A loss optimization method and related device for a multi-port interconnected low-voltage distribution network including energy storage

Abstract

This application relates to the technical field of distribution networks, and specifically provides a method for optimizing the losses of a multi-port interconnected low-voltage distribution network including energy storage and related devices. After optimizing the population using the current population optimization algorithm, the average particle distance of the offspring population can be calculated to evaluate the particle diversity degree of the offspring population through the average particle distance. If the average particle distance is less than the preset distance threshold, it indicates that the particle diversity of the offspring population is weak, and the algorithm may have premature problems, inhibiting the search process. In this case, the population optimization algorithm can be replaced and the parent population of the next iteration round can be updated. By changing the algorithm and updating the population, information such as the update mechanism and population size of the algorithm can be changed, thereby enriching the population diversity and solving the next iteration round accordingly. In this way, the optimal operation strategy of the multi-port interconnected low-voltage distribution network can be determined, and the minimum distribution network losses can be achieved.

Description

Technical Field

[0001] This application relates to the technical field of distribution networks, and particularly to a method for optimizing losses of a multi-port interconnected low-voltage distribution network including energy storage and related devices. Background Art

[0002] A multi-port interconnected low-voltage distribution network including energy storage is a low-voltage power distribution system that integrates energy storage technology and multi-port interconnection technology. It may include at least one distribution transformer, at least one energy storage device, and multiple power ports. The multiple power ports can be respectively connected to electrical equipment, distribution transformers, and energy storage devices to achieve energy scheduling between the respective power ports.

[0003] Among them, the energy storage device of the low-voltage power distribution system can store electrical energy during low load periods and release electrical energy during high load periods. By reasonably adjusting the charge and discharge strategies of the energy storage device and the load rate of the distribution transformer, the low-voltage power distribution system can reduce the energy loss of the distribution network while maintaining power balance, thereby improving the energy utilization efficiency.

[0004] Currently, the prior art uses the traditional CSO (Crisscross Optimization Algorithm) algorithm to determine the operation strategy of the low-voltage power distribution system. However, through research by the inventors, it is found that the prior art is prone to premature problems during optimization, making it difficult to obtain the optimal operation strategy of the distribution network, and thus unable to effectively reduce the distribution network losses. Summary of the Invention

[0005] The purpose of this application aims to solve at least one of the above technical defects, especially the technical defect of high distribution network losses in the prior art.

[0006] In a first aspect, an embodiment of this application provides a method for optimizing losses of a multi-port interconnected low-voltage distribution network including energy storage. The multi-port interconnected low-voltage distribution network including energy storage includes at least one energy storage device, at least one distribution transformer, and multiple power ports. The method includes:

[0007] Determine the parental population of the current iteration round; wherein, the parental population includes multiple first distribution network operation strategies;

[0008] Use the current population optimization algorithm to perform population optimization and update on the parental population of the current iteration round, and obtain the offspring population of the current iteration round; the offspring population includes multiple second distribution network operation strategies;

[0009] Calculate the average particle distance of the current iteration round according to the multiple second distribution network operation strategies; wherein, the average particle distance is used to reflect the particle diversity degree of the offspring population, and the average particle distance is positively correlated with the diversity degree;

[0010] If the average particle distance of the current iteration round is less than the preset distance threshold and the preset iteration end condition is not satisfied, then change the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm, and generate the parent population of the next iteration round according to the multiple first distribution network operation strategies and the multiple second distribution network operation strategies, and enter the next iteration round; wherein, the second optimization algorithm is different from the first optimization algorithm;

[0011] If the average particle distance of the current iteration round is less than the preset distance threshold and the iteration end condition is satisfied, then determine the optimal operation strategy among the multiple first distribution network operation strategies and the multiple second distribution network operation strategies; wherein, the optimal operation strategy includes the optimal load rate of each distribution transformer and the optimal charge and discharge strategy of the energy storage device;

[0012] Control the energy storage device and each distribution transformer respectively according to the optimal operation strategy.

[0013] In some embodiments, the changing the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm includes:

[0014] If the first optimization algorithm is the traditional CSO algorithm, then change the current population optimization algorithm from the CSO algorithm to the traditional PSO algorithm;

[0015] If the first optimization algorithm is the traditional PSO algorithm, then change the current population optimization algorithm from the traditional PSO algorithm to the ring topology CSO algorithm;

[0016] If the first optimization algorithm is the ring topology CSO algorithm, then change the current population optimization algorithm from the ring topology CSO algorithm to the ring topology PSO algorithm;

[0017] If the first optimization algorithm is the ring topology PSO algorithm, then change the current population optimization algorithm from the ring topology PSO algorithm to the NW small world CSO algorithm;

[0018] If the first optimization algorithm is the NW small world CSO algorithm, then change the current population optimization algorithm from the NW small world CSO algorithm to the NW small world PSO algorithm;

[0019] If the first optimization algorithm is the NW small-world PSO algorithm, then replace the current population optimization algorithm from the NW small-world PSO algorithm with the traditional CSO algorithm.

[0020] In some embodiments, generating the parent population for the next iteration round according to the multiple first distribution network operation strategies and the multiple second distribution network operation strategies includes:

[0021] Select M parent optimal strategies from the multiple first distribution network operation strategies according to the fitness values corresponding to the multiple first distribution network operation strategies at a first preset ratio; M is a positive integer;

[0022] Select N offspring optimal strategies from the multiple second distribution network operation strategies according to the fitness values corresponding to the multiple second distribution network operation strategies at a second preset ratio; N is a positive integer;

[0023] Use the M parent optimal strategies and the N offspring optimal strategies as the parent population for the next iteration round.

[0024] In some embodiments, calculating the average particle distance of the current iteration round according to the multiple second distribution network operation strategies includes:

[0025] Calculate the average particle distance of the current iteration round based on the following expression :

[0026]

[0027] In the formula, K is the total number of the second distribution network operation strategies, is the (t + 1)-th second distribution network operation strategy, is the t-th second distribution network operation strategy.

