High-temperature and high-humidity environment light storage direct flexible internal flexible adjustable domain calculation method

Abstract

The invention belongs to the technical field of power system optimization, and discloses a high-temperature and high-humidity environment light storage direct-flexible internal flexible adjustable domain calculation method, which comprises the following steps of: firstly, creating a flexibility prediction model of a light storage direct-flexible system; secondly, parameters of the system element model are finely adjusted to improve the precision of the system element model; then, analyzing a coupling effect among system elements, and ensuring the stability of the system under different environment changes through a robust optimization technology; and finally, verifying the effectiveness of the power demand prediction and scheduling strategy of the developed optical storage direct flexible system in the high-temperature and high-humidity environment through example simulation. According to the invention, through the adaptive robust optimization algorithm and the flexibility prediction model, the influence of the high-temperature and high-humidity environment on the optical storage direct-flexible system is effectively dealt with, and stable and efficient operation of the system is ensured; and real-time data monitoring and intelligent scheduling optimization are combined, so that the response speed and the operation efficiency of the system are improved, efficient resource configuration and green low-carbon development are promoted, and the reliability of the power system in a complex environment is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization, and in particular relates to a method for calculating a flexible and adjustable internal domain of a photovoltaic storage direct-flexible system in a high-temperature and high-humidity environment. Background Art

[0002] With the transformation of the global energy structure, the PV-storage-direct-flexible system, as a comprehensive energy structure integrating photovoltaic power generation, energy storage system, DC transmission and flexible control technology, has gradually become an important part of the modern power system. The system has the advantages of low-carbon electricity, efficient electricity use, intelligent operation, and flexible load. It is particularly suitable for coping with the fluctuating power load and the growing proportion of renewable energy in the modern power market. Therefore, the PV-storage-direct-flexible system plays an important role in reducing traditional energy consumption, improving the flexibility of power system dispatching, and ensuring the stability of the power grid.

[0003] In recent years, with the acceleration of climate change and urbanization, extreme weather has occurred frequently, especially in the hot and humid weather in summer, which has brought severe challenges to the operation of the photovoltaic storage direct-flexible system. The eastern megacities represented by Shanghai face the dual pressures of extreme high temperature and high humidity under the climate conditions of "rain and heat at the same time". This extreme weather will not only lead to increased fluctuations in power load, but also significantly affect the operating efficiency and stability of various equipment in the system. For example, high temperature weather may cause the power generation efficiency of photovoltaic power generation components to decrease, while high humidity environment may affect the chemical properties of energy storage equipment and the efficiency of the cooling system, resulting in equipment performance degradation and increased failure rate. In addition, high humidity may also affect the insulation of power components, increase the risk of short circuit, and further aggravate the difficulty of peak load regulation and load scheduling of the power system.

[0004] In this context, virtual power plant technology, as an emerging means of power dispatching, integrates distributed energy (such as photovoltaic power generation, energy storage system) and load regulation resources, and uses energy management systems and modern communication technologies to achieve dispatching functions similar to traditional power plants. Virtual power plants can respond to grid demand in real time and improve the dispatching flexibility and efficiency of power systems. However, although virtual power plant technology has improved the dispatching capabilities of power systems to a certain extent, existing technologies still face a series of challenges, especially in terms of flexible dispatching in extreme weather conditions and high temperature and high humidity environments. The following problems exist:

[0005] 1. The scheduling strategy lacks environmental adaptability: Existing scheduling methods for solar-storage direct-flexible systems mostly rely on fixed scheduling strategies that do not fully consider the impact of high temperature and high humidity environments on system performance. In hot weather, the power generation efficiency of photovoltaic modules will be significantly reduced, and high humidity environments may cause the electrochemical performance of energy storage equipment to deteriorate, affecting the charging and discharging efficiency and service life of the battery. Existing scheduling strategies fail to adjust system parameters in real time to cope with these environmental changes, resulting in delayed scheduling responses and an inability to effectively improve the operating efficiency and stability of the system.

[0006] 2. Ignoring the comprehensive impact of extreme weather: Although the solar-storage direct-flexible system takes normal climate conditions into consideration during its design, the existing scheduling models generally ignore the comprehensive impact of multiple environmental factors under extreme weather conditions of high temperature and high humidity. For example, extreme weather not only affects photovoltaic power generation, but may also have a negative impact on multiple aspects such as the battery performance of the energy storage device and the heat dissipation of the equipment. The existing technology fails to fully integrate these factors, resulting in a lack of real-time perception and response capabilities to environmental changes during the scheduling process, affecting the stability and benefits of the system.

