Multi-port interconnected low-voltage power distribution network loss optimization method and related device

Abstract

The invention relates to the technical field of power distribution network optimization, and particularly provides a multi-port interconnected low-voltage power distribution network loss optimization method and a related device, a population improvement rate is introduced as an evaluation index of CSO algorithm optimization efficiency and quality during iterative calculation, and a traditional CSO algorithm is improved accordingly, so that the algorithm can be prevented from falling into a local optimal solution problem, and the optimization efficiency and quality of the multi-port interconnected low-voltage power distribution network are improved. Therefore, a transformer load rate distribution scheme with the minimum loss can be determined, and the analysis efficiency is improved. Therefore, the line loss management efficiency and quality can be improved, the loss of the power distribution network is reduced, and optimization is realized.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network optimization, and in particular to a method for optimizing the loss of a multi-port interconnected low-voltage distribution network and related devices. Background Art

[0002] The distribution network is an important part of the power system. The loss of the distribution network is directly related to the power utilization rate, economic cost and environmental cost of the power system. As one of the important equipment of the distribution network, the loss of the distribution transformer is closely related to the loss of the distribution network, and is an important link in the line loss management of the distribution network. The transformer loss changes with the change of the load rate of the distribution transformer. When the transformer capacity is too small and the load is too large, the distribution transformer will enter a heavy load and overload state, and increase the transformer loss. When the transformer capacity is too large and the load is too small, the distribution transformer will have a capacity waste problem, which increases the operating cost of the distribution network.

[0003] In an attempt to reduce distribution network losses and operating costs, existing technologies have proposed a solution to reduce transformer losses by reducing transformer load rates by using traditional CSO (Crisscross Optimization Algorithm) for load sharing calculations.

[0004] However, the inventors have found that although the traditional CSO algorithm has a high search speed and search progress, it has the local optimal problem common in optimization algorithms. Therefore, it is impossible to determine the transformer load rate distribution plan with the minimum loss, and thus it is impossible to further reduce the distribution network loss. Summary of the invention

[0005] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the prior art cannot further reduce the loss of the distribution network.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing losses in a multi-port interconnected low-voltage distribution network, wherein the multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers, and the method includes:

[0007] Determine the parent population of the current iteration round;

[0008] According to the transformer operation data of each distribution transformer, the port operation data of each port and the parent population of the current iteration round, a vertical and horizontal crossover algorithm CSO is used to update the population and obtain the child population of the current iteration round;

[0009] Based on the parent population of the current iteration round and the child population of the current iteration round, the population improvement rate of the current iteration round is calculated; wherein the population improvement rate is used to reflect the degree of improvement of the child population relative to the parent population in the current iteration round; the population improvement rate is positively correlated with the degree of improvement;

[0010] If the population improvement rate is less than the preset improvement rate threshold, then when the preset population initialization rule is met, the parent population of the current iteration round is initialized, and the initialized parent population is used as the parent population of the next iteration round, and the next iteration round is entered;

[0011] If the population improvement rate is less than the preset improvement rate threshold, then when the population initialization rule is not met and the preset iteration end condition is not met, the parent population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered;

[0012] If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the parent population of the current iteration round, and the operation of each distribution transformer is controlled separately based on each target load rate.

[0013] In some embodiments, the parent population of the current iteration round includes m first transformer load allocation schemes, and the child population of the current iteration round includes m second transformer load allocation schemes, where m is a preset positive integer;

[0014] The calculating the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round includes:

[0015] Determining a first optimal load scheme among the m first transformer load distribution schemes, and determining a second optimal load scheme among the m second transformer load distribution schemes;

[0016] Determining a first fitness corresponding to the first optimal load solution according to a preset distribution network loss objective function;

[0017] Determining a second fitness corresponding to the second optimal load solution according to the distribution network loss objective function;

[0018] Based on the first fitness and the second fitness, a population improvement rate of the current iteration round is calculated.

[0019] In some embodiments, the calculating the population improvement rate of the current iteration round based on the first fitness and the second fitness includes:

[0020] The population improvement rate of the current iteration round is calculated based on the following expression: :

[0021]

[0022] In the formula, is the first optimal load solution, is the second optimal load solution, is the first fitness, is the second fitness, for and The Euclidean distance between is a preset constant.

[0023] In some embodiments, the distribution network loss objective function is:

[0024]

[0025] In the formula, is the distribution network loss objective function, D is the number of distribution transformers, is the iron loss of the ith distribution transformer, is the load factor of the ith distribution transformer, is the copper loss of i distribution transformers, is the active power loss of the jth port, is the penalty coefficient, The power received by the equipment to be powered corresponding to the i-th distribution transformer, is the load demand of the equipment to be powered corresponding to the i-th distribution transformer.

[0026] In some embodiments, the population initialization rule is that the number of population initialization times is less than or equal to a preset initialization times threshold; and / or, the iteration end condition is that the number of iterations is greater than or equal to a preset iteration times threshold.

