Flexible control method and system for controlling direct current equipment through optical storage direct current flexible system

Abstract

The invention provides a flexible control method and system for controlling direct current equipment through an optical storage direct current flexible system, and relates to the field of power supply methods and systems.The method comprises the steps that the direct current equipment, photovoltaic equipment, an energy storage battery and a mains supply network are connected with one another; historical power consumption data of the DC equipment is recorded, and a power consumption model of the DC equipment is established; historical power generation data of the photovoltaic equipment are recorded, and a power generation model of the photovoltaic equipment is established; according to the predicted weather data, combining a power utilization model, a power generation model, future power utilization data and future power generation data; selecting a future power supply mode according to the future power consumption data and the future power generation data, and selecting a future power transmission object; and supplying power to the direct current equipment according to the future power supply mode and the future power transmission object, and transmitting power to the future power transmission object through the photovoltaic equipment. The operation effect of the direct current equipment can be improved, the direct current equipment is protected, the overall benefit of supplying power to the direct current equipment through the optical storage direct current flexible technology is improved, and the practicability is high.

Description

Technical Field

[0001] The present application relates to the field of power supply methods and systems, and in particular to a photovoltaic-storage-direct-flexible technology, and specifically to a flexible control method and system for controlling DC equipment in a photovoltaic-storage-direct-flexible system. Background Art

[0002] "PV, storage, direct and flexible" is a new type of building energy system that is equipped with building photovoltaics and building energy storage, adopts a DC power distribution system, and the power-consuming equipment has the function of active power response. Among them, "PV" refers to the use of the building surface to develop photovoltaics, "storage" refers to connecting the building energy storage battery, "direct" refers to the realization of DC power supply inside the building, and "flexible" refers to elastic load and flexible power supply.

[0003] In the existing technology, when using photovoltaic, energy storage, direct current and flexible technology to drive DC devices such as air conditioners, in order to achieve flexible power supply, it is necessary to continuously switch the power supply source of the DC device from photovoltaic devices, energy storage batteries, and the mains network according to the current power consumption of all DC devices. This can easily lead to unstable voltage of the DC device, affect the operation of the DC device, and cause damage to the DC device. At the same time, the existing photovoltaic, energy storage, direct current and flexible technology has a low utilization rate of off-peak electricity when driving multiple DC devices, and the economic benefits of the overall power supply need to be improved. Summary of the invention

[0004] In order to solve the above-mentioned defects of the related prior art, the present application provides a flexible control method and system for controlling DC equipment in a photovoltaic storage and direct current flexible system, which can improve the operating performance of DC equipment, protect DC equipment, and enhance the overall efficiency of power supply to the DC system through photovoltaic storage and direct current flexible technology, and has strong practicality.

[0005] In order to achieve the above object, the present invention adopts the following technologies: A flexible control method for controlling a DC device in a photovoltaic-storage-direct-current-flexible system, comprising: Connect DC equipment, photovoltaic equipment, energy storage batteries, and city power networks through transmission lines; Record the historical power consumption data of DC equipment, establish and continuously update the power consumption model of DC equipment; record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment; Obtain forecast weather data via the Internet; Based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment, the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment are predicted; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply mode of the DC equipment is selected, and the future power transmission object of the photovoltaic equipment is selected; According to the future power supply mode of the DC equipment and the future power transmission target of the photovoltaic equipment, the DC equipment is powered, and power is transmitted to its future power transmission target through the photovoltaic equipment.

[0006] Furthermore, the historical power consumption data of DC equipment is recorded, and the power consumption model of DC equipment is established and continuously updated, including: Establish a historical power consumption database for DC equipment; Record the daily power consumption data of the DC equipment and store it in the historical power consumption database of the DC equipment as the historical power consumption data of the DC equipment; According to the historical power consumption data of the DC equipment, a power consumption model of the DC equipment is established by a model building method; Every time the daily power consumption data of the DC device is added to the historical power consumption database of the DC device, the power consumption model of the DC device is updated according to the newly added daily power consumption data; Record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment, including: Establish a historical power generation database for photovoltaic equipment; Record the daily power generation data and weather conditions of the photovoltaic equipment, and store them in the historical power generation database of the photovoltaic equipment as the historical power generation data of the photovoltaic equipment; According to the historical power generation data of the photovoltaic equipment, a power generation model of the photovoltaic equipment is established through a model building method; Each time the daily power generation data and weather conditions of the photovoltaic equipment are added to the historical power generation database of the photovoltaic equipment, the power generation model of the photovoltaic equipment is updated according to the newly added daily power generation data and weather conditions.

