j respectively; I ij is the current amplitude of branch ij; x ij is the reactance value of branch ij; r ij is the resistance value of branch ij, P ij is the active power at the head end of branch ij, Q ij is the reactive power at the head end of branch ij; V i is the voltage at node i.

[0017] Furthermore, the constraint condition includes a radial topology constraint, and the constraint formula is as follows:

[0018]

[0019] In the above formula, n b and n s Respectively represent the total number of nodes and root nodes in the power distribution system, z ij Indicates the switch state of branch ij.

[0020] Furthermore, the integer mahjong search algorithm comprises the following steps:

[0021] Each sparrow in the sparrow population represents a solution, and the initial population is generated by using the Sin chaotic mapping method, so that the individual solutions in the population can be evenly distributed in the search space, thereby improving the quality of the initial solution and the optimization ability of the algorithm;

[0022] The discoverers and followers are divided by fitness value, and the sparrows with better fitness are regarded as discoverers;

[0023] The discoverer selects the next search direction and explores new paths based on the current optimal solution or the optimal solution of the neighbor;

[0024] The follower learns from the discoverer and makes random jumps around the current optimal solution with a certain probability;

[0025] Evaluate the current solution according to the fitness function, and update the optimal solution and position, repeating the cycle until the preset stop condition is reached;

[0026] Update the position of the sentinel to prevent the algorithm from converging prematurely;

[0027] If the optimal position of the sparrow population is the same for several consecutive times, a disturbance mechanism is added to increase the possibility of individual sparrows jumping out of the current area.

[0028] Furthermore, the formula for generating the initial population using the Sin chaotic mapping method is as follows:

[0029]

[0030] In the above formula, q k ,q k+1 are 1×d-dimensional vectors randomly generated between 0 and 1 for the kth and k+1th elements, respectively, where q 1 Generated by initial random; x k+1 is the position of the k+1th sparrow; E and F are the maximum and minimum value matrices of the elements of each dimension in the sparrow's individual position.

[0031] A distribution network reconstruction system for maximizing the consumption rate of a wind-solar aggregation virtual power plant is applied to the distribution network reconstruction method for maximizing the consumption rate of a wind-solar aggregation virtual power plant described above, comprising:

[0032] The encoding module is used to encode each switch in the distribution network based on an ordered ring matrix and an integer encoding strategy to remove duplicate branches in each loop, eliminate a large number of infeasible solutions, and reduce the amount of algorithm calculation;

[0033] The first building module is used to establish a distribution network reconstruction model for maximizing the absorption rate of wind-solar aggregate virtual power plants; the distribution network reconstruction model includes an objective function and constraints; the objective function is to minimize the amount of abandoned wind and solar power;

[0034] The second building module is used to build an integer sparrow search algorithm suitable for integer coding strategy according to the characteristics of the distribution network reconstruction problem;

[0035] The optimization module is used to adjust the configurations of the distribution network using an integer sparrow search algorithm, optimize the objective function under constraints to obtain the optimal solution, and realize the reconstruction of the distribution network.

[0036] A computer-readable storage medium, comprising a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the distribution network reconstruction method for maximizing the absorption rate of a wind-solar aggregate virtual power plant as described above.

[0037] A processor is used to run a program, wherein when the program is running, the distribution network reconstruction method for maximizing the absorption rate of a wind-solar aggregate virtual power plant as described above is executed.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] In response to the requirement of promoting clean energy consumption under the "dual carbon" goals, this plan proposes a distribution network reconstruction model with the goal of minimizing the amount of wind and solar power abandonment, thereby improving the clean energy consumption rate of wind-solar aggregate virtual power plants; at the same time, an ordered ring matrix method is adopted to remove duplicate branches in each loop when reconstructing the distribution network, eliminate a large number of infeasible solutions, and reduce the amount of algorithm computation; according to the characteristics of the distribution network reconstruction problem, an integer sparrow search algorithm suitable for integer coding strategies is constructed, so that the sparrow algorithm, which was originally only used to solve continuous optimization problems, can be improved and applied to the distribution network reconstruction problem based on integer coding proposed in this application, with strong global search capabilities, which can avoid local optimality and improve the quality of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0041] Figure 1 The figure is a flow chart of a distribution network reconstruction method for maximizing the absorption rate of wind-solar aggregate virtual power plants. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0044] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0045] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0046] Embodiment 1

[0047] like Figure 1 The figure shows a flow chart of a distribution network reconstruction method for maximizing the consumption rate of wind-solar aggregate virtual power plants, including:

[0048] S1: Based on the ordered ring matrix, an integer coding strategy is used to encode each switch in the distribution network to remove duplicate branches in each loop, eliminate a large number of infeasible solutions, and reduce the computational complexity of the algorithm.

