Active distribution network energy storage optimization configuration method and system based on improved Parrot algorithm

Abstract

The present invention discloses a method and system for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm. The method includes: constructing a multi-objective energy storage optimization configuration model based on preset constraints, taking node voltage offset, energy storage cost, and distribution network loss as objective functions; introducing chaos theory to initialize the Parrot algorithm population; dynamically adjusting the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the Parrot algorithm during each iteration based on the individual fitness value and the current iteration number; selecting Gaussian mutation or Cauchy mutation based on the mutation probability, performing mutation operations on the Parrot individuals during the iteration process, and ultimately obtaining an improved Parrot algorithm; solving the multi-objective energy storage optimization configuration model based on the improved Parrot algorithm, and outputting the energy storage optimization configuration results. This method achieves efficient collaborative optimization of multiple objective functions, providing more accurate and comprehensive decision support for the energy storage optimization configuration of active distribution networks.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network energy storage optimization, and in particular relates to an active distribution network energy storage optimization configuration method and system based on an improved Parrot algorithm. Background Art

[0002] As a key component of smart grids, energy storage systems play an important role in balancing supply and demand, improving power quality, and stabilizing the system through their flexible charging and discharging capabilities. However, how to efficiently configure energy storage systems to maximize economic and environmental benefits has become a core issue in active distribution network research.

[0003] In recent years, traditional optimization algorithms such as genetic algorithms, particle swarm optimization, and ant colony optimization have been widely used in power system optimization due to their advantages such as parallelism, self-organization, and robustness. However, these methods still have certain technical drawbacks. Specifically, they are prone to premature convergence when dealing with multi-objective optimization problems, resulting in an inability to fully balance conflicts between objectives. Furthermore, these algorithms are extremely sensitive to parameter selection, and different parameter settings can lead to drastically different optimization results. Furthermore, due to their high computational complexity, these algorithms often struggle to quickly find the global optimal solution for high-dimensional, nonlinear, and multimodal optimization problems. Therefore, existing methods for optimizing energy storage configuration in active distribution networks still face numerous technical challenges. The Parrot Optimization Algorithm, an emerging swarm intelligence optimization algorithm, simulates the social behavior of parrots for global optimization. While it boasts advantages such as simple structure, ease of implementation, and strong global search capabilities, its application to optimizing energy storage configuration in active distribution networks also faces the challenges of high-dimensional, nonlinear, and multimodal optimization, requiring trade-offs between multiple objectives and the need to rapidly adapt to changes in distribution network operating conditions and load demand. Summary of the Invention

[0004] The present invention provides an active distribution network energy storage optimization configuration method and system based on the improved Parrot algorithm, which is used to solve at least one technical problem existing in the existing active distribution network energy storage optimization configuration method, namely, premature convergence, parameter sensitivity, large computational complexity, difficulty in handling high-dimensional nonlinear multimodality, difficulty in target balancing, and difficulty in adapting to changes.

[0005] In a first aspect, the present invention provides a method for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm, comprising:

[0006] According to the preset constraints, a multi-objective energy storage optimization configuration model is constructed with node voltage offset, energy storage cost and distribution network loss as the objective function;

[0007] Chaos theory is introduced to initialize the parrot algorithm population. In each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability, and the parrot individuals in the iteration process are mutated to finally obtain the improved parrot algorithm. The expression of the weight is:

[0008] ,

[0009] Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight;

[0010] The multi-objective energy storage optimization configuration model is solved according to the improved Parrot algorithm, and the energy storage optimization configuration result is output.

[0011] In a second aspect, the present invention provides an active distribution network energy storage optimization configuration system based on an improved Parrot algorithm, comprising:

[0012] A construction module is configured to construct a multi-objective energy storage optimization configuration model based on preset constraints and taking node voltage offset, energy storage cost, and distribution network loss as objective functions;

[0013] The processing module is configured to introduce chaos theory to initialize the parrot algorithm population. In each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability. The parrot individuals in the iteration process are mutated to finally obtain an improved parrot algorithm. The expression of the weight is:

[0014] ,

[0015] Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight;

[0016] The output module is configured to solve the multi-objective energy storage optimization configuration model according to the improved Parrot algorithm and output the energy storage optimization configuration result.

