Control method and device of facility greenhouse photoelectric energy storage system

Abstract

The application provides a control method and device for a facility greenhouse photoelectric energy storage system, and relates to the technical field of agricultural facility power supply. The application monitors the state of charge of the energy storage battery, sets a diagnostic window to obtain power time sequence data reflecting the real working mode of the load when the state of charge meets a first threshold, and analyzes the frequency spectrum components of the data based on fast Fourier transform, so as to quantitatively evaluate the fluctuation characteristics and dynamic demand of the load power. Then, an inverter power output model is constructed in combination with the real-time state of the energy storage battery, the matching degree of the output capacity of the inverter and the dynamic demand is evaluated through an adaptive algorithm, and finally, whether to perform power supply switching is decided according to the comparison result of the matching degree and a preset threshold. The method solves the problem that the power supply reliability, equipment safety and operation economy are difficult to be considered in the prior art by simply relying on the battery capacity for switching, by taking the dynamic power characteristics of the load into the switching decision basis.

Description

Technical Field

[0001] This invention relates to the field of modern agricultural power supply technology, specifically to a control method and device for a photovoltaic energy storage system for greenhouse facilities. Background Technology

[0002] In greenhouse agriculture, photovoltaic energy storage systems have become an important solution. Typically, photovoltaic panels convert solar energy into direct current (DC), which is then transmitted via a combiner box to an off-grid inverter with charge / discharge management capabilities. This inverter converts the DC to alternating current (AC) to supply the greenhouse load and simultaneously charges the lithium-ion batteries. Existing systems employ a simple threshold-based switching strategy based on state of charge (SOC). The system prioritizes battery power, automatically switching to grid power when the battery level drops to a lower threshold, while the photovoltaic system continues to charge the battery. When the battery level recovers to a higher threshold, the system switches back to battery power. This two-point control method, based on a fixed charge point, achieves basic functions such as photovoltaic charging, battery storage and discharging, and switching to grid power when battery power is insufficient. However, it has significant technical limitations in practical applications.

[0003] The main problem with existing technologies is that their power supply switching decisions rely solely on the internal state of the energy storage battery, completely ignoring the real-time power demand characteristics of the load. The power loads in greenhouses, such as irrigation pumps, rolling shutters, circulating fans, and supplemental lighting, are not constant; their power demands often exhibit randomness, intermittency, and fluctuation. When the system switches to battery power when the battery charge is sufficient, if it happens to encounter a peak load power or a period of severe fluctuation, the instantaneous output capacity of the battery and inverter may be insufficient, leading to a drop in output voltage, equipment protection shutdown, or even overload damage to the battery. Conversely, when the battery charge is low but the load is in a light and stable state, switching back to grid power will result in unnecessary electricity costs and energy waste. This switching mode makes it impossible for the system to achieve an optimal balance between power supply reliability, equipment safety, and operating economy, thus limiting the effectiveness of photovoltaic energy storage systems in the refined production management of greenhouses.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a control method and device for a greenhouse photovoltaic energy storage system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A control method for a greenhouse photovoltaic energy storage system, comprising the following steps:

[0008] Step 1: Monitor the state of charge of the energy storage battery and determine the moment when it meets the first threshold, which is marked as the first moment. Based on the first moment, go to the trace time period, set the diagnostic window, and obtain the first time-series data of the load power within the diagnostic window.

[0009] Step 2: Based on the first time series data, analyze the load power components using Fast Fourier Transform, evaluate the load power fluctuation characteristics according to the proportion of high-frequency components in the frequency components, determine the load power parameters based on the first time series data, and determine the current load demand based on the fluctuation characteristics and power parameters.

[0010] Step 3: Based on the current state of charge of the energy storage battery, construct the inverter power output model and use an adaptive load adjustment algorithm to evaluate the matching degree between the inverter's maximum output capacity and the load demand in real time.

[0011] Step 4: Based on the relationship between the matching degree and the preset threshold, determine whether power switching is required. If so, disconnect the connection between the grid and the load and use an inverter to power the load; otherwise, the load will still be powered by the grid.

[0012] Furthermore, the method used to obtain the time when the first threshold is met, calibrate the first time point, set the diagnostic window, and obtain the first time-series data is as follows:

[0013] The state of charge (SOC) values ​​of the energy storage battery are read in real time according to the set sampling rate. Based on the SOC values ​​of two adjacent samples of the energy storage battery, if the value of the current sample is less than the first threshold and the value of the next sample is greater than or equal to the first threshold, then the time of the next sample is marked as the first time.

[0014] Furthermore, for the first calibration moment, the first moment is set as the end point of the diagnostic window. The time interval formed by the fixed backward tracing is set as the diagnostic window. Within the time interval corresponding to the diagnostic window, the total power of the load is continuously collected according to the set sampling rate, and all the collected samples are arranged in chronological order to form the first time series data containing all sampling points.

[0015] The logic for setting the duration of backward look-back is as follows:

[0016] The duration of the forward tracing is determined based on the maximum working cycle of the load inside the facility greenhouse and the minimum data length required for the frequency domain analysis of the load power.

[0017] when At that time, the maximum working cycle of the load inside the facility greenhouse is set as the duration of the backward look-back;

[0018] when At that time, the minimum data length required for load power frequency domain analysis is set to the backward tracing duration;

[0019] in, This indicates the maximum operating cycle of the load inside the facility greenhouse. This indicates the minimum data length required for load power frequency domain analysis;

[0020] The maximum working cycle of the load is determined by the longest time required to complete one full working cycle among all electrical loads in the facility greenhouse.

