An energy storage management system based on photovoltaic power generation system

Abstract

The present invention relates to the technical field of photovoltaic power generation, and in particular to an energy storage management system based on a photovoltaic power generation system, comprising: a control terminal, which is a main control terminal of the system and is used to issue an execution command; a receiving module, which is used to receive power parameters generated by real-time operation in the photovoltaic power generation system; and a monitoring module, which is used to monitor in real time the meteorological information of a solar photovoltaic panel deployment area in the photovoltaic power generation system. The present invention can receive a large amount of power parameters generated by real-time operation in the photovoltaic power generation system, and store and analyze the large amount of power parameters, thereby screening the large amount of power parameters based on the analysis results to obtain characteristic power parameters for system end users to read, so that the system end users can manage the power storage in the photovoltaic power generation system more quickly based on the read power parameter content.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to an energy storage management system based on a photovoltaic power generation system. Background Art

[0002] Photovoltaic power generation system is a system that uses solar energy to generate electricity. It is mainly composed of solar panels, inverters, battery packs and control systems.

[0003] For the current energy storage management in photovoltaic power generation systems, it is often achieved by staff dynamically recording the stored electric energy through computer equipment. This method takes up a certain amount of labor costs, and the amount of power storage-related parameters obtained is huge, which is not easy for staff to read quickly, thus affecting the staff's adaptive management of energy storage in photovoltaic power generation systems based on the read power storage-related parameters. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides an energy storage management system based on a photovoltaic power generation system, which solves the technical problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] An energy storage management system based on a photovoltaic power generation system, comprising:

[0007] The control terminal is the main control terminal of the system and is used to issue execution commands;

[0008] A receiving module, used for receiving power parameters generated by real-time operation of the photovoltaic power generation system;

[0009] A monitoring module for real-time monitoring of meteorological information in the area where solar photovoltaic panels are deployed in a photovoltaic power generation system;

[0010] An analysis module is used to receive the meteorological information of the solar photovoltaic panel deployment area monitored by the monitoring module, and analyze the interference of the meteorological conditions in the solar photovoltaic panel deployment area on the operation of the solar photovoltaic panels based on the meteorological information;

[0011] A refinement module, used for receiving the operation interference of the solar photovoltaic panel analyzed by the analysis module, and performing refinement processing on the power parameters received in the receiving module based on the operation interference;

[0012] A recording module, used for receiving the power parameters obtained by the refinement processing in the refinement module, binding the meteorological information corresponding to the power parameters with the power parameters and storing them;

[0013] The system end user accesses the system through the control terminal, and reads the power parameters and the meteorological information bound to the power parameters stored in the recording module in the system.

[0014] Furthermore, the power parameters generated by real-time operation in the photovoltaic power generation system received by the receiving module include: daily output power, daily photovoltaic power generation, and power transmission loss. The receiving module is provided with submodules at the lower level, including:

[0015] A storage unit, used to obtain the power parameters received by the receiving module and store the power parameters;

[0016] A capture unit, used to traverse the power parameters stored in the storage unit and capture the power parameters that threaten to be consumed;

[0017] When the storage unit stores the acquired power parameters, the power parameters are differentiated and stored based on the attribution dates of the power parameters.

[0018] Furthermore, the capture logic of the power parameter with threat consumption in the capture unit is:

[0019]

[0020] Where: power out Output power; power loss Power transmission loss; power in is the photovoltaic power generation; t1 is the power supply duration; t2 is the power generation duration;

[0021] Based on the above formula, the capture logic operation is performed on each power parameter stored in the storage unit, and the power parameter that meets the capture logic is the capture target of the capture unit.

[0022] Furthermore, the power transmission loss power loss The formula is as follows:

[0023] power loss =P in ×k loss (1+L f +L s +L m +k V +k A );

[0024] Where: P in is the electrical input power; k loss is the power loss coefficient; L f is the cable length correction factor; L s is the cable cross-sectional area correction factor; L m k is the cable material correction factor; V is the voltage correction factor; k Ais the current correction factor;

[0025] in, P out The output power of the electric power.

