Solar energy-wind energy hybrid power supply management method and system

Abstract

According to the solar energy-wind energy mixed power supply management method and system, firstly, a model prediction method is adopted to predict the energy yield and load energy demand quantity of energy production equipment; and then optimizing and adjusting energy distribution by adopting a linear programming method according to the energy output of the energy production equipment, the load energy demand quantity, the current energy output of the energy production equipment and the state of an energy storage battery so as to ensure the balance of energy supply and demand and realize the minimization of energy cost at the same time. The energy production equipment comprises solar energy power generation equipment and wind energy power generation equipment, natural complementary natural resources are combined for power generation, negative influences on the environment can be reduced, carbon emission and emission of other pollutants are reduced, sustainable development is promoted, the energy yield and load energy demand quantity of the energy production equipment are predicted in combination with model prediction, and the energy utilization rate of the energy production equipment is improved. Energy resources can be utilized more effectively, and energy waste is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of automobiles, and in particular to a solar-wind hybrid power supply management method and system (for satellite ground stations). Background Art

[0002] As a key node connecting satellites and the earth information network, satellite ground stations have extremely high requirements for the stability and reliability of the power supply system. Their equipment, such as antennas, radio frequency equipment, signal processing units, data storage and transmission equipment, etc., require a stable power supply to ensure all-weather satellite communication services. These devices usually have high energy consumption and strict requirements on power quality, including voltage stability, frequency stability and anti-interference ability. However, the existing power supply system will have problems such as voltage fluctuation, frequency drift, harmonic interference, and the power supply equipment is old, energy consumption is high, and efficiency is low, which increases operating costs and is not conducive to energy conservation and emission reduction. At the same time, there is a lack of advanced power management systems in the station, and it is impossible to monitor and analyze power consumption in real time, making it difficult to achieve refined management and energy-saving optimization. In addition, since satellite ground stations may be located in remote areas, grid access is difficult or unstable, or even completely off-grid, there is an urgent need for independent, reliable and economical energy solutions.

[0003] Solar energy and wind energy are abundantly available in the locations where satellite ground stations are established, and they complement each other naturally - solar energy has a stable output during the day and in clear weather, while wind energy can still provide electricity at night and in cloudy weather. This complementarity helps ensure all-weather, continuous energy supply, reduces dependence on fossil fuels, and meets the requirements of environmental protection and sustainable development.

[0004] Therefore, in response to the power supply needs of satellite ground stations, a solar-wind hybrid power generation system is used as the main energy solution. Solar and wind energy can be converted into electrical energy through photovoltaic modules and wind turbines, and then stored in batteries for use by equipment in satellite ground stations. However, when designing a solar-wind hybrid power generation system, it is necessary to consider the special needs of satellite ground stations, such as the volatility of energy demand, power storage and backup energy, as well as the reliability and stability of the power generation system to ensure that a stable power supply can be provided under any circumstances, so that the supply and demand sides can be balanced while maximizing the use of solar-wind energy converted into electrical energy and minimizing energy costs. Therefore, it is necessary to design an energy management method and power supply system for solar-wind hybrid power supply. Summary of the invention

[0005] In view of the analysis of the background technology, the present invention provides a solar-wind hybrid power supply management method and system, which solves the energy supply reliability and stability problems of solar-wind hybrid power supply, and also solves the energy management problem of solar-wind hybrid power supply. By using wind energy and solar energy, two complementary natural resources, to supply energy, optimize energy allocation, ensure the balance of energy supply and demand, and maximize the use of solar-wind energy, that is, to minimize energy costs.

[0006] To achieve the above-mentioned and other related purposes, the present invention provides an energy management method for solar-wind hybrid power supply, comprising:

[0007] A solar-wind hybrid power supply management method, comprising:

[0008] Use model prediction methods to predict the energy output of production equipment and load energy demand;

[0009] According to the energy output and load energy demand, combined with the current energy output of the production equipment and the current state of the energy storage battery, the linear programming method is used to optimize the energy configuration, achieve energy supply and demand balance while minimizing energy costs.

[0010] As one embodiment of the present invention, the model used in the model prediction method is an LSTM model, and the model prediction method includes collecting historical data of the first prediction parameter and the second prediction parameter, and performing preprocessing;

[0011] Extract features that are helpful for prediction from the preprocessed historical data. The features constitute the input data set of the LSTM model. The input data set is divided into a training set and a test set. The training set is used to train the LSTM model, and the test set is used to evaluate the performance of the trained LSTM model.

[0012] The trained LSTM model is integrated into the energy management system, and the latest data corresponding to the first prediction parameter and the second prediction parameter are received in real time as input data to predict the energy output of the production equipment and the energy demand of the load.

