Method and system for evaluating load response capability of photo-thermal power generation system

Abstract

The invention relates to the technical field of photo-thermal power generation, in particular to a photo-thermal power generation system load response capability evaluation method and system, and improves the accuracy and reliability of photo-thermal power generation system load response capability evaluation. The method comprises the following steps: acquiring historical operation data of the photo-thermal power generation system; based on the historical operation data, collecting response data during each load change, including response time, response stability and maximum peak regulation capability; analyzing the historical operation data and response data during each load change, and extracting key environment parameters influencing load response; inputting the key environment parameters and the response data when the load changes into a load response evaluation model to obtain a response capability evaluation score; and comparing the responsiveness evaluation score with a preset evaluation threshold, and if the responsiveness evaluation score exceeds the preset evaluation threshold, indicating that the load responsiveness of the photo-thermal power generation system is good.

Description

Technical Field

[0001] The present invention relates to the technical field of solar thermal power generation, and in particular to a method and system for evaluating the load response capability of a solar thermal power generation system. Background Art

[0002] With the transformation of the global energy structure and the vigorous development of renewable energy, solar thermal power generation, as a clean and renewable energy utilization method, has received more and more attention and application. The solar thermal power generation system collects solar energy through collectors and converts it into thermal energy, and then drives the generator to generate electricity through a thermal cycle. This power generation method not only has the advantage of abundant resources, but also can achieve the continuity and stability of power production, which is of great significance for alleviating energy shortages and reducing carbon emissions.

[0003] However, in actual operation, the load response capability of the CSP system has become one of the key factors affecting its operating efficiency and reliability. Load response capability refers to the ability of the CSP system to quickly and stably adjust its output power to meet the needs of the power grid when faced with load changes.

[0004] At present, the evaluation of the load response capacity of the CSP system mainly relies on empirical judgment and simple data analysis. This method often lacks systematicity and scientificity, and it is difficult to accurately reflect the actual load response capacity of the CSP system. At the same time, since the CSP system is affected by a variety of environmental factors, such as solar radiation intensity, ambient temperature, wind speed, etc., these factors will have a complex impact on the load response capacity of the system. Therefore, it is difficult to comprehensively and accurately evaluate the load response capacity of the CSP system based on empirical judgment alone.

[0005] In order to overcome the shortcomings of existing evaluation methods and improve the accuracy and scientificity of the evaluation of the load response capability of CSP systems, it is urgent to develop a CSP system load response capability evaluation method based on historical operation data. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a method for evaluating the load response capability of a solar thermal power generation system, which improves the accuracy and reliability of the evaluation of the load response capability of the solar thermal power generation system.

[0007] In a first aspect, the present invention provides a method for evaluating load response capability of a solar thermal power generation system, the method comprising:

[0008] Obtain historical operation data of the CSP system;

[0009] Based on historical operation data, collect response data for each load change, including response time, response stability and maximum peak load regulation capacity;

[0010] Analyze historical operation data and response data of each load change to extract key environmental parameters that affect load response;

[0011] Input the key environmental parameters and the response data of load changes into the load response evaluation model to obtain the response capability evaluation score;

[0012] The response capability evaluation score is compared with the preset evaluation threshold. If the response capability evaluation score exceeds the preset evaluation threshold, it indicates that the load response capability of the CSP system is good; if the response capability evaluation score does not exceed the preset evaluation threshold, it indicates that the load response capability of the CSP system needs to be optimized.

[0013] Furthermore, the load response evaluation model calculates the load response capability evaluation score by the following formula:

[0014] S=α·R t +β·R s +γR p +δ·E

[0015] Among them, S represents the comprehensive response capability evaluation score, R t represents the response time, α represents the weight coefficient of the response time, R s represents the response stability, β represents the weight coefficient of response stability, R p represents the maximum peak-shaving capacity, γ represents the weight coefficient of the maximum peak-shaving capacity, E represents the key environmental parameters, and δ represents the weight coefficient of the key environmental parameters.

