Solar power generation and energy storage system and method

Abstract

The invention relates to the technical field of solar energy storage, and discloses a solar power generation and energy storage system and method, and the method comprises the following steps: collecting historical weather data, historical power utilization data and user solar equipment data; analyzing historical weather data to obtain an adjustment coefficient; analyzing the historical power consumption data, constructing a power consumption behavior model of the user, and predicting the power load data of the user in the future 72 hours; according to weather forecast data in the future 72 hours provided by a meteorological department, obtaining a photovoltaic generating capacity set in the future 72 hours; constructing a charging and discharging decision optimization model, and executing the charging and discharging strategy with the lowest cost in the group of charging and discharging strategies as an optimized charging and discharging strategy; by accurately predicting the photovoltaic power generation capacity and the user electrical load, the system can optimize the charging and discharging strategy of the energy storage equipment, it is ensured that the power generated by the system is used as much as possible, the dependence on a power grid is reduced, and the electric charge expenditure of the user is reduced.

Description

Technical Field

[0001] The present invention relates to the field of solar energy storage, and more specifically, to a solar power generation energy storage system and method. Background Art

[0002] The solar power generation energy storage method refers to storing the electric energy converted by photovoltaic panels for use when there is no sunlight (such as at night or on cloudy days); first, the solar photovoltaic (PV) panels convert the received sunlight into direct current (DC) electric energy; since household and industrial electricity is generally alternating current (AC), an inverter is required to convert the direct current into alternating current; the excess electric energy can be transported to the energy storage battery through a charge controller for storage. Modern energy storage systems may use various types of batteries such as lithium-ion, lead-acid, and sodium-sulfur. Lithium-ion batteries are preferred due to their high energy density, long life, and low self-discharge rate. An advanced energy storage system usually comes with an intelligent management system that can monitor parameters such as battery charge, health status, and temperature, and optimize the charge and discharge strategies according to the user's consumption pattern and grid electricity price; the energy storage system can be designed to be connected to the grid (grid-connected) or operate independently (off-grid). The grid-connected system can sell the excess power back to the grid when the energy storage battery is full and purchase electricity from the grid at a low electricity price to supplement the energy storage; the off-grid system relies entirely on its own power generation and energy storage; when the grid power fails, the energy storage system can provide power support as an emergency backup power supply.

[0003] However, the charge and discharge strategies in existing solar power generation energy storage systems usually follow fixed rules and cannot optimize the charge and discharge strategies according to real-time situations. Therefore, the electricity costs of existing charge and discharge strategies are sometimes high and sometimes low, which is not conducive to reducing people's electricity costs. Summary of the Invention

[0004] The present invention provides a solar power generation energy storage system and method to solve the technical problems presented in the above background art.

[0005] The present invention provides the following steps: Step 101, collect historical weather data, historical electricity consumption data, and user solar device data; Step 102, analyze the historical weather data to obtain the key factors affecting the power generation efficiency of the photovoltaic panels, combine the weather characteristics of the region where the user's solar power generation equipment is located to obtain the seasonal change law of the region, and set corresponding adjustment coefficients for each season according to the seasonal change law of the region; Step 103, analyze the historical electricity consumption data, construct the user's electricity consumption behavior model, and predict the user's electricity load data within the next 72 hours of the user; Step 104, according to the weather forecast data within the next 72 hours provided by the meteorological department, where the weather forecast data includes hourly sunshine duration information, solar radiation intensity information, and ambient temperature information, input the weather forecast data into the photovoltaic power generation model to obtain the predicted hourly photovoltaic power generation in the future. According to the adjustment coefficient in Step 102, correct the predicted hourly photovoltaic power generation in the future to obtain a set of photovoltaic power generation amounts for the next 72 hours; Step 105, construct a charge-discharge decision optimization model. After encoding the predicted electricity load data of the user within the next 72 hours and the hourly photovoltaic power generation values within the next 72 hours, input them into the charge-discharge decision optimization model. The charge-discharge decision optimization model outputs a set of charge-discharge strategies. A charge-discharge strategy includes the total charge amount and total discharge amount corresponding to each hour of the solar energy storage device within the next 72 hours. Take the charge-discharge strategy with the lowest cost in the set of charge-discharge strategies as the optimized charge-discharge strategy for execution.

