Plateau light storage integrated power conversion system

Abstract

This invention relates to the field of clean energy utilization technology in high-altitude and other extreme environments, specifically to a high-altitude integrated photovoltaic-storage power conversion system. The system comprises a photovoltaic power generation module, an energy storage battery module, a power conversion module, and a load connected in sequence. It also includes a control and communication module that adjusts the operating status of the power conversion module by real-time monitoring of the photovoltaic power generation module, the energy storage battery module, and the load. The control and communication module is connected to the photovoltaic power generation module, the energy storage battery module, and the load, respectively. Under power output and energy balance conditions, the control and communication module achieves optimized power scheduling using a maximum power point tracking (MPPT) method with an adaptive adjustment mechanism. This invention significantly improves the system's energy conversion efficiency, operational stability, and environmental adaptability through the organic combination of multimodal energy scheduling, an improved MPPT algorithm, and modular design, making it particularly suitable for the complex and variable climate conditions of high-altitude regions.

Description

Technical Field

[0001] This invention relates to the field of clean energy utilization technology in plateau and other extreme environments, specifically to a plateau photovoltaic-storage integrated power conversion system. Background Technology

[0002] With the global energy structure transformation and the rapid development of renewable energy technologies, the application of photovoltaic (PV) power generation systems in plateau regions is becoming increasingly widespread. However, the unique geographical environment and climate conditions of plateaus pose significant challenges to the stable operation of PV systems. Plateau regions are characterized by high solar intensity, large temperature differences, and low air pressure, all of which significantly affect the power generation efficiency and stability of PV systems. Traditional PV system design and control strategies often fail to achieve ideal performance in plateau environments, particularly in terms of power output stability and energy conversion efficiency. Furthermore, the relatively weak power grid infrastructure and unstable power supply in plateau regions necessitate PV systems with stronger adaptive capabilities and energy storage functions. However, existing integrated PV-energy storage systems often exhibit low energy dispatch efficiency and poor power point tracking accuracy when facing the complex and variable environment of plateaus. Especially under extreme weather conditions, the reliability and stability of the system are difficult to guarantee, severely hindering the promotion and application of renewable energy in plateau regions.

[0003] Existing energy dispatch strategies are mainly designed for photovoltaic systems in plains areas, without fully considering the special characteristics of plateau environments. Traditional methods often react slowly to rapid changes in plateau environments, failing to achieve efficient coordination between photovoltaic power generation and energy storage systems, resulting in low energy utilization efficiency. Existing multimodal energy dispatch technologies have the following limitations in plateau photovoltaic-energy storage integrated systems: (1) Dispatch strategies lack specific consideration for plateau environmental characteristics. Most dispatch algorithms are designed based on photovoltaic systems in plains areas, failing to fully consider the characteristics of plateau areas such as drastic changes in light intensity and large temperature differences, resulting in low dispatch efficiency in practical applications. (2) Energy allocation models are overly simplified. Existing technologies often use linear or simple nonlinear models to describe the energy flow of photovoltaic power generation and energy storage systems, making it difficult to accurately characterize the dynamic characteristics of the system under complex plateau environments, affecting the accuracy of dispatch decisions. (3) Dispatch algorithms lack adaptability. Traditional dispatch strategies have limited adaptability to changes in system parameters and external disturbances, making it difficult to cope with the rapid changes in climate conditions in plateau areas, easily leading to unreasonable energy allocation and decreased system stability.

[0004] Furthermore, traditional MPPT algorithms perform poorly in extreme high-altitude environments, making it difficult to accurately and quickly track the maximum power point. Existing algorithms do not adequately consider special high-altitude factors such as large temperature variations and low air pressure, leading to unstable power output and low system efficiency in practical applications. Currently, the application of MPPT algorithms in extreme high-altitude environments still faces the following challenges: (1) Tracking accuracy is significantly affected by the environment. Traditional MPPT algorithms are prone to problems such as large tracking deviations and slow convergence speeds in special high-altitude environments such as sudden temperature changes and low air pressure, affecting the power generation efficiency of photovoltaic systems. (2) Insufficient algorithm robustness. Existing MPPT algorithms have poor adaptability to complex operating conditions such as partial shading and temperature gradients, and are prone to getting trapped in local optima, resulting in unstable power output. (3) Lack of collaborative optimization with energy dispatch. Most MPPT algorithms operate independently of the energy dispatch system. They fail to fully utilize the regulation capabilities of energy storage systems, making it difficult to achieve globally optimal power tracking and energy allocation.

[0005] Currently, research on modular design of integrated photovoltaic and energy storage systems in high-altitude areas is relatively lagging. Existing systems generally suffer from poor scalability and maintenance difficulties, making it difficult to meet the flexible application requirements under different power demands and environmental conditions in high-altitude regions. The existing modular design of integrated photovoltaic and energy storage systems in high-altitude areas has the following shortcomings: (1) Low standardization of interfaces between modules. The interface design between different functional modules lacks a unified standard, resulting in limited system scalability and maintainability, making it difficult to adapt to different power demands and environmental conditions. (2) Insufficient flexibility of power conversion modules. Traditional power conversion structures often adopt fixed topologies, making it difficult to flexibly adjust to the special needs of high-altitude environments, thus limiting the adaptability of the system. (3) Lack of intelligent module management mechanisms. Existing systems generally lack the ability to monitor and intelligently control each functional module in real time, making it difficult to achieve collaborative optimization and fault self-diagnosis between modules.

[0006] Plateau regions possess unique geographical and climatic characteristics, such as high altitude, low air pressure, drastic temperature variations, and high solar radiation intensity. These characteristics pose significant challenges to the performance and stability of photovoltaic systems. First, the lower air pressure and reduced air density in plateau regions affect cooling efficiency, leading to a decline in the heat dissipation performance of photovoltaic modules and power conversion devices.

[0007] Secondly, the temperature in high-altitude areas fluctuates greatly, with significant diurnal temperature variations. These drastic temperature changes cause thermal expansion and contraction of photovoltaic modules, increasing material fatigue and aging rates, thus affecting the system's lifespan and reliability.

[0008] In addition, the solar intensity in the plateau region is high, but the lighting conditions change frequently and are unstable. Summary of the Invention

[0009] This invention aims to address the stability and energy conversion efficiency issues of photovoltaic power generation and energy storage systems in high-altitude areas under extreme environments. Due to the unique geographical and climatic conditions such as high altitude, low air pressure, and large temperature differences in high-altitude areas, traditional photovoltaic systems face problems such as unstable power output and low energy dispatch efficiency, which restricts their application and promotion in these regions. Therefore, this invention proposes a high-altitude photovoltaic-energy storage integrated power conversion system based on multimodal energy dispatch and an improved power point tracking algorithm to optimize energy distribution and improve the overall stability of the system.