[0021] The minimum data length required for the load power frequency domain analysis is determined by the minimum frequency resolution required by the Fast Fourier Transform, based on the following method:

[0022] The minimum data length is calibrated based on the ratio of coefficient 1 to the lowest frequency resolution.

[0023] Furthermore, the load power components are analyzed using Fast Fourier Transform to evaluate the fluctuation characteristics. The method used is as follows:

[0024] The power spectral density of the first time series data is obtained by performing a fast Fourier transform on the first time series data. The power of the components with frequencies higher than the preset cutoff frequency in the power spectral density is summed to obtain the total power of the high-frequency band.

[0025] The total power of the low-frequency band is obtained by summing the power corresponding to the components with frequencies lower than or equal to the preset cutoff frequency. The ratio of the total power of the high-frequency band to the total power of the low-frequency band is calculated as an indicator to quantify the load power fluctuation characteristics.

[0026] Furthermore, the power parameters of the load power include average power and peak power. The method for determining the power parameters of the load power based on the first time-series data is as follows:

[0027] The arithmetic mean of the power values ​​of all sampling points in the first time series data is calculated as the average power, and the maximum value of the power values ​​of all sampling points in the first time series data is selected as the peak power.

[0028] Furthermore, the load demand of the current load, including the equivalent power demand value, is determined based on fluctuation characteristics and power parameters, using the following method:

[0029] The product of the dynamic margin coefficient and the index of quantified load power fluctuation characteristics is used as the first coefficient, and the first coefficient is added to the coefficient 1 as the second coefficient for adjusting the peak power.

[0030] The first demand power is the product of peak power and the second coefficient, and the second demand power is the product of the preset base load coefficient and the average power.

[0031] The sum of the first and second power demands is calibrated as the equivalent power demand value.

[0032] Furthermore, the matching degree is evaluated in real time by constructing an inverter power output model and employing an adaptive load adjustment algorithm. The method used is as follows:

[0033] Based on the current state of charge of the energy storage battery, the maximum power value that the inverter can safely output in the current state is determined by the inverter power output model. The method used is as follows:

[0034] The first difference is the difference between the current state of charge of the energy storage battery and the preset low charge protection threshold of the energy storage battery, and the second difference is the difference between the preset high charge cutoff threshold and the preset low charge protection threshold of the energy storage battery.

[0035] The power attenuation ratio is based on the ratio of the first difference to the second difference.

[0036] The maximum power that the inverter can safely output under the current condition is determined by the product of the inverter's rated output power and the power attenuation ratio.

[0037] Based on the current state of charge of the energy storage battery, the theoretical sustainable power supply time of the battery under the current load demand is determined by using the equivalent power demand value and the total energy capacity of the battery. The method used is as follows:

[0038] The usable capacity ratio of the energy storage battery is calculated based on the difference between the current state of charge (SOC) value of the energy storage battery and the minimum SOC threshold that the system is allowed to discharge.

[0039] The current available energy of the energy storage battery is calculated as the product of its total energy capacity and the ratio of its available capacity.

[0040] The theoretical sustainable power supply time of an energy storage battery is determined by the ratio of currently available battery energy to the equivalent power demand.

[0041] The current real-time matching degree, including power matching degree and time matching degree, is determined through an adaptive load adjustment algorithm. The method used is as follows:

[0042] The power matching degree is obtained by comparing the maximum power value that the inverter can safely output in the current state with the equivalent power demand value, and the time matching degree is obtained by comparing the theoretically sustainable power supply time with the preset expected minimum power supply time threshold.

[0043] Furthermore, by analyzing the real-time matching degree and the preset matching degree threshold, the specific logic for determining whether a power switch is needed is as follows:

[0044] when and When the power supply capacity of the energy storage system is determined to meet the load demand, the control switching device disconnects the AC grid from the load and switches to power supply to the load by the inverter;

[0045] when or If the power supply capacity of the energy storage system is determined to be insufficient to meet the load demand, the load will continue to be supplied with power from the AC grid.

[0046] in, Indicates power matching degree, This indicates the preset power matching threshold. Indicates time matching degree. This indicates the preset time matching threshold.

[0047] The present invention also provides a control device for a photovoltaic energy storage system for a greenhouse, the control device for the photovoltaic energy storage system being used to execute the above-described control method for the photovoltaic energy storage system for a greenhouse, comprising:

[0048] The data acquisition module is used to monitor the state of charge of the energy storage battery, determine the first moment when the first threshold is met, and trace back a fixed time period based on the first moment to set a diagnostic window. Within the diagnostic window, the load power is collected by the power measurement module to generate the first time series data.

[0049] The load analysis module is used to perform a fast Fourier transform on the first time series data to obtain its power spectral density, calculate the proportion of high-frequency fluctuation energy to evaluate the fluctuation characteristics of the load power, and calculate the average power and peak power based on the first time series data. Based on the proportion of high-frequency fluctuation energy, average power and peak power, the load demand coefficient is calculated.

[0050] The system matching degree evaluation module is used to determine the maximum power value that the inverter can safely output at present based on the current state of charge of the energy storage battery through the inverter power output model, and to calculate the real-time matching degree by using an adaptive load adjustment algorithm, combined with the peak power and the proportion of high-frequency fluctuation energy.