[0007] Furthermore, based on the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the photovoltaic device, the future power consumption data of the DC device and the future power generation data of the photovoltaic device are predicted, including: According to the power consumption model of DC equipment, predict the power consumption B of DC equipment during the day on day n n ; Based on the forecast weather data for the nth day obtained from the Internet and the power generation model of the photovoltaic equipment, the power generation C of the photovoltaic equipment on the nth day is predicted n ; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply methods of the DC equipment include: Calculate the predicted maximum power consumption W of the DC equipment during the day on day n Bn =B n +Z Bn , where Z Bn is the first buffer amount; calculate the predicted maximum power supply W of the photovoltaic equipment during the day on the nth day Cn =C n-Z Cn , where Z Cn is the second buffer amount; After entering the daytime of the nth day, turn on the photovoltaic equipment and record the current energy storage battery power A 白n ; Calculate the predicted maximum power supply W of the energy storage battery during the day on day n A白n =A 白n -Z A白n , where Z A白n is the third buffer amount; Judge W Bn Is it greater than W? Cn +W An : If yes, the DC equipment is powered by photovoltaic equipment, energy storage batteries, and the mains network; if no, determine W Bn Is it greater than W? Cn ; Judge W Bn Is it greater than W? Cn When: If yes, the DC device is powered by the photovoltaic device and the energy storage battery together; if not, the DC device is powered by the photovoltaic device and it is determined whether the energy storage battery is fully loaded; When judging whether the energy storage battery is fully loaded: if so, the excess power generated by the photovoltaic equipment is connected to the grid; if not, the excess power generated by the photovoltaic equipment is stored in the energy storage battery, and when the energy storage battery is fully loaded, the excess power generated by the photovoltaic equipment is inverted and connected to the grid.

[0008] Furthermore, based on the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the photovoltaic device, the future power consumption data of the DC device and the future power generation data of the photovoltaic device are predicted, and the following are also included: According to the power consumption model of DC equipment, the power consumption D of DC equipment during the non-low-price period at night on the nth day is predicted. n , the power consumption of DC equipment during the low electricity price period at night on the nth day E n ; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply mode of the DC equipment is also selected as follows: Calculate the predicted maximum power consumption W of the DC equipment during the non-low-price period at night on the nth day Dn =D n +Z Dn , where Z Dn is the fourth buffer amount; calculate the predicted maximum power consumption W of the 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; calculate the predicted maximum total power consumption W of the DC equipment at night on the nth day DE =WDn +W En ; After entering the night of the nth day, turn off the photovoltaic equipment and record the current energy storage battery power A 夜n ; Calculate the maximum power supply W of the energy storage battery on the night of day n A夜n =A 夜n -Z A夜n , where Z A夜n is the sixth buffer amount; Judge W A夜n Is it greater than W? DE : If yes, the energy storage battery will power the DC device that night; if no, then determine W A夜n Is it greater than D n ; Judge W A夜n Is it greater than D n When: If yes, the energy storage battery will power the DC equipment during the non-low-price period that night, and the energy storage battery and the mains will power the DC equipment during the low-price period that night; if no, the energy storage battery and the mains will power the DC equipment during the non-low-price period that night, and the mains will power the DC equipment during the low-price period that night.