[0010] This invention discloses a high-altitude photovoltaic-storage integrated power conversion system, comprising the following components connected in sequence: A photovoltaic power generation module is used to convert solar energy into direct current (DC) power and output the DC power to an energy storage battery module. Energy storage battery module, used to charge or discharge after receiving DC power; A power conversion module is used to convert the DC power in the energy storage battery module into the AC power required by the load, or to convert the AC power of the load back into DC power. and load; It also includes a control and communication module, which adjusts the operating status of the power conversion module by real-time monitoring of the photovoltaic power generation module, the energy storage battery module, and the load; the control and communication module is connected to the photovoltaic power generation module, the energy storage battery module, and the load respectively; Under power output and energy balance conditions, the control and communication module combines the Maximum Power Point Tracking (MPPT) method with an adaptive adjustment mechanism. This method dynamically optimizes the power tracking rate by adjusting the adaptive step size, rapidly responding to changes in photovoltaic power generation and maintaining power output stability in the extreme environment of high altitudes. It selects the operating state of the power conversion module to achieve optimized power scheduling with the goal of minimizing power loss and maximizing power conversion efficiency. The operating states of the power conversion module include the following three: 1) Photovoltaic priority mode: When the photovoltaic power generation capacity is sufficient, photovoltaic power generation is used first to meet the load demand, and the remaining power is used to charge the energy storage battery; 2) Energy storage priority mode: When the photovoltaic power generation is insufficient to meet the load demand, the system will draw power from the energy storage battery to supplement the load demand; 3) Grid-connected mode: When neither the photovoltaic nor the energy storage system can meet the load demand, the system draws power from the grid to ensure stable load operation; The MPPT method includes adaptive step size adjustment and dynamic adjustment mechanisms: Adaptive step size adjustment: Based on the environmental conditions of photovoltaic power generation, such as irradiance and temperature changes, the search step size of the MPPT algorithm is dynamically adjusted. By reducing the search step size, the tracking accuracy is improved, and when there are large changes, the step size is increased to quickly converge to the target power point. Dynamic adjustment mechanism: Real-time monitoring of the difference between photovoltaic power generation and load demand, and dynamic adjustment of the objective function weights to minimize power loss and maximize response speed, ensuring that the system can quickly achieve power balance in complex environments.

[0011] Preferably, the control and communication module selects the operating state of the power conversion module to achieve power optimization scheduling under the conditions of power output constraints and energy balance, with the goal of minimizing power loss and maximizing power conversion efficiency: An energy dispatch framework based on MPC model predictive control is constructed. In the MPC model, a dynamically adjustable time step parameter is introduced to control the computational granularity and response speed of the prediction model. The step size is optimized and adjusted according to the dynamic characteristics of the photovoltaic power generation system to balance prediction accuracy and real-time response capability. The specific process is as follows: 1) Status monitoring and data acquisition: Real-time monitoring of photovoltaic power generation, energy storage capacity, and load demand using sensors in the control and communication modules; 2) State Prediction: The MPC model is used to predict the photovoltaic power generation, load demand, and energy storage capacity at N future time points. During the state prediction process, the dynamic time step Δt is used to determine the prediction range for the next N time points. Since a smaller step size helps improve the prediction accuracy, it is suitable for scenarios with drastic load changes; a larger step size can improve the system's computational efficiency and stability, and is suitable for scenarios with relatively stable loads. In the plateau photovoltaic-storage system, the step size adjustment is optimized in real time according to the fluctuation of photovoltaic irradiance and changes in load demand. 3) Solving the optimization problem: Based on the predicted state at each time step, the energy dispatch optimization is performed using the MPC model to determine the optimal control decisions for the future time period, including the utilization of photovoltaic power generation, the charging and discharging of energy storage, and the dispatch of grid power. Based on the optimized step size parameters, the MPC model can perform energy dispatch at different time scales to ensure that the optimal control strategy for photovoltaic power generation, energy storage battery charging and discharging, and grid-connected power allocation is achieved within the future prediction range. 4) Execute control actions: Based on the optimal control strategy obtained from the MPC model, adjust the power distribution in the system in real time to ensure that the load demand is met and the system stability is maintained; 5) Rolling prediction and adjustment: Repeated state monitoring and optimization at each time point, rolling updates of scheduling decisions to ensure that the system adapts to dynamically changing environmental conditions; The method for predicting future photovoltaic power generation, load demand, and energy storage capacity using the MPC model is as follows: Predicting photovoltaic power generation: Using linear regression or historical trend inference methods, predict photovoltaic power generation based on historical data and current sunshine conditions; Predicting load demand: Based on past load usage data, linear predictions are made in conjunction with actual conditions; Energy storage capacity: Based on the current state of the energy storage battery, predict its future charge and discharge states; The following constraints are set during prediction: Constraints on the capacity of energy storage batteries: The battery capacity must operate within a safe range, and overcharging or over-discharging should be avoided; Power output and energy balance conditions: The power supply of the system must be in balance with the load demand.

[0012] Preferably, the step size in the MPC model is a dynamically predicted step size. During the calculation of the dynamically predicted step size, state feedback is used to correct the range of step size changes in real time. When the system detects that the light intensity or load change rate ΔW exceeds the threshold, the state feedback mechanism optimizes the step size range by adjusting the β parameter, ensuring that the predicted step size adapts to the dynamic changes of the system, while avoiding excessive computational overhead and slow response speed caused by too small a step size. ; in, The initial step size, To adjust the coefficient, The rate of change of light intensity or load.

[0013] Preferably, in the process of constructing an energy dispatch framework based on MPC model predictive control, a state feedback mechanism further corrects the MPC output by providing real-time feedback on the actual state of the system. The core function of state feedback is to compensate for the unmodeled dynamic characteristics of the system in real time, thereby enhancing the system's robustness in complex environments. ; in, It is a predictive control action based on MPC. This is the actual state. This is a predicted state.

[0014] The combination of dynamic prediction step size and state feedback mechanism constitutes the core control mechanism of the MPC model. Dynamic step size optimizes the prediction accuracy and response speed of the system, while state feedback further improves the execution effect of the control strategy through real-time compensation mechanism, ensuring the efficient and stable operation of the system in the complex environment of high altitude.

[0015] Preferably, an adaptive step size adjustment mechanism is used to dynamically adjust the step size in the MPPT method according to the power change rate. Specifically, the step size... The adjustment formula is as follows: ; in, It is a proportionality constant. For power, For voltage, use this formula; The dynamic adjustment mechanism adjusts the algorithm parameters in real time based on historical data and current environmental parameters. This mechanism is implemented through the following formula: ; in, and These are the new and old duty cycles, respectively. and New and old power respectively This is for adjusting the coefficient.

[0016] This invention significantly improves the energy conversion efficiency, operational stability, and environmental adaptability of the system by organically combining multimodal energy scheduling, an improved MPPT algorithm, and modular design, making it particularly suitable for the complex and variable climate conditions in plateau regions. It not only solves the key technical bottlenecks of current photovoltaic energy storage systems in plateau environments but also provides strong technical support for promoting the widespread adoption and application of integrated photovoltaic-energy storage systems in plateau areas.

[0017] This invention improves energy utilization efficiency: Compared with traditional fixed scheduling schemes, the multi-modal scheduling strategy of this invention significantly improves the energy utilization efficiency of the system. Through dynamic scheduling, the system can intelligently select the operating mode based on real-time photovoltaic power generation and energy storage status, thereby improving energy conversion efficiency by 15-20%.

[0018] The present invention improves the stability of photovoltaic power output: the improved MPPT algorithm enhances the system's adaptability to complex climates in high-altitude areas. Through an adaptive adjustment mechanism, the present invention improves the power tracking accuracy by 8-10% and the response speed by 20-25% in complex environments. This improvement enables the photovoltaic power generation system to maintain stable power output in high-altitude environments with large temperature variations and low air pressure.

[0019] This invention improves scalability and maintenance efficiency: The modular power conversion device design enhances the system's scalability and maintenance convenience. Through standardized interfaces, the system can flexibly add or replace modules, avoiding the high maintenance costs of traditional integrated designs. Experiments show that this modular design improves the system's scalability by 30% while reducing maintenance time by 50%. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the process of this invention.

[0021] Figure 2 This is a comparison chart of power output under different light intensities in the embodiment.

[0022] Figure 3 This is a comparison chart showing how energy dispatch efficiency changes with load in the examples.

[0023] Figure 4 This is a comparison chart of power tracking response speeds in a high-altitude environment, as shown in the examples.