[0051] The switching control module is used to compare the real-time matching degree with the preset matching degree threshold, analyze the control switching device based on the comparison result, and select whether to supply power to the load by the inverter.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] This invention acquires continuous time-series data of load power by real-time monitoring of the state of charge of energy storage batteries and setting a diagnostic window. This provides a real and complete sample of dynamic load behavior for analysis, overcoming the limitations of traditional methods that rely on data from a single moment. Based on this time-series data, the Fast Fourier Transform is used to analyze the spectral components of power and calculate fluctuation characteristic indicators. This allows for a quantitative assessment of the degree of rapid fluctuation in load power, thereby accurately identifying the dynamic load demands that are ignored by traditional fixed threshold methods.

[0054] This invention also constructs an inverter power output model based on the current state of charge of the energy storage battery and employs an adaptive load adjustment algorithm to evaluate the matching degree between the inverter's maximum output capacity and load demand in real time, achieving precise quantification and dynamic adaptation of the real-time power supply capacity of the energy storage system. Power switching control is performed based on the relationship between the matching degree and a preset threshold, ensuring that switching actions are triggered only when the energy storage system's power supply capacity is confirmed to meet load demands, thereby guaranteeing the continuity and reliability of power supply. Attached Figure Description

[0055] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0056] Figure 2 This is a performance comparison chart between direct switching and the switching method in the embodiment;

[0057] Figure 3 This is a schematic diagram of the device module of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] Example:

[0061] Please see Figure 1 and Figure 2 A control method for a photovoltaic energy storage system in a greenhouse, specifically including:

[0062] Step 1: Monitor the state of charge of the energy storage battery and determine the moment when it meets the first threshold. Mark this moment as the first moment. Based on the first moment, go back to the trace time period, set the diagnostic window, and obtain the first time-series data of the load power within the diagnostic window.

[0063] In this embodiment, the method for obtaining the time when the first threshold is met, calibrating the first time, setting the diagnostic window, and obtaining the first time series data is as follows:

[0064] Through the controller area network bus, The sampling frequency is used to continuously read the state-of-charge (SOC) values ​​from the communication interface of the battery management system of the energy storage battery. It is based on experience from engineering practice.

[0065] At the same time, a first threshold is set, and a conditional judgment is performed every time a new state of charge value is read. The logic is as follows:

[0066] ;

[0067] in, This indicates the set first threshold, and Its value is based on statistical analysis of historical operating data and calibration combined with expert experience. Indicates at time If the system determines that the state of charge value has not met the triggering condition, it will maintain the current monitoring state and will not perform subsequent diagnostic window analysis.

[0068] When the read state of charge value satisfies the following logic: if the value of the current sample is less than the first threshold, and the value of the next sample is greater than or equal to the first threshold, then it is determined that the switching trigger threshold condition has been met. Simultaneously, the time of the next sample is marked as the first time. The formula used to mark the first time is:

[0069] ;

[0070] in, Indicates the first moment, in seconds. , and This indicates two adjacent sampling times.

[0071] Taking the first moment as the end point of the diagnostic window, a fixed forward lookup duration is set. The logic for setting the backward lookup duration is as follows:

[0072] The duration of the forward tracing is determined based on the maximum operating cycle of the load within the facility greenhouse and the minimum data length required for the frequency domain analysis of the load power. This setting ensures that the diagnostic window is used to analyze the time domain operating mode of the load and to ensure that the subsequent frequency domain analysis has sufficient frequency resolution to assess the fluctuation characteristics.

[0073] when At this time, the data length corresponding to the complete working cycle of the load is sufficient to meet the minimum frequency resolution requirement for spectrum analysis. At this time, the maximum working cycle of the load in the facility greenhouse is set as the duration of the backward tracing.

[0074] when At this time, the data length determined based on the complete working cycle of the load will result in insufficient frequency resolution of the spectrum analysis, making it impossible to effectively identify the preset low-frequency fluctuation components. In order to ensure the effectiveness of the fluctuation characteristic analysis method, the basic requirements of frequency domain analysis for data length should be met first. At this time, the minimum data length required for load power frequency domain analysis is set as the backward tracing time.

[0075] in, This indicates the maximum operating cycle of the load inside the facility greenhouse. This indicates the minimum data length required for load power frequency domain analysis;

[0076] The maximum working cycle of the load is determined by the longest time required for all electrical loads in the facility greenhouse to complete one full working cycle. This parameter is determined by the historical operating data of all load equipment in the facility greenhouse.

[0077] The minimum data length required for the load power frequency domain analysis is determined by the minimum frequency resolution required by the Fast Fourier Transform, based on the following logic:

[0078] The minimum data length is calibrated based on the ratio of coefficient 1 to the lowest frequency resolution.

[0079] The formula used is:

[0080] ;

[0081] in, Indicates the lowest frequency resolution. Indicates the minimum data length.

[0082] In this embodiment, the following settings are provided: The time interval formed The diagnostic window is set as such because, for greenhouse agricultural loads such as irrigation pumps, rolling shutters, and environmental control fans, their operating cycles are typically in the range of several minutes (tens of minutes in a greenhouse). It can effectively cover the start-up, shutdown, power changes and short-term duty cycles of most loads, ensuring that the collected data can represent the typical power characteristics of the load, while avoiding the inclusion of too much irrelevant historical data due to an excessively long window.

[0083] During the time period corresponding to the diagnostic window, the system uses the power sensing module to perform sampling at a set rate. The total power on the load bus is continuously collected, and the collected data is uploaded to the central controller via the communication bus. The system arranges all the collected sampling points in chronological order to form the first time-series data containing all sampling points, as follows:

[0084] ;

[0085] in, This represents the total length of the first time-series data, i.e., the total number of sampling points, and , Indicating the sampling frequency, in this embodiment, the first time-series data is... Total number of sampling points .