[0017] According to a third aspect, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the active distribution network energy storage optimization configuration method based on the improved Parrot algorithm according to any embodiment of the present invention.

[0018] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the program instructions are executed by a processor, the processor executes the steps of the active distribution network energy storage optimization configuration method based on the improved Parrot algorithm of any embodiment of the present invention.

[0019] The active distribution network energy storage optimization configuration method and system based on the improved Parrot algorithm of the present application adopts chaos theory for population initialization, which greatly enriches the diversity of the initial population, ensures comprehensive coverage of the search space, and effectively avoids the risk of premature convergence of the algorithm; by incorporating an adaptive dynamic weight strategy and a hybrid Gaussian-Cauchy mutation operator, the algorithm achieves flexible switching between global search and local search, further enhancing its optimization efficiency; multiple factors such as node voltage offset, energy storage cost and network loss of the distribution network are taken into consideration, and Pareto dominance and diversity maintenance strategies are used to achieve efficient collaborative optimization of multiple objective functions, providing more accurate and comprehensive decision support for the energy storage optimization configuration of the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1A flowchart of a method for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm provided by one embodiment of the present invention;

[0022] Figure 2 A schematic diagram of a standard node system for a distribution network used in an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of typical daily load and wind power and photovoltaic unit output provided in one embodiment of the present invention;

[0024] Figure 4 A schematic diagram of a 17-node energy storage operation strategy and state of charge change curve provided in one embodiment of the present invention;

[0025] Figure 5 A structural block diagram of an active distribution network energy storage optimization configuration system based on an improved Parrot algorithm provided by one embodiment of the present invention;

[0026] Figure 6 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] See also Figure 1 , which shows a flow chart of an active distribution network energy storage optimization configuration method based on the improved Parrot algorithm of the present application.

[0029] like Figure 1 As shown in FIG, the active distribution network energy storage optimization configuration method based on the improved Parrot algorithm specifically includes the following steps:

[0030] Step S101 : Based on preset constraints, a multi-objective energy storage optimization configuration model is constructed with node voltage offset, energy storage cost, and distribution network loss as objective functions.

[0031] In this step, node voltage offset is a key parameter for measuring power quality in distribution networks. Excessive node voltage offset can disrupt the normal operation of power equipment, thereby weakening system stability and reliability. Therefore, minimizing node voltage offset is a core objective function. Node voltage offset is quantified by calculating the absolute value of the difference between the actual node voltage and the rated voltage.

[0032] The expression of the sub-objective function of the distribution network node voltage deviation index is:

[0033] ,

[0034] ,

[0035] Where, is the number of nodes in the distribution network system, is the normalized node voltage offset, For nodes time The voltage, is the rated voltage;

[0036] This application selects investment cost and operation and maintenance cost as the main cost factors for energy storage systems. On the one hand, investment cost reflects the initial investment in the energy storage device. When integrating energy storage devices into the existing distribution network system, the level of investment cost will significantly affect the feasibility and economic viability of the project. On the other hand, operation and maintenance cost is related to the continuous investment in the energy storage device during the operation phase, including equipment maintenance, testing, troubleshooting, and other expenses. Its size directly affects the long-term stable operation of the energy storage system.

[0037] The expression of the sub-objective function of the energy storage system cost is:

[0038] ,

[0039] Where, is the investment cost of the energy storage system, The operation and maintenance costs of the energy storage system;

[0040] The expression for calculating the investment cost of the energy storage system is:

[0041] ,

[0042] Where, is the annual discount factor, is the fold rate, is the service life of the energy storage system, is the number of energy storage system nodes, For the The rated power of the energy storage, For the The unit power cost of energy storage, For the The rated capacity of the energy storage, For the Unit capacity cost of energy storage;

[0043] The expression for calculating the operation and maintenance cost of the energy storage system is:

[0044] ,

[0045] Where, is the annual operation and maintenance cost of the energy storage system.