[0028] In some embodiments, using the current population optimization algorithm to perform population optimization update on the parent population of the current iteration round and obtaining the offspring population of the current iteration round includes:

[0029] According to a preset objective function, use the current population optimization algorithm to perform population optimization update on the parent population of the current iteration round and obtain the offspring population of the current iteration round; wherein, the objective function is:

[0030]

[0031] In the formula, F is the distribution network loss, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i distribution transformers, is the active power loss of the j-th power port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer, and C is the number of energy storage devices, is the active power loss of the e-th energy storage port.

[0032] In some embodiments, the method includes:

[0033] If the average particle distance of the current iteration round is greater than or equal to the preset distance threshold and the iteration end condition is not satisfied, then replace the current population optimization algorithm with the traditional CSO algorithm, use the offspring population of the current iteration round as the parent population of the next iteration round, and enter the next iteration round.

[0034] In some embodiments, the iteration end condition is that the number of iterations is greater than or equal to a preset iteration number threshold.

[0035] In a second aspect, an embodiment of the present application provides a loss optimization device for a multi-port interconnected low-voltage distribution network including energy storage. The multi-port interconnected low-voltage distribution network including energy storage includes at least one energy storage device, at least one distribution transformer, and multiple power ports. The device includes:

[0036] A parent population determination module, configured to determine the parent population of the current iteration round; wherein, the parent population includes multiple first distribution network operation strategies;

[0037] A first population update module, configured to use the current population optimization algorithm to perform population optimization and update on the parent population of the current iteration round, and obtain the offspring population of the current iteration round; the offspring population includes multiple second distribution network operation strategies;

[0038] An average particle distance calculation module, configured to calculate the average particle distance of the current iteration round according to the multiple second distribution network operation strategies; wherein, the average particle distance is used to reflect the particle diversity degree of the offspring population, and the average particle distance is positively correlated with the diversity degree;

[0039] The second population update module is used to, if the average particle distance in the current iteration round is less than the preset distance threshold and the preset iteration end condition is not satisfied, change the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm, generate the parent population for the next iteration round according to the multiple first distribution network operation strategies and the multiple second distribution network operation strategies, and enter the next iteration round; wherein, the second optimization algorithm is different from the first optimization algorithm;

[0040] The optimal operation strategy determination module is used to, if the average particle distance in the current iteration round is less than the preset distance threshold and the iteration end condition is satisfied, determine the optimal operation strategy among the multiple first distribution network operation strategies and the multiple second distribution network operation strategies; wherein, the optimal operation strategy includes the optimal load rate of each distribution transformer and the optimal charge and discharge strategy of the energy storage device;

[0041] The control module is used to control the energy storage device and each distribution transformer respectively according to the optimal operation strategy.

[0042] In a third aspect, an embodiment of the present application provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the steps of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage according to any one of the above embodiments.

[0043] In a fourth aspect, an embodiment of the present application provides a computer device, which includes: one or more processors, and a memory;

[0044] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage according to any one of the above embodiments are executed.

[0045] In a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage and related devices provided in some embodiments of the present application, after population optimization is performed using the current population optimization algorithm, the average particle distance of the offspring population can be calculated to evaluate the particle diversity degree of the offspring population through the average particle distance. If the average particle distance is less than the preset distance threshold, it indicates that the particle diversity of the offspring population is weak, and the algorithm may have already had a premature problem, suppressing the search process. In this case, the population optimization algorithm can be changed and the parent population for the next iteration round can be updated, and the update mechanism and population size and other information of the algorithm can be changed through algorithm change and population update, so as to enrich the population diversity and perform the solution for the next iteration round accordingly.

[0046] This application optimizes the population diversity of the average particle distance evaluation optimization algorithm and solves the loss optimization problem of a multi-port interconnected low-voltage distribution network with energy storage, thereby avoiding premature convergence during the update process of the algorithm. Furthermore, the optimal operation strategy of the multi-port interconnected low-voltage distribution network can be determined to minimize the distribution network loss. In this way, the distribution network loss can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 FIG. 1 is a schematic diagram of a multi-port interconnected low-voltage distribution network with energy storage in an embodiment;

[0049] Figure 2 FIG. 2 is one of the schematic flowcharts of the method for optimizing the loss of a multi-port interconnected low-voltage distribution network with energy storage in an embodiment;

[0050] Figure 3 FIG. 3 is another schematic flowchart of the method for optimizing the loss of a multi-port interconnected low-voltage distribution network with energy storage in an embodiment;

[0051] Figure 4 FIG. 4 is a schematic structural diagram of a device for optimizing the loss of a multi-port interconnected low-voltage distribution network with energy storage in an embodiment;

[0052] Figure 5 FIG. 5 is an internal structural diagram of a computer device in an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all of them. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0054] The distribution network is an important part of the power system. In a multi-port interconnected low-voltage distribution network with energy storage, the distribution transformer and the energy storage device are important equipment in the distribution network. For a distribution transformer, capacity and loss are one of its important parameters, which can be used to evaluate whether the transformer is operating well. As a physical quantity that changes with the load rate of the transformer, transformer loss can be a direction for refined analysis of distribution network loss. Transformer loss is also an important link in line loss management. It is necessary to reasonably select the capacity of the distribution transformer according to the load conditions to avoid the situation of "using a big horse to pull a small cart" and capacity waste caused by too large a capacity and too small a load, and to avoid the increase in loss caused by heavy load and overload of the transformer due to too small a capacity and too large a load, so as to ensure that the transformer is in an economic operation state with the minimum loss. At the same time, the distribution transformer can be installed as close as possible to the load center to shorten the power supply radius of each user, ensure uniform load distribution, and achieve the optimal operation state.

