Solar intelligent storage system based on big data

Abstract

The invention particularly relates to a solar intelligent storage system based on big data, which comprises a first function quantization unit, a second function quantization unit, a third function quantization unit, a comprehensive variable unit, a trend analysis unit, an intervention variable generation unit and a terminal control unit, the first function quantization unit is used for collecting weather state data of a target area and quantizing the weather state data to obtain a quantized value, and the second function quantization unit is used for collecting illumination data of the target area and quantizing the illumination data to obtain a quantized value; and the third function quantization unit is used for collecting load demand change data of the target area and quantizing the load demand change data to obtain a quantized value. According to the invention, intervention control is carried out on the regional and regular photovoltaic power generation system only through big data formed by various data, and predictive adjustment can be realized through the intervention control.

Description

Technical Field

[0001] The present invention belongs to the field of solar energy application, and in particular relates to a solar energy intelligent storage system based on big data. Background Art

[0002] Related prior art, such as the solar power generation system disclosed in patent document CN102088257A, is a classic solar power generation system in the prior art. Its core is to control the output of electric energy when the light intensity is greater than the set value, and at the same time charge the excess electric energy to the storage device. When the light intensity is less than the set value and the decline rate is large, the solar panels and batteries are used to output electric energy at the same time to make up for the lack of electric energy and maintain the stability of electric energy output. In the application, this type of technology only dynamically adjusts the storage device according to the changes in the local data currently detected. In specific applications, the solar power generation system has obvious regionality, and its changes in light intensity and climate change also have obvious regularity. Based on the development of existing big data processing technology, it has become a hot demand to realize the collection and prediction of light and various data through big data processing methods to adjust the solar power generation system. However, the current existing technology lacks specific technical details and there is no such mature technology, which leads to many problems in the implementation of such technology, and it is impossible to use big data to regulate photovoltaic power generation. Summary of the invention

[0003] The purpose of the present invention is to provide a solar energy intelligent storage system based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] Solar energy intelligent storage system based on big data,

[0006] It includes a first function quantization unit, a second function quantization unit and a third function quantization unit, as well as a comprehensive variable unit, a trend analysis unit, an intervention variable generation unit and a terminal control unit, wherein the first function quantization unit is used to collect weather status data of a target area and quantify it to obtain a quantitative value, the second function quantization unit is used to collect illumination data of a target area and quantify it to obtain a quantitative value, and the third function quantization unit is used to collect load demand change data of a target area and quantify the load demand change data to obtain a quantitative value;

[0007] The comprehensive variable unit is used to jointly calculate a comprehensive variable with the above-mentioned multiple quantized data, and the comprehensive variable is used to characterize the change function of the influence of the above-mentioned multiple data on the power output of the photovoltaic power generation energy storage device, and the specific change function introduces an adjustable parameter corresponding to each data;

[0008] The trend analysis unit is used to sort, fit and determine the period of the comprehensive variables, as well as the final functional relationship determination;

[0009] An intervention variable generation unit, used to generate intervention variables according to the specific functional relationship of the trend analysis unit, and convert the intervention variables into intervention control signals and output them to the terminal control unit;

[0010] The terminal control unit intervenes and adjusts the duty ratio signal of the power storage device based on the intervention control signal.

[0011] Furthermore, the standard for the quantitative basis is to quantify the magnitude of the power impact value of the output energy storage device of photovoltaic power generation based on the above-mentioned various data.

[0012] Furthermore, the sorting refers to sorting according to time series, and fitting refers to fitting the sorted data into a specific functional relationship. The specific method used for period determination is to first determine a smaller initial period T, and then use the period to verify the above fitting function. If the verification fails, the value of T is continuously increased until the verification passes.

[0013] Furthermore, the intervention control signal ultimately generated by the intervention variable generating unit is specifically used to adjust the duty cycle signal. The specific form of adjusting the duty cycle signal is that when the duty cycle signal outputs a control signal to the energy storage device, the intervention control signal priority is configured to be higher than the duty cycle signal.

[0014] Furthermore, the specific form of adjusting the duty cycle signal is, specifically, when the local control logic of the terminal control unit believes that the duty cycle signal output is needed to control the battery charging, and when the intervention control signal logic believes that charging is not required, the intervention control signal can enable the cancellation of the charging command of the duty cycle signal; when the local control logic of the terminal control unit believes that the duty cycle signal output is needed to control the battery discharging, and when the intervention control signal logic believes that discharging is not required, the intervention control signal can enable the cancellation of the discharge command of the duty cycle signal.

