Reactive power optimization method of distribution network with distributed generation based on improved butterfly algorithm

Abstract

The present invention belongs to the technical field of power systems, and in particular to a reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm. The method comprises: step 1. considering a distribution network containing distributed power sources, establishing a reactive power optimization mathematical model for a low-voltage distribution network containing distributed power sources; step 2. obtaining node data of a distribution network system, selecting a node position where a reactive power compensation device is placed, and setting the number and value range of control variables; step 3. initializing a butterfly population; step 4. calculating the fitness value of each butterfly in its solution space, and finding the optimal butterfly and its position at this time; step 5. calculating the fragrance concentration emitted by each butterfly; step 6. selecting a search method; step 7. outputting the optimal value and parameters if the requirements are met. The present invention has a small amount of calculation, a simple process, and is easy to implement. Reactive power optimization results can be obtained in a timely manner, and the search capability is greatly improved. The system network loss is effectively reduced, the node voltage quality is improved, and the system operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm. Background Art

[0002] With the rapid development of my country's national economy, the rapid increase in power load and power grid capacity, the economic operation of the power grid has received increasing attention from the power sector. Low-voltage distribution networks still have serious three-phase imbalance, low voltage qualification rate, and low power factor. Especially in recent years, new energy represented by distributed power sources has received widespread attention and rapid development. The increasing penetration rate of distributed power sources in distribution networks will make the reactive power optimization of distribution networks more and more difficult. Reactive power compensation optimization is an important means to reduce distribution network losses, improve power supply quality, and improve the economy and safety of distribution network operation. Therefore, the study of reactive power optimization of distribution networks containing distributed power sources has important theoretical and practical value.

[0003] Distributed power supply devices refer to small modular, environmentally compatible independent power supplies with power ranging from several kilowatts to 50MW. These power supplies are owned by power departments, power users or third parties to meet the specific requirements of power systems and users. After the distributed power supply is connected to the traditional distribution network, the current changes from the original one-way flow to two-way flow, and the structure and flow distribution of the distribution network change, which correspondingly affects the line loss of the power grid; and the capacity, access location, and operation mode of the distributed power supply will have different degrees of impact on the line loss. Therefore, the present invention considers how to add reactive compensation devices to the distribution network where the location and capacity of the distributed power supply have been determined, so as to achieve the purpose of reducing the distribution network loss, ensuring the power supply quality, and improving the economic efficiency of the distribution network operation.

[0004] At present, the problem of reactive power optimization in distribution networks has attracted widespread attention from scholars at home and abroad. The selection of the objective function can be divided into two aspects: from the perspective of system economy and from the perspective of system safety. For the solution method, traditional methods generally include gradient method, quadratic programming method, Newton method and interior point method, but these methods have strict mathematical definitions and are not applicable to solving complex mathematical models. Modern intelligent methods generally include heuristic search methods such as genetic algorithms and particle swarms. Although these heuristic search methods do not rely on the properties and gradient information of the objective function, most of them have problems such as large amount of calculation, complex process and low optimization accuracy, which are not suitable for real-time regulation during the operation of the distribution network. Summary of the invention

[0005] In view of the shortcomings of the above-mentioned prior art, the present invention provides a reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm. The purpose is to solve the reactive power optimization problem of a distribution network containing distributed power sources in actual operation, and by introducing a hybrid search operator, the search capability of the algorithm is greatly improved, and the calculation amount is small and the process is simple.

[0006] The technical solution adopted by the present invention to achieve the above-mentioned purpose is:

[0007] The reactive power optimization method of a distribution network containing distributed power sources based on an improved butterfly algorithm comprises the following steps:

[0008] Step 1. Considering the distribution network containing distributed power sources, a mathematical model for reactive power optimization of the low-voltage distribution network containing distributed power sources is established;

[0009] Step 2. Obtain node data of the distribution network system, select the node location where the reactive power compensation device is placed, and set the number and value range of the control variables;

[0010] Step 3. Initialize the butterfly population and randomly generate n butterflies according to the objective function and the solution space of the problem;

[0011] Step 4. According to the fitness function, calculate the fitness value of each butterfly in its solution space and find the optimal butterfly and its position at this time;

[0012] Step 5. Calculate the concentration of fragrance emitted by each butterfly;

[0013] Step 6. Select the search method;

[0014] Step 7. If the requirements are met, output the optimal value and parameters, otherwise return to step 4 to continue the cycle;

[0015] Step 8. Output the final optimal reactive power compensation amount and the optimal value of the objective function.

