Data management system and method for integrated optical storage system

Abstract

The present invention discloses a data management system and method for a photovoltaic-storage integrated system, and belongs to the technical field of photovoltaic-storage system data management. The present invention includes: a photovoltaic-storage integrated system, an energy storage peak-shaving capability analysis and prediction module, an energy storage device capacity configuration optimization module, and an operation mode switching situation analysis module; the energy storage peak-shaving capability analysis and prediction module is used to analyze the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period; the energy storage device capacity configuration optimization module is used to determine the energy storage capacity configuration optimization results of the lithium-ion battery energy storage system and the solar cell panel; the operation mode switching situation analysis module is used to analyze the switching time and switching type of the operation mode of the photovoltaic-storage integrated system. The present invention can ensure that the lithium-ion battery energy storage system is seamlessly connected to the microgrid, thereby ensuring the stable operation of the microgrid, and improving the system's power peak-shaving effect on the microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of data management of photovoltaic storage systems, and in particular to a data management system and method for a photovoltaic storage integrated system. Background Art

[0002] With the development of my country's power system, microgrids, as a solution for integrating renewable energy, have also flourished. Microgrids integrate and utilize renewable energy technologies, including wind, photovoltaic, and tidal energy, along with energy storage and microgrid technologies for localized power loads. Whether operating in island mode (off-grid) or connected to the grid, microgrids experience supply and demand imbalances.

[0003] Nowadays, when a microgrid has an imbalance in supply and demand, the microgrid is connected to other microgrids to ensure the balance of supply and demand. However, due to the long transmission lines between the two microgrids, the microgrid has unstable power supply. At the same time, when the existing power system switches between the grid-connected mode and the off-grid mode, it cannot guarantee the stable operation of the microgrid, nor can it guarantee the power peak-shaving effect of the microgrid. In addition, the existing power system cannot make full use of renewable energy, that is, the economic performance of the existing power system is poor. Summary of the Invention

[0004] The object of the present invention is to provide a data management system and method for an integrated photovoltaic and storage system to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a data management system for an integrated photovoltaic and energy storage system, the system comprising: an integrated photovoltaic and energy storage system, an energy storage peak-shaving capacity analysis and prediction module, an energy storage device capacity configuration optimization module, and an operation mode switching situation analysis module;

[0006] The photovoltaic energy storage integrated system includes solar panels and lithium-ion battery energy storage systems. The power supply modes of the lithium-ion battery energy storage system include direct current power supply and solar panel energy storage power supply. By using the energy storage energy converted by the solar panels to charge the lithium-ion battery, the waste of photovoltaic energy is avoided, and efficient energy conversion and utilization is achieved.

[0007] The energy storage peak-shaving capacity analysis and prediction module is used to analyze the energy storage peak-shaving capacity of the lithium-ion battery energy storage system in each energy storage peak-shaving time period based on the historical power supply of the photovoltaic integrated system to the microgrid and the power quality index of the photovoltaic integrated system at each energy storage peak-shaving time point;

[0008] The energy storage device capacity configuration optimization module is used to determine whether it is necessary to optimize the energy storage capacity configuration of the lithium-ion battery energy storage system and the solar panel according to the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period, and to determine the energy storage capacity configuration optimization result of the lithium-ion battery energy storage system and the solar panel;

[0009] The operation mode switching analysis module is used to predict the real-time switching coefficient of the operation mode of the photovoltaic and storage integrated system according to the real-time power supply of the solar panels to the microgrid, and based on the prediction results, analyze the switching time and switching type of the operation mode of the photovoltaic and storage integrated system.

[0010] Furthermore, the energy storage peak-shaving capability analysis and prediction module includes an energy storage peak-shaving time period determination unit, a power quality index calculation unit, and an energy storage peak-shaving capability analysis and prediction unit;

[0011] The energy storage peak-shaving time period determination unit acquires historical power supply data of the integrated photovoltaic and energy storage system to the microgrid, where the historical power supply data includes historical real-time power supply of the solar panels to the microgrid and historical real-time power supply of the lithium-ion battery energy storage system to the microgrid. If the power supply of the lithium-ion battery energy storage system to the microgrid at time t is greater than 0, the power supply of the integrated photovoltaic and energy storage system to the microgrid at time t is added to a set to obtain an energy storage peak-shaving analysis data set. Based on the energy storage peak-shaving analysis data set, the energy storage peak-shaving time period of the lithium-ion battery energy storage system is determined.

[0012] The power quality index calculation unit uses an online power quality monitoring device installed in the photovoltaic and storage integrated system to analyze the power quality of the power generated by the photovoltaic and storage integrated system at each energy storage peak regulation time point, and calculates the power quality index of the power generated by the photovoltaic and storage integrated system at each energy storage peak regulation time point according to the formula: power quality index = power quality analyzed by the online power quality monitoring device at each energy storage peak regulation time point / standard power quality;

[0013] The energy storage peak-shaving capability analysis and prediction unit analyzes and predicts the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period based on the power quality index of the integrated photovoltaic and energy storage system at each energy storage peak-shaving time point and each energy storage peak-shaving analysis sub-data set obtained.

