A method, device and medium for dynamic assessment of meteorological and hydrological risks of thermal power systems

Abstract

This application discloses a method, device, and medium for dynamic assessment of meteorological and hydrological risks in thermal power systems, relating to the technical field of low-carbon transformation of power systems. The method dynamically couples meteorological and hydrological data of thermal power systems to determine a multi-source meteorological and hydrological data set; employs the second law of thermodynamics and the heat transfer equation to construct a dynamic heat balance equation for the cooling system of the thermal power unit; determines the water intake of the thermal power unit cooling system based on the multi-source meteorological and hydrological data set, the dynamic heat balance equation, and climate and hydrological constraints; determines the available capacity of the thermal power system under runoff and water temperature constraints based on the water intake of the thermal power unit cooling system and the available water resources; establishes a three-dimensional evaluation system based on the available capacity, and, based on the three-dimensional evaluation system, divides and evaluates the meteorological and hydrological risks of the thermal power system to determine the evaluation results. This application achieves a precise assessment of meteorological and hydrological risks in thermal power systems.

Description

Technical Field

[0001] The present application relates to the technical field of low-carbon transformation of power systems, and in particular to a method, equipment and medium for dynamic assessment of meteorological and hydrological risks of thermal power systems. Background Art

[0002] As a core pillar of the global energy system, thermal power systems play a critical role in baseload power supply and grid stability. However, extreme hydrological and meteorological events caused by climate change (such as high temperatures and droughts, sharp declines in runoff, and rising water temperatures) continue to threaten the operational safety of thermal power units, directly threatening the energy transition process and the resilience of the power system. Current thermal power meteorological and hydrological risk management technologies mainly focus on static risk assessment models and single-dimensional risk indicators. This involves assessing unit capacity losses based on historical climate mean data, while ignoring the dynamic fluctuations of meteorological and hydrological data and the nonlinear effects of extreme events. Furthermore, most studies use a single capacity factor (CF) or outage frequency as risk indicators, which cannot quantify the persistence, extremeness, and multi-factor coupling effects of the risk. Therefore, a dynamic assessment method for meteorological and hydrological risks in thermal power systems is urgently needed. Summary of the Invention

[0003] The purpose of this application is to provide a method, equipment and medium for dynamic assessment of meteorological and hydrological risks of thermal power systems, which can solve the problem that traditional methods cannot achieve accurate quantitative assessment of meteorological and hydrological risks.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] In a first aspect, the present application provides a method for dynamic assessment of meteorological and hydrological risks of a thermal power system, comprising: dynamically coupling meteorological and hydrological data of the thermal power system to determine a multi-source meteorological and hydrological data set; the thermal power system comprises multiple thermal power units and a thermal power unit cooling system; the meteorological and hydrological data comprise grid multi-scenario simulation data and historical observation data of meteorological and hydrological stations; the grid multi-scenario simulation data is determined based on a global climate model library and a global hydrological model simulation; a dynamic heat balance equation of the thermal power unit cooling system is constructed using the second law of thermodynamics and the heat transfer equation; the water intake of the thermal power unit cooling system is determined based on the multi-source meteorological and hydrological data set, the dynamic heat balance equation, and climate and hydrological constraints; the available capacity of the thermal power system under runoff and water temperature constraints is determined based on the water intake of the thermal power unit cooling system and the available water resources; a three-dimensional evaluation system is established based on the available capacity, and the meteorological and hydrological risks of the thermal power system are divided and assessed based on the three-dimensional evaluation system to determine an assessment result; the three-dimensional evaluation system comprises a meteorological and hydrological risk intensity index, a high-risk operation frequency, and an unsafe operation frequency.

[0006] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for dynamic assessment of meteorological and hydrological risks of thermal power systems.

[0007] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for dynamic assessment of meteorological and hydrological risks of thermal power systems.

[0008] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0009] This application first dynamically couples the meteorological and hydrological data of a thermal power system to determine a multi-source meteorological and hydrological dataset. This data is then integrated with grid multi-scenario simulation data, historical observation data from meteorological and hydrological stations, and physical data (i.e., geographic data of the thermal power unit). The application then employs the second law of thermodynamics and the heat transfer equation to construct a dynamic heat balance equation for the thermal power unit's cooling system. Based on the multi-source meteorological and hydrological dataset, the dynamic heat balance equation, and climate and hydrological constraints, the water intake of the thermal power unit's cooling system is determined. Furthermore, based on the water intake and available water resources of the thermal power unit's cooling system, the available capacity of the thermal power system, subject to runoff and water temperature constraints, is determined. Finally, a three-dimensional evaluation system is established based on the available capacity. Based on this three-dimensional evaluation system, the meteorological and hydrological risks of the thermal power system are classified and assessed, and the evaluation results are determined. The three-dimensional evaluation system includes a meteorological and hydrological risk intensity index, a high-risk operation frequency, and an unsafe operation frequency. This application, based on the dynamic coupling of meteorological and hydrological data with the physical characteristics of the turbine units, ultimately establishes a three-dimensional evaluation system. This system employs a multi-dimensional risk analysis based on the meteorological and hydrological risk intensity index, the frequency of high-risk operations, and the frequency of unsafe operations. This avoids the traditional approach of using a single capacity factor or outage frequency as a risk indicator, which results in an inability to quantify the persistence, extreme nature, and multi-factor coupling effects of risk. This application achieves a precise quantitative assessment of meteorological and hydrological risks, improving the scientific and targeted nature of prevention and control strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 This is a flow chart of a method for dynamic assessment of meteorological and hydrological risks of a thermal power system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0013] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0014] like Figure 1 As shown, this application provides a method for dynamic assessment of meteorological and hydrological risks of thermal power systems, including:

[0015] Step 101: Dynamically couple the meteorological and hydrological data of a thermal power system to determine multi-scenario and multi-meteorological model meteorological and hydrological data; the thermal power system includes multiple thermal power units and thermal power unit cooling systems; the meteorological and hydrological data includes grid multi-scenario simulation data and historical observation data of meteorological and hydrological stations; the grid multi-scenario simulation data is determined based on a global meteorological model library and a global hydrological model simulation.