[0009] Furthermore, based on the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the photovoltaic device, the future power consumption data of the DC device and the future power generation data of the photovoltaic device are predicted, and the following are also included: According to the power consumption model of DC equipment, predict the power consumption B of DC equipment during the day on day n+1 n+1 , DC equipment power consumption D during the non-low-price period at night on the n+1th day n+1 ; Based on the forecast weather data for the n+1th day obtained from the Internet and combined with the power generation curve of the photovoltaic equipment, the power generation C of the photovoltaic equipment on the n+1th day is predicted. n+1 ; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply mode of the DC equipment is also selected as follows: Define the expected remaining power X1 of the energy storage battery when the photovoltaic equipment is turned on during the day on day n+1; and in the judgment of W A夜n Is it 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 equipment during the day on day n+1 Cn+1 =C n+1 -Z Cn+1 , Z Cn+1 is the seventh buffer capacity; calculate the predicted maximum power supply W of the energy storage battery during the day on the n+1th 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 equipment during the day on day n+1 Bn+1 =B n+1 +Z Bn+1 , where Z Bn+1 is the ninth buffer amount; Define the expected remaining power X2 of the energy storage battery when the photovoltaic equipment is turned off at night on day n+1, and calculate X2=W Cn+1 +W X1 -W Bn+1 ; Calculate the predicted maximum power supply W of the energy storage battery at night on day n+1 X2 =X2-Z X2 , where Z X2 is the tenth buffer amount; calculate the predicted maximum power consumption W of the DC equipment during the non-low-price period at night on the n+1th day Dn+1 =D n+1 +Z Dn+1 , where Z Dn+1 is the eleventh buffer; Judge W X2 Is it less than W? Dn+1 If so, then on the night of the nth day, during the low electricity price period, the energy storage battery will be charged with electricity Y=W through the city power network. Dn+1 -W X2 ; If not, then end.

[0010] A flexible control system for controlling DC equipment by a photovoltaic storage system is connected to DC equipment, photovoltaic equipment, a city power network, and an energy storage battery, 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 equipment with the photovoltaic equipment, the mains network, and the energy storage battery; The data module is used to record the historical power consumption data of DC equipment, establish and continuously update the power consumption model of DC equipment; record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment; The Internet module is used to obtain forecast weather data through the Internet; The prediction module is used to predict the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment; The flexible control module is used to select 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; The power transmission control module is used to supply power to the DC device according to the future power supply mode of the DC device and the future power transmission target of the photovoltaic device, and transmit power to its future power transmission target through the photovoltaic device.

[0011] The beneficial effects of the present invention are: By using the model building method, the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment are established through the historical power consumption data of the DC equipment and the historical power generation data of the photovoltaic equipment. The future power consumption of the DC equipment and the power generation of the photovoltaic equipment are predicted according to the model. In this way, the future power supply mode of the DC equipment and the future power generation data of the photovoltaic equipment are selected in advance, the number of switching times of the power supply mode of the DC equipment is reduced, the operation effect of the DC equipment is improved, and the DC equipment is protected. At the same time, by reducing the overall utilization rate of the municipal power network and increasing the utilization ratio of off-peak electricity in the municipal power network, the economic benefit of the overall power supply of photovoltaic storage direct current is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of a flexible control method for controlling DC devices in a photovoltaic-storage-direct-flexible system according to an embodiment of the present application.

[0013] Figure 2 It is a flow chart of establishing a power consumption model of a DC device and a power generation model of a photovoltaic device in an embodiment of the present application.

[0014] Figure 3 The process of predicting data based on the model and selecting the power supply and transmission method in the embodiment of the present application is Figure 1 .

[0015] Figure 4 The process of predicting data based on the model and selecting the power supply and transmission method in the embodiment of the present application is Figure 2 .

[0016] Figure 5 It is a structural diagram of the flexible control system of the photovoltaic storage direct current flexible system controlling the direct current equipment in the embodiment of the present application. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions and advantages of the embodiments of the present invention more clear, the implementation modes of the present invention are described in detail below with reference to the accompanying drawings. However, the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all the embodiments.

[0018] like Figure 1 As shown, on one hand, this embodiment provides a flexible control method for controlling a DC device in a photovoltaic storage DC-flexible system, comprising the following steps: S100, connecting DC equipment, photovoltaic equipment, energy storage batteries, and a city power network to each other through transmission lines; DC equipment refers to equipment that is directly driven by direct current.

[0019] 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, etc., or machine learning models such as LightGBM, XGBoost, Prophet, etc., and deep learning methods such as LSTM, GRU, Transformer, etc.; its specific model building process can refer to the air conditioning load prediction model building method 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, combining historical power generation data with weather records to establish a regression model.

[0020] S300, obtaining forecast weather data through the Internet.

[0021] S400. Predict the future power consumption data of the DC device and the future power generation data of the photovoltaic device based on the predicted weather data, combined with the power consumption model of the DC device and the power generation model of the photovoltaic device. More specifically, predicting the future power consumption data and the future power generation data of the photovoltaic device through the model established in S200 belongs to the prior art and will not be elaborated here.