[0006] In a preferred embodiment, the historical weather data is the weather data of the area where the user's solar power generation equipment is located in the past 3 years, which includes hourly sunshine duration information, solar radiation intensity information, and ambient temperature information. The historical electricity consumption data is the electricity load data of the user in the past 3 years, and the electricity load data includes peak period information, off-peak period information, and total electricity consumption information; the user's solar equipment data includes a photovoltaic power generation model, and the photovoltaic power generation model is used to calculate the hourly photovoltaic power generation according to the hourly sunshine duration information, solar radiation intensity information, and ambient temperature information.

[0007] In a preferred embodiment, the charge-discharge decision optimization model includes an encoding layer, a feature combination layer, a hidden layer, and an output layer; Encode the predicted electricity load data of the user within the next 72 hours and the hourly photovoltaic power generation values within the next 72 hours into photovoltaic power generation features and electricity consumption behavior features through a time embedding encoding method; then input the photovoltaic power generation features and electricity consumption behavior features into the feature combination layer for feature fusion to generate a comprehensive feature; then input the comprehensive feature into the hidden layer to output hidden features, and finally input the hidden features into the fully connected layer, and the fully connected layer outputs a set of charge-discharge strategies.

[0008] In a preferred embodiment, the adjustment coefficient is set according to the seasonal change law, and its calculation formula is: Among them, represents the adjustment coefficient set for different seasons, represents the seasonal change function, and is related to season, average ambient temperature, and average solar radiation intensity, represents season, including spring, summer, autumn, and winter, represents the average ambient temperature in a specific season, represents the average solar radiation intensity in a specific season.

[0009] In a preferred embodiment, the calculation formula of the seasonal variation function is: where, represents the seasonal variation function, represents the average ambient temperature in a specific season, represents the temperature of the solar power generation device under rated working conditions, represents the average solar radiation intensity in a specific season, represents the solar radiation intensity of the solar power generation device under rated working conditions, , , and respectively represent the first, second, third, and fourth empirical parameters.

[0010] In a preferred embodiment, the calculation formula of the photovoltaic power generation model is: where, represents the photovoltaic power generation at time t, represents the effective area of the solar power generation device exposed to sunlight, represents the solar radiation intensity at time t, represents the conversion efficiency function at the ambient temperature at time t, and generally decreases with the increase of temperature.

[0011] In a preferred embodiment, the calculation formula for correcting the predicted hourly photovoltaic power generation in the future is: where, represents the corrected predicted hourly photovoltaic power generation in the future, represents the photovoltaic power generation at time t, represents the adjustment coefficient at time t.

[0012] In a preferred embodiment, the method for constructing a charge-discharge decision optimization model includes the following steps: Step 201, encoding the input data; First, encode the predicted electrical load data of the user within the next 72 hours and the hourly photovoltaic power generation values within the next 72 hours of the user; The encoding form of the photovoltaic power generation is as follows: , t = 1 to 72; The electrical load data of the user: , where \(t = 1\) to \(72\), representing the electricity demand of the user at time \(t\); Step 202, define the charge-discharge decision variables; Define two charge-discharge decision variables to describe the behavior of the energy storage device: Represents the charge amount at time \(t\); Represents the discharge amount at time \(t\); Step 203, set the objective function. The optimization objective is to find a charge-discharge strategy that minimizes the total cost, where the cost includes the cost of purchasing electricity from the grid and the operating cost of the energy storage device; The objective function for calculating the minimum cost in the charge-discharge strategy is: Among them, Represents the total cost of the \(i\)-th charge-discharge strategy, Represents the function of taking the minimum value, Represents the electricity price at time \(t\), Represents the electricity demand of the user at time \(t\), Represents the photovoltaic power generation at time \(t\), Represents the discharge amount of the \(i\)-th charge-discharge strategy at time \(t\), Represents the charge amount of the \(i\)-th charge-discharge strategy at time \(t\), Represents the operating cost of the energy storage device in the \(i\)-th charge-discharge strategy; The calculation formula for the operating cost of the energy storage device is: Among them, Represents the operating cost of the energy storage device in the \(i\)-th charge-discharge strategy, Represents the charging cost of the energy storage device in the \(i\)-th charge-discharge strategy at time \(t\), Represents the charging cost of the energy storage device in the \(i\)-th charge-discharge strategy at time \(t\), Represents the charging energy of the energy storage device in the \(i\)-th charge-discharge strategy at time \(t\), Represents the discharging energy of the energy storage device in the \(i\)-th charge-discharge strategy at time \(t\); Step 204, set the constraint conditions. The constraint conditions include: One, Among them, At time \(t\) At time \(t\) ; Two, Among them, represents the energy storage state, represents the maximum capacity; III. Among them, and respectively represent the charging amount and the discharging amount at time t, and respectively represent the maximum power of charging and discharging; Step 205, obtain the charging and discharging strategy with the lowest cost according to the linear programming or mixed integer linear programming optimization algorithm.