[0014] Furthermore, the specific method for the energy storage peak-shaving time period determination unit to determine the energy storage peak-shaving time period of the lithium-ion battery energy storage system is:

[0015] Let the energy storage peak load analysis data set = {X1, X2, ..., X nAccording to the correlation between the historical real-time power supply in the energy storage peak-shaving analysis data set, the energy storage peak-shaving analysis data set is classified and processed. The specific classification processing method is as follows: randomly select a historical real-time power supply X in the energy storage peak-shaving analysis data set. i , if |TX i -TX j |=R, then X j It's X i The associated historical real-time power supply, if |TX i -TX j |≠R, then X j Not X i The associated historical real-time power supply of X i The associated historical real-time power supply is all stored in the first energy storage peak load analysis sub-dataset, and X i Delete from the energy storage peak shaving analysis data set, repeat the above operation to obtain several energy storage peak shaving analysis sub-data sets;

[0016] Where i = 1, 2, ..., n, represents the number corresponding to each storage location in the energy storage peak-shaving analysis data set, n represents the total number of storage locations in the energy storage peak-shaving analysis data set, j = 1, 2, ..., n and j ≠ i, X i 、X j They represent the historical real-time power supply stored in the i-th and j-th storage locations in the energy storage peak regulation analysis data set, TX i TX j Respectively represent the acquisition time of the historical real-time power supply stored in the i-th and j-th storage locations in the energy storage peak-shaving analysis data set, and R represents the acquisition interval of the historical power supply data;

[0017] According to the acquisition time of each historical real-time power supply stored in the u-th energy storage peak-shaving analysis sub-dataset, the u-th energy storage peak-shaving time analysis dataset M is obtained. u , the energy storage peak regulation time period of the uth energy storage peak regulation analysis sub-data set = [minM u ,maxM u ], where u = 1, 2, ..., h, represents the number corresponding to each energy storage peak regulation analysis sub-dataset, h represents the total number, min represents the minimum value symbol, and max represents the maximum value symbol.

[0018] Furthermore, the specific method for the energy storage peak-shaving capability analysis and prediction unit to analyze and predict the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period is as follows:

[0019] The acquisition time of the historical real-time power supply of the solar panel stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset is related to the historical power supply of the microgrid. And the historical real-time power supply X stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset up To obtain;

[0020] make or Z up =0, when hour, when When Z up =0, at this time, there is a fault in the power supply equipment of the solar panel, which is conducive to real-time monitoring of the fault situation of the photovoltaic power supply equipment, avoiding the microgrid from being unable to supply stable power due to excessive damage to the photovoltaic power supply equipment, thereby improving the power supply security of the microgrid;

[0021] According to the formula For lithium-ion battery energy storage system in [minM u ,maxM u ]The energy storage peak-shaving capacity value within the energy storage peak-shaving time period is predicted, where p = 1, 2, …, q, represents the number corresponding to each storage location in the energy storage peak-shaving analysis sub-dataset, q represents the total number of storage locations in the energy storage peak-shaving analysis sub-dataset, Y1 represents the maximum power supply of the lithium-ion battery energy storage system to the microgrid, The power quality index represents the electric energy generated by the integrated photovoltaic and energy storage system at the time of collecting the historical real-time power supply stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset. By analyzing the power supply of the lithium-ion battery energy storage system to the microgrid during each energy storage peak-shaving time period, the energy storage peak-shaving capacity of the lithium-ion battery energy storage system during each energy storage peak-shaving time period is analyzed. The impact of the power supply quality on the energy storage peak-shaving capacity analysis process is taken into account during the analysis process, thereby improving the analysis accuracy.

[0022] Furthermore, the specific method for the energy storage device capacity configuration optimization module to determine the energy storage capacity configuration optimization results of the lithium-ion battery energy storage system and the solar panel is:

[0023] when When the energy storage capacity of lithium-ion battery energy storage system and solar panels needs to be optimized, When the lithium-ion battery energy storage system and solar panels have reached the peak, it indicates that there is no need to optimize the energy storage capacity of the lithium-ion battery energy storage system and solar panels. The comprehensive energy storage and peak-shaving capabilities of the lithium-ion battery energy storage system on the power grid can be used to determine whether the energy storage capacity of the lithium-ion battery energy storage system and solar panels needs to be optimized, ensuring that the optimization results are more economical and practical.

[0024] Calculate the average energy storage capacity ratio of the solar panel in each energy storage peak regulation time period. The energy storage capacity ratio of the solar panel at each energy storage peak regulation time point = the energy storage capacity of the solar panel at each energy storage peak regulation time point / the maximum energy storage capacity of the solar panel. The average energy storage capacity ratio of the solar panel in the energy storage peak regulation time period = the sum of the energy storage capacity ratios of the solar panel at each energy storage peak regulation time point in the energy storage peak regulation time period / the total number of energy storage peak regulation time points in the energy storage peak regulation time period. Obtain the energy storage peak regulation time period corresponding to the average energy storage capacity ratio of 1. Obtain the average power generation of the solar panel in each obtained energy storage peak regulation time period. A storage peak-shaving time period is randomly selected within the peak-shaving time period, and the difference between the average power generation of the solar panel within the selected storage peak-shaving time period and the average power supply of the solar panel to the microgrid within the selected storage peak-shaving time period is calculated until all the obtained storage peak-shaving time periods are selected, and a number of calculation results are obtained, and the maximum value of the calculation results is used as the energy storage capacity of the solar panel after optimization; according to the real-time energy storage status of the lithium-ion battery energy storage system and the solar panel, the energy storage capacity optimization results of the lithium-ion battery energy storage system and the solar panel are determined respectively, to ensure that the microgrid can continuously and stably supply power to users after the energy storage capacity is optimized;

[0025] The energy storage peak-shaving capability values ​​of the lithium-ion battery energy storage system obtained in each energy storage peak-shaving time period are placed in a set H, and the energy storage capacity optimization result of the lithium-ion battery energy storage system is determined according to D = Q*(2-maxH), where Q represents the energy storage capacity configured by the lithium-ion battery energy storage system before optimization, and D represents the energy storage capacity configured by the lithium-ion battery energy storage system after optimization.

