Loss optimization method for multi-port interconnected low-voltage power distribution network containing energy storage and related device

Abstract

The invention relates to the technical field of power distribution networks, and particularly provides a loss optimization method for a multi-port interconnected low-voltage power distribution network containing energy storage and a related device. After the current population optimization algorithm is adopted for population optimization, the average particle distance of the offspring population can be calculated, so that the particle diversity degree of the offspring population can be evaluated through the average particle distance. If the average particle distance is smaller than the preset distance threshold value, it is indicated that the particle diversity of the offspring population is weak, the premature problem possibly occurs in the algorithm, and the search process is restrained. Under the condition, a population optimization algorithm can be replaced, a parent population of a next iteration round can be updated, information such as an updating mechanism and a population scale of the algorithm can be changed through algorithm change and population updating, so that the population diversity can be enriched, and the solution of the next iteration round can be carried out according to the population diversity. Therefore, the optimal operation strategy of the multi-port interconnected low-voltage power distribution network can be determined, and the minimum loss of the power distribution network is realized.

Description

Technical Field

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

[0002] A multi-port interconnected low-voltage distribution network including energy storage is a low-voltage 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 each power port.

[0003] Among them, the energy storage device of the low-voltage distribution system can store electric energy when the load is low, and release electric energy when the load is peak. By reasonably adjusting the charging and discharging strategy of the energy storage device and the load rate of the distribution transformer, the low-voltage distribution system can reduce the energy loss of the distribution network while maintaining power balance, thereby improving energy utilization efficiency.

[0004] At present, the existing technology uses the traditional CSO (Crisscross Optimization Algorithm) algorithm to determine the operation strategy of the low-voltage distribution system. However, the inventors have found that the existing technology is prone to premature problems when solving the optimization, making it difficult to obtain the optimal distribution network operation strategy, and thus unable to effectively reduce the distribution network loss. Summary of the invention

[0005] The purpose of the present application is to solve at least one of the above-mentioned technical defects, especially the technical defect of high power distribution network loss in the prior art.

[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 including energy storage, wherein the 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, the method comprising:

[0007] Determine a parent population of a current iteration round; wherein the parent population includes a plurality of first distribution network operation strategies;

[0008] 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;

[0009] 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;

[0010] 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;

[0011] 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;

[0012] The energy storage device and each of the distribution transformers are controlled separately 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 a traditional CSO algorithm, then the current population optimization algorithm is changed from the CSO algorithm to a traditional PSO algorithm;

[0015] 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;

[0016] 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;

[0017] 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;

[0018] 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;

[0019] 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.

[0020] In some embodiments, 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 includes:

[0021] 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;

[0022] 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;

[0023] The M parent optimal strategies and the N child optimal strategies are used as the parent population of the next iteration round.

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

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

[0026]

[0027] 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.

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

[0029] 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:

[0030]

[0031] 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.

[0032] In some embodiments, the method comprises:

[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 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.

[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 multi-port interconnected low-voltage distribution network loss optimization device including energy storage, wherein 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, and the device includes:

[0036] 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;

[0037] 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;

[0038] 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;

[0039] 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;

[0040] 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;

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

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

[0043] 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;

[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 losses of a multi-port interconnected low-voltage distribution network including energy storage described in any of the above embodiments are performed.

[0045] In a multi-port interconnected low-voltage distribution network loss optimization method and related devices including energy storage provided in some embodiments of the present application, after the current population optimization algorithm is used for population optimization, the average particle distance of the offspring population can be calculated to evaluate the degree of particle diversity 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 a premature problem, which inhibits 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. The algorithm update mechanism and population size and other information can be changed by algorithm changes and population updates, so that the population diversity can be enriched and the next iteration round can be solved accordingly.

[0046] This application introduces 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 including energy storage, thereby avoiding premature algorithm update, and further determining the optimal operation strategy of the multi-port interconnected low-voltage distribution network to minimize the distribution network loss. In this way, the distribution network loss can be effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] Figure 1 is a schematic diagram of a multi-port interconnected low-voltage distribution network including energy storage in one embodiment;

[0049] Figure 2 One of the flow charts of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage in one embodiment;

[0050] Figure 3 2 is a flow chart of a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage in one embodiment;

[0051] Figure 4 A schematic diagram of a structure of a multi-port interconnected low-voltage distribution network loss optimization device including energy storage in one embodiment;

[0052] Figure 5 FIG. 1 is a diagram of the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] 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.