[0016] Furthermore, the structure of the load response evaluation model is:

[0017] Input layer: used to input key environmental parameters and response data when load changes;

[0018] Feature processing layer: preprocessing and feature selection of input environmental parameters and response data;

[0019] Model core layer: used to calculate the load response capability evaluation score based on key environmental parameters and response data when load changes;

[0020] Output layer: used to output load response capability evaluation scores.

[0021] Furthermore, the method for obtaining historical operation data of the solar thermal power generation system includes:

[0022] Deploy data acquisition sensors and monitoring equipment in the CSP system, and obtain the operating parameters of each area of ​​the CSP system through the deployed sensors and monitoring equipment;

[0023] Summarize the collected operating parameters of each area to obtain historical operating data;

[0024] Pre-process the collected historical operation data, including data cleaning, data verification and data conversion;

[0025] Regularly inspect and maintain data collection sensors and monitoring equipment to ensure the normal operation of the system and the accuracy of data collection;

[0026] The historical operation data is archived and saved so that it can be called upon at any time during the subsequent analysis and evaluation process.

[0027] Furthermore, response data is collected every time the load changes, including:

[0028] Through the system output power and grid dispatch instructions, the moment of load change is identified from historical operation data;

[0029] Record the system response time based on the moment of load change;

[0030] According to the moment of load change, the system response stability after load change is evaluated by monitoring the fluctuation of output power over a period of time;

[0031] According to the time of load change, the maximum peak load regulation capacity is obtained by comparing the output power of the system before and after the load change and calculating the change amount;

[0032] The collected response time, response stability and maximum peak-shaving capacity are recorded and stored.

[0033] Furthermore, the method for obtaining key environmental parameters affecting load response includes:

[0034] Extract environmental parameters of CSP systems from historical operating data;

[0035] Calculate the correlation coefficient between environmental parameters and load response data;

[0036] According to the correlation coefficient, the environmental parameters related to the load response capacity are screened out;

[0037] Physically verify the selected environmental parameters related to load responsiveness and analyze the specific impact mechanisms of these parameters on load responsiveness;

[0038] Verify the effectiveness of the selected parameters in actual operation and explain how they affect the system's response time, stability, and peak-shaving capabilities.

[0039] Furthermore, the influencing factors for setting the preset evaluation threshold include load characteristics, energy storage capacity, thermal cycle efficiency, solar radiation intensity, ambient temperature, wind speed and humidity, operating cost, technical level and environmental protection requirements.

[0040] On the other hand, the present application also provides a system for evaluating load response capability of a solar thermal power generation system, the system comprising:

[0041] Data acquisition module, to obtain historical operation data of the CSP system;

[0042] The response data collection module collects response data for each load change based on historical operation data, including response time, response stability and maximum peak load regulation capacity;

[0043] Environmental parameter extraction module, which analyzes historical operation data and response data of each load change to extract key environmental parameters that affect load response;

[0044] The load response evaluation module inputs the key environmental parameters and the response data of the load changes into the load response evaluation model to obtain the response capability evaluation score;

[0045] The evaluation result comparison module compares the response capability evaluation score with the preset evaluation threshold. If the response capability evaluation score exceeds the preset evaluation threshold, it indicates that the load response capability of the CSP system is good; if the response capability evaluation score does not exceed the preset evaluation threshold, it indicates that the load response capability of the CSP system needs to be optimized.

[0046] The optimization suggestion module provides targeted optimization suggestions and improvement measures for the CSP system based on the evaluation results.