[0026] Furthermore, the monitoring module is integrated by a positioning module and an interaction unit;

[0027] An interactive unit, used to access the Internet and obtain meteorological information on the network;

[0028] Among them, during the operation stage of the monitoring module, the positioning module obtains the location information of the deployment location of the solar photovoltaic panel in real time. Based on the area to which the obtained location information belongs, the interactive unit is used in the network to obtain the meteorological information of the area to which the location information belongs. The meteorological information includes: air quality and sunshine rate.

[0029] Furthermore, the monitoring module operates once a day, and the daily operation timestamp is consistent with the operation time of the solar photovoltaic panel. The operation interference analysis logic of the solar photovoltaic panel in the analysis module is expressed as:

[0030]

[0031] Where: γ is the interference degree; is the current daily average output power; f ch is the photoelectric conversion rate of solar photovoltaic panels; t Me The median of the current daily opening time of solar photovoltaic panels; τ is the interference factor obtained based on meteorological information;

[0032] Among them, τ=θ·ω1+Sr·ω2, θ is the air quality interference factor, that is, the higher the PM2.5 content in the air, the smaller the θ value, and vice versa; Sr is the sunshine rate; ω1 and ω2 are weights, ω2+ω1=1.

[0033] Furthermore, the refinement module is provided with submodules at the lower level, including:

[0034] A calling unit, used for calling the power parameters with threat consumption captured by the capturing unit;

[0035] The power parameters retrieved by the retrieval unit are the refinement targets processed in the refinement module.

[0036] Furthermore, in the operation phase of the refinement module, after the power parameters corresponding to the refinement target are retrieved by the retrieval unit, refinement processing is performed on each retrieved power parameter, and the refinement processing logic is expressed as follows:

[0037]

[0038] Wherein, after the power parameters retrieved by the retrieval unit are calculated through the refinement processing logic, if the refinement processing logic still does not hold, the power parameters for which the refinement processing logic does not hold are packaged and sent to the recording module.

[0039] Furthermore, the power parameters stored in the recording module, that is, the power parameters calculated by the refinement processing logic so that the refinement processing logic is invalid, the storage unit obtains the power parameters stored in the recording module in real time, uses the power parameters stored in the recording module as the retrieval target, searches internally with the retrieval target, and deletes the retrieved and stored identical power parameters.

[0040] Furthermore, the control terminal is interactively connected to a receiving module via a local area network, the receiving module is interactively connected to a storage unit and a capture unit via a local area network, the receiving module is interactively connected to a monitoring module via a local area network, the monitoring module is electrically connected to an interaction unit via a medium, the monitoring module is interactively connected to an analysis module and a refinement module via a local area network, the refinement module is interactively connected to a retrieval unit via a local area network, the retrieval unit is interactively connected to the capture unit via a local area network, and the refinement module is interactively connected to a recording module via a local area network.

[0041] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0042] 1. The present invention provides an energy storage management system based on a photovoltaic power generation system. During operation, the system can receive massive power parameters generated by real-time operation in the photovoltaic power generation system, store and analyze the massive power parameters, and screen the massive power parameters based on the analysis results to obtain characteristic power parameters for system-side users to read, so that the system-side users can manage the power storage in the photovoltaic power generation system more quickly based on the read power parameter content.

[0043] 2. During the operation of the system of the present invention, at the stage of screening and processing massive power parameters, the power parameters can be screened and refined through the set continuous processing logic, and the power parameters can be further screened based on the refined results, so that the power parameters finally provided to the system-end users by the system processing are more streamlined, which is more helpful for the system-end users to manage the power storage in the photovoltaic power generation system based on the read power parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a structural schematic diagram of an energy storage management system based on a photovoltaic power generation system;

[0046] Figure 2 Schematic diagram of the model structure of BP trained by the error back propagation algorithm in the present invention;

[0047] Figure 3 This is a flow chart of the radiation correction prediction correction method in the present invention;

[0048] The numbers in the figure represent: 1. control terminal; 2. receiving module; 21. storage unit; 22. capture unit; 3. monitoring module; 31. interaction unit; 4. analysis module; 5. refinement module; 51. retrieval unit; 6. recording module. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The present invention will be further described below in conjunction with the embodiments.

