Auxiliary peak regulation method and device for fused salt heat storage system and electronic equipment

Abstract

The invention discloses an auxiliary peak regulation method and device for a fused salt heat storage system and electronic equipment. The method comprises the following steps: collecting operation data of each device in the fused salt heat storage system and a measuring device corresponding to each device, according to the operation data, health state evaluation results of all the devices and the corresponding measuring devices are determined, and according to the health state evaluation results, the operation data of all the devices in the fused salt heat storage system, external operation requirements of the thermal power generating unit and the safety state of the thermal power generating unit, a peak regulation capacity evaluation coefficient is generated; according to the peak regulation capability evaluation coefficient, the peak regulation execution rate and the peak regulation depth constraint range of the thermal power generating unit and the extraction steam control valve regulation rate constraint range of the fused salt heat storage system are determined, the peak regulation execution rate serves as the actual regulation reference, and the peak regulation depth constraint range and the extraction steam control valve regulation rate constraint range serve as boundary limits; and the opening degrees of steam extraction control valves of steam extraction channels on the main steam side, the high-temperature reheating side and the four-extraction side in the fused salt heat storage system are adjusted correspondingly.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to an auxiliary peak-shaving method, device and electronic equipment for a molten salt thermal energy storage system. Background Technology

[0002] With the continuous increase in the proportion of renewable energy power generation, the power grid has placed higher demands on the peak-shaving flexibility and response accuracy of thermal power units. Peak-shaving control of thermal power units needs to fully consider the actual operating conditions of the molten salt thermal storage system to achieve a balance between safety and efficiency. Current peak-shaving control only focuses on basic regulation based on grid load demand, without fully incorporating the relevant operating conditions of the molten salt thermal storage system. This makes it difficult to accurately match peak-shaving commands with the actual system capacity, thus affecting the safety and adaptability of peak-shaving operations. Summary of the Invention

[0003] In view of the above problems, this application provides an auxiliary peak-shaving method, apparatus and electronic equipment for molten salt thermal storage systems.

[0004] To solve the above-mentioned technical problems, this application proposes the following solution:

[0005] In a first aspect, this application provides an auxiliary peak-shaving method for a molten salt thermal energy storage system. The method includes: collecting operating data of each device and its corresponding measuring device in the molten salt thermal energy storage system; determining the health status assessment results of each device and its corresponding measuring device based on the operating data; generating a peak-shaving capacity assessment coefficient based on the health status assessment results, the operating data of each device in the molten salt thermal energy storage system, the external operating requirements of the thermal power unit, and the safety status of the thermal power unit itself; determining the peak-shaving execution rate, the peak-shaving depth constraint range, and the extraction steam valve adjustment rate constraint range of the molten salt thermal energy storage system based on the peak-shaving capacity assessment coefficient; and adjusting the extraction steam valve openings of the main steam side, high-temperature reheat side, and fourth extraction side extraction channels in the molten salt thermal energy storage system, respectively, using the peak-shaving execution rate as the actual adjustment benchmark and the peak-shaving depth constraint range and the extraction steam valve adjustment rate constraint range as boundary limits, thereby realizing the auxiliary peak-shaving of the thermal power unit by the molten salt thermal energy storage system.

[0006] Secondly, this application provides an auxiliary peak-shaving device for a molten salt thermal storage system, the auxiliary peak-shaving device for the molten salt thermal storage system comprising:

[0007] The health assessment module is used to collect the operating data of each piece of equipment and the corresponding measuring device in the molten salt thermal storage system, and to determine the health status assessment results of each piece of equipment and the corresponding measuring device based on the operating data.

[0008] The generation module is used to generate peak-shaving capacity assessment coefficients based on the health status assessment results, the operating data of each device in the molten salt thermal storage system, the external operating requirements of the thermal power unit and the safety status of the thermal power unit itself.

[0009] The control module is used to determine the peak shaving execution rate, peak shaving depth constraint range, and extraction steam valve adjustment rate constraint range of the thermal power unit based on the peak shaving capacity assessment coefficient. Taking the peak shaving execution rate as the actual adjustment benchmark and the peak shaving depth constraint range and extraction steam valve adjustment rate constraint range as boundary limits, the module adjusts the opening of the extraction steam valves of the main steam side, high-temperature reheat side, and fourth extraction side of the molten salt thermal storage system, respectively, thereby realizing the peak shaving of the thermal power unit assisted by the molten salt thermal storage system.

[0010] To achieve the above objectives, according to a third aspect of this application, an electronic device is provided, the device including at least one processor, and at least one memory and bus connected to the processor; wherein the processor and memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the molten salt thermal storage system auxiliary peak shaving method of the first aspect described above.

[0011] By employing the above-described technical solution, the technical solution provided in this application has at least the following advantages:

[0012] This application combines health status assessment results, operational data of various devices in the molten salt thermal storage system, external operational demands of the thermal power unit, and its own safety status to generate peak-shaving capacity assessment coefficients. This process organically integrates multiple dimensions, accurately capturing internal system operating conditions while closely aligning with external grid peak-shaving demands. Simultaneously, it incorporates safety status as a core prerequisite, making the quantitative assessment of peak-shaving capacity more aligned with actual operational scenarios. Based on the peak-shaving capacity assessment coefficients, the peak-shaving execution rate, peak-shaving depth constraint range, and extraction steam valve adjustment rate constraint range of the thermal power unit are determined. Using these as benchmarks and boundaries, the opening of the extraction steam valves on the main steam side, high-temperature reheat side, and fourth extraction side is adjusted, allowing peak-shaving operations to proceed in an orderly manner within the system's safe carrying capacity. The clearly defined peak-shaving execution rate ensures that the peak-shaving response always matches the actual system capacity, avoiding operational fluctuations caused by imbalances in adjustment rhythm. The clearly defined peak-shaving depth constraint range clearly defines the reasonable range of load fluctuations, mitigating risks exceeding the system's tolerance limits from the outset. The constraint range of the extraction steam regulating valve provides a guarantee for the stable operation of the extraction steam channel. Through precise control of the three extraction steam channels, the efficient coordination between the molten salt thermal storage system and the thermal power unit is achieved, ultimately achieving a safe and reliable peak-shaving target.

[0013] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0014] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0015] Figure 1 A schematic flowchart of an auxiliary peak-shaving method for a molten salt thermal storage system provided in an embodiment of this application is shown.

