Battery energy storage management system based on photovoltaic power generation

Abstract

The present invention relates to the field of energy storage management technology, and specifically discloses a battery energy storage management system based on photovoltaic power generation, including: a power supply and consumption parameter acquisition module, used to obtain the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array; a power supply and consumption state determination module, used to obtain the current photovoltaic power supply state based on the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array; a charge and discharge mode control module, used to control the charge and discharge working mode of the photovoltaic battery energy storage system based on the current photovoltaic power supply state, and obtain the photovoltaic battery management result; to achieve the optimized management of photovoltaic power supply and enhance the stability and reliability of power supply. At the same time, it improves the energy utilization rate of photovoltaic power generation, reduces energy loss, and promotes the effective utilization and sustainable development of renewable energy.

Description

Technical Field

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

[0002] At present, with the global emphasis on and promotion of clean energy, photovoltaic power generation has been widely used as a sustainable way of obtaining energy. In order to improve the stability and availability of photovoltaic power generation, the battery energy storage system based on photovoltaic power generation has gradually become the focus of research and application. In the existing technical system, solar energy is converted into electrical energy through photovoltaic arrays, and the excess electrical energy is stored in batteries for use during peak power generation or when photovoltaic power supply is insufficient.

[0003] However, the existing technical system does not take into account the real-time and historical power demand of all photovoltaic power supply loads and the real-time output power of the photovoltaic array, which makes it impossible to accurately evaluate the current photovoltaic power supply status and make reasonable decisions. It is also impossible to intelligently and effectively control the charging and discharging working mode of the photovoltaic battery energy storage system based on the accurate power supply status, which affects the efficiency and service life of the energy storage system.

[0004] Therefore, the present invention proposes a battery energy storage management system based on photovoltaic power generation. Summary of the invention

[0005] The present invention provides a battery energy storage management system based on photovoltaic power generation, which is used to use a power supply and consumption parameter acquisition module to comprehensively acquire relevant data, providing a rich and accurate source of information for system analysis. By acquiring the real-time and historical power demand of all photovoltaic power supply loads, the changing trend of power consumption can be accurately grasped; acquiring the real-time output power of the photovoltaic array is helpful to understand the power generation capacity. The power supply and consumption state determination module accurately evaluates the current photovoltaic power supply state based on the above-mentioned rich data, so that the system can clearly know the actual power supply situation and provide a reliable basis for subsequent decision-making. Secondly, the charge and discharge mode control module scientifically controls the charge and discharge working mode of the photovoltaic battery energy storage system based on the accurate power supply state. This effectively improves the energy utilization efficiency, avoids overcharging or discharging, and prolongs the service life of the battery. On the whole, the system can realize the optimized management of photovoltaic power supply and enhance the stability and reliability of power supply. At the same time, it improves the energy utilization rate of photovoltaic power generation, reduces energy loss, and promotes the effective utilization and sustainable development of renewable energy.

[0006] The present invention provides a battery energy storage management system based on photovoltaic power generation, comprising:

[0007] The power supply and consumption parameter acquisition module is used to obtain the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array;

[0008] A power supply and consumption status determination module is used to obtain the current photovoltaic power supply status based on the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array;

[0009] The charge and discharge mode control module is used to control the charge and discharge working mode of the photovoltaic battery energy storage system based on the current photovoltaic power supply status to obtain the photovoltaic battery management result.

[0010] Preferably, the power supply and utilization parameter acquisition module includes:

[0011] The real-time power demand acquisition submodule is used to measure the real-time total current and real-time total voltage of each photovoltaic power supply load based on the intelligent sensor, and calculate the real-time total power of each photovoltaic power supply load based on the real-time total current and real-time total voltage of each photovoltaic power supply load as the real-time power demand of each photovoltaic power supply load;

[0012] The historical electricity demand acquisition submodule is used to obtain the historical electricity demand of all photovoltaic power supply loads;

[0013] The photovoltaic output acquisition submodule is used to detect the real-time output current and real-time voltage of the photovoltaic array based on the current sensor and the voltage sensor respectively;

[0014] The output power calculation submodule is used to calculate the real-time output power of the photovoltaic array based on the real-time output current and real-time output voltage of the photovoltaic array.

[0015] Preferably, the historical electricity demand acquisition submodule includes:

[0016] A historical total power acquisition unit is used to acquire the real-time total power of each photovoltaic power supply load at each statistical moment in a preset historical long period, and to take the sum of the real-time total power of all photovoltaic power supply loads at each statistical moment in the preset historical long period as the total power consumption of all photovoltaic power supply loads at each moment in the preset historical long period;

[0017] A curve fitting unit is used to fit the real-time total power of each photovoltaic power supply load at all statistical moments in a preset long historical period into a historical power curve of each photovoltaic power supply load, and to fit the total power consumption of all photovoltaic power supply loads at all statistical moments in a preset long historical period into a long-term power change curve of all photovoltaic power supply loads;

[0018] The historical power demand determination unit is used to take the historical power curve of each photovoltaic power supply load and the long-term power change curves of all photovoltaic power supply loads as the historical power demand of all photovoltaic power supply loads.

[0019] Preferably, the power supply and use status determination module includes:

[0020] The periodic property analysis submodule is used to perform periodic property analysis on the historical power demand of all photovoltaic power supply loads to obtain the periodic property analysis results of all photovoltaic power supply loads;

[0021] The power supply status determination submodule is used to determine the current photovoltaic power supply status based on the real-time power demand and periodic property analysis results of all photovoltaic power supply loads and the real-time output power of the photovoltaic array.

[0022] Preferably, the periodic property analysis submodule includes:

[0023] A first cycle property analysis unit is used to perform a cycle property analysis on a historical power curve of each photovoltaic power supply load in the historical power demand of all photovoltaic power supply loads to obtain a power consumption change cycle of each photovoltaic power supply load;

[0024] A cycle initial value determination unit, used to take the lowest common multiple of the total power variation cycle of all photovoltaic power supply loads as the total power variation cycle initial value of all photovoltaic power supply loads;

[0025] A period assumption value determination unit, used to determine a plurality of total power variation period assumption values ​​based on the total power variation period initial value;

[0026] The second periodic property analysis unit is used to divide and cluster the long-term power change curves of all photovoltaic power supply loads in the historical power demand of all photovoltaic power supply loads based on the assumed values ​​of all total power change cycles, and obtain the actual values ​​of the total power change cycles of all photovoltaic power supply loads as the periodic property analysis results of all photovoltaic power supply loads.