[0046] Distribution network losses refer to the energy lost during power transmission and distribution due to factors such as line resistance and inductance. Reducing these losses can improve energy efficiency and reduce energy waste, while also helping to enhance the operational stability and reliability of the distribution network.

[0047] The expression of the sub-objective function of distribution network loss is:

[0048] ,

[0049] Where, For nodes and nodes The conductance between For nodes The voltage amplitude, For nodes and nodes The susceptance between For nodes The voltage amplitude, For nodes The voltage phase angle, For nodes The voltage phase angle.

[0050] Taking into account node voltage deviation, energy storage cost and distribution network loss, the objective function of the multi-objective energy storage optimization configuration model is expressed as:

[0051] ,

[0052] Where, is the objective function of the multi-objective energy storage optimization configuration model, is the sub-objective function of the distribution network node voltage deviation index, is the sub-objective function of the energy storage system cost, is the sub-objective function of distribution network loss.

[0053] It should be noted that the constraints include:

[0054] The power balance constraint is expressed as:

[0055] ,

[0056] Where, For nodes The active power generation power, For nodes The active load power, For nodes Active line loss, is the active power of the energy storage system, For nodes The reactive power generation For nodes The reactive load power, For nodes Reactive line loss, is the reactive power of the energy storage system;

[0057] The node voltage constraint and branch current constraint are expressed as:

[0058] ,

[0059] Where, is the lower limit of the node voltage, For nodes time The voltage, is the upper limit of the node voltage, For nodes and nodes Time between lines The current, For nodes and nodes The upper limit of the current of the line between

[0060] Energy storage device constraints, expressed as:

[0061] ,

[0062] Where, is the lower limit of the charging and discharging power of the energy storage device, For nodes The charging and discharging power of the energy storage device, is the upper limit of the charging and discharging power of the energy storage device, is the lower limit of the rated capacity of the energy storage device, For nodes The rated capacity of the energy storage equipment, is the upper limit of the rated capacity of the energy storage device, is the lower limit of the state of charge of the energy storage device, For the moment The state of charge of the energy storage device, is the upper limit of the state of charge of the energy storage device, For the moment The state of charge of the energy storage device, is the charging efficiency of the energy storage device, is the charging power of the energy storage device, is the discharge power of the energy storage device, is the time step, is the discharge efficiency of the energy storage device, is the maximum charge and discharge rate of the energy storage system.

[0063] Step S102, introduce chaos theory to initialize the parrot algorithm population. In each iteration process, according to the individual fitness value and the current number of iterations, dynamically adjust the weights of foraging behavior, staying behavior, communication behavior and fear of strangers behavior in the parrot algorithm, and select Gaussian mutation or Cauchy mutation according to the mutation probability, perform mutation operation on the parrot individuals in the iteration process, and finally obtain the improved parrot algorithm.

[0064] In this step, set the algorithm-related parameters, including population size, maximum number of iterations, chaos parameters, mutation probability, etc.

[0065] In this embodiment, the population size The number of iterations is 80; the maximum number of iterations is 100; the spatial dimension after connecting an energy storage device Taken as 15, including the 24h operating power and access location of the energy storage system; chaos parameter Taken as 0.45; Gaussian mutation probability Take it as 0.5, the Cauchy mutation probability Take it as 0.5;

[0066] Chaotic Tent mapping is introduced to initialize the population of the Parrot algorithm, the fitness of the initial population and its reverse solution is calculated, and individuals with better fitness are selected to form the final initial population.

[0067] The traditional Parrot algorithm uses completely random population initialization, which cannot guarantee the traversability of the initial population in the search space. This results in a lack of population diversity and insufficient optimization accuracy. Therefore, chaos theory is introduced to enhance the diversity and uniformity of the initial population. Currently, the most common chaotic mapping methods are logistic mapping and tent mapping. The logistic mapping's distribution curve has the highest probability of taking values in the intervals [0, 0.1] and [0.9, 1], resulting in poor traversability. The tent mapping has better traversability than the logistic mapping, generating a more uniform distribution of values within [0, 1].