[0027] In some embodiments, the method further comprises:

[0028] If the population improvement rate is greater than or equal to the preset improvement rate threshold, then when the iteration end condition is not met, the child population of the current iteration round is used as the parent population of the next iteration round, and enters the next iteration round.

[0029] In some embodiments, the method further comprises:

[0030] If the population improvement rate is greater than or equal to the preset improvement rate threshold, then when the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the child population of the current iteration round, and the operation of each distribution transformer is controlled separately based on each target load rate.

[0031] In a second aspect, an embodiment of the present application provides a multi-port interconnected low-voltage distribution network loss optimization device, the multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers, and the device includes:

[0032] A parent population determination module is used to determine the parent population of the current iteration round;

[0033] A CSO update module, configured to update the population using a CSO algorithm according to the transformer operation data of each distribution transformer, the port operation data of each port, and the parent population of the current iteration round, and obtain the child population of the current iteration round;

[0034] An improvement rate calculation module, used to calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round; wherein the population improvement rate is used to reflect the degree of improvement of the child population relative to the parent population in the current iteration round; the population improvement rate is positively correlated with the degree of improvement;

[0035] The first iteration module is used for initializing the parent population of the current iteration round if the population improvement rate is less than a preset improvement rate threshold and when a preset population initialization rule is met, and using the initialized parent population as the parent population of the next iteration round, and entering the next iteration round;

[0036] A second iteration module is used for, if the population improvement rate is less than the preset improvement rate threshold, then when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, taking the parent population of the current iteration round as the parent population of the next iteration round and entering the next iteration round;

[0037] The first transformer control module is used to determine the target load rate corresponding to each distribution transformer according to the parent population of the current iteration round if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the iteration end condition is met, and control the operation of each distribution transformer respectively based on each target load rate.

[0038] In a third aspect, an embodiment of the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method described in any of the above embodiments.

[0039] In a fourth aspect, an embodiment of the present application provides a computer device, the computer device comprising: one or more processors, and a memory;

[0040] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the multi-port interconnected low-voltage distribution network loss optimization method described in any of the above embodiments are executed.

[0041] In a multi-port interconnected low-voltage distribution network loss optimization method and related devices provided in some embodiments of the present application, when the CSO algorithm is used to optimize the load rate of each distribution transformer in the region and the transmission power of each port, if the population improvement rate of the current iteration round is less than the preset improvement rate threshold, it indicates that the relative position relationship between the samples may have suppressed the search process and may fall into a local optimal solution. Therefore, under the condition of satisfying the population initialization rule, the present application can initialize the parent population, so that the neighborhood of all samples can be randomly redistributed, that is, the relative position relationship of the samples is changed, and the next iteration round is entered. In this way, disturbances can be added and bad search directions can be avoided in time to avoid falling into a local optimal solution.

[0042] If the population improvement rate is less than the preset improvement rate threshold, and does not meet the population initialization rules and iteration end conditions, the population may not be updated, and the parent population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered. In this way, the optimal solution after the last update can be fully utilized through the backtracking operation, thereby avoiding the algorithm from falling into the local optimal solution and avoiding repeated optimization of the algorithm, which helps to improve the solution efficiency.

[0043] In summary, this application introduces the population improvement rate as an evaluation index for the optimization efficiency and quality of the CSO algorithm during iterative calculations, and improves the traditional CSO algorithm accordingly, thereby avoiding the algorithm from falling into the problem of local optimal solutions, and then determining the transformer load rate allocation scheme with the minimum loss, and improving the analysis efficiency. In this way, the efficiency and quality of line loss management can be improved, thereby reducing the loss of the distribution network and achieving optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application 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 some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 A network topology diagram of a multi-port interconnected low-voltage distribution network in one embodiment;

[0046] Figure 2 One of the flow charts of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network in one embodiment;

[0047] Figure 3 This is a second flow chart of a method for optimizing losses in a multi-port interconnected low-voltage distribution network in one embodiment;

[0048] Figure 4 1 is a schematic diagram of the structure of a multi-port interconnected low-voltage distribution network loss optimization device in one embodiment;

[0049] Figure 5 FIG. 1 is a diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] At present, low-voltage distribution networks generally adopt a single radial operation mode. With the continuous development of power electronics technology, low-voltage distribution network closed-loop construction has been carried out for some low-voltage distribution networks with high power supply reliability, so that low-voltage distribution networks can achieve closed-loop operation. For example, Figure 1 The network topology diagram of the regional voltage distribution network based on multi-port flexible switches is shown. Figure 1 As shown, the multi-port interconnected low-voltage distribution network may include multiple ports and multiple distribution transformers. The multiple distribution transformers may be connected via multiple ports and may achieve closed-loop operation via a flexible closed-loop device.