[0051] Example 1

[0052] This embodiment is an energy storage management system based on a photovoltaic power generation system, such as Figure 1 As shown, including:

[0053] Control terminal 1 is the main control terminal of the system and is used to issue execution commands;

[0054] Receiving module 2, used to receive power parameters generated by real-time operation of the photovoltaic power generation system;

[0055] Monitoring module 3, used for real-time monitoring of meteorological information in the area where solar photovoltaic panels are deployed in the photovoltaic power generation system;

[0056] The analysis module 4 is used to receive the meteorological information of the solar photovoltaic panel deployment area monitored by the monitoring module 3, and analyze the interference of the meteorological conditions in the solar photovoltaic panel deployment area on the operation of the solar photovoltaic panels based on the meteorological information;

[0057] A refinement module 5 is used to receive the operation interference of the solar photovoltaic panel analyzed by the analysis module 4, and refine the power parameters received by the receiving module 2 based on the operation interference;

[0058] The recording module 6 is used to receive the power parameters obtained by the refinement processing in the refinement module 5, bind the meteorological information corresponding to the power parameters with the power parameters and store them;

[0059] The system end user accesses the system through the control terminal 1, and reads the power parameters and the meteorological information bound to them stored in the recording module 6 in the system;

[0060] The power parameters generated by the real-time operation of the photovoltaic power generation system received by the receiving module 2 include: daily output power, daily photovoltaic power generation, and power transmission loss. The receiving module 2 is provided with submodules at the lower level, including:

[0061] The storage unit 21 is used to obtain the power parameters received by the receiving module 2 and store the power parameters;

[0062] The capturing unit 22 is used to traverse the power parameters stored in the storage unit 21 and capture the power parameters that threaten to be consumed;

[0063] When the storage unit 21 stores the acquired power parameters, the power parameters are differentiated and stored based on the attribution date of the power parameters;

[0064] The capture logic of the power parameter with threat loss in the capture unit 22 is:

[0065]

[0066] Where: power out Output power; power loss Power transmission loss; power in is the photovoltaic power generation; t1 is the power supply duration; t2 is the power generation duration;

[0067] Based on the above formula, the capture logic operation is performed on each power parameter stored in the storage unit 21, and the power parameter that meets the capture logic is the capture target of the capture unit 22;

[0068] The monitoring module 3 is integrated by the positioning module and the interaction unit 31;

[0069] An interactive unit 31 is used to access the Internet and obtain weather information on the network;

[0070] Among them, during the operation phase of the monitoring module 3, the positioning module obtains the location information of the deployment location of the solar photovoltaic panel in real time, and based on the area to which the acquired location information belongs, the application interaction unit 31 obtains the meteorological information of the area to which the location information belongs in the network, and the meteorological information includes: air quality and sunshine rate;

[0071] The refinement module 5 is provided with submodules at the lower level, including:

[0072] The retrieving unit 51 is used to retrieve the power parameters with threat consumption captured by the capturing unit 22;

[0073] The power parameters retrieved by the retrieval unit 51 are the refinement targets processed by the refinement module 5;

[0074] During the operation phase of the refinement module 5, after the power parameters corresponding to the refinement target are retrieved by the retrieval unit 51, refinement processing is performed on each retrieved power parameter. The refinement processing logic is expressed as follows:

[0075]

[0076] Wherein, after the power parameters retrieved by the retrieval unit 51 are calculated by the refinement processing logic, if the refinement processing logic still does not hold, the power parameters for which the refinement processing logic does not hold are packaged and sent to the recording module 6;

[0077] The control terminal 1 is interactively connected to the receiving module 2 through the local area network, the receiving module 2 is interactively connected to the storage unit 21 and the capture unit 22 through the local area network, the receiving module 2 is interactively connected to the monitoring module 3 through the local area network, the monitoring module 3 is electrically connected to the interaction unit 31 through the medium, the monitoring module 3 is interactively connected to the analysis module 4 and the refinement module 5 through the local area network, the refinement module 5 is interactively connected to the retrieval unit 51 through the local area network, the retrieval unit 51 is interactively connected to the capture unit 22 through the local area network, and the refinement module 5 is interactively connected to the recording module 6 through the local area network.