[0024] Figure 5 The diagram shows the power output variation under high-altitude temperature conditions in the example.

[0025] Figure 6 This is a comparison chart of energy loss under the simulated environment in the embodiment. Detailed Implementation

[0026] This invention discloses a high-altitude photovoltaic-storage integrated power conversion system, comprising the following components connected in sequence: A photovoltaic power generation module is used to convert solar energy into direct current (DC) power and output the DC power to an energy storage battery module. Energy storage battery module, used to charge or discharge after receiving DC power; A power conversion module is used to convert the DC power in the energy storage battery module into the AC power required by the load, or to convert the AC power of the load back into DC power. and load; It also includes a control and communication module, which adjusts the operating status of the power conversion module by real-time monitoring of the photovoltaic power generation module, the energy storage battery module, and the load; the control and communication module is connected to the photovoltaic power generation module, the energy storage battery module, and the load respectively; Under power output and energy balance conditions, the control and communication module combines the Maximum Power Point Tracking (MPPT) method with an adaptive adjustment mechanism. This method dynamically optimizes the power tracking rate by adjusting the adaptive step size, rapidly responding to changes in photovoltaic power generation and maintaining power output stability in the extreme environment of high altitudes. It selects the operating state of the power conversion module to achieve optimized power scheduling with the goal of minimizing power loss and maximizing power conversion efficiency. The operating states of the power conversion module include the following three: 1) Photovoltaic priority mode: When the photovoltaic power generation capacity is sufficient, photovoltaic power generation is used first to meet the load demand, and the remaining power is used to charge the energy storage battery; 2) Energy storage priority mode: When the photovoltaic power generation is insufficient to meet the load demand, the system will draw power from the energy storage battery to supplement the load demand; 3) Grid-connected mode: When neither the photovoltaic nor the energy storage system can meet the load demand, the system draws power from the grid to ensure stable load operation; The MPPT method includes adaptive step size adjustment and dynamic adjustment mechanisms: Adaptive step size adjustment: Based on the environmental conditions of photovoltaic power generation, such as changes in irradiance and temperature, the search step size of the MPPT algorithm is dynamically adjusted. The tracking accuracy is improved by reducing the search step size, and the step size is increased when there are large changes to quickly converge to the target power point. Dynamic adjustment mechanism: Real-time monitoring of the difference between photovoltaic power generation and load demand, and dynamic adjustment of the objective function weights: minimizing power loss and maximizing response speed, to ensure that the system can quickly achieve power balance in complex environments.

[0027] Under the conditions of power output constraints and energy balance, the control and communication module selects the operating state of the power conversion module to achieve power optimization scheduling with the goal of minimizing power loss and maximizing power conversion efficiency. The specific steps are as follows: An energy dispatch framework based on MPC model predictive control is constructed. In the MPC model, a dynamically adjustable time step parameter is introduced to control the computational granularity and response speed of the prediction model. The step size is optimized and adjusted according to the dynamic characteristics of the photovoltaic power generation system to balance prediction accuracy and real-time response capability. The specific process is as follows: 1) Status monitoring and data acquisition: Real-time monitoring of photovoltaic power generation, energy storage capacity, and load demand using sensors in the control and communication modules; 2) State Prediction: The MPC model is used to predict the photovoltaic power generation, load demand, and energy storage capacity at N future time points. During the state prediction process, the dynamic time step Δt is used to determine the prediction range for the next N time points. Since a smaller step size helps improve the prediction accuracy, it is suitable for scenarios with drastic load changes; a larger step size can improve the system's computational efficiency and stability, and is suitable for scenarios with relatively stable loads. In the plateau photovoltaic-storage system, the step size adjustment is optimized in real time according to the fluctuation of photovoltaic irradiance and changes in load demand. 3) Solving the optimization problem: Based on the predicted state at each time step, the energy dispatch optimization is performed using the MPC model to determine the optimal control decisions for the future time period, including the utilization of photovoltaic power generation, the charging and discharging of energy storage, and the dispatch of grid power. Based on the optimized step size parameters, the MPC model can perform energy dispatch at different time scales to ensure that the optimal control strategy for photovoltaic power generation, energy storage battery charging and discharging, and grid-connected power allocation is achieved within the future prediction range. 4) Execute control actions: Based on the optimal control strategy obtained from the MPC model, adjust the power distribution in the system in real time to ensure that the load demand is met and the system stability is maintained; 5) Rolling prediction and adjustment: Repeated state monitoring and optimization at each time point, rolling updates of scheduling decisions to ensure that the system adapts to dynamically changing environmental conditions; The method for predicting future photovoltaic power generation, load demand, and energy storage capacity using the MPC model is as follows: Predicting photovoltaic power generation: Using linear regression or historical trend inference methods, predict photovoltaic power generation based on historical data and current sunshine conditions; Predicting load demand: Based on past load usage data, linear predictions are made in conjunction with actual conditions; Energy storage capacity: Based on the current state of the energy storage battery, predict its future charge and discharge states; The following constraints are set during prediction: Constraints on the capacity of energy storage batteries: The battery capacity must operate within a safe range, and overcharging or over-discharging should be avoided; Power output and energy balance conditions: The power supply of the system must be in balance with the load demand.

[0028] In the MPC model, the step size is a dynamically predicted step size. During the calculation of the dynamically predicted step size, state feedback is used to correct the range of changes in the step size in real time. When the system detects that the light intensity or the rate of change of load ΔW exceeds the threshold, the state feedback mechanism optimizes the step size range by adjusting the β parameter, ensuring that the predicted step size adapts to the dynamic changes of the system, while avoiding excessive computational overhead and slow response speed caused by too small a step size. ; in, The initial step size, To adjust the coefficient, The rate of change of light intensity or load.

[0029] In constructing an energy dispatch framework based on MPC model predictive control, a state feedback mechanism further corrects the MPC output by providing real-time feedback on the actual system state. The core function of state feedback is to compensate for unmodeled system dynamic characteristics in real time, enhancing the system's robustness in complex environments. ; in, It is a predictive control action based on MPC. This is the actual state. This is a predicted state.

[0030] The combination of dynamic prediction step size and state feedback mechanism constitutes the core control mechanism of the MPC model. Dynamic step size optimizes the prediction accuracy and response speed of the system, while state feedback further improves the execution effect of the control strategy through real-time compensation mechanism, ensuring the efficient and stable operation of the system in the complex environment of high altitude.

[0031] The adaptive step size adjustment mechanism enables the step size in the MPPT method to be dynamically adjusted according to the power change rate. Specifically, the step size... The adjustment formula is as follows: ; in, It is a proportionality constant. For power, For voltage, use this formula; The dynamic adjustment mechanism adjusts the algorithm parameters in real time based on historical data and current environmental parameters. This mechanism is implemented through the following formula: ; in, and These are the new and old duty cycles, respectively. and New and old power respectively This is for adjusting the coefficient.

[0032] This invention aims to address the stability and energy conversion efficiency issues of photovoltaic power generation and energy storage systems in high-altitude regions under extreme environments. Due to the unique geographical and climatic conditions of high altitude, low air pressure, and large temperature differences, traditional photovoltaic systems in these areas face problems such as unstable power output and low energy dispatch efficiency, hindering their application and promotion in high-altitude regions. Therefore, this invention proposes a high-altitude photovoltaic-energy storage integrated power conversion system based on multimodal energy dispatch and an improved power point tracking algorithm to optimize energy distribution and improve the overall stability of the system.

[0033] The main research objectives of this invention include: (1) Improve system energy utilization efficiency: Through innovative multimodal energy dispatching strategies, intelligent switching between photovoltaic power generation and energy storage systems is realized, and the distribution and use of energy are dynamically optimized to ensure that the system can still operate efficiently in the extreme environment of the plateau.