[0013] A solar power generation energy storage system includes the following modules: A data acquisition module, configured to acquire historical weather data, historical power consumption data, and user solar device data; A weather analysis module, configured to analyze historical weather data, determine key factors affecting the efficiency of the photovoltaic panel, and set adjustment coefficients according to seasonal change rules; A power consumption behavior modeling module, configured to construct a user power consumption behavior model based on historical power consumption data and predict the power load information within the next 72 hours; A photovoltaic power generation prediction module, which uses the future 72-hour weather forecast data provided by the meteorological department, combines with the photovoltaic power generation model to predict the future photovoltaic power generation amount, and corrects it by applying adjustment coefficients; A charging and discharging decision optimization module, configured to construct and solve a charging and discharging decision optimization model, and select the charging and discharging strategy with the lowest cost.

[0014] A storage medium stores non-temporary computer-readable instructions, which can execute the steps in a solar power generation energy storage method as described above when executed by a computer.

[0015] The beneficial effects of the present invention are as follows: 1. By accurately predicting the photovoltaic power generation amount and the user power load, the system of the present invention can optimize the charging and discharging strategy of the energy storage device, ensure that as much self-generated power as possible is used, reduce the dependence on the power grid, charge when the electricity price is low, and discharge when the electricity price is high or the photovoltaic output is insufficient, thereby reducing the user's electricity bill expenditure; 2. Based on historical data and real-time weather forecasts, the energy storage system of the present invention can dynamically adjust the energy storage strategy, flexibly respond to changing environmental conditions and power consumption demands. Even in the case of low photovoltaic output such as on cloudy days or at night, the energy storage system can ensure stable power supply and improve the user experience. Description of the Drawings

[0016] Figure 1 It is a flowchart of the solar power generation energy storage method of the present invention.

[0017] Figure 2 It is a flowchart of the method for constructing a charge-discharge decision optimization model of the present invention. Specific Embodiments

[0018] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, changes can be made to the functions and arrangements of the elements discussed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described relative to some examples can also be combined in other examples.

[0019] As Figure 1 shown, a solar power generation energy storage method includes the following steps: Step 101, collect historical weather data, historical electricity consumption data, and user solar device data. The historical weather data is the weather data of the area where the user's solar power generation device is located in the past 3 years, which includes hourly sunshine duration information, solar radiation intensity information, and ambient temperature information. The historical electricity consumption data is the electricity load data of the user in the past 3 years, and the electricity load data includes peak period information, off-peak period information, and total electricity consumption information; The user solar device data includes a photovoltaic power generation model, and the photovoltaic power generation model is used to calculate the hourly photovoltaic power generation according to the hourly sunshine duration information, solar radiation intensity information, and ambient temperature information; It should be noted that the user solar device data also includes various parameters of the solar energy storage device and the solar power generation device, such as rated power generation, maximum storage capacity, and rated charge-discharge power and other parameters.

[0020] In an embodiment of the present invention, the calculation formula of the photovoltaic power generation model is: Among them, represents the photovoltaic power generation at time t, represents the effective area of the solar power generation device exposed to sunlight, represents the solar radiation intensity at time t, represents the conversion efficiency function at the ambient temperature at time t, and usually decreases with the increase of temperature.

