Voltage fluctuation suppression method for distributed energy storage converters suitable for seasonal loads

Abstract

This invention discloses a method for suppressing voltage fluctuations in distributed energy storage converters suitable for seasonal loads, belonging to the field of voltage fluctuation suppression technology. The method includes: marking target areas, obtaining target area information maps, and setting seasonal unit areas based on the target area information maps; each seasonal unit area is an electricity consumption area that meets seasonal load requirements; determining each energy storage control area based on each seasonal unit area, and setting energy storage devices based on each energy storage control area; setting voltage suppression schemes based on preset energy storage benchmark adjustment principles and the energy storage devices in each energy storage control area; establishing a load forecasting model, collecting real-time data according to preset acquisition items to obtain load condition data, analyzing the load condition data through the load forecasting model to obtain load forecast data; analyzing the load forecast data according to the voltage suppression scheme to obtain control commands for each energy storage device, and controlling each energy storage device according to the control commands.

Description

Technical Field

[0001] This invention belongs to the field of voltage fluctuation suppression technology, specifically a method for suppressing voltage fluctuations in distributed energy storage converters suitable for seasonal loads. Background Technology

[0002] Seasonal load variations are a common phenomenon in power systems, such as in rural areas, cities, residential communities, and certain industries. The sharp increase in air conditioning load in summer and heating load in winter places enormous pressure on the power grid. These load variations cause voltage fluctuations, which in turn affect the normal operation of electrical equipment. Traditional grid regulation methods often struggle to respond quickly to these seasonal load fluctuations, leading to unstable grid voltage and reduced power supply reliability.

[0003] With the transformation of the energy structure and the development of smart grids, distributed energy storage converters have received widespread attention and application as an important means to improve grid flexibility and reliability. Therefore, how to better integrate distributed energy storage converters to suppress voltage fluctuations during seasonal loads has become a problem that needs to be solved.

[0004] Based on this, the present invention provides a method for suppressing voltage fluctuations in distributed energy storage converters suitable for seasonal loads. Summary of the Invention

[0005] To address the problems of the above solutions, this invention provides a method for suppressing voltage fluctuations in distributed energy storage converters suitable for seasonal loads.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for suppressing voltage fluctuations in distributed energy storage converters suitable for seasonal loads includes the following steps:

[0008] Step 1: Mark the target area, obtain the target area information map, and set up each seasonal unit area according to the target area information map; the seasonal unit area is the power consumption area that meets the seasonal load requirements;

[0009] Step 2: Determine the energy storage control area based on the seasonal unit area, and set up energy storage equipment according to the energy storage control area;

[0010] Step 3: Set voltage suppression schemes for energy storage devices in each energy storage control area according to the preset energy storage benchmark adjustment principles;

[0011] Step 4: Establish a load forecasting model, collect real-time data according to the preset collection items to obtain load condition data, and analyze the load condition data through the load forecasting model to obtain load forecast data;

[0012] Step 5: Analyze the load forecast data according to the voltage suppression scheme to obtain the control commands for each energy storage device, and control each energy storage device according to the control commands.

[0013] Furthermore, the step of setting each seasonal unit region based on the target region information map includes:

[0014] Set unit area definitions, identify the target area information map according to the unit area definitions, mark each unit area in the target area information map according to the identification results, obtain the historical electricity consumption data of each unit area, and mark it as unit data;

[0015] Set seasonal load requirements, and set a seasonality judgment model based on the seasonal load requirements;

[0016] The seasonal judgment model is used to analyze the unit data of each unit region to obtain the seasonal judgment value corresponding to each unit region.

[0017] Each of the aforementioned unit regions with a seasonality judgment value of 1 is marked as a seasonal unit region.

[0018] Furthermore, the expression for the seasonality judgment model is:

[0019]

[0020] In the formula: s represents the input data, which consists of individual data units; the output data is the seasonality judgment value PH(s).

[0021] Furthermore, the method for setting up the energy storage control area includes:

[0022] Step SA1: Obtain performance data of energy storage devices;

[0023] Step SA2: Select any seasonal unit region as the initial region; if no initial region is available,

[0024] Proceed to step SA7;

[0025] Obtain the regional association data between the initial region and each seasonal unit region; set the merging requirements;

[0026] Based on the corresponding regional correlation data, assess whether the initial region and each seasonal unit region meet the merging requirements, and mark each seasonal unit region that meets the merging requirements as the region to be evaluated in the initial region;

[0027] Step SA3: Combine the initial region with the corresponding region to be evaluated to form an initial combination; if there is no region to be evaluated, proceed to step SA2.