[0054] The distribution network is an important part of the power system. In the multi-port interconnected low-voltage distribution network including energy storage, the distribution transformer and the energy storage device are important equipment of the distribution network. For the distribution transformer, capacity and loss are one of its important parameters, which can be used to evaluate whether the transformer is in good operating condition. As a physical quantity that changes with the change of transformer load rate, transformer loss can be used as a direction for fine analysis of distribution network loss. Transformer loss is also an important part of line loss management. The capacity of the distribution transformer should be reasonably selected according to the load condition to avoid the "big horse pulling a small cart" and capacity waste caused by excessive capacity and too small load, and to avoid the increase of loss caused by transformer overload and overload caused by too small capacity and too large load, so as to ensure that the transformer is in an economical operating state with minimal loss. At the same time, the distribution transformer can be installed at the load center as much as possible to shorten the power supply radius of each user, ensure uniform load distribution, and optimal operating state.

[0055] In recent years, with the development of economy and society, people have higher and higher requirements for power supply reliability. The distribution network is directly facing users, and its impact on power supply reliability cannot be ignored. After a long period of development, the distribution network has generally formed a power supply mode of "closed-loop design, open-loop operation", and on this basis, measures such as distribution automation, equipment management, load transfer, power outage management, and transformer load management are implemented to ensure reliable power supply. Although these measures are of great help to improve the reliability of power supply in the distribution network, the open-loop power supply mode still cannot avoid short-term power outages caused by switching operations during line maintenance or failure, and cannot meet the stringent power supply requirements of important users such as high-tech industries. For this reason, 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 drawing on the closed-loop operation experience of foreign countries to achieve the closed-loop operation of the distribution network based on the ring wiring mode.

[0056] With the development of power electronics technology, back-to-back closed-loop operation technology has matured, and energy storage technology is also becoming increasingly mature. Adding energy storage devices to multi-port interconnected low-voltage distribution networks can further improve the operating efficiency and quality of the interconnected network. Therefore, it is urgent to provide a loss optimization method for a multi-port interconnected low-voltage distribution network including energy storage, so as to solve the loss optimization problem of a multi-port interconnected distribution network considering energy storage, thereby reducing the loss of the multi-port interconnected distribution network.

[0057] In some embodiments, the method provided in the present application can be applied to a multi-port interconnected low-voltage distribution network including energy storage for loss optimization. A multi-port interconnected low-voltage distribution network including energy storage may include at least one energy storage device, at least one distribution transformer, and multiple power ports. It is understood that the specific number of energy storage devices, the specific number of distribution transformers, and the specific number of power ports can be determined according to actual conditions, and the present application does not impose 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, enabling the low-voltage distribution network to achieve closed-loop operation. Figure 1 The network topology diagram of a multi-port interconnected low-voltage distribution network considering energy storage is shown. Multiple power ports can be provided by flexible multi-port equipment. Each distribution transformer and each energy storage device can be connected through the flexible multi-port equipment, and closed-loop operation can be achieved 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. Figure 1 The multi-port interconnected low-voltage distribution network including energy storage is shown to optimize losses. 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 functions, which can be but not limited to servers, desktop computers, notebook computers, laptops, tablet computers, smart phones and other devices, and the present application does not make specific restrictions on this.

[0060] like Figure 2 As shown, a method for optimizing the loss of a multi-port interconnected low-voltage distribution network including energy storage provided in 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 algorithm novelty criteria 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, so as to obtain the optimal operation strategy of the regional low-voltage distribution network, so that the loss of the low-voltage distribution network is minimized. In the solution process, population optimization can be performed through multiple iterations to determine the load rate of each distribution transformer and the charging and discharging strategy 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 may first determine the parent population of the current iteration round. It can be understood that the parent population may include multiple particles, and the particles of the parent population may be understood as the first distribution network operation strategy, which may include the load rate of each distribution transformer and the charging and discharging strategy of each energy storage device, and the charging and discharging strategy of the energy storage device may include the actual charging power and the 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 parent population of the current iteration round, including but not limited to using a random algorithm or a predetermined population initialization algorithm to generate the parent population of the first iteration round.

