Microgrid energy storage optimization method and system based on big data analysis

Abstract

The present invention relates to the field of smart grid technology, and is a method and system for optimizing microgrid energy storage based on big data analysis, comprising: obtaining a comprehensive predicted power generation using a predicted segment time, a set of wind turbine generators, and a set of solar turbine generators; obtaining an analysis power based on the predicted segment time and the comprehensive predicted power generation; obtaining an energy storage battery parameter set; if the analysis power is greater than or equal to a storage power threshold, retrieving a target storage battery group from the energy storage battery group using the energy storage battery parameter set; actively performing balanced charging on the target storage battery group to obtain an updated storage battery group; if the analysis power is less than a supply power threshold, confirming that a preset target energy supply battery group has been retrieved from the energy storage battery group using the analysis power and the energy storage battery parameter set, then using the target energy supply battery group to supply power to obtain an updated power supply battery group. The present invention can accurately and intelligently optimize the energy storage of a microgrid.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a microgrid energy storage optimization method and system based on big data analysis. Background Art

[0002] With the widespread application of renewable energy in microgrids, energy storage battery packs play a vital role in balancing microgrid power and improving power quality. Correspondingly, how to accurately and intelligently optimize the energy storage of energy storage battery packs plays an indispensable role in the normal, efficient and safe operation of energy storage battery packs.

[0003] At present, the optimization of microgrid energy storage is mainly achieved by collecting the output power data, load data and charge status data of distributed power sources, so as to improve the service life of energy storage battery packs in microgrids.

[0004] Although the above method can achieve energy storage optimization of the energy storage battery pack, the differences between the energy storage batteries in the energy storage battery pack are not taken into account when optimizing the energy storage of the energy storage battery pack. Moreover, before optimizing the energy storage of the energy storage battery pack in the microgrid, the power that can be supplied by the initial microgrid and the power required by the load are not considered. As a result, the energy storage battery pack in the microgrid has the problem of frequent charging and frequent discharging. Therefore, how to accurately and intelligently achieve energy storage optimization of the microgrid has become an urgent problem to be solved. Summary of the Invention

[0005] The present invention provides a microgrid energy storage optimization method based on big data analysis and a computer-readable storage medium, the main purpose of which is to accurately and intelligently optimize the energy storage of the microgrid.

[0006] To achieve the above objectives, the present invention provides a microgrid energy storage optimization method based on big data analysis, comprising:

[0007] receiving an energy storage optimization instruction, and determining an initial microgrid for energy storage optimization based on the energy storage optimization instruction, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes a plurality of wind turbines, the solar power generator set includes a plurality of solar power generators, the energy storage battery group includes a plurality of energy storage batteries, and the fuel-powered generator set includes a plurality of fuel-powered generators;

[0008] Obtaining a comprehensive forecast of power generation using pre-confirmed forecast segment times, wind turbine generator sets, and solar turbine generator sets;

[0009] Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power;

[0010] Obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value;

[0011] If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group;

[0012] If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack;

[0013] If the target energy supply battery group cannot be retrieved from the energy storage battery group, the target fuel power supply is obtained using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the process returns to the step of obtaining the energy storage battery parameter set of the energy storage battery group. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group.

[0014] Optionally, obtaining a comprehensive predicted power generation using the pre-confirmed predicted segment time, the wind turbine generator set, and the solar turbine generator set includes:

[0015] Perform the following operations for each wind turbine in the wind turbine set:

[0016] Obtain an initial set of influencing factors of wind turbines, and use a pre-built principal component analysis method to identify a target set of influencing factors from the initial set of influencing factors;

[0017] Acquiring an initial historical data set based on the target impact factor set, wherein the initial historical data set includes a plurality of initial historical data, wherein the initial historical data includes reference impact environment data and initial reference wind power;

[0018] Obtaining predicted impact environment data based on the predicted segment time, wind turbines, and target impact factor set, and obtaining a target reference wind power set using the predicted impact environment data, a pre-built clustering algorithm, and reference impact environment data corresponding to the initial historical data in the initial historical data set;

[0019] Acquire an electricity prediction model set for predicting power generation, and acquire a reference predicted wind power set based on the electricity prediction model set and the wind turbine;

[0020] Associating the target reference wind power set and the reference predicted wind power set to obtain a wind power generation reference node;

[0021] Summarizing the wind power generation reference nodes to obtain a wind power generation reference node set, wherein the wind power generation reference node set includes multiple wind power generation reference nodes, and the wind power generation reference nodes correspond to wind turbines one by one;

[0022] The comprehensive forecast power generation is obtained using the set of wind power reference nodes and the set of solar generators.

[0023] Optionally, obtaining a comprehensive predicted power generation using a set of wind power generation reference nodes and a set of solar power generators includes:

[0024] For each solar generator in the solar generator cluster, the following operations are performed:

[0025] Obtaining a photovoltaic influence factor set of a solar generator, and using the photovoltaic influence factor set to obtain a photovoltaic power generation reference node set, wherein the photovoltaic power generation reference node set includes a plurality of photovoltaic power generation reference nodes, and the photovoltaic power generation reference nodes include a target reference photovoltaic power set and a reference predicted photovoltaic power set;

[0026] Obtaining reference accuracy nodes of the power prediction models in the power prediction model set to obtain a reference accuracy node set, wherein the reference accuracy nodes include the number of training data and the number of recognition data;

[0027] The comprehensive predicted power generation is calculated based on the wind power generation reference node set, the reference accuracy node set and the photovoltaic power generation reference node set.

[0028] Optionally, the calculating of the comprehensive predicted power generation based on the wind power generation reference node set, the reference accuracy node set, and the photovoltaic power generation reference node set includes:

[0029] Clustering the target reference wind power in the wind power reference node set to obtain multiple clustered wind power sets, counting the amount of wind power in each of the multiple clustered wind power sets to obtain multiple statistical cluster quantities, and using the multiple statistical cluster quantities to identify a target clustered power set from the multiple clustered wind power sets, wherein the target clustered power set is the clustered wind power set corresponding to the largest statistical cluster quantity among the multiple statistical cluster quantities;

[0030] Calculate the mean value of the target cluster electricity in the target cluster electricity set to obtain the cluster wind power mean value;

[0031] The comprehensive reference wind power is calculated based on the clustered wind power mean and the reference accuracy node set. The comprehensive reference photovoltaic power is obtained based on the photovoltaic power generation reference node set. The sum of the comprehensive reference wind power and the comprehensive reference photovoltaic power is calculated to obtain the comprehensive predicted power generation.

[0032] Optionally, the comprehensive reference wind power is calculated based on the clustered wind power mean and the reference accuracy node set, and the calculation formula is as follows:

[0033] ;

[0034] in, represents the comprehensive reference wind power, are all preset coefficients. Indicates the total number of clustered wind power sets indivual, Respectively represent the number of statistical clusters The number of statistical clusters and the The number of statistical clusters, Indicates that multiple clusters of wind power are concentrated The contribution of a clustered wind power set is equivalent to represents the clustered wind power mean value, Respectively represent Cluster wind power concentration Cluster wind power and Clustered wind power, Indicates the The total amount of wind power in clusters is Clustered wind power, Indicates the reference accuracy node set The number of training data corresponding to the reference accuracy node, Indicates the total number of reference accuracy nodes Reference accuracy nodes, Indicates the reference accuracy node set The number of recognition data corresponding to the reference accuracy node, Indicates the first The reference predicted wind power corresponding to each power forecast model.

