A method and system for constructing a network of energy storage integrated with light

Abstract

The application discloses a kind of light storage integrated network construction energy storage method and system, it is related to network construction energy storage technical field.Analysis is carried out to the historical operation log of power system, and the control characteristic information of bidirectional energy storage converter of different time nodes in the past, the control characteristic information of photovoltaic controller and network construction energy storage operating characteristic information are extracted, generate light storage integrated-network construction energy storage operation standard judgment model, input real-time network construction energy storage operating characteristic information into light storage integrated-network construction energy storage operation standard judgment model, determine the control characteristic information of bidirectional energy storage converter and photovoltaic controller current control parameter, and construct linear regression model, input current control characteristic information into linear regression model, and carry out linear regression error analysis, according to linear regression error analysis result, the control characteristic information of current bidirectional energy storage converter and photovoltaic controller is adapted to optimize once, improve the stability when network construction energy storage operates.

Description

Technical Field

[0001] This invention relates to the field of grid-based energy storage technology, and in particular to a method and system for integrated photovoltaic-energy storage grid-based energy storage. Background Technology

[0002] With the rapid development of power systems, integrated photovoltaic (PV) and energy storage (ESS) power generation is gradually replacing the traditional grid-dependent operation of PV systems. An integrated PV-ESS unit combines a PV controller and a bidirectional energy storage converter to achieve a unified "photovoltaic + energy storage" solution. The system adopts a modular design, allowing for flexible configuration of PV, batteries, and loads. Intelligent algorithms prioritize the allocation of PV energy to the energy storage batteries or loads, enabling multiple operating modes, including grid-connected and off-grid operation, as well as intelligent switching between these modes. However, existing integrated PV-ESS units still suffer from a lack of systematic control methods, resulting in issues such as insufficient accuracy and flexibility in control, and poor stability during grid-connected operation.

[0003] To address the issues of inaccurate and flexible control, as well as poor grid operation stability caused by the lack of systematic control methods, there is an urgent need for a photovoltaic-storage integrated grid energy storage method and system that can perform real-time control of the photovoltaic-storage integrated unit and improve the stability of grid-connected and off-grid operation. Summary of the Invention

[0004] To address the problems of poor control accuracy and flexibility, as well as poor stability during grid-based energy storage operation, caused by the lack of a systematic control method for integrated photovoltaic and energy storage systems in existing technologies, this invention provides an integrated photovoltaic and energy storage grid-based energy storage method and system, comprising: an integrated photovoltaic and energy storage grid-based energy storage method, comprising:

[0005] Step 1: Analyze the historical operation logs of the power system, extract the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller at different time points in the past, and extract the grid-connected energy storage operation characteristic information at different time points in the past.

[0006] The control characteristic information of the bidirectional energy storage converter, the control characteristic information of the photovoltaic controller, and the operation characteristic information of the grid-connected energy storage at the same time point are associated and recorded as a set of photovoltaic-energy storage integrated grid-connected energy storage operation data, and several sets of photovoltaic-energy storage integrated grid-connected energy storage operation data at several time points are determined.

[0007] Step 2: Determine the grid construction operation standard based on historical grid construction operation requirements. Based on the grid construction operation standard, clean the data in the photovoltaic-storage integrated grid construction and energy storage operation data group that does not meet the grid construction operation standard. Generate a photovoltaic-storage integrated grid construction and energy storage operation standard judgment model based on the cleaned data.

[0008] Step 3: Obtain real-time grid-forming energy storage operation characteristic information, input the real-time grid-forming energy storage operation characteristic information into the grid-forming energy storage operation standard judgment model of the light storage integration, determine the current control parameters of the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, and build a linear regression model;

[0009] Step 4: Obtain the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller at the moment, input the current control characteristic information into the linear regression model, and perform linear regression error analysis;

[0010] According to the linear regression error analysis result, the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller at the moment is adaptively optimized once;

[0011] Step 5: Real-time update the grid-forming energy storage operation standard judgment model according to the real-time operation of the power system.