[0021] Step 102: Analyze historical weather data to obtain the key factors affecting the power generation efficiency of the photovoltaic panel. Combine the weather characteristics of the area where the user's solar power generation equipment is located to obtain the seasonal change law of this area. According to the seasonal change law of this area, set corresponding adjustment coefficients for each season; It should be noted that the key factors affecting the power generation efficiency of the photovoltaic panel include temperature and radiation intensity; In an embodiment of the present invention, the adjustment coefficient is set according to the seasonal change law, as follows: Among them, represents the adjustment coefficient set for different seasons, represents the seasonal change function and is related to season, average ambient temperature, and average solar radiation intensity, represents season, including spring, summer, autumn, and winter, represents the average ambient temperature of a specific season, represents the average solar radiation intensity of a specific season; The calculation formula of the seasonal change function is: Among them, represents the seasonal change function, represents the average ambient temperature of a specific season, represents the temperature of the solar power generation equipment under rated working conditions, represents the average solar radiation intensity of a specific season, represents the solar radiation intensity of the solar power generation equipment under rated working conditions, , , and respectively represent the first, second, third, and fourth empirical parameters; It should be noted that and can both be obtained from the operation manual of the solar power generation equipment or the product nameplate, , , and can also be determined according to experimental data or information provided by the manufacturer. These parameters will determine the influence degree of temperature and radiation intensity on the power generation efficiency.

[0022] Step 103: Analyze historical power consumption data, construct the user's power consumption behavior model, and predict the user's power load data within the next 72 hours; In an embodiment of the present invention, the method for constructing the user's power consumption behavior model includes the following steps: Step 1: Perform feature engineering on the collected historical electricity consumption data; Time features: Extract periodic patterns such as daily, weekly, and monthly, as well as information such as weekdays / weekends and holidays; External factors: Consider the impact of meteorological conditions such as temperature and humidity and electricity price changes on electricity consumption behavior; Aggregate statistics: Calculate the total daily and weekly electricity consumption, and the average electricity consumption during peak and off-peak hours; Step 2: Select the LSTM prediction model as the user's electricity consumption behavior model. Divide the historical data into a training set and a validation set, and adjust the hyperparameters of the LSTM prediction model through the Bayesian optimization method to find the best configuration.

[0023] Step 3: Use the mean squared error (MSE) metric to evaluate the accuracy of the model, and adopt k-fold cross-validation to evaluate the stability and generalization ability of the model; Step 4: Input the weather forecast data within the next 72 hours provided by the meteorological department and the electricity price change information within the next 72 hours into the trained LSTM prediction model, predict the hourly electricity load for the next 72 hours, and obtain the electricity load data of the user within the next 72 hours.

[0024] Step 104: According to the weather forecast data within the next 72 hours provided by the meteorological department, the weather forecast data includes hourly sunshine duration information, solar radiation intensity information, and ambient temperature information. Input the weather forecast data into the photovoltaic power generation model to obtain the predicted hourly photovoltaic power generation for the future. According to the adjustment coefficient in Step 102, correct the predicted hourly photovoltaic power generation for the future to obtain a set of photovoltaic power generation for the next 72 hours; It should be noted that the set of photovoltaic power generation for the next 72 hours is a set composed of the hourly photovoltaic power generation for the next 72 hours arranged in chronological order, including 72 sets of photovoltaic power generation values. That is to say, each photovoltaic power generation value corresponds to the photovoltaic power generation for a certain hour within the next 72 hours; In an embodiment of the present invention, the calculation formula for correcting the predicted hourly photovoltaic power generation for the future is: where, represents the corrected predicted hourly photovoltaic power generation for the future, represents the photovoltaic power generation at time t, represents the adjustment coefficient at time t; It should be noted that in the present invention, time t represents the time period between the (t - 1)-th time point and the t-th time point, and the length of this time period is usually represented as 1 hour; Step 105: Construct a charge-discharge decision optimization model. Encode the predicted electrical load data of the user within the next 72 hours and the hourly photovoltaic power generation values within the next 72 hours, and input them into the charge-discharge decision optimization model. The charge-discharge decision optimization model outputs a set of charge-discharge strategies. A charge-discharge strategy includes the total charge and total discharge corresponding to each hour of the solar energy storage device within the next 72 hours. Execute the charge-discharge strategy with the lowest cost in the set of charge-discharge strategies as the optimized charge-discharge strategy; The charge-discharge decision optimization model includes an encoding layer, a feature combination layer, a hidden layer, and an output layer; Encode the predicted electrical load data of the user within the next 72 hours and the hourly photovoltaic power generation values within the next 72 hours into photovoltaic power generation features and electricity consumption behavior features through a time embedding encoding method; then input the photovoltaic power generation features and electricity consumption behavior features into the feature combination layer for feature fusion to generate a comprehensive feature; then input the comprehensive feature into the hidden layer to output hidden features, and finally input the hidden features into the fully connected layer, and the fully connected layer outputs a set of charge-discharge strategies.