[0086] In this embodiment, 30 sets of switching process performance test data under different load conditions were collected, and the data are shown in Table 1:

[0087] Table 1: Performance Comparison Table between Direct Switching and Switching Methods in the Embodiments

[0088]

[0089] According to Table 1 and Figure 2As can be seen, a 1 in the direct switching column of the table indicates that a hard direct switch from operating mode to standby mode occurred under the test condition, while 0 indicates that no such switch occurred. In 30 sets of tests, the switching method in this embodiment achieved a smooth transition in all cases (the switching column in this embodiment was 0), while the direct switching method experienced a hard switch in 11 cases with high load fluctuation index (value ≥ 0.5) (the direct switching column value was 1). Regarding voltage drop, the voltage drop range of the direct switching method was 7.2V to 26.7V, while in this embodiment it was significantly suppressed to the range of 2.3V to 7.2V, with an average voltage drop reduction of approximately 70.6%, especially under conditions where the peak load power exceeded 1200W, demonstrating significant advantages. More significantly, in terms of stable power supply duration, the direct switching method takes 7 to 33 minutes, while in this embodiment it is extended to 42 to 60 minutes, with an average increase of about 142.5% in stable power supply duration. Specifically, as the load fluctuation index increases from 0.1 to 1.1, the voltage drop in direct switching intensifies significantly and the power supply duration shortens sharply, showing a clear negative correlation. In contrast, the voltage drop in this embodiment only increases slightly, while the power supply duration remains at a high level. This proves that the method of the present invention can effectively resist the impact of load fluctuations and can ensure voltage stability and system power supply continuity under different operating conditions. Its performance improvement fully meets the design expectations of active management and energy buffering of the switching process.

[0090] Step 2: Based on the first time series data, analyze the load power components using Fast Fourier Transform. Based on the proportion of high-frequency components in the frequency components, evaluate the load power fluctuation characteristics. Determine the load power parameters based on the first time series data. Based on the fluctuation characteristics and power parameters, determine the current load demand.

[0091] In this embodiment, the Fast Fourier Transform is used to analyze the load power components to evaluate the fluctuation characteristics. The method used is as follows:

[0092] The first time series data was preprocessed to eliminate the influence of non-fluctuation components. The method used was to calculate the arithmetic mean of the first time series data.

[0093] ;

[0094] in, This represents the arithmetic mean of the first time series data.

[0095] The zero-mean sequence is obtained by subtracting the mean from each data point, using the following formula:

[0096] ;

[0097] in, The new first time series data, i.e., the zero-mean series, is represented as: This method aims to remove the DC component from the signal, allowing subsequent spectral analysis to focus on the power fluctuations.

[0098] The first discrete time-series data is subjected to a Fast Fourier Transform (FFT) to transform it from the time domain to the frequency domain for frequency analysis, thereby obtaining the power spectral density of the time-series data. The method used is as follows:

[0099] To meet the requirements of the Fast Fourier Transform algorithm regarding the length of the zero-mean sequence data and to reduce spectral analysis errors caused by data truncation, the 600-point zero-mean sequence is extended to 1024 points, expanding to a length that is an integer power of 2. The reason for using 600 points is to meet the input requirements of the radix-2 fast Fourier transform algorithm, which has the highest computational efficiency at this length. Directly truncating 600 data points will cause the frequency components to be blurred. Filling the extended part with 0 can increase the number of display points of the spectrum without changing the original frequency components of the data, making the frequency curve smoother and clarifying the position of the frequency components.

[0100] Then, a Hanning window function is applied to the expanded 1024-point sequence. The Hanning window can effectively reduce the amplitude at both ends of the time-domain data, smoothly truncate the edges, and suppress spectral leakage, thereby improving the accuracy of the spectral analysis results.

[0101] The formula used is:

[0102] ;

[0103] in, ;

[0104] The formula used to obtain the windowed sequence to be transformed is:

[0105] ;

[0106] After performing a 1024-point Fast Fourier Transform on the sequence to be transformed to obtain its complex spectrum, the power spectral density is calculated. The power spectral density reflects the distribution intensity of signal power at different frequency components. Since the windowed sequence to be transformed is a real sequence, its spectrum has conjugate symmetry, and the effective information is contained in the first 513 points. Therefore, only the first 513 points need to be calculated. This corresponds to frequencies from 0 to half the sampling rate, i.e. The formula used is:

[0107] for ,

[0108] ;

[0109] for ,

[0110] ;

[0111] in, Represents power spectral density, in units of The frequency bands used for subsequent steps of frequency division and summation All refer to the values ​​calculated by the formula here. This represents the complex spectrum obtained by performing a 1024-point Fast Fourier Transform on the sequence to be transformed. Indicates its modulus value. It is a frequency index, and The constant 0.375 represents the power compensation factor of the Hanning window. While the windowing operation suppresses spectral leakage, it also leads to the attenuation of the total signal energy. The constant 0.375 can correct the energy loss caused by windowing, ensuring that the total signal power obtained from the power spectral density is consistent with the total power of the original time domain signal, thus guaranteeing the accuracy of the calculation.

[0112] To distinguish between slow changes and rapid fluctuations in load power, a cutoff frequency is set.

[0113] In this embodiment, the cutoff frequency is This value is set based on the typical working cycle and engineering experience of greenhouse agriculture.