[0012] In some embodiments of the present application, the method for analyzing the historical operation log of the power system and extracting the bidirectional energy storage converter control characteristic information and the photovoltaic controller control characteristic information at different time nodes in the past includes:

[0013] Analyze the historical operation log of the power system, determine the time period for extracting the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller according to the preset grid-forming energy storage scheme, and divide a plurality of time nodes in each time period;

[0014] The bidirectional energy storage converter control characteristic information includes: grid-connected voltage, grid-connected frequency, off-grid voltage and off-grid frequency of the bidirectional energy storage converter;

[0015] The control characteristic information of the photovoltaic controller includes: grid-connected voltage, grid-connected frequency, off-grid voltage and off-grid frequency of the photovoltaic controller.

[0016] In some embodiments of the present application, the grid operation standard includes:

[0017] Voltage requirement and frequency requirement when the power supply load of the power system is stably operated in the grid-connected operation mode;

[0018] Power balance requirement between power supply and power acceptance of the power system when the power system is operated in grid-connected mode;

[0019] Voltage requirement and frequency requirement when the power system itself is stably operated in the off-grid operation mode;

[0020] The power system meets the supply-demand balance requirement of its own load when operated in off-grid mode.

[0021] In some embodiments of the present application, the method for generating the photovoltaic storage integrated grid-connected energy storage operation standard judgment model by cleaning the data that does not meet the grid-connected operation standard in the photovoltaic storage integrated grid-connected energy storage operation data set comprises:

[0022] According to the grid-connected operation standard, the standard data range of voltage and frequency and the corresponding data range of power supply and power receiving during grid-connected operation and off-grid operation are determined, the data of the photovoltaic storage integrated grid-connected energy storage operation data set that exceeds the standard data range of voltage and frequency and the corresponding data range of power supply and power receiving is determined as abnormal data, and the abnormal data is divided into high abnormality degree and low abnormality degree according to the size of the exceeded data;

[0023] The data of high abnormality degree is screened out, and the data of low abnormality degree is adaptively corrected, and the photovoltaic storage integrated grid-connected energy storage operation standard judgment model is generated according to the corrected photovoltaic storage integrated grid-connected energy storage operation data set.

[0024] In some embodiments of the present application, the method for determining the current control parameters of the control characteristics information of the bidirectional energy storage converter and the photovoltaic controller and constructing a linear regression model comprises:

[0025] The real-time grid-connected energy storage operation characteristic information is obtained, a plurality of data in the real-time grid-connected energy storage operation characteristic information are comprehensively fitted, the comprehensive fitting value of the real-time grid-connected energy storage operation characteristic information is input into the photovoltaic storage integrated grid-connected energy storage operation standard judgment model, and the current control parameters of the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the bidirectional energy storage converter, and the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the photovoltaic controller are output;

[0026] The current control parameters of the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the bidirectional energy storage converter, and the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the photovoltaic controller are taken as independent variables, and the comprehensive fitting value of the real-time grid-connected energy storage operation characteristic information is taken as dependent variable, and a linear regression model is constructed;

[0027] The expression of the linear regression model is:

[0028] Y i =ω0+ω1x i-1 +ω2x i-2 +ω3x i-3 +ω4x i-4 +ω5x i-5 +ω6x i-6 +ω7x i-7 +ω8x i-8 ;

[0029] Wherein, Y iis the comprehensive fitting value of the real-time grid-connected energy storage operation characteristic information of the ith time node, ω0-ω8are error terms, x i-1 -x i-8 are the current control parameters of the grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency of the bidirectional energy storage converter and the grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency of the photovoltaic controller respectively at the ith time node.