[0049] Furthermore, the strategy of using integer coding to encode each switch in the distribution network means that all switches in the loop are encoded in the order of natural numbers and used as the value range of the variable parameter. When the variable takes a natural number within the range, it means that the corresponding distribution switch is turned on.

[0050] An ordered ring matrix is ​​a matrix whose elements are arranged in a cyclic manner. For example, a typical circulant matrix has the form that each row (or column) is obtained by cyclic shift of the previous row (or column), as shown in the following formula (1):

[0051]

[0052] Taking IEEE33 nodes as an example, the following Table 1 shows the integer encoding method based on the ordered ring matrix proposed in this application, where L1-L5 are the loops formed.

[0053] Table 1

[0054] Loop Switch number in the loop New Number <![CDATA[L 1 ]]> <![CDATA[S 2 ,S 3 ,S 4 ,S 5 ,S 6 ,S 7 ,S 33 ,S 20 ,S 19 ,S 18 ]]> 1-10 <![CDATA[L 2 ]]> <![CDATA[S 34 ,S 14 ,S 13 ,S 12 ]]> 1-4 <![CDATA[L 3 ]]> <![CDATA[S 21 ,S 35 ,S 11 ,S 10 ,S 9 ,S 8 ]]> 1-6 <![CDATA[L 4 ]]> <![CDATA[S 15 ,S 16 ,S 17 ,S 36 ,S 32 ,S 31 ,S 30 ,S 29 ]]> 1-8 <![CDATA[L 5 ]]> <![CDATA[S 25 ,S 26 ,S 27 ,S 28 ,S 37 ,S 24 ,S 23 ,S 22 ]]> 1-8

[0055] From the above table, we can see that the branch switch S 2 ,S 3 ,S 4 ,S 5 ,S 6 ,S 7 ,S 33 ,S 20 ,S 19 ,S 18 The corresponding branches form loop 1, so the integer codes corresponding to the switches are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. The integer codes of other loops are similar.

[0056] S2: Establish a distribution network reconstruction model for maximizing the absorption rate of wind-solar aggregate virtual power plants; the distribution network reconstruction model contains objective functions and constraints; the objective function is to minimize the amount of wind and solar power abandoned; the constraints include power flow constraints, node voltage constraints, branch current constraints, distributed power constraints, radial topology constraints and energy storage battery remaining capacity constraints. The wind-solar aggregate virtual power plant is a virtual power plant that aggregates wind turbine photovoltaic access nodes in the park distribution network. It is necessary to maximize the absorption rate of clean energy such as wind and light, that is, to minimize the amount of wind and solar power abandoned.

[0057] Furthermore, the expression of the objective function is as follows:

[0058]

[0059] In the above formula, f is the amount of wind and solar power abandoned; Φ wind , Φ pv They represent the collection of wind turbine and photovoltaic access nodes in the system respectively; They represent the output power of wind turbine and photovoltaic connected to node i respectively; They represent the actual power of wind turbine and PV injected into node i respectively.

[0060] Furthermore, the constraint condition includes a power flow constraint, and the formula is as follows:

[0061]

[0062]

[0063]

[0064]

[0065] In the above formula, P j and Q j are the active injection power and reactive injection power of node j respectively; I ij is the current amplitude of branch ij; x ij is the reactance value of branch ij; r ij is the resistance value of branch ij, P ij is the active power at the head end of branch ij, Q ij is the reactive power at the head end of branch ij; V i is the voltage at node i.

[0066] Furthermore, the node voltage constraint is:

[0067]

[0068] In the formula, Respectively represent the upper and lower limits of the voltage at node i.

[0069] Furthermore, the branch current constraint is:

[0070]

[0071] In the formula, is the maximum current allowed to flow through branch ij.

[0072] Furthermore, the DG power constraint is:

[0073]

[0074] Where: P DG,i , Q DG,i is the active power output and reactive power output of DG at node i; is the upper limit of active power output and reactive power output of DG at node i; is the lower limit of active power output and reactive power output of DG at node i.