[0047] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0049] Compared with the prior art, the present invention has the following beneficial effects: the method evaluates the load response capability of the system based on historical operation data, avoiding the subjectivity and uncertainty of traditional empirical judgment, making the evaluation result more scientific and objective; by collecting response data at each load change, the method can measure the response performance of the system from multiple perspectives, providing a more comprehensive evaluation framework;

[0050] By analyzing the relationship between historical operating data and response data, key environmental parameters that affect load response can be extracted, which helps to more accurately understand and predict the performance of the system under different conditions; by using the load response evaluation model to quantify the system's responsiveness, specific scores can be obtained, which helps managers more easily understand and track system performance changes;

[0051] By comparing with the preset evaluation threshold, it is possible to quickly determine whether the system has achieved the expected response level, so as to promptly identify areas that need improvement; for those situations that do not reach the preset threshold, optimization requirements are proposed, prompting operators to take actions to improve system performance, thus forming a continuous improvement cycle;

[0052] Since this method takes into account the influence of multiple environmental factors and is based on actual operating data, it can adapt to different operating environments and improve the universality and reliability of the evaluation;

[0053] In summary, this method improves the accuracy and reliability of the evaluation of the load response capacity of the CSP system through scientific methodology and data analysis, which has positive significance for promoting the development and application of CSP technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of the present invention;

[0055] Figure 2 is a flow chart of a method for obtaining historical operation data of a CSP system;

[0056] Figure 3 It is a flow chart to collect response data every time the load changes;

[0057] Figure 4 The present invention is a structural diagram of a load response capability evaluation system of a solar thermal power generation system. DETAILED DESCRIPTION

[0058] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0059] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0060] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0061] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0062] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0063] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0064] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0065] The present application is described below in conjunction with the drawings in the present application.

[0066] Embodiment 1: Figures 1 to 3 As shown, a method for evaluating load response capability of a solar thermal power generation system of the present invention specifically comprises the following steps:

[0067] S1. Obtain historical operation data of the CSP system;

[0068] The method for obtaining historical operation data of a solar thermal power generation system includes:

[0069] Deploy professional data acquisition sensors and monitoring equipment at key locations of the CSP system, and obtain the operating parameters of each area of ​​the CSP system through the deployed sensors and monitoring equipment; ensure that the operating status of the system can be recorded in real time and continuously, including key parameters such as output power, solar radiation intensity, ambient temperature, wind speed, system pressure, flow, etc.;

[0070] Summarize the collected operating parameters of each area to obtain historical operating data;

[0071] Preprocess the collected historical operation data, including data cleaning, data verification and data conversion; data cleaning is used to remove invalid data such as abnormal values, missing values ​​and duplicate values ​​to ensure data accuracy; data verification is used to verify the integrity and consistency of data to ensure that no errors occur during data collection and transmission; data conversion is to convert raw data into a data format suitable for subsequent analysis and evaluation;

[0072] Regularly inspect and maintain data collection sensors and monitoring equipment to ensure the normal operation of the system and the accuracy of data collection;

[0073] Regularly audit and verify historical operating data to ensure the authenticity and credibility of the data;

[0074] The historical operation data is archived and saved so that it can be called upon at any time during the subsequent analysis and evaluation process.

[0075] By deploying professional data acquisition sensors and monitoring equipment at key locations of the CSP system, multiple key parameters of the system can be recorded in real time and continuously, ensuring the comprehensiveness and accuracy of the data and providing a solid foundation for subsequent analysis and evaluation. The collected historical operation data are preprocessed, including data cleaning, data verification and data conversion, effectively removing invalid data such as outliers, missing values ​​and duplicate values, verifying the integrity and consistency of the data, and converting the original data into a data format suitable for subsequent analysis and evaluation. The data quality is improved and the reliability and availability of the data are ensured. The data acquisition sensors and monitoring equipment are regularly inspected and maintained to ensure the normal operation of the system and the accuracy of data collection. At the same time, the historical operation data is regularly audited and verified to further ensure the authenticity and credibility of the data. The credibility of the data is enhanced, providing a reliable data source for analysis and evaluation. The historical operation data is archived and saved so that the required data can be called at any time during the subsequent analysis and evaluation process. This data management method improves the accessibility and utilization of data and provides convenient data support for the optimization and improvement of the CSP system.