[0068] First, the population is initialized based on the chaotic tent map, and the expression is:

[0069] ,

[0070] Where, For the Individuals in The variable value at the iteration, For the Individuals in The variable value at the iteration, is the chaos parameter;

[0071] Determine the chaos parameters and After taking the corresponding initial value, we get A chaotic sequence;

[0072] Then, the chaotic sequence is mapped to the search space through the position mapping formula. Assuming that the search range is , To search for the lower bound, is the search upper bound, then the position vector after mapping is , the mapping formula is:

[0073] ,

[0074] Where, is the weight relationship in the position mapping process, is the position vector after mapping The elements, For the The search lower bound of dimension, For the The search upper bound of dimension;

[0075] The reverse solution is considered simultaneously in the initial population, and individuals with better fitness are retained in the initial population to improve the optimization performance. Specifically, for each generated initial position vector , calculate its inverse solution The calculation formula for the reverse solution is:

[0076] ,

[0077] Where, For the reverse solution The elements;

[0078] Finally, let the initial population generated by the chaotic tent map be , the population formed by its reverse solution is , joint population and , calculate the fitness of all individuals in the joint population, sort the individuals in ascending order according to their fitness, and retain the individuals before sorting Parrot individuals are used as the final initial population of the algorithm.

[0079] It should be noted that the adaptive dynamic weight strategy is used to dynamically adjust the weights of the four behaviors of foraging, staying, communicating, and fear of strangers in the parrot algorithm according to the individual fitness value and the current number of iterations during each iteration.

[0080] In the Parrot Optimization Algorithm, individuals randomly exhibit one of four behaviors at each iteration: foraging, staying, communicating, and fear of strangers. To balance global and local search, an adaptive dynamic weighting strategy is introduced. This dynamically adjusts the weights of the four behaviors based on the individual's fitness value and the current iteration number.

[0081] set up are the weights of the four behaviors of foraging, staying, communicating and fearing strangers, and satisfy , the expression of weight is:

[0082] ,

[0083] Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight;

[0084] Furthermore, assuming the probability of Gaussian mutation and the probability of Cauchy mutation ,satisfy , for each individual that needs to be mutated, and Choose Gaussian or Cauchy mutation;

[0085] If Gaussian variation is selected, for each dimension of an individual, the standard normal distribution Generate a random number , after mutation, the individual Position in one dimension for:

[0086] ,

[0087] Where, For the current individual Position in dimensions, is the Gaussian variable phase length,

[0088] If Cauchy variation is selected, for each dimension of an individual, the standard Cauchy distribution Generate a random number , after mutation, the individual Position in one dimension for:

[0089] ,

[0090] in, For the current individual Position in dimensions, is the asynchronous length of the Cauchy variable.

[0091] By introducing this hybrid mutation strategy, the variability and diversity of the algorithm can be effectively increased, thereby avoiding falling into local optimal solutions and improving the global search capability of the algorithm.

[0092] Step S103 , solving the multi-objective energy storage optimization configuration model according to the improved Parrot algorithm, and outputting the energy storage optimization configuration result.

[0093] In this step, a multi-objective fitness evaluation function is established, and the Pareto dominance relationship and diversity preservation strategy are used to deal with the conflicts between multiple objective functions, and the diversity of archives is maintained according to the individual crowding entropy. Specifically:

[0094] External archive initialization, create an empty external archive Used to store Pareto optimal solutions;

[0095] Establish a multi-objective fitness evaluation function and the sub-objective function of the distribution network node voltage deviation index , sub-objective function of energy storage system cost , sub-objective function of distribution network loss , calculate the sub-objective function value of each parrot individual, and compare the individual parrots Hedi individual parrots The fitness value of , if for all sub-objective functions, there are , and there exists at least one sub-objective function , making , then it is called Dominate , For the For each parrot The fitness value of the sub-objective function, For the For each parrot The fitness value of the sub-objective function, For the For each parrot The fitness value of the sub-objective function, For the For each parrot The fitness value of each sub-objective function;