[0026] Furthermore, the operation mode switching situation analysis module includes a switching coefficient prediction unit and an operation mode switching analysis unit;

[0027] The switching coefficient prediction unit obtains the real-time power supply of the solar panel to the microgrid, constructs a plane rectangular coordinate system with time as the horizontal coordinate and the power supply of the solar panel to the microgrid as the vertical coordinate, represents the obtained real-time power supply in the constructed plane rectangular coordinate system, connects each position point in the plane rectangular coordinate system in chronological order, obtains a power supply curve, and predicts the real-time switching coefficient of the photovoltaic storage integrated system operation mode based on the real-time smoothness of the power supply curve. The real-time smoothness of the power supply curve = e -|供电曲线的实时变化率| , e is a constant and e>1;

[0028] The operation mode switching analysis unit analyzes the switching time and switching type of the operation mode of the photovoltaic and storage integrated system according to the real-time switching coefficient of the operation mode of the photovoltaic and storage integrated system predicted by the switching coefficient prediction unit and the real-time operation mode of the photovoltaic and storage integrated system.

[0029] Furthermore, the specific method for the switching coefficient prediction unit to predict the real-time switching coefficient of the photovoltaic storage integrated system operation mode is:

[0030] The rate of change r of the power supply curve at time v v , the amount of power g supplied by the solar panel to the microgrid at time v v To obtain;

[0031] according to The switching coefficient for the integrated solar-energy-storage system's operating mode at time v+b is predicted, where F represents the optimized storage capacity of the solar panels, and b represents the interval between the solar panels' power supply to the microgrid. By analyzing the power supply curve to determine the solar panel's power supply to the microgrid at the next moment, the switching coefficient for the integrated solar-energy-storage system's operating mode is predicted, helping to ensure uninterrupted operation of the microgrid.

[0032] Furthermore, the specific method for the operation mode switching analysis unit to analyze the switching time and switching type of the operation mode of the photovoltaic storage integrated system is:

[0033] When 0≤γ v+b <E, the operation mode of the photovoltaic storage integrated system is off-grid operation mode, which means that the microgrid is powered only by solar panels. v+b When γ is less than 1, the operation mode of the photovoltaic energy storage integrated system is the grid-connected operation mode. The grid-connected operation mode means that the solar panels and lithium-ion battery energy storage system jointly supply power to the microgrid. v+b =E, the time v+b is used as the switching time of the photovoltaic storage integrated system operation mode;

[0034] When 0≤γ v-b <E and γ v+b =E, indicating that the operation mode switching type of the photovoltaic storage integrated system at time v+b is: switching from off-grid operation mode to grid-connected operation mode;

[0035] When E<γ v-b ≤1 and γ v+b =E, it means that the operation mode switching type of the integrated photovoltaic and storage system at time v+b is: switching the grid-connected operation mode to the off-grid operation mode, where 0.7≤E≤0.9.

[0036] A data management method for an integrated photovoltaic and storage system, the method comprising:

[0037] S10: Based on the historical power supply of the integrated photovoltaic and energy storage system to the microgrid, the energy storage peak-shaving time period of the lithium-ion battery energy storage system is determined, and the power quality index of the integrated photovoltaic and energy storage system at each energy storage peak-shaving time point is calculated using the online power quality monitoring device;

[0038] S20: Analyze the energy storage peak regulation capability of the lithium-ion battery energy storage system in each energy storage peak regulation time period;

[0039] S30: Based on the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period, determining whether it is necessary to optimize the energy storage capacity configuration of the lithium-ion battery energy storage system and the solar panel, and determining the energy storage capacity configuration optimization result of the lithium-ion battery energy storage system and the solar panel;

[0040] S40: Based on the real-time power supply of the solar panels to the microgrid, the real-time switching coefficient of the photovoltaic and storage integrated system operation mode is predicted, and based on the prediction result, the switching time and switching type of the photovoltaic and storage integrated system operation mode are analyzed.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. By integrating a lithium-ion battery energy storage system into the microgrid as a backup energy source, the present invention can, to a certain extent, ensure the stable operation of the microgrid compared to the existing technology. By using the stored energy converted by solar panels to charge the lithium-ion battery, the waste of photovoltaic energy is avoided, and efficient energy conversion and utilization is achieved.