[0022] S500: According to the future power consumption data of the DC device and the future power generation data of the photovoltaic device, a future power supply mode of the DC device is selected, and a power transmission object of the photovoltaic device is selected.

[0023] S600. Power the DC device according to the future power supply mode of the DC device and the future power transmission target of the photovoltaic device, and transmit power to the future power transmission target through the photovoltaic device; preferably, the photovoltaic device transmits power to the power transmission target in an MPPT mode.

[0024] Preferably, Figure 2 As shown, in step S200, the historical power consumption data of the DC device is recorded, and the power consumption model of the DC device is established and continuously updated, which can be refined as follows: S201. Establish a historical power consumption database of DC equipment.

[0025] S202 , recording the daily power consumption data of the DC device, and storing it in a historical power consumption database of the DC device as the historical power consumption data of the DC device.

[0026] S203. According to the historical power consumption data of the DC device, a power consumption model of the DC device is established by a model establishment method. The model establishment method has been described above.

[0027] S204. Every time the daily power consumption data of the DC device is added to the historical power consumption database of the DC device, the power consumption model of the DC device is updated according to the newly added daily power consumption data. With such a design, the power consumption model of the DC device can be continuously updated according to the increase in data volume, so as to continuously improve the accuracy of predicting the future power consumption data of the DC device through the power consumption model of the DC device.

[0028] In step S200, the historical power generation data of the photovoltaic equipment is recorded, and the power generation model of the photovoltaic equipment is established and continuously updated, which can be refined as follows: S211. Establish a historical power generation database for photovoltaic equipment.

[0029] S212, recording the daily power generation data and weather conditions of the photovoltaic equipment, and storing them in a historical power generation database of the photovoltaic equipment as the historical power generation data of the photovoltaic equipment.

[0030] S213. According to the historical power generation data of the photovoltaic equipment, a power generation model of the photovoltaic equipment is established by a model establishment method. The method for establishing the power generation model here has been explained above.

[0031] S214. Every time the daily power generation data and weather conditions of the photovoltaic equipment are added to the historical power generation database of the photovoltaic equipment, the power generation model of the photovoltaic equipment is updated according to the newly added daily power generation data and weather conditions. With such a design, the power generation model of the photovoltaic equipment can be continuously updated according to the increase in data volume, so as to continuously improve the accuracy of predicting the future power generation data of the photovoltaic equipment through the power generation model of the photovoltaic equipment.

[0032] Preferably, Figure 3 As shown, S400 includes the following specific steps: S401: According to the power consumption model of the DC equipment, predict the power consumption B of the DC equipment during the day on the nth day. n。

[0033] S402: Based on the forecast weather data for the nth day obtained from the Internet and in combination with the power generation model of the photovoltaic equipment, the power generation C of the photovoltaic equipment for the nth day is predicted. n .

[0034] S500 includes the following specific steps: S501. Calculate the predicted maximum power consumption W of the DC equipment during the day on day n. Bn =B n +Z Bn , where Z Bnis the first buffer amount; 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 mode allocation steps are strictly adopted, it is easy to cause insufficient power supply of the DC device. Therefore, the first buffer amount is set here to reduce the probability of insufficient power supply. The size of the first buffer amount is determined based on 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 amount; more specifically, since there is energy loss in the process of transmitting electric energy from the photovoltaic device to the DC device, the second buffer amount is set here, and the size of the second buffer amount can be calculated by the formula: Z Cn =C n ×(1-η1), where η1 is the efficiency of electric energy transmission from photovoltaic equipment to DC equipment.

[0035] S502: After entering the daytime of the nth day, turn on the photovoltaic equipment and record the current energy storage battery power A 白n ; Calculate the predicted maximum power supply W of the energy storage battery during the day on day n A白n =A 白n -Z A白n , where Z A白n is the third buffer amount; more specifically, since there is energy loss in the process of transmitting electric energy from the energy storage battery to the DC device, the third buffer amount is set here, and the size of the third buffer amount can be calculated by the formula: Z A白n =A 白n ×(1-η2) is used for calculation, where η2 is the efficiency of transmitting electric energy from the energy storage battery to the DC device.

[0036] S503, determine W Bn Is it greater than W? Cn +W An : If yes, the DC equipment is powered by photovoltaic equipment, energy storage batteries, and the mains network; if no, determine W Bn Is it greater than W? Cn .