[0096] Update external archives to include newly generated parrot individuals With external archives Compare the solutions in , if Dominate a solution in the archive , then delete from the archive , and Add to archive, i.e. ;like If it is dominated by the solution in the file, it will not be added to the file; if If the solution does not dominate the solution in the archive, the decision of whether to add it to the archive is based on the diversity index;

[0097] Maintaining file diversity when the file reaches maximum capacity When , the crowding entropy of each individual in the archive is calculated, for the kth sub-objective function , sort the individuals in the archive in ascending order according to the sub-objective function value;

[0098] For the sorted individual sequence, the crowding entropy of the first and last individuals is set to infinity, and the other individuals are in the sub-objective function The crowding entropy on is:

[0099] ,

[0100] Where, For individuals In k sub-objective functions The crowding entropy on For individuals For k sub-objective functions The value of For individuals For k sub-objective functions The value of For the k sub-objective functions in the archive The maximum value on For the k sub-objective functions in the archive The minimum value on ;

[0101] individual The total crowding entropy is:

[0102] ,

[0103] Where, For individuals The total crowding entropy of is the number of sub-objective functions;

[0104] The individual with the smallest crowding entropy in the archive, i.e., the least representative solution, is deleted to maintain the diversity and representativeness of the archive.

[0105] In this embodiment, the algorithm terminates based on two criteria: reaching the upper limit of the number of iterations or maintaining the maximum fitness value. If either condition is met, the algorithm terminates; otherwise, it restarts the initialization process and continues. The Pareto solution is output from an external file to obtain the optimal energy storage configuration.

[0106] In summary, the method of the present application adopts chaos theory for population initialization, which greatly enriches the diversity of the initial population, ensures comprehensive coverage of the search space, and effectively avoids the risk of premature convergence of the algorithm; by incorporating an adaptive dynamic weight strategy and a hybrid Gaussian-Cauchy mutation operator, the algorithm achieves flexible switching between global search and local search, further enhancing its optimization performance; multiple factors such as node voltage offset, energy storage cost, and network loss of the distribution network are taken into consideration, and the Pareto dominance and diversity maintenance strategies are used to achieve efficient collaborative optimization of multiple objective functions, providing more accurate and comprehensive decision support for the optimal configuration of energy storage in active distribution networks.

[0107] In a specific embodiment, see Figure 2 , the IEEE standard 33-node system is used for case verification simulation, and the distributed wind turbine with a rated capacity of 200kW is connected to nodes 13 and 20, and the distributed photovoltaic with a rated capacity of 200kW is connected to nodes 9 and 30. The energy storage system is allowed to be connected to nodes 2 to 33, and the maximum installed power is 300 kW. The typical daily load and the output of wind power and photovoltaic units are as follows Figure 3 This embodiment sets three experimental scenarios: Scenario 1, without energy storage; Scenario 2, with energy storage and the standard Parrot algorithm used for site selection and capacity determination; Scenario 3, with energy storage and the improved Parrot algorithm used for site selection and capacity determination.

[0108] In order to verify the beneficial effects of the present invention, scientific demonstration was carried out through experiments.

[0109] In this example, energy storage system parameters, along with load, wind and solar output, and other parameters, were entered into the program. Minimizing node voltage offset, energy storage cost, and distribution network losses was the goal. Considering constraints such as power balance, node voltage and branch current, and energy storage equipment, the optimal energy storage location was determined to be node 17, with a capacity of 186 MW·h. Three Pareto optimal solutions were presented for each of the three scenarios for comparative analysis. The optimization results for each scenario are shown in Table 1.

[0110] Table 1

[0111] ,

[0112] Comparing the power system performance under different scenarios, the performance of scenario 1 is It reached 1.5827, and decreased to 1.0052 in scenario 3, with a 36.49% improvement in node voltage offset. It was 2.94MW in scenario three, and it was reduced to 2.39MW in scenario three, with network loss reduced by 18.71%.

