Optical storage isolated network power supply system for water service gate power supply

Abstract

The invention relates to the technical field of optical storage power supply, in particular to an optical storage isolated network power supply system for water service gate power supply, which comprises a photovoltaic power generation module for converting solar energy into electric energy; the energy storage module comprises a battery pack and is used for storing the electric energy generated by the photovoltaic power generation module; the power management module is connected with the photovoltaic power generation module and the energy storage module and used for coordinating storage and distribution of electric energy, and the power management module further comprises a power generation fluctuation balance sub-module and a load fluctuation management sub-module; and the gate control module is used for receiving the power supply of the power management module and controlling the opening and closing operation of the water service gate. The charging and discharging strategy of the battery pack is optimized, and the negative influence of frequent charging and discharging on the service life of the battery is avoided by monitoring the state of the battery pack in real time and performing balanced charging and discharging management.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power supply, and in particular to a photovoltaic power supply system for powering water gates. Background Art

[0002] With the increasing awareness of global environmental protection and the continuous development of renewable energy technology, solar photovoltaic power generation and energy storage technology have been widely used in various fields, especially in remote areas or areas not covered by the power grid, where autonomous power supply systems have become an important solution. In water management systems, the power supply problem of gates has always been a difficult problem for managers, especially in remote reservoirs, irrigation systems, and flood control facilities, where stable and reliable power supply is essential.

[0003] Traditional power supply methods mainly rely on fuel generators or long-distance cable transmission, which is not only costly but also seriously pollutes the environment. With the development of solar photovoltaic power generation and energy storage technology, using solar energy and energy storage systems to power water gates has become a viable alternative. However, the photovoltaic power supply system currently on the market still has many shortcomings in terms of stability, sustainability and intelligent management, especially when facing problems such as weather changes, battery life and system integration, it cannot provide the best solution.

[0004] Current photovoltaic energy storage systems face significant challenges in the following areas:

[0005] Volatility of photovoltaic power generation and life management of energy storage systems: Photovoltaic power generation is greatly affected by the environment, such as weather and sunshine hours, which leads to significant volatility in power generation. Frequent charging and discharging will have a negative impact on the battery life of the energy storage system. Existing technologies make it difficult to optimize power management strategies and balance the charging and discharging frequency of energy storage systems in a fluctuating power generation environment to extend battery life.

[0006] Load fluctuation management: The operation of water gates usually requires instantaneous high current power supply, such as the start-up moment of the electric actuator when opening and closing the gate, which will generate large power load fluctuations. The existing system is not good at dealing with such instantaneous high current demand, and it is difficult to respond quickly to load changes and ensure the stability of power supply.

[0007] In view of the above problems, the present invention proposes a photovoltaic storage isolated grid power supply system for powering water gates. Summary of the invention

[0008] Based on the above objectives, the present invention provides a photovoltaic storage isolated grid power supply system for powering water gates.

[0009] The photovoltaic storage isolated grid power supply system used for powering water gates includes:

[0010] Photovoltaic modules, which convert solar energy into electricity;

[0011] An energy storage module, including a battery pack, for storing the electrical energy generated by the photovoltaic power generation module;

[0012] The power management module is connected to the photovoltaic power generation module and the energy storage module to coordinate the storage and distribution of electric energy. The power management module also includes:

[0013] The power generation fluctuation balancing submodule is used to balance the volatility of photovoltaic power generation and optimize the life of the battery pack. It monitors the battery pack status in real time, performs balanced charge and discharge management, uses prediction algorithms to predict future power generation and load demand, and optimizes the charge and discharge strategy.

[0014] The load fluctuation management submodule is used to manage the load fluctuation of the water gate to ensure the reliability of the gate operation. It is based on supercapacitors combined with power electronic converters to meet instantaneous high current requirements, quickly respond to load changes, and ensure the stability of power supply;

[0015] The gate control module is used to receive power supply from the power management module and control the opening and closing operations of the water gate.