[0114] Based on the preset cutoff frequency, the discrete power spectral density sequence obtained after the fast Fourier transform is calculated. The power corresponding to the components with frequencies higher than the preset cutoff frequency is summed to obtain the total power in the high-frequency band, and the power corresponding to the components with frequencies lower than or equal to the preset cutoff frequency is summed to obtain the total power in the low-frequency band. The logic is as follows:

[0115] Calculate the actual frequency value corresponding to each frequency index. Based on the comparison between the actual frequency value and the cutoff frequency, assign the power spectral density values ​​to two different subsets.

[0116] like The actual frequency value will then be assigned to the low-frequency component set.

[0117] like Then the actual frequency value will be assigned to the high-frequency component set.

[0118] The total power in the low-frequency band is calculated by summing the power spectral densities of all components within the low-frequency component set, using the following formula:

[0119] ;

[0120] The total power in the high-frequency band is calculated by summing the power spectral densities of all components within the aforementioned high-frequency component set, using the following formula:

[0121] ;

[0122] in, Indicates the first The actual frequency value corresponding to each frequency index, and at the same time express The corresponding actual frequency value, This indicates the preset cutoff frequency, and , This indicates the total power in the low-frequency band. This represents the total power in the high-frequency band, and both are in units of 1 / 2. , Represents the set of high-frequency components. This represents the set of low-frequency components.

[0123] The total power in the high-frequency band quantifies the fluctuating performance consumption of the load over a short time scale, while the total power in the low-frequency band quantifies the trend performance consumption of the load over a longer time scale.

[0124] The ratio of the total power in the high-frequency band to the total power in the low-frequency band is used as an indicator to quantify the load power fluctuation characteristics. The formula used is as follows:

[0125] ;

[0126] in, An indicator representing the quantitative characteristics of load power fluctuations. It is a dimensionless indicator. The higher the value, the higher the proportion of rapidly fluctuating components in the load power, the stronger the dynamic characteristics of the load, and the higher the requirement for the fast response capability of the power supply. The smaller the value, the more gradual the change in load power.

[0127] Furthermore, the power parameters of the load power include average power and peak power. The method for determining the power parameters of the load power based on the first time-series data is as follows: Calculate the arithmetic mean of the power values ​​at all sampling points in the first time-series data as the average power, based on the following formula:

[0128] ;

[0129] in, This represents the average power, which reflects the typical load level within the diagnostic window;

[0130] The peak power is selected as the maximum power value of all sampling points in the first time series data, based on the following formula:

[0131] ;

[0132] in, This represents peak power, which reflects the maximum instantaneous demand of the load within the diagnostic window.

[0133] Furthermore, the load demand of the current load, including the equivalent power demand value, is determined based on fluctuation characteristics and power parameters, according to the following logic:

[0134] The product of the dynamic margin coefficient and the index of quantified load power fluctuation characteristics is used as the first coefficient, and the first coefficient is added to the coefficient 1 as the second coefficient for adjusting the peak power.

[0135] The first demand power is the product of peak power and the second coefficient, and the second demand power is the product of the preset base load coefficient and the average power.

[0136] The sum of the first and second power demands is calibrated as the equivalent power demand value.

[0137] The formula used is:

[0138] ;

[0139] in, This represents the equivalent power demand value. Indicates peak power. An indicator representing the quantitative characteristics of load power fluctuations. The preset dynamic margin coefficient, and , Indicates average power. This represents the preset base load factor, and , Indicates the first coefficient. Indicates the second coefficient. Indicates the first required power. This indicates the second required power.

[0140] In this embodiment, This coefficient is set based on engineering experience and is used to adjust the degree of impact of load fluctuations on power margin requirements. This coefficient is set based on historical operating data statistics and system endurance guarantee requirements, and is used to characterize the weight of the load's continuous average energy consumption in the total power demand model.

[0141] When the load is completely stable and base energy consumption is negligible, i.e. and When the load fluctuates or continuous energy consumption needs to be considered, the equivalent power demand is equal to the peak power; when the load fluctuates or continuous energy consumption needs to be considered, the equivalent power demand is the peak power plus a dynamic margin determined by the fluctuation (referring to the formula). ) and the power component determined by the base load factor (referring to the formula) This ensures that the power supply system can meet the comprehensive requirements of the load in terms of both power output capacity and energy storage.

[0142] Step 3: Based on the current state of charge of the energy storage battery, construct the inverter power output model and use an adaptive load adjustment algorithm to evaluate the matching degree between the inverter's maximum output capacity and the load demand in real time.

[0143] In this embodiment, the matching degree is evaluated in real time by constructing an inverter power output model and using an adaptive load adjustment algorithm. The method used is as follows:

[0144] The actual usable output power of the inverter is limited by the state of charge of the energy storage battery. To prevent the battery from over-discharging or operating in an inefficient range, the inverter's output capacity needs to be linearly limited based on the current charge level. Therefore, based on the current state of charge of the energy storage battery, the maximum power value that the inverter can safely output in the current state is determined by the inverter power output model. The logic behind this is as follows:

[0145] The first difference is the difference between the current state of charge of the energy storage battery and the preset low charge protection threshold of the energy storage battery, and the second difference is the difference between the preset high charge cutoff threshold and the preset low charge protection threshold of the energy storage battery.

[0146] The power attenuation ratio is based on the ratio of the first difference to the second difference.

[0147] The maximum power that the inverter can safely output under the current condition is determined by the product of the inverter's rated output power and the power attenuation ratio.