[0015] A machine-readable medium is used to store functional program codes corresponding to a first function quantization unit, a second function quantization unit, a third function quantization unit, a comprehensive variable unit, a trend analysis unit, and an intervention variable generation unit.

[0016] Beneficial effects: This application only uses big data formed by various data to intervene in and control the regional regular photovoltaic power generation system. Through this intervention control, predictive regulation can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a block diagram of the system composition of this application. DETAILED DESCRIPTION

[0018] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] This application discloses a solar energy intelligent storage system based on big data, such as Figure 1 , including a first function quantization unit, a second function quantization unit and a third function quantization unit, as well as a comprehensive variable unit, a trend analysis unit, an intervention variable generation unit and a terminal control unit. The first function quantization unit, the second function quantization unit, the third function quantization unit, the comprehensive variable unit, the trend analysis unit and the intervention variable generation unit are all configured in the big data server; the terminal control unit belongs to the physical equipment of the prior art; the first function quantization unit is used to collect weather status data of the target area and quantify it to obtain a quantitative value, the second function quantization unit is used to collect illumination data of the target area and quantify it to obtain a quantitative value, and the third function quantization unit is used to collect load demand change data of the target area and quantify the load demand change data to obtain a quantitative value; the quantification basis standard is to quantify the size of the influence value of the output energy storage device power of photovoltaic power generation according to the above-mentioned various data. For example, when a certain data has a positive correlation with the power of the output energy storage device of photovoltaic power generation , then this positive correlation relationship is still maintained in quantification. For example, the weather status data will form a specific value of each data item, such as whether there is wind or no wind and whether it is sunny. The quantification value is determined by calculation between different values, for example, by weighted calculation. When determining the specific quantification value, it is only necessary to maintain a certain data, such as the positive or negative correlation between the quantification value of the weather status data and the output energy storage device power. There is no need to limit the specific quantification rule. Because when determining the weather status data or other data, when the selected data item is different from the specific data content, it is impossible to determine a unique quantification rule. However, it is very simple to maintain a positive or negative correlation. For example, it can be determined through rational analysis. For example, if the data item of sunny days in the weather status data is a positive value and the non-sunny day is a negative value, it can be determined through rational analysis that the larger the corresponding value of the data item of sunny days is, the larger the output energy storage device power is. Then, the quantification rule from the corresponding value of the data item of sunny days to the quantification value of the weather status data is kept as positive correlation;

[0020] The comprehensive variable unit is used to jointly calculate a comprehensive variable with the above-mentioned multiple quantized data, and the comprehensive variable is used to characterize the change function of the influence of the above-mentioned multiple data on the power output of the photovoltaic power generation energy storage device, and the adjustable parameters corresponding to each data are introduced into the specific change function; optionally, the parameters are adjusted in the subsequent data learning and training process, and the specific functional relationship can be determined by adjusting the parameters;

[0021] The trend analysis unit is used to sort, fit and determine the period of the comprehensive variables, as well as determine the final functional relationship. Sorting refers to sorting according to time series, fitting refers to fitting the sorted data into a specific functional relationship, and the specific method used for period determination is to first determine a smaller initial period T, and then use the period to verify the above fitting function. If the verification fails, the value of T is continuously increased until the verification passes; the trend function of the final comprehensive variable is determined by sorting, fitting and period;

[0022] An intervention variable generation unit, used to generate intervention variables according to the specific functional relationship of the trend analysis unit, and convert the intervention variables into intervention control signals and output them to the terminal control unit;

[0023] The terminal control unit intervenes and adjusts the duty ratio signal of the power storage device based on the intervention control signal;

[0024] The terminal control unit includes: a photovoltaic power generation system, the photovoltaic power generation system is used to generate electric energy through illumination, an inverter, the inverter is used to convert electric energy into alternating current and be incorporated into the power grid, and also includes a power storage device, the power storage device is used to store excess electric energy and conditionally discharge it, and also includes a control single chip microcomputer, the control single chip microcomputer is used to output a duty ratio signal to the energy storage device, and control the energy storage device to store electricity when the photovoltaic output electric energy is excessive, and vice versa to discharge it to the load;

[0025] The intervention control signal finally generated by the intervention variable generating unit is specifically used to adjust the duty cycle signal. The specific form of adjusting the duty cycle signal is that when the duty cycle signal outputs a control signal to the energy storage device, the priority of the intervention control signal is configured to be higher than the duty cycle signal. The specific form of adjusting the duty cycle signal is that, when the local control logic of the terminal control unit believes that it is necessary to output the duty cycle signal to control the charging of the battery, and when the intervention control signal logic believes that charging is not required, the intervention control signal can enable the cancellation of the charging command of the duty cycle signal; when the local control logic of the terminal control unit believes that it is necessary to output the duty cycle signal to control the discharging of the battery, and when the intervention control signal logic believes that discharging is not required, the intervention control signal can enable the cancellation of the discharge command of the duty cycle signal.