[0055] In recent years, with the development of the economic society, people's requirements for power supply reliability have become higher and higher. The distribution network directly faces users, and its impact on power supply reliability cannot be ignored. After long-term development, the distribution network generally forms a power supply mode of "closed-loop design and open-loop operation", and on this basis, measures such as distribution automation, equipment management, load transfer, outage management, and transformer load management are implemented to ensure reliable power supply. Although these measures are very helpful for improving the power supply reliability of the distribution network, during line maintenance or faults, the open-loop operation power supply mode still cannot avoid short-term power outages caused by switching operations and cannot meet the stringent requirements of important users such as high-tech industries for power supply. Therefore, in recent years, the power grid operation department has also begun to try the closed-loop operation of the medium-voltage distribution network, and optimize the primary grid structure of the distribution network by referring to foreign closed-loop operation experience to achieve the closed-loop operation of the distribution network based on the loop connection mode.

[0056] With the development of power electronics technology, the current back-to-back closed-loop operation technology has matured, and at the same time, energy storage technology has also become increasingly mature. Adding an energy storage device to a multi-port interconnected low-voltage distribution network can further improve the operation efficiency and quality of the interconnected network. Therefore, there is an urgent need to provide a loss optimization method applied to a multi-port interconnected low-voltage distribution network with energy storage to solve the loss optimization problem of a multi-port interconnected distribution network considering energy storage, and then reduce the loss of the multi-port interconnected distribution network.

[0057] In some embodiments, the method provided in this application can be applied to a multi-port interconnected low-voltage distribution network with energy storage for loss optimization. The multi-port interconnected low-voltage distribution network with energy storage may include at least one energy storage device, at least one distribution transformer, and multiple power ports. It can be understood that the specific number of energy storage devices, the specific number of distribution transformers, and the specific number of power ports can all be determined according to the actual situation, and this application does not make specific restrictions on this.

[0058] In some examples, the low-voltage distribution network adopts a single radial operation mode. With the continuous development of power electronics technology, some low-voltage distribution networks with high power supply reliability have been built in a closed-loop manner, enabling the low-voltage distribution network to operate in a closed loop. Figure 1 The network topology diagram of a multi-port interconnected low-voltage distribution network considering energy storage is shown. Multiple electrical energy ports can be provided through flexible multi-port devices. Each distribution transformer and each energy storage device can be connected through flexible multi-port devices and achieve closed-loop operation through a flexible closed-loop device.

[0059] In some embodiments, the present application provides a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage, which can Figure 1 optimize the loss of the multi-port interconnected low-voltage distribution network including energy storage as shown. The following embodiments are described by taking the application of this method 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 functions, which can be but is not limited to devices such as servers, desktop computers, laptop computers, notebook computers, tablet computers, smart phones, etc. The present application does not make specific limitations on this.

[0060] As Figure 2 shown, a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage provided by the present application may include the following steps:

[0061] S202: Determine the parent population of the current iteration round.

[0062] Specifically, the present application can introduce an algorithm novelty criterion to improve the CSO algorithm and use the improved algorithm to solve the network loss optimization model of the multi-port interconnected low-voltage distribution network considering energy storage to obtain the optimal operation strategy of the regional low-voltage distribution network, so that the loss of the low-voltage distribution network reaches the minimum. During the solution process, population optimization can be performed through multiple iterations to determine the load rates of each distribution transformer and the charge and discharge strategies of each energy storage device. In each iteration round, the computer device can execute steps S202 to S212.

[0063] In the current iteration round, the computer device can first determine the parent population of the current iteration round. It can be understood that the parent population can include multiple particles, and the particles of the parent population can be understood as the first distribution network operation strategy. The first distribution network operation strategy can include the load rate of each distribution transformer and the charge and discharge strategies of each energy storage device. The charge and discharge strategies of the energy storage device can include the actual charging power and actual discharging power of the energy storage device.

[0064] It should be noted that if the current iteration round is the first iteration round, the computer device can use any algorithm to generate the parental population of the current iteration round, including but not limited to using a random algorithm or a pre-determined population initialization algorithm to generate the parental population of the first iteration round.

[0065] S204: Use the current population optimization algorithm to optimize and update the parental population of the current iteration round, and obtain the offspring population of the current iteration round.

[0066] Among them, the current population optimization algorithm refers to the population optimization algorithm that needs to be used currently. The offspring population can include multiple particles. The particles of the offspring population can be understood as the second distribution network operation strategies. For the relevant descriptions of the second distribution network operation strategies, reference can be made to the above descriptions of the first distribution network operation strategies, which will not be elaborated herein.

[0067] In this step, the computer device can determine the current population optimization algorithm among multiple population optimization algorithms, and use the current population optimization algorithm and the parental population of the current iteration round to perform population optimization and update to obtain the offspring population of the current iteration round.

[0068] In some examples, the computer device can pre-obtain the maximum charge and discharge power of each energy storage device, the basic operation data of each distribution transformer, the maximum current carrying capacity of each power port, and the load demand of each substation area, and based on the foregoing data, use the current population optimization algorithm to perform population update, so as to obtain various second distribution network operation strategies that meet the actual requirements.

[0069] S206: Calculate the average particle distance of the current iteration round according to multiple second distribution network operation strategies.

[0070] In this step, each second distribution network operation strategy can be regarded as a particle of the offspring population. Therefore, the average particle distance can be the average distance between multiple second distribution network operation strategies. The average particle distance can reflect the particle diversity degree of the offspring population, and the average particle distance is positively correlated with the diversity degree. That is, the larger the average particle distance, the more unique the particles of the offspring population, and the stronger the particle diversity. On the contrary, the smaller the average particle distance, the higher the similarity of the particles of the offspring population, and the weaker the particle diversity.

[0071] It can be understood that this application can use any distance calculation formula to obtain the average particle distance of the current iteration round. In one example, to improve the solution efficiency and further improve the distribution network loss optimization efficiency, the computer device can calculate the average particle distance of the current iteration round based on the following expression :

[0072]

[0073] Wherein, K is the total number of the second distribution network operation strategies corresponding to the current iteration round, is the (t + 1)-th second distribution network operation strategy in the offspring population of the current iteration round, is the t-th second distribution network operation strategy in the offspring population of the current iteration round. is the Euclidean distance between two adjacent particles and .