[0075] Furthermore, the constraint condition includes a radial topology constraint, and the constraint formula is as follows:

[0076]

[0077] In the above formula, n b and n s Respectively represent the total number of nodes and root nodes in the power distribution system, z ij Indicates the switch state of branch ij.

[0078] Furthermore, the remaining capacity of the energy storage battery is constrained by:

[0079]

[0080] Where: is the remaining capacity of energy storage at node i during period t; and are the maximum and minimum capacity limits of energy storage at node i respectively; η ch , η dis are the charging and discharging efficiencies of energy storage, respectively; and are the charging power and discharging power of the energy storage at node i in period t respectively.

[0081] S3: According to the characteristics of the distribution network reconstruction problem, an integer sparrow search algorithm suitable for integer coding strategy is constructed. The algorithm includes the following steps: initializing the population with Sin chaotic mapping, the discoverer exploring new paths, the followers learning the discoverer, updating the position of the guard, and adding a disturbance mechanism.

[0082] S4: The integer sparrow search algorithm is used to adjust the configurations of the distribution network, optimize the objective function under constraints to obtain the optimal solution, and realize the reconstruction of the distribution network.

[0083] Furthermore, the integer mahjong search algorithm comprises the following steps:

[0084] S41: Each sparrow in the sparrow population represents a solution. In order to make the initial population have better randomness and universality, the Sin chaotic mapping method is used to generate the initial population, so that the individual solutions in the population can be evenly distributed in the search space, thereby improving the quality of the initial solution and improving the algorithm's optimization ability.

[0085] Furthermore, the formula for generating the initial population using the Sin chaotic mapping method is as follows:

[0086]

[0087] In the above formula, q k ,q k+1 are 1×d-dimensional vectors randomly generated between 0 and 1 for the kth and k+1th elements, respectively, where q 1 Generated by initial random; x k+ 1 is the position of the k+1th sparrow; E and F are the maximum and minimum matrices of the elements of each dimension in the position of the sparrow. Taking the IEEE33 node as an example, E = [10 4 6 8 8], F = [1 1 1 1 1].

[0088] S42: Divide the discoverers and followers by fitness value, and take the sparrow with better fitness as the discoverer.

[0089] S43: The discoverer selects the next search direction and explores new paths based on the current optimal solution or the optimal solution of the neighbor; the discoverer is responsible for finding food for the entire population and providing foraging directions for all followers. For this purpose, a search strategy is designed to enable the discoverer to move to a better position. The specific steps are as follows:

[0090] (1) Set the initial value ST of the warning value to the interval (0,1].

[0091] (2) Generate a random number r in the interval [0,1]. When r is less than the set warning value ST, the discoverer needs to make a large-scale path adjustment. The steps are as follows: First, The elements of each dimension in are randomly transformed in two different directions, and we get and As shown in formula (13); then, calculate the new individual and The fitness of the original individual and the fitness of the original individual; finally, the new position of the discoverer Updated to the best individuals among the original and new individuals.

[0092]

[0093] Where: t is the number of iterations; r 1 , r 2 are two random numbers between [0, 1]; E(d) and F(d) are the maximum and minimum values ​​that the element at the finder's position in the dth dimension can take, respectively.

[0094] When r is greater than the set warning value ST, the discoverer only needs to make local adjustments. The steps are as follows: randomly select the discoverer The two elements in are transformed in two different directions in a small range, and we get and As shown in formula (14); then, calculate the new individual and The fitness of the original individual and the fitness of the original individual; finally, the new position of the discoverer Updated to the best individuals among the original and new individuals.

[0095]

[0096] (3) As the number of iterations increases, the warning value is also changing. The warning value ST can be updated according to formula (15):

[0097] ST t+1 =ST t K (15);

[0098] Where: K is the expansion factor.

[0099] When the number of iterations is small, the fitness of the finder is relatively poor, and a large-scale path adjustment is more needed, and it is more likely to use formula (13) to update the position; as the number of iterations increases, the fitness of the finder will continue to improve. In order to increase the diversity of solutions and protect the better positions of the formed parts, it should be more suitable for local path adjustments, and it is more likely to use formula (14) to update the position. In this way, a strong global search capability can be achieved to avoid local optimality.

[0100] S44: Followers learn from the discoverer and make random jumps around the current optimal solution with a certain probability. Since the discoverer has better fitness than the followers, some followers need to learn from the discoverer during the iteration process. The specific steps are as follows:

[0101] (1) Set the hunger threshold HU, such as according to formula (16), that is:

[0102] HU = n / 2 (16);

[0103] Where: n is the number of populations.