[0076] S2. Based on historical operation data, collect response data for each load change, including response time, response stability and maximum peak load regulation capacity;

[0077] Collect response data for each load change, including:

[0078] By monitoring the system's output power and grid dispatching instructions, the moment of load change can be identified from historical operating data; when the output power changes significantly or a grid dispatching instruction is received to adjust the output power, it can be regarded as a load change event;

[0079] Once a load change event is identified, the system response time is recorded immediately; the response time is the time from when the load change occurs to when the system starts to adjust its output power;

[0080] By monitoring the fluctuation of output power over a period of time, the system's response stability after load changes can be evaluated; the standard deviation or coefficient of variation of the output power can be calculated to quantify its stability; in addition, it can also be observed whether the output power can return to a stable state in a relatively short period of time;

[0081] The maximum peak-shaving capacity refers to the maximum output power that the system can increase or decrease in a short period of time. The maximum peak-shaving capacity is obtained by comparing the output power of the system before and after the load change and calculating the change.

[0082] The collected response time, response stability and maximum peak shaving capacity shall be accurately recorded and stored in an appropriate database.

[0083] In this step, by real-time monitoring of the system's output power and grid dispatching instructions, the moment of load change can be accurately identified from historical operating data to ensure that each load change event is accurately captured; collecting the three key indicators of response time, response stability and maximum peak-shaving capacity can comprehensively reflect the response capability of the solar thermal power generation system when the load changes; the response time reflects the rapid response characteristics of the system; the response stability reveals whether the system can quickly return to a stable state after adjustment; and the maximum peak-shaving capacity measures the system's regulation potential when facing sudden load changes; the collected response time, response stability and maximum peak-shaving capacity are accurately recorded and stored in the database for subsequent data analysis and mining; it not only helps to timely discover problems and potential risks in the system, but also provides data support for the optimization and improvement of the system; through continuous monitoring and evaluation of load response capability, the shortcomings of the solar thermal power generation system during operation can be discovered in a timely manner, providing direction for the continuous improvement and optimization of the system; this helps to improve the overall performance and operating efficiency of the system, thereby better meeting grid needs and user expectations.

[0084] S3. Analyze historical operation data and response data of each load change to extract key environmental parameters that affect load response;

[0085] Methods for obtaining key environmental parameters that affect load response include:

[0086] Extract a large amount of data accumulated by the CSP system during long-term operation from historical operation data; including the system's output power, load change records, and synchronously recorded environmental parameters;

[0087] Calculate the correlation coefficient between environmental parameters and load response data using correlation analysis tools in statistical software or programming languages;

[0088] According to the size and significance level of the correlation coefficient, the environmental parameters significantly related to the load response capacity are screened out; the purpose of this step is to narrow the scope and focus on those environmental factors that have a significant impact on the load response capacity;

[0089] Physical verification of the selected key environmental parameters, that is, combining the operation mechanism of the CSP system and external environmental conditions, to analyze the specific impact mechanism of these parameters on the load response capability;

[0090] Verify the effectiveness of the selected parameters in actual operation through experiments or simulations, and explain how they affect the system's response time, stability, and peak-shaving capability;

[0091] The selected key environmental parameters and their influencing mechanisms are documented as a reference for evaluating the load response capability of CSP systems.

[0092] In this step, by extracting the output power, load change records and synchronously recorded environmental parameters of the CSP system from the historical operation data, and calculating them using the correlation analysis tools in statistical software or programming languages, the environmental parameters significantly related to the load response capability can be systematically identified; the subjectivity of empirical judgment alone is avoided, and the accuracy and scientificity of parameter identification are improved; after screening out the environmental parameters significantly related to the load response capability, this step can narrow the scope of research and focus on those environmental factors that have an important impact on the load response capability; it not only helps to deeply understand the mechanism of load response of the CSP system, but also provides targeted guidance for subsequent optimization and improvement; physical verification of the selected key environmental parameters and verification in combination with experiments or simulations can ensure the effectiveness and accuracy of the selected parameters in actual operation; it not only enhances the credibility of the evaluation method, but also provides strong support for the operation optimization and fault prediction of the CSP system; the selected key environmental parameters and their influencing mechanisms are organized into documents as a reference for the evaluation of the load response capability of the CSP system, which is helpful for the development of subsequent research and practice; it not only provides valuable information for researchers, but also provides decision-making support for system operators, and promotes the continuous improvement and development of CSP technology.