[0052] In some embodiments, the present application provides a multi-port interconnected low-voltage distribution network loss optimization method, which can be used to Figure 1The multi-port interconnected low-voltage distribution network shown is used for loss optimization. The following embodiments are described by taking the method applied to a computer device as an example. It can be understood that the computer device of the present application can be any device with data processing function, which can be but not limited to a server, a desktop computer, a notebook computer, a laptop computer, a tablet computer, a smart phone and other devices, and the present application does not make specific restrictions on this.

[0053] like Figure 2 As shown, a multi-port interconnected low-voltage distribution network loss optimization method provided by the present application may include the following steps:

[0054] S202: Determine the parent population of the current iteration round.

[0055] Specifically, the present application can utilize an adaptive memory search strategy and dynamic topology to improve the vertical and horizontal cross algorithm CSO, and use the improved CSO algorithm to solve the multi-port interconnected low-voltage distribution network optimization model considering port loss and port capacity, thereby obtaining the optimal operating load of each distribution transformer and the optimal transmission power of each port, so as to minimize the loss of the low-voltage distribution network.

[0056] In the process of solving the problem using the improved CSO algorithm, population optimization can be performed through multiple iterations to optimize the load distribution scheme of the distribution transformer. In each iteration round, the computer device can execute steps S202 to S212.

[0057] In the current iteration round, the computer device may first determine the parent population of the current iteration round. It is understood that the parent population may include one or more particles. Each particle may be a transformer load distribution scheme, which is used to indicate the load that each distribution transformer in the low-voltage distribution network needs to bear. In some examples, if the current iteration round is the first iteration round, a preset population initialization algorithm may be used to generate the parent population of the current iteration round.

[0058] S204: According to the transformer operation data of each distribution transformer, the port operation data of each port and the parent population of the current iteration round, the vertical and horizontal crossover algorithm CSO is used to update the population and obtain the child population of the current iteration round.

[0059] The transformer operation data can be used to reflect the operation performance of the distribution transformer, including but not limited to at least one of the basic operation data such as the maximum capacity of the distribution transformer and the transformer loss. The port operation data can be used to reflect the current carrying capacity and loss of the port, including but not limited to at least one of the data such as the maximum current carrying capacity of the port and the corresponding loss parameter.

[0060] In this step, the computer device may obtain in advance the transformer operation data of each distribution transformer and the port operation data of each port. Furthermore, the computer device may also collect in advance or in real time the load demand of each substation.

[0061] In the process of updating the population, the computer device may update the population using the crossover algorithm CSO according to the operation data of each transformer, the port operation data of each port and the parent population of the current iteration round, and obtain the child population of the current iteration round. It can be understood that, similar to the parent population, the child population may include one or more particles. Each particle may be a transformer load allocation scheme, which is used to indicate the load that each distribution transformer in the low-voltage distribution network needs to bear.

[0062] S206: Calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round.

[0063] Among them, the population improvement rate is used to reflect the degree of improvement of the offspring population relative to the parent population in the current iteration round. The population improvement rate is positively correlated with the degree of improvement. In other words, the higher the degree of improvement, the greater the population improvement rate; conversely, the lower the degree of improvement, the smaller the population improvement rate.

[0064] In this step, after the CSO algorithm is used to update the population, the computer device can calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round to evaluate the degree of population improvement of the current iteration round, and then determine whether the CSO algorithm has fallen into a local optimal solution.

[0065] When the CSO algorithm has fallen into a local optimum, the improvement of the offspring population is lower than that of the parent population, resulting in a lower population improvement rate. On the contrary, when the CSO algorithm has not fallen into a local optimum, the offspring population has made greater progress than the parent population, resulting in a higher population improvement rate.

[0066] S208: If the population improvement rate is less than the preset improvement rate threshold, then when the preset population initialization rule is met, the parent population of the current iteration round is initialized, and the initialized parent population is used as the parent population of the next iteration round, and the next iteration round is entered.

[0067] Among them, the population initialization rule refers to the rule used to determine whether to perform population initialization. The specific content of the rule can be determined according to the actual situation, and this application does not impose specific restrictions on this. In some examples, the population initialization rule may be that the number of population initialization times is less than or equal to the preset initialization times threshold. If the number of population initialization times is less than or equal to the initialization times threshold, it can be determined that the population initialization rule is met; if the number of population initialization times is greater than the initialization times threshold, it can be determined that the population initialization rule is not met. In this way, the algorithm can be prevented from falling into an infinite loop, thereby improving the operating efficiency and reliability of the algorithm.

[0068] In this step, if the population improvement rate of the current iteration round is less than or equal to the preset improvement rate threshold, it means that in the current iteration round, the improvement degree of the offspring population is lower than that of the parent population, and the CSO algorithm may have fallen into a local optimal solution. The relative position relationship between samples may have suppressed the search process.