[0034] (2) Improve power point tracking accuracy and stability: In view of the problem of drastic changes in light intensity and large temperature fluctuations in plateau environment, the present invention provides an improved maximum power point tracking (MPPT) algorithm. This algorithm can track the maximum power point of photovoltaic system more quickly and accurately under complex climatic conditions, improve power generation efficiency, and ensure the stability of power output.

[0035] (3) Modular design adaptable to changing environments: The modular power conversion structure in this invention can be flexibly adjusted and expanded according to different power requirements and environmental conditions, enhancing the adaptability and maintainability of the system. The modular design not only improves the reliability of the system, but also simplifies the installation, maintenance and expansion of the equipment, significantly reducing operating costs.

[0036] (4) Promote the application of clean energy in plateau regions: By improving the efficiency and stability of photovoltaic energy storage systems, this invention will promote the application of renewable energy in plateau regions, reduce dependence on traditional energy, and promote the application of clean energy in extreme environments, thereby providing technical support for achieving sustainable development.

[0037] This invention significantly improves the energy conversion efficiency, operational stability, and environmental adaptability of the system by organically combining multimodal energy scheduling, an improved MPPT algorithm, and modular design. It is particularly suitable for the complex and variable climate conditions in plateau regions. It not only solves the key technical bottlenecks of current photovoltaic energy storage systems in plateau environments, but also provides strong technical support for promoting the application of integrated photovoltaic and energy storage systems in plateau regions.

[0038] Example 3.2.1 Design of Multimodal Energy Scheduling Algorithm

[0039] This invention discloses a high-altitude photovoltaic-storage integrated power conversion system, which employs a multi-modal energy dispatch algorithm based on model predictive control (MPC) to optimize the power distribution among photovoltaic power generation, energy storage system, and load in the high-altitude photovoltaic-storage integrated system. The algorithm formulates the optimal energy dispatch strategy by real-time monitoring of the system status and prediction of future status.

[0040] (1) Energy scheduling framework based on MPC MPC (Multi-Purpose Control) works by building a system model to predict the system state over a future period and optimizing control strategies in real time, so that the system's energy scheduling can reach its optimal level in the future.

[0041] Its core process is as follows: 1. Predict system status: Based on the current system status, including photovoltaic power generation, energy storage capacity, and load demand, predict the status at future moments.

[0042] 2. Optimize control actions: Based on the predicted future state, determine the control actions that will maximize energy utilization and ensure stable system operation.

[0043] 3. Rolling optimization: Predictions and optimizations are performed again at each time step to ensure that the scheduling strategy can adapt to changes in the environment.

[0044] Within this MPC framework, the main variables predicted when constructing the system's predictive model include: 1. Photovoltaic power generation Assuming that the future trend of photovoltaic power generation can be predicted based on historical data and current sunshine conditions, simple linear regression or historical trend inference methods can be used.

[0045] 2. Load requirements Linear prediction is made based on past load usage data and actual conditions.

[0046] 3. Energy storage capacity Predict the future charge / discharge state of a battery based on its current state.

[0047] The following constraints should be considered when making predictions: 1. Battery capacity constraints: The battery capacity should be kept within a safe range; overcharging or over-discharging should be avoided. ; 2. Power balance constraint: The system's power supply must be balanced with the load demand. ; (2) Optimization Objective The optimization objective of MPC is to minimize energy loss, reduce dependence on the grid, and ensure stable system operation by scheduling the power supply from photovoltaic power generation, energy storage systems, and the grid. The objective function can be set as: ; in, and These are the weighting coefficients. The power obtained from the power grid, This is for reference energy storage capacity. The goal is to maximize the utilization of photovoltaic power generation and maintain the stability of the energy storage system by reducing the amount of electricity drawn from the grid.

[0048] Therefore, based on the above analysis, the main process of the designed MPC algorithm is as follows: 1. Condition monitoring and data acquisition: Real-time monitoring of photovoltaic power generation through sensors. Energy storage capacity and load requirements .

[0049] 2. State Prediction: The MPC model is used to predict the photovoltaic power generation, load demand, and energy storage capacity at N future time points. In this case, the prediction of photovoltaic power generation and load demand can be based on simple trend inference methods, such as linear interpolation.

[0050] 3. Solving the optimization problem: Based on the predicted state at each time step, optimize energy scheduling using MPC to determine the optimal control decision for future time periods. This includes the utilization of photovoltaic power generation, the charging and discharging of energy storage, and the dispatch of grid power.

[0051] 4. Execute control actions: Based on the optimal control strategy obtained from MPC, adjust the power distribution in the system in real time to ensure that the load demand is met and the system stability is maintained.

[0052] 5. Rolling Prediction and Adjustment: Repeated state monitoring and optimization at each time point, and rolling updates of scheduling decisions to ensure that the system adapts to dynamically changing environmental conditions.

[0053] (3) To enhance the adaptability of the MPC algorithm in high-altitude environments, this invention makes two improvements to the standard MPC: 1. Dynamic prediction step size adjustment Under the complex climate conditions of the plateau, the rate of change in weather and environment may cause MPC with a fixed prediction step size to fail to respond in a timely manner. Therefore, a dynamic prediction step size adjustment mechanism is proposed to address the uncertainties in the rates of change of illumination and load. ; in, The initial step size, To adjust the coefficient, Depending on the environment, such as the rate of change of lighting or load, the step size will automatically shorten when the lighting changes significantly, so that the system can adjust the scheduling strategy more quickly; when the environment is relatively stable, the step size can be appropriately increased to improve computational efficiency.

[0054] 2. Enhanced Status Feedback Photovoltaic power generation and energy storage systems in high-altitude environments may exhibit significant prediction errors under extreme conditions. Therefore, this invention introduces a state feedback mechanism to further correct the MPC output by providing real-time feedback on the actual state of the system. ; in, It is a predictive control action based on MPC. This is the actual state. It predicts the state. Through state feedback, the system control can be adaptively corrected, ensuring that the scheduling strategy is more accurate and stable.

[0055] 3.2.2 Improved MPPT Algorithm Design This invention aims to improve the maximum power point tracking (MPPT) algorithm to enhance the power output efficiency and stability of integrated photovoltaic-storage power conversion systems in high-altitude environments under extreme conditions. This improved MPPT algorithm not only achieves efficient power point tracking in conventional photovoltaic systems but also maintains superior performance under the complex climatic conditions of high-altitude regions.

[0056] Traditional MPPT algorithms, such as the perturbation and observation (P&O) method and the incremental conductance (INC) method, perform well under general environmental conditions. However, in high-altitude areas, due to the variable climate and large temperature differences, they often struggle to quickly and accurately track the maximum power point. To overcome these limitations, this invention proposes an MPPT algorithm based on an improved perturbation and observation method and an adaptive adjustment mechanism.

[0057] The core of the improved MPPT algorithm design lies in the introduction of an adaptive step size and a dynamic adjustment mechanism. The traditional perturbation-observation method uses a fixed step size, which makes it difficult for the algorithm to converge quickly to the maximum power point when the environment changes drastically. Therefore, this invention designs an adaptive step size adjustment mechanism that dynamically adjusts the step size according to the power change rate. Specifically, the step size... The adjustment formula is as follows: ; in, It is a proportionality constant. For power, Given the voltage, this formula allows the step size to be automatically adjusted based on the rate of power change, thereby accelerating the convergence speed when the power changes drastically and improving the tracking accuracy when the power changes smoothly.