[0035] Optionally, the step of retrieving a target storage battery group from the energy storage battery group by using the energy storage battery parameter set includes:

[0036] Obtaining a monitoring node time sequence of each energy storage battery in the energy storage battery pack, wherein the monitoring node time sequence includes multiple monitoring nodes, and the monitoring nodes include monitoring internal resistance and monitoring temperature;

[0037] Associating a monitoring node with a time corresponding to the monitoring node to obtain a fitting data node, summarizing the fitting data nodes to obtain a fitting data node set, mapping the fitting data nodes in the fitting data node set to a pre-constructed reference coordinate system to obtain a mapping coordinate set, obtaining a fitting curve using a pre-constructed curve fitting method and the mapping coordinate set, summarizing the fitting curves to obtain a fitting curve set, wherein the fitting curve set includes multiple fitting curves, and the fitting curves correspond one-to-one to the energy storage batteries;

[0038] For each fitted curve in the fitted curve set, the following operations are performed:

[0039] Identify the minimum curvature radius in the fitting curve, calculate the product of the minimum curvature radius and the preset proportion coefficient to obtain the identification range radius, and construct a fitting screening range with the fitting curve as the central axis and the identification range radius as the radius;

[0040] Counting the number of fitting curves in the fitting screening range in the fitting curve set to obtain a range curve number, and summarizing the range curve number to obtain a range curve number set;

[0041] Calculate the reference projection distance of the fitting curve. The calculation formula is as follows:

[0042] ;

[0043] in, represents the reference projection distance, Indicates that there are a total of monitoring nodes, Indicates monitoring internal resistance, Indicates monitoring temperature;

[0044] Summarizing the reference projection distances to obtain a reference projection distance set, sorting the reference projection distances in the reference projection distance set in ascending order of the reference projection distances to obtain a reference projection distance sequence, obtaining a range curve quantity sequence using the range curve quantity set and the reference projection distance sequence, and identifying a first curve quantity in the range curve quantity sequence based on a preset screening battery threshold, wherein the first curve quantity is the first range curve quantity in the range curve quantity sequence that is greater than or equal to the screening battery threshold;

[0045] Extracting a screening battery parameter set from the energy storage battery parameter set using the first curve quantity, wherein the screening battery parameter set includes a plurality of screening battery parameters, and the screening battery parameters include a battery SOH value, a remaining storage capacity, and a battery SOC value;

[0046] Clustering the screening battery parameter sets using a clustering algorithm to obtain one or more distance screening parameter sets, and counting the number of distance screening parameters in each of the one or more distance screening parameter sets to obtain one or more distance screening quantities;

[0047] If there is a distance screening number greater than or equal to the screening battery threshold in one or more distance screening numbers, then randomly extract energy storage batteries with the same number as the screening battery threshold from the multiple energy storage batteries corresponding to the distance screening number to obtain a target storage battery group;

[0048] Otherwise, the first curve quantity is skipped in the range curve quantity sequence, and the process returns to the step of confirming the first curve quantity in the range curve quantity sequence based on the preset battery screening threshold, until the target storage battery pack is obtained.

[0049] Optionally, the process of searching the energy storage battery pack by analyzing the power quantity and the energy storage battery parameter set, and if a preset target energy supply battery pack is found in the energy storage battery pack, includes:

[0050] The predicted segment time is used to obtain a battery power threshold, and the number of energy storage batteries is calculated according to the battery power threshold and the battery rated power;

[0051] The energy storage batteries in the energy storage battery group are sorted in descending order of remaining storage power to obtain an energy storage battery sequence. The number of energy storage batteries is used to obtain a search sliding window. According to the search sliding window, search storage power groups are sequentially extracted from the energy storage battery sequence, and the following operations are performed on the extracted search storage power groups:

[0052] Calculate the sum of the retrieved stored power in the retrieved stored power group to obtain the comprehensive detected power, and compare the comprehensive detected power with the analyzed power;

[0053] If the comprehensive detected power is greater than or equal to the analyzed power, the process returns to the step of sequentially extracting the search and storage power groups from the energy storage battery sequence until the comprehensive detected power is less than the analyzed power, thereby obtaining multiple target search and storage power groups, wherein the comprehensive detected power corresponding to the target search and storage power groups is greater than or equal to the analyzed power, and the following operation is performed on each of the multiple target search and storage power groups:

[0054] Calculating the variance of the retrieval storage power in the target retrieval storage power group to obtain a comprehensive detection variance, summarizing the comprehensive detection variance to obtain a comprehensive detection variance set, and using the comprehensive detection variance set to identify the target energy supply battery group, wherein the target energy supply battery group is the multiple energy storage batteries corresponding to the smallest comprehensive detection variance in the comprehensive detection variance set;

[0055] Otherwise, it is confirmed that the target energy supply battery pack does not exist in the energy storage battery pack.

[0056] Optionally, the number of energy storage batteries is calculated based on the battery power threshold and the battery rated power, and the calculation formula is as follows:

[0057] ;

[0058] in, Indicates the number of energy storage batteries, Indicates the battery power threshold, Indicates the rated power of the battery, Indicates rounding up.

[0059] Optionally, the step of obtaining a target fuel power supply using the predicted power consumption and the fuel generator set includes:

[0060] Obtain the rated power generation of each fuel generator in the fuel generator set to obtain a rated power generation set;

[0061] The generator set screening formula is constructed based on the rated power generation set and the battery power threshold. The generator set screening formula is as follows:

[0062] ;

[0063] in, Indicates the first of many fuel generators Number of fuel-fired generators, Indicates the rated power generation concentration Rated power generation, Indicates the total use A fuel-fired generator;

[0064] In combination, using the generator set screening formula, multiple initial fuel-fired generator sets are extracted from the fuel-fired generator set. The energy consumption corresponding to each of the multiple initial fuel-fired generator sets is obtained to obtain an initial energy consumption set. The initial fuel-fired generator set corresponding to the minimum initial energy consumption is identified in the initial energy consumption set to obtain the target fuel-fired generator set.

[0065] The target fuel power generation capacity is obtained by obtaining the target fuel power generation capacity, multiplying the target power generation capacity by the predicted segment time, and subtracting the product from the predicted power consumption capacity to obtain the target fuel power supply capacity.

[0066] To achieve the above objectives, the present invention further provides a microgrid energy storage optimization system based on big data analysis, comprising:

[0067] an initial microgrid confirmation module, configured to receive an energy storage optimization instruction and, based on the energy storage optimization instruction, confirm an initial microgrid for energy storage optimization, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes a plurality of wind turbines, the solar power generator set includes a plurality of solar power generators, the energy storage battery group includes a plurality of energy storage batteries, and the fuel-powered generator set includes a plurality of fuel-powered generators;

[0068] A microgrid parameter prediction module is used to obtain a comprehensive predicted power generation using a pre-confirmed prediction segment time, a set of wind turbine generators, and a set of solar generators;

[0069] Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power;

[0070] An energy storage solution building module is used to obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value;

[0071] If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group;

[0072] If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack;

[0073] The energy storage solution optimization module is used to use the predicted power consumption and the fuel generator set to obtain the target fuel power supply if the target energy supply battery group cannot be retrieved from the energy storage battery group, use the target fuel power supply as the analysis power, return to the step of obtaining the energy storage battery parameter set of the energy storage battery group, and realize energy storage optimization of the initial microgrid based on the updated storage battery group or the updated power supply battery group.

[0074] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0075] a memory storing at least one instruction; and

[0076] The processor executes the instructions stored in the memory to implement the above-mentioned microgrid energy storage optimization method based on big data analysis.

[0077] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one instruction, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned microgrid energy storage optimization method based on big data analysis.

[0078] In order to solve the problems described in the background technology, the present invention uses pre-confirmed predicted segment time, wind turbine set and solar turbine set to obtain comprehensive predicted power generation. It can be seen that the present invention takes into account the amount of electricity that can be generated by the wind turbine set and the solar turbine set in the initial microgrid before realizing energy storage optimization of the initial microgrid, that is, the comprehensive predicted power generation. When obtaining the comprehensive predicted power generation, the comprehensive reference wind power is calculated by screening historical data and using a prediction model for prediction. When calculating the comprehensive reference wind power, different weight values ​​are set in combination with different characteristics to improve the accuracy of the calculated comprehensive reference wind power, thereby improving the accuracy of energy storage optimization of the microgrid. In the present invention, if the analyzed power is greater than or equal to a preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced and charged to obtain an updated storage battery group. If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery group. If a preset target energy supply battery group is retrieved from the energy storage battery group, the target energy supply battery group is used to supply power to obtain an updated power supply battery group. It can be seen that before charging and discharging the energy storage battery pack, the present invention also considers analyzing whether the power is greater than a preset storage power threshold for judging the need to charge the energy storage battery pack, and is less than a preset supply power threshold for using the energy storage battery pack for energy supply. Before charging the energy storage batteries in the energy storage battery pack, the present invention also considers the similarity of the energy storage batteries at different time nodes and sets the number of energy storage batteries that need to be charged simultaneously. Thus, the phenomenon of overcharging when charging some energy storage batteries in the target energy storage battery pack is avoided, and the safety of charging the target energy storage battery pack is improved. When obtaining the target energy supply battery pack for power supply, the power and power required by the initial microgrid in the predicted segment time are considered, and the target energy supply battery pack with similar parameters is retrieved, thereby avoiding the phenomenon of over-discharge of some energy storage batteries in the target energy supply battery pack. If the present invention cannot retrieve the target energy supply battery group from the energy storage battery group, the target fuel power supply is obtained using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the step of obtaining the energy storage battery parameter set of the energy storage battery group is returned. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group. It can be seen that when there is no target energy supply battery group that can supply energy in the energy storage battery group, the present invention also considers the use of the fuel generator set for power supply. When the fuel generator set is used for power supply, the fuel generator set may have excess power. Therefore, the target fuel power supply is used as the analysis power, and the step of obtaining the energy storage battery parameter set of the energy storage battery group is returned to achieve the optimization of the energy storage of the microgrid. Therefore, the present invention can improve the accuracy and intelligent realization of the energy storage optimization of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A schematic diagram of a process flow of a microgrid energy storage optimization method based on big data analysis provided by one embodiment of the present invention;

[0080] Figure 2 A functional module diagram of a microgrid energy storage optimization system based on big data analysis provided by one embodiment of the present invention;

[0081] Figure 3 A schematic structural diagram of an electronic device for implementing the microgrid energy storage optimization method based on big data analysis provided in one embodiment of the present invention.