[0016] Figure 2 This application provides a schematic diagram of the structure of a thermal power unit according to an embodiment of the present application;

[0017] Figure 3 This illustration shows a schematic diagram of an auxiliary peak-shaving method for a molten salt thermal storage system provided in an embodiment of this application;

[0018] Figure 4 This illustration shows a structural schematic diagram of an auxiliary peak-shaving device for a molten salt thermal storage system provided in an embodiment of this application;

[0019] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0020] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0021] In the embodiments of this application, the terms "first," "second," etc., do not have a logical or temporal dependency, nor do they limit the quantity or execution order. It should also be understood that although the following description uses the terms "first," "second," etc., to describe various elements, these elements should not be limited by the terms. These terms are merely used to distinguish one element from another.

[0022] In this application, the term "at least one" means one or more, and the term "multiple" means two or more.

[0023] It should also be understood that the term “if” can be interpreted as “when” or “upon”, or “in response to determination” or “in response to detection”. Similarly, depending on the context, the phrase “if determination…” or “if detection [the stated condition or event]” can be interpreted as “when determination…” or “in response to determination…” or “when detection [the stated condition or event]” or “in response to detection [the stated condition or event]”.

[0024] The auxiliary peak-shaving method for molten salt thermal storage systems will be described in detail below with reference to the accompanying drawings. Figure 1 A flowchart illustrating an auxiliary peak-shaving method for a molten salt thermal energy storage system provided in this application. Specifically, it includes the following steps:

[0025] like Figure 2 As shown, the thermal power unit mainly consists of core equipment such as high-pressure cylinder, intermediate-pressure cylinder, low-pressure cylinder, boiler, condenser, deaerator, feedwater pump, and high and low-pressure heaters, forming an energy conversion and transmission system. The molten salt thermal storage system is coupled and linked with the thermal power unit through three independent extraction steam channels: the main steam side, the high-temperature reheat side, and the fourth extraction side. Its core equipment includes cold salt pumps, low-temperature molten salt tanks, high-temperature molten salt tanks, main steam superheater heat exchangers, hot reheater superheater heat exchangers, and fourth extraction superheater heat exchangers. It is also equipped with measuring devices such as temperature sensors, pressure transmitters, and level gauges to collect real-time operating parameters and system status data of each device. The installation locations of these measuring devices cover key parts such as extraction steam channels, heat exchanger inlets and outlets, molten salt tanks, and pump bodies, ensuring the comprehensiveness and timeliness of data collection, and jointly building a complete hardware architecture for molten salt thermal storage to assist in peak shaving.

[0026] This system architecture not only ensures the stable operation of the original power generation function of the thermal power unit, but also expands the peak-shaving regulation space through the heat storage and heat release characteristics of the molten salt thermal storage system. It provides a solid hardware foundation for subsequent accurate collection of operating data and health status assessment, and enables peak-shaving decisions based on the actual operating status of the system to have implementable hardware support.

[0027] Step 110: Collect the operating data of each device and the corresponding measuring device in the molten salt thermal storage system, and determine the health status assessment results of each device and the corresponding measuring device based on the operating data.

[0028] During the health status assessment phase, the assessment targets are first clearly defined as the core equipment and supporting measuring devices of the molten salt thermal storage system. The equipment includes key components directly involved in molten salt storage, heat exchange, and steam extraction, such as cold salt pumps, molten salt cold tanks, molten salt hot tanks, heat exchangers, and extraction steam control valves. The measuring devices are detection equipment used to monitor operating parameters such as temperature, pressure, and liquid level in real time; the accuracy of their data directly affects the reliability of peak-shaving control. The data acquisition phase simultaneously acquires equipment operating data and measuring device data. Equipment data includes vibration signals, start-stop frequency, and operating current of the cold salt pump; inlet and outlet temperatures and pressure differences of the heat exchanger; and opening feedback and response time of the extraction steam control valve. Measuring device data includes temperature sensor readings, pressure transmitter output signals, continuous monitoring data from the level gauge, and continuous operating time and data fluctuation records of each measuring device, ensuring that the acquired data comprehensively reflects the equipment operating status and the performance of the measuring devices.

[0029] The data preprocessing stage employs a combination of time-series segmentation and multi-scale feature extraction. Time-series segmentation divides continuously running data into segments with a fixed 1-minute time window, ensuring data timeliness while avoiding interference from single data fluctuations in feature extraction. Multi-scale feature extraction designs extraction rules tailored to the characteristics of different evaluation objects. Specifically, it extracts time-domain features from equipment vibration signals, including the maximum / minimum / average values ​​of vibration amplitude, the standard deviation of vibration frequency (reflecting vibration stability), and the peak impact during equipment start-up and shutdown (assessing the impact of start-up and shutdown shocks on the equipment). Fourier transform is used to convert the vibration signal to the frequency domain, extracting frequency-domain features such as the proportion of dominant frequency energy (assessing whether the dominant frequency of equipment operation is normal) and harmonic distortion rate (reflecting the degree of signal distortion, indirectly indicating the wear state of the equipment). From the measurement device's detection data, the absolute error between the measured value and the standard value under the same operating condition, and the mean relative error of multiple measurement results are calculated as accuracy features. The variance of the measurement data per unit time and the cumulative duration of continuous fault-free operation of the measurement device are statistically analyzed as stability features.

[0030] The construction of a health status assessment index system needs to be closely integrated with actual fault types. Common equipment-side faults include cold salt pump jamming, heat exchanger leakage, extraction steam valve sticking, and molten salt tank corrosion. Therefore, the indicators for the equipment operating status dimension need to cover dynamic response capability (corresponding to time domain characteristics) and operational stability (corresponding to frequency domain characteristics). Common faults of measuring devices include sensor drift, data distortion, and device failure. Therefore, the indicators for the measuring device performance dimension need to focus on detection accuracy (corresponding to accuracy characteristics) and operational continuity (corresponding to stability characteristics). The extracted four types of features are associated and mapped to their corresponding dimensions to form a structured health status assessment feature set, ensuring that each feature has a clear assessment target.

[0031] The application of historical sample data is crucial for improving the accuracy of assessments. Historical health sample data for equipment covers normal operation data under different years of operation and different load conditions. Fault sample data includes characteristic data of various faults such as excessive vibration of the cold salt pump, heat exchanger leakage, and stuck extraction steam valve. Statistical analysis is used to establish equipment characteristic analysis benchmarks. For example, the vibration amplitude range (0.1-0.3 mm) and main frequency energy percentage threshold (≥85%) of the cold salt pump during normal operation, and the upper limit of pressure difference (≤0.5 MPa) when the heat exchanger has no leakage. Historical health sample data for measuring devices includes test data after new device calibration and operational data that has passed periodic verification. Fault sample data includes error exceeding limits due to sensor drift and fluctuation data before device failure. Based on this, characteristic analysis benchmarks for measuring devices are established, such as the allowable absolute error range of temperature sensors (≤±0.5℃) and the benchmark value for continuous fault-free operation time (≥8760 hours).

