Distributed energy storage converter optimization method and system based on big data analysis

Abstract

The invention relates to the technical field of energy storage converters, in particular to a distributed energy storage converter optimization method and system based on big data analysis. According to the method, the load fluctuation can be predicted more accurately by combining meteorological and load historical data, the control parameters of the energy storage converter can be dynamically adjusted according to the obtained load fluctuation influence rate to adapt to the load change, the control parameters can be dynamically adjusted under different operation conditions according to the real-time energy conversion efficiency data, and the load fluctuation can be predicted more accurately. By comprehensively considering the energy conversion efficiency, the error of load prediction can be reduced, the response capability of the system is improved, and by monitoring the load value and the demand response gain in real time and adjusting the charging and discharging strategy of the distributed energy storage converter, the dynamic response capability and stability of the system are improved, and the energy conversion efficiency is improved. And the energy use efficiency can be improved, the use of stored energy can be optimized by dynamically adjusting the charging amount, and the overall economical efficiency and stability of the system can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage converters, and in particular to a distributed energy storage converter optimization method and system based on big data analysis. Background Art

[0002] Distributed energy storage converter is a power electronic device used to connect and manage distributed energy and energy storage systems. Its main function is to realize the conversion, control and optimization of electric energy so as to store and release electric energy efficiently. Its main functions include electric energy conversion, energy management, power regulation and intelligent control. It can be used in energy storage systems of homes or commercial buildings. Combined with solar power generation, it can increase the proportion of self-use electricity. Distributed energy storage converter plays a key role in realizing the efficient use of renewable energy and enhancing the flexibility and stability of power systems. With the popularization of distributed energy, its importance is becoming increasingly prominent.

[0003] Existing distributed energy storage inverters adopt a control strategy based on fixed rules. For example, according to the priority of different loads, the needs of important loads are given priority when the power supply is insufficient, and corresponding charging and discharging are performed. However, the above method fails to dynamically adjust according to real-time data and environmental changes, and lacks an effective demand response strategy. This may result in the energy storage inverter being unable to quickly adjust the charging and discharging strategy to maintain system balance when the load peaks or fluctuates rapidly. Summary of the invention

[0004] The main purpose of the present invention is to provide a distributed energy storage converter optimization method based on big data analysis, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes a distributed energy storage converter optimization method based on big data analysis, comprising:

[0006] Acquire historical environmental characteristic information and historical operating state characteristic information of the distributed energy storage converter, wherein the historical operating state characteristic information includes historical electrical characteristic information and historical energy storage characteristic information;

[0007] Acquire historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, and acquire a load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information;

[0008] Obtaining energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information, and obtaining a load forecast value according to the energy conversion efficiency and the load fluctuation impact rate;

[0009] Obtaining a demand response gain according to the load forecast value;

[0010] Acquire the real-time environmental characteristic information and the real-time operating state characteristic information of the distributed energy storage converter after adjustment, and acquire the real-time load value according to the real-time environmental characteristic information and the real-time operating state characteristic information;

[0011] The charging and discharging strategy of the distributed energy storage converter is adjusted according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced.

[0012] Preferably, the step of obtaining the load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information comprises:

[0013] Acquire multiple temperature characteristics, multiple humidity characteristics, and multiple wind speed characteristics of the historical meteorological characteristic information;

[0014] Obtaining a total meteorological variable according to a plurality of temperature characteristics, a plurality of humidity characteristics, and a plurality of wind speed characteristics, and obtaining a corresponding temperature influence coefficient, humidity influence coefficient, and wind speed influence coefficient according to each temperature characteristic, humidity characteristic, and wind speed characteristic and the total meteorological variable;

[0015] Acquire multiple power loads and multiple load change rates of the historical load characteristic information;

[0016] Obtaining a total load variable according to a plurality of power loads and a plurality of load change rates, and obtaining a corresponding power influence coefficient and a load change influence coefficient according to each power load, load change rate and the total load variable;

[0017] The load fluctuation influence rate is obtained according to multiple temperature influence coefficients, multiple humidity influence coefficients, multiple wind speed influence coefficients, multiple power influence coefficients and multiple load change influence coefficients.

[0018] Preferably, the step of acquiring energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information comprises:

[0019] Obtaining the charge and discharge rate and discharge depth of the historical energy storage characteristic information;

[0020] Obtaining the rated capacity of the distributed energy storage converter, and obtaining the charging time and the discharging time according to the rated capacity, the charging and discharging rate, and the discharge depth;

[0021] Obtaining harmonics, frequency, output power and input power of the historical electrical characteristic information;

[0022] Acquire input energy value according to the charging time, input power, harmonics and frequency;

[0023] The output energy value is calculated according to the discharge duration, discharge depth, output power, harmonics and frequency, wherein the calculation formula is:

[0024] ;

[0025] in, Represents the output energy value, Indicates the output power, Indicates the discharge time. Indicates frequency, represents harmonics, Indicates the depth of discharge;

[0026] The energy conversion efficiency is calculated according to the output energy value and the input energy value, wherein the calculation formula is:

[0027] ;

[0028] in, represents the energy conversion efficiency, Represents the output energy value, Indicates the input energy value.