[0082] Description of reference numerals:

[0083] 1. Electronic device; 10. Processor; 11. Storage; 12. Bus.

[0084] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0085] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] The present application provides a method for optimizing microgrid energy storage based on big data analysis. The execution entity of the method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the present application. In other words, the method can be executed by software or hardware installed on a terminal or server device, where the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0087] Reference Figure 1 FIG2 is a flow chart of a microgrid energy storage optimization method based on big data analysis according to an embodiment of the present invention. In this embodiment, the microgrid energy storage optimization method based on big data analysis includes:

[0088] S1. Receive an energy storage optimization instruction, and identify an initial microgrid for energy storage optimization based on the energy storage optimization instruction, wherein the initial microgrid includes a wind turbine set, a fuel-powered generator set, a solar turbine set, and an energy storage battery group, wherein the wind turbine set includes multiple wind turbines, the solar turbine set includes multiple solar generators, the energy storage battery group includes multiple energy storage batteries, and the fuel-powered generator set includes multiple fuel-powered generators.

[0089] It should be explained that the energy storage optimization instruction is used to optimize the energy storage of a microgrid. The initial microgrid refers to the microgrid to be optimized for energy storage, and the initial microgrid includes a set of wind turbines, a set of fuel-powered generators, a set of solar-powered generators, and an energy storage battery bank. The wind turbine bank includes multiple wind turbines, the solar generator bank includes multiple solar generators, the energy storage battery bank includes multiple energy storage batteries, and the fuel-powered generator bank includes multiple fuel-powered generators. Wind turbines refer to wind power equipment that converts wind energy into electricity, solar generators refer to equipment that converts solar energy into electricity, and energy storage batteries refer to batteries used to store and release electricity in the initial microgrid. Here, energy storage batteries are devices that store electricity in the initial microgrid. Fuel-powered generators are devices that generate electricity by burning diesel or gasoline. Fuel-powered generators differ from wind turbines and solar generators in that they offer stable power and can respond promptly to power the microgrid.

[0090] It is understandable that the power generated by wind turbines and solar generators is easily affected by environmental factors, resulting in unstable power generation. To maintain the normal operation of the load in the microgrid or store excess power generated by the wind turbines and solar generators, maintain the stability of the load in the microgrid and avoid waste of generated power, the energy storage battery packs used for energy storage need to be frequently charged and discharged. This, in turn, shortens the life of the energy storage battery packs, reduces their ability to store power, or causes significant safety hazards to the energy storage battery packs. Therefore, the embodiments of the present invention mainly aim to improve the intelligence and accuracy of microgrid energy storage optimization, thereby extending the life of the energy storage battery.

[0091] S2. Obtain a comprehensive predicted power generation using the pre-confirmed predicted segment time, the set of wind turbine generators, and the set of solar turbine generators.

[0092] It should be explained that the method of obtaining the comprehensive predicted power generation using the pre-confirmed predicted segment time, wind turbine generator set, and solar turbine generator set includes:

[0093] Perform the following operations for each wind turbine in the wind turbine set:

[0094] Obtain an initial set of influencing factors of wind turbines, and use a pre-built principal component analysis method to identify a target set of influencing factors from the initial set of influencing factors;

[0095] Acquiring an initial historical data set based on the target impact factor set, wherein the initial historical data set includes a plurality of initial historical data, wherein the initial historical data includes reference impact environment data and initial reference wind power;

[0096] Obtaining predicted impact environment data based on the predicted segment time, wind turbines, and target impact factor set, and obtaining a target reference wind power set using the predicted impact environment data, a pre-built clustering algorithm, and reference impact environment data corresponding to the initial historical data in the initial historical data set;

[0097] Acquire an electricity prediction model set for predicting power generation, and acquire a reference predicted wind power set based on the electricity prediction model set and the wind turbine;

[0098] Associating the target reference wind power set and the reference predicted wind power set to obtain a wind power generation reference node;

[0099] Summarizing the wind power generation reference nodes to obtain a wind power generation reference node set, wherein the wind power generation reference node set includes multiple wind power generation reference nodes, and the wind power generation reference nodes correspond to wind turbines one by one;

[0100] The comprehensive forecast power generation is obtained using the set of wind power reference nodes and the set of solar generators.

[0101] It should be understood that initial influencing factors refer to factors that may affect the power generation efficiency of wind turbines, such as wind speed, wind turbine location, air density, and temperature. Target influencing factors refer to factors that have a significant impact on wind turbine power generation efficiency. The technique of using principal component analysis to identify the target influencing factor set from the initial influencing factor set is well known in the art and will not be further elaborated here.

[0102] It should be understood that the target impact factor set corresponding to the initial historical data set is the same as the target impact factor set corresponding to the wind turbine. For example, if the target impact factor set for a wind turbine includes temperature, air density, and humidity, then the target impact factor set corresponding to the initial historical data also includes temperature, air density, and humidity. Generally speaking, before obtaining the initial historical data set, multiple wind turbines with known power generation can be evaluated to obtain the target impact factor set for each wind turbine. Then, the target impact factor set of the wind turbine to be predicted is used to extract the initial historical data set from the wind turbines with known target impact factor sets.

[0103] It should be explained that the predicted segmented time refers to the segmented time to be predicted after the preset time is divided. Optionally, if the preset time is the 24 hours corresponding to the next day, then the day is divided into three equal segments, and the next time segment corresponding to the current time is taken as the predicted segmented time. For example, after dividing the day into three equal segments, the three time segments are 0:00 to 8:00, 8:00 to 16:00, and 16:00 to 24:00. The current time segment is 0:00 to 8:00, so the predicted segmented time is 8:00 to 16:00.

[0104] It should be understood that the predicted environmental impact data refers to the value of the target impact factor corresponding to the target impact factor set at the location of the wind turbine in the predicted segmented time. For example, the predicted segmented time is 15:00-17:00, and the target impact factor set corresponding to the wind turbine includes: temperature and humidity. The predicted environmental impact data includes: temperature of 28 degrees Celsius and humidity of 40%. Here, the predicted environmental impact data indicates that the temperature at the location of the wind turbine from 15:00 to 17:00 is 28 degrees Celsius and the humidity is 40%. Here, the predicted environmental impact data can be obtained by taking the average value. For example, the temperature predicted at 15:00 is 29 degrees Celsius, the temperature predicted at 16:00 is 28 degrees Celsius, and the temperature predicted at 17:00 is 27 degrees Celsius. The temperature in the predicted environmental impact data is 28 degrees Celsius. The initial reference wind power refers to the amount of power that the wind turbine can generate under the reference environmental impact data.

[0105] It should be understood that the reference environmental impact data is obtained in the same manner as the predicted environmental impact data, and the time period corresponding to the reference environmental impact data is the same as the time period corresponding to the predicted segmented time. For example, if the predicted segmented time is 3:00 PM - 5:00 PM, the reference environmental impact data and initial reference wind power from the known wind turbine operating data for the period between 3:00 PM and 5:00 PM are used as the initial historical data.

[0106] It should be explained that the target reference wind power set is obtained by using the predicted impact environment data, the pre-built clustering algorithm, and the reference impact environment data corresponding to the initial historical data in the initial historical data set, including: clustering the predicted impact environment data and the reference impact environment data corresponding to the initial historical data in the initial historical data set using the clustering algorithm to obtain multiple clustered data sets, retrieving the reference impact environment data including the predicted impact environment data from the multiple clustered data sets, extracting the initial reference wind power corresponding to the reference impact environment data from the reference impact environment data, and obtaining the target reference wind power set. Optionally, the k-means clustering algorithm is used as the clustering algorithm. Other technologies can achieve the same effect, which will not be described in detail here.