[0028] The initial combination is analyzed by the performance data of the energy storage device to obtain performance evaluation results, which include those that meet the performance requirements and those that do not.

[0029] Step SA4: If the performance evaluation result indicates that the performance requirements are not met, the corresponding initial combination is prohibited; return to step SA3;

[0030] When the performance evaluation result meets the performance requirements, the initial combination is marked as a merged combination; the merged combination is combined with the corresponding area to be evaluated to form a new initial combination; the initial combination is analyzed by the energy storage equipment performance data to obtain a new performance evaluation result;

[0031] Step SA5: When the performance evaluation result is that the performance requirements are not met, the corresponding initial combination is prohibited, the combination method corresponding to the corresponding merged combination is output, and the process returns to step SA3;

[0032] When the performance evaluation result meets the performance requirements, the corresponding initial combination is marked as a new merged combination; the new merged combination is combined with the corresponding area to be evaluated to form a new initial combination; the initial combination is analyzed through the energy storage equipment performance data to obtain a new performance evaluation result;

[0033] Step SA6: Repeat step SA5;

[0034] Step SA7: Identify each combination method and form each candidate control scheme according to each combination method;

[0035] Each of the proposed control measures is evaluated to obtain corresponding control evaluation results, which include control evaluation qualified and control evaluation unqualified.

[0036] Step SA8: Mark each candidate control scheme in the control assessment and merging as a scheme to be applied, and determine the target application scheme from each of the schemes to be applied;

[0037] Identify the various combinations corresponding to the target application scheme, and integrate the seasonal unit areas corresponding to the combinations into an energy storage control area.

[0038] Furthermore, the method for evaluating each candidate control scheme in step SA7 includes:

[0039] Based on historical electricity consumption data of the target area, set up simulation data for the scheme; establish energy storage benchmark adjustment principles;

[0040] Based on the energy storage benchmark adjustment principles and scheme simulation data, each candidate control scheme is simulated to obtain corresponding simulation verification data; the benchmark simulation results corresponding to the scheme simulation data are identified.

[0041] The baseline simulation results are compared with each simulation validation data to determine the ranking of the inhibition effects corresponding to each simulation validation data. Based on the ranking of the inhibition effects, each candidate regulation scheme is ranked to obtain the first sequence.

[0042] Obtain the voltage suppression target, and remove all candidate control schemes that are below the voltage suppression target from the first sequence to obtain the second sequence;

[0043] The vacancy rate of each candidate control scheme in the second sequence is calculated. Candidate control schemes with vacancy rates lower than the threshold X1 are removed from the second sequence to obtain the third sequence.

[0044] The control evaluation results of each candidate control scheme in the third sequence are considered as qualified control evaluation;

[0045] The regulatory assessment results of each candidate regulatory scheme in the non-third sequence are considered as unqualified.

[0046] Furthermore, the energy storage benchmark adjustment principle is as follows: when the predicted load increases, charging is performed within a preset time period; when the predicted load decreases, discharging is performed within a preset time period.

[0047] Furthermore, the load forecasting model is built based on a deep neural network, with load condition data as input and load forecasting data as output.

[0048] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a distributed energy storage converter voltage fluctuation suppression method applicable to seasonal loads as described above.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] By optimizing the control strategy of distributed energy storage converters, voltage fluctuations caused by seasonal load changes can be effectively suppressed, significantly improving the stability of grid voltage, reducing voltage drops or rises, and ensuring the safe and stable operation of the grid. Based on seasonal load characteristics, the energy storage charging and discharging strategy can be intelligently adjusted to ensure effective support from energy storage devices during peak load periods. Distributed energy storage converters have rapid response capabilities, enabling them to quickly adjust output power, smooth voltage fluctuations caused by seasonal loads, and improve the grid's response speed and regulation capabilities. In extreme weather or emergencies, distributed energy storage can serve as an emergency power source, providing stable power to critical loads and enhancing the resilience and reliability of the grid. Attached Figure Description

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a flowchart illustrating a method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads, as described in an embodiment of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, a method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads includes:

[0055] Step 1: Identify the power grid management area, i.e., the jurisdiction, and mark it as the target area; obtain the target area information map, including information such as line distribution, power equipment, residential areas, villages, hospitals, etc.; define unit areas according to the smallest electricity consumption areas such as residential areas, factories, office buildings, shopping malls, and villages, which are the smallest electricity consumption areas; the definition can be combined with the corresponding line connections; identify the target area information map according to the unit area definition, and mark each unit area in the target area information map; obtain the historical electricity consumption data of each unit area and mark it as unit data; analyze the unit data to determine whether the corresponding unit area meets the seasonal load requirements, and mark the unit areas that meet the seasonal load requirements as seasonal unit areas;

[0056] Seasonal load requirements are defined by management based on actual management needs.