[0026] The present application only intervenes and controls the regional regular photovoltaic power generation system through the big data formed by various data. Through this intervention control, predictive regulation can be achieved (predictive regulation means predicting the later photovoltaic output power according to various data, and realizing intelligent regulation according to the prediction results. Many practical problems can be solved through this regulation. For example, when the local control logic of the terminal control unit believes that the storage device needs to be charged and discharged, in fact, this control logic is only determined according to the local current state detection value. This control logic is likely to be wrong or need to be corrected. This control logic cannot effectively predict later changes. Then, predictive early intervention can be achieved through the intelligent control of the present application. Through early intervention, simple local detection control can be significantly optimized. For example, more specifically, at the time node of regular light intensity changes, the control logic of the local terminal control unit cannot predict subsequent changes and is most likely to be misjudged. Through the processing of big data, this change can be predicted and the control of the optimized configuration can be realized in advance to avoid misjudgment.

[0027] The embodiments to be protected in this application include a solar energy intelligent storage system based on big data, including a first function quantization unit, a second function quantization unit and a third function quantization unit, as well as a comprehensive variable unit, a trend analysis unit, an intervention variable generation unit and a terminal control unit, wherein the first function quantization unit is used to collect weather status data of a target area and quantify it to obtain a quantified value, the second function quantization unit is used to collect illumination data of a target area and quantify it to obtain a quantified value, and the third function quantization unit is used to collect load demand change data of a target area and quantify the load demand change data to obtain a quantified value;

[0028] The comprehensive variable unit is used to jointly calculate a comprehensive variable with the above-mentioned multiple quantized data, and the comprehensive variable is used to characterize the change function of the influence of the above-mentioned multiple data on the power output of the photovoltaic power generation energy storage device, and the specific change function introduces an adjustable parameter corresponding to each data;

[0029] The trend analysis unit is used to sort, fit and determine the period of the comprehensive variables, as well as the final functional relationship determination;

[0030] An intervention variable generation unit, used to generate intervention variables according to the specific functional relationship of the trend analysis unit, and convert the intervention variables into intervention control signals and output them to the terminal control unit;

[0031] The terminal control unit intervenes and adjusts the duty ratio signal of the power storage device based on the intervention control signal.

[0032] Preferably, the standard for the quantification is to quantify the magnitude of the power impact value of the output energy storage device of photovoltaic power generation based on the above-mentioned various data.

[0033] Preferably, the sorting refers to sorting according to time series, and fitting refers to fitting the sorted data into a specific functional relationship. The specific method used for period determination is to first determine a smaller initial period T, and then use the period to verify the above fitting function. If the verification fails, the value of T is continuously increased until the verification passes.

[0034] Preferably, the intervention control signal ultimately generated by the intervention variable generating unit is specifically used to adjust the duty cycle signal, and the specific form of adjusting the duty cycle signal is that when the duty cycle signal outputs a control signal to the energy storage device, the intervention control signal priority is configured to be higher than the duty cycle signal.

[0035] Preferably, the specific form of adjusting the duty cycle signal is, specifically, when the local control logic of the terminal control unit believes that it is necessary to output a duty cycle signal to control battery charging, and when the intervention control signal logic believes that charging is not required, the intervention control signal can enable the cancellation of the charging command of the duty cycle signal; when the local control logic of the terminal control unit believes that it is necessary to output a duty cycle signal to control battery discharging, and when the intervention control signal logic believes that discharging is not required, the intervention control signal can enable the cancellation of the discharge command of the duty cycle signal.