[0074] For example, if the current population optimization algorithm is an algorithm in the form of a ring topology, before or after the population optimization update, the computer device will number the particles in the population. Based on the particle numbers, each pair of adjacent particles can be determined, and the average particle distance can be calculated accordingly. .

[0075] S208: If the average particle distance of the current iteration round is less than the preset distance threshold and the preset iteration end condition is not satisfied, the current population optimization algorithm is changed from the first optimization algorithm to the second optimization algorithm, and a parent population for the next iteration round is generated according to multiple first distribution network operation strategies and multiple second distribution network operation strategies, and the next iteration round is entered.

[0076] In this step, if the average particle distance of the current iteration round is less than the preset distance threshold, it indicates that the diversity is weak and the algorithm may have already had a premature problem, which suppresses the search process. In this case, the computer device can intervene by changing the population optimization algorithm and updating the initial population of the population optimization algorithm. In this way, information such as the update mechanism and the population size can be changed, so as to enrich the population diversity and realize the update of the population diversity.

[0077] Specifically, when the average particle distance of the current iteration round is less than the preset distance threshold and the preset iteration end condition is not satisfied, the computer device can change the current population optimization algorithm from the first optimization algorithm to a second optimization algorithm different from the first optimization algorithm to change the update mechanism. It should be noted that the first optimization algorithm and the second optimization algorithm can be any two different population optimization algorithms, which are not limited to the description of this application.

[0078] Moreover, when the average particle distance of the current iteration round is less than the preset distance threshold and the preset iteration end condition is not satisfied, the computer device can also construct a new initial population of the optimization algorithm according to the parent population and the offspring population of the current iteration round. In this way, when the computer device executes the next iteration round, it can perform population optimization update based on the new update mechanism and the new initial population, which is beneficial to maintaining the population diversity during the population update process of the algorithm.

[0079] It can be understood that the specific value of the preset distance threshold can be set according to the actual situation, and the present application does not make specific limitations thereto. 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 the present application does not make specific limitations thereto.

[0080] In some examples, the iteration end condition can 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 satisfied; 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 satisfied.

[0081] S210: If the average particle distance in the current iteration round is less than the preset distance threshold and the iteration end condition is satisfied, then among multiple first distribution network operation strategies and multiple second distribution network operation strategies, determine the optimal operation strategy.

[0082] Among them, the optimal operation strategy includes the optimal load rate of each distribution transformer and the optimal charge and discharge strategy of the energy storage device.

[0083] In this step, if the preset iteration end condition is satisfied, the computer device can select the optimal solution from the parent population and the offspring population in the current iteration round to obtain the optimal operation strategy that can minimize the distribution network loss.

[0084] S212: Control the energy storage device and each distribution transformer respectively according to the optimal operation strategy.

[0085] In this step, the computer device can control the load rate of each distribution transformer and the charge and discharge power of each energy storage device in the low-voltage distribution network respectively according to the optimal operation strategy, so that the low-voltage distribution network can operate under the optimal operation strategy and achieve the minimum loss.

[0086] The present application introduces the average particle distance to evaluate the population diversity of the optimization algorithm and solves the loss optimization problem of the multi-port interconnected low-voltage distribution network with energy storage, thereby avoiding the premature situation in the algorithm update process, and then determining the optimal operation strategy of the multi-port interconnected low-voltage distribution network to achieve the minimum distribution network loss. In this way, the distribution network loss can be effectively reduced.

[0087] In some embodiments, replacing the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm includes:

[0088] If the first optimization algorithm is the traditional CSO algorithm, then replace the current population optimization algorithm from the CSO algorithm to the traditional PSO algorithm;

[0089] If the first optimization algorithm is the traditional PSO algorithm, then replace the current population optimization algorithm from the traditional PSO algorithm to the ring topology CSO algorithm;

[0090] If the first optimization algorithm is the circular topology CSO algorithm, then change the current population optimization algorithm from the circular topology CSO algorithm to the circular topology PSO algorithm;

[0091] If the first optimization algorithm is the circular topology PSO algorithm, then change the current population optimization algorithm from the circular topology PSO algorithm to the NW small world CSO algorithm;

[0092] If the first optimization algorithm is the NW small world CSO algorithm, then change the current population optimization algorithm from the NW small world CSO algorithm to the NW small world PSO algorithm;

[0093] If the first optimization algorithm is the NW small world PSO algorithm, then change the current population optimization algorithm from the NW small world PSO algorithm to the traditional CSO algorithm.

[0094] In this embodiment, to fully consider the optimization ability of the algorithm and the optimization efficiency of the algorithm and balance the two, the priority ranking of the population optimization algorithm in this application can be: traditional CSO algorithm, traditional PSO (Particle Swarm Optimization) algorithm, circular topology CSO algorithm, circular topology PSO algorithm, NW small world CSO algorithm, NW small world PSO algorithm. Among them, the traditional CSO algorithm has the highest priority, the traditional PSO algorithm has the second highest priority, and so on, and the NW small world PSO algorithm has the lowest priority.

[0095] Since the computer device will also update the initial population when changing the population optimization algorithm, after changing the population optimization algorithm multiple times, the current initial population is quite different from the most original initial population, which can play a role in disturbing the optimization process. Based on this, if the current population optimization algorithm is the NW small world PSO algorithm with the lowest priority, in the case where the population optimization algorithm needs to be changed, the current population optimization algorithm can be changed to the traditional CSO algorithm with the highest priority, so as to perform population optimization update according to the traditional CSO algorithm and the initial population updated multiple times in the next iteration round.

[0096] In this way, on the one hand, population optimization algorithms with different topological structures can be used for population update, which is beneficial to increasing the population diversity during the process of updating the population by the algorithm. On the other hand, the optimization ability of the algorithm and the optimization efficiency can be balanced, so that the optimization efficiency can be improved on the basis of effectively reducing the power distribution network loss.