[0007] 3. Insufficient scheduling flexibility: The current scheduling strategy of the PV-storage direct-flexible system is difficult to cope with rapidly changing environmental conditions and load demands. In high temperature and high humidity environments, equipment performance fluctuates greatly, the uncertainty of photovoltaic power generation increases, and the charging and discharging efficiency of the energy storage system is also affected. The existing scheduling system fails to make timely dynamic adjustments when environmental conditions change, resulting in insufficient flexibility in system scheduling, making it difficult to achieve accurate scheduling and efficient operation.

[0008] 4. Slow response to environmental changes: Energy demand fluctuations and equipment performance changes in high temperature and high humidity environments are more dramatic, and existing scheduling methods often have slow response problems when dealing with these changes. For example, when the temperature rises, photovoltaic power generation decreases, and when the humidity increases, the discharge efficiency of energy storage equipment may decrease. Existing scheduling models fail to quickly identify and predict these changes, so they cannot adjust scheduling strategies in a timely manner, resulting in waste of resources or reduced scheduling efficiency.

[0009] 5. Lack of effective quantitative evaluation methods: Current scheduling models generally lack accurate quantitative methods to evaluate the impact of high temperature and high humidity environments on system performance. Existing technologies mainly rely on experience or qualitative analysis, and cannot accurately quantify the impact of environmental changes through real-time data. For example, it is impossible to accurately estimate the specific impact of temperature and humidity changes on photovoltaic power generation, energy storage devices, and load scheduling, resulting in insufficient scientificity and accuracy in scheduling decisions.

[0010] 6. Inadequate response to uncertain factors: High temperature and high humidity environment increases the uncertainty of the operation of the photovoltaic storage direct-flexible system. The existing technology lacks an effective response mechanism when facing the uncertainty of photovoltaic power generation, the performance degradation of energy storage equipment and load fluctuations. The existing scheduling method fails to fully consider these uncertainties, making it difficult for the scheduling model to cope with the rapid changes in the environment and system behavior, affecting the stability and safety of the system.

[0011] 7. Weak intelligence and real-time optimization capabilities: Although virtual power plant technology has introduced intelligent scheduling methods, the existing system's intelligent optimization capabilities in extreme weather are still insufficient. Intelligent scheduling systems often rely on preset models or historical data and lack sufficient real-time optimization capabilities. When faced with high temperature and high humidity environments, the system failed to dynamically adjust the scheduling strategy according to real-time environmental changes, resulting in insufficiently rapid and accurate scheduling responses, affecting the system's operating efficiency.

[0012] In summary, although the existing PV-storage direct-flexible system and virtual power plant technology have good performance under normal conditions, they still face many challenges under extreme weather conditions of high temperature and high humidity, especially in terms of scheduling flexibility, environmental adaptability and real-time response capabilities. These problems limit the application effect of existing technologies in dealing with complex weather conditions, and it is urgent to improve the overall performance of the system by optimizing scheduling algorithms, introducing more intelligent control systems, and strengthening real-time monitoring and analysis of environmental changes. Summary of the invention

[0013] The invention provides a method for calculating a flexible and adjustable internal domain of a light storage device in a high-temperature and high-humidity environment, so as to solve the existing technical problems.

[0014] In order to solve the above technical problems, the technical solution proposed by the present invention is:

[0015] A method for calculating a flexible internal adjustable domain of a light storage direct-flexible light storage in a high temperature and high humidity environment comprises the following steps:

[0016] S1. Create a flexibility prediction model for the PV-storage direct-flexible system;

[0017] S2. Fine-tune the component model parameters of the solar-storage direct-flexible system;

[0018] S3. Analyze the coupling effects between system components and ensure the stability of the system under various environmental changes through robust optimization;

[0019] S4. The power demand prediction and dispatching strategy of the developed PV-storage direct-flexible system in high temperature and high humidity environment is verified through example simulation.

[0020] As a further improvement of the above technical solution:

[0021] The S1 specifically includes the following steps: through literature review, technical research and collection and testing of existing models, analyze the operation data, emergency plans and intelligent trends of the power grid under high temperature and high humidity conditions; then, combine historical weather data and experimental tests to evaluate the performance of the model in extreme environments and optimize it to ensure its accuracy and reliability in real-time data processing; finally, form a comprehensive model structure.

[0022] In S1, the analysis process of the flexibility prediction model is divided into two parts: model investigation and test comparison. First, the component characteristics are obtained by studying the photoelectric effect, electrochemistry, DC power flow and flexible operation model, and then typical scenario simulation and accuracy and efficiency evaluation are carried out. Finally, the model structure is determined, including segmented models, high temperature and humidity boundary conditions and comprehensive evaluation to support the application of the system in extreme environments.

[0023] The S2 specifically includes the following steps: sampling of typical scenarios, followed by parameter sensitivity analysis, screening out the key variables that have the greatest impact on system performance, and identifying the most influential parameters through simulation tests and statistical analysis; finally, fine-tuning and optimizing the model based on the analysis results to ensure efficient and stable operation of the system in various environments.