[0027] Preferably, the first periodic property analysis unit comprises:

[0028] A time-frequency domain transformation subunit is used to perform a fast Fourier transform on the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load to obtain a frequency domain representation of the historical power sequence data of each photovoltaic power supply load;

[0029] The first cycle determination subunit is used to determine the frequency value corresponding to the high energy value in the frequency domain representation of the historical power sequence data of each photovoltaic power supply load as the historical power high energy frequency value of each photovoltaic power supply load, and take the reciprocal of the historical power high energy frequency value of each photovoltaic power supply load as the first power consumption change cycle of each photovoltaic power supply load;

[0030] A periodogram generating subunit, for generating a periodogram of the historical power sequence data of each photovoltaic power supply load based on a fast Fourier transform result of the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load;

[0031] The second period determination subunit is used to determine the frequency value corresponding to the peak value of the ordinate amplitude in the period diagram of the historical power sequence data of each photovoltaic power supply load as the main frequency value of each photovoltaic power supply load, and take the reciprocal of the main frequency value of each photovoltaic power supply load as the second power consumption change period of each photovoltaic power supply load;

[0032] The variation period determination subunit is used to determine the electric power variation period of each photovoltaic power supply load based on the historical power curve of each photovoltaic power supply load, the first electric power variation period and the second electric power variation period.

[0033] Preferably, the change period determination subunit includes:

[0034] The model fitting end is used to perform autoregressive moving average model fitting on the historical power curve of each photovoltaic power supply load to obtain an autoregressive moving average model whose fitting state meets the requirements;

[0035] A third period determination terminal is used to determine a third power consumption change period of each photovoltaic power supply load based on an autoregressive moving average model whose fitting state meets the requirements;

[0036] A change cycle determination terminal, used to determine a plurality of historical power change assumption values ​​of each photovoltaic power supply load based on a first power consumption change cycle, a second power consumption change cycle, and a third power consumption change cycle of each photovoltaic power supply load;

[0037] A time domain signal reconstruction end is used to reconstruct the time domain signal based on each historical power change assumption value of each photovoltaic power supply load and the fast Fourier transform result of the historical power curve, and obtain a reconstructed time domain graph corresponding to each historical power change assumption value of each photovoltaic power supply load;

[0038] A similarity calculation end is used to calculate the similarity between the reconstructed time domain graph corresponding to each historical power change hypothesis value of each photovoltaic power supply load and the historical power curve of the corresponding photovoltaic power supply load;

[0039] The variation period determination end is used to take the historical power variation assumption value corresponding to the maximum similarity among all historical power variation assumption values ​​of each photovoltaic power supply load as the power variation period of each photovoltaic power supply load.

[0040] Preferably, the second periodic property analysis unit comprises:

[0041] The curve division subunit is used to divide the long-term power change curves of all photovoltaic power supply loads in the historical power demand of all photovoltaic power supply loads based on all total power change cycle assumption values, and obtain a set of division curves corresponding to each total power change cycle assumption value;

[0042] The cluster analysis subunit is used to calculate the curve similarity in the partition curve set corresponding to each total power change period assumption value, and regard the total power change period assumption value corresponding to the partition curve set corresponding to the maximum curve similarity as the actual value of the total power change period of all photovoltaic power supply loads, and regard the actual value of the total power change period of all photovoltaic power supply loads as the periodic property analysis result of all photovoltaic power supply loads.

[0043] Preferably, the power supply status determination submodule includes:

[0044] A model acquisition unit, used to acquire a photovoltaic power supply status analysis model;

[0045] The power supply status determination unit is used to input the real-time power demand and periodic property analysis results of all photovoltaic power supply loads and the real-time output power of the photovoltaic array into the photovoltaic power supply status analysis model to obtain the current photovoltaic power supply status.

[0046] Preferably, the charge and discharge mode control module includes:

[0047] The first working mode setting submodule is used to control the photovoltaic battery energy storage system to start the charging working mode as a photovoltaic battery management result if the current photovoltaic power supply state is oversupply;

[0048] The second working mode setting submodule is used to control the photovoltaic battery energy storage system to start the discharge working mode as the photovoltaic battery management result if the current photovoltaic power supply status is insufficient power supply.

[0049] The beneficial effects of the present invention compared with the prior art are: the power supply and utilization parameter acquisition module is used to comprehensively acquire relevant data, providing a rich and accurate source of information for system analysis. By acquiring the real-time and historical power demand of all photovoltaic power supply loads, the changing trend of power consumption can be accurately grasped; acquiring the real-time output power of the photovoltaic array is helpful to understand the power generation capacity. The power supply and utilization status determination module accurately evaluates the current photovoltaic power supply status based on the above-mentioned rich data, so that the system can clearly know the actual power supply situation and provide a reliable basis for subsequent decision-making. Secondly, the charge and discharge mode control module scientifically controls the charge and discharge working mode of the photovoltaic battery energy storage system based on the accurate power supply status. This effectively improves the energy utilization efficiency, avoids overcharging or discharging, and prolongs the service life of the battery. On the whole, the system can realize the optimized management of photovoltaic power supply and enhance the stability and reliability of power supply. At the same time, it improves the energy utilization rate of photovoltaic power generation, reduces energy loss, and promotes the effective utilization and sustainable development of renewable energy.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in this application document.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 Schematic diagram of a battery energy storage management system based on photovoltaic power generation in an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of internal functional submodules of the power supply and utilization parameter acquisition module in an embodiment of the present invention;

[0055] Figure 3 Schematic diagram of internal functional submodules of a power supply and utilization status determination module in an embodiment of the present invention;

[0056] Figure 4 Schematic diagram of internal functional submodules of the charge and discharge mode control module in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0058] Example 1

[0059] The present invention provides reference Figure 1 ,include:

[0060] The power supply and consumption parameter acquisition module is used to obtain the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array;

[0061] A power supply and consumption status determination module is used to obtain the current photovoltaic power supply status based on the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array;

[0062] The charge and discharge mode control module is used to control the charge and discharge working mode of the photovoltaic battery energy storage system based on the current photovoltaic power supply status to obtain the photovoltaic battery management result.

[0063] In this embodiment, the photovoltaic power supply load refers to a device or system that relies on photovoltaic power generation to provide power to drive or support its operation. For example: factory machinery and lighting equipment that use photovoltaic power generation.

[0064] In this embodiment, the real-time power demand refers to the instantaneous value of the power consumed by the photovoltaic power supply load at the current moment. For example, the power consumption of the photovoltaic power supply production line currently running in the factory.