[0029] Preferably, the step of obtaining a load prediction value according to the energy conversion efficiency and the load fluctuation influence rate comprises:

[0030] Acquire multiple load data of the distributed energy storage converter within a preset time period, and acquire an initial load average value according to the multiple load data;

[0031] Obtaining a first weight factor of the energy conversion efficiency;

[0032] Obtaining a second weight factor of the load fluctuation impact rate;

[0033] The initial load mean value, energy conversion efficiency, load fluctuation impact rate, first weight factor and second weight factor are input into the load forecasting model to obtain the load forecast value, wherein the load forecasting model is:

[0034] ;

[0035] in, represents the load forecast value, represents the initial load mean, represents the energy conversion efficiency, represents the load fluctuation impact rate, represents the first weight factor, represents the second weighting factor.

[0036] Preferably, the step of obtaining the demand response gain according to the reference load value and the load forecast value comprises:

[0037] Obtaining a benchmark load value of a distributed energy storage converter;

[0038] Obtaining a reference gain according to the reference load value and the load prediction value;

[0039] The demand response gain is obtained according to the reference gain, the reference load value and the load prediction value.

[0040] Preferably, the step of adjusting the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced includes:

[0041] Obtaining a real-time load value and a load forecast value, and determining whether the real-time load value is greater than the load forecast value;

[0042] If the real-time load value is greater than the load prediction value, the discharge efficiency coefficient of the distributed energy storage converter is obtained, and the discharge amount is obtained according to the discharge efficiency coefficient and the demand response gain;

[0043] Discharging and adjusting the distributed energy storage converter according to the discharge amount until the distributed energy storage converter system responds in a balanced manner;

[0044] If the real-time load value is not less than the load prediction value, the charging efficiency coefficient of the distributed energy storage converter is obtained, and the charging amount is obtained according to the charging efficiency coefficient and the demand response gain;

[0045] The charging of the distributed energy storage converter is adjusted according to the charging amount until the distributed energy storage converter system responds in a balanced manner.

[0046] The present invention also provides a distributed energy storage converter optimization system based on big data analysis, comprising:

[0047] A first acquisition module is used to acquire historical environmental characteristic information and historical operating state characteristic information of the distributed energy storage converter, wherein the historical operating state characteristic information includes historical electrical characteristic information and historical energy storage characteristic information;

[0048] A second acquisition module is used to acquire historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, and acquire a load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information;

[0049] A third acquisition module is used to acquire energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information, and to acquire a load prediction value according to the energy conversion efficiency and the load fluctuation influence rate;

[0050] A fourth acquisition module, configured to acquire a demand response gain according to the load prediction value;

[0051] A fifth acquisition module, used to acquire the real-time environmental characteristic information and the real-time operating state characteristic information of the distributed energy storage converter after adjustment, and acquire the real-time load value according to the real-time environmental characteristic information and the real-time operating state characteristic information;

[0052] The adjustment module is used to adjust the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced.

[0053] Preferably, the second acquisition module includes:

[0054] A first acquisition unit, used to acquire a plurality of temperature characteristics, a plurality of humidity characteristics and a plurality of wind speed characteristics of the historical meteorological characteristic information;

[0055] A second acquisition unit is used to acquire a total meteorological variable according to a plurality of temperature characteristics, a plurality of humidity characteristics and a plurality of wind speed characteristics, and acquire a corresponding temperature influence coefficient, humidity influence coefficient and wind speed influence coefficient according to each temperature characteristic, humidity characteristic and wind speed characteristic and the total meteorological variable;

[0056] A third acquisition unit, configured to acquire a plurality of power loads and a plurality of load change rates of the historical load characteristic information;

[0057] A fourth acquisition unit, configured to acquire a total load variable according to a plurality of power loads and a plurality of load change rates, and acquire a corresponding power influence coefficient and a load change influence coefficient according to each power load, load change rate and the total load variable;

[0058] The fifth acquisition unit is used to acquire the load fluctuation influence rate according to multiple temperature influence coefficients, multiple humidity influence coefficients, multiple wind speed influence coefficients, multiple power influence coefficients and multiple load change influence coefficients.

[0059] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned distributed energy storage converter optimization method based on big data analysis when executing the computer program.

[0060] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned distributed energy storage converter optimization method based on big data analysis are implemented.

[0061] The beneficial effects of the present invention are as follows: by combining meteorological and load historical data, the present invention can more accurately predict load fluctuations. According to the acquired load fluctuation impact rate, the control parameters of the energy storage inverter can be dynamically adjusted to adapt to load changes. By analyzing the historical operating status, the key factors affecting the energy conversion efficiency can be identified, the charging and discharging strategies can be optimized, and the overall efficiency of the energy storage system can be improved. According to the real-time energy conversion efficiency data, the control parameters can be dynamically adjusted under different operating conditions to ensure that the system operates within the optimal efficiency range. By real-time monitoring of load values ​​and demand response gains, and adjusting the charging and discharging strategies of the distributed energy storage inverters accordingly, not only the dynamic response capability and stability of the system are improved, but also it helps to improve energy utilization efficiency and reduce costs. Dynamic adjustment of the charging amount helps to optimize the use of energy storage and improve the overall economy and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The figure is a schematic diagram of a method flow according to an embodiment of the present invention.