[0024] The sampling of typical scenarios is to first define and sample multiple typical scenarios, and then simulate and study the specific impact of these extreme conditions on the performance of the photovoltaic storage direct-flexible system by collecting real-time data during normal and high-load operations under different environments.

[0025] The fine-tuning and optimization are to sample and analyze in typical urban high-heat and high-humidity scenarios, conduct parameter sensitivity analysis and rough selection in combination with lighting conditions and basic loads, determine the key parameters affecting system performance through simulation tests and statistical analysis, establish a mapping model between parameters and temperature and humidity, further optimize the system through model fine-tuning and temperature-humidity mapping modeling, and finally perform cross-scenario optimization to ensure stable and efficient operation of the photovoltaic storage system in different environments.

[0026] The S3 specifically includes the following steps: first, through the system's component coupling analysis and dynamic simulation model, the interaction between photovoltaics, energy storage, direct current transmission and the power grid is evaluated, and the key influencing factors are identified through sensitivity analysis; then, robust optimization technology is used to perform uncertainty modeling and multi-objective optimization to ensure the optimal operation of the system under various environmental changes and equipment fluctuations, and balance cost, efficiency and reliability; finally, advanced prediction models are developed, combining historical and real-time data, and through machine learning and integrated data processing frameworks, the dispatchability of virtual power plants is optimized, the accuracy and response speed of power demand and supply forecasts are improved, and it is ensured that virtual power plants can adjust their operating strategies in real time and flexibly respond to dynamic changes in power grid demand.

[0027] The S4 specifically includes the following steps: first, generate 24-hour electricity demand data and use linear regression to predict the demand trend, then combine the robust optimization technology to set the robustness margin to 10kW, generate the optimal scheduling strategy considering demand fluctuations, and then verify the prediction accuracy of the model and the effectiveness of the scheduling strategy through demand error analysis.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. High environmental adaptability: The present invention can effectively cope with the impact of high temperature and high humidity environment on the performance of various components of the solar storage direct-flexible system, ensuring the stable operation and efficient output of the system under complex environmental conditions;

[0030] 2. Adaptive robust optimization algorithm: Adopting the adaptive robust optimization algorithm (ARO), it can adjust system parameters in real time to maximize system efficiency while reducing the negative impact of environmental changes on system performance. This algorithm is more flexible than traditional robust optimization methods and can dynamically adjust the optimization strategy according to actual conditions;

[0031] 3. Flexibility prediction model: By establishing a flexibility prediction model, a variety of environmental factors such as temperature, humidity, light intensity, load fluctuations, etc. are comprehensively considered to accurately identify the key factors affecting system operation, thereby optimizing energy scheduling and load management, and improving system response speed and flexibility;

[0032] 4. Intelligent scheduling and optimization: Combining machine learning, time series analysis and other technologies, the intelligent control system can analyze data in real time, optimize energy scheduling, balance load demand, and improve the overall operating efficiency of the system;

[0033] 5. Optimized dispatching of virtual power plants: Through the optimized dispatching mechanism of virtual power plants, the system can participate in autonomous transactions in the electricity market, use market information for flexible dispatching, ensure efficient allocation of resources, and improve economic benefits;

[0034] 6. Real-time monitoring and dynamic adjustment: The system can continuously optimize operating parameters through real-time data monitoring and feedback mechanism, enhance the robustness and adaptability of the system, and ensure stable operation under various environmental changes and uncertainties;

[0035] 7. Wide applicability: This method is not only applicable to the PV-storage direct-flexible system, but can also be customized according to different user needs (such as commercial, industrial, and household) to meet energy management and optimized scheduling in different scenarios;

[0036] 8. Promote green and low-carbon development: By optimizing the operating efficiency of the solar-storage direct-flexible system in high temperature and high humidity environments, promote the application of renewable energy, help green and low-carbon development, and reduce energy waste;

[0037] In summary, the present invention has strong practicality and adaptability in high temperature and high humidity environments, and can effectively improve the overall efficiency, stability and flexibility of the photovoltaic storage direct-flexible system to meet the power needs in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a technical roadmap of the flexibility prediction model of the solar-storage direct-flexible system in the embodiment of the present invention;

[0040] Figure 2 A technical roadmap for fine-tuning the component model parameters of the solar-storage direct-flexible system in an embodiment of the present invention;

[0041] Figure 3 It is a technical roadmap for predicting the dispatchability of a PV-storage-direct-flexible virtual power plant in an embodiment of the present invention;

[0042] Figure 4 A comparison diagram between actual power demand and predicted power demand in an embodiment of the present invention;

[0043] Figure 5 A scheduling strategy diagram based on robust optimization in an embodiment of the present invention;

[0044] Figure 6 This is a demand error analysis diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to facilitate the understanding of the present invention, the present invention will be described more comprehensively and meticulously below in conjunction with the accompanying drawings and preferred embodiments of the present invention, but the protection scope of the present invention is not limited to the following specific embodiments.