[0016] Step 102: Using the second law of thermodynamics and the heat transfer equation, a dynamic heat balance equation of the thermal power unit cooling system is constructed.

[0017] Step 103: Determine the water intake of the cooling system of the thermal power unit according to the multi-scenario and multi-meteorological model meteorological and hydrological data, the dynamic heat balance equation, and meteorological and hydrological constraints.

[0018] Step 104: Determine the available capacity of the thermal power system under runoff and water temperature constraints based on the water intake and available water resources of the thermal power unit cooling system.

[0019] Step 105: Based on the available capacity, a three-dimensional evaluation system is established. Based on the three-dimensional evaluation system, the meteorological and hydrological risks of the thermal power system are divided and evaluated to determine the evaluation results. The three-dimensional evaluation system includes a meteorological and hydrological risk intensity index, a high-risk operation frequency, and an unsafe operation frequency.

[0020] In some embodiments, step 101 specifically includes: for any thermal power unit, based on the geographic nearest neighbor matching method, matching the historical observation data of the meteorological and hydrological station with the longitude and latitude of the thermal power unit, determining the meteorological and hydrological station within the preset longitude and latitude of the thermal power unit and the historical observation data corresponding to the meteorological and hydrological station; downscaling the grid multi-scenario simulation data by bilinear interpolation method, and matching the data with the longitude and latitude of the thermal power unit to determine the multi-scenario multi-climate model meteorological and hydrological data of the thermal power unit; determining the multi-source meteorological and hydrological data set based on the multi-scenario multi-climate model meteorological and hydrological data of each thermal power unit and the historical observation data corresponding to the meteorological and hydrological station within the preset longitude and latitude of each thermal power unit.

[0021] In some embodiments, when the cooling technology type of the thermal power unit in the thermal power unit cooling system is a circulating cooling technology, the dynamic heat balance equation of the thermal power unit cooling system includes an energy conservation equation and a mass conservation equation; the thermal power unit cooling system includes a condenser and a cooling tower.

[0022] The energy conservation equation is:

[0023] .

[0024] in, is the heat input to the condenser; It is the flow rate of cold air entering the cooling tower; and are the enthalpy of hot air entering and leaving the cooling tower respectively; is the density of air; is the density of water; It is the amount of water required for the cooling system of thermal power units; and are the enthalpy of water entering and leaving the cooling tower, respectively; It is the loss of sewage discharge.

[0025] The mass conservation equation is: .

[0026] in, is the evaporation loss; and are the air humidity ratios inside and outside the cooling tower, respectively.

[0027] When the cooling technology type of the thermal power unit in the thermal power unit cooling system is the primary cooling technology, the dynamic heat balance equation of the thermal power unit cooling system is:

[0028] = .

[0029] in, It is the amount of water required for the cooling system of thermal power units; is the heat capacity of water; is the temperature rise of the water passing through the condenser.

[0030] A cooling tower is a device used to cool hot water, commonly found in industrial facilities such as power plants and chemical plants. It lowers the water's temperature through contact with air, thereby recycling water resources. A cooling tower primarily processes hot water exiting the condenser, cooling it before returning it to the system for reuse. The condenser is a crucial component in thermal power generation systems, primarily used to condense steam from the steam turbine into water for reheating in the boiler to generate high-pressure steam that drives the turbine. The condenser is typically located after the steam turbine, directly receiving low-pressure steam from the turbine's outlet.

[0031] In some embodiments, step 103 specifically includes: the climate-hydrological constraint conditions include a maximum allowable discharge temperature of cooling water set to protect hydro-ecological diversity and an environmental flow constraint ratio set to ensure water use for hydro-ecological purposes; the maximum allowable discharge temperature of cooling water for protecting hydro-ecological diversity and the environmental flow constraint ratio for ensuring water use for hydro-ecological purposes are determined based on hydro-ecological management requirements; determining a water intake of a cooling system of the thermal power unit based on the multi-source meteorological-hydrological dataset, the climate-hydrological constraint conditions, the cooling technology type of the thermal power unit, and the dynamic heat balance equation; the cooling technology type of the thermal power unit includes a primary cooling technology and a circulating cooling technology; when the cooling technology type of the thermal power unit is a primary cooling technology, the water intake of the cooling system of the thermal power unit is:

[0032] .

[0033] in, It is the amount of water required for the cooling system of thermal power units; is the net efficiency of the thermal power system; is the heat capacity of water; is the installed capacity of the thermal power system; is the waste heat loss ratio; is the temperature rise of the water passing through the condenser; ; is the maximum allowable discharge temperature of cooling water; is the runoff water temperature; It is the maximum allowable temperature rise of cooling water.

[0034] When the cooling technology type of the thermal power unit is circulating cooling technology, the water intake formula of the thermal power unit cooling system is: .

[0035] in, is the evaporation loss; = ; is the proportion of heat rejected during sensible heat transfer; is the latent heat of vaporization of water; It is the sewage loss, ; is the concentration cycle coefficient; is the drift loss, Equal to 0.

[0036] In some embodiments, step 104 specifically includes steps 201 to 203 .

[0037] Step 201: Calculate the available water resources based on the runoff in the meteorological and hydrological data and the environmental flow constraint ratio of hydrological and ecological water use.

[0038] Step 202: According to the water intake of the thermal power unit cooling system and the available water resource, the minimum value between the two is determined as the actual available water amount.

[0039] Step 203: Determine the available capacity of the thermal power system under runoff and water temperature constraints based on the actual available water volume and the cooling technology type of the thermal power unit.

[0040] In some embodiments, step 203 specifically includes: when the cooling technology type of the thermal power unit is the primary cooling technology, the available capacity of the thermal power system under the runoff and water temperature constraints is:

[0041] .

[0042] in, is the available capacity of the thermal power system under runoff and water temperature constraints; is the actual amount of water available; is the net efficiency of the thermal power system; is the heat capacity of water; is the waste heat loss ratio; is the temperature rise of the water passing through the condenser.