[0032] The feature analysis and evaluation value calculation process relies on comparative analysis using benchmark data. The real-time time-domain and frequency-domain characteristics of the equipment are compared with the equipment feature analysis benchmark. If the vibration amplitude of the cold salt pump is 0.2 mm and the main frequency energy accounts for 88%, both within the normal benchmark range, the equipment's operating status evaluation value is high. If the vibration amplitude reaches 0.5 mm and the harmonic distortion rate exceeds 10%, the equipment is judged to have an operational abnormality, and the evaluation value is correspondingly reduced. The calculation of the measurement device's evaluation value is similar. If the absolute error of the temperature sensor is 0.3℃ and the average relative error is 0.8%, both meeting the accuracy benchmark requirements, and the continuous fault-free operation time reaches 10,000 hours, the measurement device's performance evaluation value is high. If the average relative error exceeds 2%, or the data fluctuation variance is too large, it indicates a decrease in the measurement device's accuracy, and the evaluation value is lowered.

[0033] The determination of the dual-dimensional fusion weights employs a combination of the Analytic Hierarchy Process (AHP) and the entropy weight method. The AHP starts from functional importance, recognizing equipment as the core carrier of peak shaving execution and measuring devices as the foundation for data support. Therefore, subjectively, it assigns 65% weight to the equipment operating status evaluation value and 35% weight to the measuring device performance evaluation value. The entropy weight method calculates objective weights based on the information entropy of historical sample data. If the impact of equipment failures on peak shaving is significantly higher than that of measuring device anomalies in historical data, the objective weights are tilted towards the equipment side. The final fusion weight is obtained through a weighted average of the subjective and objective weights, ensuring that the weight allocation conforms to both logical relationships and actual operational data patterns. The comprehensive health status evaluation value is the result of the weighted sum of the equipment operating status evaluation value and the measuring device performance evaluation value according to the fusion weights. For example, if the equipment evaluation value is 85 points, the measuring device evaluation value is 90 points, and the fusion weights are 65%:35%, then the comprehensive evaluation value is 85 × 0.65 + 90 × 0.35 = 86.75 points.

[0034] The health status level is classified using a four-level threshold standard, which was determined through statistical analysis of a large number of historical samples: Excellent (85-100 points), Good (70-84 points), Acceptable (55-69 points), and Unacceptable (≤54 points). The reliability weight is calculated based on the deviation between the comprehensive evaluation value and the level benchmark threshold. The benchmark threshold is set as the median of each level (Excellent benchmark: 92.5 points, Good benchmark: 77 points, Acceptable benchmark: 62 points, Unacceptable benchmark: 40 points). For example, if a device has a comprehensive evaluation value of 90 points (Excellent level), the deviation is 1 - |90 - 92.5| / 92.5 ≈ 0.973, and the reliability weight is 0.97 (not less than 0.8). If the comprehensive evaluation value is 50 points (Unacceptable level), the deviation is 1 - |50 - 40| / 40 = 0.75. Since the unacceptable level weight must not exceed 0.3, the final reliability weight is 0.3. A complete health status assessment result is formed by combining the health status level and the reliability weight, such as "Cold salt pump: excellent health level, reliability weight 0.95" and "Temperature sensor: good health level, reliability weight 0.82".

[0035] The entire evaluation process establishes a closed-loop verification mechanism, comparing real-time evaluation results with historical results under similar operating conditions. If the frequency domain characteristic evaluation value of a certain device suddenly drops by 20%, the system will check for sudden changes in operating conditions or equipment malfunctions by referring to the equipment operation log. If the accuracy characteristic evaluation value of the measuring device continues to decline, it is necessary to prompt device calibration or replacement to ensure that the health status evaluation results can accurately and objectively reflect the actual situation, providing a reliable preliminary guarantee for peak shaving of molten salt thermal power units.

[0036] Step 120: Based on the health status assessment results, the operating data of each device in the molten salt thermal storage system, the external operating requirements of the thermal power unit and the safety status of the thermal power unit itself, generate the peak-shaving capacity assessment coefficient.

[0037] After completing the health status assessment of the molten salt thermal storage system equipment and measuring devices, a peak-shaving capacity assessment coefficient is generated by combining multiple key factors to provide a quantitative basis for subsequent peak-shaving control. This process integrates hardware status, system operating conditions and safety management requirements to ensure the comprehensiveness and reliability of the assessment results.

[0038] The determination of hardware evaluation sub-coefficients is based on the health status assessment results. For equipment such as cold brine pumps, molten brine tanks, heat exchangers, and extraction steam control valves, weights are assigned based on their functional importance in peak-shaving operation. As the power core of the molten brine cycle, the cold brine pump has a higher functional importance weight than the molten brine tank. If a cold brine pump has an excellent health status and a reliability weight of 0.92, its functional importance weight is 30%, and its equipment health contribution value is 0.92 × 30% = 0.276. For measuring devices such as temperature sensors and pressure transmitters, the weight allocation is based on the data accuracy in parameter monitoring. The accuracy of the main steam temperature sensor has a significant impact on peak-shaving control; the data accuracy impact weight is set at 25%. If the sensor has a good health status and a reliability weight of 0.85, its measuring device health contribution value is 0.85 × 25% = 0.2125. The weights are assigned according to the correlation importance of equipment and measuring devices to peak-shaving hardware assurance (e.g., equipment accounts for 60% and measuring devices account for 40%). The total equipment health contribution value is obtained by summing the health contribution values ​​of all equipment and the total measuring device health contribution value is obtained by summing the health contribution values ​​of all measuring devices. The hardware evaluation sub-coefficient is obtained by weighted calculation. For example, if the total equipment health contribution value is 0.85 and the total measuring device health contribution value is 0.78, then the hardware evaluation sub-coefficient is 0.85×60%+0.78×40%=0.822.