[0107] It should be understood that the power prediction model refers to a model that can predict the amount of power generated. For example, a trained neural network model is used as the power prediction model. The same effect can be achieved by using other technologies, which will not be described here. Reference predicted wind power refers to the amount of power that can be generated by a wind turbine predicted by using a current prediction model combined with the characteristics of the wind turbine and the predicted environmental impact data. Associating the target reference wind power set and the reference predicted wind power set to obtain a wind power reference node means using the target reference wind power set and the reference predicted wind power set corresponding to the wind turbine as the basis for evaluating the amount of power that the wind turbine can generate in the predicted segment time, and summarizing the target reference wind power set and the reference predicted wind power set to obtain a wind power reference node. For example, the target reference wind power set includes 11 kWh, 13 kWh and 12 kWh, and the reference predicted wind power set includes 8 kWh and 6 kWh. Then, the target reference wind power set and the reference predicted wind power set are associated to obtain wind power generation reference nodes including 11 kWh, 13 kWh, 12 kWh, 8 kWh and 6 kWh.

[0108] Furthermore, the method of obtaining the comprehensive predicted power generation using the wind power generation reference node set and the solar power generator set includes:

[0109] For each solar generator in the solar generator cluster, the following operations are performed:

[0110] Obtaining a photovoltaic influence factor set of a solar generator, and using the photovoltaic influence factor set to obtain a photovoltaic power generation reference node set, wherein the photovoltaic power generation reference node set includes a plurality of photovoltaic power generation reference nodes, and the photovoltaic power generation reference nodes include a target reference photovoltaic power set and a reference predicted photovoltaic power set;

[0111] Obtaining reference accuracy nodes of the power prediction models in the power prediction model set to obtain a reference accuracy node set, wherein the reference accuracy nodes include the number of training data and the number of recognition data;

[0112] The comprehensive predicted power generation is calculated based on the wind power generation reference node set, the reference accuracy node set and the photovoltaic power generation reference node set.

[0113] It should be noted that photovoltaic impact factors refer to factors that influence the power generation efficiency of solar generators, such as light intensity and temperature. The method for obtaining the photovoltaic impact factor set is the same as that for the target impact factor set, and will not be repeated here. The method for obtaining the photovoltaic power generation reference node set is the same as that for the wind power generation reference node set, and will not be repeated here.

[0114] It is understood that before using the power forecast model, it will be tested. This means that the data predicted by the power forecast model is compared with known data. If there is no significant difference between the predicted data and the known data, the power forecast model is deemed to be able to make a correct prediction; otherwise, the power forecast model is deemed to have made an incorrect prediction. The amount of training data refers to the amount of data used to test the power forecast model, and the amount of recognition data refers to the amount of data that the power test model can correctly recognize. For example, if the power forecast model is tested using 10 data points and the power forecast model can correctly predict 9 data points, the amount of training data is 10 and the amount of recognition data is 9. The method for obtaining the reference predicted photovoltaic power set is the same as the method for obtaining the reference predicted wind power set, and will not be repeated here. The reference predicted photovoltaic power refers to the amount of power that the solar power generation unit can generate during the predicted segment time. The method for obtaining the target reference photovoltaic power set is the same as the method for obtaining the target reference wind power set. The target reference photovoltaic power refers to the amount of power that the solar photovoltaic power generation unit can generate based on historical data.

[0115] Furthermore, the calculating of the comprehensive predicted power generation based on the wind power generation reference node set, the reference accuracy node set and the photovoltaic power generation reference node set includes:

[0116] Clustering the target reference wind power in the wind power reference node set to obtain multiple clustered wind power sets, counting the amount of wind power in each of the multiple clustered wind power sets to obtain multiple statistical cluster quantities, and using the multiple statistical cluster quantities to identify a target clustered power set from the multiple clustered wind power sets, wherein the target clustered power set is the clustered wind power set corresponding to the largest statistical cluster quantity among the multiple statistical cluster quantities;

[0117] Calculate the mean value of the target cluster electricity in the target cluster electricity set to obtain the cluster wind power mean value;

[0118] The comprehensive reference wind power is calculated based on the clustered wind power mean and the reference accuracy node set. The comprehensive reference photovoltaic power is obtained based on the photovoltaic power generation reference node set. The sum of the comprehensive reference wind power and the comprehensive reference photovoltaic power is calculated to obtain the comprehensive predicted power generation.

[0119] It should be explained that clustering the target reference wind power in the wind power reference node set to obtain multiple clustered wind power sets means clustering the target reference wind power in the wind power reference node set using a clustering algorithm to obtain multiple different sets of target reference wind power. For example, after clustering 100 target reference wind power using the k-means clustering algorithm, the 100 target reference wind power can be clustered into 5 clusters, each of which includes multiple target reference wind power, and the multiple target reference wind power contained in each cluster constitutes a clustered wind power set. The method for obtaining the comprehensive reference photovoltaic power is the same as the method for obtaining the comprehensive reference wind power, and will not be repeated here.

[0120] Furthermore, the comprehensive reference wind power is calculated based on the clustered wind power mean and the reference accuracy node set. The calculation formula is as follows:

[0121] ;

[0122] in, represents the comprehensive reference wind power, are all preset coefficients. Indicates the total number of clustered wind power sets indivual, Respectively represent the number of statistical clusters The number of statistical clusters and the The number of statistical clusters, Indicates that multiple clusters of wind power are concentrated The contribution of a clustered wind power set is equivalent to represents the clustered wind power mean value, Respectively represent Cluster wind power concentration Cluster wind power and Clustered wind power, Indicates the The total amount of wind power in clusters is Clustered wind power, Indicates the reference accuracy node set The number of training data corresponding to the reference accuracy node, Indicates the total number of reference accuracy nodes Reference accuracy nodes, Indicates the reference accuracy node set The number of recognition data corresponding to the reference accuracy node, Indicates the first The reference predicted wind power corresponding to each power forecast model.

[0123] It is understandable that the target clustered electricity set can represent the set of electricity that wind turbines can generate under the target set of influencing factors in most cases. Therefore, when calculating the comprehensive reference wind power, the clustered wind power mean and the number of statistical clusters are used as weights. The smaller the absolute difference between the clustered wind power and the clustered wind power mean, the greater the weight corresponding to the clustered wind power. When the number of statistical clusters is larger, the weight corresponding to the clustered wind power set is larger, so as to improve the accuracy of the obtained comprehensive reference wind power.

[0124] S3. Obtain the predicted power consumption of the initial microgrid based on the predicted segmented time, calculate the difference between the comprehensive predicted power generation and the predicted power consumption, and obtain the analyzed power.

[0125] It should be explained that the predicted power consumption refers to the power required by the initial microgrid within the predicted segment time. Optionally, a pre-trained neural network model is used to predict the power required by the initial microgrid to obtain the predicted power consumption. Other technologies can achieve the same effect, which will not be repeated here.

[0126] S4. Obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value.

[0127] It should be explained that the battery SOH value is an indicator that measures the battery performance relative to its new state, usually expressed as a percentage, reflecting the overall health of the battery. It is an existing technology. The battery SOC value represents the ratio of the remaining power of the battery to its rated capacity, usually expressed as a percentage. The battery rated power here is the rated output power of the battery, and the remaining storage capacity refers to the available power currently stored in the energy storage battery. Optionally, the energy storage battery parameters can be obtained through the BMS system. Other technologies can achieve the same effect, which will not be repeated here.

[0128] S5. If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group.