[0063] Figure 2 FIG. 1 is a schematic diagram of a device structure according to an embodiment of the present invention.

[0064] Figure 3 The figure is a schematic diagram of the internal structure of a computer device according to an embodiment of the present invention.

[0065] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0067] like Figure 1 As shown, the present invention provides a distributed energy storage converter optimization method based on big data analysis, comprising:

[0068] S1. Acquire historical environmental characteristic information and historical operating state characteristic information of a distributed energy storage converter, wherein the historical operating state characteristic information includes historical electrical characteristic information and historical energy storage characteristic information;

[0069] S2. Obtain historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, and obtain a load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information;

[0070] S3. Obtaining energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information, and obtaining a load forecast value according to the energy conversion efficiency and the load fluctuation impact rate;

[0071] S4. Obtaining a demand response gain according to the load prediction value;

[0072] S5. Obtaining the real-time environmental characteristic information and the real-time operating state characteristic information of the distributed energy storage converter after adjustment, and obtaining the real-time load value according to the real-time environmental characteristic information and the real-time operating state characteristic information;

[0073] S6. Adjust the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced.

[0074] As described in the above steps S1-S6, the distributed energy storage converter is a power electronic device used to connect and manage distributed energy (such as solar energy, wind energy, etc.) and energy storage systems (such as batteries). Its main function is to realize the conversion, control and optimization of electric energy so as to efficiently store and release electric energy. Its main functions include electric energy conversion, energy management, power regulation and intelligent control. It can be used in energy storage systems of homes or commercial buildings. Combined with solar power generation, it can increase the proportion of self-use electricity. Distributed energy storage converters play a key role in realizing the efficient use of renewable energy and enhancing the flexibility and stability of power systems. With the popularization of distributed energy, its importance is becoming increasingly prominent. Existing distributed energy storage converters adopt a control strategy based on fixed rules. For example, according to the priority of different loads (such as important loads and non-important loads), the needs of important loads are met first when the power supply is insufficient, and corresponding charging and discharging are performed. However, the above method fails to dynamically adjust according to real-time data and environmental changes, lacks an effective demand response strategy, and may result in the energy storage converter being unable to quickly adjust the charging and discharging strategy to maintain system balance when the load is peak or fluctuates rapidly. The present invention obtains historical environmental characteristic information of distributed energy storage inverters, and obtains the load fluctuation impact rate based on the historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, wherein the load fluctuation impact rate refers to the degree of influence of load changes on system performance or stability, and is used to evaluate the influence of the load change amplitude on the operating state of the power system, equipment or energy storage system. By combining meteorological and load historical data, load fluctuations can be predicted more accurately. This multivariate analysis can better capture the dynamic relationship between load and meteorological changes, and analyzing historical data helps to identify the pattern of load fluctuations and can foresee possible load changes in the future, so as to better adjust. According to the obtained load fluctuation impact rate, the control parameters of the energy storage inverter can be dynamically adjusted to adapt to load changes. For example, when the load fluctuation is large, the response sensitivity is increased and the adaptability of the system is improved. Understanding the load fluctuation characteristics can help Avoid overreaction, reduce frequent charging and discharging operations, improve system stability and efficiency, obtain historical operating status characteristic information of distributed energy storage inverters, and obtain energy conversion efficiency based on historical electrical characteristic information and historical energy storage characteristic information of historical operating status characteristic information, and obtain load forecast value based on energy conversion efficiency and load fluctuation impact rate. Among them, energy conversion efficiency refers to the ratio between input energy and output energy in the energy conversion process, which reflects the effectiveness of the system in converting one form of energy (such as electrical energy, thermal energy) to another. By analyzing the historical operating status, the key factors affecting the energy conversion efficiency can be identified, the charging and discharging strategies can be optimized, and the overall efficiency of the energy storage system can be improved. According to the real-time energy conversion efficiency data, the control parameters can be dynamically adjusted under different operating conditions to ensure that the system operates within the optimal efficiency range. The relationship between energy conversion efficiency and load fluctuation impact rate is used.It can more accurately predict load fluctuations, so as to make timely responses under different load conditions. By comprehensively considering the energy conversion efficiency, it can reduce the error of load prediction and improve the responsiveness of the system. According to different operating states and energy conversion efficiencies, customized control strategies can be designed to cope with various load conditions and improve response sensitivity. Accurate load prediction combined with energy efficiency monitoring can help to better balance power supply and load demand, reduce the instability caused by power supply fluctuations, reduce the risk of over-discharge or over-charging, and ensure that the energy storage system maintains stable operation when the load changes to avoid impact on the power grid. The demand response gain and the real-time load value of the distributed energy storage inverter are obtained through the load prediction value, and the charging and discharging strategy of the distributed energy storage inverter is adjusted according to the real-time load value and the demand response gain until the distributed energy storage inverter system is balanced. Among them, the demand response gain refers to the amount that can be achieved by adjusting the user's power demand (for example, reducing power consumption during peak hours) in the demand response activity. External benefits or benefits, by real-time monitoring of load changes, the charging and discharging strategies can be quickly adjusted to cope with load fluctuations, improving the flexibility and adaptability of the system. According to the real-time calculation of the demand response gain, the operating state of the energy storage system can be adjusted more accurately to ensure the balance between power supply and demand. By optimizing the charging and discharging strategy, the impact of load fluctuations on the system can be effectively reduced, the stability of power supply can be maintained, and power outages or overloads can be avoided. By dynamically adjusting the charging and discharging strategy according to the demand response gain, unnecessary charging and discharging cycles can be reduced, energy efficiency can be improved, energy loss can be reduced, and the energy storage system can be fully utilized to charge at low loads and discharge at high loads to achieve the optimal allocation of resources. By real-time monitoring of load values ​​and demand response gains, and adjusting the charging and discharging strategies of distributed energy storage inverters accordingly, not only the dynamic response capability and stability of the system are improved, but also energy efficiency and cost can be improved. Dynamic adjustment of the charging amount helps to optimize the use of energy storage and improve the overall economy and stability of the system. ,