[0046] Unless otherwise defined, all the professional terms used below have the same meanings as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention.

[0047] Unless otherwise specified, various raw materials, reagents, instruments and equipment used in the present invention can be purchased from the market or prepared by existing methods.

[0048] Embodiment: A method for calculating the flexible internal adjustable domain of light storage in a high temperature and high humidity environment, comprising the following steps:

[0049] like Figure 1 As shown in the figure, through a systematic literature review and existing technology research, key data related to power grid operation are collated and analyzed. These data cover important parameters such as faulty equipment, faulty phase, and reclosing operation. In addition, the emergency plan and accident handling process of the power grid will be studied to ensure rapid response in emergency situations.

[0050] On this basis, this study will conduct an in-depth investigation of the current status and development trends of artificial intelligence applications in real-time monitoring of power grid status and intelligent fault handling at home and abroad. This process will include comprehensive collection and analysis of existing models of different photovoltaic, energy storage, DC transmission systems and grid flexible response technologies. Through this systematic investigation, the project has laid a solid theoretical foundation and practical basis for subsequent model development.

[0051] Next, the research team will design a series of experiments, combining historical weather data and field measurement data to simulate the performance of the solar-storage direct-flexible system under high temperature and high humidity conditions. The experiments will focus on key performance indicators such as the accuracy, response time and failure rate of the model under extreme conditions. This link will provide important data for understanding the performance of the model in practical applications.

[0052] To further improve the model's performance, the study will use advanced data analysis methods, such as machine learning and statistical analysis, to evaluate the model's effectiveness and reliability when processing real-time data. Through these methods, researchers can identify key factors that affect model performance and make targeted improvements to the model.

[0053] Based on the experimental results, the research team will select the best performing model and make necessary adjustments and optimizations to ensure that the model can accurately predict the behavior of the power system under high temperature and high humidity in summer. This process involves not only the adjustment of model parameters, but also the application of optimization algorithms to ensure the stability and reliability of the model under various conditions.

[0054] Ultimately, the research will form a comprehensive model structure that can theoretically support the analysis of power systems under high temperature and high humidity conditions, and provide decision support for power grid dispatching in actual operations. This comprehensive model can not only adapt to different environmental conditions, but also provide scientific basis and data support for the optimized operation of the power system, and promote the efficient, safe and sustainable development of the power grid. Through this research, it is expected to provide innovative solutions for research and practice in related fields, and improve the operational flexibility and reliability of the photovoltaic storage direct-flexible system under extreme climate conditions.

[0055] like Figure 2As shown, the present invention will achieve parameter sensitivity analysis and model fine-tuning of the photovoltaic storage direct-flexible system under high temperature and high humidity conditions through the following detailed technical scheme. The preliminary work will focus on sampling typical scenarios, defining and collecting multiple typical scenarios that reflect high temperature, high humidity and basic load conditions in summer urban environments. These scenarios will specifically reflect areas with obvious urban heat island effects, continuous high humidity weather, and real-time data during routine and high-load operations. Through the collection of these data, the research team can simulate and deeply explore the specific impact of extreme environmental conditions on the performance of photovoltaic storage direct-flexible systems.

[0056] During this process, multiple key indicators such as sunshine intensity, ambient temperature, humidity and grid load will be monitored in detail to ensure the comprehensiveness and representativeness of the data obtained, laying a solid foundation for subsequent model construction.

[0057] After completing the data sampling of typical scenarios, the project will enter the parameter sensitivity analysis phase. In this phase, the research team will conduct a preliminary screening of a wide range of parameters to identify key variables that may have a significant impact on system performance. By designing simulation experiments, the team will observe the specific impact of these parameters on system performance when they change under different environmental conditions, and use statistical analysis methods such as variance analysis and regression analysis to process experimental data. This analysis will help identify the most influential parameters and determine the research focus and direction for subsequent model fine-tuning.

[0058] Next, the project will enter the stage of fine-tuning and fine-tuning the model. Based on the key parameters identified in the previous step, a mapping model between the parameters and temperature and humidity will be established to accurately simulate how these environmental variables affect the performance of each component of the system. Through this mapping model, researchers will be able to better understand the specific impact of different environmental conditions on the solar-storage direct-flexible system.

[0059] On this basis, the research team will use system identification technology to optimize the model response to ensure that the model can accurately reflect the dynamic behavior of the actual system. This process will not only help improve the applicability of the model, but also enhance its flexibility and reliability under different operating conditions. In addition, the research will ensure that the adjusted model can remain efficient and stable in a variety of possible operating environments through cross-scenario optimization. To achieve this goal, the team will repeatedly adjust and verify the model under different temperature and humidity conditions to ensure that it achieves the best overall performance.