[0016] Furthermore, the photovoltaic power generation module specifically includes:

[0017] A plurality of solar panels, installed in areas with sufficient sunlight, are used to convert solar energy into direct current electricity;

[0018] The maximum power point tracking controller is connected to each solar panel to track the working status of the solar panel in real time and adjust the output voltage and current of the panel so that it always works at the maximum power point to maximize the power output;

[0019] Inverters, which convert the DC power generated by solar panels into AC power;

[0020] Temperature sensors and monitoring are used to monitor the temperature of solar panels to prevent overheating and ensure the safe and stable operation of photovoltaic power generation modules.

[0021] Furthermore, the power management module determines the power distribution strategy according to the real-time power generation situation and the status of the energy storage module, controls the charging and discharging process of the power in the battery pack, optimizes the charging and discharging frequency and timing of the battery pack according to the prediction algorithm of the power generation fluctuation balancing sub-module, and is connected to the gate control module to adjust the power distribution according to the load demand of the water service gate to ensure a stable supply of power required for gate operation.

[0022] Furthermore, the prediction algorithm adopts a Bayesian structured time series BSTS model. The BSTS model predicts future power generation and load demand based on historical power generation data, environmental data, and load demand data, and optimizes the charging and discharging strategy. The historical power generation data includes daily and hourly photovoltaic power generation records, and the environmental data includes solar radiation intensity, temperature, humidity, wind speed, cloud cover, and rainfall meteorological data. The load demand data includes the operation records of water gates, including opening and closing time, duration, and operation frequency. The BSTS model specifically includes:

[0023] Preprocess historical power generation data, environmental data, and load demand data, including data cleaning, data normalization, and feature extraction, to build a data set;

[0024] Model construction: Based on historical power generation data, environmental data and load demand data, a BSTS model is constructed. Combining Bayesian statistics and time series analysis, the time dependency in the data is captured through a state space model.

[0025] Training and calibration: Use the collected historical data to train the BSTS model, perform parameter estimation and model calibration, and ensure that the model can reflect the changing patterns of power generation and load demand;

[0026] Prediction: Use the calibrated BSTS model to predict future PV generation and load demand based on current and future environmental data;

[0027] Scheduling is performed based on the charge and discharge controller, and the charge and discharge strategy of the battery pack is optimized according to the prediction results of the BSTS model.

[0028] Furthermore, the BSTS model construction includes:

[0029] State-space model:

[0030] Observation equation: y t =Z t α t +∈ t , where y t is the observed value at time t, including photovoltaic power generation or load demand, Z t is the observation matrix, α t is the state vector, ∈ t is the observation noise, assuming ∈ t ~N(0,σ 2 );

[0031] Equation of state: α t =T t α t-1 +R t η t , where T tis the transfer matrix, R t is the control matrix, η t is the state noise, usually η is assumed t ~N(0,Q);

[0032] The observation equation and the state equation define the dynamic process of the time series, describing how it evolves from one time point to the next;

[0033] Introducing Bayesian updating: By combining the prior distribution and the likelihood function, the posterior distribution of the model parameters is updated.

[0034] Furthermore, the prior distribution is expressed as: p(θ)~N(μ0,∑0), where θ is the model parameter, including all parameters α that need to be updated t ,∈ t , T t and η t , μ0 and ∑0 are the prior mean and covariance matrices respectively;

[0035] The likelihood function is expressed as:

[0036] The posterior distribution is expressed as: p(θ|y t )∝p(y t |θ)p(θ), and continuously adjust the model parameters θ by combining historical data and current observation data through Bayesian updating.

[0037] Furthermore, the prediction of future photovoltaic power generation and load demand specifically includes:

[0038] Time series forecasting: Based on current and future environmental data, future photovoltaic power generation and load demand are forecasted. The forecasting formula is: in, is the predicted value for the next k steps, is the predicted value of the state vector based on time t;

[0039] Prediction Error: Among them, MSE is the mean square error, which is used to evaluate the prediction accuracy of the model.