[0129] Furthermore, the step of retrieving a target storage battery group from the energy storage battery group by using the energy storage battery parameter set includes:

[0130] Obtaining a monitoring node time sequence of each energy storage battery in the energy storage battery pack, wherein the monitoring node time sequence includes multiple monitoring nodes, and the monitoring nodes include monitoring internal resistance and monitoring temperature;

[0131] Associating a monitoring node with a time corresponding to the monitoring node to obtain a fitting data node, summarizing the fitting data nodes to obtain a fitting data node set, mapping the fitting data nodes in the fitting data node set to a pre-constructed reference coordinate system to obtain a mapping coordinate set, obtaining a fitting curve using a pre-constructed curve fitting method and the mapping coordinate set, summarizing the fitting curves to obtain a fitting curve set, wherein the fitting curve set includes multiple fitting curves, and the fitting curves correspond one-to-one to the energy storage batteries;

[0132] For each fitted curve in the fitted curve set, the following operations are performed:

[0133] Identify the minimum curvature radius in the fitting curve, calculate the product of the minimum curvature radius and the preset proportion coefficient to obtain the identification range radius, and construct a fitting screening range with the fitting curve as the central axis and the identification range radius as the radius;

[0134] Counting the number of fitting curves in the fitting screening range in the fitting curve set to obtain a range curve number, and summarizing the range curve number to obtain a range curve number set;

[0135] Calculate the reference projection distance of the fitting curve. The calculation formula is as follows:

[0136] ;

[0137] in, represents the reference projection distance, Indicates that there are a total of monitoring nodes, Indicates monitoring internal resistance, Indicates monitoring temperature;

[0138] Summarizing the reference projection distances to obtain a reference projection distance set, sorting the reference projection distances in the reference projection distance set in ascending order of the reference projection distances to obtain a reference projection distance sequence, obtaining a range curve quantity sequence using the range curve quantity set and the reference projection distance sequence, and identifying a first curve quantity in the range curve quantity sequence based on a preset screening battery threshold, wherein the first curve quantity is the first range curve quantity in the range curve quantity sequence that is greater than or equal to the screening battery threshold;

[0139] Extracting a screening battery parameter set from the energy storage battery parameter set using the first curve quantity, wherein the screening battery parameter set includes a plurality of screening battery parameters, and the screening battery parameters include a battery SOH value, a remaining storage capacity, and a battery SOC value;

[0140] Clustering the screening battery parameter sets using a clustering algorithm to obtain one or more distance screening parameter sets, and counting the number of distance screening parameters in each of the one or more distance screening parameter sets to obtain one or more distance screening quantities;

[0141] If there is a distance screening number greater than or equal to the screening battery threshold in one or more distance screening numbers, then randomly extract energy storage batteries with the same number as the screening battery threshold from the multiple energy storage batteries corresponding to the distance screening number to obtain a target storage battery group;

[0142] Otherwise, the first curve quantity is skipped in the range curve quantity sequence, and the process returns to the step of confirming the first curve quantity in the range curve quantity sequence based on the preset battery screening threshold, until the target storage battery pack is obtained.

[0143] It should be noted that the storage power threshold is a pre-set value used to determine whether the analyzed power needs to be stored in the storage battery pack. The technology for actively balancing the target storage battery pack is currently available and will not be further described here. When calculating the reference projection distance, only the monitored internal resistance and temperature data are used.

[0144] Furthermore, the monitoring node is a node used to characterize the changes in temperature and internal resistance of the energy storage battery over time when it is working. Optionally, the monitoring node timing is obtained by the charge and discharge cycle test method. The same technical effect can be achieved by adopting other methods, which will not be repeated here. Monitoring internal resistance refers to the internal resistance of the energy storage battery, and monitoring temperature refers to the temperature of the energy storage battery. Generally speaking, the time for monitoring internal resistance corresponding to different monitoring nodes is different, and the time for monitoring temperature corresponding to different monitoring nodes is also different. Optionally, a Cartesian coordinate system is used as a reference coordinate system. Optionally, a polynomial fitting method is used as the curve fitting method. Using the first curve number to extract a screening battery parameter set from the energy storage battery parameter set includes: after confirming multiple energy storage batteries in the energy storage battery group according to the first curve number, extracting the corresponding screening battery parameters of the multiple energy storage batteries in the energy storage battery parameter set according to the confirmed multiple energy storage batteries to obtain a screening battery parameter set.

[0145] It should be explained that the fitting curve is a curve used to characterize the internal resistance of the battery and the temperature of the battery over time. Optionally, the x-axis in the Cartesian coordinate system is used as time, the y-axis is used to monitor the internal resistance, and the z-axis is used to monitor the temperature. The monitoring nodes in the monitoring node time series are mapped to the Cartesian coordinate system, and the polynomial fitting method is used to fit the monitoring nodes mapped to the Cartesian coordinate system into a curve. The minimum radius of curvature refers to the smallest radius of curvature. The technology for identifying the minimum radius of curvature in the fitting curve is an existing technology and will not be repeated here. The proportion coefficient refers to a numerical value set artificially, which is used to construct a fitting screening range. Optionally, the proportion coefficient is set to 0.8. Other technologies can achieve the same effect and will not be repeated here.

[0146] Furthermore, a fitting screening range constructed with the fitting curve as the central axis and the identification range radius as the radius is a pipeline range that changes with the fitting curve, and the cross-section of any pipe within the pipeline range is a circular cross-section. When a fitting curve exists within the fitting screening range, it indicates that the operating conditions of the energy storage battery are the same as those of the energy storage battery corresponding to the fitting curve. Here, the operating conditions refer to the internal resistance and temperature of the energy storage battery.

[0147] It should be explained that a smaller reference projection distance indicates a better operating condition for that type of energy storage battery. Using the range curve quantity set and the reference projection distance sequence to obtain the range curve quantity sequence means sorting the range curves in the range curve quantity set according to the order of the reference projection distances in the reference projection distance sequence to obtain the range curve quantity sequence. Generally speaking, energy storage batteries within the same fitting screening range have similar operating conditions.

[0148] It is understood that the battery screening threshold refers to the number of energy storage batteries used for charging simultaneously. Optionally, the battery screening threshold can be obtained by manually setting it. Other techniques can achieve the same effect and are not described here. The purpose of setting the battery screening threshold is to prevent overcharging of some batteries.

[0149] Furthermore, the method of clustering the screened battery parameter set using a clustering algorithm is the same as the method of obtaining the target reference wind power set using the predicted impact environment data and the pre-built clustering algorithm and the reference impact environment data corresponding to the initial historical data in the initial historical data set, and will not be repeated here.

[0150] It is understandable that the fitting curves within the same fitting screening range have the same operating conditions during operation, but their battery SOH values, remaining storage capacity, and battery SOC values ​​are not exactly the same. Therefore, in the present invention, the energy storage batteries in the energy storage battery pack are again screened by clustering the screening battery parameter set, so as to screen out energy storage batteries with similar battery SOH values, remaining storage capacity, and battery SOC values. Furthermore, it is ensured that the battery SOH values, operating resistance, operating temperature, remaining storage capacity, and battery SOC values ​​of all energy storage batteries in the target storage battery pack are similar, thereby improving the life of the energy storage batteries in the target storage battery pack and reducing the risk of charging the energy storage batteries. The step of skipping the first number of curves in the range curve number sequence and returning to the preset screening battery threshold, and confirming the first number of curves in the range curve number sequence, refers to skipping the first number of first curves confirmed in the range curve number sequence, and confirming the second number of first curves, the third number of first curves, and so on, in order of the order of the range curve numbers in the range curve number sequence, until the target storage battery pack is confirmed.

[0151] S6. If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack.

[0152] Furthermore, the method of searching the energy storage battery pack by analyzing the power and the energy storage battery parameter set, if a preset target energy supply battery pack is found in the energy storage battery pack, includes:

[0153] The predicted segment time is used to obtain a battery power threshold, and the number of energy storage batteries is calculated according to the battery power threshold and the battery rated power;

[0154] The energy storage batteries in the energy storage battery group are sorted in descending order of remaining storage power to obtain an energy storage battery sequence. The number of energy storage batteries is used to obtain a search sliding window. According to the search sliding window, search storage power groups are sequentially extracted from the energy storage battery sequence, and the following operations are performed on the extracted search storage power groups:

[0155] Calculate the sum of the retrieved stored power in the retrieved stored power group to obtain the comprehensive detected power, and compare the comprehensive detected power with the analyzed power;

[0156] If the comprehensive detected power is greater than or equal to the analyzed power, the process returns to the step of sequentially extracting the search and storage power groups from the energy storage battery sequence until the comprehensive detected power is less than the analyzed power, thereby obtaining multiple target search and storage power groups, wherein the comprehensive detected power corresponding to the target search and storage power groups is greater than or equal to the analyzed power, and the following operation is performed on each of the multiple target search and storage power groups:

[0157] Calculating the variance of the retrieval storage power in the target retrieval storage power group to obtain a comprehensive detection variance, summarizing the comprehensive detection variance to obtain a comprehensive detection variance set, and using the comprehensive detection variance set to identify the target energy supply battery group, wherein the target energy supply battery group is the multiple energy storage batteries corresponding to the smallest comprehensive detection variance in the comprehensive detection variance set;

[0158] Otherwise, it is confirmed that the target energy supply battery pack does not exist in the energy storage battery pack.

