A flexible control method and system for a photovoltaic-storage-direct-current-soft system to control direct-current devices

Abstract

The present invention provides a flexible control method and system for a DC device controlled by a photovoltaic-storage-direct-current-flexible system, which relates to the field of power supply methods and systems. The method includes: connecting a DC device, a photovoltaic device, a storage battery, and a mains power network to each other; recording the historical power consumption data of the DC device and establishing a power consumption model of the DC device; recording the historical power generation data of the photovoltaic device and establishing a power generation model of the photovoltaic device; according to the predicted weather data, combining the power consumption model and the power generation model, future power consumption data and future power generation data; according to the future power consumption data and future power generation data, selecting a future power supply method and a future power transmission object; supplying power to the DC device according to the future power supply method and the future power transmission object, and transmitting power to its future power transmission object through the photovoltaic device. The present invention can improve the operation effect of the DC device, protect the DC device, and improve the overall benefit of supplying power to the DC device through the photovoltaic-storage-direct-current-flexible technology, and has strong practicability.

Description

Technical Field

[0001] This application relates to the field of power supply methods and systems, particularly to the technology of photovoltaic-storage-direct-current-flexibility, and specifically to a flexible control method and system for a photovoltaic-storage-direct-current-flexibility system to control direct-current devices. Background Art

[0002] "Photovoltaic-storage-direct-current-flexibility" is a new type of building energy system that configures building photovoltaics and building energy storage, adopts a direct-current power distribution system, and the electrical equipment has the function of active power response. Among them, "photo" means developing photovoltaics using the building surface, "storage" means connecting the building's energy storage battery, "direct" means realizing direct-current power supply inside the building, and "flexibility" means elastic load and flexible power supply.

[0003] In the prior art, when using the photovoltaic-storage-direct-current-flexibility technology to drive direct-current devices such as air conditioners, in order to achieve flexible power supply, it is necessary to continuously switch the power supply source of the direct-current device among the photovoltaic device, the energy storage battery, and the mains power network according to the power consumption of all current direct-current devices. This is likely to cause unstable voltage of the direct-current device, affect the operation effect of the direct-current device, and cause damage to the direct-current device. At the same time, the existing photovoltaic-storage-direct-current-flexibility technology has a low utilization rate of valley electricity when driving multiple direct-current devices, and the overall economic benefit of power supply needs to be improved. Summary of the Invention

[0004] To solve the above-mentioned defects of the related prior art, this application provides a flexible control method and system for a photovoltaic-storage-direct-current-flexibility system to control direct-current devices, which can improve the operation effect of the direct-current device, protect the direct-current device, and enhance the overall benefit of power supply to the direct-current system by the photovoltaic-storage-direct-current-flexibility technology, and has strong practicability.

[0005] To achieve the above object, the present invention adopts the following technologies:

[0006] A flexible control method for a photovoltaic-storage-direct-current-flexibility system to control direct-current devices, comprising:

[0007] Connect the direct-current device, the photovoltaic device, the energy storage battery, and the mains power network to each other through transmission lines;

[0008] Record the historical power consumption data of the direct-current device, establish and continuously update the power consumption model of the direct-current device; record the historical power generation data of the photovoltaic device, establish and continuously update the power generation model of the photovoltaic device;

[0009] Obtain predicted weather data through the Internet;

[0010] According to the predicted weather data, combine the power consumption model of the direct-current device and the power generation model of the photovoltaic device to predict the future power consumption data of the direct-current device and the future power generation data of the photovoltaic device;

[0011] Select the future power supply method of the DC device and the future power transmission object of the PV device according to the future power consumption data of the DC device and the future power generation data of the PV device;

[0012] Power the DC device according to the future power supply method of the DC device and the future power transmission object of the PV device, and transmit power to its future power transmission object through the PV device.

[0013] Furthermore, record the historical power consumption data of the DC device, and establish and continuously update the power consumption model of the DC device, including:

[0014] Establish a historical power consumption database for the DC device;

[0015] Record the daily power consumption data of the DC device and store it in the historical power consumption database of the DC device as the historical power consumption data of the DC device;

[0016] Establish the power consumption model of the DC device by means of model establishment according to the historical power consumption data of the DC device;

[0017] Every time new daily power consumption data of the DC device is added to the historical power consumption database of the DC device, update the power consumption model of the DC device according to the newly added daily power consumption data;

[0018] Record the historical power generation data of the PV device, and establish and continuously update the power generation model of the PV device, including:

[0019] Establish a historical power generation database for the PV device;

[0020] Record the daily power generation data and weather conditions of the PV device and store them in the historical power generation database of the PV device as the historical power generation data of the PV device;

[0021] Establish the power generation model of the PV device by means of model establishment according to the historical power generation data of the PV device;

[0022] Every time new daily power generation data and weather conditions of the PV device are added to the historical power generation database of the PV device, update the power generation model of the PV device according to the newly added daily power generation data and weather conditions.