[0069] In this case, the computer device can abandon the child population of the current iteration round and promptly abandon the bad improvement direction with a small improvement rate. In addition, the computer device can determine whether the population initialization rule is currently met. If the population initialization rule is met, the parent population of the current iteration round can be initialized to randomly redistribute the fields of all samples and change the relative position relationship between the samples. After the population initialization is completed, the computer device can use the initialized parent population as the parent population of the next iteration round and enter the next iteration round. In this way, the CSO algorithm can update the population in the direction of a large improvement rate, thereby obtaining a transformer load distribution plan with minimal distribution network loss.

[0070] For example, if the parent population of the current iteration round is the first population and the child population of the current iteration round is the second population, then when the population improvement rate is less than the preset improvement rate threshold and meets the preset population initialization rule, the computer device can initialize the first population and obtain the third population, and use the third population as the parent population of the next iteration round.

[0071] S210: If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the preset iteration end condition is not met, the parent population of the current iteration round is used as the parent population of the next iteration round and enters the next iteration round.

[0072] The iteration end condition refers to the condition used to determine whether to end the iterative solution, and its specific content can be determined according to the actual situation, and this application does not impose specific restrictions on this. In some examples, the iteration end condition may be that the number of iterations is greater than or equal to a preset iteration number threshold. If the current number of iterations is less than the iteration number threshold, it is determined that the iteration end condition is not met; if the current number of iterations is greater than or equal to the iteration number threshold, it is determined that the iteration end condition is met.

[0073] In this step, if the population improvement rate of the current iteration round is less than the preset improvement rate threshold, and does not meet the population initialization rules and does not meet the iteration end conditions, the child population of the current iteration round can be abandoned, and the parent population of the current iteration round can be directly used as the parent population of the next iteration round, and enter the next iteration round.

[0074] For example, if the parent population of the current iteration round is the first population and the child population of the current iteration round is the second population, then when the population improvement rate is less than the preset improvement rate threshold and does not meet the population initialization rules and the preset iteration end conditions, the computer device can use the first population as the parent population for the next iteration round.

[0075] In this way, the undesirable improvement direction with a small improvement rate can be avoided in time, so that the CSO algorithm can update the population in the direction of a large improvement rate, thereby obtaining a transformer load distribution scheme with the minimum loss in the distribution network. At the same time, by retaining the parent population before the update as the parent population for the next iteration, the algorithm can retain the optimal solution after the last update in time, avoiding repeated optimization of the algorithm, reducing repeated search processes, and helping to improve the algorithm speed and search efficiency.

[0076] S212: If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the parent population of the current iteration round, and the operation of each distribution transformer is controlled based on each target load rate.

[0077] In this step, when the population improvement rate of the current iteration round is less than the preset improvement rate threshold, if the iteration end condition is met and the population initialization rule is not met, the computer device can determine the target transformer load distribution plan from the parent population of the current iteration round, and control the operation of each distribution transformer accordingly, so that each distribution transformer can operate according to the load rate indicated by the target transformer load distribution plan. In this way, the final load distribution plan can be determined from the last updated optimal solution with a large improvement rate.

[0078] In this application, when the CSO algorithm is used to optimize the load rate of each distribution transformer in the region and the transmission power of each port, if the population improvement rate of the current iteration round is less than the preset improvement rate threshold, it indicates that the relative position relationship between samples may have suppressed the search process and may fall into a local optimal solution. Therefore, under the condition of satisfying the population initialization rule, this application can initialize the parent population, so that the neighborhood of all samples can be randomly redistributed, that is, the relative position relationship of the samples is changed, and the next iteration round is entered. In this way, disturbances can be added and bad search directions can be avoided in time to avoid falling into a local optimal solution.

[0079] If the population improvement rate is less than the preset improvement rate threshold, and does not meet the population initialization rules and iteration end conditions, the population may not be updated, and the parent population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered. In this way, the optimal solution after the last update can be fully utilized through the backtracking operation, thereby avoiding the algorithm from falling into the local optimal solution and avoiding repeated optimization of the algorithm, which helps to improve the solution efficiency.

[0080] In summary, this application introduces the population improvement rate as an evaluation index for the optimization efficiency and quality of the CSO algorithm during iterative calculations, and improves the traditional CSO algorithm accordingly, thereby avoiding the algorithm from falling into the problem of local optimal solutions, and then determining the transformer load rate allocation scheme with the minimum loss, and improving the analysis efficiency. In this way, the efficiency and quality of line loss management can be improved, thereby reducing the loss of the distribution network and achieving optimization.

[0081] In some embodiments, the parent population of the current iteration round includes m first transformer load distribution schemes, and the child population of the current iteration round includes m second transformer load distribution schemes, where m is a preset positive integer.

[0082] Based on the parent population of the current iteration round and the child population of the current iteration round, calculate the population improvement rate of the current iteration round, including:

[0083] Step A1: determining a first optimal load scheme among m first transformer load distribution schemes, and determining a second optimal load scheme among m second transformer load distribution schemes.