[0075] In one embodiment, the step S2 of acquiring the load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information includes:

[0076] S21, obtaining a plurality of temperature characteristics, a plurality of humidity characteristics, and a plurality of wind speed characteristics of the historical meteorological characteristic information;

[0077] S22. Obtain a total meteorological variable according to a plurality of temperature characteristics, a plurality of humidity characteristics, and a plurality of wind speed characteristics, and obtain a corresponding temperature influence coefficient, humidity influence coefficient, and wind speed influence coefficient according to each temperature characteristic, humidity characteristic, and wind speed characteristic and the total meteorological variable. The calculation formula of the temperature influence coefficient is: ;in, represents the temperature influence coefficient, Indicates Temperature characteristics, represents the total meteorological variable, The serial number representing the temperature characteristic; the calculation method of humidity influence coefficient and wind speed influence coefficient is the same as that of temperature influence coefficient;

[0078] S23, obtaining multiple power loads and multiple load change rates of the historical load characteristic information;

[0079] S24, obtaining a total load variable according to multiple power loads and multiple load change rates, and obtaining a corresponding power influence coefficient and a load change influence coefficient according to each power load, load change rate and the total load variable. Similarly, the calculation method of the power influence coefficient and the load change influence coefficient is the same as the calculation method of the temperature influence coefficient.

[0080] S25. Obtain a load fluctuation influence rate according to a plurality of temperature influence coefficients, a plurality of humidity influence coefficients, a plurality of wind speed influence coefficients, a plurality of power influence coefficients, and a plurality of load change influence coefficients.

[0081] As described in the above steps S21-S25, the present invention obtains the total meteorological variable through multiple temperature characteristics, multiple humidity characteristics and multiple wind speed characteristics of historical meteorological characteristic information, and obtains the corresponding temperature influence coefficient, humidity influence coefficient and wind speed influence coefficient according to each temperature characteristic, humidity characteristic and wind speed characteristic and the total meteorological variable, wherein the total meteorological variable generally refers to a more comprehensive meteorological index or parameter obtained by integrating multiple meteorological characteristics (such as temperature, humidity, wind speed, etc.). By analyzing multiple meteorological characteristics, the impact of environmental factors on electricity demand and supply can be more comprehensively understood, which is helpful to formulate more scientific operation strategies. By understanding the impact of each meteorological characteristic on the total meteorological variable, it can help identify which characteristics have a negative impact on the load under specific conditions. The performance of load and energy storage system is most affected, thus realizing refined management. Meteorological factors such as temperature, humidity and wind speed are closely related to power demand. The influence coefficient can be used to improve the load forecasting model and improve its accuracy. According to the changes in meteorological factors, the charging and discharging strategies of energy storage inverters can be adjusted in real time to enhance the response capability to load fluctuations and maintain the balance of power supply. By accurately predicting loads and optimizing responses, the fluctuations of power grids caused by meteorological factors can be reduced and the overall stability of the system can be improved. By analyzing historical meteorological characteristic information and obtaining influence coefficients such as temperature, humidity and wind speed, the control parameters of distributed energy storage inverters can be optimized in many aspects, which not only improves the accuracy and dynamic response capability of load forecasting, but also enhances the stability and economy of the system. Benefits: The total load variable is obtained through multiple power loads and multiple load change rates of historical load characteristic information, and the corresponding power influence coefficient and load change influence coefficient are obtained according to each power load and load change rate and the total load variable. By utilizing the data of multiple power loads and load change rates, future load demand can be predicted more accurately, errors can be reduced, and scheduling strategies can be optimized. By analyzing the historical load change rate, the seasonal or temporal pattern of the load can be identified, so as to make more reasonable predictions in different time periods or climatic conditions. According to the power influence coefficient and load change influence coefficient, the charging and discharging strategy of the energy storage inverter is adjusted in real time to enable it to flexibly respond to load changes. Accurate load information and change rate analysis can help identify potential load fluctuations. Dynamically adjust the energy storage strategy in time to reduce the impact on the power grid, accurately control the charging and discharging process of the energy storage equipment, reduce unnecessary energy loss, and improve overall efficiency. Among them, the load change rate is the ratio of the increase or decrease of the load in a certain period of time to the load at the beginning of the period. The load fluctuation impact rate is calculated by summing up multiple temperature influence coefficients, multiple humidity influence coefficients, multiple wind speed influence coefficients, multiple power influence coefficients and multiple load change influence coefficients. In this way, by combining the influence of temperature, humidity, wind speed, power and load changes, we can fully understand the impact of the external environment on load fluctuations, enhance the accuracy of prediction, and dynamically adjust the charging and discharging strategy of the energy storage inverter based on the load fluctuation impact rate, so as to flexibly respond to load changes and maintain system balance.By accurately identifying load fluctuations, the impact on the power grid can be more effectively reduced and the overall stability of the system can be improved. In summary, using the load fluctuation impact rate to optimize the control parameters of distributed energy storage converters can not only improve the dynamic response capability and stability of the system, but also provide strong support for the intelligent management of power systems and the integration of renewable energy.