[0043] In some embodiments, step 203 further includes: when the cooling technology type of the thermal power unit is a circulating cooling technology, the available capacity of the thermal power system under the constraints of runoff and water temperature is:

[0044] .

[0045] in, is the available capacity of the thermal power system under runoff and water temperature constraints; is the actual amount of water available; is the net efficiency of the thermal power system; is the heat capacity of water; is the waste heat loss ratio is the wet-bulb temperature; is the approach temperature of the cooling tower; is the runoff water temperature; is the concentration cycle coefficient.

[0046] In some embodiments, step 105 specifically includes: determining a meteorological and hydrological risk intensity index based on the available capacity of the thermal power system under runoff and water temperature constraints and the installed capacity of the thermal power system; wherein the meteorological and hydrological risk intensity index represents the meteorological and hydrological risk level of the thermal power unit. Based on the meteorological and hydrological risk intensity index and different preset thresholds, the meteorological and hydrological risks of the thermal power system are classified to determine classification results; the classification results include safe, vulnerable, and high risk; safe indicates that the available capacity meets real-time scheduling requirements; vulnerable indicates that the loss of available capacity is controllable; and high risk indicates that operation within a preset load range will result in a sudden drop in efficiency and the risk of uncontrollable power outages.

[0047] The preset load range is a low load range, and the low load range may be a meteorological and hydrological risk intensity index lower than 20%, that is, the available capacity is less than 20% of the installed capacity of the unit.

[0048] Based on the meteorological and hydrological risk intensity index, high-risk operation frequency and unsafe operation frequency are determined; among them, the high-risk operation frequency represents the risk intensity of serious capacity loss of thermal power units due to extreme meteorological and hydrological conditions; the unsafe operation frequency represents the risk duration of the thermal power units being unable to operate stably according to the installed capacity due to meteorological and hydrological constraints.

[0049] The division result, the high-risk operation frequency, and the unsafe operation frequency are used as evaluation results.

[0050] This application provides a quantifiable, multi-dimensional dynamic assessment method for climate and hydrological risks through multi-source data fusion and thermodynamic coupling modeling, providing a scientific basis for the low-carbon transformation of thermal power systems, scheduling optimization under extreme weather conditions, and risk warning. Specifically, it includes (1) data fusion mechanism: establishing a synchronous acquisition framework for real-time operation data of thermal power units (such as output, cooling water flow, etc.), site-level meteorological observation data (such as temperature, relative humidity, wind speed, precipitation) and hydrological dynamic data (runoff, water temperature), and realizing multi-dimensional data fusion through timestamp alignment and spatial interpolation algorithm to ensure that the data temporal and spatial resolution matches the unit operation status. (2) Thermodynamic dynamic coupling model (thermal model): introducing the second law of thermodynamics and the heat transfer equation, constructing the dynamic heat balance equation of the thermal power unit cooling system (such as condenser, cooling tower), and quantifying the impact of hydrological conditions (such as rising cooling water temperature and falling runoff) on the power generation capacity of the thermal power unit. (3) Construction of a three-dimensional evaluation system: A three-dimensional evaluation system consisting of the Climate and Hydrological Risk Intensity Index (CRII), High-Risk Operating Frequency (HRFR), and Unsafe Operating Frequency (UORF) is established, where: ①CRII = available capacity / installed capacity; ②HRFR = the number of days when CRII exceeds the extreme risk threshold / 365×100%; ③UORF = the number of days when CRII is below the stable output limit / 365×100%. This application breaks through the limitations of traditional static risk assessment methods and, for the first time, dynamically couples meteorological and hydrological conditions with the thermodynamic characteristics of thermal power units, achieving real-time updates and early warnings of risk quantification indicators (CRII, HRFR, and UORF), providing scientific support for ensuring energy security.

[0051] In practical applications, a method for dynamic assessment of meteorological and hydrological risks of a thermal power system specifically includes the following steps.

[0052] Step 1: Coupling processing of meteorological and hydrological data.

[0053] Based on the meteorological data processing server, the corresponding meteorological and hydrological data are matched to the longitude and latitude coordinates provided by the unit's geographic data for subsequent calculations. As shown in Table 1, unit geographic data refers to the geographic spatial distribution and operational characteristics of global thermal power units acquired through remote sensing satellites and global public databases. This information is used to obtain meteorological and hydrological conditions matching the corresponding locations of global thermal power units. Unit geographic data includes the longitude and latitude of each thermal power unit. Meteorological and hydrological data are divided into two categories: historical observation data and grid multi-scenario simulation data. Historical observation data is primarily based on meteorological and hydrological data obtained from real-time observations at the site. By matching the unit's location with the observation site, the meteorological and hydrological conditions corresponding to the thermal power unit in the historical period can be obtained. Grid multi-scenario simulation data refers to grid-scale meteorological and hydrological variables under different future climate change scenarios simulated by global climate models. By matching the unit's geographic location to the corresponding global longitude and latitude grid, the meteorological and hydrological conditions corresponding to the future period can be obtained. Based on the two major categories of meteorological and hydrological data, the meteorological and hydrological conditions of global thermal power units under different climate change scenarios in historical and future periods can be obtained. This can then be used to quantify historical and current meteorological and hydrological risks, and to predict the meteorological and hydrological risks of thermal power units after experiencing different climate changes (different temperature rises) in the future.

[0054] Table 1 Basic data of meteorological and hydrological risks of thermal power units

[0055]

[0056] The specific processing process is as follows.

[0057] 1. Space-time matching algorithm.

[0058] (1) Matching historical observation data with unit coordinate points.

[0059] Using the geographic nearest neighbor matching method, the system searches for all meteorological and hydrological stations (meteorological / hydrological stations) within a preset radius (a 50km radius) using the longitude and latitude of the thermal power plant as the coordinate center. The data from the nearest station is directly mapped to the longitude and latitude of the thermal power plant. If no station exists within this radius, it is marked as "missing," triggering grid interpolation. Geographic Nearest Neighbor Matching (GNNM) is a method used in spatial data analysis that finds the closest target object or sample based on geographic location.