[0084] The first optimal load solution refers to the optimal solution among the m first transformer load allocation solutions, that is, the optimal particle of the parent population. The second optimal load solution refers to the optimal solution among the m second transformer load allocation solutions, that is, the optimal particle of the child population.

[0085] It is understood that the computer device can determine the quality of each particle in the same population in any manner, and determine the first optimal load solution and the second optimal load solution accordingly. For example, the computer device can determine the quality of the particle according to the fitness corresponding to each particle.

[0086] Step A2: Determine a first fitness corresponding to a first optimal load solution according to a preset distribution network loss objective function.

[0087] The distribution network loss objective function may be a low-voltage distribution network loss optimization objective function that considers port loss and port capacity, and its specific function may be determined according to actual conditions. The computer device may use a preset distribution network loss objective function to determine the fitness of the optimal particle of the parent generation and obtain a first fitness.

[0088] Step A3: Determine a second fitness corresponding to a second optimal load solution according to the distribution network loss objective function.

[0089] In this step, the computer device may use a preset distribution network loss objective function to determine the fitness of the optimal particle of the offspring generation and obtain a second fitness.

[0090] Step A4: Based on the first fitness and the second fitness, calculate the population improvement rate of the current iteration round.

[0091] In this step, considering that the first fitness reflects the best fitness of the parent population and the second fitness reflects the best fitness of the offspring population, the first fitness and the second fitness can be used to obtain the degree of improvement of the offspring population compared to the parent population, that is, the population improvement rate. In this way, the population improvement rate can be calculated based on the optimal values ​​of the two generations of populations, thereby improving the calculation efficiency of the population improvement rate and further improving the loss optimization efficiency of the distribution network.

[0092] In some embodiments, based on the first fitness and the second fitness, calculating the population improvement rate of the current iteration round includes:

[0093] The population improvement rate of the current iteration round is calculated based on the following expression: :

[0094]

[0095] In the formula, is the first optimal load solution, is the second best load solution, is the first fitness, is the second fitness, for and The Euclidean distance between is a preset constant.

[0096] This embodiment calculates the population improvement rate through the above expression, thereby further improving the calculation efficiency of the population improvement rate and improving the loss optimization efficiency of the distribution network.

[0097] In some embodiments, the distribution network loss objective function is:

[0098]

[0099] In the formula, is the distribution network loss objective function, D is the number of distribution transformers, is the iron loss of the ith distribution transformer, is the load factor of the ith distribution transformer, is the copper loss of the ith distribution transformer, is the active power loss of the jth port, is the penalty coefficient, The power received by the equipment to be powered corresponding to the i-th distribution transformer, is the load demand of the equipment to be powered corresponding to the i-th distribution transformer.

[0100] In this embodiment, the goal of optimizing the loss of the distribution network is to minimize the total loss of each interconnected transformer in the entire area by optimizing the load rate of each distribution transformer. In actual operation, both flexible switches and ordinary switches have a certain current carrying capacity. In order to be closer to practical applications and to further reduce the loss of the distribution network, this application adds the port capacity of multiple ports as a consideration to the optimization goal, specifically:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] In the formula, is the loss of the i-th distribution transformer; is the iron loss of the ith distribution transformer, in kw; is the load factor of the i-th distribution transformer; is the copper loss of the ith distribution transformer, in kw; is the apparent power output by the i-th distribution transformer; is the capacity of the i-th distribution transformer; is the power flowing through the corresponding port of the i-th distribution transformer. A positive number means it flows into the port, and a negative number means it flows out of the port. is the preset maximum power threshold flowing through the port; D is the number of distribution transformers in the area; is the useful power output by the i-th distribution transformer; The total power supply load demand for the region; The total power consumed by the equipment to be powered in the preset power supply area when it is working, that is, the power demand; is the current capacity of the i-th distribution transformer.

[0108]

[0109]

[0110] In the formula, is the active power loss of the jth AC / DC port; is the first loss coefficient of the jth AC / DC port; is the second loss coefficient of the jth AC / DC port; is the third loss coefficient of the jth AC / DC port; is the apparent power per unit value corresponding to the transmission power through the jth AC / DC port; is the per-unit active power value corresponding to the transmission power through the j-th AC / DC port; is the reactive power per unit value corresponding to the transmission power through the jth AC / DC port.

[0111] Therefore, the distribution network loss objective function can be:

[0112]

[0113] In the formula, N is the penalty coefficient, which penalizes whether the updated individual meets the load demand constraint, which can greatly improve the population update efficiency. The number of AC / DC ports in the interconnected system is consistent with the number of transformers.

[0114] In this way, a mathematical model of regional distribution network losses considering the conversion efficiency of multi-port flexible switches can be constructed and solved using this as the objective function to obtain a transformer load distribution scheme with lower losses.

[0115] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization method of the present application may also include the following steps:

[0116] If the population improvement rate is greater than or equal to the preset improvement rate threshold, then when the iteration end condition is not met, the child population of the current iteration round is used as the parent population of the next iteration round and enters the next iteration round.