[0037] S504, determine W Bn Is it greater than W? Cn When: If yes, the DC device is powered by the photovoltaic device and the energy storage battery together; if not, the DC device is powered by the photovoltaic device and it is determined whether the energy storage battery is fully loaded.

[0038] S505, determining whether the energy storage battery is fully loaded: if so, the excess power generated by the photovoltaic device is inverted and then connected to the grid; if not, the excess power generated by the photovoltaic device is stored in the energy storage battery, and when the energy storage battery is fully loaded, the excess power generated by the photovoltaic device is inverted and then connected to the grid; more specifically, connecting the grid to the grid means inverting the excess power generated by the photovoltaic device and transmitting it to the city power grid to improve the economic benefits of power generation by the photovoltaic device.

[0039] With this design, when the photovoltaic equipment is turned on during the day on the nth day, the economic benefit maximization plan for DC equipment power supply and photovoltaic equipment power transmission can be planned in advance, which not only avoids repeated switching of DC equipment power supply methods, but also improves the overall economic benefits of photovoltaic storage direct-flexible technology.

[0040] Preferably, Figure 3 As shown, S400 also includes the following specific steps: S411. According to the power consumption model of the DC equipment, the power consumption D of the DC equipment during the non-low-price period at night on the nth day is predicted. n , the power consumption of DC equipment during the low electricity price period at night on the nth day E n .

[0041] S500 also includes the following specific steps: S511, calculate the predicted maximum power consumption W of the DC equipment during the non-low-price period at night on the nth day Dn =D n +Z Dn , where Z Dn is the fourth buffer amount; calculate the predicted maximum power consumption W of the 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; calculate the predicted maximum total power consumption W of the DC equipment at night on the nth day DE =W Dn +W En ; More specifically, since the actual power consumption of the DC equipment during the non-low-price period and the low-price period on the night of the nth day may deviate from the predicted power consumption and may be greater than the predicted power consumption, if the subsequent power supply allocation steps are strictly adopted, it is easy to cause insufficient power supply to the DC equipment. Therefore, the fourth buffer amount and the fifth buffer amount are set here to reduce the probability of insufficient power supply. The sizes of the fourth buffer amount and the fifth buffer amount are determined based on experience.

[0042] S512. After entering the night of the nth day, turn off the photovoltaic equipment and record the current energy storage battery power A 夜n ; Calculate the maximum power supply W of the energy storage battery on the night of day n A夜n =A 夜n -ZA夜n , where Z A夜n is the sixth buffer; more specifically, since there is energy loss in the process of transmitting electric energy from the energy storage battery to the DC device, the sixth buffer is set here. The size of the sixth buffer can be calculated by the formula: Z A夜n =A 夜n ×(1-η2) for calculation.

[0043] S513, determine W A夜n Is it greater than W? DE : If yes, the energy storage battery will power the DC device that night; if no, then determine W A夜n Is it greater than W? Dn .

[0044] S514, determine W A夜n Is it greater than W? Dn When: If yes, the energy storage battery will power the DC equipment during the non-low-price period that night, and the energy storage battery and the mains will power the DC equipment during the low-price period that night; if no, the energy storage battery and the mains will power the DC equipment during the non-low-price period that night, and the mains will power the DC equipment during the low-price period that night.

[0045] With this design, when the photovoltaic equipment is shut down on the night of the nth day, the economic benefit maximization plan for the DC equipment power supply can be planned in advance, and the utilization efficiency of low-peak electricity can be improved. This not only avoids repeated switching of the power supply method of the DC equipment, but also improves the overall economic benefits of the photovoltaic storage direct-flexible technology.

[0046] Preferably, Figure 4 As shown, S400 also includes the following steps: S421. According to the power consumption model of the DC equipment, predict the power consumption B of the DC equipment during the daytime on day n+1. n+1 , DC equipment power consumption D during the non-low-price period at night on the n+1th day n+1 .

[0047] S422, based on the forecast weather data for the n+1th day obtained from the Internet and in combination with the power generation curve of the photovoltaic equipment, forecast the power generation C of the photovoltaic equipment for the n+1th day n+1 .

[0048] S500 also includes the following steps: S521, define the expected remaining power X1 of the energy storage battery when the photovoltaic equipment is turned on during the day on the n+1th day; A夜n Is it 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.