[0113] In addition, compared with Scenario 2, Scenario 3 also shows significant optimization effect. The node voltage offset is improved by 9.76%. The energy storage cost is RMB 134,700 in scenario 2 and RMB 115,300 in scenario 3, which means the energy storage cost is reduced by 14.4%. In addition, the distribution network loss in scenario 2 is 2.61MW, while scenario 3 is further reduced to 2.39MW, which is 8.43% lower than scenario 2. Figure 4 shown.

[0114] Results show that integrating energy storage and employing the improved Parrot algorithm excel in node voltage stability, energy storage cost control, and reducing distribution network losses. Compared to scenario one (without energy storage), scenario three significantly improves node voltage stability and significantly reduces voltage excursions. It also effectively reduces distribution network losses and improves energy transmission efficiency. Compared to scenario two (using the standard Parrot algorithm), scenario three demonstrates improvements in optimizing node voltage excursions, achieving a 14.4% cost reduction, and also demonstrates superior results in reducing network losses.

[0115] See also Figure 5 , which shows a structural block diagram of an active distribution network energy storage optimization configuration system based on the improved Parrot algorithm of the present application.

[0116] like Figure 5 As shown, the active distribution network energy storage optimization configuration system 200 includes a construction module 210 , a processing module 220 and an output module 230 .

[0117] The construction module 210 is configured to construct a multi-objective energy storage optimization configuration model based on preset constraints and taking node voltage offset, energy storage cost, and distribution network loss as objective functions;

[0118] The processing module 220 is configured to introduce chaos theory to initialize the parrot algorithm population. During each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability, and the parrot individuals in the iteration process are mutated to finally obtain an improved parrot algorithm. The expression of the weight is:

[0119] ,

[0120] Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight;

[0121] The output module 230 is configured to solve the multi-objective energy storage optimization configuration model according to the improved Parrot algorithm and output the energy storage optimization configuration result.

[0122] It should be understood that Figure 5 Modules and references documented in Figure 1 Therefore, the operations and features described above for the method and the corresponding technical effects also apply to Figure 5 The modules in it will not be described in detail here.

[0123] In other embodiments, embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor is caused to execute the active distribution network energy storage optimization configuration method based on the improved Parrot algorithm in any of the above method embodiments;

[0124] As an embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, and the computer-executable instructions are configured as follows:

[0125] According to the preset constraints, a multi-objective energy storage optimization configuration model is constructed with node voltage offset, energy storage cost and distribution network loss as the objective function;

[0126] Chaos theory is introduced to initialize the parrot algorithm population. In each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability, and the parrot individuals in the iteration process are mutated to finally obtain the improved parrot algorithm. The expression of the weight is:

[0127] ,

[0128] Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight;

[0129] The multi-objective energy storage optimization configuration model is solved according to the improved Parrot algorithm, and the energy storage optimization configuration result is output.

[0130] The computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the active distribution network energy storage optimization configuration system based on the improved Parrot algorithm, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory and may also include a memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, the computer-readable storage medium may optionally include a memory remotely located relative to the processor, and such remote memory may be connected to the active distribution network energy storage optimization configuration system based on the improved Parrot algorithm via a network. Examples of the aforementioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0131] Figure 6 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 6As shown, the device includes: a processor 310 and a memory 320. The electronic device may also include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330 and the output device 340 may be connected via a bus or other means. Figure 6 The example of a bus connection is taken. The memory 320 is the computer-readable storage medium mentioned above. The processor 310 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, implements the active distribution network energy storage optimization configuration method based on the improved Parrot algorithm in the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the active distribution network energy storage optimization configuration system based on the improved Parrot algorithm. The output device 340 may include a display device such as a display screen.