[0065] In this embodiment, the historical power demand refers to the record of the power consumption of the photovoltaic power supply load in the past period of time. For example, the power consumption value of the photovoltaic power supply equipment in the factory at each time period every day in the past week.

[0066] In this embodiment, the photovoltaic array is a device composed of a plurality of photovoltaic cell modules connected and arranged in a certain manner, and used to convert solar energy into electrical energy. For example: a whole composed of rows of solar panels installed on a roof.

[0067] In this embodiment, the real-time output power of the photovoltaic array refers to the size of the electric power that the photovoltaic array can generate and output at the current moment. For example, at the current moment, the electric power outputted by the solar panel as a whole is 10 kilowatts.

[0068] In this embodiment, the current photovoltaic power supply state refers to the relationship between photovoltaic power generation and load power consumption at the current moment, including whether the power supply is excessive or insufficient, etc. For example: the current photovoltaic power generation is greater than the load power consumption, and the power supply is excessive.

[0069] In this embodiment, the charging and discharging working mode refers to the working mode of charging or discharging of the photovoltaic battery energy storage system. For example, when there is an oversupply of power, the system is in the charging mode; when there is an insufficient power supply, the system is in the discharging mode.

[0070] In this embodiment, the photovoltaic battery management result is the final effect or output obtained by controlling and managing the photovoltaic battery energy storage system. For example: achieving efficient energy storage, stable power supply, etc.

[0071] The beneficial effects of the above technology are: the power supply and consumption parameter acquisition module is used to comprehensively obtain relevant data, providing a rich and accurate source of information for system analysis. By obtaining the real-time and historical power demand of all photovoltaic power supply loads, the changing trend of power consumption can be accurately grasped; obtaining the real-time output power of the photovoltaic array is helpful to understand the power generation capacity. The power supply and consumption status determination module accurately evaluates the current photovoltaic power supply status based on the above rich data, so that the system can clearly know the actual situation of power supply and provide a reliable basis for subsequent decision-making. Secondly, the charge and discharge mode control module scientifically controls the charge and discharge working mode of the photovoltaic battery energy storage system based on the accurate power supply status. This effectively improves the energy utilization efficiency, avoids overcharging or discharging, and prolongs the service life of the battery. On the whole, the system can realize the optimized management of photovoltaic power supply and enhance the stability and reliability of power supply. At the same time, it improves the energy utilization rate of photovoltaic power generation, reduces energy loss, and promotes the effective utilization and sustainable development of renewable energy.

[0072] Example 2

[0073] Based on Example 1, the power supply and consumption parameter acquisition module refers to Figure 2 ,include:

[0074] The real-time power demand acquisition submodule is used to measure the real-time total current and real-time total voltage of each photovoltaic power supply load based on the intelligent sensor, and calculate the real-time total power of each photovoltaic power supply load based on the real-time total current and real-time total voltage of each photovoltaic power supply load as the real-time power demand of each photovoltaic power supply load;

[0075] The historical electricity demand acquisition submodule is used to obtain the historical electricity demand of all photovoltaic power supply loads;

[0076] The photovoltaic output acquisition submodule is used to detect the real-time output current and real-time voltage of the photovoltaic array based on the current sensor and the voltage sensor respectively;

[0077] The output power calculation submodule is used to calculate the real-time output power of the photovoltaic array based on the real-time output current and real-time output voltage of the photovoltaic array.

[0078] In this embodiment, the intelligent sensor is a current transformer and a voltage transformer installed in a distribution box or a distribution cabinet, which is used to measure the total current and voltage of multiple loads or large loads. The current transformer selects a suitable ratio according to the load current, such as 500A / 5A, and is installed on the incoming line of the load. The voltage transformer is selected according to the system voltage level, such as 10kV / 100V, and is connected in parallel to the power bus.

[0079] In this embodiment, the real-time total current and the real-time total voltage refer to the total current consumed by each photovoltaic power supply load and the total voltage borne by each photovoltaic power supply load at the current moment. For example, the total current consumed by all photovoltaic power supply equipment in the factory is 10 amperes and the total voltage is 220 volts.

[0080] In this embodiment, the real-time total power of all photovoltaic power supply loads is calculated based on the real-time total current and real-time total voltage of all photovoltaic power supply loads by using the power calculation formula (power = current × voltage), and using the collected real-time total current and real-time total voltage data to obtain the total power consumed by all photovoltaic power supply loads at the current moment. For example: if the real-time total current is 5 amperes and the real-time total voltage is 12 volts, the real-time total power is 60 watts.

[0081] In this embodiment, the real-time output current and real-time voltage of the photovoltaic array refer to the current magnitude and voltage value when the photovoltaic array outputs electric energy at the current moment. For example, at this moment, the output current of the photovoltaic array is 8 amperes and the output voltage is 18 volts.

[0082] In this embodiment, the real-time output power of the photovoltaic array is calculated based on the real-time output current and real-time output voltage of the photovoltaic array. The power calculation formula is also used to determine the magnitude of the electric power outputted at the current moment by the real-time output current and real-time output voltage data of the photovoltaic array obtained. For example: if the real-time output current is 6 amperes and the real-time output voltage is 15 volts, the real-time output power is 90 watts.

[0083] The beneficial effects of the above technologies are as follows: the real-time power demand acquisition submodule uses intelligent sensors to measure and calculate the real-time total power of all photovoltaic power supply loads as real-time power demand, realizing accurate monitoring and quantification of real-time power consumption. The historical power demand acquisition submodule obtains the historical power consumption data of the load, which provides an important reference for analyzing power consumption trends and patterns, and helps to make more comprehensive power supply and consumption planning. The photovoltaic output acquisition submodule detects the real-time output current and voltage of the photovoltaic array through current sensors and voltage sensors respectively, ensuring the accuracy and reliability of the acquired data. The output power calculation submodule calculates the real-time output power of the photovoltaic array based on the detected real-time current and voltage, providing key data for evaluating photovoltaic power generation capacity. Overall, the various submodules of the power supply and consumption parameter acquisition module work together to fully and accurately obtain the parameters related to power supply and consumption, providing a solid data foundation for the subsequent power supply and consumption status determination and charge and discharge mode control, which helps to improve the operation efficiency and stability of the entire photovoltaic battery energy storage management system.