[0093] S4. Inputting the key environmental parameters and the response data when the load changes into the load response evaluation model to obtain the response capability evaluation score;

[0094] The structure of the load response evaluation model is:

[0095] Input layer: used to input key environmental parameters and response data when load changes; key environmental parameters are parameters that have a significant impact on the load response capability of the CSP system; load change response data include response time, response stability and maximum peak load regulation capability, etc., reflecting the actual performance of the system when facing load changes;

[0096] Feature processing layer: pre-process the input environmental parameters and response data to eliminate the impact of dimensional differences and outliers on the model; feature selection is also required to reduce model complexity and improve computational efficiency;

[0097] Model core layer: According to the characteristics and data features of the CSP system, a suitable machine learning algorithm is selected as the core of the model to calculate the load response capability evaluation score based on key environmental parameters and response data when the load changes;

[0098] Output layer: The model outputs the load response capability evaluation score, which is a quantitative evaluation of the load response capability of the CSP system under current environmental conditions and load changes;

[0099] The formula for calculating the load response capability evaluation score of the load response evaluation model is:

[0100] S=α·R t +β·R s +γR p +δ·E

[0101] Among them, S represents the comprehensive response capability evaluation score, R t represents the response time, α represents the weight coefficient of the response time, R s represents the response stability, β represents the weight coefficient of response stability, R p represents the maximum peak-shaving capacity, γ represents the weight coefficient of the maximum peak-shaving capacity, E represents the key environmental parameters, and δ represents the weight coefficient of the key environmental parameters.

[0102] In this step, by integrating the response data of key environmental parameters and load changes, a systematic evaluation of the load response capability of the CSP system is achieved; the limitations of previous reliance on empirical judgment and simple data analysis are overcome, and the accuracy and scientificity of the evaluation are improved; the model outputs a specific response capability evaluation score, which is a quantitative evaluation of the load response capability of the CSP system under current environmental conditions and load changes; the evaluation results are more objective and comparable, which is convenient for monitoring and optimizing the performance of the system; the feature processing layer preprocesses the input data, eliminates the influence of dimensional differences and outliers on the model, and improves the accuracy and reliability of the data; at the same time, feature selection reduces the complexity of the model, improves the calculation efficiency, and makes the model more concise and efficient; by introducing the weight coefficients of response time, response stability and maximum peak-shaving capacity, as well as the weight coefficients of key environmental parameters, the model can comprehensively consider the influence of various factors on the load response capability, making the evaluation results more comprehensive and accurate; step S4 realizes a systematic and quantitative evaluation of the load response capability of the CSP system by constructing a load response evaluation model and calculating the response capability evaluation score, and improves the accuracy and scientificity of the evaluation.

[0103] S5. Compare the response capability evaluation score with the preset evaluation threshold. If the response capability evaluation score exceeds the preset evaluation threshold, it indicates that the load response capability of the CSP system is good; if the response capability evaluation score does not exceed the preset evaluation threshold, it indicates that the load response capability of the CSP system needs to be optimized.

[0104] The factors affecting the setting of the preset evaluation threshold include:

[0105] Load characteristics: The load characteristics of power grids in different regions are different. In some regions, the peak-to-valley difference is large, and the requirements for load response speed are also higher; therefore, the preset evaluation threshold needs to consider the specific load requirements of the local power grid;

[0106] Energy storage capacity: The capacity of the thermal energy storage facilities in the CSP system determines the system's response capability during non-sunshine periods or peak load periods; the preset evaluation threshold should take into account the system's energy storage capacity and its impact on load response;

[0107] Thermodynamic cycle efficiency: The efficiency of the thermodynamic cycle system affects the speed and stability of the system response; the preset evaluation threshold should take into account the thermodynamic cycle efficiency of the system;

[0108] Solar radiation intensity: Changes in solar radiation intensity will affect the system's thermal energy collection efficiency, and thus affect load response capabilities; the preset evaluation threshold needs to take into account changes in solar radiation intensity in different seasons and regions;

