Method for regulating low-voltage distributed photovoltaic grid points

Abstract

The application relates to the field of photovoltaic power generation, and discloses a regulation and control method for a low-voltage distributed photovoltaic grid-connected point, which comprises the following steps: S1, collecting real-time data of photovoltaic array output power, photovoltaic array voltage, photovoltaic array current, power grid frequency and state of charge of an energy storage system based on a model predictive control framework; S2, calculating a control strategy by using a prediction model to minimize a performance index; and S3, adjusting a reference voltage and a power factor of an inverter according to an optimization result to realize optimal control of photovoltaic grid connection. By introducing the model predictive control framework, combining real-time data acquisition and a dynamic optimization strategy, and ensuring that the photovoltaic system can continuously track a maximum power point under various environmental conditions, the overall power generation efficiency of the photovoltaic system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation, specifically to a regulation method for low-voltage distributed photovoltaic grid-connected points. BACKGROUND

[0002] With the increasing global energy demand and the rising environmental protection awareness, the development of renewable energy technology has attracted more and more attention. Among them, solar photovoltaic power generation as a clean and renewable energy form has been widely used and rapidly developed. However, the widespread application of photovoltaic power generation systems has brought many technical challenges, especially in the regulation of low-voltage distributed photovoltaic grid-connected points.

[0003] Firstly, solar photovoltaic power generation is directly affected by light conditions, and its output has obvious intermittency and volatility. Cloud cover, day-night changes and seasonal changes will all cause significant fluctuations in photovoltaic output power. This instability brings huge dispatching pressure to the power grid, especially in areas with high photovoltaic power penetration, the power grid needs to have stronger adaptability to respond to the rapid changes of photovoltaic output. In addition, the grid-connected inverter is a key device for photovoltaic systems to access the grid, and its performance directly affects the grid quality and stability of the photovoltaic system. The traditional grid-connected inverter control method often only focuses on maximum power point tracking (MPPT), lacks real-time response capability to grid demand, and is difficult to ensure stable operation of the system under different operating conditions.

[0004] Secondly, after the distributed photovoltaic system is connected to the low-voltage distribution network, due to the instability of photovoltaic power generation, it may cause fluctuations in grid voltage and frequency, affecting power quality and grid stability. Voltage fluctuations and frequency deviations not only affect the user's electrical equipment, but also may cause the protection device of the power grid to malfunction, and in severe cases may even cause the collapse of the local power grid. The integration of energy storage systems plays an important role in distributed photovoltaic systems, which can effectively balance power generation and load demand, and improve the flexibility and stability of the system. However, how to efficiently manage the charging and discharging process of the energy storage system and optimize the use of the energy storage system to support the stable operation of the photovoltaic power generation system and the demand response of the power grid is still a problem to be solved.

[0005] Finally, with the increasing number of distributed photovoltaic systems, how to realize real-time monitoring, fault diagnosis and intelligent scheduling of these systems is the key to improving the efficiency and reliability of the system. Traditional monitoring methods often only provide basic device operation data, lack intelligent analysis and optimization functions, and are difficult to cope with complex and variable operating environments. The existing technology mainly exists problems such as insufficient response capability, lack of coordinated control and non-optimized energy management in the regulation of low-voltage distributed photovoltaic grid-connected points, and new technical means and methods are needed to solve these challenges and improve the overall performance of the photovoltaic system and the stability of the power grid. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a regulation method for low-voltage distributed photovoltaic grid-connected points, which introduces a model predictive control framework, combines real-time data acquisition and dynamic optimization strategy, and ensures that the photovoltaic system can continuously track the maximum power point under various environmental conditions, thereby improving the overall power generation efficiency of the photovoltaic system.