[0084] Example 3

[0085] Based on Example 2, the historical electricity demand acquisition submodule includes:

[0086] A historical total power acquisition unit is used to acquire the real-time total power of each photovoltaic power supply load at each statistical moment in a preset historical long period, and to take the sum of the real-time total power of all photovoltaic power supply loads at each statistical moment in the preset historical long period as the total power consumption of all photovoltaic power supply loads at each moment in the preset historical long period;

[0087] A curve fitting unit is used to fit the real-time total power of each photovoltaic power supply load at all statistical moments in a preset long historical period into a historical power curve of each photovoltaic power supply load, and to fit the total power consumption of all photovoltaic power supply loads at all statistical moments in a preset long historical period into a long-term power change curve of all photovoltaic power supply loads;

[0088] The historical power demand determination unit is used to take the historical power curve of each photovoltaic power supply load and the long-term power change curves of all photovoltaic power supply loads as the historical power demand of all photovoltaic power supply loads.

[0089] In this embodiment, the preset historical long period is a pre-set relatively long time range for collecting and analyzing historical electricity consumption data, for example, the past year, the past few months, etc.

[0090] In this embodiment, the statistical time is a specific time point predetermined for recording and counting electricity consumption data, for example, every hour of the day, such as 8 o'clock, 12 o'clock, etc.

[0091] In this embodiment, the total power consumption refers to the sum of the power consumed by all photovoltaic power supply loads in a certain period of time. For example, in the past week, the sum of the power consumed by all photovoltaic power supply loads is 1000 kWh.

[0092] In this embodiment, the historical power curve is a curve formed by connecting the power consumption values ​​of each photovoltaic power supply load at different times in a preset long historical period, which is used to intuitively display the change of power consumption over time. For example: a curve formed by the power consumption values ​​of a photovoltaic power supply load at different times every day in the past month.

[0093] In this embodiment, the long-term power change curve is a curve formed by connecting the total power consumption values ​​of all photovoltaic power supply loads at each statistical moment in a preset historical long period, which is used to reflect the long-term change trend of the overall power consumption. For example: in the past six months, the curve formed by the total power consumption values ​​of all photovoltaic power supply loads at each moment on a fixed date every month.

[0094] The beneficial effects of the above technology are: the historical power demand acquisition submodule can comprehensively and deeply process and analyze historical power consumption data. The historical total power acquisition unit obtains the real-time total power of each photovoltaic power supply load at each statistical moment in the preset historical long period and calculates the sum, which lays a solid data foundation for subsequent analysis and allows the system to have a comprehensive grasp of the overall historical power consumption. The curve fitting unit fits the complex real-time total power data into an intuitive historical power curve and a long-term power change curve, realizes the regularization and modeling of the data, and helps to discover the periodicity, trend and other characteristics hidden in the data. The historical power demand determination unit determines these curves as historical power demand, making the description of historical power consumption more accurate and intuitive, and providing a reliable basis for predicting future power demand. This series of operations work together to deeply explore the potential value of historical power consumption data, and provide strong support for optimizing power supply and consumption strategies and reasonably controlling charging and discharging modes, thereby improving the intelligence level, energy utilization efficiency, stability and adaptability of the photovoltaic battery energy storage management system, and effectively ensuring the reliable supply and efficient utilization of photovoltaic power generation.

[0095] Example 4

[0096] Based on Example 1, the power supply and consumption status determination module refers to Figure 3 ,include:

[0097] The periodic property analysis submodule is used to perform periodic property analysis on the historical power demand of all photovoltaic power supply loads to obtain the periodic property analysis results of all photovoltaic power supply loads;

[0098] The power supply status determination submodule is used to determine the current photovoltaic power supply status based on the real-time power demand and periodic property analysis results of all photovoltaic power supply loads and the real-time output power of the photovoltaic array.

[0099] In this embodiment, the periodic property analysis result refers to the conclusion on the periodic characteristics of electricity demand after in-depth study and calculation of the historical electricity demand of all photovoltaic power supply loads. This may include the periodic regularity of the change in the power consumption of each photovoltaic power supply load, such as the duration of the cycle, the strength of the periodicity, whether there are multiple different cycles superimposed, etc. For example, the analysis results may show that the power consumption of a certain load shows a regularity of one cycle every 24 hours, or the total power consumption of multiple loads shows a characteristic of one cycle per week with a significant peak on weekends. These results help to more accurately predict future electricity demand, thereby optimizing photovoltaic power supply and energy storage management.

[0100] The beneficial effects of the above technologies are as follows: The periodic property analysis submodule performs periodic property analysis on the historical power demand of the photovoltaic power supply load, which helps to discover the regularity and periodic characteristics of power consumption. This provides an important basis for accurately predicting future power demand, enabling the system to prepare and plan in advance. The power supply status determination submodule comprehensively considers the real-time power demand, the periodic property analysis results, and the real-time output power of the photovoltaic array to determine the current photovoltaic power supply status, achieving a comprehensive and accurate assessment of the power supply situation. Through the collaborative work of these two submodules, the status of photovoltaic power supply can be more accurately grasped, and the system operation strategy can be adjusted and optimized in time. It helps to improve energy utilization efficiency, ensure the stability and reliability of power supply, and avoid insufficient or excessive power supply. At the same time, it provides key decision support for the reasonable control of the charging and discharging mode of the photovoltaic battery energy storage system, further improving the performance and benefits of the entire system.

[0101] Example 5

[0102] Based on Example 4, the periodic property analysis submodule includes:

[0103] A first cycle property analysis unit is used to perform a cycle property analysis on a historical power curve of each photovoltaic power supply load in the historical power demand of all photovoltaic power supply loads to obtain a power consumption change cycle of each photovoltaic power supply load;

[0104] A cycle initial value determination unit, used to take the lowest common multiple of the total power variation cycle of all photovoltaic power supply loads as the total power variation cycle initial value of all photovoltaic power supply loads;

[0105] A period assumption value determination unit, used to determine a plurality of total power variation period assumption values ​​based on the total power variation period initial value;

[0106] The second periodic property analysis unit is used to divide and cluster the long-term power change curves of all photovoltaic power supply loads in the historical power demand of all photovoltaic power supply loads based on the assumed values ​​of all total power change cycles, and obtain the actual values ​​of the total power change cycles of all photovoltaic power supply loads as the periodic property analysis results of all photovoltaic power supply loads.

[0107] In this embodiment, the power consumption variation cycle refers to the time interval during which the power consumption of the photovoltaic power supply load presents a repetitive change. For example, the power consumption of a certain load may repeat a similar variation pattern every 12 hours, so 12 hours is its power consumption variation cycle.

[0108] In this embodiment, the initial value of the total power variation cycle is a starting estimated value of the total power variation cycle of all photovoltaic power supply loads that is initially set or calculated when performing cycle analysis.