[0104] (2) When the follower is before the hunger threshold HU, it means that the current individual has good fitness and needs to learn further from the discoverer: randomly select a discoverer and replace the corresponding position of the follower with the random two-dimensional elements of the discoverer.

[0105] (3) When the follower is behind the hunger threshold HU, it means that the fitness of the current individual is very poor, so a new individual is generated randomly.

[0106] S45: Evaluate the current solution according to the fitness function, and update the optimal solution and position, repeating the cycle until the preset stop condition is reached.

[0107] S46: Update the sentinel position to prevent the algorithm from converging prematurely.

[0108] The position update of the alerter can prevent the algorithm from converging too early, and its position can be updated according to formula (17). When the fitness of the alert value is greater than the current global optimum, the search is performed near the global optimum; when the fitness of the alert value is less than or equal to the current global optimum, in order to avoid falling into the local optimum, a small range of changes can be made near the global optimum.

[0109]

[0110] Where: f k , f g are the fitness value of the current sparrow individual and the current global optimal fitness value respectively; β is the step size control parameter, with a mean of 0, which conforms to the random normal distribution; is the current global optimal individual; W is a random number that satisfies the normal distribution; L is a 1×d matrix with all elements being 1.

[0111] S47: If the optimal position of the sparrow population is the same for several consecutive times, a disturbance mechanism is added to increase the possibility of individual sparrows jumping out of the current area.

[0112] If the optimal position of the sparrow population is the same for 5 consecutive times and does not change, the algorithm may have found the global optimal solution or may have fallen into the local optimal solution. To avoid falling into the local optimum, disturbance can be added to increase the possibility of individual sparrows jumping out of the current area. The disturbance added in this application is to introduce a Cauchy distribution random number near the current position to change the position, as shown in formula (18).

[0113]

[0114] Where: P(0,1) is the standard form of the Cauchy distribution.

[0115] In this solution, the maximization of the virtual power plant consumption rate is reflected in the pursuit of minimizing the amount of wind and solar power abandonment, directly improving the utilization rate of wind and solar resources. In the model, constraints are added to ensure that the voltage and power meet the operating standards, while dealing with actual physical limitations such as node voltage, branch current and power balance.

[0116] Sparrow search algorithms and other algorithms have strong global search capabilities, which can avoid local optimality, improve the quality of solutions, and make the solution of the entire model feasible and have good performance. Fitness function: By optimizing the objective function, the consumption rate is quantitatively measured, and the performance of the algorithm can be directly obtained by improving the fitness. Reliability: Various power grid constraints are comprehensively considered to ensure that the safety and stability requirements of the power system are not violated during the optimization process.

[0117] The integer sparrow search algorithm simulates the process of sparrows foraging. The algorithm can fully search the solution space and avoid falling into the local optimum. The algorithm uses random initialization and dynamic adjustment mechanisms to adapt to the changing optimization environment. The integer sparrow search algorithm cooperates with the objective function and constraints to reconstruct the distribution network. The algorithm optimizes the objective function to minimize the amount of wind and solar power abandonment, thereby improving the wind and solar resource consumption rate. During the reconstruction process, the integer sparrow search algorithm adjusts various configurations to achieve the optimization of the objective function. During the search process, the algorithm evaluates the fitness of the solution to ensure that the solution is legal within the physical and operational constraints (such as voltage, power flow, and topology constraints). By combining constraints in the fitness function, the algorithm automatically avoids solutions that do not meet the conditions. Using the dynamic adjustment mechanism of the sparrow search algorithm, the strategy can be flexibly adjusted according to the current state of the distribution network. Through continuous learning and updating mechanisms, the algorithm can adapt to changes in load and power generation conditions and reconstruct the distribution network to the optimal state.

[0118] In response to the requirement of promoting clean energy consumption under the "dual carbon" goals, this plan proposes a distribution network reconstruction model with the goal of minimizing the amount of wind and solar power abandonment, thereby improving the clean energy consumption rate of wind-solar aggregate virtual power plants; at the same time, an ordered ring matrix method is adopted to remove duplicate branches in each loop when reconstructing the distribution network, eliminate a large number of infeasible solutions, and reduce the amount of algorithm computation; according to the characteristics of the distribution network reconstruction problem, an integer sparrow search algorithm suitable for integer coding strategies is constructed, so that the sparrow algorithm, which was originally only used to solve continuous optimization problems, can be improved and applied to the distribution network reconstruction problem based on integer coding proposed in this application, with strong global search capabilities, which can avoid local optimality and improve the quality of the solution.