[0159] It should be noted that when the analyzed power level is less than the supply power threshold, it indicates that the initial microgrid needs to be powered by the energy storage batteries in the energy storage battery pack or by a fuel generator. The battery power threshold refers to the power required by the energy storage battery pack to supply the initial microgrid. Optionally, multiple power values ​​consumed by the initial microgrid are obtained by predicting the segment time, and the maximum value of these multiple power values ​​is used as the battery power threshold.

[0160] It is understandable that the size of the retrieval sliding window is the same as the number of energy storage batteries. For example, if the number of energy storage batteries is 5, a sliding window with a window size of 5 is used as the retrieval sliding window. Generally speaking, when using a sliding window, it is also necessary to set the sliding step size of the sliding window. For example, there are 6 remaining storage capacities in the energy storage battery sequence, namely: 9, 8, 6, 4, 3 and 2, and the number of energy storage batteries is 3, then the analysis power is 15. Using the retrieval sliding window, 4 retrieval storage power groups can be extracted from the energy storage battery sequence, and the 4 retrieval storage power groups are respectively: 9, 8 and 6, 8, 6 and 4, 6, 4 and 3, 4, 3 and 2. The comprehensive detection power is calculated based on the retrieval storage power group, and after comparing it with the analysis power, the third and fourth retrieval storage power groups are eliminated, that is, two target retrieval storage power groups are retained, namely: 9, 8 and 6, 8, 6 and 4.

[0161] Furthermore, the number of energy storage batteries is calculated based on the battery power threshold and the battery rated power, and the calculation formula is as follows:

[0162] ;

[0163] in, Indicates the number of energy storage batteries, Indicates the battery power threshold, Indicates the rated power of the battery, Indicates rounding up.

[0164] S7. If the target energy supply battery group cannot be retrieved from the energy storage battery group, the target fuel power supply is obtained by using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the process returns to the step of obtaining the energy storage battery parameter set of the energy storage battery group. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group.

[0165] It should be explained that the method of obtaining the target fuel power supply using the predicted power consumption and the fuel generator set includes:

[0166] Obtain the rated power generation of each fuel generator in the fuel generator set to obtain a rated power generation set;

[0167] The generator set screening formula is constructed based on the rated power generation set and the battery power threshold. The generator set screening formula is as follows:

[0168] ;

[0169] in, Indicates the first of many fuel generators Number of fuel-fired generators, Indicates the rated power generation concentration Rated power generation, Indicates the total use A fuel-fired generator;

[0170] In combination, using the generator set screening formula, multiple initial fuel-fired generator sets are extracted from the fuel-fired generator set. The energy consumption corresponding to each of the multiple initial fuel-fired generator sets is obtained to obtain an initial energy consumption set. The initial fuel-fired generator set corresponding to the minimum initial energy consumption is identified in the initial energy consumption set to obtain the target fuel-fired generator set.

[0171] The target fuel power generation capacity is obtained by obtaining the target fuel power generation capacity, multiplying the target power generation capacity by the predicted segment time, and subtracting the product from the predicted power consumption capacity to obtain the target fuel power supply capacity.

[0172] It should be explained that the rated power generation refers to the maximum power that a fuel generator can continuously and stably output under rated conditions. Obtaining the energy consumption corresponding to each of the multiple initial fuel generator sets refers to the energy consumption required by the initial fuel generator in the predicted segment time obtained by combining the fixed energy consumption of the initial fuel generator in the initial fuel generator set and the unit energy consumption that changes with time. This is a prior art and will not be described in detail here. The target power generation refers to the sum of the power generation corresponding to the target fuel generator in the target fuel generator set. The product of the target power generation and the predicted segment time is used to characterize the amount of electricity that can be generated by the target power generation within the predicted segment time. The difference between this amount and the predicted power consumption refers to the amount of electricity remaining after supplying the initial microgrid, that is, the target fuel power supply.

[0173] Furthermore, the target fuel power is used as the analysis power to determine whether the target fuel power satisfies the storage conditions, that is, whether it is greater than or equal to the storage power threshold, so as to achieve the storage of the target fuel power. Generally speaking, the initial microgrid generally also includes a temporary power supply unit, such as a supercapacitor. When storing the target fuel power, it is also possible to consider storing the target fuel power in a temporary power supply unit, and the method of storing the target fuel power in the temporary power supply unit is the same as the method of storing the target fuel power in the target storage battery pack, which will not be repeated here. For example, when the analysis power is greater than zero and the analysis power is less than the storage power threshold or the analysis power is less than zero and the analysis power is greater than or equal to the supply power threshold, the temporary power supply unit can be used to supply power or the temporary power supply unit can be used to store power.

[0174] In order to solve the problems described in the background technology, the present invention uses pre-confirmed predicted segment time, wind turbine set and solar turbine set to obtain comprehensive predicted power generation. It can be seen that the present invention takes into account the amount of electricity that can be generated by the wind turbine set and the solar turbine set in the initial microgrid before realizing energy storage optimization of the initial microgrid, that is, the comprehensive predicted power generation. When obtaining the comprehensive predicted power generation, the comprehensive reference wind power is calculated by screening historical data and using a prediction model for prediction. When calculating the comprehensive reference wind power, different weight values ​​are set in combination with different characteristics to improve the accuracy of the calculated comprehensive reference wind power, thereby improving the accuracy of energy storage optimization of the microgrid. In the present invention, if the analyzed power is greater than or equal to a preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced and charged to obtain an updated storage battery group. If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery group. If a preset target energy supply battery group is retrieved from the energy storage battery group, the target energy supply battery group is used to supply power to obtain an updated power supply battery group. It can be seen that before charging and discharging the energy storage battery pack, the present invention also considers analyzing whether the power is greater than a preset storage power threshold for judging the need to charge the energy storage battery pack, and is less than a preset supply power threshold for using the energy storage battery pack for energy supply. Before charging the energy storage batteries in the energy storage battery pack, the present invention also considers the similarity of the energy storage batteries at different time nodes and sets the number of energy storage batteries that need to be charged simultaneously. Thus, the phenomenon of overcharging when charging some energy storage batteries in the target energy storage battery pack is avoided, and the safety of charging the target energy storage battery pack is improved. When obtaining the target energy supply battery pack for power supply, the power and power required by the initial microgrid in the predicted segment time are considered, and the target energy supply battery pack with similar parameters is retrieved, thereby avoiding the phenomenon of over-discharge of some energy storage batteries in the target energy supply battery pack. If the present invention cannot retrieve the target energy supply battery group from the energy storage battery group, the target fuel power supply is obtained using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the step of obtaining the energy storage battery parameter set of the energy storage battery group is returned. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group. It can be seen that when there is no target energy supply battery group that can supply energy in the energy storage battery group, the present invention also considers the use of the fuel generator set for power supply. When the fuel generator set is used for power supply, the fuel generator set may have excess power. Therefore, the target fuel power supply is used as the analysis power, and the step of obtaining the energy storage battery parameter set of the energy storage battery group is returned to achieve the optimization of the energy storage of the microgrid. Therefore, the present invention can improve the accuracy and intelligent realization of the energy storage optimization of the microgrid.

[0175] like Figure 2 , which is a functional module diagram of a microgrid energy storage optimization system based on big data analysis provided by one embodiment of the present invention.

[0176] The microgrid energy storage optimization system 100 based on big data analysis described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the microgrid energy storage optimization system 100 based on big data analysis can include an initial microgrid confirmation module 101, a microgrid parameter prediction module 102, an energy storage solution construction module 103, and an energy storage solution optimization module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function. These are stored in the electronic device's memory.

[0177] The initial microgrid confirmation module 101 is configured to receive an energy storage optimization instruction and, based on the energy storage optimization instruction, confirm an initial microgrid for energy storage optimization, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes multiple wind turbines, the solar power generator set includes multiple solar power generators, the energy storage battery group includes multiple energy storage batteries, and the fuel-powered generator set includes multiple fuel-powered generators;

[0178] The microgrid parameter prediction module 102 is used to obtain a comprehensive predicted power generation using the pre-confirmed prediction segment time, the wind turbine generator set and the solar turbine generator set;

[0179] Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power;

[0180] The energy storage solution building module 103 is used to obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value;

[0181] If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group;

[0182] If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack;

[0183] The energy storage solution optimization module 104 is configured to, if a target energy supply battery pack cannot be retrieved from the energy storage battery pack, use the predicted power consumption and the fuel generator set to obtain a target fuel supply capacity, use the target fuel supply capacity as the analysis power capacity, return to the step of obtaining the energy storage battery parameter set of the energy storage battery pack, and optimize the energy storage of the initial microgrid based on the updated storage battery pack or the updated power supply battery pack.