[0097] In some examples, the computer device can pre-construct an algorithm pool and respectively determine the numbers of each population optimization algorithm in the algorithm pool. The computer device can change the current population optimization algorithm by changing the current algorithm number.

[0098] For example, the algorithm pool may include a traditional CSO algorithm (numbered 1), a traditional PSO algorithm (numbered 2), a ring topology CSO algorithm (numbered 3), a ring topology PSO algorithm (numbered 4), an NW small world CSO algorithm (numbered 5), and an NW small world PSO algorithm (numbered 6). When it is necessary to change the population optimization algorithm, the computer device can perform the calculation of H=(H + 1) with the current algorithm number H to update the number, so as to achieve the purpose of changing the population optimization algorithm.

[0099] In some embodiments, generating the parental population for the next iteration round according to a plurality of first distribution network operation strategies and a plurality of second distribution network operation strategies includes:

[0100] Selecting M parental optimal strategies from the plurality of first distribution network operation strategies according to the fitness values corresponding to the plurality of first distribution network operation strategies at a first preset ratio; M is a positive integer;

[0101] Selecting N offspring optimal strategies from the plurality of second distribution network operation strategies according to the fitness values corresponding to the plurality of second distribution network operation strategies at a second preset ratio; N is a positive integer;

[0102] Taking the M parental optimal strategies and the N offspring optimal strategies as the parental population for the next iteration round.

[0103] In this embodiment, both the first preset ratio and the second preset ratio can be determined according to actual factors, and the present application does not make specific limitations thereon. For ease of description, in some embodiments of the present application, the first preset ratio is 50% and the second preset ratio is 50% as an example for illustration.

[0104] Specifically, if the average particle distance in the current iteration round is less than the preset distance threshold and the preset iteration end condition is not satisfied, the computer device can replace the population optimization algorithm, and use the optimal 50% particles after update and the optimal 50% particles before update as the initial population of the new optimization algorithm. In this way, it can play a disturbing role in the optimization process, which is beneficial to improving the diversity of particles, and thus the operation strategy with the minimum loss can be determined.

[0105] In some embodiments, using the current population optimization algorithm to perform population optimization update on the parental population of the current iteration round and obtaining the offspring population of the current iteration round includes:

[0106] According to the preset objective function, using the current population optimization algorithm to perform population optimization update on the parental population of the current iteration round and obtaining the offspring population of the current iteration round; wherein, the objective function is:

[0107]

[0108] In the formula, F is the distribution network loss, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i-th distribution transformer, is the active power loss of the j-th power port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer, C is the number of energy storage devices, is the active power loss of the e-th energy storage port.

[0109] In this application, since the multi-port interconnected low-voltage distribution network includes energy storage devices, an optimization objective function of the low-voltage distribution network loss considering energy storage loss can be established. At the same time, the goal of optimizing the distribution network loss is to optimize the load rates of each distribution transformer and the charge and discharge strategies of each energy storage device, so that the total loss of the entire low-voltage distribution network reaches the minimum. In the actual operation process, both ordinary switches and flexible switches have a certain current-carrying capacity. Therefore, to be closer to the actual application scenario and further reduce the distribution network loss, this embodiment can comprehensively consider the port capacity of multiple ports and energy storage loss to generate an optimization objective. Specifically as follows:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] In the formula, is the loss of the i-th distribution transformer; is the iron loss of the i-th distribution transformer, and the unit can be kW; is the load rate of the i-th distribution transformer; is the copper loss of the i-th distribution transformer, and the unit can be 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 port corresponding to the i-th distribution transformer. A positive number means flowing into the port, and a negative number means flowing out of the port; is the pre-set maximum power threshold flowing through the port; D is the number of distribution transformers; is the useful power output by the i-th distribution transformer; is the power of the e-th energy storage device; is the total power supply load demand of the area; is the received power of the power supply equipment corresponding to the i-th distribution transformer; is the current capacity of the i-th distribution transformer;

[0117]

[0118]

[0119] In the formula, is the active power loss of the j-th AC / DC port (the AC / DC port is the electrical energy port connecting the distribution transformer); is the first loss coefficient of the j-th AC / DC port; is the second loss coefficient of the j-th AC / DC port; is the third loss coefficient of the j-th AC / DC port; is the per-unit value of the apparent power corresponding to the transmission power passing through the j-th AC / DC port; is the per-unit value of the active power corresponding to the transmission power passing through the j-th AC / DC port; is the per-unit value of the reactive power corresponding to the transmission power passing through the j-th AC / DC port.

[0120] The loss model of the DC / DC converter is as follows:

[0121]

[0122] In the formula, is the active power loss of the e-th DC / DC port (the DC / DC port is the energy storage port, that is, the electrical energy port connecting the energy storage device); is the first loss coefficient of the e-th DC / DC port; is the second loss coefficient of the e-th DC / DC port; is the per-unit value of the apparent power corresponding to the transmission power passing through the e-th DC / DC port; is the third loss coefficient of the e-th DC / DC port.

[0123]

[0124]

[0125] In the formula, is the actual charging power of the e-th DC / DC port; is the maximum charging power of the e-th DC / DC port; is the actual discharge power of the e-th DC / DC port; is the maximum discharge power of the e-th DC / DC port.

[0126] In some examples, the number of AC / DC ports in the low-voltage distribution network can be the same as the number of distribution transformers, and the number of DC / DC ports can be the same as the number of energy storage devices.

[0127] Combined with the above expressions, the objective function can be:

[0128]

[0129] In the formula, F is the distribution network loss, is to minimize the distribution network loss, D is the number of distribution transformers, is the loss of the i-th distribution transformer, is the active power loss of the j-th electrical energy port, is the active power loss of the e-th energy storage port, C is the number of energy storage devices, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i-th distribution transformer. N is a penalty coefficient, which penalizes whether the updated individual meets the load demand constraint, which can greatly improve the population update efficiency.