[0016] Furthermore, in step 1, a distribution network including distributed power sources is considered, and a reactive power optimization mathematical model of a low-voltage distribution network including distributed power sources is established, including the following steps:

[0017] Step (101) considers the economic efficiency of system operation and selects an objective function that minimizes the sum of the total active power loss cost of the target distribution network and the cost of configuring the compensation device:

[0018] minC loss +C Q

[0019] Among them, C loss is the economic loss of electricity for 24 hours. The specific calculation formula is as follows:

[0020] Closs =c high ×P Lhigh ×t high +c flat ×P Lflat ×t flat +c low ×P Llow ×t low

[0021] Among them, P Lhigh , P Lflat , P Llow are the network losses under three load modes: peak, flat and low; t high ,t flat ,t low are the operating time of the three load modes respectively; c high 、c flat 、c low They are the electricity prices corresponding to the three load modes;

[0022] Among them, C Q is the cost of reactive power compensation equipment, and the specific calculation formula is as follows:

[0023]

[0024] Among them, λ k is a 0-1 variable, which determines whether the node k is equipped with a reactive power compensation device; μ q The cost of reactive power compensation per unit capacity; Q k is the maximum capacity of reactive power compensation required by node k; η q is the investment in the control part of the reactive power compensation device; L q The service life of the reactive power compensation equipment;

[0025] Step (102) Power flow calculation equation:

[0026]

[0027]

[0028] Among them, P i , Q j are the active power and reactive power injected into the node respectively; U i , U j are the node voltages of nodes i and j respectively; G ij , B ij are branch conductance and susceptance respectively; δ ij is the voltage phase difference between nodes i and j.

[0029] Furthermore, the number and value range of the control variables are set as follows:

[0030] U imin ≤U i ≤U imax

[0031] Q Cimin ≤Q Ci ≤Q Cimax

[0032] Among them, U imax and U imin are respectively the upper and lower limits of the allowable voltage amplitude fluctuation of node i; Q Cimin and Q Cimax are respectively the upper and lower limits of the reactive power generated by the reactive power compensation device.

[0033] Further, in step 4, according to the fitness function, calculate the fitness value of each butterfly in its solution space, and find the optimal butterfly and its position at this time, where the position of the butterfly represents the reactive power compensation amount Q Ci , so it is necessary to meet the reactive power constraint range conditions given in step 2.

[0034] Further, in step 5, calculate the fragrance concentration emitted by each butterfly, and the formula for calculating the fragrance concentration is as follows:

[0035] f = cI a

[0036] Among them, f is the fragrance concentration perceived by other butterflies; c is the perception modality; I is the stimulus intensity; a is the power exponent depending on the perception modality, reflecting different degrees of releasing fragrance.

[0037] Further, in step 6, select the search method, including:

[0038] In order to reduce the influence of the external environment, adopt the method of comparing the switching probability p with the random number r generated before each iteration to determine the search method: if p > r, then perform global search; if p < r, then perform local search:

[0039] (1) Global search:

[0040] The butterfly individuals with low fragrance concentration fly towards the position of the butterfly with the best fragrance concentration. The formula for global search is as follows:

[0041]

[0042] Among them, is the position of the i-th butterfly at the t-th iteration; g * is the position of the current optimal butterfly; f i is the fragrance concentration of the i-th butterfly; r is a random number between 0 and 1;

[0043] (2) Local search:

[0044]

[0045] in, is the Cauchy distribution operator;

[0046] Corrected position based on individual butterflies The power flow is calculated for the current butterfly position, the 24-hour power economic loss and reactive power compensation equipment cost are calculated, and the current optimal position and fitness value of the butterfly are obtained.

[0047] Furthermore, if the requirements are met in step 7, the optimal value and parameters are output, otherwise the loop is continued by returning to step 4, which means: if the pre-set requirements of the algorithm are met, the program stops executing and outputs the optimal value and corresponding parameters, otherwise the loop is continued by returning to step 4.

[0048] Furthermore, the optimal reactive power compensation amount refers to the optimal solution; the optimal value of the objective function refers to the minimum cost of system operation, that is, the 24-hour electricity economic loss and reactive power compensation equipment costs.

[0049] A computer device comprises a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the method for reactive power optimization of a distribution network containing distributed power sources based on an improved butterfly algorithm is implemented.