[0043] 2. The present invention predicts the power supply situation of the integrated photovoltaic and storage system to the microgrid during each energy storage peak-shaving time period, and the energy storage peak-shaving capacity value of the lithium-ion battery energy storage system during each energy storage peak-shaving time period. During the prediction process, the present invention can monitor the power supply failure of the solar panel to avoid the inability of the microgrid to supply power stably due to excessive damage to the photovoltaic power supply equipment, thereby improving the power supply security of the microgrid. Combined with the average energy storage capacity ratio of the solar panel in each energy storage peak-shaving time period, the energy storage capacity optimization results of the lithium-ion battery energy storage system and the solar panel are determined, ensuring that renewable energy can be fully utilized and improving the use effect of the system.

[0044] 3. The present invention calculates the real-time smoothness of the power supply curve by the real-time rate of change of the power supply curve, which is conducive to analyzing the power supply situation of the solar panel to the microgrid at the next moment. Combined with the real-time power supply situation of the solar panel to the microgrid, the switching coefficient of the photovoltaic energy storage integrated system operation mode at the next moment is predicted, ensuring the seamless integration of the lithium-ion battery energy storage system into the microgrid, thereby ensuring the stable operation of the microgrid and improving the system's power peak regulation effect on the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] 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:

[0046] Figure 1 This is a schematic diagram of the working principle and structure of the data management system and method for the integrated photovoltaic and storage system of the present invention;

[0047] Figure 2 It is a schematic diagram of the workflow of the data management system and method for the integrated photovoltaic and storage system of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0049] See also Figure 1 and Figure 2 The present invention provides a technical solution: a data management system for an integrated photovoltaic and energy storage system, the system comprising: an integrated photovoltaic and energy storage system, an energy storage peak-shaving capability analysis and prediction module, an energy storage device capacity configuration optimization module, and an operation mode switching analysis module;

[0050] The integrated solar-storage system includes solar panels and lithium-ion battery energy storage systems;

[0051] The energy storage peak-shaving capacity analysis and prediction module is used to analyze the energy storage peak-shaving capacity of the lithium-ion battery energy storage system during each energy storage peak-shaving time period based on the historical power supply of the integrated photovoltaic and storage system to the microgrid and the power quality index of the integrated photovoltaic and storage system at each energy storage peak-shaving time point. The energy storage peak-shaving time period refers to the time period when the power supply of the solar panels to the microgrid cannot meet the power supply demand of the microgrid and the lithium-ion battery energy storage system is required to supply power to the microgrid;

[0052] The energy storage peak-shaving capability analysis and prediction module includes an energy storage peak-shaving time period determination unit, a power quality index calculation unit, and an energy storage peak-shaving capability analysis and prediction unit;

[0053] The energy storage peak-shaving time period determination unit obtains historical power supply data of the integrated photovoltaic and energy storage system to the microgrid. The historical power supply data includes the historical real-time power supply of the solar panels to the microgrid and the historical real-time power supply of the lithium-ion battery energy storage system to the microgrid. If the power supply of the lithium-ion battery energy storage system to the microgrid at time t is greater than 0, the power supply of the integrated photovoltaic and energy storage system to the microgrid at time t is added to the set to obtain an energy storage peak-shaving analysis data set. Based on the energy storage peak-shaving analysis data set, the energy storage peak-shaving time period of the lithium-ion battery energy storage system is determined. The specific method is as follows:

[0054] Let the energy storage peak load analysis data set = {X1, X2, ..., X n According to the correlation between the historical real-time power supply in the energy storage peak-shaving analysis data set, the energy storage peak-shaving analysis data set is classified and processed. The specific classification processing method is as follows: randomly select a historical real-time power supply X in the energy storage peak-shaving analysis data set. i , if |TX i -TX j |=R, then X j It's X i The associated historical real-time power supply, if |TX i -TX j |≠R, then X j Not X i The associated historical real-time power supply of X i The associated historical real-time power supply is all stored in the first energy storage peak load analysis sub-dataset, and X i Delete from the energy storage peak shaving analysis data set, repeat the above operation to obtain several energy storage peak shaving analysis sub-data sets;

[0055] Where i = 1, 2, ..., n, represents the number corresponding to each storage location in the energy storage peak-shaving analysis data set, n represents the total number of storage locations in the energy storage peak-shaving analysis data set, j = 1, 2, ..., n and j ≠ i, X i 、X j They represent the historical real-time power supply stored in the i-th and j-th storage locations in the energy storage peak regulation analysis data set, TX i TX j Respectively represent the acquisition time of the historical real-time power supply stored in the i-th and j-th storage locations in the energy storage peak-shaving analysis data set, and R represents the acquisition interval of the historical power supply data;

[0056] According to the acquisition time of each historical real-time power supply stored in the u-th energy storage peak-shaving analysis sub-dataset, the u-th energy storage peak-shaving time analysis dataset M is obtained. u , the energy storage peak regulation time period of the uth energy storage peak regulation analysis sub-data set = [minM u ,maxM u ], where u = 1, 2, ..., h, represents the number corresponding to each energy storage peak-shaving analysis sub-dataset, h represents the total number, min represents the minimum value symbol, and max represents the maximum value symbol;

[0057] The power quality index calculation unit utilizes an online power quality monitoring device installed in the photovoltaic and storage integrated system. The online power quality monitoring device is used to monitor and analyze the quality of the electric energy generated by the photovoltaic and storage integrated system. The power quality of the electric energy generated by the photovoltaic and storage integrated system at each energy storage peak regulation time point is analyzed. According to the power quality index = power quality analyzed by the online power quality monitoring device at each energy storage peak regulation time point / standard power quality, the standard power quality refers to the quality of the electric energy generated by the photovoltaic and storage integrated system analyzed by the online power quality monitoring device when all the electric energy indicators monitored by the online power quality monitoring device are within the set standard range. The electric energy indicators include voltage deviation, frequency deviation, voltage fluctuation and flicker, three-phase voltage imbalance, etc. The power quality index of the electric energy generated by the photovoltaic and storage integrated system at each energy storage peak regulation time point is calculated. The energy storage peak regulation time point refers to a time point within the energy storage peak regulation period. The value range of the power quality index is [0,1].