[0007] To achieve the above object, the present application is realized by the following technical scheme: a regulation method for low-voltage distributed photovoltaic grid-connected points, comprising the following steps:

[0008] Based on the model predictive control framework, real-time data of photovoltaic array output power P pv (t), photovoltaic array voltage V pv (t), photovoltaic array current I pv (t), grid frequency f grid (t) and state of charge SOC(t) of the energy storage system are collected;

[0009] A prediction model is used:

[0010] x t+1 =Ax t +Bu t +Ed t +w t

[0011] Wherein, x t is the system state vector, u t is the control input, d t is the external disturbance, A, B, E is the system parameter matrix, w t is the process noise;

[0012] A control strategy is calculated to minimize the performance index:

[0013]

[0014] Wherein, P mpp，t is the theoretical maximum power point, f nom is the nominal frequency of the grid, SOC target is the target energy storage state, and α1, α2, α3 are weight coefficients;

[0015] According to the optimization result, the reference voltage V ref，t and power factor PF t of the inverter are adjusted to realize the optimization control of photovoltaic grid connection.

[0016] Preferably, the external disturbance d t includes solar irradiance G sun，t and ambient temperature T amb，t, for adjusting the prediction of the photovoltaic system state vector x t in the following way:

[0017] Using the solar irradiance G sun，t and ambient temperature T amb，t data obtained from environmental sensors, in combination with the measured values of photovoltaic voltage V pv (t) and current I pv (t), the calculation of photovoltaic output power P pv，t in the prediction model is dynamically adjusted according to the temperature coefficient and irradiance coefficient of the photovoltaic cell:

[0018]

[0019] where P STC is the photovoltaic output power under standard test conditions, β temp is the temperature coefficient of the photovoltaic cell, T amb，t is the current ambient temperature, T STC is the temperature under standard test conditions, G sun，t is the current solar irradiance, G STC is the solar irradiance under standard test conditions.

[0020] Preferably, the external disturbance d t modifies the update of the state vector x t by affecting the system parameter matrix E, where the specific value of E is dynamically adjusted based on historical data statistics and real-time environmental feedback.

[0021] Preferably, the system state vector x t includes light intensity and temperature data collected from environmental sensors, which are used to update the system parameter matrices A, B, E in the prediction model in real time to adapt to environmental changes and optimize the performance of the photovoltaic system, where the parameter adjustment is based on regression analysis between real-time data and historical data.

[0022] Preferably, the method further comprises solving the MPC problem using a nonlinear programming solver, where the solver is selected from IPOPT or SQP.

[0023] Preferably, the control input u t also includes charge and discharge control instructions for the energy storage system, which achieve optimal energy distribution by optimizing the predicted value of the state of charge SOC of the energy storage system, and the optimization is specifically performed by the following formula:

[0024]

[0025] where η charge and η discharge are the charging and discharging efficiencies, respectively, and Pcharge and P discharge are the charging and discharging power, respectively, and Δt is the time interval.

[0026] Preferably, the control strategy is updated at the beginning of each control period based on the current demand of the grid and the predicted external conditions, and the updating operation includes recalculating the weight factors α1, α2, α3 in the optimization objective J(u) to adapt to the real-time demand changes of the grid.

[0027] Preferably, the voltage and current constraints are set to ensure the safe operation of the photovoltaic system, specifically:

[0028] V pvmin ≤V pv (t)≤V pv，max

[0029] I pvmin ≤I pv (t)≤I pv，max

[0030] where V pv，min and V pv，max are the minimum and maximum voltage limits allowed by the inverter, I pv，min and I pv，max are the minimum and maximum current limits allowed.

[0031] Preferably, the optimization of the state of charge SOC(t) of the energy storage system takes into account the predicted photovoltaic power generation and the expected grid load, and the optimization strategy is adjusted through the following model:

[0032] SOC t+1 = SOC t + η·(P pv，t -P grid，t )·Δt

[0033] where η is the energy conversion efficiency, P pv，t is the photovoltaic power generation, P grid，t is the grid-connected output power, and Δt is the time interval.