[0065] S204: Using the current population optimization algorithm, perform population optimization update on the parent population of the current iteration round, and obtain the child population of the current iteration round.

[0066] Among them, the current population optimization algorithm refers to the population optimization algorithm currently required to be adopted. The offspring population may include multiple particles, and the particles of the offspring population can be understood as the second distribution network operation strategy. The relevant description of the second distribution network operation strategy can refer to the above description of the first distribution network operation strategy, and this application will not repeat it here.

[0067] In this step, the computer device may determine the current population optimization algorithm from among multiple population optimization algorithms, and perform population optimization update using the current population optimization algorithm and the parent population of the current iteration round to obtain the child population of the current iteration round.

[0068] In some examples, the computer equipment can pre-acquire the maximum charging and discharging power of each energy storage device, the basic operating data of each distribution transformer, the maximum current carrying capacity of each power port, and the load demand of each substation, and based on the aforementioned data, use the current population optimization algorithm to update the population, thereby obtaining each second distribution network operation strategy that meets actual requirements.

[0069] S206: Calculate the average particle distance of the current iteration round according to the 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 degree of particle diversity of the offspring population, and the average particle distance is positively correlated with the degree of diversity. That is, the larger the average particle distance, the more unique the particles of the offspring population are, and the stronger the particle diversity is. Conversely, the smaller the average particle distance, the higher the particle similarity of the offspring population, and the weaker the particle diversity is.

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

[0072]

[0073] Where K is the total number of second distribution network operation strategies corresponding to the current iteration round, is the t+1th second distribution network operation strategy in the sub-generation population of the current iteration round, It is the t-th second distribution network operation strategy in the child population of the current iteration round. For two adjacent particles and The Euclidean distance.

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

[0075] S208: If the average particle distance of the current iteration round is less than the preset distance threshold and does not meet the preset iteration end condition, the current population optimization algorithm is changed 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.

[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 premature problems, which inhibit the search process. In this case, the computer device can intervene by replacing the population optimization algorithm and updating the initial population of the population optimization algorithm. In this way, the update mechanism and information such as the population size can be changed, thereby enriching the population diversity and realizing the update of the population diversity.

[0077] Specifically, when 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 computer device may replace 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 may be any two different population optimization algorithms, which are not limited to the description of this application.

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

[0079] It is understood that the specific number of the preset distance threshold can be set according to the actual situation, and this application does not impose specific restrictions on this. 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.

[0080] 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.

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

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

[0083] In this step, if the preset iteration end condition is met, the computer device can select the optimal solution from the parent population and the child population of 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 separately according to the optimal operation strategy.

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

[0086] This application introduces 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 including energy storage, thereby avoiding premature algorithm update, and further determining the optimal operation strategy of the multi-port interconnected low-voltage distribution network to minimize the 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, the current population optimization algorithm is changed from the CSO algorithm to the traditional PSO algorithm;

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

[0090] If the first optimization algorithm is a ring topology CSO algorithm, the current population optimization algorithm is changed from the ring topology CSO algorithm to the ring topology PSO algorithm;

[0091] If the first optimization algorithm is a ring topology PSO algorithm, the current population optimization algorithm is changed from the ring topology PSO algorithm to the NW small world CSO algorithm;

[0092] If the first optimization algorithm is the NW small world CSO algorithm, the current population optimization algorithm is changed 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, the current population optimization algorithm is replaced from the NW small-world PSO algorithm to the traditional CSO algorithm.

[0094] In this embodiment, in order to fully consider the algorithm's optimization ability and the algorithm's optimization efficiency to balance the two, the priority of the population optimization algorithm in this application can be: traditional CSO algorithm, traditional PSO (Particle Swarm Optimization) algorithm, ring topology CSO algorithm, ring 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, 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 already quite different from the 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, then if 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 that in the next iteration round, the population optimization update is performed according to the traditional CSO algorithm and the initial population that has been updated multiple times.

[0096] In this way, on the one hand, the population optimization algorithm with different topological structures can be used to update the population, which is conducive to maintaining the diversity of the population in the process of increasing the algorithm's update population. On the other hand, the algorithm's optimization ability and optimization efficiency can be balanced, thereby improving the optimization efficiency on the basis of effectively reducing the loss of the distribution network.