[0060] Through this comprehensive research program, the project aims to gain a deeper understanding of the operational efficiency and reliability of the PV-storage direct-flexible system under high temperature and high humidity conditions. The ultimate goal is to provide more scientific and precise support for energy management in practical applications. Through in-depth analysis of key parameters and fine-tuning of the model, the research will provide strong data support for the optimized operation of the power system and promote the flexibility and stability of the power system under extreme climate conditions.

[0061] like Figure 3 As shown, the present invention first conducts component coupling analysis to comprehensively evaluate the interaction between the components of the system. By constructing a dynamic simulation model, the research team will deeply analyze the mutual influence between photovoltaic power generation, energy storage systems, DC transmission technology and flexible response of the power grid, especially when different environmental conditions change, how each component affects each other's performance and stability will become the focus of the analysis. In addition, the project will also carry out parameter sensitivity analysis to identify key influencing factors such as temperature, humidity and light intensity, and evaluate the specific impact of these parameter changes on the overall system performance. In order to ensure the accuracy of the coupling analysis, the team will adopt a technical cross-validation method to ensure the consistency between the model prediction results and the actual operation data, thereby improving the reliability and practicality of the model.

[0062] Secondly, for robust optimization modeling of aggregate models, the project plans to develop a comprehensive optimization framework to cope with uncertainties such as weather changes and equipment performance fluctuations. The framework will use robust optimization techniques to ensure that the system can maintain optimal operation under various environmental conditions. By designing a multi-objective optimization strategy, the project will achieve a balance between cost, efficiency and reliability, thereby minimizing operating costs and environmental impact while meeting electricity demand. This optimization strategy will promote efficient management of energy production and consumption, ensuring the sustainability and flexibility of the power system.

[0063] In order to verify the effectiveness of the optimization model, the study will use simulation data for testing and adjust the model parameters based on the test results to improve the accuracy and stability of the model. The ultimate goal is to achieve a power system that can adapt to various complex environmental conditions and enhance its ability to respond to emergencies.

[0064] In terms of dispatchability prediction algorithms, the project will also develop advanced prediction models to estimate the dispatchability of virtual power plants. These models will be based on historical and real-time data, using machine learning and statistical analysis methods to accurately predict changes in power demand and supply. By optimizing the prediction algorithms, the models will improve accuracy and response speed, especially by applying deep learning networks to process large-scale data sets, so as to quickly adapt to dynamic changes in the environment.

[0065] In addition, the project will develop an integrated data processing framework to integrate information from multiple data sources. This framework will ensure that the algorithm can effectively access and analyze all relevant data, and seamlessly integrate the prediction algorithm into the real-time monitoring system, so that the virtual power plant can dynamically adjust its operation strategy according to grid conditions and prediction results. Through this integration, the virtual power plant will be able to manage resources more effectively, optimize operating efficiency, and better meet electricity demand in actual applications.

[0066] In the example of the present invention, a whole day (24 hours) of power demand data is generated to simulate the actual operation. First, we generate time series data ranging from 0 to 24 hours. In order to simulate the changes in power demand during the day and night, a sine function and random noise are used. The power demand during the day is set between 80kW and 100kW:

[0067]

[0068] Where Dday(t) represents the daytime electricity demand at time t (unit: hour), σ 2 represents the variance of random noise. The night demand is set to 30kW, and the following model is used:

[0069] Dnight(t)=30+N(0,σ 2 ) (2)

[0070] Random fluctuations make demand data more realistic. In order to predict electricity demand, we use linear regression method. By constructing the independent variable matrix X (including constant terms and time variables), it can be expressed as:

[0071]

[0072] On this basis we calculated the linear regression coefficient:

[0073] β=(X T X) -1 X T y (4)

[0074] The resulting forecast demand curve shows the overall trend of electricity demand. In the robust optimization process, the robustness margin is set to 10kW, and the optimal dispatch strategy is generated based on the predicted power demand. This strategy takes into account potential demand fluctuations to ensure the stable operation of the system under uncertainty. The optimization objective can be expressed as:

[0075]

[0076] like Figure 4As shown in the figure, it shows the comparison between actual power demand and predicted power demand. The dotted line represents the actual demand, while the solid line represents the predicted demand. It can be seen that the prediction model reflects the changing trend of demand well. Figure 5 As shown in the figure, the gray solid line is the predicted demand, and the black solid line is the scheduling strategy based on robust optimization. Through this strategy, the system increases the robustness margin based on the predicted demand and improves its ability to respond to demand fluctuations. Figure 6 This bar chart shows the hourly demand error E(t) = D pred (t)-D actual (t), the visualization of the error helps to analyze the accuracy of model prediction and the effectiveness of the scheduling strategy. The above results show that the power demand forecasting and scheduling strategy based on adaptive robust optimization can effectively improve the flexibility and reliability of the solar-storage direct-flexible system in high temperature and high humidity environments, providing theoretical support for practical applications.