[0082] In one embodiment, the step S3 of acquiring energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information includes:

[0083] S31, obtaining the charge and discharge rate and discharge depth of the historical energy storage characteristic information;

[0084] S32, obtaining the rated capacity of the distributed energy storage converter, and obtaining the charging time and the discharging time according to the rated capacity, the charging and discharging rate and the discharge depth;

[0085] S33, obtaining harmonics, frequency, output power and input power of the historical electrical characteristic information;

[0086] S34, obtaining an input energy value according to the charging duration, input power, harmonics and frequency;

[0087] S35. Calculate the output energy value according to the discharge duration, discharge depth, output power, harmonics and frequency, wherein the calculation formula is:

[0088] ;

[0089] in, Represents the output energy value, Indicates the output power, Indicates the discharge time. Indicates frequency, represents harmonics, Indicates the depth of discharge;

[0090] S36, calculating the energy conversion efficiency according to the output energy value and the input energy value, wherein the calculation formula is:

[0091] ;

[0092] in, represents the energy conversion efficiency, Represents the output energy value, Indicates the input energy value.

[0093] As described in the above steps S31-S36, the present invention obtains the rated capacity of the distributed energy storage converter, and obtains the charging time and the discharging time according to the rated capacity and the charging and discharging rate and the discharge depth of the historical energy storage characteristic information, wherein the charging and discharging rate generally refers to the rate at which the battery is charged or discharged, and the discharge depth refers to the proportion of the amount of electricity discharged during the discharge process of the battery to the total capacity of the battery. The charging time is calculated as the ratio of the product value between the charging and discharging rate and the discharge depth to the charging and discharging rate, and the discharge time is first calculated by subtracting the discharge depth from 1, and then the difference is multiplied by the product value between the charging and discharging rate and the charging and discharging rate. In this way, by accurately calculating the charging and discharging time, the operation plan of the energy storage device can be arranged more effectively to ensure that the energy storage system can respond quickly when needed. Understanding the charging and discharging time helps to predict the availability of the energy storage system and reduce the response time affected by delays. In the case of load fluctuations, the operating state of the energy storage device can be adjusted in time according to the calculated charging and discharging time, effectively maintaining the load balance of the power grid and improving the stability of the system. By obtaining the harmonics, frequency, output power and output of the historical electrical characteristic information, the energy storage system can be operated in a timely manner. The input power is calculated, and the input energy value is obtained according to the charging time, input power, harmonics and frequency. The output energy value is calculated according to the discharge time, discharge depth, output power, harmonics and frequency. The energy conversion efficiency is then calculated according to the output energy value and the input energy value. By calculating the input and output energy values ​​and the energy conversion efficiency, the performance of the energy storage system can be effectively evaluated, thereby identifying potential efficiency improvement points. By understanding the energy conversion efficiency, the charging and discharging time and strategy can be adjusted in a targeted manner to improve the energy utilization rate of the overall system. According to the real-time input and output energy data, the control parameters of the energy storage converter can be dynamically adjusted to quickly respond to load changes and maintain grid stability. By monitoring harmonics and frequency and optimizing control parameters, the impact of harmonics on system stability can be reduced and power quality can be maintained. Accurate energy calculation and adjustment can effectively balance the grid load and reduce fluctuations caused by uneven load. Optimizing energy conversion efficiency can reduce energy loss and reduce operating costs, thereby improving overall economic benefits. Therefore, by systematically acquiring and analyzing historical electrical characteristic information and optimizing the control parameters of distributed energy storage converters accordingly, the dynamic response balance stability can be significantly improved.

[0094] In one embodiment, the step S3 of obtaining a load prediction value according to the energy conversion efficiency and the load fluctuation impact rate includes:

[0095] S37, obtaining a plurality of load data within a preset time period of the distributed energy storage converter, and obtaining an initial load mean value according to the plurality of load data;

[0096] S38, obtaining a first weight factor of the energy conversion efficiency;

[0097] S39, obtaining a second weight factor of the load fluctuation influence rate;

[0098] S310: Input the initial load mean value, energy conversion efficiency, load fluctuation impact rate, first weight factor and second weight factor into a load forecasting model to obtain a load forecast value, wherein the load forecasting model is:

[0099] ;

[0100] in, represents the load forecast value, represents the initial load mean, represents the energy conversion efficiency, represents the load fluctuation impact rate, represents the first weight factor, represents the second weighting factor.