[0184] In detail, each module in the microgrid energy storage optimization system 100 based on big data analysis in the embodiment of the present invention adopts the same Figure 1 The same technical means are used as the microgrid energy storage optimization method based on big data analysis described in the previous section and can produce the same technical effects, so they will not be repeated here.

[0185] like Figure 3 , which is a structural diagram of an electronic device for implementing a microgrid energy storage optimization method based on big data analysis provided by an embodiment of the present invention.

[0186] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a microgrid energy storage optimization method program based on big data analysis.

[0187] The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard drive, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard drive of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 11 includes both the internal storage unit of the electronic device 1 and external storage devices. The memory 11 can be used not only to store application software installed in the electronic device 1 and various data, such as the code of a microgrid energy storage optimization method based on big data analysis, but also to temporarily store data that has been output or is about to be output.

[0188] In some embodiments, the processor 10 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (control unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a microgrid energy storage optimization method program based on big data analysis) and accesses data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0189] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0190] Figure 3 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the electronic device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0191] For example, although not shown, the electronic device 1 may further include a power supply (e.g., a battery) to power various components. Preferably, the power supply may be logically connected to the at least one processor 10 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management via the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not further described here.

[0192] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0193] Optionally, the electronic device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or a display unit, and is used to display information processed by the electronic device 1 and to display a visual user interface.

[0194] The microgrid energy storage optimization method program based on big data analysis stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0195] receiving an energy storage optimization instruction, and determining an initial microgrid for energy storage optimization based on the energy storage optimization instruction, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes a plurality of wind turbines, the solar power generator set includes a plurality of solar power generators, the energy storage battery group includes a plurality of energy storage batteries, and the fuel-powered generator set includes a plurality of fuel-powered generators;

[0196] Obtaining a comprehensive forecast of power generation using pre-confirmed forecast segment times, wind turbine generator sets, and solar turbine generator sets;

[0197] Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power;

[0198] Obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value;

[0199] If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group;

[0200] If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack;

[0201] If the target energy supply battery group cannot be retrieved from the energy storage battery group, the target fuel power supply is obtained using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the process returns to the step of obtaining the energy storage battery parameter set of the energy storage battery group. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group.

[0202] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0203] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0204] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0205] receiving an energy storage optimization instruction, and determining an initial microgrid for energy storage optimization based on the energy storage optimization instruction, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes a plurality of wind turbines, the solar power generator set includes a plurality of solar power generators, the energy storage battery group includes a plurality of energy storage batteries, and the fuel-powered generator set includes a plurality of fuel-powered generators;

[0206] Obtaining a comprehensive forecast of power generation using pre-confirmed forecast segment times, wind turbine generator sets, and solar turbine generator sets;

[0207] Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power;

[0208] Obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value;

[0209] If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group;

[0210] If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack;

[0211] If the target energy supply battery group cannot be retrieved from the energy storage battery group, the target fuel power supply is obtained using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the process returns to the step of obtaining the energy storage battery parameter set of the energy storage battery group. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group.

[0212] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only exemplary, and actual implementations may have other division methods.

[0213] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0214] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0215] 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 present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A microgrid energy storage optimization method based on big data analysis, characterized in that: The method comprises: receiving an energy storage optimization instruction, and determining an initial microgrid for energy storage optimization based on the energy storage optimization instruction, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes a plurality of wind turbines, the solar power generator set includes a plurality of solar power generators, the energy storage battery group includes a plurality of energy storage batteries, and the fuel-powered generator set includes a plurality of fuel-powered generators; Obtaining a comprehensive forecast of power generation using pre-confirmed forecast segment times, wind turbine generator sets, and solar turbine generator sets; Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power; Obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value; If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group; If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack; If the target energy supply battery group cannot be retrieved from the energy storage battery group, the target fuel power supply is obtained using the predicted power consumption and the fuel generator set. The target fuel power supply is used as the analysis power, and the process returns to the step of obtaining the energy storage battery parameter set of the energy storage battery group. The energy storage optimization of the initial microgrid is achieved based on the updated storage battery group or the updated power supply battery group.

2. The microgrid energy storage optimization method based on big data analysis according to claim 1, characterized in that: The method of obtaining the comprehensive predicted power generation using the pre-confirmed predicted segment time, the wind turbine generator set, and the solar turbine generator set includes: Perform the following operations for each wind turbine in the wind turbine set: Obtain an initial set of influencing factors of wind turbines, and use a pre-built principal component analysis method to identify a target set of influencing factors from the initial set of influencing factors; Acquiring an initial historical data set based on the target impact factor set, wherein the initial historical data set includes a plurality of initial historical data, wherein the initial historical data includes reference impact environment data and initial reference wind power; Obtaining predicted impact environment data based on the predicted segment time, wind turbines, and target impact factor set, and obtaining a target reference wind power set using the predicted impact environment data, a pre-built clustering algorithm, and reference impact environment data corresponding to the initial historical data in the initial historical data set; Acquire an electricity prediction model set for predicting power generation, and acquire a reference predicted wind power set based on the electricity prediction model set and the wind turbine; Associating the target reference wind power set and the reference predicted wind power set to obtain a wind power generation reference node; Summarizing the wind power generation reference nodes to obtain a wind power generation reference node set, wherein the wind power generation reference node set includes multiple wind power generation reference nodes, and the wind power generation reference nodes correspond to wind turbines one by one; The comprehensive forecast power generation is obtained using the set of wind power reference nodes and the set of solar generators.

3. The microgrid energy storage optimization method based on big data analysis according to claim 2, characterized in that: The method of obtaining a comprehensive predicted power generation using a set of wind power generation reference nodes and a set of solar power generators includes: For each solar generator in the solar generator cluster, the following operations are performed: Obtaining a photovoltaic influence factor set of a solar generator, and using the photovoltaic influence factor set to obtain a photovoltaic power generation reference node set, wherein the photovoltaic power generation reference node set includes a plurality of photovoltaic power generation reference nodes, and the photovoltaic power generation reference nodes include a target reference photovoltaic power set and a reference predicted photovoltaic power set; Obtaining reference accuracy nodes of the power prediction models in the power prediction model set to obtain a reference accuracy node set, wherein the reference accuracy nodes include the number of training data and the number of recognition data; The comprehensive predicted power generation is calculated based on the wind power generation reference node set, the reference accuracy node set and the photovoltaic power generation reference node set.

4. The microgrid energy storage optimization method based on big data analysis according to claim 3, characterized in that: The calculating of the comprehensive predicted power generation based on the wind power generation reference node set, the reference accuracy node set and the photovoltaic power generation reference node set includes: Clustering the target reference wind power in the wind power reference node set to obtain multiple clustered wind power sets, counting the amount of wind power in each of the multiple clustered wind power sets to obtain multiple statistical cluster quantities, and using the multiple statistical cluster quantities to identify a target clustered power set from the multiple clustered wind power sets, wherein the target clustered power set is the clustered wind power set corresponding to the largest statistical cluster quantity among the multiple statistical cluster quantities; Calculate the mean value of the target cluster electricity in the target cluster electricity set to obtain the cluster wind power mean value; The comprehensive reference wind power is calculated based on the clustered wind power mean and the reference accuracy node set. The comprehensive reference photovoltaic power is obtained based on the photovoltaic power generation reference node set. The sum of the comprehensive reference wind power and the comprehensive reference photovoltaic power is calculated to obtain the comprehensive predicted power generation.

5. The microgrid energy storage optimization method based on big data analysis according to claim 4, characterized in that: The comprehensive reference wind power is calculated based on the clustered wind power mean and the reference accuracy node set. The calculation formula is as follows: ； in, represents the comprehensive reference wind power, are all preset coefficients. Indicates the total number of clustered wind power sets indivual, Respectively represent the number of statistical clusters The number of statistical clusters and the The number of statistical clusters, Indicates that multiple clusters of wind power are concentrated The contribution of a clustered wind power set is equivalent to represents the clustered wind power mean value, Respectively represent Cluster wind power concentration Cluster wind power and Clustered wind power, Indicates the The total amount of wind power in clusters is Clustered wind power, Indicates the reference accuracy node set The number of training data corresponding to the reference accuracy node, Indicates the total number of reference accuracy nodes Reference accuracy nodes, Indicates the reference accuracy node set The number of recognition data corresponding to the reference accuracy node, Indicates the first The reference predicted wind power corresponding to each power forecast model.