[0077] The photovoltaic storage direct-flexible system of the present invention comprises a photovoltaic module, an energy storage device, a control unit and a sensor.

[0078] Photovoltaic modules are used to capture solar energy and convert it into electrical energy. The design of photovoltaic modules takes into account the working characteristics in high temperature and high humidity environments. They use efficient photovoltaic materials and modular design to maintain high energy conversion efficiency under changing climate conditions. At the same time, these photovoltaic modules have good heat dissipation performance to cope with the impact of high temperatures.

[0079] Energy storage devices are used to store the electricity generated by photovoltaic modules to cope with fluctuations in load demand. The energy storage device can be a battery pack, supercapacitor or other type of energy storage technology with high energy density and long cycle life. In high temperature and high humidity environments, the design of the energy storage device ensures that it can still operate normally under extreme conditions and has efficient charging and discharging performance.

[0080] The control unit optimizes the operation strategy of photovoltaic modules and energy storage devices by monitoring the system status and environmental conditions in real time. The control unit uses an advanced adaptive scheduling algorithm to dynamically adjust the working status of photovoltaic modules and the charging and discharging strategy of energy storage devices based on real-time data to achieve optimal energy efficiency. This algorithm combines machine learning and data analysis technology, enabling the system to make quick and accurate decisions under different meteorological conditions and load requirements.

[0081] Sensors are used to collect meteorological data and equipment operation data in high temperature and high humidity environments, including light intensity, ambient temperature, humidity, and load changes. These sensors have high sensitivity and fast response capabilities, and can transmit data to the control unit in real time, providing a basis for the intelligent scheduling of the system. The sensor data can not only be used to optimize the operation of the system, but also to predict and warn potential failures and improve the reliability of the system.

[0082] The control unit uses an adaptive robust optimization algorithm that can adjust to uncertainties and dynamic environmental conditions. The algorithm has strong robustness and can effectively deal with the uncertainty and noise of sensor data under high temperature and high humidity conditions, thereby ensuring the stability and reliability of the system.

[0083] The design of the PV-storage direct-flexible system also takes into account a variety of energy management strategies, which can be flexibly adjusted according to real-time load demand. This flexibility not only improves the economy of the system, but also enhances its support for the power grid and ensures the continuity and stability of power supply.

[0084] The present invention constructs a flexibility prediction model, comprehensively considers the impact of high temperature and high humidity environment on the system, and uses an adaptive robust optimization algorithm to dynamically adjust system parameters to ensure its efficient operation under extreme environmental conditions. This method realizes real-time optimization and scheduling of the photovoltaic storage direct-flexible system, improves the response speed and flexibility of the system, can effectively deal with uncertainties and external interference in the power system, and ensures stable operation of the system.

[0085] By establishing a flexibility prediction model, we dynamically analyze and model the impact of high temperature and high humidity environments on the PV-storage direct-flexible system, ensuring the adaptability and stability of the system under various environmental conditions, thereby achieving efficient energy scheduling and load management. The flexibility prediction model comprehensively considers a variety of environmental factors, including but not limited to temperature, humidity, light intensity, load fluctuations, equipment aging, etc. Through data collection and analysis, we identify key influencing factors and establish a multi-dimensional prediction model to achieve more accurate flexibility prediction.

[0086] The flexibility prediction model is trained using machine learning algorithms to improve the model's ability to adapt to environmental changes, and can adjust itself based on historical data to achieve dynamic prediction and evaluation of future operating states. The adaptive robust optimization algorithm uses a multi-objective optimization strategy to maximize system operating efficiency and flexibility while ensuring system security and stability, and optimize scheduling plans, including but not limited to minimizing energy losses, balancing load demands, and improving response speed.

[0087] The PV-storage direct-flexible system consists of photovoltaic modules, energy storage devices and loads, which are coordinated through an intelligent control system. The intelligent control system dynamically adjusts the operating status of each part through real-time data analysis and optimization algorithm scheduling to cope with load changes and environmental interference. The intelligent control system combines edge computing with cloud computing to achieve real-time and intelligent data processing, allowing the system to quickly respond to external changes and improve the overall flexibility and operating efficiency of the system.

[0088] Flexible and adjustable domain computing includes multiple sub-modules such as load demand forecasting, energy scheduling optimization, system status monitoring and evaluation. Each sub-module forms a closed-loop control through mutual cooperation and feedback mechanism to ensure the stable operation and flexible response of the system. The load demand forecasting module uses time series analysis and deep learning algorithms to analyze historical load data and accurately predict future load demand changes, thereby providing an effective basis for energy scheduling optimization.