[0109] Ambient temperature: Changes in ambient temperature will affect the transfer efficiency of thermal energy and the thermal cycle of the system; the preset evaluation threshold should take into account the impact of ambient temperature on the system's responsiveness;

[0110] Wind speed and humidity: Wind speed and humidity will also affect the heat exchange efficiency of the system, and thus affect the load response capability; the preset evaluation threshold needs to take these environmental factors into account;

[0111] Operating cost: Operation and maintenance costs are important considerations; the preset evaluation threshold should take into account the operating cost of the system under different load response requirements;

[0112] Technical level: Different technical levels determine the optimal load response capability that the system can achieve; the preset evaluation threshold should be set according to the current technical level to ensure that the evaluation criteria are implementable;

[0113] Environmental protection requirements: Environmental protection requirements and policies will also have an impact on the preset evaluation thresholds; if the environmental protection requirements are strict, then the preset evaluation thresholds need to be increased accordingly to ensure that the system meets the requirements in terms of environmental protection;

[0114] The optimization method of load response capability of CSP system includes:

[0115] Improve collector design: Use efficient thermal energy storage technology to improve the thermal energy utilization rate of the system and ensure stable thermal energy output during peak load periods; optimize the layout and angle of the collector to maximize the efficiency of solar energy reception, thereby improving the overall power generation efficiency and enhancing the load response capability of the system;

[0116] Enhanced control system: Introduce advanced control strategies to monitor system status in real time and adjust output power, improve the system's response speed and stability to load changes; use machine learning algorithms to predict future load demand, and adjust the system's operating status in advance based on the prediction results to respond more effectively to grid demand;

[0117] Optimize operation strategy: Set dynamic adjustment strategy according to load changes to optimize the use of heat sources; coordinate the CSP system with other renewable energy sources to achieve load balance, reduce dependence on a single power source, and improve the reliability and flexibility of the overall system;

[0118] System maintenance and upgrade: Regular inspection and maintenance of key components such as collectors, thermal storage systems and generators ensures that the equipment is always in optimal operating condition, reduces the risk of failure, and improves the overall responsiveness of the system;

[0119] Environmental adaptability design: Establish an environmental monitoring system to obtain real-time data on solar radiation, temperature, wind speed, etc., dynamically adjust system operating parameters according to environmental changes, and enhance system adaptability; design anti-interference measures based on the impact of different environmental factors on the solar thermal power generation system to ensure the stable operation of the system under extreme weather conditions;

[0120] Data feedback and optimization: Use historical operating data and real-time monitoring data to establish a feedback mechanism, regularly evaluate the effectiveness of optimization measures, and continuously adjust and improve optimization strategies to adapt to new load demands and environmental conditions; combine big data analysis and use data mining technology to identify key factors that affect load responsiveness, and adjust operating strategies accordingly to achieve refined management.

[0121] In this step, by setting a preset evaluation threshold and comparing the response capacity evaluation score of the solar thermal power generation system with it, the load response capacity of the system can be scientifically evaluated; it is more accurate than traditional empirical judgment and simple data analysis, and can comprehensively consider multiple influencing factors, thereby ensuring the objectivity and reliability of the evaluation results; according to the comparison results of the response capacity evaluation score and the preset evaluation threshold, the shortcomings of the load response capacity of the solar thermal power generation system can be clearly identified; it is helpful to formulate targeted optimization strategies, thereby effectively improving the load response capacity of the system; by optimizing the load response capacity of the solar thermal power generation system, not only the operating efficiency and reliability of the system can be improved, but also the operating and maintenance costs can be reduced; as a clean and renewable energy utilization method, the optimization of the load response capacity of solar thermal power generation helps to reduce carbon emissions and environmental pollution, and meets the requirements of environmental protection and sustainable development; by improving the load response capacity of the system, it can better match the needs of the power grid, reduce dependence on traditional fossil energy, and promote the transformation and upgrading of the energy structure.