[0025] It should be noted that the m-th element in the comprehensive feature includes both the power generation of the photovoltaic power generation device at the m-th hour and the electricity consumption of the user at the m-th hour. That is to say, the feature combination layer splices and combines the photovoltaic power generation amount and the user's electricity consumption at each time in chronological order.

[0026] In an embodiment of the present invention, as Figure 2 shown, the method for constructing a charge-discharge decision optimization model includes the following steps: Step 201: Encode the input data; First, encode the predicted electrical load data of the user within the next 72 hours and the hourly photovoltaic power generation values within the next 72 hours; The encoding form of the photovoltaic power generation amount is as follows: , t = 1 to 72; The user's electrical load data: , t = 1 to 72, indicating the required electricity consumption of the user at time t; Step 202: Define charge-discharge decision variables; Define two charge-discharge decision variables to describe the behavior of the energy storage device: represents the charge amount at time t (when the energy storage device charges from the power grid or the photovoltaic system); represents the discharge amount at time t (when the energy storage device supplies power to the load); Step 203: Set the objective function. The goal is to find a charge-discharge strategy that minimizes the total cost, where the cost includes the cost of purchasing electricity from the grid and the operating cost of the energy storage device. The objective function for calculating the charge-discharge strategy that minimizes the cost is: Where, represents the total cost of the i-th charge-discharge strategy, represents the function for taking the minimum value, represents the electricity price at time t, represents the electricity demand of the user at time t, represents the photovoltaic power generation at time t, represents the discharge amount of the i-th charge-discharge strategy at time t, represents the charge amount of the i-th charge-discharge strategy at time t, represents the operating cost of the energy storage device in the i-th charge-discharge strategy; The calculation formula for the operating cost of the energy storage device is: Where, represents the operating cost of the energy storage device in the i-th charge-discharge strategy, represents the charging cost of the energy storage device in the i-th charge-discharge strategy at time t, represents the charging cost of the energy storage device in the i-th charge-discharge strategy at time t, represents the charging energy of the energy storage device in the i-th charge-discharge strategy at time t, represents the discharging energy of the energy storage device in the i-th charge-discharge strategy at time t; Step 204: Set the constraint conditions. The constraint conditions include: One, Where, represents the electrical load data of the user at time t, represents the photovoltaic power generation at time t, represents the discharge amount at time t, represents the charge amount at time t; Two, Where, represents the energy storage state, represents the maximum capacity; Three, Where, , respectively represent the charging amount and discharging amount at time t, , respectively represent the maximum power of charging and discharging; Step 205, obtain the charging and discharging strategy with the lowest cost according to the linear programming or mixed integer linear programming optimization algorithm.

[0027] A solar power generation energy storage system includes the following modules: A data acquisition module, configured to acquire historical weather data, historical electricity consumption data, and user solar device data; A weather analysis module, configured to analyze historical weather data, determine key factors affecting the efficiency of the photovoltaic panel, and set an adjustment coefficient according to the seasonal change rule; An electricity consumption behavior modeling module, configured to construct a user electricity consumption behavior model based on historical electricity consumption data and predict the electricity load information within the next 72 hours; A photovoltaic power generation prediction module, using the future 72-hour weather forecast data provided by the meteorological department, combining with the photovoltaic power generation model to predict the future photovoltaic power generation amount, and correcting it by applying the adjustment coefficient; A charging and discharging decision optimization module, configured to construct and solve a charging and discharging decision optimization model, and select the charging and discharging strategy with the lowest cost.