[0034] The present application also provides a regulating device for low-voltage distributed photovoltaic grid-connected points, comprising:

[0035] A data acquisition module configured with multiple sensors for real-time collection of the output power of the photovoltaic array, the photovoltaic array voltage, the photovoltaic array current, the grid frequency, and the state of charge of the energy storage system, as well as external environmental conditions such as solar radiation and ambient temperature;

[0036] A prediction model processing module containing one or more microprocessors configured with algorithms to execute the prediction model;

[0037] An optimization control module for calculating a control strategy to minimize a performance index;

[0038] An inverter control module for receiving the output of the optimization control module and adjusting the reference voltage and power factor of the inverter accordingly to adapt to the requirements of photovoltaic grid connection.

[0039] The present application provides a regulation method for low-voltage distributed photovoltaic grid connection points. It has the following advantages:

[0040] 1. The present application introduces a model predictive control (MPC) framework, combined with real-time data acquisition and dynamic optimization strategy, to ensure that the photovoltaic system can continuously track the maximum power point (MPPT) under various environmental conditions, thereby improving the overall power generation efficiency of the photovoltaic system.

[0041] 2. The present application dynamically adjusts the output power and power factor of the inverter, so that the photovoltaic system can respond to the real-time requirements of the power grid, provide frequency regulation and voltage support, significantly improve the frequency stability and power quality of the power grid, and reduce the impact of power grid fluctuations on system operation.

[0042] 3. The present application adopts a SOC optimization strategy based on real-time and predicted data, the system can intelligently adjust the charging and discharging process of the energy storage device, ensure efficient use of energy, balance photovoltaic power generation and grid load demand, and improve the utilization rate and economy of the energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 It is one of the method flowcharts of the present application;

[0044] Figure 2 It is a schematic diagram of the device structure of the present application.

[0045] Among them, 100, data acquisition module; 200, prediction model processing module; 300, optimization control module; 400, inverter control module. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Please refer to the drawings of the present application Figure 1 The embodiments of the present application provide a regulation method for low-voltage distributed photovoltaic grid connection points, which uses a model predictive control (MPC) framework to achieve efficient energy management and optimization control between the photovoltaic system and the power grid.

[0048] Specifically, the method includes the following steps:

[0049] S1, Real-time data acquisition

[0050] Firstly, through the data acquisition module, real-time collection of key parameters of the photovoltaic system, including:

[0051] Photovoltaic array output power P pv (t): the current power generation of the photovoltaic system.

[0052] Photovoltaic array voltage V pv (t): the working voltage of the photovoltaic panel.

[0053] Photovoltaic array current I pv (t): the working current of the photovoltaic panel.

[0054] Grid frequency f grid (t): the frequency of the current grid.

[0055] State of charge SOC(t) of the energy storage system: the current power state of the energy storage device.

[0056] These data are obtained in real time through the sensor network and the data acquisition system, and are transmitted to the central control unit. These data are the basis for subsequent prediction model and control strategy calculation, ensuring that the system can respond to environmental changes and grid demands in real time.

[0057] S2, Prediction model and control strategy calculation

[0058] Using the model predictive control (MPC) framework, a dynamic prediction model of the photovoltaic system is established, as follows:

[0059] x t+1 = Ax t + Bu t + Ed t + w t

[0060] Where:

[0061] x t is the system state vector, including P pv (t), V pv (t), I pv (t), f grid (t), and SOC(t);

[0062] u t is the control input, including the inverter reference voltage V ref，t and the power factor PF t ;

[0063] d tis the external disturbance, including solar irradiance G sun，t and ambient temperature T amb，t ;

[0064] AB, E is the system parameter matrix, obtained through historical data and system identification;

[0065] w t is the process noise, used to represent the uncertainty of the system.

[0066] On this basis, the control strategy is calculated to minimize the performance index by solving the following optimization problem:

[0067]

[0068] Where:

[0069] P mppt is the theoretical maximum power point, predicted by the MPPT algorithm;

[0070] f nom is the grid nominal frequency, usually 50Hz or 60Hz;

[0071] SOC target is the target energy storage state, generally set between 50%-80% to maintain the optimal working state of the battery;

[0072] α1, α2, α3 are weight coefficients, used to balance the importance of each control objective.