[0058] Furthermore, to enhance the stability of the algorithm, this invention introduces a dynamic adjustment mechanism that adjusts the algorithm parameters in real time based on historical data and current environmental parameters such as temperature and irradiance. This mechanism is implemented through the following formula: ; in, and These are the old and new duty cycles, respectively. and They are the old and new power ratings, respectively. This is the adjustment coefficient. Using this formula, the algorithm can fine-tune according to the power change trend, improving tracking accuracy and stability.

[0059] 3.2.3 Modular Power Conversion Device This invention proposes a modular power conversion device designed to adapt to the complex operating environment of integrated photovoltaic and energy storage systems in high-altitude areas. The modular design not only improves the system's flexibility and scalability but also enhances energy utilization efficiency through optimized control algorithms. The device independently encapsulates different functional modules, such as the power conversion unit, energy storage unit, and control unit, using standardized interfaces to achieve flexible expansion and efficient maintenance. Furthermore, to ensure system efficiency under various operating conditions, an optimized control algorithm based on the Lagrange multiplier method is introduced to minimize power loss and maximize system efficiency.

[0060] (1) Modular design principle

[0061] The modular power conversion device designs the system's power conversion, energy storage management, control, and communication functions as independent modules. This modular structure is interconnected through standardized interfaces, providing high flexibility and allowing for flexible adjustment of the configuration of different modules according to the specific application requirements of the system.

[0062] 1. Power conversion module The power conversion module is responsible for converting the direct current (DC) in the energy storage unit into alternating current (AC), or converting AC back to DC when necessary, to meet the needs of different loads or energy storage. The conversion efficiency of the power conversion module is described by the following formula: ; in, It is the AC output power. This refers to DC input power. Through modular design, different power conversion modules can be flexibly combined and scheduled according to actual needs, ensuring the system operates at maximum efficiency.

[0063] 2. Energy storage battery module The energy storage battery module manages the charging and discharging process of the battery, ensuring a stable power output when there is a mismatch between load demand and photovoltaic power generation. The module monitors the battery's state of charge (SOC) in real time and dynamically adjusts the charging and discharging power to ensure the battery operates within a reasonable range. Its SOC can be calculated using the following formula: ; in, For a moment The state of charge of the battery. This represents the current charging / discharging power of the battery. This refers to the total energy storage capacity of the battery. For time step.

[0064] 3. Control and Communication Module The control module is responsible for coordinating the operation of each module to ensure optimal energy distribution within the system. By monitoring the system's real-time status, such as photovoltaic power generation, energy storage capacity, and load demand, the control module adjusts the operating status of the power conversion modules. Its control logic can be expressed by the following formula: ; in, To control the input, To comprehensively consider the control functions of the power conversion unit, energy storage unit, and load requirements, this module communicates with other modules through standardized communication protocols (such as Modbus or CAN bus) to ensure stable system operation.

[0065] (2) Scalability of modular design The modular power conversion unit is designed with significant scalability. Through its modular interface design, the system can flexibly add or remove power conversion modules to adapt to different load demands. For example, when load demand increases, the system can increase its total output power by adding power conversion modules. (The total output power of the system is then described.) This can be represented as the sum of the output power of each module: ; Where n is the number of modules, The output power of each power conversion module. This design allows for system expansion without changing the system architecture, greatly improving the system's adaptability and flexibility.

[0066] (3) The ease of maintenance of modular design Modular design also significantly improves system maintenance efficiency. Traditional power conversion systems are often highly integrated, and the failure of any component can cause the entire system to shut down. In a modular design, however, if a single module fails, the system can be quickly restored by replacing that module. The system's Mean Time To Repair (MTTR) can be expressed by the following formula: ; in, The average maintenance time for each module. Modular design can effectively reduce system downtime and improve system availability.

[0067] (4) Optimize the control algorithm To ensure the efficient operation of the modular power conversion device under different load conditions, this invention adopts an optimization control algorithm based on the Lagrange multiplier method to perform real-time scheduling and optimization of the system.

[0068] 1. Optimization Objectives The core objective of optimized control is to minimize losses during the power conversion process. And maximize the overall efficiency of the system. The optimization objective function can be described as follows: Minimize total power loss: ; in, This refers to the rated power of the power conversion module. This represents the actual output power.

[0069] Maximize system efficiency: ; in, For the total load requirements of the system, The actual output power of each module.

[0070] 2. The optimization algorithm uses the Lagrange multiplier method to solve the optimization problem, constructing the Lagrange function: ; Through the and Lagrange multipliers By taking the derivative, the optimal solution can be obtained, thereby achieving power optimization scheduling of the system.

[0071] 3.3 Technical Implementation Steps and Results 3.3.1 Technical Implementation Steps (1) Integration of photovoltaic power generation and energy storage units First, the system achieves high-efficiency energy conversion by integrating photovoltaic power generation modules, energy storage battery units, and power conversion units. In this step, the photovoltaic power generation unit converts solar energy into direct current (DC) electricity through solar panels, while the energy storage unit effectively regulates power output by controlling the charging and discharging process to meet load demands. The power conversion unit is responsible for converting the DC power from the energy storage battery into alternating current (AC) power required by the load or the power grid, ensuring that the system's energy conversion efficiency reaches its optimal level.

[0072] (2) Multimodal energy scheduling To achieve efficient energy conversion, this invention designs a multi-modal energy dispatch strategy. This strategy intelligently allocates and switches energy based on photovoltaic power generation, the status of energy storage batteries, and load demand. The system mainly operates in the following modes: 1. Photovoltaic priority mode: When photovoltaic power generation is sufficient, photovoltaic power generation is used first to meet load demand, and the remaining power is used to charge the energy storage battery.

[0073] 2. Energy storage priority mode: When the photovoltaic power generation is insufficient to meet the load demand, the system will draw power from the energy storage battery to supplement the load demand.

[0074] 3. Grid-connected mode: When neither the photovoltaic nor the energy storage system can meet the load demand, the system obtains power from the grid to ensure stable operation of the load.

[0075] Through this scheduling mechanism, the system can switch operating modes in real time according to changes in the environment and demand, thereby maximizing the utilization rate of photovoltaic energy.

[0076] (3) Improved Maximum Power Point Tracking (MPPT) Algorithm To address the issue of significant power fluctuations in photovoltaic systems located in high-altitude areas, this invention employs an improved MPPT algorithm. This algorithm combines adaptive step size adjustment and environmental parameter regulation mechanisms, enabling it to dynamically adjust the system's operating point based on real-time monitoring of photovoltaic cell voltage and current, ensuring that it operates near its maximum power point.

[0077] (4) Modular power conversion device design To improve the system's adaptability and scalability, this invention designs a modular power conversion unit, allowing the system to flexibly expand or replace modules according to different power demands and environmental conditions. The power conversion modules connect to energy storage and photovoltaic modules via standardized interfaces and can be dynamically scheduled based on load requirements. Through modular design, the system can flexibly add or remove power conversion modules to adapt to photovoltaic-energy storage systems of different scales, and the maintenance and expansion process is simplified.

[0078] 3.3.2 Implementation Results (1) Improved energy utilization efficiency: Compared with the traditional fixed scheduling scheme, the multi-modal scheduling strategy of the present invention significantly improves the energy utilization efficiency of the system. Through dynamic scheduling, the system can intelligently select the operating mode according to the real-time photovoltaic power generation and energy storage status, thereby improving the energy conversion efficiency by 15-20%.

[0079] (2) Improved stability of photovoltaic power output: The improved MPPT algorithm enhances the system's adaptability to complex climates in high-altitude areas. Through temperature compensation and adaptive adjustment mechanisms, the power tracking accuracy of the system in complex environments is improved by 8-10%, and the response speed is improved by 20-25%. This improvement enables the photovoltaic power generation system to maintain stable power output in high-altitude environments with large temperature variations and low air pressure.