[0028] A storage medium stores non-temporary computer-readable instructions, which can execute the steps in a solar power generation energy storage method as described above when executed by a computer.

[0029] The above describes the embodiments of this embodiment, but this embodiment is not limited to the above specific implementation manners. The above specific implementation manners are merely illustrative and not restrictive. Under the inspiration of this embodiment, those of ordinary skill in the art can also make many forms, all of which fall within the protection scope of this embodiment.

Claims

1. A solar energy generation and energy storage method, characterized in that: The following steps are involved: Step 101, collecting historical weather data, historical electricity consumption data and user solar energy equipment data; Step 102, analyzing historical weather data to obtain key factors affecting the power generation efficiency of photovoltaic panels, combining the weather characteristics of the area where the user's solar power generation equipment is located to obtain the seasonal variation law of the area, and setting a corresponding adjustment coefficient for each season according to the seasonal variation law of the area; Step 103, analyzing historical electricity consumption data, constructing a user's electricity consumption behavior model, and predicting the user's electricity load data within the next 72 hours; Step 104: based on the weather forecast data for the next 72 hours provided by the meteorological department, the weather forecast data includes hourly sunshine time information, solar radiation intensity information and ambient temperature information, the weather forecast data is input into the photovoltaic power generation model to obtain the predicted photovoltaic power generation per hour in the future, and the predicted photovoltaic power generation per hour in the future is corrected according to the adjustment coefficient in step 102 to obtain the photovoltaic power generation set for the next 72 hours; Step 105, constructing a charging and discharging decision optimization model, encoding the predicted user's electric load data within the next 72 hours and the photovoltaic power generation value per hour within the next 72 hours and inputting them into the charging and discharging decision optimization model, the charging and discharging decision optimization model outputs a set of charging and discharging strategies, one charging and discharging strategy includes the total charging amount and total discharging amount corresponding to the solar energy storage device per hour within the next 72 hours, and executing the charging and discharging strategy with the lowest cost in the set of charging and discharging strategies as the optimized charging and discharging strategy.

2. A solar energy power generation and energy storage method according to claim 1, characterized in that: The historical weather data refers to the weather data of the area where the user's solar power generation equipment is located in the past three years, which includes hourly sunshine time information, solar radiation intensity information and ambient temperature information. The historical electricity consumption data refers to the user's electricity load data in the past three years, and the electricity load data includes peak period information, off-peak period information and total electricity consumption information; the user's solar equipment data includes a photovoltaic power generation model, which is used to calculate the hourly photovoltaic power generation based on the hourly sunshine time information, solar radiation intensity information and ambient temperature information.

3. A solar energy power generation and energy storage method according to claim 2, characterized in that: The charging and discharging decision optimization model includes a coding layer, a feature combination layer, a hidden layer, and an output layer; The predicted electric load data of the user within the next 72 hours and the photovoltaic power generation value per hour within the next 72 hours are encoded into photovoltaic power generation characteristics and electricity consumption behavior characteristics through the time embedding coding method; the photovoltaic power generation characteristics and electricity consumption behavior characteristics are then input into the feature combination layer for feature fusion to generate a comprehensive feature; the comprehensive feature is then input into the hidden layer to output the hidden feature, and finally the hidden feature is input into the fully connected layer, and the fully connected layer outputs a set of charging and discharging strategies.

4. A solar energy power generation and energy storage method according to claim 3, characterized in that: The adjustment coefficient is set according to the law of seasonal changes, and its calculation formula is: in, Represents the adjustment coefficient set for different seasons, represents a seasonal variation function and is related to the season, average ambient temperature, and average solar radiation intensity. Indicates the seasons, including spring, summer, autumn and winter. represents the average ambient temperature for a particular season, Represents the average solar radiation intensity in a specific season.