[0057] In one embodiment, determining whether a corresponding unit area meets the requirements of seasonal load can be done based on existing data assessment techniques or manually.

[0058] In one embodiment, a method for determining whether a corresponding unit area meets the requirements of seasonal load includes:

[0059] Set seasonal load requirements, and based on these requirements, set up a seasonality assessment model. This model analyzes the input data to determine whether it meets the seasonality requirements. The expression for the seasonality assessment model is: In the formula: s represents the input data, which consists of individual data units; the output data is the seasonality judgment value PH(s).

[0060] The seasonal judgment model is used to analyze the unit data of each unit region to obtain the seasonal judgment value corresponding to each unit region; the unit region with a seasonal judgment value of 1 is marked as a seasonal unit region.

[0061] Step 2: Determine the energy storage control area based on the seasonal unit area, and set up energy storage equipment according to the energy storage control area;

[0062] For example, distributed energy storage converters and other related energy storage devices can be set up in each energy storage control area for subsequent voltage suppression control based on the distributed energy storage converters.

[0063] In one embodiment, the energy storage control zone can be set up manually or by existing methods.

[0064] In one embodiment, the method for setting up an energy storage control zone includes:

[0065] Step SA1: Obtain energy storage device performance data, i.e., data such as the adjustment range that the energy storage device can make during load adjustment, to determine the size of the area it can handle;

[0066] Step SA2: Select any seasonal unit region as the initial region; acquire the regional correlation data between the initial region and other seasonal unit regions, including the seasonal unit regions corresponding to the other initial regions. The regional correlation data includes the location relationship and line connection relationship between the corresponding seasonal unit regions, etc., to determine whether the corresponding seasonal unit regions can be voltage suppressed and regulated by the same energy storage device, and set merging requirements based on whether voltage suppression and regulation can be achieved by the same energy storage device; evaluate whether the initial region meets the merging requirements with other seasonal unit regions.

[0067] Each seasonal unit area that meets the merging requirements is marked as an area to be evaluated;

[0068] If there is no initial region, proceed to step SA7;

[0069] Step SA3: Combine the initial region with the corresponding region to be evaluated to form an initial combination, and the combination can be selected arbitrarily; analyze the initial combination through the performance data of the energy storage device to determine whether the historical electricity consumption data corresponding to the initial combination will exceed the performance requirements of the energy storage device, that is, exceed the control limit; obtain the performance evaluation results, including whether the performance requirements are met or not.

[0070] When there is no area to be evaluated, that is, an initial combination cannot be formed, step SA2;

[0071] Step SA4: When the performance evaluation result is that the performance requirements are not met, the corresponding initial combination is prohibited, that is, the initial region and the region to be evaluated corresponding to the initial combination will not appear in the future; return to step SA3;

[0072] When the performance evaluation result is that the performance requirements are met, the corresponding initial combination is marked as a merged combination; the merged combination is combined with the corresponding area to be evaluated to form a new initial combination, without repeating the combination; the new initial combination is analyzed through the energy storage equipment performance data to obtain the performance evaluation result, including whether the performance requirements are met or not.

[0073] Step SA5: When the performance evaluation result is that the performance requirements are not met, the corresponding initial combination is prohibited, and the combination method corresponding to the corresponding merged combination is output, that is, the merged combination in the corresponding initial combination; return to step SA3;

[0074] When the performance evaluation result is that the performance requirements are met, the corresponding initial combination is marked as a new merged combination; the new merged combination is combined with the corresponding area to be evaluated to form a new initial combination; the performance data of the energy storage equipment is analyzed to obtain the performance evaluation result, including whether the performance requirements are met or not.

[0075] Step SA6: Repeat step SA5.

[0076] Step SA7: Identify the available combination methods, each of which corresponds to an energy storage device configuration. Based on each combination method, generate candidate control schemes, which are the combinations of these methods, essentially representing the energy storage device's operational area layout. Each combination method corresponding to a candidate control scheme may not include all seasonal unit areas, but the included combinations cannot overlap. Overlap refers to multiple combinations within a candidate control scheme sharing the same seasonal unit area. The available candidate control schemes can be determined based on the existing data arrangement, such as using an enumeration method.

[0077] Each candidate control scheme is evaluated to determine whether it meets the regional control requirements. The regional control requirements are set by the management personnel based on the voltage suppression requirements of the target area. The corresponding control evaluation results are obtained, including whether the control evaluation is qualified or unqualified.