[0060] (2) Grid-scale climate change multi-scenario simulation data are matched with the coordinate points of thermal power units.

[0061] For each thermal power unit's longitude and latitude coordinates, global meteorological / hydrological data covering a 0.5×0.5 grid is converted to unit location data using a bilinear interpolation algorithm. The unit location is the unit's longitude and latitude. By mapping these locations, the meteorological and hydrological data observed at that site are considered the meteorological and hydrological conditions experienced by the unit. Meteorological and hydrological risks are subsequently calculated based on these meteorological and hydrological conditions.

[0062] (3) Time alignment: Data with different time resolutions (such as hourly observation data and daily pattern data) are uniformly downsampled into daily data sets, with 00:00 every day as the reference point, and the average value (temperature, relative humidity) or cumulative value (runoff) is used for aggregation.

[0063] 2. Data quality control.

[0064] (1) Outlier filtering: The input data is checked for a physical range (e.g., Ta∈[-50,60]℃, RH∈[0,100]%). Data outside the range is marked as invalid and linear interpolation of adjacent time period data is used to determine the interpolated simulation value of the thermal power unit. The linear interpolation filling method uses the valid data points before and after the outlier to linearly calculate the intermediate value according to the time interval ratio for filling.

[0065] Historical observational data refers only to the historical period, while grid-scale multi-scenario simulation data includes meteorological and hydrological data from the historical and future periods, as well as different climate models. Matching historical observation site data yields meteorological and hydrological variables observed at the site during the historical period. Matching grid-scale simulation data yields meteorological and hydrological variables simulated by the model for both the historical and future periods, i.e., multi-scenario, multi-meteorological model meteorological and hydrological data for thermal power units. (This involves two processing issues: 1) Grid-scale multi-scenario simulation data may contain outliers, so the interpolated simulated values ​​of the thermal power units are filtered for outlier quality control. Unsatisfactory values ​​are filled using linear interpolation from adjacent time periods. 2) Grid-scale multi-scenario simulation data are converted to unit locations through interpolation, which may include bias. The site observation data are assumed to be accurate, and then the interpolated data are compared with observation data from the same location and the same period. If they match, the interpolation is considered valid; if not, manual review is performed.)

[0066] Verification of interpolation results: Observed data and interpolated data from the same thermal power unit's latitude and longitude, taken during the same period, are compared and the root mean square error (RMSE) is calculated. If the RMSE exceeds a threshold, a manual review process is triggered. In other words, interpolated data, based on the thermal power unit's interpolated simulation values ​​and historical observations, is ultimately quality-verified.

[0067] In practical applications, multiple sets of meteorological and hydrological data are involved. Model simulations, in particular, encompass different scenarios, meteorological patterns, and hydrological models. Therefore, after data processing, these data need to be categorized to construct a multi-scenario database. Each set of data can be used to calculate a meteorological and hydrological risk, reflecting the uncertainty of the meteorological and hydrological risks of thermal power units under climate change.

[0068] 3. Construction of multi-source meteorological and hydrological datasets.

[0069] (1) Scenario definition: For historical periods, different sources (station observations and model simulations) are distinguished; for future periods, different shared socioeconomic pathways (e.g., SSP1-2.6, SSP3-7.0, SSP5-8.5) and climate models (e.g., GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, ukesM1-0-11) and hydrological models (e.g., WaterGAP, PCR-GLOBWB) are used for model simulation scenarios. Among them, Shared Socioeconomic Pathways (SSPs) are a set of frameworks used in climate change research to describe possible scenarios of future socioeconomic development. They are designed to assess the potential impact of human activities on future climate change and provide a basis for formulating response strategies.

[0070] (2) Scenario-labeled storage: For example, data from the historical period (1980-2014) and the future period (2015-2100) are divided according to the time axis, and different data sources, shared socioeconomic paths, climate patterns, and hydrological model labels are subdivided to form multiple scenario data sets.

[0071] (3) Time index construction: A hierarchical index based on year, month, and day is established for each scenario dataset to support rapid data extraction by time period (e.g., “data for June 30, 2030”). Finally, a multi-source meteorological and hydrological dataset is obtained, as shown in Table 2.

[0072] Table 2 Variables of multi-source meteorological and hydrological datasets

[0073]

[0074] The key functions of Step 1 are: 1. Ensuring spatiotemporal consistency: Interpolation and matching algorithms are used to resolve spatial mismatches between meteorological / hydrological data and turbine locations, avoiding the systematic biases caused by the coarse resolution of traditional methods. 2. Multi-scenario compatibility: Standardized storage of historical and future scenario data provides a data foundation for subsequent analysis of the long-term impact of climate change on turbine risks. 3. Data traceability: The data_source, scenario, climate_model, and hydro_model fields are used to distinguish between observed and simulated data, ensuring the interpretability of risk assessment results. This provides the basic data input for subsequent available capacity calculations, which is temporally and spatially aligned.

[0075] Step 2: Dynamic calculation framework of available capacity under meteorological and hydrological constraints.

[0076] The calculation is primarily based on the unit-scale, multi-scenario, multi-meteorological model meteorological and hydrological dataset prepared in Step 1 and the unit's environmental regulations (the administrative region and watershed unit's requirements for hydrological and ecological management). The meteorological and hydrological constraints include: the maximum allowable cooling water discharge temperature Tlmax (°C, a known quantity) to ensure hydrological and ecological diversity; and the environmental flow rate ratio to ensure hydrological and ecological water use. γ (%, known amount). The calculation process is as follows.

[0077] 1. Calculate the water intake requirements of the water-cooling unit 。

[0078] Cooling technologies for water-cooled units are categorized into two main types: primary cooling and recirculating cooling. The second law of thermodynamics and heat transfer equations are used to construct the dynamic heat balance equation for the thermal power unit's cooling system (such as the condenser and cooling tower). Calculations are performed using the corresponding branch-based calculation method based on the unit's cooling technology. Meteorological and hydrological conditions, such as ambient temperature, relative humidity, and water temperature, affect the water demand of the water-cooled unit. Branch 1 represents primary cooling technology, while branch 2 represents recirculating cooling technology. Water-cooled units within thermal power units utilize two cooling technologies: primary cooling and recirculating cooling. These two technologies operate differently, and therefore their water demand calculations differ. For each thermal power unit, first determine its cooling technology type. If it utilizes primary cooling, use branch 1 for calculations; if it utilizes recirculating cooling, use branch 2.