[0109] In this embodiment, based on the initial value of the total power variation period, multiple total power variation period assumption values ​​are determined, which means that based on the estimated value of the total power variation period initially obtained, multiple possible period value assumptions are derived through a certain method or algorithm. For example, the initial value of the total power variation period is 8 hours. Based on this, it is possible to determine assumption values ​​such as 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, etc., so as to further analyze and verify, so as to find the total power variation period that best meets the actual situation.

[0110] In this embodiment, the actual value of the total power variation period is a value that is finally determined after analysis, verification and calculation of various assumed values ​​and can accurately reflect the actual variation period of the total power of all photovoltaic power supply loads.

[0111] The beneficial effects of the above technology are as follows: the first cycle property analysis unit analyzes the historical power curve of each photovoltaic power supply load to obtain the power consumption change cycle, which provides a basis for the subsequent in-depth study of power consumption patterns. The cycle initial value determination unit cleverly uses the lowest common multiple of the total power change cycle of all loads as the initial value, setting a meaningful starting point for subsequent analysis. The cycle assumption value determination unit determines multiple assumption values ​​based on the initial value, expanding the possibility and comprehensiveness of the analysis. The second cycle property analysis unit divides and clusters the long-term power change curve based on these assumption values, and finally obtains the actual value of the total power change cycle, realizing the accurate analysis of the cycle properties. These operations of the entire cycle property analysis submodule can deeply explore the cycle properties in the historical power demand of photovoltaic power supply loads, providing key support for accurately grasping the power consumption pattern, optimizing the power supply strategy and improving the system performance. It helps to more accurately predict power demand, rationally allocate energy resources, and improve the stability and energy efficiency of the photovoltaic power supply system.

[0112] Example 6

[0113] On the basis of Embodiment 5, the first periodic property analysis unit includes:

[0114] A time-frequency domain transformation subunit is used to perform a fast Fourier transform on the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load to obtain a frequency domain representation of the historical power sequence data of each photovoltaic power supply load;

[0115] The first cycle determination subunit is used to determine the frequency value corresponding to the high energy value in the frequency domain representation of the historical power sequence data of each photovoltaic power supply load as the historical power high energy frequency value of each photovoltaic power supply load, and take the reciprocal of the historical power high energy frequency value of each photovoltaic power supply load as the first power consumption change cycle of each photovoltaic power supply load;

[0116] A periodogram generating subunit, for generating a periodogram of the historical power sequence data of each photovoltaic power supply load based on a fast Fourier transform result of the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load;

[0117] The second period determination subunit is used to determine the frequency value corresponding to the peak value of the ordinate amplitude in the period diagram of the historical power sequence data of each photovoltaic power supply load as the main frequency value of each photovoltaic power supply load, and take the reciprocal of the main frequency value of each photovoltaic power supply load as the second power consumption change period of each photovoltaic power supply load;

[0118] The variation period determination subunit is used to determine the electric power variation period of each photovoltaic power supply load based on the historical power curve of each photovoltaic power supply load, the first electric power variation period and the second electric power variation period.

[0119] In this embodiment, a fast Fourier transform (FFT) is performed on the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load to obtain a frequency domain representation of the historical power sequence data of each photovoltaic power supply load, which is:

[0120] Fast Fourier transform (FFT) is applied to the historical power sequence data contained in the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load to convert the time domain signal into a frequency domain signal. The spectrum diagram obtained by fast Fourier transform can observe the energy distribution of the power signal at different frequencies. There is an inverse relationship between the frequency value and the period, so the peak frequency corresponding to the possible period can be found from the spectrum diagram to determine the period. For example, if a high energy value is found at a frequency of f = 0.0417 Hz (corresponding to a period of 24 hours, i.e., one day) in the spectrum diagram, it indicates that the power of the load may have a daily cycle variation.

[0121] In this embodiment, the historical power sequence data refers to a set of data consisting of power values ​​of each photovoltaic power supply load recorded in chronological order.

[0122] In this embodiment, the high energy value is the amplitude (ie, the energy value) when the vertical coordinate value in the spectrum graph obtained by fast Fourier transform is the largest.

[0123] In this embodiment, the frequency value corresponding to the high energy value is usually the value with the highest energy in the frequency domain representation.

[0124] In this embodiment, the fast Fourier transform result of the historical power curve refers to a frequency domain diagram that can reflect the power frequency characteristics obtained after fast Fourier transform processing.

[0125] In this embodiment, based on the fast Fourier transform result of the historical power curve of each photovoltaic power load in the historical power demand of each photovoltaic power load, a periodogram of the historical power series data of each photovoltaic power load is generated. This is a method for estimating the power spectral density (PSD), which is obtained by performing a Fourier transform on the power time series and then calculating the square of the transform result. The periodogram shows the power distribution of the power signal at different frequencies, similar to the spectrum diagram in spectrum analysis. By finding the peak in the periodogram, the main frequency components in the power signal can be determined, and then the corresponding period can be calculated. For example, if a peak value of the ordinate is found in the periodogram at a frequency of f = 0.000116 Hz (corresponding to a period of 86400 seconds, i.e., one day), it indicates that the power of the load may have a daily periodic variation.

[0126] The beneficial effects of the above technology are as follows: The time-frequency domain transformation subunit converts the historical power curve into a frequency domain representation by performing a fast Fourier transform on the historical power curve, providing a new perspective and data form for subsequent analysis. The first period determination subunit determines the frequency value corresponding to the high energy value from the frequency domain representation, and then obtains the first power consumption change period, providing a quantitative method for period analysis. The periodogram generated by the periodogram generation subunit can more intuitively display the periodic characteristics of the power sequence data. The second period determination subunit determines the main frequency value in the periodogram and obtains the second power consumption change period, further enriching the means of period analysis. The change period determination subunit comprehensively considers the historical power curve and the two power consumption change periods to determine the final power consumption change period, making the result more accurate and reliable. These subdivision operations of the first period property analysis unit realize a comprehensive, in-depth and accurate analysis of the periodic properties of the historical power curve of the photovoltaic power supply load. It helps to grasp the periodic law of electricity consumption more accurately, and provides strong data support and technical guarantee for optimizing power supply and consumption strategies, improving energy utilization efficiency and ensuring power supply stability.