[0101] As described in the above steps S37-S310, the present invention obtains multiple load data within a preset time period of the distributed energy storage inverter, and obtains an initial load average based on the multiple load data, obtains a first weight factor of energy conversion efficiency and a second weight factor of load fluctuation influence rate, and inputs the initial load average, energy conversion efficiency, load fluctuation influence rate, the first weight factor and the second weight factor into the load prediction model to obtain a load prediction value. By analyzing multiple load data, a more accurate initial load average can be obtained, and the reliability and accuracy of load prediction can be enhanced. By using energy conversion efficiency and load fluctuation influence rate as weight factors, the prediction model can be dynamically adjusted according to actual conditions, thereby improving the flexibility of load prediction. Through the load prediction value, the load prediction model can be more accurately adjusted. The control parameters of the energy storage inverter can achieve more efficient charging and discharging management and optimize energy use. Accurate load forecasting helps to quickly respond to changes in grid demand, maintain dynamic balance, and reduce system instability caused by load fluctuations. By considering the load fluctuation impact rate, it can better cope with load changes and ensure the stable operation of the system under various load conditions. The load prediction value can help the energy storage system better balance the load and reduce fluctuations in the grid caused by uneven load. Therefore, by obtaining and analyzing load data and related factors, the use of load forecasting models can significantly improve the control parameter optimization capabilities of distributed energy storage inverters, thereby enhancing the dynamic response balance stability of the system, improving overall efficiency, reducing costs, and supporting the efficient use of renewable energy.

[0102] In one embodiment, the step S4 of obtaining the demand response gain according to the reference load value and the load forecast value includes:

[0103] S41, obtaining a reference load value of a distributed energy storage converter;

[0104] S42, obtaining a reference gain according to the reference load value and the load prediction value;

[0105] S43, obtaining a demand response gain according to the benchmark gain, benchmark load value and load forecast value, and the calculation formula is: ;in, represents the demand response gain, represents the reference gain, represents the load forecast value, Indicates the base load value.

[0106] As described in the above steps S41-S43, the present invention obtains a benchmark load value of the distributed energy storage inverter, obtains a benchmark gain according to the benchmark load value and the load prediction value, and obtains a demand response gain according to the benchmark gain, the benchmark load value and the load prediction value. Since the benchmark load value provides a reference point, by comparing with the load prediction value, the load state of the system can be better evaluated to ensure the effectiveness of the control strategy. By calculating the benchmark gain, the control strategy can be dynamically adjusted according to the actual load changes, so that the system can respond to load fluctuations more flexibly. The calculation of the benchmark gain and the demand response gain helps to formulate more accurate control parameters so that the inverter can achieve optimal performance under different load conditions. The accurate gain value enables the control system to respond quickly to load changes, reduce hysteresis, and maintain the stability of the power grid. By analyzing the benchmark gain and the demand response gain, the charging and discharging process of the energy storage system can be more reasonably allocated to improve the overall energy utilization rate.

[0107] In one embodiment, the step S6 of adjusting the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced includes:

[0108] S61, obtaining a real-time load value and a load prediction value, and determining whether the real-time load value is greater than the load prediction value;

[0109] S62: If the real-time load value is greater than the load prediction value, the discharge efficiency coefficient of the distributed energy storage converter is obtained, and the discharge amount is obtained according to the discharge efficiency coefficient and the demand response gain;

[0110] S63, adjusting the discharge of the distributed energy storage converter according to the discharge amount until the distributed energy storage converter system responds to a balance;

[0111] S64. If the real-time load value is not less than the load prediction value, the charging efficiency coefficient of the distributed energy storage converter is obtained, and the charging amount is obtained according to the charging efficiency coefficient and the demand response gain;

[0112] S65. Adjust the charging of the distributed energy storage converter according to the charging amount until the distributed energy storage converter system responds in a balanced manner.

[0113] As described in the above steps S101-S103, the present invention obtains the real-time load value and the load prediction value, and determines whether the real-time load value is greater than the load prediction value. If the real-time load value is greater than the load prediction value, the discharge efficiency coefficient of the distributed energy storage inverter is obtained, and the discharge amount is obtained according to the discharge efficiency coefficient and the demand response gain, and the distributed energy storage inverter is adjusted according to the discharge amount until the distributed energy storage inverter system responds to the balance. If the real-time load value is not less than the load prediction value, the charging efficiency coefficient of the distributed energy storage inverter is obtained, and the charging amount is obtained according to the charging efficiency coefficient and the demand response gain, and the distributed energy storage inverter is charged according to the charging amount until the distributed energy storage inverter system responds to the balance. The distributed energy storage inverter system responds in a balanced manner. Based on the comparison between the real-time load and the predicted load, the system can quickly determine whether discharge or charging is needed to ensure timely response to changes in grid demand. By combining the discharge efficiency coefficient and the charging efficiency coefficient, the system can perform charging and discharging operations under optimal conditions to improve the overall energy utilization rate. The charge and discharge amount calculated according to the efficiency coefficient ensures that the energy storage system operates in the most efficient state, thereby reducing unnecessary energy loss. Dynamic adjustment of the discharge or charge amount can effectively balance the grid load and avoid grid fluctuations and potential failures caused by uneven load. Discharging at high load helps reduce peak load pressure, alleviate the burden on the grid, and improve power supply reliability.