[0023] Furthermore, according to the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the PV device, predict the future power consumption data of the DC device and the future power generation data of the PV device, including:

[0024] Predict the power consumption B of the DC device during the day on the nth day according to the power consumption model of the DC device n ; According to the predicted weather data on the nth day obtained from the Internet, combined with the power generation model of the PV device, predict the power generation C of the PV device on the nth dayn ;

[0025] Selecting the future power supply mode of the DC device according to the future power consumption data of the DC device and the future power generation data of the photovoltaic device includes:

[0026] Calculating the predicted maximum power consumption W of the DC device during the day on the nth day Bn =B n +Z Bn , where Z Bn is the first buffer; calculating the predicted maximum power supply W of the photovoltaic device during the day on the nth day Cn =C n -Z Cn , where Z Cn is the second buffer;

[0027] After entering the day on the nth day, turn on the photovoltaic device and record the current battery charge A 白n ; calculating the predicted maximum power supply W of the battery during the day on the nth day A白n =A 白n -Z A白n , where Z A白n is the third buffer;

[0028] Judge whether W Bn is greater than W Cn +W A白n : If so, supply power to the DC device through the photovoltaic device, the battery, and the mains network together; if not, judge whether W Bn is greater than W Cn ;

[0029] Judge whether W Bn is greater than W Cn : If so, supply power to the DC device through the photovoltaic device and the battery together; if not, supply power to the DC device through the photovoltaic device and judge whether the battery is full;

[0030] When judging whether the battery is full: If so, feed the excess power generation of the photovoltaic device back to the grid; if not, store the excess power generation of the photovoltaic device in the battery, and when the battery is full, invert the excess power generation of the photovoltaic device and then feed it back to the grid.

[0031] Furthermore, predicting the future power consumption data of the DC device and the future power generation data of the photovoltaic device according to the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the photovoltaic device, further includes:

[0032] Predicting the power consumption D of the DC device during the non-low electricity price period at night on the nth day according to the power consumption model of the DC device n and the power consumption E of the DC device during the low electricity price period at night on the nth dayn ;

[0033] Selecting the future power supply mode of the DC device according to the future power consumption data of the DC device and the future power generation data of the photovoltaic device further includes:

[0034] Calculating the predicted maximum power consumption W of the DC device during the non-low electricity price period at night on the nth day Dn =D n +Z Dn , where Z Dn is the fourth buffer quantity; calculating the predicted maximum power consumption W of the DC device during the low electricity price period at night on the nth day En =E n +Z En , where Z En is the fifth buffer quantity; calculating the predicted maximum total power consumption W of the DC device at night on the nth day DE =W Dn +W En ;

[0035] After entering the night of the nth day, turn off the photovoltaic device and record the current battery power A 夜n ; calculating the maximum power supply W of the battery during the night of the nth day A夜n =A 夜n -Z A夜n , where Z A夜n is the sixth buffer quantity;

[0036] Judge whether W A夜n is greater than W DE : If so, the battery supplies power to the DC device that night; if not, judge whether W A夜n is greater than D n ;

[0037] Judge whether W A夜n is greater than D n : If so, the battery supplies power to the DC device during the non-low electricity price period that night, and the battery and the mains supply power to the DC device together during the low electricity price period that night; if not, the battery and the mains supply power to the DC device together during the non-low electricity price period that night, and the mains supplies power to the DC device during the low electricity price period that night.

[0038] Furthermore, predicting the future power consumption data of the DC device and the future power generation data of the photovoltaic device according to the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the photovoltaic device, further includes:

[0039] According to the power consumption model of the DC device, predicting the power consumption B of the DC device during the day on the (n + 1)th day n+1 and the power consumption D of the DC device during the non-low electricity price period at night on the (n + 1)th day n+1 ;

[0040] Predict the power generation C of the photovoltaic device on the (n + 1)-th day according to the predicted weather data on the (n + 1)-th day obtained from the Internet and in combination with the power generation curve of the photovoltaic device n+1 ;

[0041] Selecting the future power supply mode of the DC device according to the future power consumption data of the DC device and the future power generation data of the photovoltaic device further includes:

[0042] Define the expected remaining power X1 of the energy storage battery when the photovoltaic device is turned on during the day on the (n + 1)-th day; and when judging whether W A夜n is greater than W DE , if the judgment is yes, then X1 = W A夜n - W DE ; if the judgment is no, then X1 = 0;

[0043] Calculate the predicted maximum power supply W of the photovoltaic device during the day on the (n + 1)-th day Cn+1 = C n+1 - Z Cn+1 , where Z Cn+1 is the seventh buffer quantity; calculate the predicted maximum power supply W of the energy storage battery during the day on the (n + 1)-th day X1 = X1 - Z X1 , where Z A白n+1 is the eighth buffer quantity; calculate the predicted maximum power consumption W of the DC device during the day on the (n + 1)-th day Bn+1 = B n+1 + Z Bn+1 , where Z Bn+1 is the ninth buffer quantity;