[0040] Furthermore, the charging and discharging strategy includes:

[0041] Charging plan: When the power generation is expected to be sufficient, arrange the charging plan of the battery pack in advance;

[0042] Charging strategy: Among them, P charge is the charging power, P pv is the photovoltaic power generation power, P load is the load demand power, is the maximum charging power of the battery;

[0043] Discharge plan: When power generation is expected to be insufficient, arrange the discharge plan of the battery group in advance;

[0044] Discharge strategy: Among them, P discharge is the discharge power, is the maximum discharge power of the battery;

[0045] Dynamically adjust the charge and discharge frequency: optimize the service life of the battery pack by adjusting the charge and discharge frequency;

[0046] Dynamic adjustment formula: f adjust =αf current +(1-α)f predicted , where f adjust is the adjusted charge and discharge frequency, f current is the current charge and discharge frequency, f predicted is the predicted charge and discharge frequency, α is the adjustment coefficient (0 <a<1)。

[0047] Furthermore, the supercapacitor unit in the load fluctuation management submodule is connected in parallel with the battery pack in the energy storage module to quickly provide high power output when the load demands an instantaneous large current; the power electronic converter is connected to the supercapacitor unit and the battery pack to convert energy between different power sources to ensure that the system can respond quickly when the load changes; a dynamic load management controller is introduced and connected to the power management module to monitor the operating status and power demand of the water gate in real time and dynamically adjust the energy distribution between the supercapacitor unit and the battery pack; the charge and discharge controller is connected to the supercapacitor unit to charge the supercapacitor when the power demand is low to ensure that it can provide instantaneous high power output when needed.

[0048] Furthermore, the dynamic load management controller includes a real-time load demand P load , calculated as: P load =P base +P peak , where P base is the base load demand, P peak It is the part of instantaneous load demand increase;

[0049] When the energy distribution meets the instantaneous high current demand, the energy distribution of the supercapacitor and the battery pack is calculated by the following formula:

[0050] P total =P cap +P battery , where P total is the total load demand, P capis the power provided by the supercapacitor, P battery is the power provided by the battery pack.

[0051] Beneficial effects of the present invention:

[0052] In the present invention, the power generation fluctuation balancing submodule adopts a Bayesian structure time series model and combines historical power generation data and environmental data to accurately predict future power generation and load demand, optimize the charging and discharging strategy of the battery pack, and perform balanced charging and discharging management by real-time monitoring of the battery pack status to avoid the negative impact of frequent charging and discharging on battery life, thereby improving the system's adaptability to photovoltaic power generation fluctuations, thereby extending the life of the battery pack and ensuring the stable operation of the system.

[0053] In the present invention, the load fluctuation management submodule is based on the combined use of supercapacitors and power electronic converters to meet instantaneous large current demands and quickly respond to load changes. The supercapacitor quickly provides high power output when the load demand increases instantaneously through its high capacitance and voltage, while the power electronic converter performs efficient energy conversion between different power sources to ensure that the system can be quickly adjusted when the load changes. The dynamic load management controller monitors the operating status and power demand of the water gate in real time, dynamically adjusts energy distribution, improves the system's ability to cope with instantaneous load fluctuations, and ensures the reliability of the water gate operation.

[0054] The present invention collects and analyzes environmental data and load demand data in real time. Through intelligent battery management and fast charge and discharge controller, the energy management strategy of battery pack and supercapacitor is dynamically optimized to ensure the optimal operating state under different working conditions. Bayesian updating combines prior distribution and likelihood function to dynamically adjust model parameters and improve prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic diagram of system function modules according to an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of the Bayesian structured time series BSTS model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0059] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0060] like Figure 1 and Figure 2 As shown in the figure, the photovoltaic storage isolated grid power supply system for powering water gates includes:

[0061] Photovoltaic modules, which convert solar energy into electricity;

[0062] An energy storage module, including a battery pack, for storing the electrical energy generated by the photovoltaic power generation module;

[0063] The power management module is connected to the photovoltaic power generation module and the energy storage module to coordinate the storage and distribution of electric energy. Photovoltaic power generation is greatly affected by the environment, such as weather and sunshine time, which leads to significant fluctuations in power generation. Frequent charging and discharging will have a negative impact on the battery life of the energy storage battery pack. It is necessary to solve how to optimize the power management strategy in a fluctuating power generation environment and balance the charging and discharging frequency of the energy storage battery pack to extend the battery life. The power management module also includes:

[0064] The power generation fluctuation balancing submodule is used to balance the volatility of photovoltaic power generation and optimize the life of the battery pack. It monitors the battery pack status in real time, performs balanced charge and discharge management, uses prediction algorithms to predict future power generation and load demand, and optimizes the charge and discharge strategy.