[0127] Example 7

[0128] On the basis of Embodiment 6, the change period determination subunit includes:

[0129] The model fitting end is used to perform autoregressive moving average model fitting on the historical power curve of each photovoltaic power supply load to obtain an autoregressive moving average model whose fitting state meets the requirements;

[0130] A third period determination terminal is used to determine a third power consumption change period of each photovoltaic power supply load based on an autoregressive moving average model whose fitting state meets the requirements;

[0131] A change cycle determination terminal, used to determine a plurality of historical power change assumption values ​​of each photovoltaic power supply load based on a first power consumption change cycle, a second power consumption change cycle, and a third power consumption change cycle of each photovoltaic power supply load;

[0132] A time domain signal reconstruction end is used to reconstruct the time domain signal based on each historical power change assumption value of each photovoltaic power supply load and the fast Fourier transform result of the historical power curve, and obtain a reconstructed time domain graph corresponding to each historical power change assumption value of each photovoltaic power supply load;

[0133] A similarity calculation end is used to calculate the similarity between the reconstructed time domain graph corresponding to each historical power change hypothesis value of each photovoltaic power supply load and the historical power curve of the corresponding photovoltaic power supply load;

[0134] The variation period determination end is used to take the historical power variation assumption value corresponding to the maximum similarity among all historical power variation assumption values ​​of each photovoltaic power supply load as the power variation period of each photovoltaic power supply load.

[0135] In this embodiment, an autoregressive moving average model is fitted to the historical power curve of each photovoltaic power supply load, and an autoregressive moving average model whose fitting state meets the requirements is obtained:

[0136] By fitting the model to the historical power data, the estimated parameter values ​​of the model can be obtained. Then the residual sequence of the model is analyzed. If the residual sequence is approximately white noise (that is, there is no autocorrelation), it means that the model fits well.

[0137] In this embodiment, based on the autoregressive moving average model whose fitting state meets the requirements, the third power consumption change period of each photovoltaic power supply load is determined: in the fitted ARIMA model, the order of the autoregressive part and the order of the moving average part may be related to the power change period. For example, if in the fitted ARIMA (p, d, q) model, the order of the autoregressive part is equal to 1 and the autoregressive coefficient is significantly non-zero at the lag operator equal to 24 (corresponding to one day), it may indicate that the load power has a daily cycle change.

[0138] In this embodiment, the multiple historical power change assumption values ​​of each photovoltaic power supply load are determined based on the first power consumption change cycle, the second power consumption change cycle, and the third power consumption change cycle of each photovoltaic power supply load. The three cycle values ​​obtained before are combined to set multiple possible historical power change values. The assumption value can be generated by setting a certain range and step size near these three known cycle values. For example: it is known that the first cycle is 10 hours, the second cycle is 12 hours, and the third cycle is 8 hours. The step size is set to 1 hour. The generated assumption values ​​may be 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 13 hours, etc.

[0139] In this embodiment, the time domain signal is reconstructed based on the fast Fourier transform result of each historical power change assumption value and the historical power curve of each photovoltaic power supply load, and the reconstructed time domain image corresponding to each historical power change assumption value of each photovoltaic power supply load is obtained by reconstructing the signal image in the time domain using the assumption value and the transformation result. In order to verify whether the period determined from the spectrum diagram is accurate, you can try to reconstruct the time domain signal according to the identified peak frequency. Each frequency component (sine wave and cosine wave determined by the peak frequency) is superimposed according to its amplitude and phase to obtain a reconstructed time domain signal. Then compare the reconstructed signal with the original time domain signal. If the two have a high degree of similarity in shape, amplitude change, etc., it means that the determined period is reasonable; if there is a large difference, it may be necessary to review the spectrum diagram analysis process to check whether important frequency components are missed or there is a misjudgment.

[0140] In this embodiment, calculating the similarity between the reconstructed time domain image corresponding to each historical power change hypothesis value of each photovoltaic power supply load and the historical power curve of the corresponding photovoltaic power supply load is to compare the similarity between the reconstructed image and the actual historical power curve. Common similarity calculation methods, such as cosine similarity, mean square error, etc., can be used to compare the power value difference between the reconstructed time domain image and the actual historical power curve at the corresponding time point. For example: by calculating the mean square error of the two, the smaller the mean square error, the higher the similarity.

[0141] The beneficial effects of the above technology are as follows: the model fitting end obtains an accurate model by fitting the autoregressive moving average model to the historical power curve, providing an effective tool for the subsequent determination of the cycle. The third cycle determination end determines the third power consumption change cycle based on the fitting model, enriching the dimension of the cycle analysis. The change cycle determination end determines multiple historical power change assumptions based on multiple cycles, increasing the comprehensiveness and accuracy of the analysis. The time domain signal reconstruction end reconstructs the time domain signal for different assumptions and converts the assumptions into a time domain graph that can be intuitively compared. The similarity calculation end calculates the similarity between the reconstructed time domain graph and the historical power curve, providing a quantitative basis for determining the final cycle. The change cycle determination end selects the assumption value corresponding to the maximum similarity as the power consumption change cycle, ensuring the reliability and accuracy of the results. A series of operations of the entire change cycle determination subunit realizes the accurate, comprehensive and reliable determination of the power consumption change cycle of the photovoltaic power supply load. It is helpful to deeply explore the periodic law of power consumption, and provides key and accurate information support for optimizing power supply and consumption strategies, improving energy utilization efficiency, and ensuring power supply stability and reliability, thereby significantly improving the performance and benefits of the photovoltaic power supply system.

[0142] Example 8

[0143] On the basis of Embodiment 5, the second periodic property analysis unit comprises:

[0144] The curve division subunit is used to divide the long-term power change curves of all photovoltaic power supply loads in the historical power demand of all photovoltaic power supply loads based on all total power change cycle assumption values, and obtain a set of division curves corresponding to each total power change cycle assumption value;

[0145] The cluster analysis subunit is used to calculate the curve similarity in the partition curve set corresponding to each total power change period assumption value, and regard the total power change period assumption value corresponding to the partition curve set corresponding to the maximum curve similarity as the actual value of the total power change period of all photovoltaic power supply loads, and regard the actual value of the total power change period of all photovoltaic power supply loads as the periodic property analysis result of all photovoltaic power supply loads.

[0146] In this embodiment, based on all total power variation period assumption values, the long-term power variation curves of all photovoltaic-powered loads in the historical power demand of all photovoltaic-powered loads are divided.

[0147] In this embodiment, the divided curve set refers to a set of multiple partial long-term power change curves obtained by dividing the long-term power change curves of all photovoltaic power supply loads into curve segments according to the assumed value of each total power change cycle. For example, if the assumed value of the total power change cycle is 8 hours, the long-term power change curve is divided into curve segments at time intervals of 8 hours to obtain a set of multiple partial long-term power change curves with a duration of 8 hours.