[0013] As one embodiment of the present invention, the first prediction parameter includes but is not limited to the historical energy production data and weather forecast data of the production equipment; the second prediction parameter includes but is not limited to the historical energy demand data, activity plan and temperature data of the load.

[0014] As one embodiment of the present invention, the features include but are not limited to time series features, meteorological features and historical energy output of production equipment; the feature extraction method includes but is not limited to importance evaluation method and recursive feature elimination method.

[0015] As one embodiment of the present invention, when the LSTM model is trained with the data of the training set, the LSTM model parameters are adjusted through cross-validation (hyperparameter tuning) to reduce the risk of overfitting and improve the prediction accuracy.

[0016] As one embodiment of the present invention, when the data of the test set is used to evaluate the performance of the trained LSTM model, the evaluation indicators include but are not limited to mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and determination coefficient R2.

[0017] As one embodiment of the present invention, the decision variables of the linear programming method include: the amount of additional energy produced (x1), the amount of electricity released by the battery (x2) and the amount of electricity stored in the battery (x3), wherein the amount of additional energy produced (x1) is the energy output of the additional functional equipment;

[0018] The objective functions of linear programming methods include: minimizing cost or maximizing efficiency, which can be simplified to balancing cost and supply and demand;

[0019] The constraints of the linear programming method include:

[0020] (1) Energy supply and demand balance constraint: the energy output of the current production equipment + (x1) - (x3) = predicted load energy demand - (x2);

[0021] (2) Energy storage battery status constraint: battery capacity lower limit ≤ battery initial power + (x3) - (x2) ≤ battery capacity upper limit;

[0022] (3) Production and release restrictions: (x1), (x2) and (x3) are non-negative numbers.

[0023] As one embodiment of the present invention, the linear programming model of the linear programming method is simplified as follows:

[0024] Decision variables of the linear programming method: (P_{current}) is the energy output of the current production equipment; (P_{predicted}) is the predicted energy output of the production equipment; (D_{predicted}) is the predicted load energy demand; (B_{initial}) is the initial state (power) of the battery; (B_{min}) and (B_{max}) are the lower and upper limits of the battery capacity, respectively;

[0025] Objective function: [\text{Minimize}0]

[0026] The constraints include:

[0027] (1) Energy supply and demand balance: [P_{current}+P_{predicted}+x1-x2=D_{predicted}];

[0028] (2) Energy storage battery state constraint: [B_{min}\leq B_{initial}+x3-x2\leq B_{max}];

[0029] (3) Non-negative variable constraints: [x1,x2,x3\geq 0].

[0030] As one embodiment of the present invention, a solar-wind hybrid power supply system using the energy management method described in the claims includes:

[0031] Equipment module, the equipment module includes power generation equipment and load, the power generation equipment includes solar power generation equipment and wind power generation equipment;

[0032] An energy storage battery module, the energy storage battery module includes an energy storage battery and a battery management system (BMS), the battery management system manages the storage and release of energy of the energy storage battery; and

[0033] Energy Management System (EMS), the energy management system includes a model algorithm module and a management module; the model module includes an LSTM model and a linear programming model. The LSTM model predicts the energy output of the production equipment and the energy demand of the load. The linear programming model optimizes the energy allocation plan according to the energy output of the production equipment, the energy demand of the load, the current energy output of the production equipment and the information of the energy storage battery to ensure the balance of energy supply and demand; the management module monitors the operating status and parameters of the load, production equipment and energy storage battery in real time, and adjusts the connection and disconnection of the load and production equipment according to the energy allocation plan optimized by the model algorithm module, and controls the charging and discharging of the energy storage battery.

[0034] As one embodiment of the present invention, the management module includes a monitoring module, an adjustment conversion module and a fault monitoring module;

[0035] The monitoring module monitors the operating status and parameters of the load, power generation equipment and energy storage battery in real time, and schedules the connection and disconnection of the load and power generation equipment according to the energy allocation plan optimized by the linear programming model, and controls the charging and discharging of the energy storage battery through the battery management system;

[0036] The regulating conversion module converts the electric energy generated by the power generation equipment to adapt the power supply energy of the load; and

[0037] The fault monitoring module monitors the system fault conditions of loads, production equipment, energy storage batteries and energy management systems in real time, and cuts off the system-related circuits in time when a system fault is detected.