[0050] A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm are implemented.

[0051] The present invention has the following beneficial effects and advantages:

[0052] The reactive power optimization method of a low-voltage distribution network containing distributed power sources based on a hybrid search strategy butterfly optimization algorithm proposed by the present invention greatly improves the search ability of the algorithm by introducing a hybrid search operator. Compared with traditional algorithms such as genetic algorithms and particle swarm algorithms, it has the advantages of fewer parameters, small amount of calculation, simple process, easy implementation, strong optimization ability, etc., and can obtain reactive power optimization results in time. Compared with the traditional butterfly algorithm, the introduced hybrid search operator greatly improves the search ability of the algorithm compared with the traditional butterfly algorithm. In the actual operation of the distribution network, the reactive power optimization results can be obtained in time to perform reactive power compensation, thereby effectively reducing the system network loss, improving the quality of the node voltage, and reducing the system operation cost. Combining the above advantages of the algorithm in solving the reactive power optimization problem of a low-voltage distribution network containing distributed power sources, it can be seen that the algorithm can effectively solve the reactive power optimization problem of a low-voltage distribution network containing distributed power sources in the actual operation process, has great practical application value, and is suitable for real-time regulation during the operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0054] Figure 1 It is a flow chart of the hybrid search strategy butterfly optimization algorithm of the present invention;

[0055] Figure 2 The present invention improves the IEEE-33 node standard power distribution system;

[0056] Figure 3 is the typical daily characteristic curve of the load of the present invention;

[0057] Figure 4 It is the typical daily characteristic curve of photovoltaic and wind power of the present invention;

[0058] Figure 5 It is a comparison diagram of network loss under different optimization algorithms of the present invention;

[0059] Figure 6 This is a comparison diagram of the lowest node voltage under different optimization algorithms of the present invention. DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0062] Refer to the following Figure 1-Figure 6 The technical solutions of some embodiments of the present invention are described.

[0063] Example 1

[0064] The present invention provides an embodiment, which is a reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm. Figure 2 As shown, the present invention improves the IEEE-33 node standard power distribution system. The IEEE-33 node system is a standard example test system containing 33 nodes. It is usually a simplified model of an actual power system and is used for performance testing of different algorithms under different research directions in the field of power systems. The steps to achieve reactive power optimization on the test system include the following parts. First, a distributed power source is configured at a part of the nodes in the test system. Then, a part of the nodes in the test system are selected as reactive power compensation nodes. Then, a reactive power optimization mathematical model is established for the test system, and an optimization algorithm is introduced to solve it. Finally, the optimal reactive power compensation device capacity is obtained, so that the 24-hour electricity economic loss and reactive power compensation equipment costs are minimized.

[0065] Based on this, the present invention provides a reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm, specifically a reactive power optimization method for a low-voltage distribution network containing distributed power sources based on a hybrid search strategy butterfly optimization algorithm, such as Figure 1 FIG. 1 is a flow chart of a hybrid search strategy butterfly optimization algorithm of the present invention, which specifically includes the following steps:

[0066] Step 1. Considering the distribution network containing distributed power sources, a mathematical model for reactive power optimization of the low-voltage distribution network containing distributed power sources is established;

[0067] Specifically, it starts from the economic perspective of distribution network operation, and takes the minimization of the sum of the total active power loss cost and the cost of configuring compensation devices of the target distribution network as the objective function.

[0068] The establishment of a reactive power optimization mathematical model for a low-voltage distribution network containing distributed power sources comprises the following steps:

[0069] Step (101) considers the economic efficiency of system operation and selects an objective function that minimizes the sum of the total active power loss cost of the target distribution network and the cost of configuring the compensation device:

[0070] minC loss +C Q

[0071] Among them, C loss is the economic loss of electricity for 24 hours. The specific calculation formula is as follows:

[0072] C loss =c high ×P Lhigh ×t high +c flat ×P Lflat ×t flat +c low ×P Llow ×t low

[0073] Among them, P Lhigh , P Lflat , P Llow are the network losses under three load modes: peak, flat and low; t high ,t flat ,t low are the operating time of the three load modes respectively; c high 、c flat 、c low These are the electricity prices corresponding to the three load modes.

[0074] Among them, C Q is the cost of reactive power compensation equipment, and the specific calculation formula is as follows:

[0075]

[0076] Among them, λ k is a 0-1 variable, which determines whether the node k is equipped with a reactive power compensation device; μ q The cost of reactive power compensation per unit capacity; Q k is the maximum capacity of reactive power compensation required by node k; η q is the investment in the control part of the reactive power compensation device; L q It is the service life of the reactive power compensation equipment.