[0114] like Figure 2 As shown, the present invention also provides a distributed energy storage converter optimization system based on big data analysis, including:

[0115] A first acquisition module is used to acquire historical environmental characteristic information and historical operating state characteristic information of the distributed energy storage converter, wherein the historical operating state characteristic information includes historical electrical characteristic information and historical energy storage characteristic information;

[0116] A second acquisition module is used to acquire historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, and acquire a load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information;

[0117] A third acquisition module is used to acquire energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information, and to acquire a load prediction value according to the energy conversion efficiency and the load fluctuation influence rate;

[0118] A fourth acquisition module, configured to acquire a demand response gain according to the load prediction value;

[0119] A fifth acquisition module, used to acquire the real-time environmental characteristic information and the real-time operating state characteristic information of the distributed energy storage converter after adjustment, and acquire the real-time load value according to the real-time environmental characteristic information and the real-time operating state characteristic information;

[0120] The adjustment module is used to adjust the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced.

[0121] In one embodiment, the second acquisition module includes:

[0122] A first acquisition unit, used to acquire a plurality of temperature characteristics, a plurality of humidity characteristics and a plurality of wind speed characteristics of the historical meteorological characteristic information;

[0123] A second acquisition unit is used to acquire a total meteorological variable according to a plurality of temperature characteristics, a plurality of humidity characteristics and a plurality of wind speed characteristics, and acquire a corresponding temperature influence coefficient, humidity influence coefficient and wind speed influence coefficient according to each temperature characteristic, humidity characteristic and wind speed characteristic and the total meteorological variable;

[0124] A third acquisition unit, configured to acquire a plurality of power loads and a plurality of load change rates of the historical load characteristic information;

[0125] A fourth acquisition unit, configured to acquire a total load variable according to a plurality of power loads and a plurality of load change rates, and acquire a corresponding power influence coefficient and a load change influence coefficient according to each power load, load change rate and the total load variable;

[0126] The fifth acquisition unit is used to acquire the load fluctuation influence rate according to multiple temperature influence coefficients, multiple humidity influence coefficients, multiple wind speed influence coefficients, multiple power influence coefficients and multiple load change influence coefficients.

[0127] like Figure 3 As shown, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned distributed energy storage converter optimization method based on big data analysis are implemented.

[0128] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned distributed energy storage converter optimization method based on big data analysis are implemented.

[0129] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0130] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0131] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distributed energy storage converter optimization method based on big data analysis, characterized in that: include: Acquire historical environmental characteristic information and historical operating state characteristic information of the distributed energy storage converter, wherein the historical operating state characteristic information includes historical electrical characteristic information and historical energy storage characteristic information; Acquire historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, and acquire a load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information; Obtaining energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information, and obtaining a load forecast value according to the energy conversion efficiency and the load fluctuation impact rate; Obtaining a demand response gain according to the load forecast value; Acquire the real-time environmental characteristic information and the real-time operating state characteristic information of the distributed energy storage converter after adjustment, and acquire the real-time load value according to the real-time environmental characteristic information and the real-time operating state characteristic information; The charging and discharging strategy of the distributed energy storage converter is adjusted according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced.

2. The distributed energy storage converter optimization method based on big data analysis according to claim 1 is characterized in that: The step of obtaining the load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information comprises: Acquire multiple temperature characteristics, multiple humidity characteristics, and multiple wind speed characteristics of the historical meteorological characteristic information; Obtaining a total meteorological variable according to a plurality of temperature characteristics, a plurality of humidity characteristics, and a plurality of wind speed characteristics, and obtaining a corresponding temperature influence coefficient, humidity influence coefficient, and wind speed influence coefficient according to each temperature characteristic, humidity characteristic, and wind speed characteristic and the total meteorological variable; Acquire multiple power loads and multiple load change rates of the historical load characteristic information; Obtaining a total load variable according to a plurality of power loads and a plurality of load change rates, and obtaining a corresponding power influence coefficient and a load change influence coefficient according to each power load, load change rate and the total load variable; The load fluctuation influence rate is obtained according to multiple temperature influence coefficients, multiple humidity influence coefficients, multiple wind speed influence coefficients, multiple power influence coefficients and multiple load change influence coefficients.