[0117] In this embodiment, if the population improvement rate is greater than or equal to the preset improvement rate threshold, it indicates that in the current iteration round, the child population is more improved than the parent population, and the CSO algorithm is currently updating the population in the direction of the greater improvement. In this case, if the preset iteration end condition is not met, the computer device can use the child population of the current iteration round as the parent population of the next iteration round, so that the next iteration round can search in the direction of the higher improvement, thereby obtaining a transformer load distribution scheme with lower loss.

[0118] For example, if the parent population of the current iteration round is the first population and the child population of the current iteration round is the second population, then when the population improvement rate is greater than or equal to the preset improvement rate threshold and the iteration end condition is not met, the second population will be used as the parent population of the next iteration round and enter the next iteration round.

[0119] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization method of the present application may also include the following steps:

[0120] If the population improvement rate is greater than or equal to the preset improvement rate threshold, then when the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the child population of the current iteration round, and the operation of each distribution transformer is controlled based on each target load rate.

[0121] In this embodiment, when the population improvement rate of the current iteration round is greater than or equal to the preset improvement rate threshold, if the iteration end condition is met, the computer device can determine the target transformer load distribution plan from the child population of the current iteration round, and control the operation of each distribution transformer accordingly, so that each distribution transformer can operate according to the load rate indicated by the target transformer load distribution plan. In this way, the final load distribution plan can be determined from the last updated optimal solution with a large improvement rate to reduce the loss of the distribution network.

[0122] To facilitate understanding of the solution of the present application, a specific example is provided below for illustration.

[0123] like Figure 3 As shown, this example provides a method for optimizing the loss of a multi-port interconnected low-voltage distribution network, which specifically includes the following steps:

[0124] S302: Determine the parameters of the CSO algorithm;

[0125] S304: Initialize the number of iterations T, for example, T=1;

[0126] S306: Initialize the total population size M and the population size m, for example, M=m;

[0127] S308: Initialize the population;

[0128] S310: Calculate population fitness;

[0129] S312: Perform lateral operations and adopt a greedy strategy to compete with the parent generation and update the population;

[0130] S314: Execute vertical operation and adopt greedy strategy to compete with the parent generation, and update the population to obtain the child population;

[0131] S316: Calculate the population improvement rate based on the optimal particle of the offspring population and the optimal particle of the parent population;

[0132] S318: judging whether the improvement rate requirement is met according to the population improvement rate, if not, executing step S320, otherwise, executing step S326;

[0133] S320: Do not update the population, that is, discard the offspring population and keep the parent population;

[0134] S322: Determine whether M is less than or equal to 2m, if so, execute step S324, otherwise, execute step S328;

[0135] S324: Accumulate the total number of populations M, and then execute step S308;

[0136] S326: Use the offspring population as the parent population for the next iteration;

[0137] S328: Determine whether the maximum number of iterations has been reached, if so, execute step S334, otherwise, execute step S330;

[0138] S330: Record the current population size M and the best particle in the population. The optimal population size can be further determined according to the improvement rate criterion, which can further improve the optimization speed and indicate the direction for optimization.

[0139] S332: Accumulate the number of iterations T, and then execute step S312;

[0140] S334: Output an optimal allocation plan, and control the load rate of each distribution transformer according to the optimal allocation plan.

[0141] This example has at least the following beneficial effects:

[0142] (1) Compared with the traditional single-radiation operation mode of low-voltage distribution network, this application proposes a new network structure of low-voltage distribution network, which can further improve the power supply reliability of low-voltage distribution network;

[0143] (2) For the first time, the improvement rate is used as an evaluation method for the optimization efficiency and quality of the CSO algorithm, and the load rate of the distribution transformers in the region is optimized to minimize the distribution network loss in the region, thereby improving the efficiency of the loss analysis of the interconnected low-voltage distribution network. At the same time, the intelligent optimization algorithm is used to solve the network loss optimization problem of the multi-port interconnected system distribution network, which can solve the current gap in the method of solving the network loss problem of the multi-port interconnected system distribution network, and improve the efficiency of the loss analysis of the interconnected low-voltage distribution network;

[0144] (3) The regional loss optimization problem is creatively transformed into the optimization of transformer load rate and port load transmission, and the optimal load rate combination of all transformers in the region is obtained, making the optimization result more scientific.

[0145] The multi-port interconnected low-voltage distribution network loss optimization device provided in an embodiment of the present application is described below. The multi-port interconnected low-voltage distribution network loss optimization device described below and the multi-port interconnected low-voltage distribution network loss optimization method described above can be referenced to each other.