[0039] The calculation of the sub-coefficients for system operation evaluation is closely integrated with grid demand and actual operating conditions. The operating data of each device in the molten salt thermal storage system includes the liquid level in the cold salt tank, the liquid level in the hot salt tank, the terminal temperature difference of the heat exchanger, and the thermal shock temperature of the heat exchanger. These parameters directly determine the adjustment boundary of the extraction steam rate. When the molten salt thermal storage system is at its maximum extraction steam rate (e.g., the maximum designed extraction steam rate of 100 t / h) and the thermal power unit is at its highest load (e.g., the rated load of 600 MW), the maximum load reduction range (e.g., 150 MW) and the corresponding peak shaving duration (e.g., continuous peak shaving for 30 minutes at a rate of 5 MW / min) are determined by combining key operating parameters such as sufficient liquid level in the cold salt tank and normal terminal temperature difference of the heat exchanger. When the molten salt thermal storage system is at its minimum extraction rate (e.g., a design minimum extraction rate of 20 t / h) and the thermal power unit is at its lowest load (e.g., a minimum stable load of 200 MW), the maximum load increase (e.g., 120 MW) and the corresponding peak-shaving duration (e.g., continuous peak shaving for 30 minutes at a rate of 4 MW / min) are determined based on safety margins such as the hot salt tank level and the heat exchanger thermal shock temperature. To meet the grid load peak-shaving demand, if the grid requires a load reduction of 120 MW and a peak-shaving rate of 4 MW / min, the matching degree with the maximum load reduction of 150 MW and the corresponding peak-shaving duration of 37.5 minutes (150 MW ÷ 4 MW / min) is 120 ÷ 150 = 0.8. If the grid requires a load increase of 100 MW and a peak-shaving rate of 3 MW / min, the matching degree with the maximum load increase of 120 MW and the corresponding peak-shaving duration of 40 minutes (120 MW ÷ 3 MW / min) is 100 ÷ 120 ≈ 0.833. Calculate the matching degree between the real-time steam extraction rate of the molten salt thermal storage system and the current steam extraction demand. If the real-time steam extraction rate is 80t / h and the current steam extraction demand corresponding to the load reduction is 75t / h, the matching degree is 80÷75≈1.067. The average comprehensive matching degree is (0.8+0.833)÷2≈0.817. With the matching degree of 1.067, the sub-coefficient of system operation evaluation is determined to be 0.817×1.067≈0.872.

[0040] The determination of the safety management assessment sub-coefficient is based on the triggering of safety events in the thermal power unit's own safety status, combined with the execution of a preset set of safety events and a set of limiting events. The preset set of safety events includes turbine tripping, boiler MFT, generator disconnection, shutdown of both cold salt pumps, blockage of the extraction steam heat exchanger circuit, blockage of the molten salt side of the heat exchanger, excessively high temperature on the extraction steam side of the molten salt system, excessively high pressure on the extraction steam side of the molten salt system, mismatch between the main steam extraction and hot reheat extraction, excessive pressure difference between the first-stage extraction pressure and the high-pressure exhaust pressure, and excessively high temperature on the molten salt side of the molten salt thermal storage system. Once any of these events is triggered, the peak-shaving capacity assessment coefficient is immediately set to zero, and the extraction steam control valve of the molten salt thermal storage system is closed to terminate peak-shaving operations and ensure system safety. The preset set of limiting events includes low cold salt tank level in the molten salt system, high hot salt tank level in the molten salt system, abnormal main steam temperature of the thermal power unit, abnormal main steam pressure of the thermal power unit, and RB triggering of the thermal power unit. When any of these events is triggered, the safety management assessment sub-coefficient is adjusted downward according to the impact degree corresponding to the event type and preset rules. The event impact level is divided according to the weight of the limiting event's impact on the safety and stability of the peak-shaving operation of the molten salt thermal storage system. For example, low cold salt tank level and high hot salt tank level have a significant impact on peak shaving and are classified as Level 1 impact; slight abnormalities in main steam temperature and pressure have a relatively small impact on peak shaving and are classified as Level 2 impact. Corresponding reduction ratios are configured for different impact levels: Level 1 impact level is configured with a reduction ratio of 30%, and Level 2 impact level is configured with a reduction ratio of 15%. If the current event of low cold salt tank level in the molten salt system (Level 1 impact) is triggered, the preset baseline value is 1.0, then the reduced safety management assessment sub-coefficient is 1.0 × (1 - 30%) = 0.7. The adjusted safety management assessment sub-coefficient shall not be lower than the preset minimum threshold (e.g., 0.5) to ensure the basic safety boundary of peak-shaving operation. When no event in the safety event set or the limit event set is triggered, the safety management assessment sub-coefficient shall be determined according to the preset benchmark value (e.g., 1.0).

[0041] The weighted average of the hardware evaluation sub-coefficient, system operation evaluation sub-coefficient, and safety management evaluation sub-coefficient is determined based on the real-time operating conditions of the thermal power unit, with different weight allocations for different operating conditions. For example, when the unit is operating under high-load stable conditions, the system operating status has a more critical impact on peak-shaving capacity, so the system operation evaluation sub-coefficient is assigned a weight of 50%, the hardware evaluation sub-coefficient a weight of 35%, and the safety management evaluation sub-coefficient a weight of 15%. When the unit is operating under low-load fluctuating conditions, hardware reliability and safety management are more important, so the hardware evaluation sub-coefficient is assigned a weight of 40%, the safety management evaluation sub-coefficient a weight of 25%, and the system operation evaluation sub-coefficient a weight of 35%. The peak-shaving capacity evaluation coefficient is obtained by weighting the three sub-coefficients according to the determined weights. If, under a certain operating condition, the weighted average is 50% for system operation evaluation, 35% for hardware evaluation, and 15% for safety management evaluation, with corresponding sub-coefficients of 0.872, 0.822, and 1.0 respectively, then the peak-shaving capacity evaluation coefficient is 0.872×50%+0.822×35%+1.0×15%=0.436+0.2877+0.15=0.8737.

[0042] Step 130: Based on the peak-shaving capacity assessment coefficient, determine the peak-shaving execution rate of the thermal power unit, the peak-shaving depth constraint range, and the steam extraction valve regulation rate constraint range of the molten salt thermal storage system.

[0043] After the peak-shaving capacity assessment coefficient is generated, it serves as the core basis for determining the peak-shaving execution rate, peak-shaving depth constraint range, and extraction steam regulating valve adjustment rate constraint range of the thermal power unit, in conjunction with various constraints. Based on these parameters, the opening of the three-channel extraction steam regulating valve is then precisely adjusted.

[0044] Determining the peak-shaving execution rate requires integrating the peak-shaving capacity assessment coefficient, the original set load rate of thermal power units, and the real-time peak-shaving rate demand of the power grid. The original set load rate of thermal power units serves as the inherent regulation basis for the units. For example, the original set load rate of a 600MW unit is 5MW / min. The real-time peak-shaving rate demand of the power grid changes dynamically based on fluctuations in renewable energy. If the power grid currently requires a faster response, the demand rate will be higher than the original set value, and vice versa. The original set load rate is corrected by the peak-shaving capacity assessment coefficient, and then fine-tuned in conjunction with the real-time demand of the power grid to form the final peak-shaving execution rate. For example, if the peak-shaving capacity assessment coefficient is 0.9, the original set load rate is 5MW / min, and the real-time demand rate of the power grid is consistent with the original set value, then the peak-shaving execution rate is 5 × 0.9 = 4.5MW / min, ensuring that the rate matches both the actual peak-shaving capacity of the system and the power grid demand.