[0044] Define the expected remaining power X2 of the energy storage battery when the photovoltaic device is turned off at night on the (n + 1)-th day, and calculate X2 = W Cn+1 + W X1 - W Bn+1 ;

[0045] Calculate the predicted maximum power supply W of the energy storage battery at night on the (n + 1)-th day X2 = X2 - Z X2 , where Z X2 is the tenth buffer quantity; calculate the predicted maximum power consumption W of the DC device during the non-low electricity price period at night on the (n + 1)-th day Dn+1 = D n+1 + Z Dn+1 , where Z Dn+1 is the eleventh buffer quantity;

[0046] Judge whether W X2 is less than W Dn+1 , if so, then during the low electricity price period on the night of the n-th day, charge the energy storage battery with the electricity quantity Y = W Dn+1 - W X2 ; if not, then end.

[0047] A flexible control system for a photovoltaic-storage-direct-current-soft system to control direct-current devices, which is connected to direct-current devices, photovoltaic devices, the mains power network, and energy storage batteries, includes a power transmission connection module, a data module, an Internet module, a prediction module, a flexible control module, and a power transmission control module:

[0048] The power transmission connection module is used to connect the direct-current devices to the photovoltaic devices, the mains power network, and the energy storage batteries;

[0049] The data module is used to record the historical power consumption data of the direct-current devices, establish and continuously update the power consumption model of the direct-current devices; record the historical power generation data of the photovoltaic devices, establish and continuously update the power generation model of the photovoltaic devices;

[0050] The Internet module is used to obtain predicted weather data through the Internet;

[0051] The prediction module is used to predict the future power consumption data of the direct-current devices and the future power generation data of the photovoltaic devices according to the predicted weather data, combined with the power consumption model of the direct-current devices and the power generation model of the photovoltaic devices;

[0052] The flexible control module is used to select the future power supply mode of the direct-current devices according to the future power consumption data of the direct-current devices and the future power generation data of the photovoltaic devices;

[0053] The power transmission control module is used to supply power to the direct-current devices according to the future power supply mode of the direct-current devices and the future power transmission objects of the photovoltaic devices, and transmit power to the future power transmission objects through the photovoltaic devices.

[0054] The beneficial effects of the present invention are as follows:

[0055] By using the method of model establishment, through the historical power consumption data of the direct-current devices and the historical power generation data of the photovoltaic devices, the power consumption model of the direct-current devices and the power generation model of the photovoltaic devices are established, and the future power consumption of the direct-current devices and the future power generation of the photovoltaic devices are predicted according to the models, so as to select in advance the future power supply mode of the direct-current devices and the future power generation data of the photovoltaic devices, reduce the switching times of the power supply mode of the direct-current devices, improve the operation effect of the direct-current devices, and protect the direct-current devices; at the same time, by reducing the overall utilization rate of the mains power network, increasing the utilization ratio of the low-valley electricity in the mains power network, and improving the economic benefits of the overall power supply of the photovoltaic-storage-direct-current-soft system. Description of the Drawings

[0056] Figure 1 It is a flowchart of the flexible control method for a photovoltaic-storage-direct-current-soft system to control direct-current devices in an embodiment of the present application.

[0057] Figure 2 It is a flowchart of the establishment of the power consumption model of the direct-current devices and the establishment of the power generation model of the photovoltaic devices in an embodiment of the present application.

[0058] Figure 3 is the process of predicting data according to the model and selecting power supply and transmission methods in the embodiments of the present application Figure 1 .

[0059] Figure 4 is the process of predicting data according to the model and selecting power supply and transmission methods in the embodiments of the present application Figure 2 .

[0060] Figure 5 is the structural diagram of the flexible control system for controlling DC equipment in the flexible optical storage direct current and flexible (FODF) system in the embodiments of the present application Specific embodiments

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following describes the embodiments of the present invention in detail with reference to the accompanying drawings. However, the embodiments described herein are only a part of the embodiments of the present invention, rather than all of the embodiments

[0062] As Figure 1 shown, on the one hand, this embodiment provides a flexible control method for controlling DC equipment in an FODF system, including the following steps

[0063] S100. Connect the DC equipment, photovoltaic equipment, energy storage battery, and mains power network to each other through transmission lines; the DC equipment refers to the equipment directly driven by direct current

[0064] S200. Record the historical power consumption data of the DC equipment, establish and continuously update the power consumption model of the DC equipment; record the historical power generation data of the photovoltaic equipment, establish and continuously update the power generation model of the photovoltaic equipment; more specifically, the method for establishing the power consumption model of the DC equipment here can adopt traditional statistical models such as ARIMA, SARIMA, exponential smoothing method, etc., or machine learning models such as LightGBM, XGBoost, Prophet, etc., or deep learning methods such as LSTM, GRU, Transformer, etc.; the specific model establishment process can refer to, for example, the method for establishing an air-conditioning load prediction model disclosed in Patent No. CN115828543A; more specifically, the method for establishing the power generation model of the photovoltaic equipment here can adopt the method of establishing a regression model, and combine the historical power generation data with the weather record to establish a regression model

[0065] S300. Obtain the predicted weather data through the Internet

[0066] S400. Based on the predicted weather data and combined with the power consumption model of DC devices and the power generation model of photovoltaic devices, predict the future power consumption data of DC devices and the future power generation data of photovoltaic devices; more specifically, predicting the future power consumption data and the future power generation data of photovoltaic devices through the model established in S200 belongs to the prior art, so it will not be elaborated here.