[0030] In some embodiments of the present application, the method for obtaining the current control characteristic information, inputting the current control characteristic information into the linear regression model, and performing linear regression error analysis includes:

[0031] The grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency of the current bidirectional energy storage converter and the grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency of the photovoltaic controller are obtained and input into the linear regression equation as independent variables, and the sample observation value is calculated according to the linear regression equation;

[0032] The residual error between the sample observation value and the sample prediction value is calculated, and the residual error is subjected to regression error analysis with the preset error standard.

[0033] In some embodiments of the present application, the expression for performing regression error analysis is:

[0034] S i = C i × [ | (Y i-0 - Y i-1 ) | - Δ i ];

[0035] Wherein, S i is the error weight corresponding to the ith time node, C i is the conversion parameter configured according to the current grid-connected operation requirement of the ith time node, Y i-0 is the sample observation value of the ith time node, Y i-1 is the sample prediction value of the ith time node, and Δ i is the preset error standard.

[0036] In some embodiments of the present application, the method for performing adaptive optimization of the control characteristic information of the current bidirectional energy storage converter and the photovoltaic controller according to the linear regression error analysis result includes:

[0037] If the error weight corresponding to the ith time node is greater than the preset error threshold, the corresponding current control characteristic information is determined to be optimized, and the control characteristic information of the current bidirectional energy storage converter and the photovoltaic controller is subjected to adaptive optimization according to the sample prediction value of the linear regression model.

[0038] In some embodiments of the present application, an integrated photovoltaic and energy storage grid-forming energy storage system is also disclosed, comprising:

[0039] A data set generation module is configured to analyze the historical operation log of the power system, extract the control characteristic information of the bidirectional energy storage converter and the control characteristic information of the photovoltaic controller at different time nodes in the past, and extract the grid-forming energy storage operation characteristic information at different time nodes in the past, associate the control characteristic information of the bidirectional energy storage converter, the control characteristic information of the photovoltaic controller and the grid-forming energy storage operation characteristic information at the same time node, record as a set of integrated photovoltaic and energy storage-grid-forming energy storage operation data, and determine the integrated photovoltaic and energy storage-grid-forming energy storage operation data set at several time nodes.

[0040] A standard judgment model generation module is configured to determine the grid-forming operation standard according to the historical grid-forming operation requirement, clean the data in the integrated photovoltaic and energy storage-grid-forming energy storage operation data set that does not meet the grid-forming operation standard according to the grid-forming operation standard, and generate an integrated photovoltaic and energy storage-grid-forming energy storage operation standard judgment model according to the cleaned data.

[0041] A linear regression model construction module is configured to obtain real-time grid-forming energy storage operation characteristic information, input the real-time grid-forming energy storage operation characteristic information into the integrated photovoltaic and energy storage-grid-forming energy storage operation standard judgment model, determine the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, and construct a linear regression model.

[0042] A linear regression error analysis module is configured to obtain the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, input the current control characteristic information into the linear regression model, and perform linear regression error analysis, and according to the linear regression error analysis result, perform adaptive optimization on the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller.

[0043] A model updating module is configured to update the integrated photovoltaic and energy storage-grid-forming energy storage operation standard judgment model in real time according to the real-time operation of the power system.

[0044] The beneficial effects of the present application are:

[0045] 1. The present application extracts the control characteristic data of the bidirectional energy storage converter and the photovoltaic controller, associates it with the grid-forming energy storage operation characteristic information to generate an integrated photovoltaic and energy storage-grid-forming energy storage operation standard judgment model, constructs a linear regression model by obtaining real-time grid-forming energy storage operation characteristic information and current control parameters, performs linear regression error analysis on the current control characteristic information, improves the accuracy of error judgment on the current control characteristic information, and optimizes the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller according to the error analysis result, greatly improves the stability of the grid-forming energy storage system in grid-connected operation and off-grid operation.