[0079] Branch 1: One-step cooling technology.

[0080] Condenser temperature rise constraint: .

[0081] in, is the temperature rise across the condenser (°C, unknown); is the maximum allowable discharge temperature of cooling water (°C, known quantity); is the runoff water temperature (°C, a known quantity); is the maximum allowable temperature rise of cooling water (℃, a known quantity).

[0082] The water intake formula is: .

[0083] in, is the water intake required by the thermal power system (m 3 / s, unknown quantity); is the density of water (1000 kg / m 3 , known quantity); is the heat capacity of water (4.184 J / g / °C, a known quantity); is the installed capacity of the thermal power system (MW, a known quantity); is the net efficiency of the thermal power system (%, a known quantity); is the waste heat loss ratio (%, a known quantity).

[0084] Branch 2: Circulation cooling technology.

[0085] Condenser water demand: Recirculating cooling technology requires water to compensate for three types of water consumption in the cooling system: evaporation loss, blowdown loss, and drift loss. Drift loss refers to the liquid water spray escaping from the cooling tower and can be counted as zero and ignored.

[0086] .

[0087] in, Evaporation loss is the main source of water demand and is related to the heat load entering the condenser.

[0088] .

[0089] in, is the proportion of heat removed during sensible heat transfer (%, known quantity); is the latent heat of vaporization of water (2.45 MJ / kg, a known quantity).

[0090] in, Blowdown loss includes the small amount of sewage discharged to prevent the accumulation of pollutants (chlorides) in the cooling system, which can be represented by evaporation loss and the number of cooling tower concentration cycles.

[0091] .

[0092] Substitute the calculation formula of evaporation loss and blowdown loss into the energy / mass balance equation and iteratively solve to obtain the water intake of the circulating cooling technology Wop .

[0093] Conservation of Energy: .

[0094] Conservation of mass: .

[0095] in, is the flow rate of cold air entering the tower (kg / s, a known quantity); and is the enthalpy of the hot air entering and leaving the cooling tower (MJ / kg, a known quantity); and is the humidity ratio of the air inside and outside the cooling tower (a known quantity); is the wet-bulb temperature (°C, a known quantity); is the approach temperature of the cooling tower (°C, a known quantity); The density of air (under standard conditions (0°C, 1 standard atmosphere (1 atm) is about 1.29 kg / m 3 , known quantity).

[0096] 2. Determine the amount of available water resources.

[0097] Calculate the available runoff (available water resources) based on the runoff in the meteorological and hydrological dataset in step 1 and the environmental flow constraint ratio that ensures water use for hydrological ecology: .in, is the maximum proportion of runoff that can be used for power generation after accounting for environmental flows (%, a known quantity); is the runoff at the location of the thermal power system (m 3 / s, known quantity).

[0098] 3. Calculate available capacity .

[0099] Actual available water volume: Compare the available water resources with the water intake requirement of the water-cooling unit, and determine the smaller value of the two as the actual available water volume, that is: ;in, is the amount of available water resources, that is, the available runoff under environmental flow constraints (m 3 / s, known quantity).

[0100] Dynamically calculate available capacity: Based on the actual available water volume under thermodynamic theory and available water resource constraints, the proportion of installed capacity that can be met is calculated, representing the available capacity. Because the water intake calculation methods for primary and recirculating cooling technologies differ, the available capacity calculation corresponds to the water intake calculation, and is subdivided into Branch 1 (primary cooling technology) and Branch 2 (recirculating cooling technology).

[0101] Understandably, water-cooled units within thermal power plants utilize two cooling technologies: primary cooling and recirculating cooling. Their operating principles differ, and therefore their water demand calculations, and thus the resulting available capacity calculations, differ. For each thermal power unit, first determine its cooling technology type. If it utilizes primary cooling, use branch 1 for the calculation; if it utilizes recirculating cooling, use branch 2.

[0102] Branch 1: One-step cooling technology.

[0103] .

[0104] Branch 2: Circulation cooling technology.

[0105] .

[0106] in, is the water intake required by the thermal power system (m 3 / s, unknown quantity); is the heat input to the condenser (J / MWh, unknown quantity); is the density of water (1000 kg / m 3 , known quantity); is the heat capacity of water (4.184 J / g / °C, a known quantity); ; is the temperature rise across the condenser (°C, unknown); is the installed capacity of the thermal power system (MW, a known quantity); is the net efficiency of the thermal power system (%, a known quantity); is the proportion of waste heat loss (%, known quantity); is the maximum allowable discharge temperature of cooling water (°C, known quantity); is the runoff water temperature (°C, a known quantity); is the maximum allowable temperature rise of cooling water (°C, known quantity); is the available capacity of the thermal power system under runoff and water temperature constraints (MW, unknown); is the actual available water volume of the thermal power system (m 3 / s, unknown quantity); is the evaporation loss (m 3 / s, unknown quantity); is the latent heat of vaporization of water (2.45 MJ / kg, a known quantity); is the concentration cycle coefficient (known quantity); and is the enthalpy of the water entering the cooling tower and the water leaving the cooling tower (MJ / kg, known quantity); is the flow rate of cold air entering the tower (kg / s, known quantity); and are the enthalpies of the hot air entering and leaving the cooling tower (MJ / kg, known quantity); and is the air humidity ratio inside and outside the cooling tower (known quantity); is the wet bulb temperature (°C, known quantity); is the approach temperature of the cooling tower (°C, known quantity).

[0107] In practical applications, quantify the maximum heat dissipation capacity of the cooling system under meteorological and hydrological constraints to provide thermodynamic boundary conditions for the available capacity calculation; quantify the dynamic limitations of meteorological and hydrological conditions and regulatory constraints on the actual available output of the unit.