[0038] The beneficial technical effects of the present invention include at least:

[0039] The solar-wind hybrid power supply management method and system of the present invention first adopts a model prediction method to predict the energy output of the production equipment and the load energy demand; then, according to the energy output of the production equipment, the load energy demand, the current energy output of the production equipment and the current energy storage battery status, a linear programming method is used to optimize and adjust the energy distribution to ensure the balance of energy supply and demand. The production equipment includes solar power generation equipment and wind power generation equipment. Combining natural complementary natural resources for power generation can reduce the negative impact on the environment, reduce carbon emissions and other pollutant emissions, and promote sustainable development. Combining the model prediction in the machine learning algorithm to predict the energy output of the production equipment and the load energy demand can make more effective use of energy resources and avoid energy waste. Furthermore, the linear programming method is used to optimize and adjust the energy distribution to ensure the balance of energy supply and demand. Minimizing the use of batteries can maximize the utilization rate of wind-solar energy, minimize energy costs, and maximize the energy conversion efficiency of wind-solar energy into electrical energy.

[0040] Therefore, the solar-wind hybrid power supply management method and system of the present invention avoids energy waste or energy shortage problems caused by energy supply shortage or oversupply, while maximizing the use of wind energy and solar energy, two complementary and abundantly available natural energy sources, to minimize energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the solar-wind hybrid power supply management method of the present invention;

[0042] Figure 2 This is a system architecture diagram of the solar-wind hybrid power supply system of the present invention;

[0043] Figure 3 A schematic diagram of the principle of a solar device of a solar-wind hybrid power supply system according to an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of the principle of a wind energy device in a solar-wind hybrid power supply system according to an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the principle of a solar-wind hybrid power supply system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following is an explanation of the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the features in the following embodiments and the embodiments can be combined with each other without conflict. It should also be understood that the terms used in the embodiments of the present invention are intended to describe specific embodiments, rather than to limit the scope of protection of the present invention. The test methods for which specific conditions are not indicated in the following examples are usually carried out under conventional conditions or according to the conditions recommended by the manufacturers.

[0047] See also Figures 1 to 5 . It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical content disclosed by the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description, and are not used to limit the scope of the implementation of the present invention. Changes or adjustments in their relative relationships should also be regarded as the scope of the implementation of the present invention without substantially changing the technical content.

[0048] When numerical ranges are given in the embodiments, it should be understood that unless otherwise specified in the present invention, both endpoints of each numerical range and any value between the two endpoints can be selected. Unless otherwise defined, all technical and scientific terms used in the present invention are familiar to those skilled in the art and the description of the present invention, and any method, device and material of the prior art similar or equivalent to the method, device and material in the embodiments of the present invention can also be used to implement the present invention.

[0049] See also Figure 1 To achieve the above-mentioned purpose and other related purposes, the present invention provides a solar-wind hybrid power supply management method, comprising:

[0050] A model prediction method is used to predict the energy production of the capacity equipment and the energy demand of the load, and the prediction parameters are respectively a first prediction parameter and a second prediction parameter; the first prediction parameter includes but is not limited to the historical energy production data and weather forecast data of the capacity equipment; the second prediction parameter includes but is not limited to the historical energy demand data, activity plan and temperature data of the load.

[0051] According to the energy output of the production equipment and the energy demand of the load, combined with the current energy output of the production equipment and the current status of the energy storage battery, the linear programming method is used to optimize and adjust the energy distribution to ensure the balance of energy supply and demand while minimizing energy costs.

[0052] As one of the embodiments of the present invention, the model used in the model prediction method is an LSTM (Long Short-Term Memory) model, which specifically includes the following contents: collecting historical data of the first prediction parameter and the second prediction parameter, and preprocessing. Extract features that are helpful for prediction from the preprocessed historical data, and the features constitute the input data set of the LSTM model. The input data set is divided into a training set and a test set, and the data of the training set is used to train the LSTM model. The LSTM model parameters (hyperparameter tuning) are adjusted by cross-validation to reduce the risk of overfitting and improve the prediction accuracy; the data of the test set is used to evaluate the performance of the trained LSTM model, and commonly used evaluation indicators include but are not limited to mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and determination coefficient R2. The trained LSTM model is integrated into the energy management system, and the latest data corresponding to the first prediction parameter and the second prediction parameter are received in real time as input data. Based on the trained LSTM model, the energy output and load energy demand of the production capacity equipment are predicted, which can be the energy output and load energy demand of the production capacity equipment in the future.

[0053] Wherein, collecting historical data of the first prediction parameter and the second prediction parameter includes:

[0054] a. Collect historical data: The historical data of the first prediction parameter includes but is not limited to weather data (such as temperature, humidity, wind speed, solar radiation intensity, etc.), equipment operation records, maintenance history, energy production records, etc.

[0055] b. The historical data of the second prediction parameter includes but is not limited to historical load data, activity plans, temperature data, etc. to achieve the function of predicting future power demand.

[0056] Data preprocessing includes: cleaning data, handling missing values ​​and outliers; normalizing or standardizing data to eliminate dimensional effects and improve model training efficiency.