[0132] The electronic device can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0133] As an embodiment, the electronic device is applied to an active distribution network energy storage optimization configuration system based on the improved Parrot algorithm, and is used for a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0134] According to the preset constraints, a multi-objective energy storage optimization configuration model is constructed with node voltage offset, energy storage cost and distribution network loss as the objective function;

[0135] Chaos theory is introduced to initialize the parrot algorithm population. In each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability, and the parrot individuals in the iteration process are mutated to finally obtain the improved parrot algorithm. The expression of the weight is:

[0136] ,

[0137] Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight;

[0138] The multi-objective energy storage optimization configuration model is solved according to the improved Parrot algorithm, and the energy storage optimization configuration result is output.

[0139] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods of each embodiment or certain portions of the embodiments.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing energy storage configuration in active distribution networks based on an improved Parrot algorithm, characterized in that: include: According to the preset constraints, a multi-objective energy storage optimization configuration model is constructed with node voltage offset, energy storage cost and distribution network loss as the objective function. The objective function of the multi-objective energy storage optimization configuration model is expressed as: ， Where, is the objective function of the multi-objective energy storage optimization configuration model, is the sub-objective function of the distribution network node voltage deviation index, is the sub-objective function of the energy storage system cost, is the sub-objective function of distribution network loss; Among them, the expression of the sub-objective function of the distribution network node voltage deviation index is: ， ， Where, is the number of nodes in the distribution network system, is the normalized node voltage offset, For nodes time The voltage, is the rated voltage; The expression of the sub-objective function of the energy storage system cost is: ， Where, is the investment cost of the energy storage system, The operation and maintenance costs of the energy storage system; The expression of the sub-objective function of distribution network loss is: ， Where, For nodes and nodes The conductance between For nodes The voltage amplitude, For nodes and nodes The susceptance between For nodes The voltage amplitude, For nodes The voltage phase angle, For nodes The voltage phase angle; Chaos theory is introduced to initialize the parrot algorithm population. In each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability, and the parrot individuals in the iteration process are mutated to finally obtain the improved parrot algorithm. The expression of the weight is: ， Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight; The multi-objective energy storage optimization configuration model is solved according to the improved Parrot algorithm, and the energy storage optimization configuration result is output.

2. The method for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm according to claim 1, characterized in that: in, The expression for calculating the investment cost of the energy storage system is: ， Where, is the annual discount factor, is the fold rate, is the service life of the energy storage system, is the number of energy storage system nodes, For the The rated power of the energy storage, For the The unit power cost of energy storage, For the The rated capacity of the energy storage, For the Unit capacity cost of energy storage; The expression for calculating the operation and maintenance cost of the energy storage system is: ， Where, is the annual operation and maintenance cost of the energy storage system.

3. The method for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm according to claim 1, characterized in that: The constraints include: The power balance constraint is expressed as: ， Where, For nodes The active power generation power, For nodes The active load power, For nodes Active line loss, is the active power of the energy storage system, For nodes The reactive power generation, For nodes The reactive load power, For nodes Reactive line loss, is the reactive power of the energy storage system; The node voltage constraint and branch current constraint are expressed as: ， Where, is the lower limit of the node voltage, For nodes time The voltage, is the upper limit of the node voltage, For nodes and nodes Time between lines The current, For nodes and nodes The upper limit of the current of the line between Energy storage device constraints, expressed as: ， Where, is the lower limit of the charging and discharging power of the energy storage device, For nodes The charging and discharging power of the energy storage device, is the upper limit of the charging and discharging power of the energy storage device, is the lower limit of the rated capacity of the energy storage device, For nodes The rated capacity of the energy storage equipment, is the upper limit of the rated capacity of the energy storage device, is the lower limit of the state of charge of the energy storage device, For the moment The state of charge of the energy storage device, is the upper limit of the state of charge of the energy storage device, For the moment The state of charge of the energy storage device, is the charging efficiency of the energy storage device, is the charging power of the energy storage device, is the discharge power of the energy storage device, is the time step, is the discharge efficiency of the energy storage device, is the maximum charge and discharge rate of the energy storage system.