[0097] In some examples, the computer device may pre-build an algorithm pool and determine the numbers of the respective population optimization algorithms in the algorithm pool. The computer device may 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), a NW small world CSO algorithm (numbered 5), and a NW small world PSO algorithm (numbered 6). When it is necessary to change the population optimization algorithm, the computer device may perform H=(H+1) calculation on the current algorithm number H to implement the number update, so as to achieve the purpose of changing the population optimization algorithm.

[0099] In some embodiments, generating a parent 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] According to the fitness values ​​corresponding to the multiple first distribution network operation strategies, M parent optimal strategies are selected from the multiple first distribution network operation strategies according to a first preset ratio; M is a positive integer;

[0101] 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;

[0102] The M optimal strategies of the parents and the N optimal strategies of the children are used as the parent population for the next iteration.

[0103] In this embodiment, both the first preset ratio and the second preset ratio can be determined according to actual factors, and this application does not impose any specific restrictions on this. For ease of description, some embodiments of this application are described by taking the first preset ratio as 50% and the second preset ratio as 50% as an example.

[0104] Specifically, if the average particle distance of the current iteration round is less than the preset distance threshold and does not meet the preset iteration end condition, the computer device can replace the population optimization algorithm and use the best 50% of particles after the update and the best 50% of particles before the 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 conducive to improving the diversity of particles, so as to determine the operation strategy with the least loss.

[0105] In some embodiments, the current population optimization algorithm is used to perform population optimization update on the parent population of the current iteration round, and the child population of the current iteration round is obtained, including:

[0106] According to the preset objective function, the current population optimization algorithm is used to optimize and update the parent population of the current iteration round, and obtain the child population of the current iteration round; where the objective function for:

[0107]

[0108] 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.

[0109] In the present application, since the multi-port interconnected low-voltage distribution network includes an energy storage device, a low-voltage distribution network loss optimization objective function that takes into account energy storage losses can be established. At the same time, the goal of the distribution network loss optimization is to optimize the load rate of each distribution transformer and the charging and discharging strategy of each energy storage device, so that the total loss of the entire low-voltage distribution network is minimized. In the actual operation process, both ordinary switches and flexible switches have a certain current carrying capacity. Therefore, in order to be closer to the actual application scenario and further reduce the distribution network loss, this embodiment can comprehensively consider the port capacity and energy storage loss of the multi-port to generate an optimization target. 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 ith distribution transformer, the unit may be kW; is the load factor of the i-th distribution transformer; is the copper loss of the ith distribution transformer, 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 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; is the useful power output by the i-th distribution transformer; is the power of the e-th energy storage device; The total power supply load demand for the region; The power received by the device to be powered 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 jth AC / DC port (the AC / DC port is the power port connected to the distribution transformer); 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.

[0120] The DC / DC converter loss model 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 an energy storage port, i.e., an energy port connected to an 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 apparent power per unit value corresponding to the transmission power 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 of the low-voltage distribution network may be consistent with the number of distribution transformers, and the number of DC / DC ports may be consistent with the number of energy storage devices.

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

[0128]

[0129] Where F is the distribution network loss, To minimize the distribution network loss, D is the number of distribution transformers, is the loss of the ith distribution transformer, is the active power loss of the jth power port, is the active power loss of the e-th energy storage port, C is the number of energy storage devices, 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, is the iron loss of the ith distribution transformer, is the load factor of the ith distribution transformer, is the copper loss of the i distribution transformer. N is the 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 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 changed to 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.

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

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

[0134] like Figure 3 As 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 algorithm parameters and an algorithm pool; wherein the algorithm pool may include a traditional CSO algorithm, a traditional PSO algorithm, a ring topology CSO algorithm, a ring topology PSO algorithm, a NW small world CSO algorithm, and a NW small world PSO algorithm.

[0136] S304: Initialize the population.

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

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

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

[0140] S312: Calculate population fitness.

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

[0142] S316: Calculate the average particle distance based on each particle of the offspring population.

[0143] S318: Determine whether the novelty index requirement is met based on the average particle distance, if not, execute step S320, if yes, execute step S326.

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

[0145] S322:H=H+1.

[0146] S324: The population is updated, and the updated population is used as the initial population of the new algorithm.