[0058] The energy storage peak-shaving capability analysis and prediction unit analyzes and predicts the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period based on the power quality index of the integrated photovoltaic and energy storage system at each energy storage peak-shaving time point and the obtained energy storage peak-shaving analysis sub-datasets. The specific method is as follows:

[0059] The acquisition time of the historical real-time power supply of the solar panel stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset is related to the historical power supply of the microgrid. And the historical real-time power supply X stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset up To obtain;

[0060] make or Z up =0, when hour, when When Z up =0, then the solar panel power supply equipment has a fault;

[0061] According to the formula For lithium-ion battery energy storage system in [minM u,maxM u ]The energy storage peak-shaving capacity value within the energy storage peak-shaving time period is predicted, where p = 1, 2, …, q, represents the number corresponding to each storage location in the energy storage peak-shaving analysis sub-dataset, q represents the total number of storage locations in the energy storage peak-shaving analysis sub-dataset, Y1 represents the maximum power supply of the lithium-ion battery energy storage system to the microgrid, The power quality index of the electric energy generated by the photovoltaic and energy storage integrated system at the time of collecting the historical real-time power supply stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset;

[0062] The energy storage equipment capacity configuration optimization module is used to determine whether it is necessary to optimize the energy storage capacity configuration of the lithium-ion battery energy storage system and the solar panel based on the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period, and to determine the energy storage capacity configuration optimization results of the lithium-ion battery energy storage system and the solar panel. The specific method is as follows:

[0063] when When the energy storage capacity of lithium-ion battery energy storage system and solar panels needs to be optimized, When , it means that there is no need to optimize the energy storage capacity of the lithium-ion battery energy storage system and solar panels;

[0064] Calculate the average energy storage capacity ratio of the solar panel in each energy storage peak regulation time period. The energy storage capacity ratio of the solar panel at each energy storage peak regulation time point = the energy storage capacity of the solar panel at each energy storage peak regulation time point / the maximum energy storage capacity of the solar panel. The average energy storage capacity ratio of the solar panel in the energy storage peak regulation time period = the sum of the energy storage capacity ratios of the solar panel at each energy storage peak regulation time point in the energy storage peak regulation time period / the total number of energy storage peak regulation time points in the energy storage peak regulation time period. Obtain the energy storage peak regulation time period corresponding to the average energy storage capacity ratio of 1, and calculate the average power generation of the solar panel in each obtained energy storage peak regulation time period. The amount is obtained, and an energy storage peak-shaving time period is randomly selected within the obtained energy storage peak-shaving time period. The difference between the average power generation of the solar panel in the selected energy storage peak-shaving time period and the average power supply of the solar panel to the microgrid in the selected energy storage peak-shaving time period is calculated. The average power supply = the historical power supply of the solar panel to the microgrid at each energy storage peak-shaving time point in the selected energy storage peak-shaving time period / the total number of energy storage peak-shaving time points in the selected energy storage peak-shaving time period. Several calculation results are obtained until all the obtained energy storage peak-shaving time periods are selected. The maximum value of the calculation results is used as the energy storage capacity of the solar panel after optimization.

[0065] The energy storage peak-shaving capability values ​​of the lithium-ion battery energy storage system in each energy storage peak-shaving time period are placed into a set H, and the energy storage capacity optimization result of the lithium-ion battery energy storage system is determined according to D = Q*(2-maxH), where Q represents the energy storage capacity configured by the lithium-ion battery energy storage system before optimization, and D represents the energy storage capacity configured by the lithium-ion battery energy storage system after optimization;

[0066] The operation mode switching analysis module is used to predict the real-time switching coefficient of the operation mode of the photovoltaic and storage integrated system according to the real-time power supply of the solar panels to the microgrid. Based on the prediction results, the switching time and switching type of the operation mode of the photovoltaic and storage integrated system are analyzed;

[0067] The operation mode switching situation analysis module includes a switching coefficient prediction unit and an operation mode switching analysis unit;

[0068] The switching coefficient prediction unit obtains the real-time power supply of the solar panel to the microgrid, constructs a plane rectangular coordinate system with time as the horizontal axis and the power supply of the solar panel to the microgrid as the vertical axis, represents the obtained real-time power supply in the constructed plane rectangular coordinate system, connects each position point in the plane rectangular coordinate system in chronological order, and obtains the power supply curve. According to the real-time smoothness of the power supply curve, the real-time switching coefficient of the photovoltaic storage integrated system operation mode is predicted. The real-time smoothness of the power supply curve = e -|供电曲线的实时变化率| The calculation method of the curve change rate is the existing technology, e is a constant and e>1, and the specific prediction method is:

[0069] The rate of change r of the power supply curve at time v v , the amount of power g supplied by the solar panel to the microgrid at time v v To obtain;

[0070] according to The switching coefficient of the photovoltaic and energy storage integrated system operation mode at time v+b is predicted, where F represents the optimized energy storage capacity of the solar panel and b represents the interval between the solar panel's power supply to the microgrid.