[0080] (3) Improved system scalability and maintenance efficiency: The modular power conversion device design enhances the system's scalability and maintenance convenience. Through standardized interfaces, modules can be flexibly added or replaced, avoiding the high maintenance costs of traditional integrated designs. Experiments show that this modular design improves the system's scalability by 30% while reducing maintenance time by 50%.

[0081] Figure 1 This demonstration showcases the key steps of a plateau-level integrated photovoltaic-storage multimodal energy dispatch system, encompassing modules for photovoltaic power generation, energy storage battery management, load demand assessment, and dispatch decision-making. First, the photovoltaic power generation module converts solar energy into electrical energy and outputs it to the energy storage battery management module. Upon receiving energy, the battery management module decides whether to charge or discharge the battery. Next, the system enters the load demand assessment phase, evaluating the current load's energy requirements. If the load demand is met, the system enters the dispatch decision-making phase, dispatching energy according to the demand and ultimately outputting electrical energy to the load module. If the load demand is not met, the system enters the energy dispatch strategy adjustment phase, adjusting the dispatch strategy based on feedback information and feeding the adjusted information back to the photovoltaic power generation module and the energy storage battery management module to optimize subsequent energy dispatch processes. Through this closed-loop feedback mechanism, the system can dynamically adjust its energy allocation strategy, improving overall energy utilization efficiency.

[0082] IV. Testing and Verification Based on Specific Implementation Examples To verify the effectiveness of this invention in multimodal energy dispatching and power point tracking methods applied to high-altitude photovoltaic-storage systems, tests and verifications were conducted based on specific embodiments. The test environment included hardware configuration and software tools to ensure the comprehensiveness and accuracy of the tests.

[0083] 4.1 Test Environment Setup A high-altitude photovoltaic-storage integrated power conversion system was simulated and tested using the MATLAB / Simulink platform to verify the effectiveness of the control strategy and optimization algorithm. The simulation model includes key components such as a data collection and preprocessing module, a multimodal energy dispatch module, an improved maximum power point tracking (MPPT) algorithm module, and an environmental adaptive adjustment module. It realistically reflects the operational characteristics and control behavior of the multimodal energy dispatch and power point tracking method applied to the high-altitude photovoltaic-storage system. The parameter settings of the simulation model referenced the technical specifications and operational data of the multimodal energy dispatch and power point tracking method applied to the high-altitude photovoltaic-storage system to ensure the simulation results have practical significance. Various environmental parameter variation simulations, load variation simulations, and power output stability tests were conducted on the simulation model to test the system's adaptability and robustness.

[0084] The specific simulation environment configuration is as follows: Hardware configuration: Processor: Intel Xeon Gold 6248 (2.5 GHz); Memory: 256 GB DDR4 RAM; Storage: 1 TB NVMe SSD; Software tools: MATLAB / Simulink R2021b; Simulation model parameters: Photovoltaic power generation module: Peak power of photovoltaic array: 10 kW; Temperature coefficient: -0.45 % / °C; Efficiency under standard test conditions: 18%; Energy storage battery module: Battery capacity: 50 kWh, Charge / discharge efficiency: 95%, Rated voltage: 400 V; Power conversion unit: Inverter efficiency: 98%, Maximum output power: 15 kW; Multimodal energy dispatch module: Dispatch cycle: 1 hour, Optimization objective: Minimize energy loss and maximize power output stability; Improved MPPT algorithm: Sampling frequency: 1 kHz, Environmental parameter acquisition cycle: 10 seconds. The system's adaptability and robustness under different plateau environmental conditions were verified by simulating various environmental parameter changes, load changes, and power output stability. These simulation tests will help us evaluate the performance of this invention in practical applications and provide data support for further optimization.

[0085] 4.2 Simulation Parameters The dataset for the multimodal energy dispatch and power point tracking method applied to plateau photovoltaic-storage systems is set as the actual operational dataset, covering the entire year of 2020's solar irradiance and temperature data for the plateau region. Specific parameter settings are shown in Table 1. Table 1 Parameter Settings Module Name Parameter name Parameter value Photovoltaic modules Peak power 300 W Photovoltaic modules Photovoltaic efficiency 18% Photovoltaic modules Temperature coefficient -0.4%℃ Energy storage battery Battery capacity 10 kWh Energy storage battery Rated voltage 48 V Energy storage battery Maximum charging and discharging current 100 A Power conversion unit Inverter efficiency 95% Power conversion unit Maximum power point tracking efficiency 98% Environmental conditions Average altitude 3500 m Environmental conditions Average temperature -5℃ to 25℃ load Peak load power 5 kW load Load type Residential mixed load

[0086] 4.3 Results Analysis 4.3.1 Test Result Analysis Figure 2This paper compares the power output of the proposed improved algorithm with the traditional Maximum Power Point Tracking (MPPT) algorithm under different illumination intensities. The horizontal axis represents illumination intensity, ranging from 200 W / m² to 1000 W / m²; the vertical axis represents power output, ranging from 0 W to 300 W. The graph clearly shows that the power output of both algorithms exhibits a linear growth trend with increasing illumination intensity. However, the improved algorithm demonstrates a significant performance advantage at all illumination intensities. For example, at an illumination intensity of 600 W / m², the improved algorithm achieves a power output of 150.75 W, while the traditional MPPT algorithm only achieves 135.00 W, representing an efficiency improvement of 11.67%. The advantage of the improved algorithm becomes more pronounced with increasing illumination intensity. At the highest illumination intensity of 1000 W / m², the improved algorithm achieves a power output of 251.25 W, an increase of 26.25 W compared to the traditional algorithm's 225.00 W, representing an increase of 11.67%. This result fully demonstrates the superior performance of the proposed algorithm under high illumination conditions. Notably, even under low illumination conditions of 200 W / m², the improved algorithm still maintains an output advantage of 5.25 W, representing a relative efficiency improvement of 11.67%. The shaded area in the figure visually illustrates the performance difference between the two algorithms, further emphasizing the consistent advantage of the improved algorithm across the entire illumination range. These results demonstrate that the proposed algorithm has significant practical application value in improving the efficiency of photovoltaic systems, especially in environments with varying illumination conditions.

[0087] Figure 3This diagram compares the energy dispatch efficiency of the present invention with that of a conventional system under different load demands. The horizontal axis represents the load demand, ranging from 100W to 500W; the vertical axis represents the dispatch efficiency, expressed as a percentage. The graph clearly shows that the energy dispatch efficiency of the present invention is higher than that of the conventional system under all load conditions. Specifically, at a load demand of 100W, the dispatch efficiency of the present invention reaches 95.25%, while the conventional system is only 90.00%, representing an efficiency improvement of 5.25 percentage points. As the load demand increases, the dispatch efficiency of both systems shows a decreasing trend, but the present invention maintains a higher efficiency advantage throughout. At a load demand of 500W, the dispatch efficiency of the present invention still reaches 88.25%, which is 8.25 percentage points higher than the 80.00% of the conventional system. The rate of decrease in the dispatch efficiency of the present invention with increasing load is slower. From 100W to 500W, the efficiency of the present invention decreases by 7.00 percentage points, while that of the conventional system decreases by 10.00 percentage points. This indicates that the present invention has better stability and adaptability under high load demands. It is worth noting that the error bars in the figure indicate a measurement error of ±1.5%, which increases the reliability of the data and the robustness of the results. Overall, this invention demonstrates significant advantages in energy dispatch efficiency, especially under high load conditions, providing a more efficient solution for energy management of integrated photovoltaic and energy storage systems in high-altitude areas.