[0089] The energy dispatch optimization module comprehensively considers electricity price fluctuations, user needs and environmental factors, and dynamically adjusts the charging and discharging strategies of energy storage devices to maximize economic benefits and improve the response speed of the system. By combining with the optimization dispatch mechanism of the virtual power plant, the system can autonomously participate in transactions in the power market, use market information for flexible dispatch, and achieve efficient allocation of resources.

[0090] The implementation process of the method includes data collection, model building, optimization calculation and scheduling execution, and each step is automated to improve the operating efficiency and accuracy of the system. During operation, the system can continuously optimize and adjust operating parameters through real-time monitoring and feedback mechanisms to improve the robustness and adaptability of the system and ensure stability in complex environments.

[0091] Through integration with other renewable energy systems, a wider range of energy management and optimal scheduling can be achieved, promoting the application of renewable energy in high temperature and high humidity environments, and promoting green and low-carbon development. The flexible prediction model and adaptive robust optimization algorithm can be customized according to user needs to meet application requirements in different scenarios, including but not limited to energy management in commercial, industrial and household fields.

[0092] Through cooperation with the State Grid and related institutions, we will promote the application of this method, provide technical support and solutions for power systems in high temperature and high humidity environments, and improve the overall efficiency and safety of power systems.

[0093] The present invention has high environmental adaptability, can effectively cope with the impact of high temperature and high humidity environment on equipment performance, and ensure the stable operation of the system under complex conditions; the adaptive robust optimization algorithm can optimize the system operation in real time, maximize the utilization efficiency of renewable energy, and reduce energy losses caused by environmental changes; the system collects and analyzes real-time data to realize the calculation of the flexible and adjustable domain within the system, ensures rapid response and optimization adjustment under different operating conditions, and improves the level of intelligence; in the case of sensitivity to electricity demand and market changes, the photovoltaic storage system of the present invention can quickly adjust the operating strategy, reduce operating costs, and achieve efficient energy management.

[0094] The applicability of traditional photovoltaic-storage-direct-flexible system models under different climatic conditions is still insufficient. High temperature and high humidity weather can significantly affect the efficiency of photovoltaic power generation, energy storage equipment and direct current transmission. Therefore, it is necessary to evaluate and select models suitable for flexibility prediction under these extreme conditions. The technical route of the present invention includes the investigation of existing models, operation constraint analysis, and the establishment of a dynamic mapping relationship between the influence of temperature and humidity on model parameters, and the design of effective simulation tests to compare the performance of different models under extreme weather conditions and ensure the accuracy and stability of their predictions, providing a model structure suitable for flexibility prediction in high temperature and high humidity environments in summer for subsequent research of the present invention.

[0095] The operating efficiency of the PV-storage direct-flexible system in a high temperature and high humidity environment is affected by a variety of parameters. The key point of the present invention is to conduct sensitivity analysis on these parameters and identify the parameters that have the greatest impact on system performance. By collecting and analyzing data in a typical summer scenario, sensitive parameters can be identified to fine-tune and optimize the model. Based on these findings, the model is fine-tuned to study how the parameters change with temperature, humidity, and operating status, and a precise parameter change model is identified through a system identification method to improve scheduling flexibility and accuracy in extreme environments.

[0096] With the changes in electricity demand and extreme weather, the operating efficiency and response speed of virtual power plants have become increasingly important. Traditional virtual power plant flexibility aggregation prediction models usually only consider the output constraints of different distributed energy sources and the voltage and current thermal effects of the lines, but ignore the in-depth consideration of the coupling relationship between different components and technologies. The photovoltaic storage direct current flexible virtual power plant aggregation flexibility prediction model constraints developed by the present invention comprehensively consider the coupling dynamics of photovoltaic power generation, battery energy storage and direct current transmission technology, and establish a comprehensive prediction model that can effectively reflect these complex relationships, which is the core of the research. In addition, under the uncertainty caused by high temperature and high humidity, robust optimization methods are applied to ensure that the system can maintain optimal operation under various environmental conditions.

[0097] Traditional optimization methods generally use robust optimization (Robust Optimization, RO), whose uncertainty set is pre-defined, that is, we have a fixed definition for all possible uncertainty values, which may not fully reflect the actual situation and is relatively conservative. The adaptive robust optimization (Adaptive Robust Optimization, ARO) proposed in the present invention allows the uncertainty set to be adjusted according to the actual situation during the optimization process, which means that the optimization scheme can be adaptively adjusted according to the actual situation, so as to better adapt to the changing conditions, reduce the decision-making risk, and ensure that the goal can still be achieved under variable conditions. In addition, ARO shows good adaptability. As the environment and conditions change, the system requirements and constraints may change. ARO can adapt to new requirements and constraints by continuously updating and adjusting the model, thereby improving the overall performance of the system. This adaptability ensures the effectiveness and reliability of the optimization results in different situations, giving it greater advantages in practical applications.