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

[0148] S328: T=T+1.

[0149] S330: Outputting 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 that includes energy storage. By optimizing the load rate of each transformer in the area and the charging and discharging of energy storage, the total loss of transformers in the area is minimized, and the current line loss management efficiency and quality are further improved. This is achieved by the following specific technical means:

[0151] (1) Use multi-port flexible switches to construct a low-voltage substation interconnection network structure with energy storage.

[0152] (2) The novelty index is used to evaluate the diversity of the optimization algorithm population and to solve the distribution network loss optimization problem of a multi-port interconnected system with energy storage, determine the optimal load rate of each transformer in the region, and minimize the regional transformer loss.

[0153] (3) The use of intelligent optimization algorithms to solve the loss optimization problem of multi-port interconnected distribution networks with energy storage can address the current gap in methods for solving transformer loss optimization problems 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 low-voltage distribution network, this application proposes a new network structure of low-voltage distribution network including energy storage, which can further improve the power supply reliability of low-voltage distribution network;

[0156] (2) For the first time, the novelty index was introduced into the population evaluation of the CSO algorithm and the PSO algorithm, and the improved hybrid algorithm was used for the first time to optimize the load factor of the distribution transformers in the region and the energy storage and port transmission capacity, thereby minimizing the loss in the region and improving the efficiency of regional loss analysis;

[0157] (3) The transformer loss optimization problem is creatively transformed into the optimization of the transformer load rate, and the optimal load rate combination of all transformers in the area is obtained, making the optimization result more scientific.

[0158] The following is a description of a multi-port interconnected low-voltage distribution network loss optimization device including energy storage provided in an embodiment of the present application. The multi-port interconnected low-voltage distribution network loss optimization device including energy storage described below and the multi-port interconnected low-voltage distribution network loss optimization method including energy storage described above can be referenced to each other.

[0159] In some embodiments, the present application provides a multi-port interconnected low-voltage distribution network loss optimization device including energy storage, which is applied to 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.

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

[0161] A parent population determination module 402 is used to determine the parent population of the current iteration round; wherein the parent population includes a plurality of first distribution network operation strategies;

[0162] The first population update module 404 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 multiple second distribution network operation strategies;

[0163] The average particle distance calculation module 406 is 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;

[0164] A second population updating module 408 is configured 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;

[0165] The optimal operation strategy determination module 410 is used to determine the optimal operation strategy among the multiple first distribution network operation strategies and the multiple 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 the optimal load rate of each of the distribution transformers and the optimal charging and discharging strategy of the energy storage device 400;

[0166] The control module 412 is used to control the energy storage device 400 and each of the distribution transformers according to the optimal operation strategy.

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

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

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

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

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

[0172] a fifth changing unit, configured to change the current population optimization algorithm from the NW small world CSO algorithm to a NW small world PSO algorithm if the first optimization algorithm is the NW small world CSO algorithm;

[0173] A sixth replacement unit is configured to replace the current population optimization algorithm from the NW small-world PSO algorithm to 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] A parent optimal strategy selection unit, configured to select M parent 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 and in accordance with a first preset ratio; M is a positive integer;

[0176] A sub-generation optimal strategy selection unit, configured to select N sub-generation 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 and in accordance with a second preset ratio; N is a positive integer;

[0177] The strategy combination unit is used to use the M optimal strategies of the parent generation and the N optimal strategies of the child generation as the parent generation population of the next iteration round.

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

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

[0180]

[0181] Where K is the total number of the second distribution network operation strategies, is the t+1th 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] A population optimization updating unit is used to perform population optimization updating on the parent population of the current iteration round according to a preset objective function and adopt the current population optimization algorithm to obtain the child population of the current iteration round; wherein the objective function for:

[0184]

[0185] 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 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 of the current iteration round is greater than or equal to the preset distance threshold and the iteration end condition is not met, and use the child population of the current iteration round as the parent population of 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 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 a multi-port interconnected low-voltage distribution network loss optimization method including energy storage in any embodiment.

[0190] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of a method for optimizing losses of a multi-port interconnected low-voltage distribution network including energy storage as in any embodiment.

[0191] 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 executable 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 a multi-port interconnected low-voltage distribution network loss optimization method including energy storage 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 Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM, or the like.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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 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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