[0078] In this embodiment, the control terminal 1 controls the receiving module 2 to operate and receive the power parameters generated by the real-time operation of the photovoltaic power generation system, the monitoring module 3 is post-operated to monitor the meteorological information of the solar photovoltaic panel deployment area in the photovoltaic power generation system in real time, the analysis module 4 synchronously receives the meteorological information of the solar photovoltaic panel deployment area monitored by the monitoring module 3, and analyzes the interference of the meteorological in the solar photovoltaic panel deployment area on the operation of the solar photovoltaic panels based on the meteorological information, and then the refinement module 5 receives the operation interference of the solar photovoltaic panels analyzed by the analysis module 4, and refines the power parameters received in the receiving module 2 based on the operation interference, and finally receives the power parameters obtained by the refinement processing in the refinement module 5 through the recording module 6, binds the meteorological information corresponding to the power parameters to the power parameters and stores them, and the system end user accesses the system through the control terminal 1, and reads the power parameters stored in the recording module 6 and the meteorological information bound thereto in the system.

[0079] The power parameters received by the receiving module 2 are further processed by the submodules set at the lower level of the receiving module 2, and the power parameters received in the system are continuously screened by the capture logic of the power parameters with threat consumption and the power parameter refinement processing logic, so that the power parameters finally obtained by the system operation can be more streamlined for system end users to read;

[0080] In order to further improve the precision of the power parameter processing logic in this system, the irradiance analysis and prediction of the solar photovoltaic panel deployment area is further introduced. Figure 2 and Figure 3 As shown:

[0081] The BP neural network model consists of an input layer, a hidden layer, and an output layer. According to the irradiance prediction model, its learning process is the forward learning process of the irradiance sample signal and the reverse learning process of the irradiance error.

[0082] First, the irradiance sample is sent to the input layer, and after the hidden layer learning process, it is sent to the output layer: the output value is compared with the expected result. If it does not meet the requirements, it will enter the reverse learning process of the irradiance error, which is apportioned to all units in each layer, and the weights of each unit are corrected to generate a new model output. If the error does not meet the requirements, the reverse cycle will continue until the error meets the expected value requirements. The model structure and learning process are as follows Figure 2 Content shown;

[0083] In order to ensure the data mining capability and prediction accuracy of the BP neural network model, the input model parameters should not be too many or too few. Too many will affect the model data control, and too few will affect the model prediction accuracy.

[0084] The neural network model based on historical irradiance data uses 24 historical irradiances of the same type on the previous day as the input. The number of neurons in the input layer is determined according to the input dimension and output.

[0085] In the design of the number of hidden layers of the neural network, in order to obtain any non - linear mapping relationship, the number of neurons in a single hidden layer requires a relatively large number, but its computational efficiency will decrease sharply with the increase in the number of neurons. Therefore, the number of layers is designed to be two. The number of neurons in a single hidden layer can be determined according to the empirical formula. Among them, L is the number of neurons in the input layer, I is the number of neurons in the output layer, p is a constant and I < p < 10, then y is between 7 and 17. The number of neurons in the double hidden layer can be initially determined by the number of input samples and the above - mentioned empirical formula of the single hidden layer, and then through the method of cross - validation, with the goal of obtaining the minimum mean absolute bias MABE and root mean square error RMSE, to determine the number of hidden layers and the number of neurons in the hidden layer. Among them, RMSE and MABE are shown in formulas (1) and (2):

[0086]

[0087]

[0088] In the formula, Gs,i is the predicted irradiance value; gs,i is the measured irradiance value, i is the sampling point, representing time, i = 1,..., m, where m is the number of sampling points from 06:00 to 08:00, m = 24; s is the day of the year, that is, the serial number of the date in the year. For example, on January 1st, s is 1, on January 7th, s is 7, and the maximum value of the day of the year in a leap year is 366;

[0089] In the neural network model prediction system based on historical data, the input value is the historical irradiance data of 1 day or 2 days before the prediction. Since the factors affecting irradiance also include other meteorological elements such as atmospheric scattering, reflection, air mass, temperature, humidity, etc., it is very difficult to accurately represent the change law of irradiance of the same day type with only one historical irradiance data parameter. Therefore, meteorological parameters such as irradiance mean, irradiance variance, temperature mean, and day of the year are introduced in the selection of model input parameters. 4.3.3 BP neural network model prediction simulation analysis

[0090] By constructing a BP neural network irradiance prediction model based on the day - type correction method for the input model, Figure 3 which is its flow chart.