[0073] Through this step, the system can predict the future state change and optimize the current control input to achieve high efficiency energy management and grid stability at the same time.

[0074] S3, inverter adjustment and optimization control

[0075] According to the calculation results of the optimization control strategy in step S2, the working parameters of the inverter are adjusted in real time:

[0076] Inverter reference voltage V ref，t : According to the optimization results, adjust the working voltage of the photovoltaic array to make it as close as possible to the maximum power point voltage, to ensure that the photovoltaic system operates at the highest efficiency.

[0077] Power factor PF t : According to the grid demand, dynamically adjust the power factor of the inverter to optimize power quality and grid stability.

[0078] By adjusting the reference voltage and power factor of the inverter, the system can respond to the instantaneous demand of the grid in real time, maintain the stability of the grid frequency and the quality of the power.

[0079] The invention provides an efficient low-voltage distributed photovoltaic grid-connected point regulation method by introducing a model predictive control (MPC) framework, combining real-time data acquisition and dynamic optimization strategy. This method not only improves the power generation efficiency of photovoltaic system, but also enhances the stability and energy management capability of power grid, with wide application prospect and significant practical benefit. Through this integrated control method, photovoltaic system can better adapt to environmental changes and grid demand, achieving higher energy utilization rate and system reliability.

[0080] In a preferred embodiment of the invention, the operating state of the photovoltaic system is adjusted by changes in external environmental factors to improve its power generation efficiency and better adapt to changing environmental conditions. By accurately measuring external disturbances such as solar irradiance and ambient temperature, and using these data to dynamically adjust the photovoltaic output power in the prediction model, this embodiment can effectively optimize the performance of the photovoltaic system.

[0081] In this embodiment, the external disturbance d t includes solar irradiance G sun，t and ambient temperature T amb，t , which are the main factors affecting the output performance of photovoltaic cells, so accurate monitoring of these parameters is crucial for optimizing the performance of the photovoltaic system. The adjustment method includes:

[0082] 1. Data acquisition

[0083] Real-time measurement of solar irradiance G sun，t and ambient temperature T amb，t using environmental sensors. These sensors are deployed near the photovoltaic array to ensure the accuracy and real-time nature of the data, providing reliable input for the control system.

[0084] 2. Adjust the photovoltaic output power in the prediction model

[0085] Using the collected solar irradiance and ambient temperature data, combined with the measured values of photovoltaic voltage V pv (t) and current I pv (t), the calculation formula of photovoltaic output power is dynamically adjusted as follows:

[0086]

[0087] Where:

[0088] P STC is the photovoltaic output power under standard test conditions;

[0089] β temp is the temperature coefficient describing the effect of temperature change on photovoltaic output power;

[0090] T ambt is the current ambient temperature;

[0091] T STC is the temperature of standard test conditions, usually set to 25℃;

[0092] G sunt is the current solar irradiance;

[0093] G STC is the solar irradiance under standard test conditions, usually set to 1000 W / m 2 .

[0094] 3. Optimized dynamic adjustment

[0095] This dynamic adjustment method allows the photovoltaic system to adjust its output according to real-time environmental conditions, thereby optimizing performance under different environments. By adjusting model parameters in real time to adapt to changes in external environment, the system can more effectively respond to factors such as cloud coverage, temperature changes, etc., maximizing power generation efficiency.

[0096] By introducing a dynamic adjustment mechanism for external disturbance factors, this embodiment not only improves the efficiency and output stability of the photovoltaic system, but also enhances the system's adaptability to environmental changes, achieving efficient and sustainable energy management.