[0108] Step 3: Three-dimensional evaluation system for meteorological and hydrological risks of thermal power units.

[0109] Based on the available capacity calculated in Branch 1 (primary cooling) and Branch 2 (circulating cooling) in Step 2 to determine the three-dimensional evaluation system.

[0110] 3.1 Definition of risk indicators.

[0111] ① Meteorological and hydrological risk intensity index (CRII):

[0112] .

[0113] The physical meaning of CRII: Reflects the proportion of the available capacity of the unit under meteorological and hydrological conditions and environmental regulations to the installed capacity, and characterizes the meteorological and hydrological risk level of the unit.

[0114] Threshold standard:

[0115] Safe (CRII ≥ 0.9): The available capacity meets the real-time scheduling requirements.

[0116] Vulnerable (0.2 < CRII < 0.9): Capacity loss is controllable, but scheduling intervention is required.

[0117] High risk (CRII ≤ 0.2): Low load operation leads to a sharp drop in efficiency and the risk of uncontrollable power outages.

[0118] ② High risk operation frequency (HRFR):

[0119] HRFR = × 100 ; .

[0120] Physical meaning: Quantify the risk intensity of the unit suffering serious capacity loss due to extreme meteorological and hydrological conditions (such as high temperature and drought).

[0121] ③ Unsafe Operating Frequency (UORF):

[0122] UORF= ×100 .

[0123] Physical meaning: Characterizes the risk duration of the unit being unable to operate stably at installed capacity due to meteorological and hydrological constraints.

[0124] 3.2 Methods for assessing spatiotemporal heterogeneity.

[0125] Time dimension: Based on the daily CRII series, the annual average CRII (annual average meteorological and hydrological risk intensity, representing the annual average risk level), HRFR (annual high-risk operation frequency, representing the extreme risk intensity throughout the year) and UORF (unsafe operation frequency, representing the risk duration throughout the year) are calculated.

[0126] Spatial dimension: Compare the CRII distribution, HRFR hotspots, and UORF cumulative effects of units in different regions to identify areas with high meteorological and hydrological risks.

[0127] In practical applications, by dynamically coupling meteorological and hydrological constraints with unit operation data, a three-dimensional indicator system of CRII-HRFR-UORF is constructed to quantitatively assess the intensity, frequency and persistence of meteorological and hydrological risks of thermal power units, achieve accurate classification of risk levels and graded early warning, and support power system resilience planning and adaptive decision-making.

[0128] Compared with existing technologies, this application achieves core advantages in thermal power meteorological and hydrological risk assessment, including multi-dimensional accuracy improvement, risk response calculation optimization, comprehensive enhancement of meteorological and hydrological risk identification, and improvement of multi-time scale risk assessment capabilities. The specific technical advantages and sources are as follows.

[0129] 1. Improving the spatiotemporal matching accuracy of meteorological and hydrological data: Step 1 of this application utilizes a bilinear interpolation algorithm in conjunction with the geographic nearest neighbor matching method. Traditional methods, however, employ only a single interpolation method (such as the nearest neighbor method) or direct mapping of coarse-grid data, resulting in significant errors in meteorological and hydrological parameters at thermal power unit locations. This application utilizes the bilinear interpolation algorithm in Step 1 (for gridded multi-scenario simulation data) and the priority matching rule for sites within a 50km radius (for observational data) to increase the spatial resolution of the data from 0.5 grids (approximately 55km) to the thermal power unit location level, where the location is the latitude and longitude. This lays the foundation for the dynamic calculation of available capacity in Step 2, avoiding risk misjudgments due to data bias.

[0130] 2. Improving the timeliness of available capacity predictions for thermal power units and optimizing computational efficiency: Dynamic coupling of the thermal model with real-time operating data in step 2 of this application. Existing technologies often use static thermal efficiency coefficients or monthly average meteorological data, which cannot capture the impact of short-term (e.g., daily) hydrological fluctuations on the cooling system, resulting in delayed capacity predictions. This application introduces a dynamic heat balance equation and a sliding time window mechanism (e.g., daily updates) in step 2 to achieve daily available capacity calculations. The CRII index in step 3 can reflect the real-time impact of extreme weather (e.g., a sharp drop in cooling efficiency on hot days).

[0131] 3. Enhanced Comprehensiveness of Meteorological and Hydrological Risk Identification: The synergistic effect of the three-dimensional assessment system (CRII, HRFR, and UORF) and the dynamic threshold calibration method in Step 3 of this application are demonstrated. Traditional methods rely on a single indicator (such as a temperature threshold) or a fixed risk threshold, failing to distinguish the combined impacts of long-term climate change and short-term extreme events. This application achieves comprehensive risk coverage through the following technical features in Step 3: the CRII index quantifies the nonlinear impact of cooling water temperature / flow on available capacity; the HRFR index assesses extremely high-risk operating conditions; and the UORF index quantifies the duration of risk.

[0132] 4. Multi-timescale risk assessment capabilities (simultaneously covering short-, medium-, and long-term risks): This application leverages the multi-scenario data construction in step 1 and the three-dimensional assessment system in step 3 to implement a multi-dimensional risk assessment model. Existing technologies typically only support historical data or single climate scenario analysis, making it difficult to assess the cumulative risk of future climate change over the unit's lifetime (30-50 years). Through standardized storage of SSP scenarios in step 1 and interannual HRFR trend analysis in step 3, this application achieves: short-term (1-3 days): CRII early warning based on real-time data; medium-term (1-5 years): prediction of high-risk periods for units based on HRFR thresholds; and long-term (10-50 years): quantification of future UORF growth trends based on SSP scenarios. This supports power plants in planning low-carbon transition paths (e.g., preemptively deploying energy storage systems to mitigate long-term UORF increases).

[0133] This application systematically addresses the shortcomings of traditional methods in data accuracy, calculation timeliness, risk coverage and multi-scale analysis through high-precision data fusion in step 1, dynamic coupling of thermal models in step 2, and a three-dimensional evaluation system in step 3, and provides high-confidence decision support for the low-carbon transformation of the power system.