[0130] In some embodiments, a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage provided by the present application may further include the following steps:

[0131] If the average particle distance in the current iteration round is greater than or equal to the preset distance threshold and the iteration end condition is not satisfied, then change the current population optimization algorithm to the traditional CSO algorithm, use the offspring population in the current iteration round as the parent population in the next iteration round, and enter the next iteration round.

[0132] In this embodiment, if the average particle distance in the current iteration round is greater than or equal to the preset distance threshold, it indicates that the particle diversity degree of the offspring population in the current iteration round meets the novelty requirement. In this case, the computer device can use the offspring population in the current iteration round as the parent population in the next iteration round, and replace the current population optimization algorithm with the traditional CSO algorithm to perform population optimization and update using the traditional CSO algorithm in the next iteration round. In this way, the optimization ability and efficiency of the algorithm can be balanced, so that the optimization efficiency can be improved on the basis of effectively reducing the distribution network loss.

[0133] To facilitate the understanding of the solution of this application, a specific example is used for illustration below.

[0134] As Figure 3 shown, this example provides a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage, which may include the following steps:

[0135] S302: Determine the algorithm parameters and the algorithm pool; among them, the algorithm pool may include the traditional CSO algorithm, the traditional PSO algorithm, the ring topology CSO algorithm, the ring topology PSO algorithm, the NW small world CSO algorithm, and the NW small world PSO algorithm.

[0136] S304: Initialize the population.

[0137] S306: Number the optimization algorithms in the algorithm pool; among them, the number of the traditional CSO algorithm is 1, the number of the traditional PSO algorithm is 2, the number of the ring topology CSO algorithm is 3, the number of the ring topology PSO algorithm is 4, the number of the NW small world CSO algorithm is 5, and the number of the NW small world PSO algorithm is 6.

[0138] S308: Initialize the number of iterations T. For example, let T = 1.

[0139] S310: Let the current algorithm number H = 1.

[0140] S312: Calculate the population fitness.

[0141] S314: Determine the current population optimization algorithm in the algorithm pool according to the current algorithm number, and use the current population optimization algorithm to update the population.

[0142] S316: Calculate the average particle distance according to each particle of the offspring population.

[0143] S318: Judge whether the novelty index requirement is met according to the average particle distance. If not, execute step S320. If so, execute step S326.

[0144] S320: Determine if H ≤ 5. If yes, execute step S322; otherwise, execute step S326.

[0145] S322: H = H + 1.

[0146] S324: Update the population and use the updated population as the initial population of the new algorithm.

[0147] S326: Determine if the maximum number of iterations has been reached. If yes, execute S330; otherwise, execute step S328.

[0148] S328: T = T + 1.

[0149] S330: Output the optimal operation plan. The computer device can control each distribution transformer and each energy storage device according to the optimal operation plan.

[0150] This example is used to optimize the losses of a multi-port interconnected distribution network with energy storage. By optimizing the load rates of each transformer in the area and the charge and discharge of the energy storage, the total losses of the transformers in the area are minimized, further improving the current line loss management efficiency and line loss management quality. It is achieved by the following specific technical means:

[0151] (1) Use a multi-port flexible switch to construct an interconnected network structure of a low-voltage substation area with energy storage.

[0152] (2) Use the novelty index evaluation to optimize the population diversity of the algorithm, solve the problem of optimizing the distribution network losses of a multi-port interconnected system with energy storage, determine the optimal load rate of each transformer in the area, and minimize the losses of the area transformers.

[0153] (3) Use an intelligent optimization algorithm to solve the problem of optimizing the losses of a multi-port interconnected distribution network with energy storage, which can fill the gap in the current methods for solving the problem of optimizing transformer losses and improve the efficiency of transformer loss analysis.

[0154] Compared with the prior art, this example has at least the following beneficial effects:

[0155] (1) Compared with the traditional single-radiation operation mode of the low-voltage distribution network, this application proposes a new network structure of the low-voltage distribution network with energy storage, which can further improve the power supply reliability of the low-voltage distribution network;

[0156] (2) For the first time, the novelty index is introduced into the population evaluation of the CSO algorithm and the PSO algorithm, and the improved hybrid algorithm is first used to optimize the load rate of the distribution transformers in the area, the energy storage, and the port transmission capacity, achieving the minimum losses in the area and improving the efficiency of area loss analysis;

[0157] (3)Creatively transform the transformer loss optimization problem into the optimization of the transformer load rate, obtain the optimal load rate combination of all transformers in the area, and make the optimization result more scientific.

[0158] Next, a loss optimization device for a multi-port interconnected low-voltage distribution network with energy storage provided by an embodiment of the present application will be described. The loss optimization device for a multi-port interconnected low-voltage distribution network with energy storage described below can be correspondingly referred to the loss optimization method for a multi-port interconnected low-voltage distribution network with energy storage described above.

[0159] In some embodiments, the present application provides a loss optimization device for a multi-port interconnected low-voltage distribution network with energy storage, which is applied to a multi-port interconnected low-voltage distribution network with energy storage. Among them, the multi-port interconnected low-voltage distribution network with energy storage includes at least one energy storage device, at least one distribution transformer, and multiple power ports.

[0160] As Figure 4 shown, a loss optimization device 400 for a multi-port interconnected low-voltage distribution network with energy storage includes:

[0161] A parental population determination module 402, configured to determine the parental population of the current iteration round; wherein, the parental population includes multiple first distribution network operation strategies;

[0162] A first population update module 404, configured to use the current population optimization algorithm to perform population optimization update on the parental population of the current iteration round, and obtain the offspring population of the current iteration round; the offspring population includes multiple second distribution network operation strategies;

[0163] An average particle distance calculation module 406, configured to calculate the average particle distance of the current iteration round according to the multiple second distribution network operation strategies; wherein, the average particle distance is used to reflect the particle diversity degree of the offspring population, and the average particle distance is positively correlated with the diversity degree;

[0164] A second population update module 408, configured to, if the average particle distance of the current iteration round is less than a preset distance threshold and does not meet the preset iteration end condition, change the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm, and generate the parental population of the next iteration round according to the multiple first distribution network operation strategies and the multiple second distribution network operation strategies, and enter the next iteration round; wherein, the second optimization algorithm is different from the first optimization algorithm;

[0165] The optimal operation strategy determination module 410 is configured to determine an optimal operation strategy from the multiple first distribution network operation strategies and the multiple second distribution network operation strategies if the average particle distance in the current iteration round is less than the preset distance threshold and the iteration end condition is satisfied; wherein, the optimal operation strategy includes the optimal load rate of each distribution transformer and the optimal charge and discharge strategy of the energy storage device 400;

[0166] The control module 412 is configured to control the energy storage device 400 and each distribution transformer respectively according to the optimal operation strategy.