[0046] 2. The application improves the flexibility of using the photovoltaic storage integrated grid storage energy operation standard judgment model to judge the current control parameters by updating the photovoltaic storage integrated grid storage energy operation standard judgment model in real time according to the real-time operation of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 is a schematic diagram of the steps of a photovoltaic storage integrated grid storage energy method in the embodiment of the application;

[0048] Figure 2 is a schematic diagram of the module connection of a photovoltaic storage integrated grid storage energy system in the embodiment of the application. DETAILED DESCRIPTION

[0049] The technical solutions of the application will be further described below with reference to the drawings and embodiments.

[0050] The technical solutions of the application will be further described below with reference to the drawings and embodiments.

[0051] Embodiment:

[0052] The purpose of the application is to provide a photovoltaic storage integrated grid storage energy method and system.

[0053] A photovoltaic storage integrated grid storage energy method, referring to Figure 1 , comprising:

[0054] S1: Analyzing the historical operation log of the power system, extracting the control characteristic information of the bidirectional storage converter and the control characteristic information of the photovoltaic controller at different time nodes in the past, and extracting the grid storage energy operation characteristic information at different time nodes in the past.

[0055] Correlate the bidirectional storage converter control characteristic information, the control characteristic information of the photovoltaic controller and the grid storage energy operation characteristic information at the same time node, record as a set of photovoltaic storage integrated grid storage energy operation data, and determine the photovoltaic storage integrated grid storage energy operation data set at several time nodes.

[0056] S2: Determine the network operation standard according to the historical network operation requirements, clean the data in the light storage integrated-network energy storage operation data set that does not meet the network operation standard according to the network operation standard, and generate a light storage integrated-network energy storage operation standard judgment model according to the cleaned data.

[0057] It should be understood that the data that does not meet the standard here may be abnormal data obtained when reading data abnormally, or the value deviating from the standard in the original data is too large, so it cannot be introduced into the standard judgment model as a standard value. Therefore, the data that does not meet the standard needs to be cleaned.

[0058] S3: Obtain real-time network energy storage operation characteristic information, input the real-time network energy storage operation characteristic information into the light storage integrated-network energy storage operation standard judgment model, determine the current control parameters of the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, and construct a linear regression model.

[0059] It should be understood that the construction of the linear regression model here refers to a multiple linear regression model. In the study of the problem of affecting the network operation stability, the dependent variable is the network energy storage operation characteristic information, and the change of the dependent variable is affected by several important factors. At this time, the multiple influencing factors of the control characteristic information are used as independent variables to explain the change of the dependent variable. Since there is a linear relationship between the multiple independent variables and the dependent variable, the regression model to be constructed is a multiple linear regression model.

[0060] S4: Obtain the control characteristic information of the current bidirectional energy storage converter and the photovoltaic controller, input the current control characteristic information into the linear regression model, and perform linear regression error analysis.

[0061] According to the linear regression error analysis result, the control characteristic information of the current bidirectional energy storage converter and the photovoltaic controller is adaptively optimized.

[0062] S5: Real-time update the light storage integrated-network energy storage operation standard judgment model according to the real-time operation of the power system.

[0063] In some embodiments of the present application, the method for analyzing the historical operation log of the power system and extracting the bidirectional energy storage converter control characteristic information and the photovoltaic controller control characteristic information at different time nodes in the past includes:

[0064] The historical operation log of the power system is analyzed, the time period for extracting the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller is determined according to the preset network energy storage scheme, and a plurality of time nodes are divided in each time period.

[0065] The control characteristic information of the bidirectional energy storage converter includes grid-connected voltage, grid-connected frequency, off-grid voltage, and off-grid frequency of the bidirectional energy storage converter.

[0066] The control characteristic information of the photovoltaic controller includes grid-connected voltage, grid-connected frequency, off-grid voltage, and off-grid frequency of the photovoltaic controller.

[0067] In some embodiments of the present application, the grid-formation operation standard includes:

[0068] The voltage requirement and the frequency requirement when the power system supplies the load stably in the grid-connected operation mode.

[0069] The power balance requirement between power supply and power acceptance of the power system in the grid-connected operation.