[0097] In a preferred embodiment of the invention, the external disturbance d t influences the system parameter matrix E to dynamically adjust the prediction model, thereby optimizing the update of the state vector x t of the photovoltaic system. This method effectively combines real-time environmental data and historical statistical analysis to achieve more accurate and adaptive photovoltaic output control. The dynamic adjustment process is as follows:

[0098] According to the data obtained from real-time environmental sensors (solar irradiance and environmental temperature), as well as statistical analysis through historical data, the value of the E matrix is dynamically adjusted. This adjustment is based on regression analysis and pattern recognition of historical data to identify the specific impact of environmental variables on photovoltaic performance.

[0099] By adjusting the value of E in real time through environmental feedback, the model can reflect the specific impact of environmental changes on the photovoltaic system in real time, thereby improving the accuracy of the prediction and the response speed of the system.

[0100] The embodiment of the invention provides a highly adaptive method by combining external environmental disturbances with dynamic adjustment of the system parameter matrix E, to adjust the operation of the photovoltaic system in real time, optimize performance and respond to environmental changes. This method not only enhances the economic efficiency and reliability of the system, but also improves the overall efficiency of energy management. By regulating the E matrix, the invention can ensure that the photovoltaic system always maintains optimal operating conditions under varying environmental conditions.

[0101] In a preferred embodiment of the invention, a process is described for dynamically adjusting the system parameter matrices A, B, E, which are directly used in the predictive model controlling the photovoltaic system. By collecting environmental sensor data in real-time and combining these data with historical data for regression analysis, the system can adapt to environmental changes and optimize performance. The specific steps are as follows:

[0102] 1. Definition of system state vector and data collection

[0103] In this embodiment, the system state vector x t includes the light intensity G sun，t and the ambient temperature T amb，t , which are collected by environmental sensors in real time. Light intensity and temperature are the main environmental factors affecting the performance of photovoltaic cells, and monitoring these variables is the basis for adjusting the parameters of the predictive model.

[0104] 2. Dynamic adjustment of parameter matrices A, B, E

[0105] Integrate the data collected in real time with historical data. Historical data includes system performance data under the same environmental conditions, such as photovoltaic output power, system efficiency, etc.

[0106] Use statistical and machine learning techniques (such as linear regression, support vector machines or neural networks) to establish regression models between environmental factors and system performance. These models evaluate the relationship between variables in historical and real-time data to determine the optimal values of parameter matrices A, B, E.

[0107] According to the results of regression analysis, dynamically update the parameter matrices A, B, E in the predictive model. For example, if the analysis shows that an increase in temperature leads to a decrease in output power, adjust the matrix E accordingly to compensate for this effect in the model.

[0108] The predictive model adjusts the control strategy in real time according to the new parameter matrices to maximize output or optimize other performance indicators.

[0109] Through the above steps, the photovoltaic system can dynamically adjust its operation strategy according to the actual environmental conditions, achieving higher energy efficiency and more stable performance. This method enables the system to adapt to rapidly changing environmental conditions, such as fluctuations in light intensity caused by cloud cover and the impact of daily temperature differences on system efficiency.

[0110] By implementing this data-driven dynamic parameter adjustment method, the photovoltaic system can more accurately match environmental conditions, improving energy conversion efficiency and reducing energy loss, while increasing the reliability and economic benefits of the system. In addition, this method provides a highly adaptive operation mechanism for photovoltaic systems, enabling them to maintain optimal performance in a changing environment.

[0111] In one preferred embodiment of the invention, the solution process for the Model Predictive Control (MPC) problem is detailed, particularly the application of a nonlinear programming solver to implement the optimization of the control strategy. This embodiment highlights specifically the solvers employed within the MPC framework, such as IPOPT (Interior Point OPTimizer) or SQP (Sequential Quadratic Programming), which are used to handle the complex photovoltaic system control problem.

[0112] The solution process is as follows:

[0113] Model establishment: Establish a mathematical model representing the dynamic behavior of the photovoltaic system, including state transition equations and external disturbance effects.

[0114] Optimization execution: Use the IPOPT or SQP solver to solve the MPC problem. The solver needs to handle the nonlinear relationships and dynamic changes in the model, approaching the global optimal solution through iterative methods.