[0077] Step (102) Power flow calculation equation:

[0078]

[0079]

[0080] Among them, P i , Q j are the active power and reactive power injected into the node respectively; U i , U j are the node voltages of nodes i and j respectively; G ij , B ij are branch conductance and susceptance respectively; δij is the voltage phase difference between nodes i and j.

[0081] Step 2. Obtain the node data of the distribution network system, select the node positions for placing reactive power compensation devices, and set the number and value ranges of control variables;

[0082] The setting of the number and value ranges of the control variables is as follows:

[0083] U imin ≤U i ≤U imax

[0084] Q Cimin ≤Q Ci ≤Q Cimax

[0085] where U imax and U imin are the upper and lower limits of the allowable voltage amplitude fluctuations of node i respectively; Q Cimin and Q Cimax are the upper and lower limits of the reactive power generated by the reactive power compensation device respectively.

[0086] Step 3. Initialize the butterfly population, and randomly generate n butterflies according to the objective function and the solution space of the problem.

[0087] Step 4. According to the fitness function, calculate the fitness value of each butterfly in its solution space, so as to find the optimal butterfly and its position at this time.

[0088] The position of the butterfly represents the reactive power compensation amount Q Ci , so it is necessary to meet the reactive power constraint range conditions given in Step 2.

[0089] Step 5. Calculate the fragrance concentration emitted by each butterfly.

[0090] The formula for calculating the fragrance concentration is as follows:

[0091] f = cI a

[0092] where f is the fragrance concentration perceived by other butterflies; c is the perception modality; I is the stimulation intensity; a is the power exponent depending on the perception modality, reflecting different degrees of releasing fragrance.

[0093] Step 6. Select the search method;

[0094] To minimize the influence of the external environment as much as possible, a method of comparing the switching probability p with the random number r generated before each iteration is adopted to determine which search method to use. If p > r, global search is executed; if p < r, local search is executed.

[0095] (1) Global search:

[0096] Butterflies with low fragrance concentration fly to the position of butterflies with the best fragrance concentration. The global search formula is as follows:

[0097]

[0098] in, is the position of the i-th butterfly at the t-th iteration; g * is the current optimal butterfly position; f i is the fragrance concentration of the i-th butterfly; r is a random number between 0 and 1.

[0099] (2) Local search:

[0100]

[0101] in, is the Cauchy distribution operator.

[0102] Corrected position based on individual butterflies The power flow calculation is performed for the current butterfly position respectively. The power flow calculation formula is the calculation equation in step (102). The 24-hour power economic loss and reactive power compensation equipment cost are calculated to obtain the current optimal position and fitness value of the butterfly.

[0103] Step 7. If the algorithm meets the pre-set requirements, the program stops executing and outputs the optimal value and corresponding parameters, otherwise returns to step 4 to continue the loop.

[0104] Step 8. Output the final optimal reactive power compensation amount and the optimal value of the objective function.

[0105] The optimal solution is the optimal reactive power compensation amount, and the optimal value is the minimum cost of system operation, that is, the 24-hour electricity economic loss and reactive power compensation equipment cost.

[0106] Example 2

[0107] The present invention further provides an embodiment, which is a reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm. A numerical example of the present invention is given for simulation calculation as follows:

[0108] like Figure 2As shown in the figure, the present invention improves the IEEE-33 node standard power distribution system. In the improved IEEE 33 node system, the system rated voltage is 12.66kV, and the node voltage allowable range is 0.95-1.05pu; among them, node 10 is connected to 200kW distributed wind power, and nodes 13 and 23 are connected to 200kW distributed photovoltaic, and the reactive output of these three nodes is between -0.15-0.45Mvar; the six nodes 7, 8, 24, 25, 30, and 32 with higher load levels and lower voltage levels are taken as reactive compensation points, and the capacity of the reactive compensation equipment is 200kMVar×5.

[0109] like Figure 3 The typical daily characteristic curve of the load of the present invention is shown in FIG. Considering the node load changes within a day, the total network load is 3715kW+j2300kVar. It can be seen that the peak load period is 19:00-22:00; the valley period is 11:00-18:00 and 23:00-24:00; and the valley period is 01:00-10:00.