4. The method for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm according to claim 1, characterized in that: The improved parrot algorithm is finally obtained by selecting Gaussian mutation or Cauchy mutation according to the mutation probability and performing mutation operation on the parrot individuals in the iterative process. Assuming Gaussian variation probability and the probability of Cauchy mutation ,satisfy , for each individual that needs to be mutated, and Choose Gaussian or Cauchy mutation; If Gaussian variation is selected, for each dimension of an individual, the standard normal distribution Generate a random number , after mutation, the individual Position in one dimension for: ， Where, For the current individual Position in dimensions, is the Gaussian variable phase length, If Cauchy variation is selected, for each dimension of an individual, the standard Cauchy distribution Generate a random number , after mutation, the individual Position in one dimension for: ， in, For the current individual Position in dimensions, is the asynchronous length of the Cauchy variable.

5. The method for optimizing energy storage configuration in an active distribution network based on an improved Parrot algorithm according to claim 1, characterized in that: Solving the multi-objective energy storage optimization configuration model according to the improved Parrot algorithm and outputting the energy storage optimization configuration result includes: External archive initialization, create an empty external archive Used to store Pareto optimal solutions; Establish a multi-objective fitness evaluation function and the sub-objective function of the distribution network node voltage deviation index , sub-objective function of energy storage system cost , sub-objective function of distribution network loss , calculate the sub-objective function value of each parrot individual, and compare the individual parrots Hedi individual parrots The fitness value of , if for all sub-objective functions, there are , and there exists at least one sub-objective function , making , then it is called Dominate , For the For each parrot The fitness value of the sub-objective function, For the For each parrot The fitness value of the sub-objective function, For the For each parrot The fitness value of the sub-objective function, For the For each parrot The fitness value of each sub-objective function; Update external archives to include newly generated parrot individuals With external archives Compare the solutions in , if Dominate a solution in the archive , then delete from the archive , and Add to archive, i.e. ;like If it is dominated by the solution in the file, it will not be added to the file; if If the solution does not dominate the solution in the archive, the decision of whether to add it to the archive is based on the diversity index; Maintaining file diversity when the file reaches maximum capacity When , the crowding entropy of each individual in the archive is calculated, for the kth sub-objective function , sort the individuals in the archive in ascending order according to the sub-objective function value; For the sorted individual sequence, the crowding entropy of the first and last individuals is set to infinity, and the other individuals are in the sub-objective function The crowding entropy on is: ， Where, For individuals In k sub-objective functions The crowding entropy on For individuals For k sub-objective functions The value of For individuals For k sub-objective functions The value of For the k sub-objective functions in the archive The maximum value on For the k sub-objective functions in the archive The minimum value on ; individual The total crowding entropy is: ， Where, For individuals The total crowding entropy of is the number of sub-objective functions; The individual with the smallest crowding entropy in the archive, i.e., the least representative solution, is deleted to maintain the diversity and representativeness of the archive.

6. A system for optimizing the configuration of energy storage in an active distribution network based on an improved Parrot algorithm according to any one of claims 1 to 5, characterized in that: The system comprises: A construction module is configured to construct a multi-objective energy storage optimization configuration model based on preset constraints and taking node voltage offset, energy storage cost, and distribution network loss as objective functions; The processing module is configured to introduce chaos theory to initialize the parrot algorithm population. In each iteration, the weights of foraging behavior, staying behavior, communication behavior, and fear of strangers in the parrot algorithm are dynamically adjusted according to the individual fitness value and the current iteration number. Gaussian mutation or Cauchy mutation is selected according to the mutation probability. The parrot individuals in the iteration process are mutated to finally obtain an improved parrot algorithm. The expression of the weight is: ， Where, For individuals in The first iteration is executed The fitness value of the behavior, is the maximum number of iterations, For the The adaptive weight of the behavior, is the current iteration number, and Both are first adjustment parameters, used to adjust the degree of influence of fitness value changing with the number of iterations when calculating weights. The second adjustment parameter is used to adjust the influence of the number of iterations when calculating the weight; The output module is configured to solve the multi-objective energy storage optimization configuration model according to the improved Parrot algorithm and output the energy storage optimization configuration result.

7. An electronic device, characterized in that: include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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