[0057] Features that are helpful for prediction are extracted from the preprocessed historical data. The features constitute the input data set of the LSTM model, including:

[0058] a. Select or construct features that are helpful for prediction: such as time series features (such as hours, seasons, holidays), meteorological characteristics, historical production, etc.

[0059] b. Feature selection: Use methods such as feature importance evaluation and recursive feature elimination to select the features that are most valuable for prediction.

[0060] Continuous learning and optimization of the LSTM model include:

[0061] a. Regularly retrain the LSTM model with new data to adapt to new features brought about by environmental changes or system upgrades.

[0062] b. Implement an online learning mechanism so that the LSTM model can continuously learn new data during operation and gradually improve the prediction accuracy.

[0063] As one embodiment of the present invention, the code of the above-mentioned solar-wind hybrid power supply energy management method based on Python and a common database can be as follows:

[0064]

[0065]

[0066]

[0067] As one embodiment of the present invention, the decision variables of the linear programming method include: the additional energy produced (x1), the amount of electricity released by the battery (x2) and the amount of electricity stored in the battery (x3), wherein the additional energy produced (x1) is the energy output of the additional functional equipment (which can be the power supply of the interface of the mains power grid or the fuel generator).

[0068] The objective functions of the linear programming method include: minimizing cost or maximizing efficiency, which can be simplified to balancing cost and supply and demand. Possible objective functions are to ensure the lowest total cost of the power supply system or to minimize battery usage while meeting demand. Minimizing cost means minimizing energy cost. When minimizing battery usage, wind and solar energy will be maximized, that is, efficiency will be maximized, and energy cost will be minimized.

[0069] The constraints of the linear programming method include:

[0070] (1) Energy supply and demand balance constraint: the energy output of the current production equipment + (x1) - (x3) = predicted load energy demand - (x2);

[0071] (2) Energy storage battery status constraint: battery capacity lower limit ≤ battery initial power + (x3) - (x2) ≤ battery capacity upper limit;

[0072] (3) Production and release restrictions: (x1), (x2) and (x3) are non-negative numbers.

[0073] As one embodiment of the present invention, the linear programming model of the linear programming method is simplified as follows:

[0074] Decision variables of the linear programming method: (P_{current}) is the energy output of the current production equipment; (P_{predicted}) is the predicted energy output of the production equipment; (D_{predicted}) is the predicted load energy demand; (B_{initial}) is the initial state (power) of the battery; (B_{min}) and (B_{max}) are the lower and upper limits of the battery capacity, respectively;

[0075] Objective function: [\text{Minimize}0],

[0076] The constraints include:

[0077] (1) Energy supply and demand balance: [P_{current}+P_{predicted}+x1-x2=D_{predicted}];

[0078] (2) Energy storage battery state constraint: [B_{min}\leq B_{initial}+x3-x2\leq B_{max}];

[0079] (3) Non-negative variable constraints: [x1,x2,x3\geq 0].

[0080] As one embodiment of the present invention, the above linear programming method is based on the code of import pulp as follows:

[0081] #Define the problem

[0082] prob=pulp.LpProblem("DynamicEnergyScheduling",pulp.LpMinimize)

[0083] #Define decision variables

[0084] x1=pulp.LpVariable("x1",lowBound=0)#Additional energy produced

[0085] x2=pulp.LpVariable("x2",lowBound=0)#Electricity released from the battery

[0086] x3=pulp.LpVariable("x3",lowBound=0)#The amount of electricity stored in the battery

[0087] #Objective function (here simplified to minimize a constant, it should actually be replaced by a specific cost function)

[0088] prob+=0

[0089] #Constraints

[0090] prob+=P_current+P_predicted+x1-x2==D_predicted,"supply and demand balance"

[0091] prob+=B_min<=B_initial+x3-x2<=B_max,"battery status constraint"

[0092] #Solution

[0093] prob.solve()

[0094] # Output results

[0095] print("Status:",pulp.LpStatus[prob.status])

[0096] print("Additional energy production:",x1.varValue)

[0097] print("Battery discharge:",x2.varValue)

[0098] print("Storing power into the battery:",x3.varValue).

[0099] As one embodiment of the present invention, please refer to Figure 2 To achieve the above-mentioned purpose and other related purposes, the present invention also includes a solar-wind hybrid power supply system using the above-mentioned power supply management method, including a device module, an energy storage battery module and an energy management system (EMS). The power supply system should also have an interface with the mains power grid or a fuel generator so as to access an external power supply when the solar and wind energy supply is seriously insufficient. The EMS can automatically identify and seamlessly switch to the backup power supply to ensure uninterrupted power supply.

[0100] The equipment module includes power generation equipment and loads, and the power generation equipment includes solar power generation equipment and wind power generation equipment.