[0078] Step SA8: Mark each candidate control scheme in the control assessment as a scheme to be applied. Subsequently, the management personnel will determine the target application scheme based on the actual situation. The determination of the target application scheme needs to be based on various practical factors such as whether energy storage is allowed in each seasonal unit area and cost considerations.

[0079] Identify the various combinations corresponding to the target application scheme, and integrate the seasonal unit areas corresponding to the combination into an energy storage control area.

[0080] In one implementation, the method for evaluating the control options in step SA7 includes:

[0081] Based on the historical electricity consumption data of the target area, the simulation data of the scheme is set up. That is, representative data is set up as simulation data based on the historical electricity consumption data, which is used to verify and simulate each scheme and simulate the voltage suppression effect after the scheme is applied.

[0082] The energy storage benchmark adjustment principle is established by utilizing the fast response characteristics of distributed energy storage converters. This involves charging in advance when a load increase is predicted and discharging when a load decrease is predicted, thereby balancing the instantaneous power difference in the power grid and suppressing voltage fluctuations. Specifically, charging occurs within a preset time period when a load increase is predicted, and discharging occurs within a preset time period when a load decrease is predicted. The specific preset time period is determined based on its charging and discharging performance to ensure that it meets operational requirements.

[0083] Based on the energy storage benchmark adjustment principle and the simulation data of the scheme, each candidate control scheme is simulated to obtain the corresponding simulation verification data. That is, according to the energy storage benchmark adjustment principle, the power grid of the original target area is adjusted in combination with the candidate control scheme, and the voltage suppression after the adjustment is simulated and marked as simulation verification data. Generally, it is represented by load curves such as voltage and current, which is more intuitive.

[0084] Identify the voltage data corresponding to the simulation data of the scheme and mark it as the baseline simulation result;

[0085] The baseline simulation results are compared with the simulation verification data to determine the corresponding suppression effect ranking, which ranks the magnitude of the reduction in the impact of load fluctuations such as grid voltage and current. The greater the effect, the higher the ranking. The candidate control schemes are ranked according to the suppression effect ranking to obtain the first sequence. The voltage suppression target is obtained. The voltage suppression target is set by the management based on the needs and expectations, and is used to indicate the standard that the suppression effect should not be lower than. The candidate control schemes that are lower than the voltage suppression target are removed from the first sequence to obtain the second sequence.

[0086] Based on the simulation process and simulation verification results, the vacancy rate corresponding to each candidate control scheme in the second sequence is calculated. The vacancy rate is calculated based on whether each energy storage device is used and the proportion of its performance used during use to its total performance, which is used to measure its resource utilization. Candidate control schemes with vacancy rates lower than the threshold X1 are removed from the second sequence to obtain the third sequence.

[0087] The control evaluation results of each candidate control scheme in the third sequence are considered as qualified control evaluation;

[0088] The regulatory assessment results of each candidate regulatory scheme in the non-third sequence are considered as unqualified.

[0089] Step 3: Set voltage suppression schemes for energy storage devices in each energy storage control area according to the preset energy storage benchmark adjustment principles;

[0090] Simulation tests are conducted based on historical electricity consumption data to determine the optimal control method, including the setting of preset time periods, the criteria for determining load increases / decreases, and relevant data such as charging and discharging parameters. The settings are implemented by professionals and subject to expert review.

[0091] Step 4: Establish a load forecasting model. This model is built based on historical load data from the power grid and is used to predict load change trends over a future period based on various factors such as seasons and weather conditions. Specifically, it uses existing forecasting technologies; for example, it is built using neural networks such as DNNs. A corresponding training set is manually created for training. The training set includes input and output data. The input data consists of prediction condition data, which are various data related to load forecasting; the output data is the load forecasting data. The successfully trained load forecasting model is then analyzed.

[0092] Based on the load condition data, each collection item is preset, and real-time data is collected according to each preset collection item to obtain load condition data. The load condition data is then analyzed through the load prediction model to obtain the corresponding load prediction data.

[0093] Step 5: Analyze the load forecast data according to the voltage suppression scheme to obtain the control commands for each energy storage device. Control each energy storage device according to the control commands to balance the instantaneous power difference of the power grid and thus suppress voltage fluctuations.