[0148] The formula used is:

[0149] ;

[0150] in, This represents the maximum power that the inverter can safely output under the current conditions, serving as a direct comparison benchmark for assessing whether it can meet the load's power requirements. This indicates the inverter's rated output power, which is the maximum power the inverter can continuously output when the battery is fully charged. It is a parameter on the equipment's nameplate. This indicates the current state of charge (SOC) of the energy storage battery, provided in real time by the battery management system. This indicates the preset low-charge protection threshold for the energy storage battery. When the battery's state of charge falls below this threshold, the inverter should stop discharging to prevent battery damage. This indicates the preset high-charge cutoff threshold for the energy storage battery. When the battery's state of charge exceeds this threshold, the system considers the battery to have sufficient charge. Indicates the first difference. Indicates the second difference. This indicates the power attenuation ratio.

[0151] In this embodiment, a low-charge protection threshold is set based on the characteristics of lithium batteries. High battery power cutoff threshold setting .

[0152] To ensure a meaningful operating time after switching to inverter power and to avoid frequent switching, it is necessary to assess the battery's energy sustainability under current load demands. Therefore, based on the current state of charge of the energy storage battery, the theoretical sustainable power supply time of the battery under current load demands is determined using the equivalent power demand and the total battery energy capacity. The underlying logic is as follows:

[0153] The usable capacity ratio of the energy storage battery is calculated based on the difference between the current state of charge (SOC) value of the energy storage battery and the minimum SOC threshold that the system is allowed to discharge.

[0154] The current available energy of the energy storage battery is calculated as the product of its total energy capacity and the ratio of its available capacity.

[0155] The theoretical sustainable power supply time of an energy storage battery is determined by the ratio of currently available battery energy to the equivalent power demand.

[0156] The formula used is:

[0157] ;

[0158] in, This represents the theoretically sustainable power supply time. It characterizes the time that a battery can sustain operation from its current charge to its minimum allowable charge, assuming the load always operates at the equivalent power demand. This represents the minimum state of charge threshold that the system is allowed to discharge. This indicates the percentage of usable capacity of the energy storage battery. This indicates the percentage of usable capacity of the energy storage battery.

[0159] In this embodiment, it is set to This threshold is slightly higher than the low battery protection threshold, providing a safety buffer for the system. This indicates the total energy capacity of the energy storage battery, which is an inherent parameter of the battery.

[0160] The current real-time matching degree, including power matching degree and time matching degree, is determined by an adaptive load adjustment algorithm. The method used is as follows: the power matching degree is obtained by comparing the maximum power that the inverter can safely output in the current state with the equivalent power demand value. The formula used is:

[0161] ;

[0162] Wherein, power matching degree is represented, when This indicates that the inverter's maximum output capacity can cover the equivalent power demand of the load. This indicates that the inverter's capacity is insufficient.

[0163] The time matching degree is obtained by comparing the theoretically sustainable power supply time with the preset expected minimum power supply time threshold, based on the following formula:

[0164] ;

[0165] in, Indicates time matching degree. This represents the preset minimum expected power supply time threshold, and the unit is seconds.

[0166] In this embodiment, the time limit is set according to the typical duration of greenhouse load operation. That is, 30 minutes. This threshold defines the shortest duration that the system considers a valid off-grid power supply to last, in order to avoid invalid or frequent switching due to insufficient power supply time.

[0167] when This indicates that the battery energy is expected to support the load operation for at least the shortest possible time. This indicates that the energy supply time is insufficient.

[0168] The critical value 1 used to determine the power matching degree and the time matching degree are both mathematical critical points for determining whether the supply meets the demand.

[0169] Step 4: Based on the relationship between the matching degree and the preset threshold, determine whether power switching is required. If so, disconnect the connection between the grid and the load and use an inverter to power the load; otherwise, the load will still be powered by the grid.

[0170] In this embodiment, the real-time matching degree and the preset matching degree threshold are analyzed. The specific analysis content is as follows:

[0171] The power matching threshold is used to determine whether the inverter's instantaneous power output capability is sufficient. Based on the fundamental principles of power supply safety in circuit systems, the inverter must be able to provide power no less than the load demand to ensure stable output voltage and normal equipment operation.

[0172] The specific logic used to determine whether a power switch is needed is as follows:

[0173] when and When the power supply capacity of the energy storage system is determined to meet the load demand, the control switching device disconnects the AC grid from the load and switches to power supply to the load by the inverter;

[0174] when or If the power supply capacity of the energy storage system is determined to be insufficient to meet the load demand, the load will continue to be supplied with power from the AC grid.

[0175] in, Indicates power matching degree, This indicates the preset power matching threshold. Indicates time matching degree. This indicates the preset time matching threshold.

[0176] This embodiment is set This value is the rigid theoretical lower limit to ensure that the power supply system does not overload.

[0177] Power matching is satisfied only when the inverter’s maximum safe output power is at least equal to the load’s equivalent power requirement.

[0178] The time matching threshold is used to determine whether the battery has sufficient energy reserves. This ensures that switching to off-grid power supply mode has practical operational significance and avoids frequent switching back to the grid due to insufficient power, which could cause frequent mode oscillations and damage to the equipment.

[0179] In this embodiment, the setting is This value is the basic logical requirement to ensure that the system can continue to work stably for a meaningful period of time after it is disconnected from the network.

[0180] Time matching is satisfied only when the theoretically sustainable power supply time of the battery reaches at least the preset expected minimum power supply time.

[0181] In this embodiment, the power matching degree characterizes the sufficiency of the inverter's maximum output power relative to the load's power demand, and the time matching degree characterizes the sufficiency of the battery's energy reserves relative to the expected continuous power supply time.