[0115] Real-time update: At each MPC control period, update the model parameters based on the latest system state and environmental data, and re-execute the optimization to adapt to possible environmental changes and system disturbances.

[0116] In summary, this embodiment provides an effective method for dynamic management and optimization of photovoltaic systems by integrating advanced mathematical optimization techniques, contributing to the overall efficiency and reliability of the system.

[0117] In one preferred embodiment of the invention, energy management is optimized by dynamically adjusting the charge and discharge control instructions of the energy storage system. This process not only involves accurate prediction of the state of charge (SOC) of the energy storage system, but also includes effective energy distribution strategies to ensure maximum energy efficiency of the photovoltaic system and grid stability.

[0118] The definition of control input and the optimization process are as follows:

[0119] Control input u t Including the charge and discharge control instructions of the energy storage system, which are adjusted based on the prediction of the state of charge (SOC) of the energy storage system. Specifically, the control instructions determine how much energy should be injected into the energy storage system (charging) or extracted from the energy storage system (discharging) in each time interval.

[0120] The dynamic adjustment formula of the SOC of the energy storage system is as follows:

[0121]

[0122] Where:

[0123] η charge and η discharge are the charging and discharging efficiencies, respectively;

[0124] P charge and P discharge are the charging and discharging powers, respectively;

[0125] Δt is the time interval, typically the length of the control period.

[0126] The energy optimization strategy is as follows:

[0127] 1. Decision on energy input and output:

[0128] Based on the real-time generation capacity of the photovoltaic system and the demand of the grid, calculate the required charging or discharging power. Increase charging power when sunlight is abundant, and increase discharging power when demand peaks or sunlight is insufficient.

[0129] 2. Optimize the predicted value of SOC:

[0130] Use advanced prediction algorithms (such as time series analysis, machine learning models) to predict future changes in SOC, so as to achieve optimal use of energy without affecting system stability and battery health.

[0131] 3. Implement dynamic adjustment:

[0132] Based on the prediction results and real-time data, dynamically adjust the charging and discharging instructions to adapt to rapidly changing market demand and environmental conditions. For example, reduce night-time discharging when predicting abundant sunlight the next day, or increase energy storage utilization when predicting overcast days.

[0133] Through highly integrated control strategies and advanced prediction technologies, the performance of photovoltaic systems in energy management and system efficiency can be significantly improved.

[0134] In a preferred embodiment of the invention, by periodically updating the control strategy, especially the weight factors α1, α2, α3 in the optimization target J(u), the real-time demand of the grid and the changes in external environmental conditions are adapted. This process ensures that the photovoltaic system not only responds to immediate grid demand, but also adapts to environmental changes, optimizing system performance.

[0135] The process of updating the control strategy is as follows:

[0136] At the beginning of each control period, the system evaluates the current grid demand, environmental conditions such as weather changes, and predicts future trends over a period of time. The length of the control period can be set according to the dynamic response ability of the system and the flexibility of the grid operation, usually ranging from a few minutes to a few hours.

[0137] Weight factors:

[0138] α1: controls the weight of the difference between photovoltaic output power and the theoretical maximum power point, optimizing photovoltaic power generation efficiency.

[0139] α2: controls the weight of grid frequency stability, responding to grid demand and frequency regulation service.

[0140] α3: controls the optimization weight of the state of charge (SOC) of the energy storage system, managing energy storage and release.

[0141] Data-driven weight adjustment:

[0142] Data collection: the system collects and analyzes data about grid demand, weather forecast, photovoltaic performance and energy storage status.

[0143] Prediction model: use data prediction model (may include machine learning algorithm) to predict the grid demand and photovoltaic output change in the short term.

[0144] Weight calculation: based on the prediction results and real-time data of the grid, dynamically adjust the weight factor. For example, in the case of high grid load prediction, α2 may be increased to emphasize the importance of frequency stability.

[0145] Recalculation of optimization target:

[0146] According to the new weight factor and system state, recalculate the control strategy to ensure the realization of the optimization target in the new control cycle.