5. A solar energy power generation and energy storage method according to claim 4, characterized in that: The calculation formula of the seasonal variation function is: in, represents the seasonal variation function, represents the average ambient temperature for a particular season, Indicates the temperature of solar power generation equipment under rated working conditions. represents the average solar radiation intensity in a particular season, Indicates the solar radiation intensity of solar power generation equipment under rated working conditions. , , and Represent the first, second, third and fourth empirical parameters respectively.

6. A solar energy power generation and energy storage method according to claim 5, characterized in that: The calculation formula of the photovoltaic power generation model is: in, represents the photovoltaic power generation at time t, Indicates the effective area of ​​solar power generation equipment exposed to sunlight. represents the solar radiation intensity at time t, represents the conversion efficiency function at ambient temperature at time t, and generally decreases with increasing temperature.

7. A solar energy power generation and energy storage method according to claim 6, characterized in that: The calculation formula for the revised predicted future hourly photovoltaic power generation is: in, represents the revised forecast of future hourly photovoltaic power generation, represents the photovoltaic power generation at time t, represents the adjustment coefficient at time t.

8. A solar energy power generation and energy storage method according to claim 7, characterized in that: The method for constructing a charging and discharging decision optimization model comprises the following steps: Step 201, encoding input data; First, the predicted electric load data of the user in the next 72 hours and the photovoltaic power generation value per hour in the next 72 hours are encoded; The encoding format of photovoltaic power generation is as follows: , t=1 to 72; User's electrical load data: , t = 1 to 72, indicating the power demand of the user at time t; Step 202, defining charging and discharging decision variables; Two charging and discharging decision variables are defined to describe the behavior of the energy storage device: represents the charge amount at time t; Indicates the discharge amount at time t; Step 203, setting an objective function, the optimization goal is to find a charging and discharging strategy that minimizes the total cost, where the cost includes the cost of purchasing electricity from the power grid and the operating cost of the energy storage device; The objective function for minimizing the cost in calculating the charging and discharging strategy is: in, represents the total cost of the i-th charging and discharging strategy, represents the function of taking the minimum value, represents the electricity price at time t, represents the power demand of the user at time t, represents the photovoltaic power generation at time t, represents the discharge amount of the i-th charge-discharge strategy at time t, represents the charge amount of the i-th charge-discharge strategy at time t, represents the operating cost of the energy storage equipment in the i-th charging and discharging strategy; The calculation formula for the operating cost of energy storage equipment is: in, represents the operating cost of the energy storage equipment in the i-th charging and discharging strategy, represents the charging cost of the energy storage device at time t for the i-th charging and discharging strategy, represents the charging cost of the energy storage device at time t for the i-th charging and discharging strategy, represents the charging energy of the energy storage device at time t for the i-th charging and discharging strategy, represents the discharged energy of the energy storage device at time t for the i-th charging and discharging strategy; Step 204, setting constraints, which include: one, in, At time t At time t ; two, in, Indicates the energy storage state. Indicates the maximum capacity; three, in, , They represent the charge and discharge amounts at time t, respectively. , Respectively represent the maximum power of charging and discharging; Step 205 , obtaining a charge and discharge strategy with the lowest cost according to a linear programming or mixed integer linear programming optimization algorithm.

9. A solar power generation and energy storage system, used to execute a solar power generation and energy storage method as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: Data collection module, used to collect historical weather data, historical electricity consumption data and user solar equipment data; Weather analysis module, which is used to analyze historical weather data, determine the key factors affecting the efficiency of photovoltaic panels, and set adjustment coefficients according to seasonal changes; The electricity consumption behavior modeling module is used to build a user electricity consumption behavior model based on historical electricity consumption data and predict the electricity load information within the next 72 hours; The photovoltaic power generation prediction module uses the weather forecast data for the next 72 hours provided by the meteorological department, combined with the photovoltaic power generation model to predict the future photovoltaic power generation, and applies the adjustment coefficient for correction; The charging and discharging decision optimization module is used to build and solve the charging and discharging decision optimization model and select the charging and discharging strategy with the lowest cost.

10. A storage medium, characterized in that: It stores non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are executed by a computer, the steps in the solar power generation and energy storage method as claimed in claim 8 can be executed.

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