[0182] when When this occurs, it indicates that the maximum power output of the inverter under the current state has reached or exceeded the equivalent power demand of the load, and the system's power output capability meets the power conditions for stable power supply; when When the battery's theoretical continuous power supply time has reached or exceeded the preset minimum expected power supply time, it means that the system's continuous power supply is sufficient to meet the continuous operation requirements of the load.

[0183] Therefore, if and only if and When both conditions are met, it is determined that the energy storage system meets the comprehensive needs of the load under the power conditions of stable power supply and the continuous operation requirements of the load. At this time, switching to the inverter to supply power to the load has reliability and stability.

[0184] when When this occurs, it indicates that the maximum power output of the inverter in its current state does not meet the equivalent power demand of the load, and switching at this time carries an overload risk; when When this occurs, it indicates that the system's energy storage cannot guarantee the expected continuous power supply duration, and switching will lead to power outages or frequent mode switching.

[0185] Therefore, when or If any of the conditions is met, it is determined that the power supply capacity of the energy storage system is insufficient. In order to ensure the continuity of power supply, the load is supplied with power from the AC grid.

[0186] When the decision logic determines that a switch is required, the central controller sends a control signal to the switching device composed of interlocking contactors in the system through the digital output interface according to the preset safety sequence. The switching action follows the principle of "disconnect first, then connect" to ensure electrical safety. The specific steps are as follows: the control switching device first disconnects the connection between the AC power grid and the load bus. After confirming that the power grid side has been completely disconnected, the controller closes the switch connecting the inverter output terminal and the load bus. The power supply of the load is switched from the AC power grid to the energy storage system inverter, and the system enters the off-grid operation mode.

[0187] Please see Figure 3 The present invention also provides a control device for a photovoltaic energy storage system for a greenhouse, the control device being used to implement the above-mentioned control method for the photovoltaic energy storage system for a greenhouse, specifically including:

[0188] The data acquisition module is used to monitor the state of charge of the energy storage battery, determine the first moment when the first threshold is met, and trace back a fixed time period based on the first moment to set a diagnostic window. Within the diagnostic window, the load power is collected by the power measurement module to generate the first time series data.

[0189] The load analysis module is used to perform a fast Fourier transform on the first time series data to obtain its power spectral density, calculate the proportion of high-frequency fluctuation energy to evaluate the fluctuation characteristics of the load power, and calculate the average power and peak power based on the first time series data. Based on the proportion of high-frequency fluctuation energy, average power and peak power, the load demand coefficient is calculated.

[0190] The system matching degree evaluation module is used to determine the maximum power value that the inverter can safely output at present based on the current state of charge of the energy storage battery through the inverter power output model, and to calculate the real-time matching degree by using an adaptive load adjustment algorithm, combined with the peak power and the proportion of high-frequency fluctuation energy.

[0191] The switching control module is used to compare the real-time matching degree with the preset matching degree threshold, analyze the control switching device based on the comparison result, and select whether to supply power to the load by the inverter.

[0192] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0193] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0194] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0195] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A control method for a photovoltaic energy storage system in a greenhouse, characterized in that, Specifically, it includes: Step 1: Monitor the state of charge of the energy storage battery and determine the moment when it meets the first threshold, which is marked as the first moment. Based on the first moment, go to the trace time period, set the diagnostic window, and obtain the first time-series data of the load power within the diagnostic window. Step 2: Based on the first time series data, analyze the load power components using Fast Fourier Transform, evaluate the load power fluctuation characteristics according to the proportion of high-frequency components in the frequency components, determine the load power parameters based on the first time series data, and determine the current load demand based on the fluctuation characteristics and power parameters. Step 3: Based on the current state of charge of the energy storage battery, construct the inverter power output model and use an adaptive load adjustment algorithm to evaluate the matching degree between the inverter's maximum output capacity and the load demand in real time. Step 4: Based on the relationship between the matching degree and the preset threshold, determine whether power switching is required. If so, disconnect the connection between the grid and the load and use the inverter to power the load; otherwise, the load will still be powered by the grid. For the first calibration moment, the first moment is taken as the end point of the diagnostic window. The time interval formed by the fixed backward tracing is set as the diagnostic window. Within the time interval corresponding to the diagnostic window, the total power of the load is continuously collected according to the set sampling rate, and all the collected samples are arranged in chronological order to form the first time series data containing all sampling points. The logic for setting the duration of backward look-back is as follows: The duration of the forward tracing is determined based on the maximum working cycle of the load inside the facility greenhouse and the minimum data length required for the frequency domain analysis of the load power. when At that time, the maximum working cycle of the load inside the facility greenhouse is set as the duration of the backward look-back; when At that time, the minimum data length required for load power frequency domain analysis is set to the backward tracing duration; in, This indicates the maximum operating cycle of the load inside the facility greenhouse. This indicates the minimum data length required for load power frequency domain analysis; The maximum working cycle of the load is determined by the longest time required to complete one full working cycle among all electrical loads in the facility greenhouse. The minimum data length required for the load power frequency domain analysis is determined by the minimum frequency resolution required by the Fast Fourier Transform, based on the following method: The minimum data length is calibrated based on the ratio of coefficient 1 to the lowest frequency resolution.