[0101] Solar power generation equipment: Satellite ground stations are equipped with solar photovoltaic arrays, which consist of multiple solar panels. These panels use semiconductor materials (such as silicon) to directly convert the received sunlight into direct current (DC). Solar panels are usually mounted on tracking brackets and can be adjusted according to the position of the sun to improve the efficiency of light energy capture. When there is sufficient sunlight, the solar photovoltaic array generates electricity to supply ground station equipment or store it in batteries. The output power of the solar panel (P_{pv}) can be expressed by the following formula: [P_{pv}=I_{sc}\times V_{oc}\times FF], (I_{sc}) is the short-circuit current, (V_{oc}) is the open-circuit voltage, and (FF) is the fill factor.

[0102] See Figure 3 As shown, the working principle of the solar power generation equipment in one embodiment of the present invention is as follows: sunlight shines on the solar panel (solar cell array), and the excited photons interact with the atoms in the semiconductor material. When the energy of the photons is high enough, it can excite electrons from the atoms to form free electron and hole pairs. Under the action of the electric field of the PN junction, the free electrons and holes move to the N-type and P-type semiconductors respectively, forming a potential difference and generating a direct current. The controller monitors the state of the battery panel, adjusts the current and voltage of the battery panel charging the battery, prevents overcharging or discharging, and protects the battery. When there is sufficient sunshine, the excess electric energy is stored in the battery for power supply at night or on cloudy days. The inverter converts the direct current stored in the battery into alternating current to meet the power demand of the AC load in the station. The monitoring, alarm and other equipment in the station use direct current. The communication equipment, air conditioning, lighting and other equipment in the station use alternating current. Through this process, the solar equipment converts solar energy into electric energy that can be used by the station. Wind power generation equipment: The ground station is also equipped with wind turbines, which generally use horizontal or vertical axis wind turbines. The appropriate model and specifications are selected according to the local wind conditions. Wind turbines drive the rotor to rotate by capturing wind energy, and then convert mechanical energy into electrical energy through the built-in generator, which is also output as direct current. Wind energy is an effective supplement to solar energy, especially in cloudy days or at night when solar energy is insufficient, to ensure the continuity of power supply.

[0103] Wind power generation equipment: The ground station is also equipped with a wind turbine generator set, which generally adopts horizontal axis or vertical axis wind turbines. The appropriate model and specifications are selected according to the local wind conditions. The wind turbine drives the rotor to rotate by capturing wind energy, and then converts the mechanical energy into electrical energy through the built-in generator, which is also output as direct current. Wind energy is an effective supplement to solar energy, especially in cloudy days or at night when solar energy is insufficient, to ensure the continuity of power supply. As the core device of the wind power generation system, the most critical component of the wind turbine is the wind wheel (also known as wind blade or blade). The wind wheel is usually designed with multiple blades and can rotate efficiently under the blowing of wind. The output power (P_{wind}) of the wind turbine is usually described by Betz's law, and the simplified formula is: [P_{wind}=\frac{1}{2}\rho Av^3C_p], where (\rho) is the air density, (A) is the swept area of ​​the wind wheel, (v) is the wind speed, and (C_p) is the wind energy utilization coefficient.

[0104] See Figure 4 As shown, the working principle of the wind power generation equipment in one embodiment of the present invention is as follows: when the wind blows through the wind wheel, the kinetic energy of the wind is transferred to the wind wheel, causing the wind wheel to rotate. The wind energy is converted into mechanical energy, and the main shaft of the wind wheel is connected to the generator at the same time. The generator usually contains two parts, the stator and the rotor. When the rotor rotates in the magnetic field, according to the principle of electromagnetic induction, the wire cuts the magnetic flux lines to generate electromotive force, thereby forming a current in the closed circuit. The mechanical energy is further converted into electrical energy. The converter converts the variable voltage and frequency AC generated by the wind turbine into DC. The control module monitors the system status in real time and implements various protection measures to ensure the safe operation of the system. The battery converts the unstable wind energy into chemical energy and stores it through the charging process, and then converts the chemical energy into AC through the discharging process for use in the station. The inverter converts DC into AC to meet the load requirements of the site. The monitoring and alarm equipment in the station uses DC, while communication equipment, air conditioning, lighting and other equipment use AC. Through the above process, wind power generation equipment converts wind energy into electrical energy, providing clean and stable energy supply for the site. At the same time, through the cooperation of equipment such as batteries and inverters, the safe and stable operation of the system is ensured.