6. The microgrid energy storage optimization method based on big data analysis according to claim 5, characterized in that: The method of retrieving a target storage battery group from the energy storage battery group by using the energy storage battery parameter set includes: Obtaining a monitoring node time sequence of each energy storage battery in the energy storage battery pack, wherein the monitoring node time sequence includes multiple monitoring nodes, and the monitoring nodes include monitoring internal resistance and monitoring temperature; Associating a monitoring node with a time corresponding to the monitoring node to obtain a fitting data node, summarizing the fitting data nodes to obtain a fitting data node set, mapping the fitting data nodes in the fitting data node set to a pre-constructed reference coordinate system to obtain a mapping coordinate set, obtaining a fitting curve using a pre-constructed curve fitting method and the mapping coordinate set, summarizing the fitting curves to obtain a fitting curve set, wherein the fitting curve set includes multiple fitting curves, and the fitting curves correspond one-to-one to the energy storage batteries; For each fitted curve in the fitted curve set, the following operations are performed: Identify the minimum curvature radius in the fitting curve, calculate the product of the minimum curvature radius and the preset proportion coefficient to obtain the identification range radius, and construct a fitting screening range with the fitting curve as the central axis and the identification range radius as the radius; Counting the number of fitting curves in the fitting screening range in the fitting curve set to obtain a range curve number, and summarizing the range curve number to obtain a range curve number set; Calculate the reference projection distance of the fitting curve. The calculation formula is as follows: ； in, represents the reference projection distance, Indicates that there are a total of monitoring nodes, Indicates monitoring internal resistance, Indicates monitoring temperature; Summarizing the reference projection distances to obtain a reference projection distance set, sorting the reference projection distances in the reference projection distance set in ascending order of the reference projection distances to obtain a reference projection distance sequence, obtaining a range curve quantity sequence using the range curve quantity set and the reference projection distance sequence, and identifying a first curve quantity in the range curve quantity sequence based on a preset screening battery threshold, wherein the first curve quantity is the first range curve quantity in the range curve quantity sequence that is greater than or equal to the screening battery threshold; Extracting a screening battery parameter set from the energy storage battery parameter set using the first curve quantity, wherein the screening battery parameter set includes a plurality of screening battery parameters, and the screening battery parameters include a battery SOH value, a remaining storage capacity, and a battery SOC value; Clustering the screening battery parameter sets using a clustering algorithm to obtain one or more distance screening parameter sets, and counting the number of distance screening parameters in each of the one or more distance screening parameter sets to obtain one or more distance screening quantities; If there is a distance screening number greater than or equal to the screening battery threshold in one or more distance screening numbers, then randomly extract energy storage batteries with the same number as the screening battery threshold from the multiple energy storage batteries corresponding to the distance screening number to obtain a target storage battery group; Otherwise, the first curve quantity is skipped in the range curve quantity sequence, and the process returns to the step of confirming the first curve quantity in the range curve quantity sequence based on the preset battery screening threshold, until the target storage battery pack is obtained.

7. The microgrid energy storage optimization method based on big data analysis according to claim 6, characterized in that: The method of searching the energy storage battery pack by analyzing the power and the energy storage battery parameter set, and finding a preset target energy supply battery pack in the energy storage battery pack, includes: The predicted segment time is used to obtain a battery power threshold, and the number of energy storage batteries is calculated according to the battery power threshold and the battery rated power; The energy storage batteries in the energy storage battery group are sorted in descending order of remaining storage power to obtain an energy storage battery sequence. The number of energy storage batteries is used to obtain a search sliding window. According to the search sliding window, search storage power groups are sequentially extracted from the energy storage battery sequence, and the following operations are performed on the extracted search storage power groups: Calculate the sum of the retrieved stored power in the retrieved stored power group to obtain the comprehensive detected power, and compare the comprehensive detected power with the analyzed power; If the comprehensive detected power is greater than or equal to the analyzed power, the process returns to the step of sequentially extracting the search and storage power groups from the energy storage battery sequence until the comprehensive detected power is less than the analyzed power, thereby obtaining multiple target search and storage power groups, wherein the comprehensive detected power corresponding to the target search and storage power groups is greater than or equal to the analyzed power, and the following operation is performed on each of the multiple target search and storage power groups: Calculating the variance of the retrieval storage power in the target retrieval storage power group to obtain a comprehensive detection variance, summarizing the comprehensive detection variance to obtain a comprehensive detection variance set, and using the comprehensive detection variance set to identify the target energy supply battery group, wherein the target energy supply battery group is the multiple energy storage batteries corresponding to the smallest comprehensive detection variance in the comprehensive detection variance set; Otherwise, it is confirmed that the target energy supply battery pack does not exist in the energy storage battery pack.

8. The microgrid energy storage optimization method based on big data analysis according to claim 7, characterized in that: The calculation formula for calculating the number of energy storage batteries based on the battery power threshold and the battery rated power is as follows: ； in, Indicates the number of energy storage batteries, Indicates the battery power threshold, Indicates the rated power of the battery, Indicates rounding up.

9. The microgrid energy storage optimization method based on big data analysis according to claim 8, characterized in that: The method of obtaining a target fuel power supply using the predicted power consumption and the fuel generator set includes: Obtain the rated power generation of each fuel generator in the fuel generator set to obtain a rated power generation set; The generator set screening formula is constructed based on the rated power generation set and the battery power threshold. The generator set screening formula is as follows: ； in, Indicates the first of many fuel generators Number of fuel-fired generators, Indicates the rated power generation concentration Rated power generation, Indicates the total use A fuel-fired generator; In combination, using the generator set screening formula, multiple initial fuel-fired generator sets are extracted from the fuel-fired generator set. The energy consumption corresponding to each of the multiple initial fuel-fired generator sets is obtained to obtain an initial energy consumption set. The initial fuel-fired generator set corresponding to the minimum initial energy consumption is identified in the initial energy consumption set to obtain the target fuel-fired generator set. The target fuel power generation capacity is obtained by obtaining the target fuel power generation capacity, multiplying the target power generation capacity by the predicted segment time, and subtracting the product from the predicted power consumption capacity to obtain the target fuel power supply capacity.

10. A microgrid energy storage optimization system based on big data analysis, characterized in that: The system comprises: an initial microgrid confirmation module, configured to receive an energy storage optimization instruction and, based on the energy storage optimization instruction, confirm an initial microgrid for energy storage optimization, wherein the initial microgrid includes a wind turbine generator set, a fuel-powered generator set, a solar power generator set, and an energy storage battery group, wherein the wind turbine generator set includes a plurality of wind turbines, the solar power generator set includes a plurality of solar power generators, the energy storage battery group includes a plurality of energy storage batteries, and the fuel-powered generator set includes a plurality of fuel-powered generators; A microgrid parameter prediction module is used to obtain a comprehensive predicted power generation using a pre-confirmed prediction segment time, a set of wind turbine generators, and a set of solar generators; Obtaining the predicted power consumption of the initial microgrid based on the predicted segmented time, calculating the difference between the comprehensive predicted power generation and the predicted power consumption to obtain the analyzed power; An energy storage solution building module is used to obtain an energy storage battery parameter set of the energy storage battery group, wherein the energy storage battery parameter set includes multiple energy storage battery parameters, and the energy storage battery parameters correspond one-to-one to the energy storage batteries. The energy storage battery parameters include: battery SOH value, remaining storage capacity, battery rated power and battery SOC value; If the analyzed power is greater than or equal to the preset storage power threshold, the target storage battery group is retrieved from the energy storage battery group using the energy storage battery parameter set, and the target storage battery group is actively balanced charged to obtain an updated storage battery group; If the analyzed power is less than the preset supply power threshold, the analyzed power and the energy storage battery parameter set are used to search in the energy storage battery pack. If a preset target energy supply battery pack is retrieved in the energy storage battery pack, the target energy supply battery pack is used to supply power to obtain an updated power supply battery pack; The energy storage solution optimization module is used to use the predicted power consumption and the fuel generator set to obtain the target fuel power supply if the target energy supply battery group cannot be retrieved from the energy storage battery group, use the target fuel power supply as the analysis power, return to the step of obtaining the energy storage battery parameter set of the energy storage battery group, and realize energy storage optimization of the initial microgrid based on the updated storage battery group or the updated power supply battery group.

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