[0067] S500. Based on the future power consumption data of DC devices and the future power generation data of photovoltaic devices, select the future power supply mode of DC devices and select the power transmission objects of photovoltaic devices.

[0068] S600. Supply power to DC devices according to the future power supply mode of DC devices and the future power transmission objects of photovoltaic devices, and transmit power to its future power transmission objects through photovoltaic devices; preferably, the photovoltaic device uses the MPPT method to transmit power to the power transmission object.

[0069] Preferably, as Figure 2 shown, in step S200, recording the historical power consumption data of DC devices and establishing and continuously updating the power consumption model of DC devices can be refined as:

[0070] S201. Establish a historical power consumption database for DC devices.

[0071] S202. Record the daily power consumption data of DC devices and store it in the historical power consumption database of DC devices as the historical power consumption data of DC devices.

[0072] S203. According to the historical power consumption data of DC devices, establish the power consumption model of DC devices through the model establishment method, which has been elaborated above.

[0073] S204. Every time a new daily power consumption data of DC devices is added to the historical power consumption database of DC devices, update the power consumption model of DC devices according to the newly added daily power consumption data; designed in this way, the power consumption model of DC devices can be continuously updated according to the increase in the data volume to continuously improve the accuracy of predicting the future power consumption data of DC devices through the power consumption model of DC devices.

[0074] In step S200, recording the historical power generation data of photovoltaic devices and establishing and continuously updating the power generation model of photovoltaic devices can be refined as:

[0075] S211. Establish a historical power generation database for photovoltaic devices.

[0076] S212. Record the daily power generation data and weather conditions of photovoltaic devices and store them in the historical power generation database of photovoltaic devices as the historical power generation data of photovoltaic devices.

[0077] S213. Establish a power generation model for the photovoltaic device by means of model establishment based on the historical power generation data of the photovoltaic device. The method for establishing the power generation model has been described above.

[0078] S214. Every time the daily power generation data and weather conditions of the photovoltaic device are newly added to the historical power generation database of the photovoltaic device, update the power generation model of the photovoltaic device according to the newly added daily power generation data and weather conditions; with such a design, the power generation model of the photovoltaic device can be continuously updated according to the increase in the amount of data, so as to continuously improve the accuracy of predicting the future power generation data of the photovoltaic device through the power generation model of the photovoltaic device.

[0079] Preferably, as Figure 3 shown, S400 includes the following specific steps:

[0080] S401. Predict the power consumption B of the DC device during the day on the nth day according to the power consumption model of the DC device n。

[0081] S402. Predict the power generation C of the photovoltaic device on the nth day according to the predicted weather data on the nth day obtained from the Internet and in combination with the power generation model of the photovoltaic device n .

[0082] S500 includes the following specific steps:

[0083] S501. Calculate the predicted maximum power consumption W of the DC device during the day on the nth day Bn = B n + Z Bn , where Z Bn is the first buffer quantity; more specifically, since the actual power consumption of the DC device during the day on the nth day may deviate from the predicted power consumption and may be greater than the predicted power consumption, if the subsequent power supply method allocation steps are strictly adopted, it is easy to cause insufficient power supply for the DC device. Therefore, a first buffer quantity is set here to reduce the probability of insufficient power supply. The size of the first buffer quantity is determined according to experience; calculate the predicted maximum power supply W of the photovoltaic device during the day on the nth day Cn = C n - Z Cn , where Z Cn is the second buffer quantity; more specifically, since there is energy loss in the process of transmitting electric energy from the photovoltaic device to the DC device, a second buffer quantity is set here. The size of the second buffer quantity can be calculated by the formula: Z Cn = C n × (1 - η1), where η1 is the transmission efficiency of electric energy from the photovoltaic device to the DC device.

[0084] S502. After entering the day on the nth day, turn on the photovoltaic device and record the current battery energy storage level A 白n;

[0085] Calculate the predicted maximum power supply W of the energy storage battery during the day on the nth day A白n = A 白n - Z A白n , where Z A白n is the third buffer quantity; more specifically, since there is energy loss during the process of electric energy being transmitted from the energy storage battery to the DC device, the third buffer quantity is set here. The size of the third buffer quantity can be calculated by the formula: Z A白n = A 白n ×(1 - η2), where η2 is the transmission efficiency of electric energy from the energy storage battery to the DC device.