[0049] S522: Calculate the predicted maximum power supply W of the photovoltaic equipment during the day on day n+1 Cn+1 =C n+1 -Z Cn+1 , Z Cn+1 is the seventh buffer; more specifically, since there is energy loss in the process of transmitting electric energy from the photovoltaic device to the DC device, the seventh buffer is set here. The size of the seventh buffer can be calculated by the formula: Z Cn+1 =C n+1 ×(1-η1) is used for calculation; the predicted maximum power supply W of the energy storage battery during the day on day n+1 is calculated X1 =X1-Z X1 , where Z X1 is the eighth buffer; more specifically, since there is energy loss in the process of transmitting electric energy from the energy storage battery to the DC device, the eighth buffer is set here. The size of the eighth buffer can be calculated by the formula: Z X1 =X1×(1-η2) to calculate; calculate the predicted maximum power consumption W of the DC equipment during the day on day n+1 Bn+1 =B n+1 +Z Bn+1 , where Z Bn+1 is the ninth buffer amount; more specifically, since the actual power consumption of the DC equipment during the day on the (n+1) day may deviate from the predicted power consumption and may be greater than the predicted power consumption, if the subsequent power supply allocation steps are strictly adopted, it is easy to cause insufficient power supply to the DC equipment. Therefore, the ninth buffer amount is set here to reduce the probability of insufficient power supply. The size of the ninth buffer amount is determined based on experience.

[0050] S523, define the expected remaining power X2 of the energy storage battery when the photovoltaic equipment is turned off at night on the n+1th day, and calculate X2=W Cn+1 +W X1 -W Bn+1 .

[0051] S524, calculating the predicted maximum power supply W of the energy storage battery at night on day n+1 X2 =X2-Z X2 , where Z X2 is the tenth buffer; more specifically, since there is energy loss in the process of transmitting electric energy from the energy storage battery to the DC device, the tenth buffer is set here. The size of the tenth buffer can be calculated by the formula: Z X2 =X2×(1-η2) for calculation; calculate the predicted maximum power consumption W of the DC equipment during the non-low-price period at night on the n+1th day Dn+1 =D n+1 +Z Dn+1 , where Z Dn+1is the eleventh buffer amount; more specifically, since the actual power consumption of the DC equipment during the non-low-price period on the night of the n+1 day may deviate from the predicted power consumption and may be greater than the predicted power consumption, if the subsequent power supply allocation steps are strictly adopted, it is easy to cause insufficient power supply to the DC equipment. Therefore, the eleventh buffer amount is set here to reduce the probability of insufficient power supply. The size of the eleventh buffer amount is determined based on experience.

[0052] S525, judge W X2 Is it less than W? Dn+1 If so, then on the night of the nth day, during the low electricity price period, the energy storage battery will be charged with electricity Y=W through the city power network. Dn+1 -W X2 ; If not, then end.

[0053] Such a design, through advance forecasting and planning, can further improve the utilization efficiency of off-peak electricity, reduce the use of non-off-peak electricity, and improve the overall economic benefits of solar-storage direct-flexible technology.

[0054] Preferably, the DC device adopts a DC device driven by DC power. Such a design reduces the inversion processing of the current at the photovoltaic device end and the energy storage battery end, and only requires rectification processing of the current at the mains network end; and because the frequency of using the mains network as a power supply method is relatively low, the overall current loss is reduced and the economic benefit is improved.

[0055] Further preferably, the method further comprises the following steps: S700: Introduce an alarm variable G.

[0056] S701. When the number of DC devices directly driven by the photovoltaic device reaches the maximum value that can be driven, the value of G is set to 1; when the number of DC devices directly driven by the photovoltaic device does not reach the maximum value that can be driven, the value of G is set to 0.

[0057] S702: When a DC device is turned on during the day on the nth day, determine whether the value of G is 1: If G=1, regardless of whether the power supply mode is preset, the DC device is directly powered by the energy storage battery or the mains network; If G=0, the DC device is powered according to the preset power supply mode.

[0058] During the actual operation of the DC device, a situation is unavoidable: during a certain period of time during the day on the nth day, the power generation power of the photovoltaic device is insufficient 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. However, through the above-mentioned method S700~S702, an alarm variable G can be set to force the selection of a power supply method that is not in the preset in the above situation, thereby improving the stability of the method.