[0110] like Figure 4 The typical daily characteristic curves of photovoltaic and wind power of the present invention are shown in FIG. The typical daily output of distributed photovoltaic and distributed wind power is considered.

[0111] Considering the economic efficiency of system operation, taking the minimization of the sum of the total active power loss cost and the cost of configuring compensation devices of the target distribution network as the objective function, the following dynamic reactive power optimization problem of the distribution network is obtained.

[0112] min C loss +C Q

[0113]

[0114]

[0115] U imin ≤U i ≤U imax

[0116] Q Cimin ≤Q Ci ≤Q Cimax

[0117] Among them, the node voltage U i The constraint range is 0.95-1.05pu, and the reactive compensation amount Q Ci The constraint range is 0-0.1MW.

[0118] In order to fully verify the effectiveness of the hybrid search strategy butterfly optimization algorithm (HSBOA) in solving the dynamic reactive power optimization problem of distribution network, the traditional algorithms particle swarm optimization (PSO) and genetic algorithm (GA) are introduced for comparative analysis. The number of iterations of the algorithm is unified as 200. The sensing mode of the hybrid search strategy butterfly optimization algorithm is c=0.01, the power index a=0.1; the population size of GA is 100, the crossover rate is 0.003, and the mutation rate is 0.8; the population size of PSO is 25, and the inertia weight is 0.95.

[0119] The algorithm program was written and simulated and tested in MATLAB 2016. The 24-hour electricity economic loss and reactive compensation equipment cost without reactive compensation equipment and with reactive compensation equipment obtained by different optimization algorithms are shown in Table 1.

[0120] Table 1 Comparison of 24-hour electricity economic losses and reactive power compensation equipment costs under different optimization algorithms

[0121] Optimization Algorithm 24-hour operation cost (yuan) Without reactive power compensation equipment 2326.4 HSBOA 1984.6 PSO 2095.7 GA 2094.3

[0122] As can be seen from Table 1, the 24-hour operation cost of the hybrid search strategy butterfly optimization algorithm (HSBOA) is significantly lower than that of other algorithms, indicating that the obtained solution is optimal and ensures the economic efficiency of the distribution network operation.

[0123] like Figure 5 As shown in FIG. 1 , it is a comparison diagram of network loss under different optimization algorithms of the present invention; Figure 6 As shown, it is a comparison diagram of the lowest node voltage under different optimization algorithms of the present invention. The change curves of the system network loss and node voltage within a day can be obtained respectively. It can be seen from the figure that under the hybrid search strategy butterfly optimization algorithm (HSBOA), the system network loss is significantly reduced in each period of a day, and the lowest node voltage is also significantly improved, and meets the constraint range of 0.95-1.05pu. It is proved that the hybrid search strategy butterfly optimization algorithm (HSBOA) has significantly reduced the system network loss and improved the safety of the distribution network while ensuring the economy of the distribution network operation. At the same time, compared with other traditional algorithms, the hybrid search strategy butterfly optimization algorithm obtains better optimization results. Therefore, the correctness of this model and algorithm is proved.

[0124] Example 3

[0125] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, the reactive power optimization method for a distribution network containing distributed power sources based on the improved butterfly algorithm described in Embodiments 1 to 2 is implemented.

[0126] Example 4

[0127] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the reactive power optimization method for a distribution network containing distributed power sources based on the improved butterfly algorithm described in Example 1 or 2 are implemented.

[0128] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0129] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0130] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0131] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0132] 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm is characterized by: The following steps are involved: Step 1. Considering the distribution network with distributed power sources, a mathematical model of reactive power optimization of low-voltage distribution network with distributed power sources is established; Step 2. Obtain the node data of the distribution network system, select the node location where the reactive power compensation device is placed, and set the number and value range of the control variables; Step 3. Initialize the butterfly population and randomly generate n butterflies according to the objective function and the solution space of the problem; Step 4. Calculate the fitness value of each butterfly in its solution space according to the fitness function, and find the optimal butterfly and its position at this time; Step 5. Calculate the fragrance concentration emitted by each butterfly; Step 6. Select the search method, including: to reduce the influence of the external environment, adopt the method of comparing the switching probability p with the random number r generated before each iteration to determine the search method: if p > r, perform global search, if p < r, perform local search: (1) Global search: The butterfly individuals with low fragrance concentration fly towards the position of the butterfly with the best fragrance concentration. The formula for global search is as follows: Wherein, is the position of the i-th butterfly at the t-th iteration; g* is the position of the current optimal butterfly; fi is the fragrance concentration of the i-th butterfly; r is a random number between 0 and 1; (2) Local search: Wherein, is the Cauchy distribution operator; According to the corrected position of the butterfly individual Perform power flow calculations on the current butterfly position respectively, calculate the 24-hour electric energy economic loss and the cost of reactive power compensation equipment, and obtain the current optimal position and fitness value of the butterfly; Step 7. If the requirements are met, output the optimal value and parameters, otherwise return to Step 4 to continue the loop; Step 8. Output the final optimal reactive power compensation amount and the optimal value of the objective function.