[0119] Embodiment 2

[0120] A distribution network reconstruction system for maximizing the consumption rate of a wind-solar aggregation virtual power plant is applied to the distribution network reconstruction method for maximizing the consumption rate of a wind-solar aggregation virtual power plant described above, comprising:

[0121] The encoding module is used to encode each switch in the distribution network based on an ordered ring matrix and an integer encoding strategy to remove duplicate branches in each loop, eliminate a large number of infeasible solutions, and reduce the amount of algorithm calculation;

[0122] The first building module is used to establish a distribution network reconstruction model for maximizing the absorption rate of wind-solar aggregate virtual power plants; the distribution network reconstruction model includes an objective function and constraints; the objective function is to minimize the amount of abandoned wind and solar power;

[0123] The second building module is used to build an integer sparrow search algorithm suitable for integer coding strategy according to the characteristics of the distribution network reconstruction problem;

[0124] The optimization module is used to adjust the configurations of the distribution network using an integer sparrow search algorithm, optimize the objective function under constraints to obtain the optimal solution, and realize the reconstruction of the distribution network.

[0125] Embodiment 3

[0126] A computer-readable storage medium, comprising a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the distribution network reconstruction method for maximizing the absorption rate of a wind-solar aggregate virtual power plant as described above.

[0127] Embodiment 4

[0128] A processor is used to run a program, wherein when the program is running, the distribution network reconstruction method for maximizing the absorption rate of a wind-solar aggregate virtual power plant as described above is executed.

[0129] The present application provides a distribution network reconstruction method for maximizing the absorption rate of a wind-solar aggregation virtual power plant, including: based on an ordered ring matrix, an integer coding strategy is used to encode each switch in the distribution network to remove duplicate branches in each loop, eliminate a large number of infeasible solutions, and reduce the amount of algorithm calculations; a distribution network reconstruction model for maximizing the absorption rate of a wind-solar aggregation virtual power plant is established; the distribution network reconstruction model contains an objective function and constraints; the objective function is to minimize the amount of wind and solar abandonment; according to the characteristics of the distribution network reconstruction problem, an integer sparrow search algorithm suitable for an integer coding strategy is constructed; the integer sparrow search algorithm is used to adjust the various configurations of the distribution network, and the objective function is optimized under constraints to obtain the optimal solution, thereby realizing the reconstruction of the distribution network. In response to the requirement of promoting clean energy consumption under the "dual carbon" goals, this plan proposes a distribution network reconstruction model with the goal of minimizing the amount of wind and solar power abandonment, thereby improving the clean energy consumption rate of wind-solar aggregate virtual power plants; at the same time, an ordered ring matrix method is adopted to remove duplicate branches in each loop when reconstructing the distribution network, eliminate a large number of infeasible solutions, and reduce the amount of algorithm computation; according to the characteristics of the distribution network reconstruction problem, an integer sparrow search algorithm suitable for integer coding strategies is constructed, so that the sparrow algorithm, which was originally only used to solve continuous optimization problems, can be improved and applied to the distribution network reconstruction problem based on integer coding proposed in this application, with strong global search capabilities, which can avoid local optimality and improve the quality of the solution.

[0130] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0131] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0132] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A distribution network reconstruction method for maximizing the consumption rate of wind-solar aggregate virtual power plants, characterized in that: include: Based on the ordered ring matrix, an integer coding strategy is used to encode each switch in the distribution network to remove repeated branches in each loop, eliminate a large number of infeasible solutions, and reduce the computational complexity of the algorithm. Establish a distribution network reconstruction model for maximizing the absorption rate of wind-solar aggregate virtual power plants; the distribution network reconstruction model includes an objective function and constraints; the objective function is to minimize the amount of wind and solar abandonment; According to the characteristics of distribution network reconstruction problem, an integer sparrow search algorithm suitable for integer coding strategy is constructed; The integer sparrow search algorithm is used to adjust the configurations of the distribution network, and the objective function is optimized under constraints to obtain the optimal solution and realize the reconstruction of the distribution network.

2. The distribution network reconstruction method for maximizing the absorption rate of wind-solar aggregate virtual power plants according to claim 1 is characterized in that: The strategy of using integer coding to encode each switch in the distribution network means that all switches in the loop are encoded in the order of natural numbers and used as the value range of the variable parameter. When the variable takes a natural number within the range, it means that the corresponding distribution switch is turned on.