[0105] As one embodiment of the present invention, the energy storage battery module includes an energy storage battery and a battery management system (BMS), and the battery management system manages the storage and release of energy of the energy storage battery. Since the output of solar energy and wind energy is affected by weather conditions, in order to ensure the power supply during periods of insufficient sunlight or no wind, the system is equipped with a large-capacity battery pack. Energy storage batteries (such as lead-acid, lithium-ion or lithium iron phosphate batteries, etc.) are used to store excess electrical energy and release it when needed. Specifically, the battery management system (BMS) is responsible for detecting and managing various key parameters of the energy storage battery, obtaining the voltage of the energy storage battery through the voltage sensor of the battery management system, obtaining the charge and discharge current through the current sensor, and measuring the temperature of the energy storage battery through the temperature sensor. Then, the SOC of the energy storage battery is estimated through these important parameters. The estimation method adopts a machine learning algorithm. Finally, the battery management system sends the SOC and other parameters to the energy management system through the serial communication protocol CAN bus, and the energy management system determines the charge and discharge of the energy storage battery based on this information. The energy management system monitors the battery status to ensure that its charge and discharge process is carried out within a safe range to prevent overcharging or deep discharge from shortening the battery life.

[0106] As one of the embodiments of the present invention, the energy management system includes a model algorithm module and a management module. The model module includes an LSTM model and a linear programming model. The LSTM model predicts the energy output of the production equipment and the energy demand of the load, which can be the energy output and load energy demand in a future time window. The linear programming model optimizes the energy allocation plan according to the energy output of the production equipment, the energy demand of the load, the current energy output of the production equipment and the information of the energy storage battery to ensure the balance of energy supply and demand within the above time window. The management module monitors the operating status and parameters of the load, production equipment and energy storage battery in real time, and adjusts the connection and disconnection of the load and production equipment according to the energy allocation plan optimized by the model algorithm module, and controls the charging and discharging of the energy storage battery.

[0107] As one embodiment of the present invention, the management module includes a monitoring module, an adjustment and conversion module and a fault monitoring module.

[0108] The monitoring module monitors the operating status and parameters of the load, production equipment and energy storage battery in real time, and schedules the connection and disconnection of the load and production equipment according to the energy allocation plan optimized by the linear programming model, and controls the charging and discharging of the energy storage battery through the battery management system. The monitoring module can transmit system data to the central monitoring center or cloud platform through wired or wireless communication technology (such as GPRS, satellite communication, etc.). Operation and maintenance personnel can remotely monitor the system operation status, receive alarm information, and perform remote diagnosis and control, which greatly improves the maintenance efficiency and reliability of the system.

[0109] The regulating and conversion module converts the electric energy generated by the energy generation equipment to adapt the power supply energy of the load. Specifically, the direct current generated by solar energy and wind energy usually needs to be converted into alternating current (AC) by an inverter for use by various AC loads in the ground station. In addition, it is also responsible for managing the direct current-to-direct current (DC-DC) converter to adapt the power supply to equipment of different voltage levels. The power electronic equipment in the power supply system should have a wide voltage input range to adapt to the fluctuating characteristics of solar energy and wind energy output.

[0110] The fault monitoring module monitors the system faults of loads, production equipment, energy storage batteries and energy management systems in real time, and cuts off the relevant system circuits in time when a system fault is detected. Specifically, a statistical method (Z-score) is used, and its parameters are sensor readings, historical fault data, etc. The code of the processing process is as follows:

[0111] Data preprocessing and Z-score anomaly detection:

[0112] First, the sensor readings and historical fault data need to be processed. Outliers are identified by calculating the Z-score, a statistic that indicates how many standard deviations a value is from the mean.

[0113] import numpy as np

[0114] #Assume sensor_readings is a list containing sensor readings

[0115] sensor_readings = [25.6, 26.2, 24.8, 30.1, 45.0, 27.3, 28.9, 60.0] # Sample data

[0116] # Calculate the mean and standard deviation

[0117] mean=np.mean(sensor_readings)

[0118] std_dev=np.std(sensor_readings)

[0119] # Calculate the Z-score for each reading

[0120] z_scores=[(x-mean) / std_dev for x in sensor_readings]

[0121] #Define the abnormal threshold, usually 3

[0122] threshold=3

[0123] #Filter out outliers

[0124] anomalous_indices=[i for i,z_score in enumerate(z_scores)if abs(z_score)>threshold]

[0125] anomalous_readings=[sensor_readings[i]for iin anomalous_indices]

[0126] print("Anomalous readings:",anomalous_readings)

[0127] Real-time monitoring and response logic:

[0128] Next, through a real-time monitoring loop, anomaly detection is performed whenever a new sensor reading comes in, and actions are taken when an anomaly is found, such as sending an alert or attempting automatic recovery.