2. The reactive power optimization method for a distribution network containing distributed power sources based on the improved butterfly algorithm according to claim 1 is characterized in that: In step 1, a distribution network including distributed power sources is considered, and a reactive power optimization mathematical model of a low-voltage distribution network including distributed power sources is established, including the following steps: Step (101) considers the economic efficiency of system operation and selects an objective function that minimizes the sum of the total active power loss cost of the target distribution network and the cost of configuring the compensation device: minCloss+C Q Among them, Closs is the economic loss of electricity in 24 hours, and the specific calculation formula is as follows: Closs=high×P L high×thigh+cflat×P L flat×tflat+clow×P L low × low Among them, P L high, P L flat、P L low is the network loss under the three load modes of peak, flat and low; thigh, tflat and tlow are the operation time of the three load modes; chigh, cflat and clow are the electricity prices corresponding to the three load modes; Among them, C Q is the cost of reactive power compensation equipment, and the specific calculation formula is as follows: Among them, λk is a 0-1 variable that determines whether node k is equipped with a reactive power compensation device; μq is the cost of reactive power compensation per unit capacity; Qk is the maximum capacity of reactive power compensation required by node k; ηq is the investment in the control part of the reactive power compensation device; Lq is the service life of the reactive power compensation equipment; Step (102) Power flow calculation equation: Among them, Pi and Qj are the active power and reactive power injected into the node respectively; Ui and Uj are the node voltages of nodes i and j respectively; Gij and Bij are the branch conductance and susceptance respectively; δij is the voltage phase difference between nodes i and j.

3. The reactive power optimization method for a distribution network containing distributed power sources based on the improved butterfly algorithm according to claim 1 is characterized in that: The number and value range of the control variables are as follows: Uimin≤Ui≤Uimax Q C imin≤Q C i≤Q C imax Among them, U imax , U imin are the upper and lower limits of the voltage amplitude fluctuation allowed at node i; Q Cimin , Q Cimax They are respectively the upper and lower limits of the reactive power emitted by the reactive compensation device.

4. The reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm according to claim 1 is characterized in that: in step 4, according to the fitness function, the fitness value of each butterfly in its solution space is calculated, and the optimal butterfly and its position at this time are found, wherein the position of the butterfly represents the reactive compensation amount Q C i, so the reactive power constraint range conditions given in step 2 need to be met.

5. The reactive power optimization method for a distribution network containing distributed power sources based on the improved butterfly algorithm according to claim 1 is characterized in that: Step 5 calculates the fragrance concentration emitted by each butterfly, and the fragrance concentration calculation formula is as follows: f=cIa Among them, f is the fragrance concentration perceived by other butterflies; c is the perception mode; I is the stimulus intensity; a is the power exponent that depends on the perception mode, reflecting the different degrees of fragrance release.

6. The reactive power optimization method for a distribution network containing distributed power sources based on the improved butterfly algorithm according to claim 1 is characterized in that: If the requirements are met in step 7, the optimal value and parameters are output, otherwise the program returns to step 4 to continue the loop, which means: if the pre-set requirements of the algorithm are met, the program stops executing and outputs the optimal value and corresponding parameters, otherwise it returns to step 4 to continue the loop.

7. The reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm according to claim 1 is characterized in that: The optimal reactive power compensation amount refers to the optimal solution; the optimal value of the objective function refers to the minimum cost of system operation, that is, the 24-hour electricity economic loss and reactive power compensation equipment costs.

8. A computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein: When the processor executes the computer program, the method for reactive power optimization of a distribution network containing distributed power sources based on the improved butterfly algorithm as described in any one of claims 1 to 7 is implemented.

9. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of the reactive power optimization method for a distribution network containing distributed power sources based on an improved butterfly algorithm as described in any one of claims 1 to 7.

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