[0059] like Figure 5 As shown, another aspect of the present application provides a flexible control system for controlling DC devices by a photovoltaic storage system, which is connected to DC devices, photovoltaic devices, a mains network, and energy storage batteries, including a power transmission module, a data module, an Internet module, a prediction module, and a flexible control module: The transmission module is used to connect DC equipment, photovoltaic equipment, energy storage batteries, and the mains network through transmission lines.

[0060] The data module is used to record the historical power consumption data of DC equipment, establish and continuously update the power consumption model of DC equipment; record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment.

[0061] The Internet module is used to obtain forecast weather data through the Internet.

[0062] The prediction module is used to predict the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment.

[0063] 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.

[0064] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application.

Claims

1. A flexible control method for controlling DC devices in a photovoltaic-storage-flexible DC system, characterized in that: include: Connect DC equipment, photovoltaic equipment, energy storage batteries, and city power networks through transmission lines; Record the historical power consumption data of DC equipment, establish and continuously update the power consumption model of DC equipment; record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment; Obtain forecast weather data via the Internet; Based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment, the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment are predicted; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply mode of the DC equipment is selected, and the future power transmission object of the photovoltaic equipment is selected; According to the future power supply mode of the DC equipment and the future power transmission target of the photovoltaic equipment, the DC equipment is powered, and power is transmitted to its future power transmission target through the photovoltaic equipment.

2. According to claim 1, a flexible control method for controlling DC devices in a photovoltaic-storage-flexible DC system is characterized in that: Record the historical power consumption data of DC equipment, establish and continuously update the power consumption model of DC equipment, including: Establish a historical power consumption database for DC equipment; Record the daily power consumption data of the DC equipment and store it in the historical power consumption database of the DC equipment as the historical power consumption data of the DC equipment; According to the historical power consumption data of the DC equipment, a power consumption model of the DC equipment is established by a model building method; Every time the daily power consumption data of the DC device is added to the historical power consumption database of the DC device, the power consumption model of the DC device is updated according to the newly added daily power consumption data; Record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment, including: Establish a historical power generation database for photovoltaic equipment; Record the daily power generation data and weather conditions of the photovoltaic equipment, and store them in the historical power generation database of the photovoltaic equipment as the historical power generation data of the photovoltaic equipment; According to the historical power generation data of the photovoltaic equipment, a power generation model of the photovoltaic equipment is established through a model building method; Each time the daily power generation data and weather conditions of the photovoltaic equipment are added to the historical power generation database of the photovoltaic equipment, the power generation model of the photovoltaic equipment is updated according to the newly added daily power generation data and weather conditions.

3. According to claim 1, a flexible control method for controlling DC devices in a photovoltaic-storage-flexible DC system is characterized in that: Based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment, the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment are predicted, including: According to the power consumption model of DC equipment, predict the power consumption B of DC equipment during the day on day n n ; Based on the forecast weather data for the nth day obtained from the Internet and the power generation model of the photovoltaic equipment, the power generation C of the photovoltaic equipment on the nth day is predicted n ; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply methods of the DC equipment include: Calculate the predicted maximum power consumption W of the DC equipment during the day on day n Bn =B n +Z Bn , where Z Bn is the first buffer amount; calculate the predicted maximum power supply W of the photovoltaic equipment during the day on the nth day Cn =C n -Z Cn , where Z Cn is the second buffer amount; After entering the daytime of the nth day, turn on the photovoltaic equipment and record the current energy storage battery power A 白n ; Calculate the predicted maximum power supply W of the energy storage battery during the day on day n A白n =A 白n -Z A白n , where Z A白n is the third buffer amount; Judge W Bn Is it greater than W? Cn +W An : If yes, the DC equipment is powered by photovoltaic equipment, energy storage batteries, and the mains network; if no, determine W Bn Is it greater than W? Cn ; Judge W Bn Is it greater than W? Cn When: If yes, the DC device is powered by the photovoltaic device and the energy storage battery together; if not, the DC device is powered by the photovoltaic device and it is determined whether the energy storage battery is fully loaded; When judging whether the energy storage battery is fully loaded: if so, the excess power generated by the photovoltaic equipment is connected to the grid; if not, the excess power generated by the photovoltaic equipment is stored in the energy storage battery, and when the energy storage battery is fully loaded, the excess power generated by the photovoltaic equipment is inverted and connected to the grid.