[0086] S503. Judge whether W Bn is greater than W Cn + W A白n : If so, supply power to the DC device together through the photovoltaic device, the energy storage battery, and the mains power network; if not, judge whether W Bn is greater than W Cn .

[0087] S504. Judge whether W Bn is greater than W Cn : If so, supply power to the DC device together through the photovoltaic device and the energy storage battery; if not, supply power to the DC device through the photovoltaic device and judge whether the energy storage battery is full.

[0088] S505. Judge whether the energy storage battery is full: If so, invert the excess power generation of the photovoltaic device and feed the surplus power into the grid; if not, store the excess power generation of the photovoltaic device in the energy storage battery, and when the energy storage battery is full, invert the excess power generation of the photovoltaic device and feed the surplus power into the grid; more specifically, feeding the surplus power into the grid means inverting the excess power generation of the photovoltaic device and transmitting it to the mains power network to improve the economic benefits of the photovoltaic device power generation.

[0089] With such a design, when the photovoltaic device is turned on during the day on the nth day, the economic benefit maximization plan for DC device power supply and photovoltaic device power transmission can be planned in advance, which not only avoids repeatedly switching the power supply mode of the DC device but also improves the overall economic benefits of the photovoltaic-storage-direct-current-soft technology.

[0090] Preferably, as Figure 3 shown, S400 further includes the following specific steps:

[0091] S411. According to the power consumption model of the DC device, predict the power consumption D n of the DC device during the non-peak electricity price period at night on the nth day and the power consumption E n of the DC device during the peak electricity price period at night on the nth day.

[0092] S500 also includes the following specific steps:

[0093] S511. Calculate the predicted maximum power consumption W of DC equipment during the non-low electricity price period at night on the nth day Dn =D n +Z Dn , where Z Dn is the fourth buffer quantity; calculate the predicted maximum power consumption W of DC equipment during the low electricity price period at night on the nth day En =E n +Z En , where Z En is the fifth buffer quantity; calculate the predicted maximum total power consumption W of DC equipment during the night on the nth day DE =W Dn +W En ; More specifically, since the actual power consumption of DC equipment during the non-low electricity price period and the low electricity price period at night on the nth day may deviate from the predicted power consumption and may be greater than the predicted power consumption, if the subsequent power supply method allocation steps are strictly adopted, it is likely to lead to insufficient power supply for DC equipment. Therefore, the fourth buffer quantity and the fifth buffer quantity are set here to reduce the probability of insufficient power supply. The sizes of the fourth buffer quantity and the fifth buffer quantity are determined according to experience.

[0094] S512. After entering the night of the nth day, turn off the photovoltaic equipment and record the current battery power A 夜n ; calculate the maximum power supply W of the battery during the night of the nth day A夜n =A 夜n -Z A夜n , where Z A夜n is the sixth buffer quantity; More specifically, since there is energy loss during the process of transmitting electric energy from the battery to DC equipment, the sixth buffer quantity is set here. The size of the sixth buffer quantity can be calculated by the formula: Z A夜n =A 夜n ×(1 - η2).

[0095] S513. Determine whether W A夜n is greater than W DE : If so, the battery supplies power to the DC equipment that night; if not, determine whether W A夜n is greater than W Dn .

[0096] S514. Determine whether W A夜n is greater than W Dn : If so, the battery supplies power to the DC equipment during the non-low electricity price period that night, and the battery and the mains supply power to the DC equipment together during the low electricity price period that night; if not, the battery and the mains supply power to the DC equipment together during the non-low electricity price period that night, and the mains supplies power to the DC equipment during the low electricity price period that night.

[0097] With such a design, when the photovoltaic device is turned off on the night of the nth day, the economic benefit maximization plan for the power supply of the DC device can be planned in advance, and the utilization efficiency of the valley electricity is improved. This not only avoids repeatedly switching the power supply mode of the DC device, but also improves the overall economic benefit of the photovoltaic-storage-direct-current-flexible technology.

[0098] Preferably, as Figure 4 shown, S400 further includes the following steps:

[0099] S421. According to the power consumption model of the DC device, predict the power consumption B of the DC device during the day of the (n + 1)th day n+1 , the power consumption D of the DC device during the non-valley electricity period at night of the (n + 1)th day n+1 .

[0100] S422. According to the predicted weather data of the (n + 1)th day obtained from the Internet, combined with the power generation curve of the photovoltaic device, predict the power generation C of the photovoltaic device on the (n + 1)th day n+1 .

[0101] S500 further includes the following steps:

[0102] S521. Define the expected remaining electricity X1 of the energy storage battery when the photovoltaic device is turned on during the day of the (n + 1)th day; when judging whether W A夜n is greater than W DE , if the judgment is yes, then X1 = W A夜n -W DE ; if the judgment is no, then X1 = 0.