[0129] def monitor_and_react(sensor_reading):

[0130]

[0131] Figure 5 As shown, the working principle of the solar-wind hybrid power supply system in one embodiment of the present invention is schematically shown: the power bus and the communication bus divide the system into the left and right sides, and the left side is a solar power generation device and a wind power generation device arranged in parallel. The electric energy generated by them is transmitted to the power bus, and distributed to the energy storage battery on the right side through the power bus and for normal use by the ground station. At the same time, the power bus should also have an interface with the city power grid or a fuel generator so as to access an external power supply when the supply of solar energy and wind energy is seriously insufficient. Among them, all transformers, converters and inverters are used to adjust the voltage and frequency so that the generated electric energy matches the power grid and is stable and reliable, and can be used normally by the ground station. The communication bus can monitor the operating status and parameters of solar equipment, wind power generation equipment, energy storage batteries and ground station loads in real time through local monitoring or cloud platform remote monitoring, and control the charging and discharging of energy storage batteries through an integrated battery management system. Through data analysis, the energy output trend is predicted, and the access and disconnection of each energy source is intelligently scheduled according to the load demand and the energy storage status of the energy storage battery to optimize energy distribution.

[0132] In summary, the energy management method and power supply system of the solar-wind hybrid power supply of the present invention realizes the efficient collection, conversion, storage and distribution of multiple energy sources by integrating solar photovoltaic, wind power generation, energy storage device (energy storage battery) and advanced energy management system, ensuring that ground station equipment obtains stable and green power supply under various climatic conditions. It not only improves the safety, stability and economy of energy supply, but also enhances the environmental adaptability and sustainable development level of the site, showing the following significant advantages compared with the existing technology:

[0133] 1) Enhance the self-sufficiency and independence of energy supply: Compared with relying entirely on traditional power grids or a single renewable energy source, the hybrid power supply system combines two renewable resources, solar and wind energy, to significantly reduce dependence on external energy, especially in remote areas or satellite ground stations with inconvenient grid access, significantly improving the energy self-sufficiency rate and independent operation capabilities of the site. After using software-defined tasks, it does not rely on the hardware itself to identify the task, but uses automated task loading and execution to complete it.

[0134] 2) Improve energy efficiency and power supply stability: Solar energy and wind energy are naturally complementary in terms of time and season. The hybrid system can flexibly switch or use both energy sources at the same time under different meteorological conditions, ensuring that a certain level of power generation can be maintained during periods of insufficient sunlight or low wind, effectively smoothing out fluctuations in the output of a single energy source, improving overall energy efficiency, minimizing energy costs, and ensuring that the power supply of satellite ground stations is more stable and reliable.

[0135] 3) Integrated energy management system optimizes resource allocation and energy storage management: Integrated systems are usually equipped with advanced energy management systems (EMS), which can monitor the operating status of solar photovoltaic, wind turbines and energy storage equipment in real time, and intelligently dispatch energy production and consumption based on factors such as weather forecasts, load demand and battery status. EMS can optimize power distribution, give priority to the use of renewable energy, charge or discharge energy storage devices in a timely manner, reduce energy waste, reduce battery use, maximize the use of wind-solar energy, extend the life of energy storage equipment, and seamlessly switch to backup power or external power grid when necessary to ensure uninterrupted power supply.

[0136] 4) Strong environmental adaptability and reduced operation and maintenance costs: The hybrid power supply system can adapt to a wider range of geographical and climatic conditions. Whether in sunny desert areas, windy coastal areas, or inland areas with distinct seasons and changeable weather, it can maximize the capture of locally available renewable energy through the reasonable configuration of photovoltaic modules and wind turbines. This high adaptability reduces dependence on specific environmental conditions and reduces operation and maintenance interventions caused by energy supply problems, thereby saving operation and maintenance costs and manpower investment.

[0137] 5) Energy saving, emission reduction and sustainability advantages: The use of solar and wind power hybrid power supply significantly reduces fossil fuel consumption and greenhouse gas emissions, meeting global requirements for green energy and low-carbon operations. This is conducive to fulfilling corporate social responsibility and improving environmental protection image for facilities such as satellite ground stations that operate for a long time and have a large demand for electricity. At the same time, it also conforms to the future energy development trend and reduces cost risks and policy compliance pressures for the long-term operation of the site.

[0138] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A solar-wind hybrid power supply management method, characterized in that: include: Use model prediction methods to predict the energy output of production equipment and load energy demand; According to the energy output and the load energy demand, combined with the current energy output of the production equipment and the current state of the energy storage battery, a linear programming method is used to optimize energy configuration to achieve a balance between energy supply and demand while minimizing energy costs.

2. The solar-wind hybrid power supply management method according to claim 1, characterized in that: The model used in the model prediction method is an LSTM model, and the model prediction method includes collecting historical data of the first prediction parameter and the second prediction parameter and performing preprocessing; Extracting features useful for prediction from the preprocessed historical data, wherein the features constitute an input data set for the LSTM model, dividing the input data set into a training set and a test set, using the training set to train the LSTM model, and using the test set to evaluate the performance of the trained LSTM model; The trained LSTM model is integrated into the energy management system, and the latest data corresponding to the first prediction parameter and the second prediction parameter are received in real time as input data to predict the energy output of the production equipment and the energy demand of the load.