[0070] The voltage requirement and the frequency requirement when the power system supplies the load stably in the off-grid operation mode.

[0071] The supply-demand balance requirement of the power system to meet the load in the off-grid operation.

[0072] In some embodiments of the present application, the method for cleaning the data that does not meet the grid-formation operation standard in the photovoltaic and energy storage integrated grid-formation energy storage operation data set and generating a photovoltaic and energy storage integrated grid-formation energy storage operation standard judgment model includes:

[0073] According to the grid-formation operation standard, the standard data range of voltage and frequency and the corresponding data range of power supply and power acceptance in the grid-connected operation and the off-grid operation are determined, the data of the photovoltaic and energy storage integrated grid-formation energy storage operation data set that exceeds the standard data range of voltage and frequency and the corresponding data range of power supply and power acceptance is determined as abnormal data, and the abnormal data is divided into high abnormality degree and low abnormality degree according to the size of the exceeding data.

[0074] The data of high abnormality degree is screened out, the data of low abnormality degree is adaptively corrected, and the photovoltaic and energy storage integrated grid-formation energy storage operation standard judgment model is generated according to the corrected photovoltaic and energy storage integrated grid-formation energy storage operation data set.

[0075] In some embodiments of the present application, the method for determining the current control parameters of the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller and constructing a linear regression model includes:

[0076] The real-time grid-formation energy storage operation characteristic information is obtained, a number of data in the real-time grid-formation energy storage operation characteristic information are comprehensively fitted, the comprehensive fitting value of the real-time grid-formation energy storage operation characteristic information is input into the photovoltaic and energy storage integrated grid-formation energy storage operation standard judgment model, and the current control parameters of the grid-connected voltage, the grid-connected frequency, the off-grid voltage, and the off-grid frequency of the bidirectional energy storage converter and the grid-connected voltage, the grid-connected frequency, the off-grid voltage, and the off-grid frequency of the photovoltaic controller are output.

[0077] The current control parameters of the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the bidirectional energy storage converter, the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the photovoltaic controller are taken as independent variables, and the comprehensive fitting value of the real-time grid-constructed energy storage operation characteristic information is taken as dependent variable, to construct a linear regression model.

[0078] The expression of the linear regression model is:

[0079] Y i = ω0+ ω1x i-1 + ω2x i-2 + ω3x i-3 + ω4x i-4 + ω5x i-5 + ω6x i-6 + ω7x i-7 + ω8x i-8 ;

[0080] Wherein, Y i is the comprehensive fitting value of the real-time grid-constructed energy storage operation characteristic information at the i th time node, ω0- ω8 is an error term, and x i-1 -x i-8 are the current control parameters of the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the bidirectional energy storage converter, the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the photovoltaic controller at the i th time node.

[0081] It should be understood that the multivariate linear regression model can be constructed by using modeling software, and in the specific values input in the software, the current control parameters of the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the bidirectional energy storage converter, the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the photovoltaic controller are taken as independent variables x, and the comprehensive fitting value of the real-time grid-constructed energy storage operation characteristic information is taken as dependent variable y.

[0082] In some embodiments of the present application, the method of obtaining the current control characteristic information, inputting the current control characteristic information into the linear regression model, and performing linear regression error analysis includes:

[0083] The grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the bidirectional energy storage converter, the grid-connected voltage, the grid-connected frequency, the off-grid voltage, the off-grid frequency of the photovoltaic controller are obtained and taken as independent variables input into the linear regression equation, and the sample observation value is calculated according to the linear regression equation.

[0084] The residual error of the sample observation value and the sample prediction value is calculated, and the residual error is subjected to regression error analysis with the preset error standard.