2. The control method for a photovoltaic energy storage system for a greenhouse according to claim 1, characterized in that, The method used to obtain the time when the first threshold is met, calibrate the first time point, set the diagnostic window, and obtain the first time series data is as follows: The state of charge (SOC) values ​​of the energy storage battery are read in real time according to the set sampling rate. Based on the SOC values ​​of two adjacent samples of the energy storage battery, if the value of the current sample is less than the first threshold and the value of the next sample is greater than or equal to the first threshold, then the time of the next sample is marked as the first time.

3. The control method for a photovoltaic energy storage system for a greenhouse according to claim 1, characterized in that, The fast Fourier transform is used to analyze the load power components to evaluate fluctuation characteristics. The method used is as follows: The power spectral density of the first time series data is obtained by performing a fast Fourier transform on the first time series data. The power of the components with frequencies higher than the preset cutoff frequency in the power spectral density is summed to obtain the total power of the high-frequency band. The total power of the low-frequency band is obtained by summing the power corresponding to the components with frequencies lower than or equal to the preset cutoff frequency. The ratio of the total power of the high-frequency band to the total power of the low-frequency band is calculated as an indicator to quantify the load power fluctuation characteristics.

4. The control method for a photovoltaic energy storage system for a greenhouse according to claim 3, characterized in that, The power parameters of the load power include average power and peak power. The method for determining the power parameters of the load power based on the first time-series data is as follows: The arithmetic mean of the power values ​​of all sampling points in the first time series data is calculated as the average power, and the maximum value of the power values ​​of all sampling points in the first time series data is selected as the peak power.

5. The control method for a photovoltaic energy storage system for a greenhouse according to claim 4, characterized in that, The method used to determine the current load demand, including the equivalent power demand value, based on fluctuation characteristics and power parameters is as follows: The product of the dynamic margin coefficient and the index of quantified load power fluctuation characteristics is used as the first coefficient, and the first coefficient is added to the coefficient 1 as the second coefficient for adjusting the peak power. The first demand power is the product of peak power and the second coefficient, and the second demand power is the product of the preset base load coefficient and the average power. The sum of the first and second power demands is calibrated as the equivalent power demand value.

6. The control method for a photovoltaic energy storage system for a greenhouse according to claim 5, characterized in that, The method used to construct an inverter power output model and evaluate the matching degree in real time by employing an adaptive load adjustment algorithm is as follows: Based on the current state of charge of the energy storage battery, the maximum power value that the inverter can safely output in the current state is determined by the inverter power output model. The method used is as follows: The first difference is the difference between the current state of charge of the energy storage battery and the preset low charge protection threshold of the energy storage battery, and the second difference is the difference between the preset high charge cutoff threshold and the preset low charge protection threshold of the energy storage battery. The power attenuation ratio is based on the ratio of the first difference to the second difference. The maximum power that the inverter can safely output under the current condition is calibrated by the product of the inverter's rated output power and the power attenuation ratio. Based on the current state of charge of the energy storage battery, the theoretical sustainable power supply time of the battery under the current load demand is determined by using the equivalent power demand value and the total energy capacity of the battery. The method used is as follows: The usable capacity ratio of the energy storage battery is calculated based on the difference between the current state of charge (SOC) value of the energy storage battery and the minimum SOC threshold that the system is allowed to discharge. The current available energy of the energy storage battery is calculated as the product of its total energy capacity and the ratio of its available capacity. The theoretical sustainable power supply time of the energy storage battery is determined by the ratio of the currently available battery energy to the equivalent power demand. The current real-time matching degree, including power matching degree and time matching degree, is determined through an adaptive load adjustment algorithm. The method used is as follows: The power matching degree is obtained by comparing the maximum power value that the inverter can safely output in the current state with the equivalent power demand value, and the time matching degree is obtained by comparing the theoretically sustainable power supply time with the preset expected minimum power supply time threshold.

7. The control method for a photovoltaic energy storage system for a greenhouse according to claim 6, characterized in that, The specific logic for determining whether a power switch is needed is based on analyzing the real-time matching degree and the preset matching degree threshold: when and When the power supply capacity of the energy storage system is determined to meet the load demand, the control switching device disconnects the AC grid from the load and switches to power supply to the load by the inverter; when or If the power supply capacity of the energy storage system is determined to be insufficient to meet the load demand, the load will continue to be supplied with power from the AC grid. in, Indicates power matching degree, This indicates the preset power matching threshold. Indicates time matching degree. This indicates the preset time matching threshold.

8. A device for a photovoltaic energy storage system for greenhouse facilities, characterized in that, The device of the photovoltaic energy storage system is used to implement the control method of the photovoltaic energy storage system for greenhouses according to any one of claims 1-7, specifically including: The data acquisition module is used to monitor the state of charge of the energy storage battery, determine the first moment when the first threshold is met, and trace back a fixed time period based on the first moment to set a diagnostic window. Within the diagnostic window, the load power is collected by the power measurement module to generate the first time series data. The load analysis module is used to perform a fast Fourier transform on the first time series data to obtain its power spectral density, calculate the proportion of high-frequency fluctuation energy to evaluate the fluctuation characteristics of the load power, and calculate the average power and peak power based on the first time series data. Based on the proportion of high-frequency fluctuation energy, average power and peak power, the load demand coefficient is calculated. The system matching degree evaluation module is used to determine the maximum power value that the inverter can safely output at present based on the current state of charge of the energy storage battery through the inverter power output model, and to calculate the real-time matching degree by using an adaptive load adjustment algorithm, combined with the peak power and the proportion of high-frequency fluctuation energy. The switching control module is used to compare the real-time matching degree with the preset matching degree threshold, analyze the control switching device based on the comparison result, and select whether to supply power to the load by the inverter.

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