[0146] In some embodiments, Figure 4 As shown, the present application provides a multi-port interconnected low-voltage distribution network loss optimization device 400, which is applied to a multi-port interconnected low-voltage distribution network including at least two ports and at least two distribution transformers, and the device 400 includes:

[0147] A parent population determination module 402 is used to determine the parent population of the current iteration round;

[0148] A CSO updating module 404 is used to update the population using a CSO algorithm according to the transformer operation data of each distribution transformer, the port operation data of each port and the parent population of the current iteration round, and obtain the child population of the current iteration round;

[0149] An improvement rate calculation module 406 is used to calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round; wherein the population improvement rate is used to reflect the degree of improvement of the child population relative to the parent population in the current iteration round; the population improvement rate is positively correlated with the degree of improvement;

[0150] The first iteration module 408 is used to initialize the parent population of the current iteration round if the population improvement rate is less than a preset improvement rate threshold and to use the initialized parent population as the parent population of the next iteration round and enter the next iteration round when a preset population initialization rule is met;

[0151] The second iteration module 410 is used for, if the population improvement rate is less than the preset improvement rate threshold, taking the parent population of the current iteration round as the parent population of the next iteration round and entering the next iteration round when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied;

[0152] The first transformer control module 412 is used to determine the target load rate corresponding to each distribution transformer according to the parent population of the current iteration round if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the iteration end condition is met, and control the operation of each distribution transformer based on each target load rate.

[0153] In some embodiments, the parent population of the current iteration round includes m first transformer load allocation schemes, and the child population of the current iteration round includes m second transformer load allocation schemes, where m is a preset positive integer. The improvement rate calculation module 406 of the present application includes:

[0154] An optimal load scheme determining unit, configured to determine a first optimal load scheme among the m first transformer load distribution schemes, and to determine a second optimal load scheme among the m second transformer load distribution schemes;

[0155] A first fitness calculation unit, configured to determine a first fitness corresponding to the first optimal load solution according to a preset distribution network loss objective function;

[0156] A second fitness calculation unit, configured to determine a second fitness corresponding to the second optimal load solution according to the distribution network loss objective function;

[0157] An improvement rate calculation unit is used to calculate the population improvement rate of the current iteration round based on the first fitness and the second fitness.

[0158] In some embodiments, the improvement rate calculation unit of the present application is used to calculate the population improvement rate of the current iteration round based on the following expression: :

[0159]

[0160] In the formula, is the first optimal load solution, is the second optimal load solution, is the first fitness, is the second fitness, for and The Euclidean distance between is a preset constant.

[0161] In some embodiments, the distribution network loss objective function is:

[0162]

[0163] In the formula, is the distribution network loss objective function, D is the number of distribution transformers, is the iron loss of the ith distribution transformer, is the load factor of the ith distribution transformer, is the copper loss of i distribution transformers, is the active power loss of the jth port, is the penalty coefficient, The power received by the equipment to be powered corresponding to the i-th distribution transformer, is the load demand of the equipment to be powered corresponding to the i-th distribution transformer.

[0164] In some embodiments, the population initialization rule is that the number of population initialization times is less than or equal to a preset initialization times threshold; and / or, the iteration end condition is that the number of iterations is greater than or equal to a preset iteration times threshold.

[0165] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization device 400 of the present application further includes:

[0166] The third iteration module is used to use the child population of the current iteration round as the parent population of the next iteration round and enter the next iteration round if the population improvement rate is greater than or equal to the preset improvement rate threshold and the iteration end condition is not met.

[0167] In some embodiments, the multi-port interconnected low-voltage distribution network loss optimization device 400 of the present application further includes:

[0168] The second transformer control module is used to determine the target load rate corresponding to each distribution transformer according to the child population of the current iteration round if the population improvement rate is greater than or equal to the preset improvement rate threshold, and control the operation of each distribution transformer based on each target load rate when the iteration end condition is met.

[0169] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method in any embodiment.

[0170] In one embodiment, the present application also provides a computer device, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method in any embodiment.

[0171] Indicatively, Figure 5 The internal structure diagram of a computer device provided in an embodiment of the present application is shown in FIG. 1 . In one example, the computer device may be a server. Figure 5 , the computer device 900 includes a processing component 902, which further includes one or more processors, and a memory resource represented by a memory 901, for storing instructions that can be executed by the processing component 902, such as an application. The application stored in the memory 901 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 902 is configured to execute instructions to perform the steps of the method described in any of the above embodiments.

[0172] The computer device 900 may further include a power supply component 903 configured to perform power management of the computer device 900, a wired or wireless network interface 904 configured to connect the computer device 900 to a network, and an input / output (I / O) interface 905. The computer device 900 may operate based on an operating system stored in the memory 901, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0173] Those skilled in the art will understand that the internal structure of the computer device shown in the present application is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0174] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or equipment including the elements. Herein, "one", "one", "said", "the" and "it" may also include plural forms, unless the context clearly indicates another way. A plurality refers to at least two cases, such as 2, 3, 5 or 8, etc. "And / or" includes any and all combinations of the relevant listed items.

[0175] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can refer to each other.