[0167] In some embodiments, the second population update module 408 of the present application includes:

[0168] The first replacement unit is configured to replace the current population optimization algorithm from the CSO algorithm with the traditional PSO algorithm if the first optimization algorithm is the traditional CSO algorithm;

[0169] The second replacement unit is configured to replace the current population optimization algorithm from the traditional PSO algorithm with the ring topology CSO algorithm if the first optimization algorithm is the traditional PSO algorithm;

[0170] The third replacement unit is configured to replace the current population optimization algorithm from the ring topology CSO algorithm with the ring topology PSO algorithm if the first optimization algorithm is the ring topology CSO algorithm;

[0171] The fourth replacement unit is configured to replace the current population optimization algorithm from the ring topology PSO algorithm with the NW small world CSO algorithm if the first optimization algorithm is the ring topology PSO algorithm;

[0172] The fifth replacement unit is configured to replace the current population optimization algorithm from the NW small world CSO algorithm with the NW small world PSO algorithm if the first optimization algorithm is the NW small world CSO algorithm;

[0173] The sixth replacement unit is configured to replace the current population optimization algorithm from the NW small world PSO algorithm with the traditional CSO algorithm if the first optimization algorithm is the NW small world PSO algorithm.

[0174] In some embodiments, the second population update module 408 of the present application includes:

[0175] The parental optimal strategy selection unit is configured to select M parental optimal strategies from the multiple first distribution network operation strategies according to the fitness values corresponding to the multiple first distribution network operation strategies at a first preset ratio; M is a positive integer;

[0176] The offspring optimal strategy selection unit is configured to select N offspring optimal strategies from the multiple second distribution network operation strategies according to the fitness values corresponding to the multiple second distribution network operation strategies and in accordance with a second preset ratio; N is a positive integer;

[0177] The strategy combination unit is configured to use the M parent optimal strategies and the N offspring optimal strategies as the parent population for the next iteration round.

[0178] In some embodiments, the average particle distance calculation module 406 of the present application includes:

[0179] The distance calculation unit is configured to calculate the average particle distance of the current iteration round based on the following expression :

[0180]

[0181] In the formula, K is the total number of second distribution network operation strategies, is the (t + 1)-th second distribution network operation strategy, is the t-th second distribution network operation strategy.

[0182] In some embodiments, the first population update module 404 of the present application includes:

[0183] The population optimization update unit is configured to perform population optimization update on the parent population of the current iteration round according to a preset objective function and by using the current population optimization algorithm, and obtain the offspring population of the current iteration round; wherein, the objective function is:

[0184]

[0185] In the formula, F is the distribution network loss, D is the number of distribution transformers, is the iron loss of the i-th distribution transformer, is the load rate of the i-th distribution transformer, is the copper loss of the i-th distribution transformer, is the active power loss of the j-th power port, is the penalty coefficient, is the received power of the power supply equipment corresponding to the i-th distribution transformer, is the load demand of the power supply equipment corresponding to the i-th distribution transformer, C is the number of energy storage devices 400, is the active power loss of the e-th energy storage port.

[0186] In some embodiments, a multi-port interconnected low-voltage distribution network loss optimization device 400 including energy storage further includes:

[0187] The third population update module is used to replace the current population optimization algorithm with the traditional CSO algorithm if the average particle distance in the current iteration round is greater than or equal to the preset distance threshold and the iteration end condition is not satisfied, use the offspring population in the current iteration round as the parent population in the next iteration round, and enter the next iteration round.

[0188] In some embodiments, the iteration end condition is that the number of iterations is greater than or equal to a preset iteration number threshold.

[0189] In one embodiment, the present application further provides a storage medium storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage as described in any embodiment.

[0190] In one embodiment, the present application further provides a computer device storing computer-readable instructions, which when executed by one or more processors, cause the one or more processors to execute the steps of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage as described in any embodiment.

[0191] Schematically, Figure 5 is an internal structure schematic diagram of a computer device provided by an embodiment of the present application. In one example, the computer device may be a server. Referring to Figure 5 , the computer device 900 includes a processing component 902, which further includes one or more processors, and memory resources represented by a memory 901 for storing instructions executable by the processing component 902, such as application programs. The application programs 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 a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage as described in any of the above embodiments.

[0192] 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 WindowsServer TM, Mac OS XTM, Unix TM, Linux TM, Free BSDTM or the like.

[0193] Those skilled in the art can understand that the internal structure of the computer device shown in this application is only a block diagram of the part of the structure related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0194] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element. In this article, "a", "an", "the", "this" and "its" may also include the plural form, unless the context clearly indicates otherwise. A plurality means at least two cases, such as 2, 3, 5 or 8, etc. "And / or" includes any and all combinations of the related listed items.