[0071] The operation mode switching analysis unit analyzes the switching time and switching type of the operation mode of the photovoltaic and storage integrated system based on the real-time switching coefficient of the operation mode of the photovoltaic and storage integrated system predicted by the switching coefficient prediction unit and the real-time operation mode of the photovoltaic and storage integrated system. The specific method is as follows:

[0072] When 0≤γ v+b < E, the operation mode of the photovoltaic storage integrated system is off-grid operation mode, which means that the microgrid is powered only by solar panels. v+bWhen γ is less than 1, the operation mode of the photovoltaic energy storage integrated system is the grid-connected operation mode. The grid-connected operation mode means that the solar panels and lithium-ion battery energy storage system jointly supply power to the microgrid. v+b =E, the time v+b is used as the switching time of the photovoltaic storage integrated system operation mode;

[0073] When 0≤γ v-b <E and γ v+b =E, indicating that the operation mode switching type of the photovoltaic storage integrated system at time v+b is: switching from off-grid operation mode to grid-connected operation mode;

[0074] When E<γ v-b ≤1 and γ v+b =E, it means that the operation mode switching type of the integrated photovoltaic and storage system at time v+b is: switching the grid-connected operation mode to the off-grid operation mode, where 0.7≤E≤0.9.

[0075] A data management method for an integrated photovoltaic and storage system, the method comprising:

[0076] S10: Based on the historical power supply of the integrated photovoltaic and energy storage system to the microgrid, the energy storage peak-shaving time period of the lithium-ion battery energy storage system is determined, and the power quality index of the integrated photovoltaic and energy storage system at each energy storage peak-shaving time point is calculated using the online power quality monitoring device;

[0077] S20: Analyze the energy storage peak regulation capability of the lithium-ion battery energy storage system in each energy storage peak regulation time period;

[0078] S30: Based on the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period, determining whether it is necessary to optimize the energy storage capacity configuration of the lithium-ion battery energy storage system and the solar panel, and determining the energy storage capacity configuration optimization result of the lithium-ion battery energy storage system and the solar panel;

[0079] S40: Based on the real-time power supply of the solar panels to the microgrid, the real-time switching coefficient of the photovoltaic and storage integrated system operation mode is predicted, and based on the prediction result, the switching time and switching type of the photovoltaic and storage integrated system operation mode are analyzed.

[0080] Example 1: Assume that the rate of change of the power supply curve at time v is r v =4, the amount of power supplied by the solar panel to the microgrid at time v is g v =500 kWh, the optimized energy storage capacity of the solar panel F = 800 kWh, and the collection interval of the solar panel's power supply to the microgrid is 2 minutes. Then the switching coefficient of the photovoltaic storage integrated system operation mode at time v+2 is:

[0081]

[0082] The switching coefficient of the photovoltaic and storage integrated system operation mode at time v+2 is 0.61.

[0083] Example 2: Assume that the first energy storage peak-shaving analysis sub-dataset = {450, 550}, and the historical real-time power supply of the solar panel stored in the first and second storage locations in the first energy storage peak-shaving analysis sub-dataset is collected at the time of the historical power supply of the microgrid: The historical real-time power supply stored in the first energy storage peak load analysis sub-dataset is X 11 =450, X 12 =550;

[0084] because Can Z 11 =400, Z 12 =450;

[0085] Assume that the maximum power supply of the lithium-ion battery energy storage system to the microgrid is Y1=500, and the power quality index of the electric energy generated by the integrated photovoltaic and storage system at the time of collecting the historical real-time power supply stored in the first and second storage locations in the first energy storage peak-shaving analysis sub-dataset is: The energy storage peak-shaving capability of the lithium-ion battery energy storage system in the [minM1, maxM1] energy storage peak-shaving time period is:

[0086]

[0087] The energy storage peak-shaving capability value of the lithium-ion battery energy storage system in the [minM1, maxM1] energy storage peak-shaving time period is 0.81.