3. The distributed energy storage converter optimization method based on big data analysis according to claim 1 is characterized in that: The step of acquiring energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information comprises: Obtaining the charge and discharge rate and discharge depth of the historical energy storage characteristic information; Obtaining the rated capacity of the distributed energy storage converter, and obtaining the charging time and the discharging time according to the rated capacity, the charging and discharging rate, and the discharge depth; Obtaining harmonics, frequency, output power and input power of the historical electrical characteristic information; Acquire input energy value according to the charging time, input power, harmonics and frequency; The output energy value is calculated according to the discharge duration, discharge depth, output power, harmonics and frequency, wherein the calculation formula is: ； in, Represents the output energy value, Indicates the output power, Indicates the discharge time. Indicates frequency, represents harmonics, Indicates the depth of discharge; The energy conversion efficiency is calculated according to the output energy value and the input energy value, wherein the calculation formula is: ； in, represents the energy conversion efficiency, Represents the output energy value, Indicates the input energy value.

4. The distributed energy storage converter optimization method based on big data analysis according to claim 1 is characterized in that: The step of obtaining a load prediction value according to the energy conversion efficiency and the load fluctuation influence rate comprises: Acquire multiple load data of the distributed energy storage converter within a preset time period, and acquire an initial load average value according to the multiple load data; Obtaining a first weight factor of the energy conversion efficiency; Obtaining a second weight factor of the load fluctuation impact rate; The initial load mean value, energy conversion efficiency, load fluctuation impact rate, first weight factor and second weight factor are input into the load forecasting model to obtain the load forecast value, wherein the load forecasting model is: ； in, represents the load forecast value, represents the initial load mean, represents the energy conversion efficiency, represents the load fluctuation impact rate, represents the first weight factor, represents the second weighting factor.

5. The distributed energy storage converter optimization method based on big data analysis according to claim 1 is characterized in that: The step of obtaining the demand response gain according to the reference load value and the load forecast value comprises: Obtaining a benchmark load value of a distributed energy storage converter; Obtaining a reference gain according to the reference load value and the load prediction value; The demand response gain is obtained according to the reference gain, the reference load value and the load prediction value.

6. The distributed energy storage converter optimization method based on big data analysis according to claim 1 is characterized in that: The step of adjusting the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced includes: Obtaining a real-time load value and a load forecast value, and determining whether the real-time load value is greater than the load forecast value; If the real-time load value is greater than the load prediction value, the discharge efficiency coefficient of the distributed energy storage converter is obtained, and the discharge amount is obtained according to the discharge efficiency coefficient and the demand response gain; Discharging and adjusting the distributed energy storage converter according to the discharge amount until the distributed energy storage converter system responds in a balanced manner; If the real-time load value is not less than the load prediction value, the charging efficiency coefficient of the distributed energy storage converter is obtained, and the charging amount is obtained according to the charging efficiency coefficient and the demand response gain; The charging of the distributed energy storage converter is adjusted according to the charging amount until the distributed energy storage converter system responds in a balanced manner.

7. A distributed energy storage converter optimization system based on big data analysis, characterized in that: include: A first acquisition module is used to acquire historical environmental characteristic information and historical operating state characteristic information of the distributed energy storage converter, wherein the historical operating state characteristic information includes historical electrical characteristic information and historical energy storage characteristic information; A second acquisition module is used to acquire historical meteorological characteristic information and historical load characteristic information of the historical environmental characteristic information, and acquire a load fluctuation impact rate according to the historical meteorological characteristic information and the historical load characteristic information; A third acquisition module is used to acquire energy conversion efficiency according to the historical electrical characteristic information and the historical energy storage characteristic information, and to acquire a load prediction value according to the energy conversion efficiency and the load fluctuation influence rate; A fourth acquisition module, configured to acquire a demand response gain according to the load prediction value; A fifth acquisition module, used to acquire the real-time environmental characteristic information and the real-time operating state characteristic information of the distributed energy storage converter after adjustment, and acquire the real-time load value according to the real-time environmental characteristic information and the real-time operating state characteristic information; The adjustment module is used to adjust the charging and discharging strategy of the distributed energy storage converter according to the real-time load value and the demand response gain until the distributed energy storage converter system is balanced.

8. The distributed energy storage converter optimization system based on big data analysis according to claim 7 is characterized in that: The second acquisition module includes: A first acquisition unit, used to acquire a plurality of temperature characteristics, a plurality of humidity characteristics and a plurality of wind speed characteristics of the historical meteorological characteristic information; A second acquisition unit is used to acquire a total meteorological variable according to a plurality of temperature characteristics, a plurality of humidity characteristics and a plurality of wind speed characteristics, and acquire a corresponding temperature influence coefficient, humidity influence coefficient and wind speed influence coefficient according to each temperature characteristic, humidity characteristic and wind speed characteristic and the total meteorological variable; A third acquisition unit, configured to acquire a plurality of power loads and a plurality of load change rates of the historical load characteristic information; A fourth acquisition unit, configured to acquire a total load variable according to a plurality of power loads and a plurality of load change rates, and acquire a corresponding power influence coefficient and a load change influence coefficient according to each power load, load change rate and the total load variable; The fifth acquisition unit is used to acquire the load fluctuation influence rate according to multiple temperature influence coefficients, multiple humidity influence coefficients, multiple wind speed influence coefficients, multiple power influence coefficients and multiple load change influence coefficients.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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