[0091] Example 2

[0092] At the specific implementation level, on the basis of Example 1, this example refers to Figure 1 to further specifically describe a energy storage management system based on a photovoltaic power generation system in Example 1:

[0093] Power transmission loss loss The formula is as follows:

[0094] power loss =P in ×k loss (1+L f +L s +L m +k V +k A );

[0095] Where: P in is the electrical input power; k loss is the power loss coefficient; L f is the cable length correction factor; L s is the cable cross-sectional area correction factor; L m k is the cable material correction factor; V is the voltage correction factor; k A is the current correction factor;

[0096] in, P out The output power of the electric power.

[0097] The above formula calculation provides necessary operation data support for the capture logic of power parameters that threaten power consumption and the refinement logic of power parameters.

[0098] like Figure 1 As shown, the monitoring module 3 operates once a day, and the daily operation timestamp is consistent with the operation time of the solar photovoltaic panel. The operation interference analysis logic of the solar photovoltaic panel in the analysis module 3 is expressed as:

[0099]

[0100] Where: γ is the interference degree; is the current daily average output power; f ch is the photoelectric conversion rate of solar photovoltaic panels; t Me The median of the current daily opening time of solar photovoltaic panels; τ is the interference factor obtained based on meteorological information;

[0101] Among them, τ=θ·ω1+Sr·ω2, θ is the air quality interference factor, that is, the higher the PM2.5 content in the air, the smaller the θ value, and vice versa; Sr is the sunshine rate; ω1 and ω2 are weights, ω2+ω1=1.

[0102] The above formula calculation provides necessary operation data support for the refined processing logic of power parameters.

[0103] like Figure 1 As shown, the power parameters stored in the recording module 6 are the power parameters that are calculated by the refinement processing logic so that the refinement processing logic is not valid. The storage unit 21 obtains the power parameters stored in the recording module 6 in real time, and uses the power parameters stored in the recording module 6 as the retrieval target. The retrieval target is used internally to search and delete the retrieved stored identical power parameters.

[0104] Through the above configuration, the power parameters stored in the storage unit 21 are further managed.

[0105] In summary, during the operation of the system in the above embodiment, it is possible to receive massive power parameters generated by the real-time operation of the photovoltaic power generation system, store and analyze the massive power parameters, and then screen the massive power parameters based on the analysis results to obtain characteristic power parameters for system-side users to read, so that the system-side users can manage the power storage in the photovoltaic power generation system more quickly based on the read power parameter content; and during the operation of the system, for the stage of screening and processing massive power parameters, the continuous processing logic set up can screen and refine the power parameters, and then further screen the power parameters based on the refined results, so that the power parameters finally provided to the system-side users by the system processing are more streamlined, which is more helpful for the system-side users to manage the power storage in the photovoltaic power generation system based on the read power parameters.