[0065] The load fluctuation management submodule is used to manage the load fluctuation of the water gate to ensure the reliability of the gate operation. It is based on supercapacitors combined with power electronic converters to meet instantaneous high current requirements, quickly respond to load changes, and ensure the stability of power supply;

[0066] The gate control module is used to receive power supply from the power management module and control the opening and closing operations of the water gate.

[0067] Photovoltaic power generation modules specifically include:

[0068] A plurality of solar panels, installed in areas with sufficient sunlight, are used to convert solar energy into direct current electricity;

[0069] The maximum power point tracking controller is connected to each solar panel to track the working status of the solar panel in real time and adjust the output voltage and current of the panel so that it always works at the maximum power point to maximize the power output;

[0070] Inverters, which convert the DC power generated by solar panels into AC power;

[0071] Temperature sensors and monitoring are used to monitor the temperature of solar panels to prevent overheating and ensure the safe and stable operation of photovoltaic power generation modules.

[0072] The power management module determines the power distribution strategy based on the real-time power generation situation and the status of the energy storage module, controls the charging and discharging process of power in the battery pack, optimizes the charging and discharging frequency and timing of the battery pack based on the prediction algorithm of the power generation fluctuation balance submodule, and connects with the gate control module to adjust the power distribution according to the load demand of the water service gate to ensure a stable supply of power required for gate operation.

[0073] The prediction algorithm uses the Bayesian structured time series BSTS model. The BSTS model predicts future power generation and load demand based on historical power generation data, environmental data, and load demand data, and optimizes the charging and discharging strategy. The historical power generation data includes daily and hourly photovoltaic power generation records, and the environmental data includes solar radiation intensity, temperature, humidity, wind speed, cloud cover, and rainfall meteorological data. The load demand data includes the operation records of water gates, including opening and closing time, duration, and operation frequency. The BSTS model specifically includes:

[0074] Preprocess historical power generation data, environmental data, and load demand data, including data cleaning, data normalization, and feature extraction, to build a data set;

[0075] Model construction: Based on historical power generation data, environmental data and load demand data, a BSTS model is constructed. Combining Bayesian statistics and time series analysis, the time dependency in the data is captured through a state space model.

[0076] Training and calibration: Use the collected historical data to train the BSTS model, perform parameter estimation and model calibration to ensure that the model can reflect the changing patterns of power generation and load demand;

[0077] Prediction: Use the calibrated BSTS model to predict future PV generation and load demand based on current and future environmental data;

[0078] Scheduling is performed based on the charge and discharge controller, and the charge and discharge strategy of the battery pack is optimized according to the prediction results of the BSTS model, including:

[0079] Charging plan: When the power generation is expected to be sufficient, arrange the charging plan of the battery pack in advance to ensure that the battery pack stores electricity in an efficient state;

[0080] Discharge plan: When power generation is expected to be insufficient, arrange the battery discharge plan in advance to ensure that the power supply demand of the water gate is met;

[0081] Dynamic adjustment: Dynamically adjust the charge and discharge frequency and time to avoid the negative impact of frequent charge and discharge on battery life;

[0082] By regularly updating the forecasting model and utilizing new historical power generation data, environmental data, and load demand data, the BSTS model is continuously optimized and adjusted to improve forecasting accuracy and adaptability.

[0083] The BSTS model consists of a state space model, Bayesian updating, and time series analysis and prediction. The construction of the BSTS model includes:

[0084] State-space model:

[0085] Observation equation: y t =Z t α t +∈ t , where y t is the observed value at time t, including photovoltaic power generation or load demand, Z t is the observation matrix, α t is the state vector, ∈ t is the observation noise, assuming ∈ t ~N(0,σ 2 );

[0086] Equation of state: α t =T t α t-1 +R t η t , where T t is the transfer matrix, R t is the control matrix, η t is the state noise, usually assuming that η t ~N(0,Q);

[0087] The observation equation and the state equation define the dynamic process of the time series, describing how it evolves from one time point to the next;

[0088] Introducing Bayesian updating: By combining the prior distribution and the likelihood function, the posterior distribution of the model parameters is updated.