[0088] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0089] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A data management system for an integrated photovoltaic and storage system, characterized by: The system includes: a photovoltaic storage integrated system, an energy storage peak-shaving capability analysis and prediction module, an energy storage equipment capacity configuration optimization module, and an operation mode switching situation analysis module; The integrated photovoltaic and energy storage system includes solar panels and a lithium-ion battery energy storage system. The power supply mode of the lithium-ion battery energy storage system includes direct current power supply and solar panel energy storage power supply; The energy storage peak-shaving capacity analysis and prediction module is used to analyze the energy storage peak-shaving capacity of the lithium-ion battery energy storage system in each energy storage peak-shaving time period based on the historical power supply of the photovoltaic integrated system to the microgrid and the power quality index of the photovoltaic integrated system at each energy storage peak-shaving time point; The energy storage device capacity configuration optimization module is used to determine whether it is necessary to optimize the energy storage capacity configuration of the lithium-ion battery energy storage system and the solar panel according to the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period, and to determine the energy storage capacity configuration optimization result of the lithium-ion battery energy storage system and the solar panel; The operation mode switching analysis module is used to predict the real-time switching coefficient of the operation mode of the photovoltaic and storage integrated system according to the real-time power supply of the solar panels to the microgrid, and analyze the switching time and switching type of the operation mode of the photovoltaic and storage integrated system based on the prediction results; The operation mode switching situation analysis module includes a switching coefficient prediction unit and an operation mode switching analysis unit; The switching coefficient prediction unit obtains the real-time power supply of the solar panel to the microgrid, constructs a plane rectangular coordinate system with time as the horizontal coordinate and the power supply of the solar panel to the microgrid as the vertical coordinate, represents the obtained real-time power supply in the constructed plane rectangular coordinate system, and connects each position point in the plane rectangular coordinate system in time sequence to obtain a power supply curve. The real-time smoothness of the power supply curve is equal to e -|供电曲线的实时变化率| , e is a constant and e>1. According to the real-time smoothness of the power supply curve, the real-time switching coefficient of the photovoltaic storage integrated system operation mode is predicted. The specific method is: The rate of change r of the power supply curve at time v v , the amount of power g supplied by the solar panel to the microgrid at time v v To obtain; according to The switching coefficient of the photovoltaic and energy storage integrated system operation mode at time v+b is predicted, where F represents the optimized energy storage capacity of the solar panel and b represents the interval between the solar panel's power supply to the microgrid. The operation mode switching analysis unit analyzes the switching time and switching type of the operation mode of the photovoltaic and storage integrated system based on the real-time switching coefficient of the operation mode of the photovoltaic and storage integrated system predicted by the switching coefficient prediction unit and the real-time operation mode of the photovoltaic and storage integrated system. The specific method is as follows: When 0≤γ v+b <E, the operation mode of the photovoltaic storage integrated system is off-grid operation mode, which means that the microgrid is powered only by solar panels. v+b When γ is less than 1, the operation mode of the photovoltaic energy storage integrated system is the grid-connected operation mode. The grid-connected operation mode means that the solar panels and lithium-ion battery energy storage system jointly supply power to the microgrid. v+b =E, the v+b moment is taken as the switching time of the photovoltaic storage integrated system operation mode; When 0≤γ v-b <E and γ v+b =E, indicating that the operation mode switching type of the photovoltaic storage integrated system at time v+b is: switching from off-grid operation mode to grid-connected operation mode; When E<γ v-b ≤1 and γ v+b =E, it means that the operation mode switching type of the integrated photovoltaic and storage system at time v+b is: switching the grid-connected operation mode to the off-grid operation mode, where 0.7≤E≤0.

9.

2. The data management system for an integrated optical-storage system according to claim 1, characterized in that: The energy storage peak-shaving capability analysis and prediction module includes an energy storage peak-shaving time period determination unit, a power quality index calculation unit, and an energy storage peak-shaving capability analysis and prediction unit; The energy storage peak-shaving time period determination unit acquires historical power supply data of the integrated photovoltaic and energy storage system to the microgrid, where the historical power supply data includes historical real-time power supply of the solar panels to the microgrid and historical real-time power supply of the lithium-ion battery energy storage system to the microgrid. If the power supply of the lithium-ion battery energy storage system to the microgrid at time t is greater than 0, the power supply of the integrated photovoltaic and energy storage system to the microgrid at time t is added to a set to obtain an energy storage peak-shaving analysis data set. Based on the energy storage peak-shaving analysis data set, the energy storage peak-shaving time period of the lithium-ion battery energy storage system is determined. The power quality index calculation unit uses an online power quality monitoring device installed in the photovoltaic and storage integrated system to analyze the power quality of the power generated by the photovoltaic and storage integrated system at each energy storage peak regulation time point. According to the formula: power quality index = power quality analyzed by the online power quality monitoring device at each energy storage peak regulation time point / standard power quality, the power quality index of the power generated by the photovoltaic and storage integrated system at each energy storage peak regulation time point is calculated. The standard power quality refers to the quality of the power generated by the photovoltaic and storage integrated system analyzed by the online power quality monitoring device when all power indicators monitored by the online power quality monitoring device are within the set standard range; The energy storage peak-shaving capability analysis and prediction unit analyzes and predicts the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period based on the power quality index of the integrated photovoltaic and energy storage system at each energy storage peak-shaving time point and each energy storage peak-shaving analysis sub-data set obtained.