[0106] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy storage management system based on a photovoltaic power generation system, characterized in that: include: The control terminal (1) is the main control terminal of the system and is used to issue execution commands; A receiving module (2) is used to receive power parameters generated by real-time operation of the photovoltaic power generation system; A monitoring module (3) is used to monitor in real time the meteorological information of the area where the solar photovoltaic panels are deployed in the photovoltaic power generation system; An analysis module (4) is used to receive the meteorological information of the solar photovoltaic panel deployment area monitored by the monitoring module (3), and analyze the interference of the meteorological conditions in the solar photovoltaic panel deployment area on the operation of the solar photovoltaic panels based on the meteorological information; A refinement module (5) is used to receive the operation interference of the solar photovoltaic panel analyzed by the analysis module (4), and to perform refinement processing on the power parameters received by the receiving module (2) based on the operation interference; A recording module (6) is used to receive the power parameters obtained by the refinement processing in the refinement module (5), bind the meteorological information corresponding to the power parameters with the power parameters, and store them; The system end user accesses the system through the control terminal (1) and reads the power parameters and the meteorological information bound thereto stored in the recording module (6) in the system; The power parameters generated in real time during operation of the photovoltaic power generation system received by the receiving module (2) include: daily output power, daily photovoltaic power generation, and power transmission loss. The receiving module (2) is provided with submodules at the lower level, including: A storage unit (21) is used to obtain the power parameters received by the receiving module (2) and store the power parameters; A capture unit (22) is used to traverse the power parameters stored in the storage unit (21) and capture power parameters that threaten power consumption; When the storage unit (21) stores the acquired power parameters, the power parameters are differentiated and stored based on the attribution date of the power parameters; The capture logic of the power parameter with threat of loss in the capture unit (22) is: Where: power ou t is the output power; power loss Power transmission loss; power in is the photovoltaic power generation; t1 is the power supply duration; t2 is the power generation duration; Based on the above formula, the capture logic operation is performed on each power parameter stored in the storage unit (21), and the power parameter that meets the capture logic is the capture target of the capture unit (22); The monitoring module (3) operates once a day, and the daily operation timestamp is consistent with the operation time of the solar photovoltaic panel. The operation interference analysis logic of the solar photovoltaic panel in the analysis module (4) is expressed as follows: Where: γ is the interference degree; is the current daily average output power; f ch is the photoelectric conversion rate of solar photovoltaic panels; t Me The median of the current daily opening time of solar photovoltaic panels; τ is the interference factor obtained based on meteorological information; Among them, τ=θ·ω1+Sr·ω2, θ is the air quality interference factor, that is, the higher the PM2.5 content in the air, the smaller the value of θ, and vice versa; Sr is the sunshine rate; ω1 and ω2 are weights, ω2+ω1=1; The refinement module (5) is provided with submodules at the lower level, including: A calling unit (51) is used to call the power parameters with threat consumption captured by the capturing unit (22); The power parameter retrieved by the retrieval unit (51) is the refinement target processed by the refinement module (5); During the operation phase of the refinement module (5), after the power parameters corresponding to the refinement target are retrieved by the retrieval unit (51), refinement processing is performed on each retrieved power parameter. The refinement processing logic is represented as follows: Wherein, after the power parameters retrieved by the retrieval unit (51) are calculated through the refinement processing logic, if the refinement processing logic still does not hold, the power parameters for which the refinement processing logic does not hold are packaged and sent to the recording module (6).

2. The energy storage management system based on the photovoltaic power generation system according to claim 1, characterized in that: The power transmission loss power loss The formula is as follows: power loss =P in ×k loss (1+L f +L s +L m +k V +k A ); Where: P in is the electrical input power; k loss is the power loss coefficient; L f is the cable length correction factor; L s is the cable cross-sectional area correction factor; L m k is the cable material correction factor; V is the voltage correction factor; k A is the current correction factor; in, P out The output power of the electric power.

3. The energy storage management system based on a photovoltaic power generation system according to claim 1, characterized in that: The monitoring module (3) is integrated with a positioning module and an interaction unit (31); An interactive unit (31) is used to access the Internet and obtain meteorological information on the network; During the operation phase of the monitoring module (3), the positioning module acquires the location information of the deployment location of the solar photovoltaic panel in real time. Based on the area to which the acquired location information belongs, the interactive unit (31) is used to acquire the meteorological information of the area to which the location information belongs in the network. The meteorological information includes: air quality and sunshine rate.

4. The energy storage management system based on a photovoltaic power generation system according to claim 1, characterized in that: The power parameters stored in the recording module (6) are power parameters that are calculated using the refinement processing logic so that the refinement processing logic is invalid. The storage unit (21) obtains the power parameters stored in the recording module (6) in real time, uses the power parameters stored in the recording module (6) as a retrieval target, performs a search internally using the retrieval target, and deletes the retrieved stored identical power parameters.

5. The energy storage management system based on the photovoltaic power generation system according to claim 1, characterized in that: The control terminal (1) is interactively connected to a receiving module (2) via a local area network; the receiving module (2) is interactively connected to a storage unit (21) and a capture unit (22) via a local area network; the receiving module (2) is interactively connected to a monitoring module (3) via a local area network; the monitoring module (3) is electrically connected to an interaction unit (31) via a medium; the monitoring module (3) is interactively connected to an analysis module (4) and a refinement module (5) via a local area network; the refinement module (5) is interactively connected to a retrieval unit (51) via a local area network; the retrieval unit (51) is interactively connected to the capture unit (22) via a local area network; the refinement module (5) is interactively connected to a recording module (6) via a local area network.

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