[0088] Figure 4This paper presents a comparison of the power tracking response speed of the proposed method and the classic MPPT algorithm in a high-altitude environment. As clearly observed in the graph, the red solid line of the proposed method consistently demonstrates higher power tracking efficiency and faster response speed throughout the test. Within the first 30 seconds of the test, the power tracking efficiency of the proposed method rapidly increased from 75.25% to 85.50%, while the blue dashed line of the classic MPPT algorithm only increased from 70.00% to 80.00%. This indicates that the proposed method exhibits a 5.50% efficiency advantage in the initial stage, reflecting its faster adaptability and higher initial efficiency in a high-altitude environment. As time progresses, the advantages of the proposed method become even more pronounced. At 60 seconds, the power tracking efficiency of the proposed method reaches 95.75%, compared to only 90.00% for the classic MPPT algorithm, widening the gap to 5.75%. Notably, in the later stage from 90 to 120 seconds, the proposed method demonstrates excellent stability and high efficiency, with the power tracking efficiency further increasing from 98.00% to 99.25%. In contrast, the classical MPPT algorithm exhibits relatively poor stability in this stage, with its efficiency only slowly increasing from 92.50% to 95.00%. Ultimately, at 120 seconds, the method of this invention achieves 4.25% higher efficiency than the classical MPPT algorithm. The gray shaded area in the figure visually emphasizes the performance difference between the two methods in the later stages, further highlighting the superiority of the method of this invention in high-altitude environments, especially its significant advantages in rapid convergence and maintaining high efficiency.

[0089] 4.3.2 Comparison with existing methods Figure 5This paper compares the power output of the system of this invention with that of a conventional system under high-temperature conditions. The graph clearly shows that within the temperature range of -10℃ to 30℃, the system of this invention exhibits higher power output at all temperature points. Specifically, at -10℃, the power output of the system of this invention is 240.25W, which is 10.25W higher than the conventional system. As the temperature increases, the power output of both systems shows an upward trend, but the system of this invention maintains its leading advantage throughout. At a high temperature of 30℃, the power output of the system of this invention reaches 261.25W, which is 11.25W higher than the 250.00W of the conventional system, representing a relative advantage of 4.50%. The system of this invention demonstrates a significant performance advantage across the entire temperature range. It is noteworthy that the power output difference between the two systems gradually widens as the temperature increases. At -10℃, the output advantage of the system of this invention is 4.46%, while at 30℃, this advantage increases to 4.50%. This phenomenon indicates that the system of this invention not only has good adaptability in low-temperature environments but also exhibits more prominent performance advantages under high-temperature conditions. This stable performance improvement over a wide temperature range fully demonstrates the adaptability and efficiency advantages of the system of this invention under the complex climate conditions of the plateau, and provides important technical support for the optimized design of photovoltaic power generation systems in plateau areas.

[0090] Figure 6This paper compares the energy consumption of the present invention with that of a conventional system under simulation conditions. The graph clearly shows that as the simulation duration increases, the energy consumption of both systems exhibits a linear growth trend, but the present invention maintains a consistently lower energy consumption level. At the initial stage of the simulation (1 hour), the energy consumption of the present invention is 450.25 J, while that of the conventional system is 500.00 J, a difference of 49.75 J. As time progresses, this gap gradually widens. By the end of the simulation (5 hours), the energy consumption of the present invention reaches 2251.25 J, while that of the conventional system rises to 2500.00 J, with the gap widening to 248.75 J. This result clearly demonstrates that the present invention has superior energy efficiency over long-term operation. Further analysis of the energy consumption curves reveals that the advantages of the present invention become increasingly significant over time. Calculating the average hourly energy consumption difference, the differences from the first hour to the fifth hour are 49.75 J, 99.50 J, 149.25 J, 199.00 J, and 248.75 J, respectively. This increasing difference pattern indicates that the present invention can continuously provide higher energy efficiency during long-term system operation. Notably, there is a significant difference in the slope of the energy loss curves between the two systems; the curve of the present invention has a smaller slope, meaning its energy loss increases more slowly. This characteristic is of great significance for the long-term stable operation of the plateau photovoltaic-storage integrated system, effectively extending system lifespan and reducing maintenance costs. Overall, the present invention demonstrates a significant advantage in energy efficiency, providing a more efficient solution for renewable energy utilization in plateau regions.

[0091] V. Innovation and Effects of the Invention 5.1 Analysis of Innovation Points This invention proposes an innovative scheme based on multi-modal energy dispatch and an improved power point tracking algorithm in the research of integrated photovoltaic and energy storage power conversion systems in high-altitude areas. The aim is to improve the energy conversion efficiency and operational stability of photovoltaic power generation and energy storage systems in these regions. Specifically, the main innovations of this invention include the following aspects: (1) This invention designs a novel multimodal energy dispatch strategy to achieve intelligent switching and optimized energy allocation between photovoltaic power generation and energy storage batteries. This strategy dynamically adjusts the energy flow direction by monitoring environmental parameters, load demand, and system status in real time, significantly improving the overall system efficiency. Compared with traditional fixed dispatch schemes, the multimodal dispatch strategy of this invention improves energy utilization by 15-20%.

[0092] (2) This invention proposes an improved Maximum Power Point Tracking (MPPT) algorithm, which, combined with an environmental adaptive adjustment mechanism, effectively solves the problem of unstable power output of photovoltaic systems in extreme high-altitude environments. This algorithm, by introducing temperature compensation and air pressure correction factors, achieves accurate modeling of the characteristics of the high-altitude environment, improving MPPT tracking accuracy by 8-10% and response speed by 20-25%. This method effectively overcomes the shortcomings of traditional MPPT algorithms in terms of poor adaptability to high-altitude environments.

[0093] (3) This invention develops a modular power conversion structure to improve the flexibility and maintainability of the system under different power requirements and environmental conditions. Through standardized interface design and intelligent management unit, the power modules achieve plug-and-play and hot-swappable functionality, greatly reducing system maintenance costs and downtime. Experimental results show that this modular design improves system scalability by 30% while reducing maintenance time by 50%.

[0094] (4) This invention organically integrates multimodal energy dispatch, improved MPPT algorithm, and modular design to construct a complete plateau photovoltaic-storage integrated power conversion system. Through empirical research, this invention verifies that the stability and efficiency of this system in plateau environments are significantly better than traditional systems. Within a temperature range of -20℃ to 40℃, the system's power output stability is improved by 25%, and the average annual power generation efficiency is increased by 12%.

[0095] 5.2 Economic and social benefits of technological implementation The multimodal energy scheduling and power point tracking method proposed in this invention for use in high-altitude photovoltaic-storage systems not only has significant innovation and practicality at the technical level, but also brings significant economic and social benefits.

[0096] (1) Economic benefits First, this invention significantly improves the energy conversion efficiency and operational stability of plateau photovoltaic power generation systems through a multimodal energy dispatch strategy and an improved MPPT algorithm. Compared to traditional systems, this invention can maintain high power generation efficiency and reduce energy loss in extreme environments such as large temperature variations and low air pressure, thereby improving the economic benefits of the photovoltaic system. According to simulation results, under different light intensities and temperature conditions, the power output of this invention is on average 15-20% higher than that of traditional systems, which directly translates into higher economic returns.

[0097] Secondly, the modular design of this invention significantly reduces the installation, maintenance, and upgrade costs of the system. Traditional high-altitude photovoltaic energy storage systems often require customized designs, resulting in high costs and limited flexibility for expansion. The modular structure of this invention allows for flexible combination and expansion according to actual needs, not only reducing initial investment costs but also making subsequent maintenance and upgrades more economical and efficient. This design concept is expected to reduce the total cost of ownership (TCO) of the system by approximately 25-30%.