[0089] The prior distribution is expressed as: p(θ)~N(μ0,∑0), where θ is the model parameter, including all parameters α that need to be updated t ,∈ t , T t and η t , μ0 and ∑0 are the prior mean and covariance matrices respectively;

[0090] The likelihood function is expressed as:

[0091] The posterior distribution is expressed as: p(θ|y t )∝p(y t |θ)p(θ), and continuously adjust the model parameters θ by combining historical data and current observation data through Bayesian updating.

[0092] The updated parameters include:

[0093] State vector parameter α t : Represents the state of the system at time t, such as photovoltaic power generation, environmental factors (temperature, wind speed, etc.) and implicit state of load demand. In the state space model, these state parameters describe the dynamic process and capture the trend changes of the time series.

[0094] Observation noise parameter ∈ t : represents the noise in the observation equation, assuming a normal distribution N(0, σ 2 ), where σ is the parameter that needs to be updated and the observation noise parameter is used to explain the error between the observation value and the state vector.

[0095] State transition parameter T t : describes how the state vector is transferred from one time point to the next time point, the state transfer matrix T t Parameters that can contain trend and seasonal components need to be estimated and calibrated via Bayesian updating.

[0096] Control noise parameter η t : represents the noise in the state equation, assuming a normal distribution N(0,Q), where Q is the covariance matrix that needs to be updated, and the control noise parameters are used to explain the uncertainty in the state transfer.

[0097] Predicting future photovoltaic power generation and load demand specifically includes:

[0098] Time series forecasting: Based on current and future environmental data, future photovoltaic power generation and load demand are forecasted. The forecasting formula is: in, is the predicted value for the next k steps, is the predicted value of the state vector based on time t;

[0099] Prediction Error: Among them, MSE is the mean square error, which is used to evaluate the prediction accuracy of the model.

[0100] The charging and discharging strategies include:

[0101] Charging plan: When the power generation is expected to be sufficient, arrange the charging plan of the battery pack in advance;

[0102] Charging strategy: Among them, P charge is the charging power, P pv is the photovoltaic power generation power, P load is the load demand power, is the maximum charging power of the battery;

[0103] Discharge plan: When power generation is expected to be insufficient, arrange the discharge plan of the battery group in advance;

[0104] Discharge strategy: Among them, P discharge is the discharge power, is the maximum discharge power of the battery;

[0105] Dynamically adjust the charge and discharge frequency: optimize the service life of the battery pack by adjusting the charge and discharge frequency;

[0106] Dynamic adjustment formula: f adjust =αf current +(1-α)f predicted , where f adjust is the adjusted charge and discharge frequency, f current is the current charge and discharge frequency, f predicted is the predicted charge and discharge frequency, α is the adjustment coefficient (0 <a<1)。

[0107] The supercapacitor unit in the load fluctuation management submodule is connected in parallel with the battery pack in the energy storage module to quickly provide high power output when the load demands an instantaneous high current; the power electronic converter is connected to the supercapacitor unit and the battery pack to convert energy between different power sources to ensure that the system can respond quickly when the load changes; a dynamic load management controller is introduced and connected to the power management module to monitor the operating status and power demand of the water gate in real time and dynamically adjust the energy distribution between the supercapacitor unit and the battery pack; the charge and discharge controller is connected to the supercapacitor unit to charge the supercapacitor when the power demand is low to ensure that it can provide instantaneous high power output when needed.

[0108] The energy storage capacity E of the supercapacitor is calculated as: Where C is the capacitance of the supercapacitor and V is the voltage of the capacitor.

[0109] The instantaneous power output P of the supercapacitor cap Calculated by the following formula:

[0110] P cap =VI, where V is the voltage of the capacitor and I is the discharge current of the capacitor.

[0111] The energy conversion efficiency η of a power electronic converter is calculated by the following formula:

[0112] Among them, P out is the output power of the converter, P in is the input power of the converter.

[0113] If the power electronic converter needs to convert the voltage of the supercapacitor into the voltage V required by the system out , the conversion formula is: V out =ηV in , where V in is the voltage of the supercapacitor.