[0176] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the loss of a multi-port interconnected low-voltage distribution network, characterized in that: The multi-port interconnected low-voltage distribution network includes at least two ports and at least two distribution transformers, and the method includes: Determine the parent population of the current iteration round; According to the transformer operation data of each distribution transformer, the port operation data of each port and the parent population of the current iteration round, a vertical and horizontal crossover algorithm CSO is used to update the population and obtain the child population of the current iteration round; Based on the parent population of the current iteration round and the child population of the current iteration round, the population improvement rate of the current iteration round is calculated; wherein the population improvement rate is used to reflect the degree of improvement of the child population relative to the parent population in the current iteration round; the population improvement rate is positively correlated with the degree of improvement; If the population improvement rate is less than the preset improvement rate threshold, then when the preset population initialization rule is met, the parent population of the current iteration round is initialized, and the initialized parent population is used as the parent population of the next iteration round, and the next iteration round is entered; If the population improvement rate is less than the preset improvement rate threshold, then when the population initialization rule is not met and the preset iteration end condition is not met, the parent population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered; If the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the parent population of the current iteration round, and the operation of each distribution transformer is controlled separately based on each target load rate.

2. The method according to claim 1, characterized in that The parent population of the current iteration round includes m first transformer load allocation schemes, and the child population of the current iteration round includes m second transformer load allocation schemes, where m is a preset positive integer; The calculating the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round includes: Determining a first optimal load scheme among the m first transformer load distribution schemes, and determining a second optimal load scheme among the m second transformer load distribution schemes; Determining a first fitness corresponding to the first optimal load solution according to a preset distribution network loss objective function; Determining a second fitness corresponding to the second optimal load solution according to the distribution network loss objective function; Based on the first fitness and the second fitness, a population improvement rate of the current iteration round is calculated.

3. The method according to claim 2, characterized in that The calculating the population improvement rate of the current iteration round based on the first fitness and the second fitness includes: The population improvement rate of the current iteration round is calculated based on the following expression: : In the formula, is the first optimal load solution, is the second optimal load solution, is the first fitness, is the second fitness, for and The Euclidean distance between is a preset constant.

4. The method according to claim 2, characterized in that: The distribution network loss objective function is: In the formula, is the distribution network loss objective function, D is the number of distribution transformers, is the iron loss of the ith distribution transformer, is the load factor of the ith distribution transformer, is the copper loss of i distribution transformers, is the active power loss of the jth port, is the penalty coefficient, The power received by the equipment to be powered corresponding to the i-th distribution transformer, is the load demand of the equipment to be powered corresponding to the i-th distribution transformer.

5. The method according to claim 1, characterized in that The population initialization rule is that the number of population initialization times is less than or equal to a preset initialization times threshold; and / or the iteration end condition is that the number of iterations is greater than or equal to a preset iteration times threshold.

6. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: If the population improvement rate is greater than or equal to the preset improvement rate threshold, then when the iteration end condition is not met, the child population of the current iteration round is used as the parent population of the next iteration round, and enters the next iteration round.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: If the population improvement rate is greater than or equal to the preset improvement rate threshold, then when the iteration end condition is met, the target load rate corresponding to each distribution transformer is determined according to the child population of the current iteration round, and the operation of each distribution transformer is controlled separately based on each target load rate.

8. A multi-port interconnected low-voltage distribution network loss optimization device, characterized in that: The multi-port interconnected low-voltage distribution network comprises at least two ports and at least two distribution transformers, and the device comprises: A parent population determination module is used to determine the parent population of the current iteration round; A CSO update module, configured to update the population using a CSO algorithm according to the transformer operation data of each distribution transformer, the port operation data of each port, and the parent population of the current iteration round, and obtain the child population of the current iteration round; An improvement rate calculation module, used to calculate the population improvement rate of the current iteration round based on the parent population of the current iteration round and the child population of the current iteration round; wherein the population improvement rate is used to reflect the degree of improvement of the child population relative to the parent population in the current iteration round; the population improvement rate is positively correlated with the degree of improvement; The first iteration module is used for initializing the parent population of the current iteration round if the population improvement rate is less than a preset improvement rate threshold and when a preset population initialization rule is met, and using the initialized parent population as the parent population of the next iteration round, and entering the next iteration round; A second iteration module is used for, if the population improvement rate is less than the preset improvement rate threshold, then when the population initialization rule is not satisfied and the preset iteration end condition is not satisfied, taking the parent population of the current iteration round as the parent population of the next iteration round and entering the next iteration round; The first transformer control module is used to determine the target load rate corresponding to each distribution transformer according to the parent population of the current iteration round if the population improvement rate is less than the preset improvement rate threshold, when the population initialization rule is not met and the iteration end condition is met, and control the operation of each distribution transformer respectively based on each target load rate.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the multi-port interconnected low-voltage distribution network loss optimization method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the multi-port interconnected low-voltage distribution network loss optimization method as described in any one of claims 1 to 7 are performed.

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