[0103] S522. Calculate the predicted maximum power supply W Cn+1 = C n+1 - Z Cn+1 of the photovoltaic device during the day of the (n + 1)th day, where Z Cn+1 is the seventh buffer quantity; more specifically, since there is energy loss during the process of delivering electric energy from the photovoltaic device to the DC device, the seventh buffer quantity is set here, and the size of the seventh buffer quantity can be calculated by the formula: Z Cn+1 = C n+1 ×(1 - η1); calculate the predicted maximum power supply W X1 = X1 - Z X1 of the energy storage battery during the day of the (n + 1)th day, where Z X1 is the eighth buffer quantity; more specifically, since there is energy loss during the process of delivering electric energy from the energy storage battery to the DC device, the eighth buffer quantity is set here, and the size of the eighth buffer quantity can be calculated by the formula: Z X1 = X1×(1 - η2); calculate the predicted maximum power consumption W Bn+1 = B n+1 + Z Bn+1, where Z Bn+1 is the ninth buffer quantity; more specifically, since there may be a deviation between the actual power consumption of the DC device during the day on the (n + 1)-th day and the predicted power consumption, and it may be greater than the predicted power consumption, if the subsequent power supply method allocation steps are strictly adopted, it is easy to cause insufficient power supply for the DC device. Therefore, the ninth buffer quantity is set here to reduce the probability of insufficient power supply, and the size of the ninth buffer quantity is determined according to experience.

[0104] S523. Define the expected remaining power X2 of the energy storage battery when the photovoltaic device is turned off at night on the (n + 1)-th day, and calculate X2 = W Cn+1 +W X1 -W Bn+1 .

[0105] S524. Calculate the predicted maximum power supply W X2 = X2 - Z X2 , where Z X2 is the tenth buffer quantity; more specifically, since there is energy loss during the process of transmitting electric energy from the energy storage battery to the DC device, the tenth buffer quantity is set here, and the size of the tenth buffer quantity can be calculated by the formula: Z X2 = X2×(1 - η2); calculate the predicted maximum power consumption W Dn+1 = D n+1 + Z Dn+1 , where Z Dn+1 is the eleventh buffer quantity; more specifically, since there may be a deviation between the actual power consumption of the DC device during the non-peak electricity price period at night on the (n + 1)-th day and the predicted power consumption, and it may be greater than the predicted power consumption, if the subsequent power supply method allocation steps are strictly adopted, it is easy to cause insufficient power supply for the DC device. Therefore, the eleventh buffer quantity is set here to reduce the probability of insufficient power supply, and the size of the eleventh buffer quantity is determined according to experience.

[0106] S525. Judge whether W X2 is less than W Dn+1 , if so, then during the low electricity price period on the night of the n-th day, charge the energy storage battery with the electricity quantity Y = W Dn+1 - W X2 ; if not, then end.

[0107] With such a design, through advance prediction and planning, the utilization efficiency of low valley electricity can be further improved, the usage amount of non-low valley electricity can be reduced, and the overall economic benefit of the PV-storage-DC-soft technology can be improved.

[0108] Preferably, the DC device is a DC device driven by direct current. With such a design, the inversion process of the current at the photovoltaic device end and the energy storage battery end is reduced, and only the rectification process of the current needs to be performed at the mains network end. Since the frequency of using the mains network as the power supply method in this method is relatively small, the overall current loss is reduced and the economic benefit is improved.

[0109] Further preferably, the method further includes the following steps:

[0110] S700: Introduce an alarm variable G.

[0111] S701: When the number of DC devices directly driven by the photovoltaic device reaches the maximum value that can be driven, set the value of G to 1; when the number of DC devices directly driven by the photovoltaic device does not reach the maximum value that can be driven, set the value of G to 0.

[0112] S702: When turning on a DC device during the day on the nth day, determine whether the value of G is 1:

[0113] If G = 1, regardless of whether the power supply method is pre-set or not, directly supply power to the DC device using the energy storage battery or the mains network;

[0114] If G = 0, supply power to the DC device according to the pre-set power supply method.

[0115] During the actual operation of the DC device, it is inevitable that a situation will occur: during a certain period of time during the day on the nth day, the power generation power of the photovoltaic device is not sufficient to match the power consumption of the current DC device. If the pre-set power supply strategy is followed, it is easy to cause voltage instability. Through the methods of S700~S702 above, by setting an alarm variable G, it is possible to forcibly select a power supply method not in the preset in the above situation, improving the stability of this method.

[0116] As Figure 5 shown, on the other hand, the present application provides a flexible control system for controlling a DC device in a photovoltaic-storage-direct-current-flexible system, which is connected to the DC device, the photovoltaic device, the mains network, and the energy storage battery, and includes a power transmission module, a data module, an Internet module, a prediction module, and a flexible control module:

[0117] The power transmission module is used to connect the DC device, the photovoltaic device, the energy storage battery, and the mains network to each other through transmission lines.

[0118] The data module is used to record the historical power consumption data of the DC device, establish and continuously update the power consumption model of the DC device; record the historical power generation data of the photovoltaic device, establish and continuously update the power generation model of the photovoltaic device.

[0119] The Internet module is used to obtain predicted weather data through the Internet.