3. The management method of solar-wind hybrid power supply according to claim 2, characterized in that: The first prediction parameter includes but is not limited to the historical energy production data and weather forecast data of the production equipment; the second prediction parameter includes but is not limited to the historical energy demand data, activity plan and temperature data of the load.

4. The management method of solar-wind hybrid power supply according to claim 2, characterized in that: The features include but are not limited to time series features, meteorological features and the historical energy output of the energy production equipment; the feature extraction method includes but is not limited to importance evaluation method and recursive feature elimination method.

5. The method for managing solar-wind hybrid power supply according to claim 2, characterized in that: When the data of the training set is used to train the LSTM model, the LSTM model parameters are adjusted through cross-validation (hyperparameter tuning) to reduce the risk of overfitting and improve prediction accuracy.

6. The method for managing solar-wind hybrid power supply according to claim 5, characterized in that: When the data of the test set is used to evaluate the performance of the trained LSTM model, the evaluation indicators include but are not limited to mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and determination coefficient R2.

7. The method for managing solar-wind hybrid power supply according to claim 1, characterized in that: The decision variables of the linear programming method include: the amount of additional energy produced (x1), the amount of electricity released by the battery (x2) and the amount of electricity stored in the battery (x3), wherein the amount of additional energy produced (x1) is the energy output of the additional functional equipment; The objective functions of the linear programming method include: minimizing cost or maximizing energy conversion efficiency, which can be simplified as balancing cost and supply and demand; The constraints of the linear programming method include: (1) Energy supply and demand balance constraint: the energy output of the current production equipment + (x1) - (x3) = predicted load energy demand - (x2); (2) Energy storage battery status constraint: battery capacity lower limit ≤ battery initial power + (x3) - (x2) ≤ battery capacity upper limit; (3) Production and release restrictions: (x1), (x2) and (x3) are non-negative numbers.

8. The method for managing solar-wind hybrid power supply according to claim 7, characterized in that: The linear programming model of the linear programming method is simplified as follows: The decision variables of the linear programming method are: (P_{current}) is the energy output of the current production equipment; (P_{predicted}) is the predicted energy output of the production equipment; (D_{predicted}) is the predicted load energy demand; (B_{initial}) is the initial state (power) of the battery; (B_{min}) and (B_{max}) are the lower and upper limits of the battery capacity, respectively; Objective function: [\text{Minimize}0] The constraints include: (1) Energy supply and demand balance: [P_{current}+P_{predicted}+x1-x2=D_{predicted}]; (2) Energy storage battery state constraint: [B_{min}\leq B_{initial}+x3-x2\leq B_{max}]; (3) Non-negative variable constraints: [x1,x2,x3\geq 0].

9. A solar-wind hybrid power supply system using the energy management method according to any one of claims 1 to 8, characterized in that: include: Equipment module, the equipment module includes energy generation equipment and load, the energy generation equipment includes solar energy power generation equipment and wind energy power generation equipment; An energy storage battery module, wherein the energy storage battery module includes an energy storage battery and a battery management system (BMS), wherein the battery management system manages the storage and release of energy of the energy storage battery; as well as An energy management system (EMS), the energy management system comprising a model algorithm module and a management module; the model algorithm module comprises an LSTM model and a linear programming model, the LSTM model predicts the energy output of the energy production equipment and the load energy demand, and the linear programming model optimizes the energy allocation plan according to the energy output of the energy production equipment, the load energy demand, the current energy output of the energy production equipment and the information of the energy storage battery to ensure the balance of energy supply and demand; The management module monitors the operating status and parameters of the load, the energy generation equipment and the energy storage battery in real time, and adjusts the connection and disconnection of the load and the energy generation equipment according to the energy allocation plan optimized by the model algorithm module, and controls the charging and discharging of the energy storage battery.

10. The solar-wind hybrid power supply system according to claim 9, characterized in that: The management module includes a monitoring module, an adjustment conversion module and a fault monitoring module; The monitoring module monitors the operating status and parameters of the load, the energy generation equipment and the energy storage battery in real time, and schedules the connection and disconnection of the load and the energy generation equipment according to the energy allocation scheme optimized by the linear programming model, and controls the charging and discharging of the energy storage battery through the battery management system; The regulating and converting module converts the electric energy generated by the energy generating device to adapt to the power supply electric energy of the load; as well as The fault monitoring module monitors the system fault conditions of the load, the energy generation equipment, the energy storage battery and the energy management system in real time, and cuts off the system-related circuits in time when the system fault is detected.

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