4. According to claim 1, a flexible control method for controlling DC devices in a photovoltaic-storage-flexible DC system is characterized in that: Based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment, the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment are predicted, which also includes: According to the power consumption model of DC equipment, the power consumption D of DC equipment during the non-low-price period at night on the nth day is predicted. n , the power consumption of DC equipment during the low electricity price period at night on the nth day E n ; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply mode of the DC equipment is also selected as follows: Calculate the predicted maximum power consumption W of the DC equipment during the non-low-price period at night on the nth day Dn =D n +Z Dn , where Z Dn is the fourth buffer amount; calculate the predicted maximum power consumption W of the 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; calculate the predicted maximum total power consumption W of the 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 energy storage battery power A 夜n ; Calculate the maximum power supply W of the energy storage battery on the night of day n A夜n =A 夜n -Z A夜n , where Z A夜n is the sixth buffer amount; Judge W A夜n Is it greater than W? DE : If yes, the energy storage battery will power the DC device that night; if no, then determine W A夜n Is it greater than D n ; Judge W A夜n Is it greater than D n When: If yes, the energy storage battery will power the DC equipment during the non-low-price period that night, and the energy storage battery and the mains will power the DC equipment during the low-price period that night; if no, the energy storage battery and the mains will power the DC equipment during the non-low-price period that night, and the mains will power the DC equipment during the low-price period that night.

5. According to claim 4, a flexible control method for controlling DC devices in a photovoltaic-storage-flexible DC system is characterized in that: Based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment, the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment are predicted, which also includes: According to the power consumption model of DC equipment, predict the power consumption B of DC equipment during the day on day n+1 n+1 , DC equipment power consumption D during the non-low-price period at night on the n+1th day n+1 ; Based on the forecast weather data for the n+1th day obtained from the Internet and combined with the power generation curve of the photovoltaic equipment, the power generation C of the photovoltaic equipment on the n+1th day is predicted. n+1 ; According to the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment, the future power supply mode of the DC equipment is also selected as follows: Define the expected remaining power X1 of the energy storage battery when the photovoltaic equipment is turned on during the day on day n+1; and in the judgment of W A夜n Is it 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 equipment during the day on day n+1 Cn+1 =C n+1 -Z Cn+1 , Z Cn+1 is the seventh buffer capacity; calculate the predicted maximum power supply W of the energy storage battery during the day on the n+1th 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 equipment during the day on day n+1 Bn+1 =B n+1 +Z Bn+1 , where Z Bn+1 is the ninth buffer amount; Define the expected remaining power X2 of the energy storage battery when the photovoltaic equipment is turned off at night on day n+1, and calculate X2=W Cn+1 +W X1 -W Bn+1 ; Calculate the predicted maximum power supply W of the energy storage battery at night on day n+1 X2 =X2-Z X2 , where Z X2 is the tenth buffer amount; calculate the predicted maximum power consumption W of the DC equipment during the non-low-price period at night on the n+1th day Dn+1 =D n+1 +Z Dn+1 , where Z Dn+1 is the eleventh buffer; Judge W X2 Is it less than W? Dn+1 If so, then on the night of the nth day, during the low electricity price period, the energy storage battery will be charged with electricity Y=W through the city power network. Dn+1 -W X2 ; If not, then end.

6. A flexible control system for controlling DC devices by a photovoltaic storage system, connected to DC devices, photovoltaic devices, a mains network, and energy storage batteries, characterized in that: Including power transmission connection module, data module, Internet module, prediction module, flexible control module, and power transmission control module: The power transmission connection module is used to connect the DC equipment with the photovoltaic equipment, the mains network, and the energy storage battery; The data module is used to record the historical power consumption data of DC equipment, establish and continuously update the power consumption model of DC equipment; record the historical power generation data of photovoltaic equipment, establish and continuously update the power generation model of photovoltaic equipment; The Internet module is used to obtain forecast weather data through the Internet; The prediction module is used to predict the future power consumption data of the DC equipment and the future power generation data of the photovoltaic equipment based on the predicted weather data, combined with the power consumption model of the DC equipment and the power generation model of the photovoltaic equipment; The flexible control module is used to select 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; The power transmission control module is used to supply power to the DC device according to the future power supply mode of the DC device and the future power transmission target of the photovoltaic device, and transmit power to its future power transmission target through the photovoltaic device.

Images