3. The distribution network reconstruction method for maximizing the absorption rate of wind-solar aggregate virtual power plants according to claim 1 is characterized in that: The expression of the objective function is as follows: In the above formula, f is the amount of wind and solar power abandoned; Φ wind , Φ pv Respectively represent the collection of wind turbines and photovoltaic access nodes in the system; P i wind , P i pv Respectively represent the output power of wind turbine and photovoltaic connected to node i; P i wind,r , P i pv,r They represent the actual power of wind turbine and PV injected into node i respectively.

4. The distribution network reconstruction method for maximizing the absorption rate of wind-solar aggregate virtual power plants according to claim 1 is characterized in that: The constraints include power flow constraints, which are as follows: In the above formula, P j and Q j are the active injection power and reactive injection power of node j respectively; I ij is the current amplitude of branch ij; x ij is the reactance value of branch ij; r ij is the resistance value of branch ij, P ij is the active power at the head end of branch ij, Q ij is the reactive power at the head end of branch ij; V i is the voltage at node i.

5. The distribution network reconstruction method for maximizing the absorption rate of wind-solar aggregate virtual power plants according to claim 1 is characterized in that: The constraint condition includes radial topology constraint, and the constraint formula is as follows: In the above formula, n b and n s Respectively represent the total number of nodes and root nodes in the power distribution system, z ij Indicates the switch state of branch ij.

6. The distribution network reconstruction method for maximizing the consumption rate of wind-solar aggregate virtual power plants according to claim 1 is characterized in that: The integer sparrow search algorithm comprises the following steps: Each sparrow in the sparrow population represents a solution, and the initial population is generated by using the Sin chaotic mapping method, so that the individual solutions in the population can be evenly distributed in the search space, thereby improving the quality of the initial solution and the optimization ability of the algorithm; The discoverers and followers are divided by fitness value, and the sparrows with better fitness are regarded as discoverers; The discoverer selects the next search direction and explores new paths based on the current optimal solution or the optimal solution of the neighbor; The follower learns from the discoverer and makes random jumps around the current optimal solution with a certain probability; Evaluate the current solution according to the fitness function, and update the optimal solution and position, repeating the cycle until the preset stop condition is reached; Update the position of the sentinel to prevent the algorithm from converging prematurely; If the optimal position of the sparrow population is the same for several consecutive times, a disturbance mechanism is added to increase the possibility of individual sparrows jumping out of the current area.

7. The distribution network reconstruction method for maximizing the absorption rate of wind-solar aggregate virtual power plants according to claim 6 is characterized in that: The formula for generating the initial population using the Sin chaotic mapping method is as follows: In the above formula, q k ,q k+1 are 1×d-dimensional vectors randomly generated between 0 and 1 for the kth and k+1th elements, respectively, where q1 is generated randomly at the beginning; x k+1 is the position of the k+1th sparrow; E and F are the maximum and minimum value matrices of the elements of each dimension in the sparrow's individual position.

8. A distribution network reconstruction system for maximizing the consumption rate of wind-solar aggregate virtual power plants, characterized in that: The distribution network reconstruction method for maximizing the absorption rate of a wind-solar aggregate virtual power plant as described in any one of claims 1 to 7 comprises: The encoding module is used to encode each switch in the distribution network based on an ordered ring matrix and an integer encoding strategy to remove duplicate branches in each loop, eliminate a large number of infeasible solutions, and reduce the amount of algorithm calculation; The first building module is used to establish a distribution network reconstruction model for maximizing the absorption rate of wind-solar aggregate virtual power plants; the distribution network reconstruction model includes an objective function and constraints; the objective function is to minimize the amount of abandoned wind and solar power; The second building module is used to build an integer sparrow search algorithm suitable for integer coding strategy according to the characteristics of the distribution network reconstruction problem; The optimization module is used to adjust the configurations of the distribution network using an integer sparrow search algorithm, optimize the objective function under constraints to obtain the optimal solution, and realize the reconstruction of the distribution network.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein, when the program is running, the device where the computer-readable storage medium is located is controlled to execute the distribution network reconstruction method for maximizing the absorption rate of the wind-solar aggregate virtual power plant as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein when the program is running, the distribution network reconstruction method for maximizing the absorption rate of the wind-solar aggregate virtual power plant as described in any one of claims 1 to 7 is executed.

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