[0045] The determination of the peak-shaving depth constraint range is related to the peak-shaving capacity assessment coefficient, the rated load of thermal power units, and the safety margin of key operating parameters of the molten salt thermal storage system. The rated load of thermal power units is the basic boundary of the peak-shaving depth. The safety margin of key operating parameters of the molten salt thermal storage system, such as the cold salt tank level, hot salt tank level, heat exchanger terminal temperature difference, and heat exchanger thermal shock temperature, determines the upper and lower limits of the peak-shaving depth. For example, for a unit with a rated load of 600MW, a peak-shaving capacity assessment coefficient of 0.8, sufficient cold salt tank level, heat exchanger terminal temperature difference within a safe range, and a correction coefficient corresponding to the safety margin of 1.05, the upper limit of the peak-shaving depth constraint range is 600 × 0.8 × 1.05 = 504MW. The lower limit is determined by combining the minimum stable load and parameter safety margin. If the minimum stable load is 200MW and the safety margin correction coefficient is 0.95, then the lower limit is 200 × 0.8 × 0.95 = 152MW. This clarifies the load fluctuation range during the unit's peak-shaving process to avoid exceeding the safe operating range.

[0046] The determination of the constraint range for the extraction steam control valve's regulation rate is based on the peak-shaving capacity assessment coefficient, the maximum regulation rate of the extraction steam control valve in the molten salt thermal storage system, and the operational stability requirements of the extraction steam channel. The maximum regulation rate of the extraction steam control valve is the equipment design limit; for example, the maximum regulation rate of the main steam side extraction steam control valve is 5% / s. This is scaled down by combining the peak-shaving capacity assessment coefficient with the operational stability requirements of the extraction steam channel to avoid sudden changes in channel pressure and temperature caused by excessively rapid regulation. If the peak-shaving capacity assessment coefficient is 0.85 and the correction coefficient corresponding to the channel stability requirements is 0.9, then the constraint range for the extraction steam control valve's regulation rate is 5% / s × 0.85 × 0.9 = 3.825% / s, limiting the maximum change in valve opening per unit time.

[0047] Step 140: Using the peak shaving execution rate as the actual adjustment benchmark and the peak shaving depth constraint range and the extraction steam valve adjustment rate constraint range as boundary limits, adjust the opening of the extraction steam valves of the main steam side, high temperature reheat side and fourth extraction side extraction steam channels in the molten salt thermal storage system respectively.

[0048] During the regulation of the control valve opening, the peak shaving execution rate is used as the actual regulation benchmark, and the peak shaving depth constraint range and the extraction steam control valve regulation rate constraint range are used as boundary limits. Regulation is performed separately for the main steam side, the high-temperature reheat side, and the fourth extraction side extraction steam channels. When regulating the main steam side extraction steam channel, the valve opening is adjusted in real time according to the peak shaving execution rate, combined with the corresponding peak shaving depth constraint range and extraction steam control valve regulation rate constraint range, to ensure that the extraction steam volume matches the peak shaving demand. Simultaneously, a portion of the main steam after heat exchange returns to the thermal power unit's low-temperature reheat pipeline to ensure reheater flow, and a portion condenses into condensate which returns to the deaerator. The regulation of the high-temperature reheat side extraction steam channel needs to be synchronized with the main steam side extraction volume. The opening is dynamically adjusted according to the peak shaving execution rate to maintain the ratio of main steam extraction to hot reheat extraction between 0.8 and 1.2, avoiding system fluctuations caused by flow mismatch. The regulation of the fourth extraction side extraction steam channel, combined with its own constraint range and unit load status, precisely controls the opening to ensure that the pressure difference between the extraction steam pressure and the high-pressure exhaust pressure does not exceed 15% of the rated pressure. Throughout the regulation process, the temperature and pressure on the molten salt side must be monitored in real time to ensure that the temperature does not exceed the rated operating temperature of the molten salt and the pressure does not exceed the design pressure of the heat exchanger. Simultaneously, the operating data of each device in the molten salt thermal storage system must be monitored in conjunction with the system, and the valve opening should be dynamically fine-tuned based on changes in the cold and hot salt tank levels to ensure the safety and stability of peak-shaving operation. Furthermore, the regulation process must be adapted to the unit's AGC operating modes (normal mode, support mode, and load tracking mode). The regulation strategy should be flexibly adjusted according to the grid load peak-shaving requirements under different modes to achieve efficient assistance from the molten salt thermal storage system for peak-shaving of thermal power units.

[0049] like Figure 3 As shown, the core logic of molten salt thermal energy storage system assisting thermal power units in peak shaving is achieved through three-layer collaborative control: The first layer is the correction of the basic coal quantity. The molten salt thermal energy storage system dynamically corrects the original basic coal quantity line controlled by the boiler based on the real-time steam extraction and peak shaving demand, while optimizing fuel supply in conjunction with other dynamic feedforward signals to ensure boiler combustion stability during peak shaving; The second layer is the coordinated adjustment of sliding pressure and pressure rate. Based on the peak shaving capacity assessment coefficient, the original unit sliding pressure curve and pressure rate setting are corrected to ensure that the unit pressure parameters are accurately matched with the peak shaving execution rate and steam extraction regulation demand, avoiding operational risks caused by pressure fluctuations; The third layer is the enhancement of fault interlocking logic. When the molten salt thermal energy storage system triggers a fault, the unit interlocking increase and decrease logic are synchronously linked, supplementing the original interlocking logic with molten salt-side fault constraints to prevent peak shaving operations from exceeding the system safety boundary. The three-layer control logic forms a closed-loop collaboration, comprehensively adapting to the peak shaving assistance needs of the molten salt thermal energy storage system from fuel supply and parameter matching to safety protection, enabling thermal power units to ensure both adjustment flexibility and operational safety and stability when responding to changes in grid load.

[0050] Based on the complete logic of the above-described auxiliary peak-shaving method for molten salt thermal storage systems, this application also provides a corresponding auxiliary peak-shaving device for molten salt thermal storage systems. The embodiments of this device correspond to the aforementioned method embodiments. For ease of reading, this embodiment will not repeat the details of the aforementioned method embodiments one by one, but it should be clear that the device in this embodiment can correspondingly implement all the contents of the aforementioned method embodiments. Specifically, as follows... Figure 4 As shown, the auxiliary peak-shaving device 400 of the molten salt thermal storage system includes:

[0051] The health assessment module 410 is used to collect the operating data of each piece of equipment and the corresponding measuring device in the molten salt thermal storage system, and to determine the health status assessment results of each piece of equipment and the corresponding measuring device based on the operating data.