[0036] The quantitative value corresponding to the weather status data in the target area is a is the adjustable parameter corresponding to the weather status data, x1 is the time variable, m1 is the precision coefficient, n1 is its maximum value, and the quantitative value corresponding to the illumination data of the target area b is the adjustable parameter corresponding to the illumination data, x2 is the time variable, m2 is the precision coefficient, n2 is its maximum value, and the quantitative value corresponding to the load demand change data in the target area c is the adjustable parameter corresponding to the load demand change data, x3 is the time variable, m3 is the precision coefficient, and n3 is its maximum value, wherein the quantized values ​​are all processed as function values ​​or polynomials about the time variable, wherein the precision coefficients are all used to adjust the period of the quantized values, and the adjustable parameters are also used to adjust the period of the quantized values. Preferably, a=19.93*10 -8 , b=72.72*10 -6 , c = 24.24*10 -7 , the larger the value of the precision coefficient, the more obvious the periodicity of the corresponding quantization value, n1 = 1000000; n2 = 10000000;

[0037] n3=100000; the above quantified data are used together to calculate a comprehensive variable,

[0038] Specifically, the weighted sum of the quantized values ​​is calculated.

[0039] It is understandable that the first function quantization unit, the second function quantization unit, the third function quantization unit, the comprehensive variable unit, the trend analysis unit, and the intervention variable generation unit functions of the present application can also be implemented by program code, and the corresponding program code is stored in a machine-readable medium, which can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In order to provide interaction with the user, the first function quantization unit, the second function quantization unit, the third function quantization unit, the comprehensive variable unit, the trend analysis unit, and the intervention variable generation unit described herein can be implemented on a computer, and the computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0040] The above-described embodiments only express some implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. Solar energy intelligent storage system based on big data, characterized by: It includes a first function quantization unit, a second function quantization unit and a third function quantization unit, as well as a comprehensive variable unit, a trend analysis unit, an intervention variable generation unit and a terminal control unit, wherein the first function quantization unit is used to collect weather status data of a target area and quantify it to obtain a quantitative value, the second function quantization unit is used to collect illumination data of a target area and quantify it to obtain a quantitative value, and the third function quantization unit is used to collect load demand change data of a target area and quantify the load demand change data to obtain a quantitative value; The comprehensive variable unit is used to jointly calculate a comprehensive variable with the above-mentioned multiple quantized data, and the comprehensive variable is used to characterize the change function of the influence of the above-mentioned multiple data on the power output of the photovoltaic power generation energy storage device, and the specific change function introduces an adjustable parameter corresponding to each data; The trend analysis unit is used to sort, fit and determine the period of the comprehensive variables, as well as the final functional relationship determination; An intervention variable generation unit, used to generate intervention variables according to the specific functional relationship of the trend analysis unit, and convert the intervention variables into intervention control signals and output them to the terminal control unit; The terminal control unit intervenes and adjusts the duty ratio signal of the power storage device based on the intervention control signal.

2. The solar energy intelligent storage system based on big data according to claim 1 is characterized in that: The standard for quantitative basis is to quantify the magnitude of the power impact value of the output energy storage device of photovoltaic power generation based on the above-mentioned various data.

3. The solar energy intelligent storage system based on big data according to claim 1 is characterized in that: The sorting here refers to sorting according to time series, and fitting refers to fitting the sorted data into a specific functional relationship. The specific method used for period determination is to first determine a smaller initial period T, and then use this period to verify the above fitting function. If the verification fails, the value of T is continuously increased until the verification passes.

4. The solar energy intelligent storage system based on big data according to claim 1 is characterized in that: The intervention control signal ultimately generated by the intervention variable generation unit is specifically used to adjust the duty cycle signal. The specific form of adjusting the duty cycle signal is that when the duty cycle signal outputs a control signal to the energy storage device, the priority of the intervention control signal is configured to be higher than the duty cycle signal.

5. The solar energy intelligent storage system based on big data according to claim 1 is characterized in that: The specific form of adjusting the duty cycle signal is, specifically, when the local control logic of the terminal control unit believes that the duty cycle signal output is needed to control the battery charging, and when the intervention control signal logic believes that charging is not required, the intervention control signal can enable the cancellation of the charging command of the duty cycle signal; when the local control logic of the terminal control unit believes that the duty cycle signal output is needed to control the battery discharging, and when the intervention control signal logic believes that discharging is not required, the intervention control signal can enable the cancellation of the discharge command of the duty cycle signal.

6. A machine-readable medium, characterized in that Used to store functional program codes corresponding to the first function quantization unit, the second function quantization unit, the third function quantization unit, the comprehensive variable unit, the trend analysis unit, and the intervention variable generation unit.

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