[0114] The dynamic load management controller includes the real-time load demand P load , calculated as: P load =P base +P peak , where P base is the base load demand, P peak It is the part of instantaneous load demand increase;

[0115] Energy distribution When meeting the instantaneous high current demand, the energy distribution of the supercapacitor and the battery pack is calculated by the following formula:

[0116] P total =P cap +P battery , where P total is the total load demand, P cap is the power provided by the supercapacitor, P battery is the power provided by the battery pack.

[0117] The calculation of the load fluctuation management submodule generally involves the following aspects:

[0118] 1. Energy storage and instantaneous power output: Supercapacitors store energy through their high capacitance and voltage, and quickly release energy when needed, providing instantaneous high power output.

[0119] formula For calculating the energy storage of supercapacitors, the formula P cap =VI is used to calculate its instantaneous power output.

[0120] 2. Energy conversion efficiency: The power electronic converter converts energy between the supercapacitor and the load, and its efficiency is calculated by the formula Calculation to ensure the efficiency of the energy conversion process, the converter output voltage is calculated by the formula V out =ηV in Calculated to meet the voltage requirements of the load.

[0121] 3. Dynamic load management:

[0122] Load demand is monitored and calculated in real time, using the formula P load =P base +P peak The load demand is estimated. The energy distribution between the supercapacitor and the battery pack is calculated by the formula P total =P cap +P battery Calculations are performed to ensure smooth supply of load demand.

[0123] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present invention is limited to these examples. Under the concept of the present invention, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0124] The present invention is intended to cover all such substitutions, modifications and variations that fall within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The photovoltaic storage isolated grid power supply system used for powering water gates is characterized by: include: Photovoltaic modules, which convert solar energy into electricity; An energy storage module, including a battery pack, for storing the electrical energy generated by the photovoltaic power generation module; The power management module is connected to the photovoltaic power generation module and the energy storage module to coordinate the storage and distribution of electric energy. The power management module also includes: The power generation fluctuation balancing submodule is used to balance the volatility of photovoltaic power generation and optimize the life of the battery pack. It monitors the battery pack status in real time, performs balanced charge and discharge management, uses prediction algorithms to predict future power generation and load demand, and optimizes the charge and discharge strategy. The load fluctuation management submodule is used to manage the load fluctuation of the water gate to ensure the reliability of the gate operation. It is based on supercapacitors combined with power electronic converters to meet instantaneous high current requirements, quickly respond to load changes, and ensure the stability of power supply; The gate control module is used to receive power supply from the power management module and control the opening and closing operations of the water gate.

2. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 1 is characterized in that: The photovoltaic power generation module specifically includes: A plurality of solar panels, installed in areas with sufficient sunlight, are used to convert solar energy into direct current electricity; The maximum power point tracking controller is connected to each solar panel to track the working status of the solar panel in real time and adjust the output voltage and current of the panel so that it always works at the maximum power point to maximize the power output; Inverters, which convert the DC power generated by solar panels into AC power; Temperature sensors and monitoring are used to monitor the temperature of solar panels to prevent overheating and ensure the safe and stable operation of photovoltaic power generation modules.

3. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 1 is characterized in that: The power management module determines the power distribution strategy based on the real-time power generation situation and the status of the energy storage module, controls the charging and discharging process of the power in the battery pack, optimizes the charging and discharging frequency and timing of the battery pack based on the prediction algorithm of the power generation fluctuation balancing submodule, and is connected to the gate control module to adjust the power distribution according to the load demand of the water service gate to ensure a stable supply of power required for gate operation.

4. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 1 is characterized in that: The prediction algorithm adopts the Bayesian structured time series BSTS model. The BSTS model predicts future power generation and load demand based on historical power generation data, environmental data and load demand data, and optimizes the charging and discharging strategy. The historical power generation data includes daily and hourly photovoltaic power generation records, the environmental data includes solar radiation intensity, temperature, humidity, wind speed, cloud cover, and rainfall meteorological data, and the load demand data includes the operation records of water gates, including opening and closing time, duration, and operation frequency. The BSTS model specifically includes: Preprocess historical power generation data, environmental data, and load demand data, including data cleaning, data normalization, and feature extraction, to build a data set; Model construction: Based on historical power generation data, environmental data and load demand data, a BSTS model is constructed. Combining Bayesian statistics and time series analysis, the time dependency in the data is captured through a state space model. Training and calibration: Use the collected historical data to train the BSTS model, perform parameter estimation and model calibration, and ensure that the model can reflect the changing patterns of power generation and load demand; Prediction: Use the calibrated BSTS model to predict future PV generation and load demand based on current and future environmental data; Scheduling is performed based on the charge and discharge controller, and the charge and discharge strategy of the battery pack is optimized according to the prediction results of the BSTS model.

5. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 4 is characterized in that: The BSTS model construction includes: State-space model: Observation equation: y t =Z t α t +∈ t , where y t is the observed value at time t, including photovoltaic power generation or load demand, Z t is the observation matrix, α t is the state vector, ∈ t is the observation noise; Equation of state: α t =T t α t-1 +R t η t , where T t is the transfer matrix, R t is the control matrix, η t is the state noise, usually assuming that η t ~N(0,Q); The observation equation and the state equation define the dynamic process of the time series, describing how it evolves from one time point to the next; Introducing Bayesian updating: By combining the prior distribution and the likelihood function, the posterior distribution of the model parameters is updated.

6. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 5 is characterized in that: The prior distribution is expressed as: p(θ)~N(μ0,∑0), where θ is a model parameter, μ0 and ∑0 are the prior mean and covariance matrix respectively; The likelihood function is expressed as: The posterior distribution is expressed as: p(θ|y t )∝p(y t |θ)p(θ), and continuously adjust the model parameters θ by combining historical data and current observation data through Bayesian updating.

7. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 6 is characterized in that: The prediction of future photovoltaic power generation and load demand specifically includes: Time series forecasting: Based on current and future environmental data, future photovoltaic power generation and load demand are forecasted. The forecasting formula is: in, is the predicted value for the next k steps, is the predicted value of the state vector based on time t; Prediction Error: Among them, MSE is the mean square error, which is used to evaluate the prediction accuracy of the model.

8. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 7 is characterized in that: The charging and discharging strategy includes: Charging plan: When the power generation is expected to be sufficient, arrange the charging plan of the battery pack in advance; Charging strategy: Among them, P ch arge is the charging power, P pv is the photovoltaic power generation power, P load is the load demand power, is the maximum charging power of the battery; Discharge plan: When power generation is expected to be insufficient, arrange the discharge plan of the battery group in advance; Discharge strategy: Among them, P disch arge is the discharge power, is the maximum discharge power of the battery; Dynamically adjust the charge and discharge frequency: optimize the service life of the battery pack by adjusting the charge and discharge frequency; Dynamic adjustment formula: f adjust =αf current +(1-α)f predicted , where f adjust is the adjusted charge and discharge frequency, f cutrrent is the current charge and discharge frequency, f predicted is the predicted charge and discharge frequency, α is the adjustment coefficient (0 <a<1)。 9. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 4 is characterized in that: The supercapacitor unit in the load fluctuation management submodule is connected in parallel with the battery pack in the energy storage module to quickly provide high power output when the load requires an instantaneous high current; the power electronic converter is connected to the supercapacitor unit and the battery pack to convert energy between different power sources; a dynamic load management controller is introduced and connected to the power management module to monitor the operating status and power demand of the water gate in real time and dynamically adjust the energy distribution between the supercapacitor unit and the battery pack; The charge and discharge controller is connected to the supercapacitor unit to charge the supercapacitor when the power demand is low.

10. The photovoltaic storage isolated grid power supply system for water gate power supply according to claim 9, characterized in that: The dynamic load management controller includes a real-time load demand P load , calculated as: P load =P base +P peak , where P base is the base load demand, P peak It is the part of instantaneous load demand increase; When the energy distribution meets the instantaneous high current demand, the energy distribution of the supercapacitor and the battery pack is calculated by the following formula: P total =P cap +P battery , where P total is the total load demand, P cap is the power provided by the supercapacitor, P battery is the power provided by the battery pack.

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