[0122] Embodiment 2: Figure 4 As shown, a load response capability evaluation system of a solar thermal power generation system of the present invention specifically includes the following modules;

[0123] Data acquisition module, to obtain historical operation data of the CSP system;

[0124] The response data collection module collects response data for each load change based on historical operation data, including response time, response stability and maximum peak load regulation capacity;

[0125] Environmental parameter extraction module, which analyzes historical operation data and response data of each load change to extract key environmental parameters that affect load response;

[0126] The load response evaluation module inputs the key environmental parameters and the response data of the load changes into the load response evaluation model to obtain the response capability evaluation score;

[0127] The evaluation result comparison module compares the response capability evaluation score with the preset evaluation threshold. If the response capability evaluation score exceeds the preset evaluation threshold, it indicates that the load response capability of the CSP system is good; if the response capability evaluation score does not exceed the preset evaluation threshold, it indicates that the load response capability of the CSP system needs to be optimized.

[0128] The optimization suggestion module provides targeted optimization suggestions and improvement measures for the CSP system based on the evaluation results.

[0129] The evaluation system forms a complete and systematic evaluation process through the collaborative work of multiple modules; it overcomes the limitations of traditional experience judgment and simple data analysis, and improves the accuracy and scientificity of the evaluation; the system not only considers the operating data of the CSP system itself, but also deeply analyzes the key environmental parameters that affect the load response, making the evaluation more accurate and able to truly reflect the load response capability of the CSP system;

[0130] By collecting historical operation data and response data when load changes in real time, the system can dynamically evaluate the load response capability of the CSP system; enabling the system to promptly detect and respond to potential problems, thereby improving the reliability and stability of the system;

[0131] According to the evaluation results, the optimization suggestion module can provide targeted optimization suggestions and improvement measures for the CSP system, which not only helps to improve the load response capability of the system, but also improves the overall operation efficiency and reliability of the system.

[0132] In summary, the above-mentioned CSP system not only improves the accuracy and scientificity of the evaluation of the load response capacity of the CSP system, but also provides strong support for the continuous improvement of the system.

[0133] Of course, the above parameters are only some typical parameters, and other parameters that can be obtained and used are also within the protection scope of the present invention.

[0134] Please take the bank account opening business as an example and help me sort out the complete process of the business handling path planning model planning path. You must follow the principle of seeking truth from facts and not talk nonsense. Remember to use mermaid syntax to output

[0135] Based on your understanding, please convert the above method into a systematic description, such as "xxx module, used for xxxx". Please be realistic and logical, and do not talk nonsense.

[0136] Please introduce S1 in detail in combination with business processing and technical solutions in the field of big data. The other steps do not need to be introduced. Please be realistic and not talk nonsense.

[0137] The various variations and specific embodiments of the method for evaluating the load response capability of a solar thermal power generation system in the aforementioned embodiment 1 are also applicable to the system for evaluating the load response capability of a solar thermal power generation system in the present embodiment. Through the aforementioned detailed description of the method for evaluating the load response capability of a solar thermal power generation system, those skilled in the art can clearly know the implementation method of the system for evaluating the load response capability of a solar thermal power generation system in the present embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0138] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating load response capability of a solar thermal power generation system, characterized in that: The method comprises: Obtain historical operation data of the CSP system; Based on historical operation data, collect response data for each load change, including response time, response stability and maximum peak load regulation capacity; Analyze historical operation data and response data of each load change to extract key environmental parameters that affect load response; Input the key environmental parameters and the response data of load changes into the load response evaluation model to obtain the response capability evaluation score; The response capability evaluation score is compared with the preset evaluation threshold. If the response capability evaluation score exceeds the preset evaluation threshold, it indicates that the load response capability of the CSP system is good; if the response capability evaluation score does not exceed the preset evaluation threshold, it indicates that the load response capability of the CSP system needs to be optimized.