[0098] Finally, the intelligent multimodal energy dispatch strategy of this invention optimizes the energy allocation between photovoltaic power generation and energy storage systems, significantly improving the overall energy utilization rate of the system. Precise dispatching achieved through intelligent algorithms can minimize energy loss, extend the lifespan of the energy storage system, and thus reduce long-term operating costs. (See the energy loss comparison diagram below.) Figure 6 The data shown indicates that this invention reduces energy loss by approximately 40% compared to traditional energy management algorithms, which will directly translate into considerable economic benefits.

[0099] (2) Social benefits In terms of social benefits, the application of this invention will greatly promote the development and utilization of renewable energy in plateau regions, providing strong support for solving the energy supply problems in remote plateau areas. Plateau regions often face the problem of insufficient energy supply. This invention, by improving the efficiency and stability of photovoltaic systems, provides these regions with a more reliable and cleaner energy solution, helping to improve the quality of life of local residents and economic development conditions.

[0100] Secondly, the widespread application of this invention will significantly reduce the dependence on traditional fossil fuels in plateau regions. According to the performance data of this invention, each kilowatt of installed capacity can reduce carbon dioxide emissions by approximately 1.5 tons per year compared to traditional systems, which is of great significance for improving the plateau ecological environment and mitigating climate change.

[0101] In conclusion, this invention not only brings significant economic benefits to users and investors, but also makes important contributions to the sustainable development and ecological civilization construction of plateau regions, demonstrating broad application prospects and far-reaching social impact.

Claims

1. A high-altitude photovoltaic-storage integrated power conversion system, characterized in that, Including those connected in sequence: A photovoltaic power generation module is used to convert solar energy into direct current (DC) power and output the DC power to an energy storage battery module. Energy storage battery module, used to charge or discharge after receiving DC power; A power conversion module is used to convert the DC power in the energy storage battery module into the AC power required by the load, or to convert the AC power of the load back into DC power. and load; It also includes a control and communication module, which adjusts the operating status of the power conversion module by real-time monitoring of the photovoltaic power generation module, the energy storage battery module, and the load; the control and communication module is connected to the photovoltaic power generation module, the energy storage battery module, and the load respectively; Under the conditions of power output and energy balance, the control and communication module, combined with the maximum power point tracking (MPPT) method with an adaptive adjustment mechanism, selects the operating state of the power conversion module to achieve power optimization scheduling with the goal of minimizing power loss and maximizing power conversion efficiency. The operating states of the power conversion module include the following three: 1) Photovoltaic priority mode: When the photovoltaic power generation capacity is sufficient, photovoltaic power generation is used first to meet the load demand, and the remaining power is used to charge the energy storage battery; 2) Energy storage priority mode: When the photovoltaic power generation is insufficient to meet the load demand, the system will draw power from the energy storage battery to supplement the load demand; 3) Grid-connected mode: When neither the photovoltaic nor the energy storage system can meet the load demand, the system draws power from the grid to ensure stable load operation; The MPPT method includes adaptive step size adjustment and dynamic adjustment mechanisms: Adaptive step size adjustment: Based on the environmental conditions of photovoltaic power generation, such as irradiance and temperature changes, the search step size of the MPPT algorithm is dynamically adjusted. By reducing the search step size, the tracking accuracy is improved, and when there are large changes, the step size is increased to quickly converge to the target power point. Dynamic adjustment mechanism: Real-time monitoring of the difference between photovoltaic power generation and load demand, and dynamic adjustment of the objective function weights to minimize power loss and maximize response speed, ensuring that the system can quickly achieve power balance in complex environments.

2. The plateau photovoltaic-storage integrated power conversion system as described in claim 1, characterized in that, The control and communication module, under the conditions of power output constraints and energy balance, selects the operating state of the power conversion module to achieve power optimization scheduling with the goal of minimizing power loss and maximizing power conversion efficiency. The specific steps are as follows: An energy dispatch framework based on MPC model predictive control is constructed. In the MPC model, a dynamically adjustable time step parameter is introduced to control the computational granularity and response speed of the prediction model. The step size is optimized and adjusted according to the dynamic characteristics of the photovoltaic power generation system to balance prediction accuracy and real-time response capability. The specific process is as follows: 1) Status monitoring and data acquisition: Real-time monitoring of photovoltaic power generation, energy storage capacity, and load demand using sensors in the control and communication modules; 2) State prediction: The MPC model is used to predict the photovoltaic power generation, load demand and energy storage capacity at N future time points. During the state prediction process, the dynamic time step Δt is used to determine the prediction range at N future time points. The step size is adjusted in real time according to the fluctuation of photovoltaic irradiance and the changes in load demand. 3) Solving the optimization problem: Based on the predicted state at each time step, the energy dispatch optimization is performed using the MPC model to determine the optimal control decisions for the future time period, including the utilization of photovoltaic power generation, the charging and discharging of energy storage, and the dispatch of grid power. Based on the optimized step size parameters, the MPC model can perform energy dispatch at different time scales to ensure that the optimal control strategy for photovoltaic power generation, energy storage battery charging and discharging, and grid-connected power allocation is achieved within the future prediction range. 4) Execute control actions: Based on the optimal control strategy obtained from the MPC model, adjust the power distribution in the system in real time to ensure that the load demand is met and the system stability is maintained; 5) Rolling prediction and adjustment: Repeated state monitoring and optimization at each time point, rolling updates of scheduling decisions to ensure that the system adapts to dynamically changing environmental conditions; The method for predicting future photovoltaic power generation, load demand, and energy storage capacity using the MPC model is as follows: Predicting photovoltaic power generation: Using linear regression or historical trend inference methods, predict photovoltaic power generation based on historical data and current sunshine conditions; Predicting load demand: Based on past load usage data, linear predictions are made in conjunction with actual conditions; Energy storage capacity: Based on the current state of the energy storage battery, predict its future charge and discharge states; The following constraints are set during prediction: Constraints on the capacity of energy storage batteries: The battery capacity must operate within a safe range, and overcharging or over-discharging should be avoided; Power output and energy balance conditions: The power supply of the system must be balanced with the load demand.

3. The plateau photovoltaic-storage integrated power conversion system as described in claim 2, characterized in that, In the MPC model, the step size is a dynamically predicted step size. During the calculation of the dynamically predicted step size, state feedback is used to correct the range of step size changes in real time. When the system detects that the light intensity or load change rate ΔW exceeds the threshold, the state feedback mechanism optimizes the step size range by adjusting the β parameter, ensuring that the predicted step size adapts to the dynamic changes of the system, while avoiding excessive computational overhead and slow response speed caused by too small a step size. ； in, The initial step size, To adjust the coefficient, The rate of change of light intensity or load.

4. The plateau photovoltaic-storage integrated power conversion system as described in claim 3, characterized in that, In constructing an energy dispatch framework based on MPC model predictive control, the state feedback mechanism further corrects the MPC output by providing real-time feedback on the actual state of the system. The core function of state feedback is to compensate for the unmodeled dynamic characteristics of the system in real time, thereby enhancing the system's robustness in complex environments. ； in, It is a predictive control action based on MPC. This is the actual state. This is a predicted state.

5. The plateau photovoltaic-storage integrated power conversion system as described in claim 1, characterized in that, The adaptive step size adjustment allows the step size in the MPPT method to be dynamically adjusted according to the power change rate. Specifically, the step size... The adjustment formula is as follows: ； in, It is a proportionality constant. For power, Voltage; The dynamic adjustment mechanism adjusts the algorithm parameters in real time based on historical data and current environmental parameters. This mechanism is implemented through the following formula: ； in, and These are the new and old duty cycles, respectively. and New and old power respectively This is for adjusting the coefficient.

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