[0134] Regarding the technical solution of this application, the existing alternative technologies or methods are as follows, but they have significant defects in systematicity, accuracy or feasibility.

[0135] Alternative 1: Traditional static empirical model, specific technical description:

[0136] Step 1: Use historical average meteorological and hydrological data and ignore real-time dynamic changes.

[0137] Step 2: Calculate the available capacity based on the linear regression model with fixed design parameters.

[0138] Step 3: Assess risk using a single capacity factor (CF).

[0139] Defect analysis includes the following.

[0140] Lack of dynamics: Unable to capture the nonlinear impact of extreme events (such as a 100-year drought) on available capacity.

[0141] One-sided decision-making basis: The CF indicator cannot distinguish risk types (step 3), resulting in a mismatch between strategy and risk.

[0142] Strong human subjectivity: Experience-driven decisions lack quantitative verification, and adaptive strategies have low reliability.

[0143] Alternative 2: Purely data-driven AI model, the technical description is as follows:

[0144] Step 1: Directly input the original meteorological data without multi-source data fusion and spatial interpolation.

[0145] Step 2: A black-box deep learning model (e.g., LSTM) predicts available capacity.

[0146] Step 3: Output risk probability, without clear indicator definition.

[0147] The defect analysis is as follows.

[0148] Mechanism disconnection: Ignoring the unit physical model (step 2), the available capacity prediction error is large.

[0149] High data dependence: Sensitive to the distribution of training data, and extrapolation fails for newly built units or extreme climates.

[0150] Existing alternatives suffer from significant deficiencies in data dynamics, model accuracy, and decision-making scientificity, hindering the precise quantification and scientific prevention and control of climate and hydrological risks. This technical solution, through innovations such as multi-source data fusion, physical-data hybrid modeling, and multi-dimensional risk analysis, offers irreplaceable systemic advantages and provides a viable risk assessment solution for the thermal power industry in response to meteorological and hydrological changes under climate change.

[0151] The thermal power industry urgently needs to break away from the traditional paradigm of "static assessment and empirical decision-making" and address the following core issues: ① How to dynamically quantify the available capacity of units under the coupled effects of multiple factors? Traditional static models cannot capture the real-time interactive effects of water temperature, runoff, and environmental regulations. ② How to build a multi-dimensional risk indicator system to support precise prevention and control? A single indicator is insufficient to guide the coordinated optimization of the multiple objectives of "supply guarantee, emission reduction, and resilience."

[0152] To address these issues, this application proposes a comprehensive approach: "Dynamic Coupling - Multidimensional Assessment - Risk Decision-Making." Specifically, this approach includes: 1. Dynamic Coupling Drive: This integrates a physical process-based thermal model with real-time meteorological and hydrological data to address the dynamic biases inherent in traditional static models. 2. Multidimensional Risk Analysis: This approach utilizes a three-dimensional CRII-HRFR-UORF evaluation system to quantify risk intensity, extremity, and persistence, supporting tiered prevention and control.

[0153] Under the dual pressures of climate crisis and energy transformation, this application breaks through the static, one-sided and experience-dependent bottlenecks of traditional methods through three major innovations: dynamic coupling modeling, multi-dimensional risk analysis, and scientific decision-making optimization, and provides the thermal power industry with a systematic solution from risk quantification to prevention and control implementation to cope with climate change.

[0154] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0155] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0156] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0157] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRdM), magnetic random access memory (MRdM), ferroelectric random access memory (FRdM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RdM) or external cache memory, etc. By way of illustration and not limitation, RdM may be in various forms, such as static random access memory (SRdM) or dynamic random access memory (DRdM).

[0158] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0159] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0160] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for dynamic assessment of meteorological and hydrological risks of thermal power systems, characterized by: include: Dynamically coupling meteorological and hydrological data of a thermal power system is processed to determine a multi-source meteorological and hydrological data set; the thermal power system includes a plurality of thermal power units and a thermal power unit cooling system; The meteorological and hydrological data include grid multi-scenario simulation data and historical observation data of meteorological and hydrological stations; the grid multi-scenario simulation data is determined based on the global climate model library and the global hydrological model simulation; Using the second law of thermodynamics and the heat transfer equation, a dynamic heat balance equation of the thermal power unit cooling system is constructed; determining the water intake of the thermal power unit cooling system based on the multi-source meteorological and hydrological dataset, the dynamic heat balance equation, and climatic and hydrological constraints, wherein the climatic and hydrological constraints include a maximum allowable cooling water discharge temperature set to protect hydrological and ecological diversity and an environmental flow rate constraint ratio set to ensure hydrological and ecological water use; Determining the available capacity of the thermal power system under runoff and water temperature constraints based on the water intake and available water resources of the thermal power unit cooling system; Based on the available capacity, a three-dimensional evaluation system is established, and according to the three-dimensional evaluation system, meteorological and hydrological risks of the thermal power system are divided and evaluated, and an evaluation result is determined; The three-dimensional evaluation system includes meteorological and hydrological risk intensity index, high-risk operation frequency and unsafe operation frequency.

2. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 1, characterized in that: Dynamically couple the meteorological and hydrological data of the thermal power system to determine the multi-source meteorological and hydrological data set, including: For any thermal power unit, based on the geographic nearest neighbor matching method, the historical observation data of the meteorological and hydrological stations are matched with the longitude and latitude of the thermal power unit, and the meteorological and hydrological stations within the preset longitude and latitude range of the thermal power unit and the historical observation data corresponding to the meteorological and hydrological stations are determined; The grid multi-scenario simulation data is downscaled by bilinear interpolation and matched with the longitude and latitude of the thermal power unit to determine the multi-scenario multi-climate model meteorological and hydrological data of the thermal power unit; A multi-source meteorological and hydrological dataset is determined based on the meteorological and hydrological data of each thermal power unit in multiple scenarios and multiple climate models and the historical observation data corresponding to the meteorological and hydrological stations within the preset longitude and latitude range of each thermal power unit.

3. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 1, characterized in that: When the cooling technology type of the thermal power unit in the thermal power unit cooling system is a circulating cooling technology, the dynamic heat balance equation of the thermal power unit cooling system includes an energy conservation equation and a mass conservation equation; the thermal power unit cooling system includes a condenser and a cooling tower; The energy conservation equation is: ; in, is the heat input to the condenser; It is the flow rate of cold air entering the cooling tower; and are the enthalpy of hot air entering and leaving the cooling tower respectively; is the density of air; is the density of water; It is the amount of water required for the cooling system of thermal power units; and are the enthalpy of water entering and leaving the cooling tower, respectively; It is the loss of sewage discharge; The mass conservation equation is: ; in, is the evaporation loss; and are the air humidity ratios inside and outside the cooling tower respectively; When the cooling technology type of the thermal power unit in the thermal power unit cooling system is the primary cooling technology, the dynamic heat balance equation of the thermal power unit cooling system is: = ； in, It is the amount of water required for the cooling system of thermal power units; is the heat capacity of water; is the temperature rise of the water passing through the condenser; is the density of water.

4. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 3, characterized in that: Determining the water intake of the cooling system of the thermal power unit according to the multi-source meteorological and hydrological dataset, the dynamic heat balance equation, and the climate and hydrological constraints, specifically includes: The maximum allowable discharge temperature of cooling water for protecting hydro-ecological diversity and the environmental flow constraint ratio for ensuring hydro-ecological water use are determined based on the management requirements of hydro-ecology; determining a water intake of a cooling system of the thermal power unit according to the multi-source meteorological and hydrological dataset, the climate and hydrological constraints, the cooling technology type of the thermal power unit, and the dynamic heat balance equation; the cooling technology type of the thermal power unit including a primary cooling technology and a circulating cooling technology; When the cooling technology type of the thermal power unit is primary cooling technology, the water intake of the thermal power unit cooling system is: ; in, It is the amount of water required for the cooling system of thermal power units; is the net efficiency of the thermal power system; is the heat capacity of water; is the installed capacity of the thermal power system; is the waste heat loss ratio; is the temperature rise of the water passing through the condenser; ; is the maximum allowable discharge temperature of cooling water; is the runoff water temperature; It is the maximum allowable temperature rise of cooling water; When the cooling technology type of the thermal power unit is circulating cooling technology, the water intake formula of the thermal power unit cooling system is: ; in, is the evaporation loss; = ; is the proportion of heat rejected during sensible heat transfer; is the latent heat of vaporization of water; It is the sewage loss, ; is the concentration cycle coefficient; is the drift loss, Equal to 0.

5. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 3, characterized in that: Based on the water intake and available water resources of the thermal power unit cooling system, the available capacity of the thermal power system under runoff and water temperature constraints is determined, specifically including: Calculate the available water resources based on the runoff volume in the meteorological and hydrological data and the environmental flow constraint ratio of hydrological and ecological water use; Determine the actual available water amount based on the water intake of the thermal power unit cooling system and the available water resources; Based on the actual available water volume and the cooling technology type of the thermal power unit, the available capacity of the thermal power system under the constraints of runoff and water temperature is determined.

6. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 5, characterized in that: Based on the actual available water volume and the cooling technology type of the thermal power unit, determine the available capacity of the thermal power system under the constraints of runoff and water temperature, specifically including: When the cooling technology type of the thermal power unit is primary cooling technology, the available capacity of the thermal power system under the constraints of runoff and water temperature is: ; in, is the available capacity of the thermal power system under runoff and water temperature constraints; is the actual amount of water available; is the net efficiency of the thermal power system; is the heat capacity of water; is the waste heat loss ratio; is the temperature rise of the water passing through the condenser.

7. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 5, characterized in that: Based on the actual available water volume and the cooling technology type of the thermal power unit, determine the available capacity of the thermal power system under the constraints of runoff and water temperature, specifically including: When the cooling technology type of the thermal power unit is circulating cooling technology, the available capacity of the thermal power system under the constraints of runoff and water temperature is: ； in, is the available capacity of the thermal power system under runoff and water temperature constraints; is the actual amount of water available; is the net efficiency of the thermal power system; is the heat capacity of water; is the waste heat loss ratio; is the wet-bulb temperature; is the approach temperature of the cooling tower; is the runoff water temperature; is the concentration cycle coefficient.

8. The method for dynamic assessment of meteorological and hydrological risks of thermal power systems according to claim 7, characterized in that: Based on the available capacity, a three-dimensional evaluation system is established. Based on the three-dimensional evaluation system, the meteorological and hydrological risks of the thermal power system are divided and evaluated, and the evaluation results are determined, including: Determining a meteorological and hydrological risk intensity index based on the available capacity of the thermal power system under runoff and water temperature constraints and the installed capacity of the thermal power system; wherein the meteorological and hydrological risk intensity index represents the meteorological and hydrological risk level of the thermal power unit; Based on the meteorological and hydrological risk intensity index and different preset thresholds, the meteorological and hydrological risks of the thermal power system are divided and the division results are determined; the division results include safe, vulnerable and high risk; safe means that the available capacity meets the real-time scheduling requirements; vulnerable means that the loss of available capacity is controllable; high risk means that operation within the preset load range may lead to a sudden drop in efficiency and the risk of uncontrollable power outages; Based on the meteorological and hydrological risk intensity index, a high-risk operation frequency and an unsafe operation frequency are determined; wherein the high-risk operation frequency represents the risk intensity of severe capacity loss of the thermal power unit due to extreme meteorological and hydrological conditions; and the unsafe operation frequency represents the risk duration of the thermal power unit being unable to stably operate at installed capacity due to meteorological and hydrological constraints; The division result, the high-risk operation frequency, and the unsafe operation frequency are used as evaluation results.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for dynamic assessment of meteorological and hydrological risks of a thermal power system according to any one of claims 1 to 8.

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 method for dynamic assessment of meteorological and hydrological risks of a thermal power system according to any one of claims 1 to 8 is implemented.

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