[0052] The generation module 420 is used to generate peak-shaving capacity assessment coefficients based on the health status assessment results, the operating data of each device in the molten salt thermal storage system, the external operating requirements of the thermal power unit and the safety status of the thermal power unit itself.

[0053] The control module 430 is used to determine the peak shaving execution rate, peak shaving depth constraint range, and extraction steam valve adjustment rate constraint range of the thermal power unit based on the peak shaving capacity evaluation coefficient. Taking the peak shaving execution rate as the actual adjustment benchmark and the peak shaving depth constraint range and extraction steam valve adjustment rate constraint range as boundary limits, the control module adjusts the opening of the extraction steam valves of the main steam side, high temperature reheat side, and fourth extraction side of the molten salt thermal storage system, respectively, thereby realizing the peak shaving of the thermal power unit assisted by the molten salt thermal storage system.

[0054] Furthermore, such as Figure 4 As shown, the health assessment module 410 is specifically used to perform time-series segmentation and multi-scale feature extraction on the operating data to obtain time-domain features reflecting the dynamic characteristics of equipment operation, frequency-domain features reflecting the stability of equipment operation, precision features reflecting the detection accuracy of the measuring device, and stability features reflecting the continuous operation of the measuring device. Based on the fault types of each device and measuring device, a health status assessment index system is constructed. The time-domain and frequency-domain features are mapped to the equipment operating status dimension of the health status assessment index system, and the precision and stability features are mapped to the measuring device performance dimension of the health status assessment index system, forming a health status assessment feature set. Based on the historical health sample data and fault sample data of each device and measuring device, the features of each dimension in the health status assessment feature set are analyzed respectively. The analysis results are fused through dual-dimensional weight allocation to obtain the health status assessment results of each device and its corresponding measuring device.

[0055] Furthermore, such as Figure 4As shown, the health assessment module 410 is specifically used to establish equipment feature analysis benchmarks based on historical health sample data and fault sample data of each device, and to establish measurement device feature analysis benchmarks based on historical health sample data and fault sample data of each measuring device; to determine the equipment operating status evaluation value based on the equipment feature analysis benchmarks, time domain characteristics, and frequency domain characteristics; to determine the measurement device performance evaluation value based on the measurement device feature analysis benchmarks, accuracy characteristics, and stability characteristics; to determine the dual-dimensional fusion weight of the equipment operating status evaluation value and the measurement device performance evaluation value based on the analytic hierarchy process (AHP) and entropy weight method; to determine the comprehensive health status evaluation value based on the dual-dimensional fusion weight, the equipment operating status evaluation value, and the measurement device performance evaluation value; to determine the health status level of each device and the corresponding measuring device based on the preset health status level classification threshold and the comprehensive health status evaluation value; to determine the reliability weight based on the deviation between the comprehensive health status evaluation value and the corresponding level benchmark threshold; and to form the health status assessment result of each device and the corresponding measuring device based on the health status level and the reliability weight.

[0056] Furthermore, such as Figure 4 As shown, the generation module 420 is specifically used to determine the hardware evaluation sub-coefficient based on the health status assessment results of each device and its corresponding measuring device; to determine the system operation evaluation sub-coefficient based on the grid load peak-shaving demand in the external operating requirements of the thermal power unit, the operating data of each device in the molten salt thermal storage system and the real-time steam extraction, combined with the current load status of the thermal power unit; to determine the safety management evaluation sub-coefficient based on the safety event triggering situation in the safety status of the thermal power unit, combined with the preset safety event set and the limit event set; to determine the weighted weights of the hardware evaluation sub-coefficient, the system operation evaluation sub-coefficient, and the safety management evaluation sub-coefficient based on the real-time operating conditions of the thermal power unit; and to weight the hardware evaluation sub-coefficient, the system operation evaluation sub-coefficient, and the safety management evaluation sub-coefficient based on the weighted weights to obtain the peak-shaving capacity evaluation coefficient.

[0057] Furthermore, such as Figure 4 As shown, the generation module 420 is specifically used to determine the health contribution value of each device based on its health status level and reliability weight, combined with the proportion of the device's functional importance in the peak-shaving operation of the molten salt thermal storage system; to determine the health contribution value of each measuring device based on its health status level and reliability weight, combined with the influence weight of the measuring device's data accuracy in parameter monitoring; and to weight the health contribution values ​​of the devices and the measuring devices according to the correlation importance of the devices and measuring devices to the peak-shaving hardware guarantee, thereby obtaining the hardware evaluation sub-coefficient.

[0058] Furthermore, such as Figure 4As shown, the generation module 420 is specifically used to determine the maximum load reduction range and corresponding peak shaving duration based on the operating data of each device in the molten salt thermal storage system, the maximum steam extraction rate, and the highest load status of the thermal power unit; to determine the maximum load increase range and corresponding peak shaving duration based on the operating data of each device in the molten salt thermal storage system, the minimum steam extraction rate, and the lowest load status of the thermal power unit; to match the maximum load reduction range, the maximum load increase range, and the corresponding peak shaving duration according to the peak shaving range requirements and peak shaving rate requirements in the power grid load peak shaving demand; and to determine the system operation evaluation sub-coefficient based on the matching results and the adaptability of the real-time steam extraction rate of the molten salt thermal storage system to the current steam extraction demand.

[0059] Furthermore, such as Figure 4 As shown, the generation module 420 is specifically used to: set the peak-shaving capacity assessment coefficient to zero and close the extraction steam control valve of the molten salt thermal storage system if any event in the preset safety event set is triggered; if any event in the preset restricted event set is triggered, adjust the safety management assessment sub-coefficient according to the impact degree corresponding to the event type and preset rules; if no event in the safety event set or restricted event set is triggered, determine the safety management assessment sub-coefficient according to the preset benchmark value; adjust the safety management assessment sub-coefficient according to the impact degree corresponding to the event type and preset rules, including: classifying the event impact level according to the impact weight of the restricted event on the safety and stability of the peak-shaving operation of the molten salt thermal storage system; configuring the corresponding safety management assessment sub-coefficient reduction ratio for different impact levels; adjusting the safety management assessment sub-coefficient according to the impact level of the triggered restricted event and the corresponding reduction ratio, wherein the adjusted safety management assessment sub-coefficient is not lower than the preset minimum threshold to ensure the basic safety boundary of peak-shaving operation.