[0148] In this embodiment, calculating the curve similarity in the divided curve set corresponding to each total power variation period assumed value means comparing the similarity between the curves in each set of curves obtained by dividing the assumed value. The similarity calculation can be achieved by comparing the shape, trend, value distribution and other characteristics of the curves. Common similarity calculation methods such as cosine similarity, mean square error, etc. can be used.

[0149] The beneficial effects of the above technology are as follows: the curve division subunit divides the long-term power change curve according to the assumed value of the total power change cycle, which provides a basis for the subsequent cluster analysis, making the analysis more targeted and organized. The cluster analysis subunit can accurately find the most representative assumed value of the total power change cycle by calculating the similarity of the curves in the divided curve set, and determine it as the actual value of the total power change cycle. The two subunits of the second cycle property analysis unit cooperate with each other to achieve accurate analysis and determination of the total power change cycle of the photovoltaic power supply load. It helps to deeply understand the overall periodic law of electricity consumption and provides key parameter basis for the rational planning and optimization of photovoltaic power supply systems. It can improve the scientificity and effectiveness of energy management, ensure the stability and reliability of power supply, and at the same time improve energy utilization efficiency and reduce system operating costs.

[0150] Example 9

[0151] On the basis of Embodiment 4, the power supply status determination submodule includes:

[0152] A model acquisition unit, used to acquire a photovoltaic power supply status analysis model;

[0153] The power supply status determination unit is used to input the real-time power demand and periodic property analysis results of all photovoltaic power supply loads and the real-time output power of the photovoltaic array into the photovoltaic power supply status analysis model to obtain the current photovoltaic power supply status.

[0154] In this embodiment, the photovoltaic power supply status analysis model is a tool or algorithm for evaluating and judging the current operating status of the photovoltaic power supply system. It determines whether the photovoltaic power supply is in a state of excess, shortage or balance by collecting and analyzing data related to photovoltaic power generation and load power consumption. For example, a factory has installed a photovoltaic power supply system. The model will collect the real-time output power of the photovoltaic array, the real-time power demand of each device in the factory as a load, and the historical power consumption data over the past period of time. Then, through the comprehensive processing and calculation of these data, the current power supply status is obtained.

[0155] The photovoltaic power supply status analysis model is trained using a large amount of historical data including the real-time output power of the photovoltaic array, the real-time power demand of all photovoltaic power supply loads, and the historical power demand, as well as the corresponding photovoltaic power supply status annotated for each data sample, such as oversupply, undersupply or balanced status, as training samples. By using these rich and accurate labeled training samples to train the model, the model can learn the complex relationship between different input features and power supply status, so as to accurately predict the current photovoltaic power supply status. For example, the photovoltaic output power and load power demand data of a power consumption area in different seasons, weather and time periods of the year are collected, and the power supply status at each moment is annotated in detail. These data can be input into the model as training samples for training to improve the model's adaptability and prediction accuracy to various complex situations.

[0156] The beneficial effects of the above technology are: the photovoltaic power supply status analysis model obtained by the model acquisition unit provides an effective tool and method for accurately determining the power supply status. The power supply status determination unit inputs the relevant real-time power demand, periodic property analysis results and real-time output power into the model, and with the help of the powerful analysis ability of the model, it can quickly and accurately obtain the current photovoltaic power supply status. This method avoids complex manual analysis and calculations, and improves the efficiency and accuracy of power supply status determination. It helps to promptly discover problems and potential risks in power supply, and provides a reliable basis for timely adjustment and optimization of power supply and utilization strategies. It can better ensure the stability and reliability of photovoltaic power supply, improve energy utilization efficiency, meet users' electricity needs, and promote the effective utilization and sustainable development of photovoltaic energy.

[0157] Embodiment 10:

[0158] Based on Example 1, the charge and discharge mode control module refers to Figure 4 ,include:

[0159] The first working mode setting submodule is used to control the photovoltaic battery energy storage system to start the charging working mode as a photovoltaic battery management result if the current photovoltaic power supply state is oversupply;

[0160] The second working mode setting submodule is used to control the photovoltaic battery energy storage system to start the discharge working mode as the photovoltaic battery management result if the current photovoltaic power supply status is insufficient power supply.

[0161] The beneficial effects of the above technology are as follows: the first working mode setting submodule controls the photovoltaic battery energy storage system to start the charging working mode when the current photovoltaic power supply state is overpowered. This helps to make full use of excess electrical energy to charge the energy storage system, realize energy storage, avoid energy waste, and improve the comprehensive utilization efficiency of energy. The second working mode setting submodule controls the energy storage system to start the discharge working mode when the power supply is insufficient, timely supplements the power supply gap, ensures the stability and continuity of the power supply, and meets the power demand of the load. Through the synergy of these two submodules, the charging and discharging mode control module can flexibly and accurately switch the working mode according to the real-time photovoltaic power supply state. It effectively balances the supply of photovoltaic power generation and the demand of the load, and optimizes the distribution and utilization of energy. It enhances the reliability and adaptability of the photovoltaic power supply system, reduces dependence on the external power grid, and enhances the value and competitiveness of photovoltaic energy in practical applications.

[0162] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A battery energy storage management system based on photovoltaic power generation, characterized in that: include: The power supply and consumption parameter acquisition module is used to obtain the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array; A power supply and consumption status determination module is used to obtain the current photovoltaic power supply status based on the real-time power demand and historical power demand of each photovoltaic power supply load and the real-time output power of the photovoltaic array; The charge and discharge mode control module is used to control the charge and discharge working mode of the photovoltaic battery energy storage system based on the current photovoltaic power supply status and obtain the photovoltaic battery management result; The power supply and use status determination module includes: The periodic property analysis submodule is used to perform periodic property analysis on the historical power demand of all photovoltaic power supply loads to obtain the periodic property analysis results of all photovoltaic power supply loads; The power supply status determination submodule is used to determine the current photovoltaic power supply status based on the real-time power demand and periodic property analysis results of all photovoltaic power supply loads and the real-time output power of the photovoltaic array; Among them, the periodic property analysis submodule includes: A first cycle property analysis unit is used to perform a cycle property analysis on a historical power curve of each photovoltaic power supply load in the historical power demand of all photovoltaic power supply loads to obtain a power consumption change cycle of each photovoltaic power supply load; A cycle initial value determination unit, used to take the lowest common multiple of the total power variation cycle of all photovoltaic power supply loads as the total power variation cycle initial value of all photovoltaic power supply loads; A period assumption value determination unit, used to determine a plurality of total power variation period assumption values ​​based on the total power variation period initial value; The second periodic property analysis unit is used to divide and cluster the long-term power change curves of all photovoltaic power supply loads in the historical power demand of all photovoltaic power supply loads based on the assumed values ​​of all total power change cycles, and obtain the actual values ​​of the total power change cycles of all photovoltaic power supply loads as the periodic property analysis results of all photovoltaic power supply loads.