[0147] The embodiment of the present application provides a flexible method to adapt to the rapidly changing grid and environmental conditions, by intelligently adjusting the control strategy, to ensure the optimal operation of the photovoltaic system and the stability of the grid.

[0148] In a preferred embodiment of the present application, the safe operation of the photovoltaic system is ensured by setting constraints on voltage and current. This constraint setting is critical because it helps to prevent the photovoltaic system from exceeding the safe working parameters of its electrical equipment during operation, thereby avoiding potential equipment damage and safety risks.

[0149] The constraints of voltage and current are set to ensure the safe operation of the photovoltaic system, specifically:

[0150] V pvmin ≤V pv (t)≤V pv，max

[0151] I pvmin ≤I pv (t)≤I pv，max

[0152] Where, V pv，min and V pv，maxis the minimum and maximum voltage limit allowed by the inverter, I pv，min and I pv，max are the minimum and maximum current limit allowed.

[0153] This embodiment not only emphasizes the safety of photovoltaic system operation, but also improves the overall efficiency and reliability of the system. By precisely controlling the range of voltage and current, it ensures the long-term operation of the photovoltaic system in the best state, while minimizing maintenance costs and potential failure risks.

[0154] In a preferred embodiment of the invention, the state of charge (SOC) of the energy storage system is optimized by predicting photovoltaic power generation and grid load. This strategy adjusts the SOC through a quantitative model to adapt to changes in energy supply and demand, ensuring system efficiency and stability.

[0155] The state of charge (SOC) update model of the energy storage system is defined as:

[0156] SOC t+1 = SOC t + η·(P pv，t -P grid，t )·Δt

[0157] Where:

[0158] SOC t is the state of charge of the energy storage system at time t.

[0159] η is the energy conversion efficiency, taking into account the loss of energy during conversion.

[0160] P pvt is the photovoltaic power generation at time t.

[0161] P gridt is the grid-connected output power at time t, i.e. the actual amount of electricity sent to the grid.

[0162] Δt is the time interval, indicating the length of the control period.

[0163] This model determines whether the energy storage system should be charged or discharged in each control period by comparing photovoltaic power generation with grid-connected output power:

[0164] If P pv，t is greater than P grid，t , it indicates that photovoltaic power generation exceeds the current grid demand, and the excess electricity can be used to charge the energy storage system.

[0165] Conversely, if P pv，t is less than P grid，t , electricity needs to be taken from the energy storage system to meet the additional demand of the grid.

[0166] The SOC management model enables the photovoltaic power generation system to interact with the power grid more effectively, realizes optimal distribution of energy, and enhances the overall performance and reliability of the system.

[0167] The control device for the low-voltage distributed photovoltaic grid-connected point described below can be correspondingly referred to the control method for the low-voltage distributed photovoltaic grid-connected point described above.

[0168] Please refer to the accompanying Figure 2 The application also provides a control device for a low-voltage distributed photovoltaic grid-connected point, comprising:

[0169] The data acquisition module 100 is configured with a plurality of sensors for real-time collection of the output power of the photovoltaic array, the photovoltaic array voltage, the photovoltaic array current, the grid frequency, and the state of charge of the energy storage system, as well as external environmental conditions such as solar radiation and ambient temperature;

[0170] The prediction model processing module 200 comprises one or more microprocessors and is configured with an algorithm to execute a prediction model;

[0171] The optimization control module 300 is used to calculate a control strategy to minimize the performance index;

[0172] The inverter control module 400 is used to receive the output of the optimization control module and adjust the reference voltage and power factor of the inverter accordingly to adapt to the needs of photovoltaic grid connection.

[0173] The device of the embodiment can be used to execute the method embodiments described above, and has similar principles and technical effects, which will not be described here again.

[0174] Although embodiments of the application have been shown and described, it is to be understood that the application is not limited to these embodiments. It will be obvious to a person skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirit of the application, and the scope of the application is defined by the appended claims and their equivalents.