[0085] In some embodiments of the present application, the expression of the regression error analysis is:

[0086] S i =C i ×[|(Y i-0 -Y i-1 )|-Δ i ];

[0087] Among them, S i Let C be the error weight corresponding to the i-th time node. i Y is the conversion parameter configured for the i-th time node according to the current network operation requirements. i-0 Y represents the sample observation value at the i-th time point. i-1 Let Δ be the predicted value of the sample at time point i. i This is a preset error standard.

[0088] It is important to understand that in the formula |(Y i-0 -Y i-1 The specific meaning of the )| item is the difference between the sample observation value and the sample prediction value. Reflected on the curve, it is the difference between the sample observation value and the curve. If it is above the curve, it is a positive value, and if it is below the curve, it is a negative value. The absolute value needs to be taken to convert it into a positive value, and then the difference is calculated with the preset error standard.

[0089] In some embodiments of this application, a method for adaptively optimizing the control characteristic information of the current bidirectional energy storage converter and photovoltaic controller based on the results of linear regression error analysis includes:

[0090] If the error weight corresponding to the i-th time node is greater than the preset error threshold, the corresponding current control feature information is determined to need optimization, and adaptive optimization is performed on the current control feature information of the bidirectional energy storage converter and photovoltaic controller based on the sample prediction value of the linear regression model.

[0091] It is important to understand that optimization can be achieved by adjusting the control feature information up or down. If the deviation from the curve is above, it is adjusted downwards; if the deviation from the curve is below, it is adjusted upwards. The specific adjustment value is related to the error weight corresponding to the i-th time node, which can better achieve efficient optimization.

[0092] In some embodiments of this application, an integrated photovoltaic-storage grid-connected energy storage system is also disclosed, see reference. Figure 2 It includes: a data set generation module, a standard judgment model generation module, a linear regression model construction module, a linear regression error analysis module, and a model update module.

[0093] The data set generation module is configured to analyze the historical operation log of the power system, extract the control characteristic information of the bidirectional energy storage converter and the control characteristic information of the photovoltaic controller at different time nodes in the past, extract the network-constructed energy storage operation characteristic information at different time nodes in the past, associate the control characteristic information of the bidirectional energy storage converter, the control characteristic information of the photovoltaic controller and the network-constructed energy storage operation characteristic information at the same time node, record as a group of integrated photovoltaic and energy storage-network-constructed energy storage operation data, and determine the integrated photovoltaic and energy storage-network-constructed energy storage operation data set at a plurality of time nodes.

[0094] The standard judgment model generation module is configured to determine the network-constructed operation standard according to the historical network-constructed operation requirement, clean the data in the integrated photovoltaic and energy storage-network-constructed energy storage operation data set that does not meet the network-constructed operation standard according to the network-constructed operation standard, and generate an integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model according to the cleaned data.

[0095] The linear regression model construction module is configured to obtain real-time network-constructed energy storage operation characteristic information, input the real-time network-constructed energy storage operation characteristic information into the integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model, determine the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, and construct a linear regression model.

[0096] The linear regression error analysis module is configured to obtain the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, input the current control characteristic information into the linear regression model, perform linear regression error analysis, and perform adaptive optimization on the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller according to the linear regression error analysis result.

[0097] The model updating module is configured to update the integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model in real time according to the real-time operation of the power system.

[0098] The beneficial effects of the present application are as follows:

[0099] 1. The present application extracts the control characteristic data of the bidirectional energy storage converter and the photovoltaic controller, associates the data with the network-constructed energy storage operation characteristic information to generate an integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model, constructs a linear regression model by obtaining real-time network-constructed energy storage operation characteristic information and current control characteristic information, performs linear regression error analysis on the current control characteristic information, improves the accuracy of error judgment on the current control characteristic information, optimizes the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller according to the error analysis result, and greatly improves the stability of the network-constructed energy storage system in grid-connected operation and off-grid operation.

[0100] 2. The application improves the flexibility of judging the current control parameters by using the light storage integrated network energy storage operation standard judgment model through real-time updating of the light storage integrated network energy storage operation standard judgment model according to the real-time operation of the power system.