[0060] Furthermore, such as Figure 4 As shown, the control module 430 is specifically used to determine the peak shaving execution rate based on the peak shaving capacity assessment coefficient and the original set load rate of the thermal power unit, combined with the real-time peak shaving rate demand of the power grid; to determine the peak shaving depth constraint range based on the peak shaving capacity assessment coefficient, the rated load of the thermal power unit, and the safety margin of the key operating parameters of the molten salt thermal storage system; and to determine the extraction steam control valve regulation rate constraint range based on the peak shaving capacity assessment coefficient and the maximum regulation rate of the extraction steam control valve of the molten salt thermal storage system, combined with the operational stability requirements of the extraction steam channel.

[0061] Optionally, the auxiliary peak-shaving device for the molten salt thermal storage system may be an electronic device with data processing capabilities, or a functional module within the electronic device, without limitation.

[0062] For example, the electronic device can be a server, which can be a single server or a server cluster consisting of multiple servers. As another example, the electronic device can be a mobile phone, tablet computer, desktop computer, laptop computer, handheld computer, notebook computer, ultra-mobile personal computer (UMPC), netbook, as well as cellular phones, personal digital assistants (PDAs), augmented reality (AR) devices, virtual reality (VR) devices, and other terminal devices. As yet another example, the electronic device can also be a recording device, video surveillance equipment, etc. This application does not impose any special limitations on the specific form of the electronic device.

[0063] The following example uses electronic equipment as an auxiliary peak-shaving device in a molten salt thermal storage system. Figure 5 As shown, Figure 5 The hardware structure of an electronic device 500 provided in this application.

[0064] like Figure 5 As shown, the electronic device 500 includes a processor 510, a communication line 520, and a communication interface 530.

[0065] Optionally, the electronic device 500 may also include a memory 540. The processor 510, memory 540, and communication interface 530 can be connected via a communication line 520.

[0066] The processor 510 can be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 510 can also be any other device with processing capabilities, such as a circuit, device, or software module, without limitation.

[0067] In one example, processor 510 may include one or more CPUs, for example Figure 5 CPU0 and CPU1 in the CPU.

[0068] As an optional implementation, the electronic device 500 may include multiple processors; for example, in addition to processor 510, it may also include processor 570. A communication line 520 is used to transmit information between the components included in the electronic device 500.

[0069] Communication interface 530 is used for communication with other devices or other communication networks. These other communication networks can be Ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), etc. Communication interface 530 can be a module, circuit, transceiver, or any device capable of enabling communication.

[0070] Memory 540 is used to store instructions. These instructions can be computer programs.

[0071] The memory 540 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and / or instructions; it may also be a random access memory (RAM) or other type of dynamic storage device capable of storing information and / or instructions; it may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, etc., without limitation.

[0072] It should be noted that the memory 540 can exist independently of the processor 510, or it can be integrated with the processor 510. The memory 540 can be used to store instructions, program code, or some data, etc. The memory 540 can be located inside or outside the electronic device 500, without restriction.

[0073] The processor 510 is configured to execute instructions stored in the memory 540 to implement the communication method provided in the following embodiments of this application. For example, when the electronic device 500 is a terminal or a chip in a terminal, the processor 510 can execute instructions stored in the memory 540 to implement the steps performed by the sending end in the following embodiments of this application.

[0074] As an optional implementation, the electronic device 500 also includes an output device 550 and an input device 560. The output device 550 can be a display screen, speaker, or other device capable of outputting data from the electronic device 500 to the user. The input device 560 can be a keyboard, mouse, microphone, joystick, or other device capable of inputting data into the electronic device 500.

[0075] It should be pointed out that, Figure 5 The structure shown does not constitute a limitation on the electronic device, except... Figure 5 In addition to the components shown, the electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0076] The auxiliary peak-shaving device and application scenarios for molten salt thermal storage systems described in this application are intended to more clearly illustrate the technical solutions of this application and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of auxiliary peak-shaving devices for molten salt thermal storage systems and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0077] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for auxiliary peak shaving in a molten salt thermal storage system, characterized in that, Applied to thermal power units, the thermal power units including molten salt thermal storage systems, the method includes: Collect operational data of each device and its corresponding measuring device in the molten salt thermal storage system, and determine the health status assessment results of each device and its corresponding measuring device based on the operational data. Based on the health status assessment results, the operating data of each device in the molten salt thermal storage system, the external operating requirements of the thermal power unit, and the safety status of the thermal power unit itself, a peak-shaving capacity assessment coefficient is generated. Based on the peak-shaving capacity evaluation coefficient, determine the peak-shaving execution rate, peak-shaving depth constraint range, and extraction steam regulating valve adjustment rate constraint range of the thermal power unit. Using the peak shaving execution rate as the actual adjustment benchmark and the peak shaving depth constraint range and the extraction steam valve adjustment rate constraint range as boundary limits, the opening of the extraction steam valves of the main steam side, high temperature reheat side and four extraction side extraction steam channels in the molten salt thermal storage system are adjusted respectively, thereby realizing the molten salt thermal storage system to assist the thermal power unit in peak shaving.

2. The method according to claim 1, characterized in that, Based on the operational data, the health status assessment results of each device and its corresponding measuring device are determined, including: The operational data is divided into time series and multi-scale features are extracted to obtain time-domain features reflecting the dynamic characteristics of equipment operation, frequency-domain features reflecting the stability of equipment operation, precision features reflecting the detection accuracy of the measuring device, and stability features reflecting the continuous operation of the measuring device. A health status assessment index system is constructed based on the fault types of each device and measuring device. The time domain features and the frequency domain features are associated and mapped to the device operation status dimension of the health status assessment index system, and the accuracy features and the stability features are associated and mapped to the measuring device performance dimension of the health status assessment index system, thus forming a health status assessment feature set. Based on historical health sample data and fault sample data of each device and measuring device, the features of each dimension in the health status assessment feature set are analyzed respectively. The analysis results are fused by dual-dimensional weight allocation to obtain the health status assessment results of each device and the corresponding measuring device.

3. The method according to claim 2, characterized in that, Based on historical health and fault sample data of each device and measuring device, the features of each dimension in the health status assessment feature set are analyzed. The analysis results are then fused using a two-dimensional weight allocation to obtain the health status assessment results for each device and its corresponding measuring device, including: A benchmark for equipment feature analysis is established based on historical health sample data and fault sample data of each device; a benchmark for measurement device feature analysis is established based on historical health sample data and fault sample data of each measurement device. Based on the equipment feature analysis benchmark, the time domain features, and the frequency domain features, the equipment operating status evaluation value is determined; Based on the characteristic analysis benchmark of the measuring device, the accuracy characteristics, and the stability characteristics, the performance evaluation value of the measuring device is determined; Based on the analytic hierarchy process and the entropy weight method, the dual-dimensional fusion weight of the equipment operation status evaluation value and the measurement device performance evaluation value is determined. The comprehensive health status evaluation value is determined based on the dual-dimensional fusion weights, the equipment operation status evaluation value, and the measurement device performance evaluation value. Based on the preset health status level classification threshold and the comprehensive health status evaluation value, the health status level of each device and the corresponding measuring device is determined. The reliability weight is determined based on the deviation between the comprehensive health status evaluation value and the corresponding level benchmark threshold. Based on the health status level and the reliability weight, a health status assessment result is generated for each device and its corresponding measuring device.