Claims

1. A control method for low-voltage distributed photovoltaic grid-connected points, characterized in that, Includes the following steps: Based on a model predictive control framework, the output power of the photovoltaic array is collected. Photovoltaic array voltage Photovoltaic array current Grid frequency State of charge of energy storage system and solar radiation and ambient temperature Real-time data; Using predictive models: in, It is the system state vector, which includes the output power of the photovoltaic array. Photovoltaic array voltage Photovoltaic array current Grid frequency State of charge of energy storage systems ; It is the control input, which includes the inverter reference voltage. and power factor ; It is an external disturbance, including solar radiation. and ambient temperature ; It is the system parameter matrix. It is process noise; Construct and solve to minimize the following performance metrics The goal is to solve an optimization problem to obtain the optimal inverter reference voltage. and power factor Optimal control input : in, It is the theoretical maximum power point. It is the nominal frequency of the power grid. It is the target energy storage state. These are weighting coefficients; According to the optimal control input Adjust the inverter's reference voltage and power factor This is to achieve optimized control of photovoltaic grid connection.

2. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 1, characterized in that, The external disturbance Including solar radiation and ambient temperature Used to adjust the state vector of the photovoltaic system The prediction is adjusted as follows: Solar irradiance obtained using environmental sensors and ambient temperature Data, combined with photovoltaic voltage and current The measured values ​​are used to dynamically adjust the photovoltaic output power in the prediction model according to the temperature coefficient and irradiance coefficient of the photovoltaic cells. Calculation: in, This is the photovoltaic output power under standard test conditions. It is the temperature coefficient of photovoltaic cells. This is the current ambient temperature. The temperature is under standard test conditions. This is the current solar irradiance. This is the solar irradiance under standard test conditions.

3. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 2, characterized in that, The external disturbance By influencing the system parameter matrix To modify the state vector The update, in which The specific value is dynamically adjusted based on historical data statistics and real-time environmental feedback.

4. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 1, characterized in that, The method also includes using a nonlinear programming solver to solve the MPC problem, wherein the solver is selected from IPOPT or SQP.

5. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 1, characterized in that, The control input It also includes charging and discharging control commands for the energy storage system. These commands optimize energy allocation by optimizing the predicted state of charge (SOC) of the energy storage system. The specific optimization is performed using the following formula: in, and These refer to the charging and discharging efficiencies, respectively. and These are the charging and discharging power, respectively. It is a time interval.

6. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 1, characterized in that, At the beginning of each control cycle, the control strategy is updated based on the current demand of the power grid and the predicted external conditions. The update operation includes recalculating the optimization objective. Weighting factors To adapt to real-time changes in the power grid's demand.

7. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 1, characterized in that, The voltage and current constraints are set to ensure the safe operation of the photovoltaic system, specifically: in, and These are the minimum and maximum voltage limits allowed by the inverter. and These are the minimum and maximum allowable current limits.

8. The regulation method for low-voltage distributed photovoltaic grid-connected points according to claim 1, characterized in that, The state of charge of the energy storage system The optimization takes into account the predicted photovoltaic power generation and the expected grid load, and the optimization strategy is adjusted through the following model: in, It is energy conversion efficiency. It is photovoltaic power generation. It is the grid-connected output power. It is a time interval.

9. A control device for low-voltage distributed photovoltaic grid-connected points, used to implement the method as described in any one of claims 1-8, characterized in that, include: The data acquisition module is equipped with multiple sensors to collect data in real time, including the output power of the photovoltaic array, the voltage of the photovoltaic array, the current of the photovoltaic array, the grid frequency, the state of charge of the energy storage system, and external environmental conditions such as solar irradiance and ambient temperature. The prediction model processing module contains one or more microprocessors configured with algorithms to execute the prediction model; The optimization control module is used to calculate the control strategy to minimize performance metrics; The inverter control module receives the output from the optimization control module and adjusts the inverter's reference voltage and power factor accordingly to meet the requirements of photovoltaic grid connection.

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