[0101] The above is only one embodiment of the present application, but cannot limit the scope of the present application, any structural changes made according to the present application, as long as the essence of the present application is not lost, should be considered to fall within the scope of the present application and be restricted. Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the system and the related description described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0102] Finally, it should be noted that: the above examples are used to illustrate the technical solutions of the present application rather than to limit it, although the present application 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 application can still be modified or replaced by the equivalent, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for constructing a network of energy storage integrated with light, characterized in that, Comprise: Step 1: analysis of power system historical operation log, extract the past different time node's bidirectional energy storage converter control characteristic information and photovoltaic controller control characteristic information, and extract the past different time node's network construction energy storage operation characteristic information; The bidirectional energy storage converter control characteristic information, the control characteristic information of the photovoltaic controller and the network construction energy storage operation characteristic information of the same time node are associated, recorded as a group of integrated light storage-network construction energy storage operation data, and the integrated light storage-network construction energy storage operation data group under several time nodes is determined; Step 2: according to the historical network construction operation requirement, the network construction operation standard is determined, and the data in the integrated light storage-network construction energy storage operation data group that does not meet the network construction operation standard is cleaned according to the network construction operation standard, and the integrated light storage-network construction energy storage operation standard judgment model is generated according to the cleaned data; Step 3: obtain real-time network construction energy storage operation characteristic information, input the real-time network construction energy storage operation characteristic information into the integrated light storage-network construction energy storage operation standard judgment model, determine the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, and construct a linear regression model; Step 4: obtain the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller at present, input the current control characteristic information into the linear regression model, and carry out linear regression error analysis; According to the linear regression error analysis result, the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller at present is adaptively optimized once; Step 5: according to the real-time operation of the power system, the integrated light storage-network construction energy storage operation standard judgment model is updated in real time. 2.The method of claim 1, wherein, The method for analyzing the power system historical operation log and extracting the bidirectional energy storage converter control characteristic information and the photovoltaic controller control characteristic information at different time nodes in the past comprises: Analyze the power system historical operation log, determine the time period for extracting the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller according to the preset network construction energy storage scheme, and divide a plurality of time nodes in each time period; The bidirectional energy storage converter control characteristic information includes: grid-connected voltage, grid-connected frequency, off-grid voltage and off-grid frequency of the bidirectional energy storage converter; The control characteristic information of the photovoltaic controller includes: grid-connected voltage, grid-connected frequency, off-grid voltage and off-grid frequency of the photovoltaic controller. 3.The method of claim 1, wherein, The network construction operation standard includes: Voltage requirement and frequency requirement when power system power supply load is stably operated in grid-connected operation mode; Power balance requirement between power system power supply and power acceptance in grid-connected operation; Voltage requirement and frequency requirement when power system itself load is stably operated in off-grid operation mode; Power supply and demand balance requirement of power system to meet its own load in off-grid operation. 4.The method of claim 1, wherein, The method for cleaning the data in the integrated light storage-network construction energy storage operation data group that does not meet the network construction operation standard and generating the integrated light storage-network construction energy storage operation standard judgment model comprises: According to the network operation standard, the standard data range of voltage and frequency in grid-connected operation and off-grid operation and the corresponding data range of power supply and power receiving are determined, and the data of the optical storage integrated network energy storage operation data set exceeding the voltage and frequency standard data range and the power supply and power receiving corresponding data range is determined as abnormal data, and the abnormal data is divided into high abnormality degree and low abnormality degree according to the size of the exceeding data. The data of high abnormality degree is screened out, and the data of low abnormality degree is adaptively corrected, and the optical storage integrated network energy storage operation standard judgment model is generated according to the corrected optical storage integrated network energy storage operation data set.