4. The method according to any one of claims 1-3, characterized in that, Based on the health status assessment results, the operating data of each device in the molten salt thermal storage system, the external operating requirements of the thermal power unit, and the safety status of the thermal power unit itself, a peak-shaving capacity assessment coefficient is generated, including: Based on the health status assessment results of each device and its corresponding measuring device, determine the hardware assessment sub-coefficient; Based on the grid load peak-shaving demand in the external operating requirements of thermal power units, the operating data of each device in the molten salt thermal storage system and the real-time steam extraction, and combined with the current load status of the thermal power units, the system operation evaluation sub-coefficient is determined. Based on the safety event triggering situation in the safety status of the thermal power unit itself, and in combination with the preset set of safety events and the set of restricted events, the safety management assessment sub-coefficients are determined; Based on the real-time operating conditions of the thermal power unit, determine the weighted weights of the hardware evaluation sub-coefficient, the system operation evaluation sub-coefficient, and the safety management evaluation sub-coefficient; Based on the weighted values, the hardware evaluation sub-coefficient, the system operation evaluation sub-coefficient, and the security management evaluation sub-coefficient are weighted to obtain the peak-shaving capability evaluation coefficient.

5. The method according to claim 4, characterized in that, Based on the health status assessment results of each device and its corresponding measuring device, hardware assessment sub-coefficients are determined, including: Based on the health status level and reliability weight of each device, and combined with the proportion of the device's functional importance in the peak-shaving operation of the molten salt thermal storage system, the health contribution value of the device is determined. Based on the health status level and reliability weight of each measuring device, and combined with the influence weight of the data accuracy of the measuring device in parameter monitoring, the health contribution value of the measuring device is determined. Based on the correlation importance of equipment and measurement devices to peak-shaving hardware assurance, weights are assigned to the health contribution values ​​of equipment and measurement devices, and a hardware evaluation sub-coefficient is obtained by weighting them.

6. The method according to claim 4, characterized in that, Based on the grid load peak-shaving demand in the external operating requirements of thermal power units, the operating data of each device in the molten salt thermal storage system, and the real-time steam extraction rate, combined with the current load status of the thermal power units, the system operation evaluation sub-coefficients are determined, including: Based on the operating data of each device in the molten salt thermal storage system, the maximum steam extraction rate, and the peak load status of the thermal power unit, determine the maximum load reduction range and the corresponding peak shaving duration; Based on the operating data of each device in the molten salt thermal storage system, the minimum steam extraction rate, and the minimum load status of the thermal power unit, determine the maximum load increase and the corresponding peak shaving duration; Based on the peak shaving amplitude and peak shaving rate requirements in the power grid load peak shaving demand, the maximum load reduction amplitude, the maximum load increase amplitude, and the corresponding peak shaving duration are matched respectively. Based on the matching results and the compatibility between the real-time steam extraction volume of the molten salt thermal storage system and the current steam extraction demand, the sub-coefficients for system operation evaluation are determined.

7. The method according to claim 4, characterized in that, Based on the safety event triggering conditions within the thermal power unit's own safety status, and in conjunction with a pre-set set of safety events and a set of limiting events, safety management assessment sub-coefficients are determined, including: If any event in the preset set of safety events is triggered, the peak-shaving capacity assessment coefficient will be set to zero, and the extraction steam control valve of the molten salt thermal storage system will be shut down. If any event in the preset restricted event set is triggered, the safety management assessment sub-coefficient will be lowered according to the impact level corresponding to the event type and preset rules. If no event in the set of safety events or the set of restricted events is triggered, the safety management assessment sub-coefficient is determined according to the preset benchmark value; Based on the impact level corresponding to the event type, the sub-coefficients of the safety management assessment are adjusted downward according to preset rules, including: The impact levels of events are classified according to the weight of their impact on the safety and stability of peak-shaving operation of molten salt thermal energy storage systems. Configure corresponding reduction ratios for safety management assessment sub-coefficients for different impact levels; Based on the impact level of the triggered restriction event, the safety management assessment sub-coefficient is adjusted according to the corresponding reduction ratio. The adjusted safety management assessment sub-coefficient is not lower than the preset minimum threshold to ensure the basic safety boundary of peak shaving operation.

8. The method according to claim 1, characterized in that, Based on the peak-shaving capacity assessment coefficient, the peak-shaving execution rate, peak-shaving depth constraint range, and extraction steam regulating valve adjustment rate constraint range of the thermal power unit are determined, including: The peak-shaving execution rate is determined based on the peak-shaving capacity assessment coefficient and the original set load rate of the thermal power unit, combined with the real-time peak-shaving rate requirement of the power grid. Based on the peak-shaving capacity assessment coefficient, the rated load of the thermal power unit, and the safety margin of the key operating parameters of the molten salt thermal storage system, the peak-shaving depth constraint range is determined. Based on the peak-shaving capacity assessment coefficient and the maximum adjustment rate of the extraction steam control valve of the molten salt thermal storage system, combined with the operational stability requirements of the extraction steam channel, the constraint range of the extraction steam control valve adjustment rate is determined.

9. An auxiliary peak-shaving device for a molten salt thermal storage system, characterized in that, The device includes: The health assessment module is used to collect the operating data of each device and the corresponding measuring device in the molten salt thermal storage system, and to determine the health status assessment results of each device and the corresponding measuring device based on the operating data. The generation module is used to generate peak-shaving capacity evaluation coefficients based on the health status assessment results, the operating data of each device in the molten salt thermal storage system, the external operating requirements of the thermal power unit, and the safety status of the thermal power unit itself. The control module is used to determine the peak shaving execution rate, peak shaving depth constraint range, and extraction steam valve adjustment rate constraint range of the thermal power unit based on the peak shaving capacity evaluation coefficient. Using the peak shaving execution rate as the actual adjustment benchmark and the peak shaving depth constraint range and extraction steam valve adjustment rate constraint range as boundary limits, the module adjusts the opening of the extraction steam valves of the main steam side, high-temperature reheat side, and fourth extraction side of the molten salt thermal storage system, respectively, thereby realizing the molten salt thermal storage system to assist the thermal power unit in peak shaving.

10. An electronic device, characterized in that, The device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the molten salt thermal storage system auxiliary peak shaving method as described in any one of claims 1-8.

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