[0094] By optimizing the control strategy of distributed energy storage converters, voltage fluctuations caused by seasonal load changes can be effectively suppressed, significantly improving the stability of grid voltage, reducing voltage drops or rises, and ensuring the safe and stable operation of the grid. Based on seasonal load characteristics, the energy storage charging and discharging strategy can be intelligently adjusted to ensure effective support from energy storage devices during peak load periods. Distributed energy storage converters have rapid response capabilities, enabling them to quickly adjust output power, smooth voltage fluctuations caused by seasonal loads, and improve the grid's response speed and regulation capabilities. In extreme weather or emergencies, distributed energy storage can serve as an emergency power source, providing stable power to critical loads and enhancing the resilience and reliability of the grid.

[0095] This application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above embodiment of a distributed energy storage converter voltage fluctuation suppression method suitable for seasonal loads.

[0096] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0098] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0099] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

[0100] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0101] For ease of description, the above apparatus is described in terms of function, with each unit described separately. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware components. Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

Claims

1. A method for suppressing voltage fluctuations in distributed energy storage converters suitable for seasonal loads, characterized in that, Includes the following steps: Step 1: Mark the target area, obtain the target area information map, and set up each seasonal unit area according to the target area information map; the seasonal unit area is the power consumption area that meets the seasonal load requirements; Step 2: Determine the energy storage control area based on the seasonal unit area, and set up energy storage equipment according to the energy storage control area; Step 3: Set voltage suppression schemes for energy storage devices in each energy storage control area according to the preset energy storage benchmark adjustment principles; Step 4: Establish a load forecasting model, collect real-time data according to the preset collection items to obtain load condition data, and analyze the load condition data through the load forecasting model to obtain load forecast data; Step 5: Analyze the load forecast data according to the voltage suppression scheme to obtain control commands for each energy storage device, and control each energy storage device according to the control commands; The method for setting up the energy storage control zone includes: Step SA7: Identify the combination of each seasonal unit region, and form each candidate control scheme according to the combination. Each of the proposed control measures is evaluated to obtain corresponding control evaluation results, which include control evaluation qualified and control evaluation unqualified. Step SA8: Mark each candidate control scheme in the control assessment and merging as a scheme to be applied, and determine the target application scheme from each of the schemes to be applied; Identify the various combinations corresponding to the target application scheme, and integrate the seasonal unit areas corresponding to the combinations into an energy storage and control area; The methods for evaluating each candidate control scheme in step SA7 include: Based on historical electricity consumption data of the target area, set up simulation data for the scheme; establish energy storage benchmark adjustment principles; Based on the energy storage benchmark adjustment principles and scheme simulation data, each candidate control scheme is simulated to obtain corresponding simulation verification data; the benchmark simulation results corresponding to the scheme simulation data are identified. The baseline simulation results are compared with each simulation validation data to determine the ranking of the inhibition effects corresponding to each simulation validation data. Based on the ranking of the inhibition effects, each candidate regulation scheme is ranked to obtain the first sequence. Obtain the voltage suppression target, and remove all candidate control schemes that are below the voltage suppression target from the first sequence to obtain the second sequence; The vacancy rate of each candidate control scheme in the second sequence is calculated. Candidate control schemes with vacancy rates lower than the threshold X1 are removed from the second sequence to obtain the third sequence. The control evaluation results of each candidate control scheme in the third sequence are considered as qualified control evaluation; The regulatory assessment results of each candidate regulatory scheme in the non-third sequence are considered as unqualified.

2. The method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads according to claim 1, characterized in that, The step of setting each seasonal unit region based on the target region information map includes: Set unit area definitions, identify the target area information map according to the unit area definitions, mark each unit area in the target area information map according to the identification results, obtain the historical electricity consumption data of each unit area, and mark it as unit data; Set seasonal load requirements, and set a seasonality judgment model based on the seasonal load requirements; The seasonality judgment model is used to analyze the unit data of each unit region to obtain the seasonality judgment value corresponding to each unit region; the seasonality judgment value is 1 or 0. Each of the aforementioned unit regions with a seasonality judgment value of 1 is marked as a seasonal unit region.

3. The method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads according to claim 2, characterized in that, The expression for the seasonality judgment model is: ； In the formula: s represents the input data, which consists of individual data units; the output data is the seasonality judgment value PH(s).

4. The method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads according to claim 1, characterized in that, The energy storage baseline adjustment principle is as follows: when the predicted load increases, charging is performed within a preset time period; when the predicted load decreases, discharging is performed within a preset time period.

5. The method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads according to claim 1, characterized in that, The load forecasting model is based on a deep neural network. The input data is load condition data, and the output data is load forecast data.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a method for suppressing voltage fluctuations in a distributed energy storage converter suitable for seasonal loads, as described in any one of claims 1 to 5.

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