2. The battery energy storage management system based on photovoltaic power generation according to claim 1 is characterized in that: The power supply and consumption parameter acquisition module includes: The real-time power demand acquisition submodule is used to measure the real-time total current and real-time total voltage of each photovoltaic power supply load based on the intelligent sensor, and calculate the real-time total power of each photovoltaic power supply load based on the real-time total current and real-time total voltage of each photovoltaic power supply load as the real-time power demand of each photovoltaic power supply load; The historical electricity demand acquisition submodule is used to obtain the historical electricity demand of all photovoltaic power supply loads; The photovoltaic output acquisition submodule is used to detect the real-time output current and real-time voltage of the photovoltaic array based on the current sensor and the voltage sensor respectively; The output power calculation submodule is used to calculate the real-time output power of the photovoltaic array based on the real-time output current and real-time output voltage of the photovoltaic array.

3. The battery energy storage management system based on photovoltaic power generation according to claim 2 is characterized in that: The historical electricity demand acquisition submodule includes: A historical total power acquisition unit is used to acquire the real-time total power of each photovoltaic power supply load at each statistical moment in a preset historical long period, and to take the sum of the real-time total power of all photovoltaic power supply loads at each statistical moment in the preset historical long period as the total power consumption of all photovoltaic power supply loads at each moment in the preset historical long period; A curve fitting unit is used to fit the real-time total power of each photovoltaic power supply load at all statistical moments in a preset long historical period into a historical power curve of each photovoltaic power supply load, and to fit the total power consumption of all photovoltaic power supply loads at all statistical moments in a preset long historical period into a long-term power change curve of all photovoltaic power supply loads; The historical power demand determination unit is used to take the historical power curve of each photovoltaic power supply load and the long-term power change curves of all photovoltaic power supply loads as the historical power demand of all photovoltaic power supply loads.

4. The battery energy storage management system based on photovoltaic power generation according to claim 1, characterized in that: The first cycle property analysis unit includes: A time-frequency domain transformation subunit is used to perform a fast Fourier transform on the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load to obtain a frequency domain representation of the historical power sequence data of each photovoltaic power supply load; The first cycle determination subunit is used to determine the frequency value corresponding to the high energy value in the frequency domain representation of the historical power sequence data of each photovoltaic power supply load as the historical power high energy frequency value of each photovoltaic power supply load, and take the reciprocal of the historical power high energy frequency value of each photovoltaic power supply load as the first power consumption change cycle of each photovoltaic power supply load; A periodogram generating subunit, for generating a periodogram of the historical power sequence data of each photovoltaic power supply load based on a fast Fourier transform result of the historical power curve of each photovoltaic power supply load in the historical power demand of each photovoltaic power supply load; The second period determination subunit is used to determine the frequency value corresponding to the peak value of the ordinate amplitude in the period diagram of the historical power sequence data of each photovoltaic power supply load as the main frequency value of each photovoltaic power supply load, and take the reciprocal of the main frequency value of each photovoltaic power supply load as the second power consumption change period of each photovoltaic power supply load; The variation period determination subunit is used to determine the electric power variation period of each photovoltaic power supply load based on the historical power curve of each photovoltaic power supply load, the first electric power variation period and the second electric power variation period.

5. The battery energy storage management system based on photovoltaic power generation according to claim 4 is characterized in that: The change cycle determination subunit includes: The model fitting end is used to perform autoregressive moving average model fitting on the historical power curve of each photovoltaic power supply load to obtain an autoregressive moving average model whose fitting state meets the requirements; A third period determination terminal is used to determine a third power consumption change period of each photovoltaic power supply load based on an autoregressive moving average model whose fitting state meets the requirements; A change cycle determination terminal, used to determine a plurality of historical power change assumption values ​​of each photovoltaic power supply load based on a first power consumption change cycle, a second power consumption change cycle, and a third power consumption change cycle of each photovoltaic power supply load; A time domain signal reconstruction end is used to reconstruct the time domain signal based on each historical power change assumption value of each photovoltaic power supply load and the fast Fourier transform result of the historical power curve, and obtain a reconstructed time domain graph corresponding to each historical power change assumption value of each photovoltaic power supply load; A similarity calculation end is used to calculate the similarity between the reconstructed time domain graph corresponding to each historical power change hypothesis value of each photovoltaic power supply load and the historical power curve of the corresponding photovoltaic power supply load; The variation period determination end is used to take the historical power variation assumption value corresponding to the maximum similarity among all historical power variation assumption values ​​of each photovoltaic power supply load as the power variation period of each photovoltaic power supply load.

6. The battery energy storage management system based on photovoltaic power generation according to claim 1, characterized in that: The second cycle property analysis unit includes: The curve division subunit is used to divide the long-term power change curves of all photovoltaic power supply loads in the historical power demand of all photovoltaic power supply loads based on all total power change cycle assumption values, and obtain a set of division curves corresponding to each total power change cycle assumption value; The cluster analysis subunit is used to calculate the curve similarity in the partition curve set corresponding to each total power change period assumption value, and regard the total power change period assumption value corresponding to the partition curve set corresponding to the maximum curve similarity as the actual value of the total power change period of all photovoltaic power supply loads, and regard the actual value of the total power change period of all photovoltaic power supply loads as the periodic property analysis result of all photovoltaic power supply loads.

7. The battery energy storage management system based on photovoltaic power generation according to claim 1, characterized in that: The power supply status determination submodule includes: A model acquisition unit, used to acquire a photovoltaic power supply status analysis model; The power supply status determination unit is used to input the real-time power demand and periodic property analysis results of all photovoltaic power supply loads and the real-time output power of the photovoltaic array into the photovoltaic power supply status analysis model to obtain the current photovoltaic power supply status.

8. The photovoltaic power generation-based battery energy storage management system according to claim 1, characterized in that: The charge and discharge mode control module includes: The first working mode setting submodule is used to control the photovoltaic battery energy storage system to start the charging working mode as a photovoltaic battery management result if the current photovoltaic power supply state is oversupply; The second working mode setting submodule is used to control the photovoltaic battery energy storage system to start the discharge working mode as the photovoltaic battery management result if the current photovoltaic power supply status is insufficient power supply.

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