[0098] The present invention can extensively investigate the relevant models of existing photovoltaic, energy storage, direct current transmission and flexible interactive technologies, identify the deficiencies of current models under high temperature and high humidity conditions, analyze the operating characteristics of the photovoltaic, energy storage, direct current transmission and flexible interactive systems under high temperature and high humidity environments, including photovoltaic power generation efficiency, energy storage performance and grid response capabilities, understand the impact of different environmental factors on system performance, and based on system characteristics, construct a prediction model that comprehensively considers photovoltaic, energy storage and grid flexible response, set the mapping relationship between model parameters and environmental factors (temperature, humidity, etc.), collect real-time data under high temperature and high humidity conditions, perform data analysis to identify key parameters and their impact on system flexibility, through simulation tests and historical data.

Claims

1. A method for calculating the flexible internal adjustable domain of light storage in a high temperature and high humidity environment, characterized in that: The following steps are involved: S1. Create a flexibility prediction model for the PV-storage direct-flexible system; S2. Fine-tune the component model parameters of the solar-storage direct-flexible system; S3. Analyze the coupling effects between system components and ensure the stability of the system under various environmental changes through robust optimization; S4. The power demand prediction and dispatching strategy of the developed PV-storage direct-flexible system in high temperature and high humidity environment is verified through example simulation.

2. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 1 is characterized in that: The S1 specifically includes the following steps: through literature review, technical research and collection and testing of existing models, analyze the operation data, emergency plans and intelligent trends of the power grid under high temperature and high humidity conditions; then, combine historical weather data and experimental tests to evaluate the performance of the model in extreme environments and optimize it to ensure its accuracy and reliability in real-time data processing; finally, form a comprehensive model structure.

3. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 2 is characterized in that: In S1, the analysis process of the flexibility prediction model is divided into two parts: model investigation and test comparison. First, the component characteristics are obtained by studying the photoelectric effect, electrochemistry, DC power flow and flexible operation model, and then typical scenario simulation and accuracy and efficiency evaluation are carried out. Finally, the model structure is determined, including segmented models, high temperature and humidity boundary conditions and comprehensive evaluation to support the application of the system in extreme environments.

4. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 1 is characterized in that: The S2 specifically includes the following steps: sampling of typical scenarios, followed by parameter sensitivity analysis, screening out the key variables that have the greatest impact on system performance, and identifying the most influential parameters through simulation tests and statistical analysis; finally, fine-tuning and optimizing the model based on the analysis results to ensure efficient and stable operation of the system in various environments.

5. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 4 is characterized in that: The sampling of typical scenarios is to first define and sample multiple typical scenarios, and then simulate and study the specific impact of these extreme conditions on the performance of the photovoltaic storage direct-flexible system by collecting real-time data during normal and high-load operations under different environments.

6. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 5 is characterized in that: The fine-tuning and optimization are to sample and analyze in typical urban high-heat and high-humidity scenarios, conduct parameter sensitivity analysis and rough selection in combination with lighting conditions and basic loads, determine the key parameters affecting system performance through simulation tests and statistical analysis, establish a mapping model between parameters and temperature and humidity, further optimize the system through model fine-tuning and temperature-humidity mapping modeling, and finally perform cross-scenario optimization to ensure stable and efficient operation of the photovoltaic storage system in different environments.

7. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 1 is characterized in that: The S3 specifically includes the following steps: first, through the system's component coupling analysis and dynamic simulation model, the interaction between photovoltaics, energy storage, direct current transmission and the power grid is evaluated, and the key influencing factors are identified through sensitivity analysis; then, robust optimization technology is used to perform uncertainty modeling and multi-objective optimization to ensure the optimal operation of the system under various environmental changes and equipment fluctuations, and balance cost, efficiency and reliability; finally, advanced prediction models are developed, combining historical and real-time data, and through machine learning and integrated data processing frameworks, the dispatchability of virtual power plants is optimized, the accuracy and response speed of power demand and supply forecasts are improved, and it is ensured that virtual power plants can adjust their operating strategies in real time and flexibly respond to dynamic changes in power grid demand.

8. The method for calculating the flexible internal adjustable range of light storage in a high temperature and high humidity environment according to claim 1, characterized in that: The S4 specifically includes the following steps: first, generate 24-hour electricity demand data and use linear regression to predict the demand trend, then combine the robust optimization technology to set the robustness margin to 10kW, generate the optimal scheduling strategy considering demand fluctuations, and then verify the prediction accuracy of the model and the effectiveness of the scheduling strategy through demand error analysis.

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