2. A method for evaluating load response capability of a solar thermal power generation system according to claim 1, characterized in that: The formula for calculating the load response capability evaluation score of the load response evaluation model is: S=α·R t +β·R s +γ·R p +δ·E Among them, S represents the comprehensive response capability evaluation score, R t represents the response time, α represents the weight coefficient of the response time, R s represents the response stability, β represents the weight coefficient of response stability, R p represents the maximum peak-shaving capacity, γ represents the weight coefficient of the maximum peak-shaving capacity, E represents the key environmental parameters, and δ represents the weight coefficient of the key environmental parameters.

3. A method for evaluating load response capability of a solar thermal power generation system according to claim 2, characterized in that: The structure of the load response evaluation model is: Input layer: used to input key environmental parameters and response data when load changes; Feature processing layer: preprocessing and feature selection of input environmental parameters and response data; Model core layer: used to calculate the load response capability evaluation score based on key environmental parameters and response data when load changes; Output layer: used to output load response capability evaluation scores.

4. A method for evaluating load response capability of a solar thermal power generation system according to claim 1, characterized in that: The method for obtaining historical operation data of a solar thermal power generation system includes: Deploy data acquisition sensors and monitoring equipment in the CSP system, and obtain the operating parameters of each area of ​​the CSP system through the deployed sensors and monitoring equipment; Summarize the collected operating parameters of each area to obtain historical operating data; Pre-process the collected historical operation data, including data cleaning, data verification and data conversion; Regularly inspect and maintain data collection sensors and monitoring equipment to ensure the normal operation of the system and the accuracy of data collection; The historical operation data is archived and saved so that it can be called upon at any time during the subsequent analysis and evaluation process.

5. A method for evaluating load response capability of a solar thermal power generation system according to claim 1, characterized in that: Collect response data for each load change, including: Through the system output power and grid dispatch instructions, the moment of load change is identified from historical operation data; Record the system response time based on the moment of load change; According to the moment of load change, the system response stability after load change is evaluated by monitoring the fluctuation of output power over a period of time; According to the time of load change, the maximum peak load regulation capacity is obtained by comparing the output power of the system before and after the load change and calculating the change amount; The collected response time, response stability and maximum peak-shaving capacity are recorded and stored.

6. A method for evaluating load response capability of a solar thermal power generation system according to claim 1, characterized in that: Methods for obtaining key environmental parameters that affect load response include: Extract environmental parameters of CSP systems from historical operating data; Calculate the correlation coefficient between environmental parameters and load response data; According to the correlation coefficient, the environmental parameters related to the load response capacity are screened out; Physically verify the selected environmental parameters related to load responsiveness and analyze the specific impact mechanisms of these parameters on load responsiveness; Verify the effectiveness of the selected parameters in actual operation and explain how they affect the system's response time, stability, and peak-shaving capabilities.

7. A method for evaluating load response capability of a solar thermal power generation system according to claim 1, characterized in that: The influencing factors for setting the preset evaluation threshold include load characteristics, energy storage capacity, thermal cycle efficiency, solar radiation intensity, ambient temperature, wind speed and humidity, operating cost, technical level and environmental protection requirements.

8. A system for evaluating load response capability of a solar thermal power generation system, characterized in that: The system comprises: Data acquisition module, to obtain historical operation data of the CSP system; The response data collection module collects response data for each load change based on historical operation data, including response time, response stability and maximum peak load regulation capacity; Environmental parameter extraction module, which analyzes historical operation data and response data of each load change to extract key environmental parameters that affect load response; The load response evaluation module inputs the key environmental parameters and the response data of the load changes into the load response evaluation model to obtain the response capability evaluation score; The evaluation result comparison module compares the response capability evaluation score with the preset evaluation threshold. If the response capability evaluation score exceeds the preset evaluation threshold, it indicates that the load response capability of the CSP system is good; if the response capability evaluation score does not exceed the preset evaluation threshold, it indicates that the load response capability of the CSP system needs to be optimized. The optimization suggestion module provides targeted optimization suggestions and improvement measures for the CSP system based on the evaluation results.

9. An electronic device for evaluating the load response capability of a solar thermal power generation system, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and characterized in that: When the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

Images