5. The method of claim 1, wherein, The method for determining the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller and constructing the linear regression model comprises: Obtain real-time network energy storage operation characteristic information, synthesize and fit a plurality of data in the real-time network energy storage operation characteristic information, input the synthesized and fitted value of the real-time network energy storage operation characteristic information into the optical storage integrated network energy storage operation standard judgment model, and output the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller. The current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller is used as the independent variable, and the synthesized and fitted value of the real-time network energy storage operation characteristic information is used as the dependent variable to construct a linear regression model. The expression of the linear regression model is: Y i = ω0+ ω1x i-1 + ω2x i-2 + ω3x i-3 + ω4x i-4 + ω5x i-5 +ω6x i-6 +ω7x i-7 +ω8x i-8 ; wherein Y i is the comprehensive fitting value of the real-time network-constructing energy storage operation characteristic information of the i th time node, ω0-ω8are error terms, x i-1 -x i-8 are the current control parameters of the grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency of the bidirectional energy storage converter and the grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency data of the photovoltaic controller, respectively. 6.The method of claim 1, wherein, The method for obtaining the current control characteristic information, inputting the current control characteristic information into the linear regression model, and performing linear regression error analysis comprises: Obtain the grid-connected voltage, grid-connected frequency, off-grid voltage, off-grid frequency, and grid-connected voltage, grid-connected frequency, off-grid voltage, and off-grid frequency data of the photovoltaic controller of the bidirectional energy storage converter, and input them into the linear regression equation as independent variables, and calculate the sample observation value according to the linear regression equation. Calculate the residual error of the sample observation value and the sample prediction value, and perform regression error analysis on the residual error and the preset error standard.

7. The method of claim 6, wherein the method further comprises: The expression of the regression error analysis is: S i = C i × [ | (Y i-0 - Y i-1 ) | - Δ i ]; Wherein, S i is the error weight corresponding to the i th time node, C i is the conversion parameter configured according to the current network operation requirement of the i th time node, Y i-0 is the sample observation value of the i th time node, Y i-1 is the sample prediction value of the i th time node, Δ i is the preset error standard. 8.The method of claim 1, wherein, According to the linear regression error analysis result, the method for adaptively optimizing the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller once comprises: If the error weight corresponding to the i th time node is greater than the preset error threshold, the corresponding current control characteristic information is determined to be optimized, and the control characteristic information of the bidirectional energy storage converter and the photovoltaic controller is adaptively optimized according to the sample prediction value of the linear regression model.

9. An integrated optical storage and grid energy system, comprising: Comprise: The data set generation module is configured to analyze the historical operation log of the power system, extract the control characteristic information of the bidirectional energy storage converter and the control characteristic information of the photovoltaic controller at different time nodes in the past, extract the network-constructed energy storage operation characteristic information at different time nodes in the past, associate the control characteristic information of the bidirectional energy storage converter, the control characteristic information of the photovoltaic controller and the network-constructed energy storage operation characteristic information at the same time node, record as a group of integrated photovoltaic and energy storage-network-constructed energy storage operation data, and determine the integrated photovoltaic and energy storage-network-constructed energy storage operation data sets at a plurality of time nodes; The standard judgment model generation module is configured to determine the network-constructed operation standard according to the historical network-constructed operation requirement, clean the data in the integrated photovoltaic and energy storage-network-constructed energy storage operation data set that does not meet the network-constructed operation standard according to the network-constructed operation standard, and generate the integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model according to the cleaned data; The linear regression model construction module is configured to obtain real-time network-constructed energy storage operation characteristic information, input the real-time network-constructed energy storage operation characteristic information into the integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model, determine the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, and construct a linear regression model; The linear regression error analysis module is configured to obtain the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller, input the current control characteristic information into the linear regression model, perform linear regression error analysis, and perform adaptive optimization on the current control characteristic information of the bidirectional energy storage converter and the photovoltaic controller according to the linear regression error analysis result; The model updating module is configured to update the integrated photovoltaic and energy storage-network-constructed energy storage operation standard judgment model in real time according to the real-time operation of the power system.

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