[0120] The prediction module is used to predict the future power consumption data of the DC device and the future power generation data of the photovoltaic device according to the predicted weather data in combination with the power consumption model of the DC device and the power generation model of the photovoltaic device.

[0121] The flexible control module is used to select the future power supply mode of the DC device and the power transmission object of the photovoltaic device according to the future power consumption data of the DC device and the future power generation data of the photovoltaic device.

[0122] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Obviously, those skilled in the art can make various changes and deformations to the present application without departing from the spirit and scope of the present application.

Claims

1. A flexible control method for a DC device controlled by a photovoltaic-storage-direct-current-flexible system, characterized in that, Including: Interconnecting DC devices, photovoltaic devices, energy storage batteries, and the mains power network with each other via transmission lines; Recording the historical power consumption data of DC devices, establishing and continuously updating the power consumption models of DC devices; recording the historical power generation data of photovoltaic devices, establishing and continuously updating the power generation models of photovoltaic devices; Obtaining predicted weather data via the Internet; Based on the predicted weather data, combining the power consumption models of DC devices and the power generation models of photovoltaic devices, predicting the future power consumption data of DC devices and the future power generation data of photovoltaic devices; Based on the future power consumption data of DC devices and the future power generation data of photovoltaic devices, selecting the future power supply methods for DC devices and selecting the future power transmission targets for photovoltaic devices; According to the future power supply methods for DC devices and the future power transmission targets for photovoltaic devices, supplying power to DC devices and transmitting power to their future power transmission targets via photovoltaic devices; Based on the predicted weather data, combining the power consumption models of DC devices and the power generation models of photovoltaic devices, predicting the future power consumption data of DC devices and the future power generation data of photovoltaic devices includes: Predict the power consumption B of DC devices during the day on the nth day according to the power consumption model of DC devices n ; Predict the power generation C of PV devices on the nth day according to the predicted weather data on the nth day obtained from the Internet and combining with the power generation model of PV devices n ; Based on the future power consumption data of DC devices and the future power generation data of photovoltaic devices, selecting the future power supply methods for DC devices includes: Calculate the predicted maximum power consumption W of DC devices during the day on the nth day Bn =B n +Z Bn , where Z Bn is the first buffer; Calculate the predicted maximum power supply W of PV devices during the day on the nth day Cn =C n -Z Cn , where Z Cn is the second buffer; After entering the daytime of the nth day, turn on the photovoltaic equipment and record the current battery power A of the energy storage battery 白n ; Calculate the predicted maximum power supply W of the energy storage battery during the daytime of the nth day A白n =A 白n -Z A白n , where Z A白n is the third buffer quantity; Determine W Bn Is it greater than W Cn +W A白n : If so, supply power to the DC device together through the photovoltaic device, energy storage battery, and mains power network; if not, determine W Bn Is it greater than W Cn ; Determine W Bn whether it is greater than W Cn If it is, the photovoltaic device and the energy storage battery supply power to the DC device together; if not, the photovoltaic device supplies power to the DC device, and determine whether the energy storage battery is fully loaded; Judging whether the energy storage battery is full: if so, feeding the excess power generation of the photovoltaic device into the grid; if not, storing the excess power generation of the photovoltaic device into the energy storage battery, and when the energy storage battery is full, then inverting the excess power generation of the photovoltaic device and feeding it into the grid; Based on the predicted weather data, combining the power consumption models of DC devices and the power generation models of photovoltaic devices, predicting the future power consumption data of DC devices and the future power generation data of photovoltaic devices further includes: Predict the power consumption D of DC equipment during the non-low electricity price period at night on the nth day according to the power consumption model of DC equipment n and the power consumption E of DC equipment during the low electricity price period at night on the nth day n ; Based on the future power consumption data of DC devices and the future power generation data of photovoltaic devices, selecting the future power supply methods for DC devices further includes: Calculate the predicted maximum power consumption W of DC equipment during the non-low electricity price period at night on the nth day Dn =D n +Z Dn , where Z Dn is the fourth buffer quantity; Calculate the predicted maximum power consumption W of DC equipment during the low electricity price period at night on the nth day En =E n +Z En , where Z En is the fifth buffer quantity; Calculate the predicted maximum total power consumption W of DC equipment at night on the nth day DE =W Dn +W En ; After entering the night of the nth day, turn off the photovoltaic equipment and record the current power of the energy storage battery A 夜n ; Calculate the maximum power supply W of the energy storage battery on the night of the nth day A夜n =A 夜n -Z A夜n , where Z A夜n is the sixth buffer volume; Determine W A夜n Is it greater than W DE : If so, the energy storage battery supplies power to the DC device that night; if not, determine W A夜n Is it greater than D n ; Determine W A夜n Is it greater than D n If so, during the non-low electricity price period at night, the energy storage battery supplies power to the DC equipment, and during the low electricity price period at night, the energy storage battery and the mains power supply the DC equipment together; if not, during the non-low electricity price period at night, the energy storage battery and the mains power supply the DC equipment together, and during the low electricity price period at night, the mains power supplies power to the DC equipment; Based on the predicted weather data, combining the power consumption models of DC devices and the power generation models of photovoltaic devices, predicting the future power consumption data of DC devices and the future power generation data of photovoltaic devices further includes: Predict the DC device power consumption B during the day on the (n + 1)-th day according to the power consumption model of DC devices n+1 and the DC device power consumption D during the non-low electricity price period at night on the (n + 1)-th day n+1 ; Predict the power generation C of the photovoltaic device on the (n + 1)-th day according to the predicted weather data on the (n + 1)-th day obtained from the Internet and in combination with the power generation curve of the photovoltaic device n+1 ; Based on the future power consumption data of DC devices and the future power generation data of photovoltaic devices, selecting the future power supply methods for DC devices further includes: Define the expected remaining power X1 of the energy storage battery when the photovoltaic device is turned on during the day on the (n + 1)-th day; and when judging whether W A夜n is greater than W DE , if the judgment is yes, then X1 = W A夜n -W DE ; if the judgment is no, then X1 = 0; Calculate the predicted maximum power supply W of the photovoltaic device during the day on the (n + 1)-th day Cn+1 =C n+1 -Z Cn+1 , Z Cn+1 is the seventh buffer; Calculate the predicted maximum power supply W of the energy storage battery during the day on the (n + 1)-th day X1 =X1-Z X1 , where Z A白n+1 is the eighth buffer; Calculate the predicted maximum power consumption W of the DC device during the day on the (n + 1)-th day Bn+1 =B n+1 +Z Bn+1 , where Z Bn+1 is the ninth buffer; Define the expected remaining power X2 of the energy storage battery when the photovoltaic device is turned off on the (n + 1)-th night, and calculate X2 = W Cn+1 +W X1 -W Bn+1 ; Calculate the predicted maximum power supply W of the energy storage battery on the night of the (n + 1)-th day X2 = X2 - Z X2 , where Z X2 is the tenth buffer quantity; calculate the predicted maximum power consumption W of the DC equipment during the non-low electricity price period on the night of the (n + 1)-th day Dn+1 = D n+1 + Z Dn+1 , where Z Dn+1 is the eleventh buffer quantity; Determine W X2 Is it less than W Dn+1 , if so, during the low electricity price period on the nth night, charge the energy storage battery with the mains network with the electricity quantity Y = W Dn+1 -W X2 ; if not, end.