[0195] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0196] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments shown herein, but will be accorded 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 including energy storage, characterized in that: A multi-port interconnected low-voltage distribution network including energy storage includes at least one energy storage device, at least one distribution transformer and a plurality of power ports, and the method includes: Determine a parent population of a current iteration round; wherein the parent population includes a plurality of first distribution network operation strategies; Using the current population optimization algorithm, the parent population of the current iteration round is optimized and updated, and the child population of the current iteration round is obtained; the child population includes a plurality of second distribution network operation strategies; According to the plurality of second distribution network operation strategies, the average particle distance of the current iteration round is calculated; wherein the average particle distance is used to reflect the particle diversity degree of the offspring population, and the average particle distance is positively correlated with the diversity degree; If the average particle distance of the current iteration round is less than a preset distance threshold and does not meet the preset iteration end condition, the current population optimization algorithm is replaced from the first optimization algorithm to the second optimization algorithm, and according to the multiple first distribution network operation strategies and the multiple second distribution network operation strategies, the parent population of the next iteration round is generated, and the next iteration round is entered; wherein the second optimization algorithm is different from the first optimization algorithm; If the average particle distance of the current iteration round is less than the preset distance threshold and the iteration end condition is met, then an optimal operation strategy is determined among the multiple first distribution network operation strategies and the multiple second distribution network operation strategies; wherein the optimal operation strategy includes an optimal load rate of each of the distribution transformers and an optimal charging and discharging strategy of the energy storage device; The energy storage device and each of the distribution transformers are controlled separately according to the optimal operation strategy.

2. The method according to claim 1, characterized in that The step of changing the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm includes: If the first optimization algorithm is a traditional CSO algorithm, then the current population optimization algorithm is changed from the CSO algorithm to a traditional PSO algorithm; If the first optimization algorithm is the traditional PSO algorithm, then changing the current population optimization algorithm from the traditional PSO algorithm to a ring topology CSO algorithm; If the first optimization algorithm is the ring topology CSO algorithm, then changing the current population optimization algorithm from the ring topology CSO algorithm to the ring topology PSO algorithm; If the first optimization algorithm is the ring topology PSO algorithm, then the current population optimization algorithm is changed from the ring topology PSO algorithm to the NW small world CSO algorithm; If the first optimization algorithm is the NW small world CSO algorithm, then the current population optimization algorithm is changed from the NW small world CSO algorithm to the NW small world PSO algorithm; If the first optimization algorithm is the NW small-world PSO algorithm, the current population optimization algorithm is replaced from the NW small-world PSO algorithm to the traditional CSO algorithm.

3. The method according to claim 1, characterized in that The step of generating a parent population for the next iteration round according to the plurality of first distribution network operation strategies and the plurality of second distribution network operation strategies comprises: According to the fitness values ​​corresponding to the plurality of first distribution network operation strategies, M parent optimal strategies are selected from the plurality of first distribution network operation strategies according to a first preset ratio; M is a positive integer; According to the fitness values ​​corresponding to the plurality of second distribution network operation strategies, and in accordance with a second preset ratio, N sub-generation optimal strategies are selected from the plurality of second distribution network operation strategies; N is a positive integer; The M parent optimal strategies and the N child optimal strategies are used as the parent population of the next iteration round.

4. The method according to claim 1, characterized in that: The step of calculating the average particle distance of the current iteration round according to the plurality of second distribution network operation strategies includes: The average particle distance of the current iteration round is calculated based on the following expression: : Where K is the total number of the second distribution network operation strategies, is the t+1th second distribution network operation strategy, is the tth second distribution network operation strategy.

5. The method according to claim 1, characterized in that The adopting the current population optimization algorithm to perform population optimization update on the parent population of the current iteration round and obtain the child population of the current iteration round includes: According to the preset objective function, the current population optimization algorithm is used to perform population optimization update on the parent population of the current iteration round, and obtain the child population of the current iteration round; wherein the objective function for: In the formula, F is the distribution network loss, 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 power 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, C is the number of energy storage devices, is the active power loss of the e-th energy storage port.

6. The method according to any one of claims 1 to 5, characterized in that: The method comprises: If the average particle distance of the current iteration round is greater than or equal to the preset distance threshold and the iteration end condition is not met, the current population optimization algorithm is replaced with the traditional CSO algorithm, and the child population of the current iteration round is used as the parent population of the next iteration round, and the next iteration round is entered.

7. The method according to any one of claims 1 to 5, characterized in that: The iteration end condition is that the number of iterations is greater than or equal to a preset iteration number threshold.

8. A multi-port interconnected low-voltage distribution network loss optimization device including energy storage, characterized in that: A multi-port interconnected low-voltage distribution network including energy storage includes at least one energy storage device, at least one distribution transformer and a plurality of power ports, wherein the device includes: A parent population determination module, used to determine the parent population of the current iteration round; wherein the parent population includes a plurality of first distribution network operation strategies; A first population update module is used to use the current population optimization algorithm to perform population optimization update on the parent population of the current iteration round, and obtain the child population of the current iteration round; the child population includes a plurality of second distribution network operation strategies; An average particle distance calculation module, used to calculate the average particle distance of the current iteration round according to the multiple second distribution network operation strategies; wherein the average particle distance is used to reflect the particle diversity degree of the offspring population, and the average particle distance is positively correlated with the diversity degree; A second population updating module is used to change the current population optimization algorithm from the first optimization algorithm to the second optimization algorithm if the average particle distance of the current iteration round is less than a preset distance threshold and does not meet the preset iteration end condition, and generate a parent population of the next iteration round according to the multiple first distribution network operation strategies and the multiple second distribution network operation strategies, and enter the next iteration round; wherein the second optimization algorithm is different from the first optimization algorithm; An optimal operation strategy determination module, configured to determine an optimal operation strategy among the plurality of first distribution network operation strategies and the plurality of second distribution network operation strategies if the average particle distance of the current iteration round is less than the preset distance threshold and the iteration end condition is met; wherein the optimal operation strategy includes an optimal load rate of each of the distribution transformers and an optimal charging and discharging strategy of the energy storage device; A control module is used to control the energy storage device and each of the distribution transformers respectively according to the optimal operation strategy.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of a method for optimizing losses in a multi-port interconnected low-voltage distribution network including energy storage 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, which, when executed by the one or more processors, perform the steps of a method for optimizing losses in a multi-port interconnected low-voltage distribution network including energy storage as described in any one of claims 1 to 7.

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