3. The data management system for an integrated optical-storage system according to claim 2, characterized in that: The specific method for the energy storage peak-shaving time period determination unit to determine the energy storage peak-shaving time period of the lithium-ion battery energy storage system is: Let the energy storage peak load analysis data set = {X1, X2,…, X n According to the correlation between the historical real-time power supply in the energy storage peak-shaving analysis data set, the energy storage peak-shaving analysis data set is classified and processed. The specific classification processing method is as follows: randomly select a historical real-time power supply X in the energy storage peak-shaving analysis data set. i , if |TX i -TX j |=R, then X j It's X i The associated historical real-time power supply, if |TX i -TX j |≠R, then X j Not X i The associated historical real-time power supply of X i The associated historical real-time power supply is all stored in the first energy storage peak load analysis sub-dataset, and X i Delete from the energy storage peak shaving analysis data set, repeat the above operation to obtain several energy storage peak shaving analysis sub-data sets; Where i = 1, 2, ..., n, represents the number of each storage location in the energy storage peak-shaving analysis dataset, n represents the total number of storage locations in the energy storage peak-shaving analysis dataset, j = 1, 2, ..., n and j ≠ i, X i 、X j They represent the historical real-time power supply stored in the i-th and j-th storage locations in the energy storage peak regulation analysis data set, TX i TX j Respectively represent the acquisition time of the historical real-time power supply stored in the i-th and j-th storage locations in the energy storage peak-shaving analysis data set, and R represents the acquisition interval of the historical power supply data; According to the acquisition time of each historical real-time power supply stored in the u-th energy storage peak-shaving analysis sub-dataset, the u-th energy storage peak-shaving time analysis dataset M is obtained. u , the energy storage peak regulation time period of the uth energy storage peak regulation analysis sub-data set = [minM u ,maxM u ], where u=1,2,…,h represents the number corresponding to each energy storage peak regulation analysis sub-dataset, h represents the total number, min represents the minimum value symbol, and max represents the maximum value symbol.

4. The data management system for an integrated optical-storage system according to claim 3, characterized in that: The specific method for the energy storage peak-shaving capability analysis and prediction unit to analyze and predict the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period is as follows: The acquisition time of the historical real-time power supply of the solar panel stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset is related to the historical power supply of the microgrid. , and the historical real-time power supply X stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset up To obtain; make or ,when hour, ,when hour, ,At this time, there is a fault in the power supply equipment of the solar panel; According to the formula For lithium-ion battery energy storage system in [minM u ,maxM u ]The energy storage peak-shaving capacity value within the energy storage peak-shaving time period is predicted, where p=1,2,…,q, represents the number corresponding to each storage location in the energy storage peak-shaving analysis sub-dataset, q represents the total number of storage locations in the energy storage peak-shaving analysis sub-dataset, Y1 represents the maximum power supply of the lithium-ion battery energy storage system to the microgrid, Represents the power quality index of the electric energy generated by the integrated photovoltaic and energy storage system at the time of collecting the historical real-time power supply stored in the p-th storage location in the u-th energy storage peak-shaving analysis sub-dataset.

5. The data management system for an integrated optical-storage system according to claim 4, characterized in that: The specific method for the energy storage device capacity configuration optimization module to determine the energy storage capacity configuration optimization results of the lithium-ion battery energy storage system and the solar panel is: when When the energy storage capacity of lithium-ion battery energy storage system and solar panels needs to be optimized, When , it means that there is no need to optimize the energy storage capacity of the lithium-ion battery energy storage system and solar panels; The average energy storage capacity ratio of the solar panel in each energy storage peak-shaving time period is calculated. The energy storage capacity ratio of the solar panel at each energy storage peak-shaving time point = the energy storage capacity of the solar panel at each energy storage peak-shaving time point / the maximum energy storage capacity of the solar panel. The average energy storage capacity ratio of the solar panel in the energy storage peak-shaving time period = the sum of the energy storage capacity ratios of the solar panel at each energy storage peak-shaving time point in the energy storage peak-shaving time period / the total number of energy storage peak-shaving time points in the energy storage peak-shaving time period. The energy storage peak-shaving time period corresponding to the average energy storage capacity ratio being 1 is obtained. The average power generation of the solar panel in each obtained energy storage peak-shaving time period is obtained. A storage peak-shaving time period is randomly selected within the obtained energy storage peak-shaving time period. The difference between the average power generation of the solar panel in the selected energy storage peak-shaving time period and the average power supply of the solar panel to the microgrid in the selected energy storage peak-shaving time period is calculated until all the obtained energy storage peak-shaving time periods are selected and several calculation results are obtained. The maximum value of the calculation results is used as the energy storage capacity of the solar panel after optimization. The energy storage peak regulation capability values ​​of the lithium-ion battery energy storage system in each energy storage peak regulation time period are put into the set H, and according to The energy storage capacity optimization result of the lithium-ion battery energy storage system is determined, where Q represents the energy storage capacity configured by the lithium-ion battery energy storage system before optimization, and D represents the energy storage capacity configured by the lithium-ion battery energy storage system after optimization.

6. A data management method for an integrated photovoltaic and storage system, applied to the data management system for the integrated photovoltaic and storage system according to any one of claims 1 to 5, characterized in that: The method comprises: S10: Based on the historical power supply of the integrated photovoltaic and energy storage system to the microgrid, the energy storage peak-shaving time period of the lithium-ion battery energy storage system is determined, and the power quality index of the integrated photovoltaic and energy storage system at each energy storage peak-shaving time point is calculated using the online power quality monitoring device; S20: Analyze the energy storage peak regulation capability of the lithium-ion battery energy storage system in each energy storage peak regulation time period; S30: Based on the energy storage peak-shaving capability of the lithium-ion battery energy storage system in each energy storage peak-shaving time period, determining whether it is necessary to optimize the energy storage capacity configuration of the lithium-ion battery energy storage system and the solar panel, and determining the energy storage capacity configuration optimization result of the lithium-ion battery energy storage system and the solar panel; S40: Based on the real-time power supply of the solar panels to the microgrid, the real-time switching coefficient of the photovoltaic and storage integrated system operation mode is predicted, and based on the prediction result, the switching time and switching type of the photovoltaic and storage integrated system operation mode are analyzed.

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