2. The flexible control method for controlling DC equipment in a photovoltaic-storage-direct-current-soft (PV-SD-DC-S) system according to claim 1, wherein, Recording the historical power consumption data of DC devices, establishing and continuously updating the power consumption models of DC devices includes: Establishing a historical power consumption database for DC devices; Recording the daily power consumption data of DC devices and storing it in the historical power consumption database of DC devices as the historical power consumption data of DC devices; Based on the historical power consumption data of DC devices, establishing the power consumption models of DC devices by means of model establishment; Every time new daily power consumption data of DC devices is added to the historical power consumption database of DC devices, the power consumption models of DC devices are updated once according to the newly added daily power consumption data; Recording the historical power generation data of photovoltaic devices, establishing and continuously updating the power generation models of photovoltaic devices includes: Establishing a historical power generation database for photovoltaic devices; Recording the daily power generation data and weather conditions of photovoltaic devices and storing them in the historical power generation database of photovoltaic devices as the historical power generation data of photovoltaic devices; Based on the historical power generation data of photovoltaic devices, establishing the power generation models of photovoltaic devices by means of model establishment; Each time the daily power generation data and weather conditions of a photovoltaic device are newly added to the historical power generation database of the photovoltaic device, the power generation model of the photovoltaic device is updated according to the newly added daily power generation data and weather conditions.

3. A flexible control system for a DC device in a photovoltaic-storage-direct-current-flexible system, connected to the DC device, photovoltaic device, mains power network, and energy storage battery, characterized in that, Including a power transmission connection module, a data module, an Internet module, a prediction module, a flexible control module, and a power transmission control module: The power transmission connection module is used to connect the DC device to the photovoltaic device, the mains power network, and the energy storage battery; The data module is used to record the historical power consumption data of the DC device, establish and continuously update the power consumption model of the DC device; record the historical power generation data of the photovoltaic device, establish and continuously update the power generation model of the photovoltaic device; The Internet module is used to obtain predicted weather data through the Internet; The prediction module is used to predict the future power consumption data of the DC device and the future power generation data of the photovoltaic device according to the predicted weather data, in combination with the power consumption model of the DC device and the power generation model of the photovoltaic device; The flexible control module is used to select the future power supply method of the DC device according to the future power consumption data of the DC device and the future power generation data of the photovoltaic device; The power transmission control module is used to supply power to the DC device according to the future power supply method of the DC device and the future power transmission object of the photovoltaic device, and transmit power to its future power transmission object through the photovoltaic device.

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