High-precision runoff type hydroelectric resource technical potential dynamic measuring and calculating method and related device

Abstract

The invention provides a high-precision runoff type hydroelectric resource technical potential dynamic measuring and calculating method and a related device, and belongs to the technical field of renewable energy source development and utilization, and the method comprises the following steps: calculating cumulative flow based on ERA5 data and DEM data; relative flow is calculated in combination with ecological flow requirements; water purification head and water head utilization efficiency is obtained through hydrological analysis and hydraulic geometric model correction; comprehensive efficiency is calculated by considering water turbine type characteristics; determining installation potential based on the river parameters and the water turbine layout parameters; and power generation potential and capacity factors are dynamically calculated by fusing multiple factors. According to the method, the installation density, the water head utilization efficiency and the efficiency curve of different water turbine types are comprehensively considered for the first time, hourly high-spatial-resolution data are adopted, the hydroelectric resource potential is quantified from the spatial grid scale, the defects that in the prior art, technical characteristics of water turbines are not considered, and the spatial-temporal resolution is low are overcome, and the measurement and calculation result is more accurate.

Description

Technical Field

[0001] This invention relates to the field of renewable energy development and utilization technology, and in particular to a high-precision dynamic calculation method and related apparatus for the technical potential of runoff-type hydropower resources. Background Technology

[0002] Hydropower is a technology that combines water turbines with the energy of water discharged or pumped from rivers and reservoirs to convert it into electricity. It is mainly divided into four categories: reservoir-type, run-of-river-type, diversion-type, and pumped-storage hydropower. As of 2024, the global installed capacity of hydropower (excluding pumped storage) reached 128.3 million kilowatts, with 15 million kilowatts added that year. In 2023, the global weighted average levelized cost of electricity (LCOE) was only US$0.057 per kilowatt-hour, highlighting the important role of hydropower in the renewable energy system.

[0003] However, hydropower resource development faces multiple challenges, including uneven resource distribution, diverse characteristics, technological maturity constraints, economic feasibility limitations, environmental protection requirements, and policy guidance. Meanwhile, hydropower stations worldwide are generally aging, with operating hydropower stations averaging nearly 40 years of service life and decommissioned hydropower stations averaging about 60 years. Therefore, conducting accurate hydropower resource assessments is crucial for optimizing the operating efficiency of aging units, scientifically planning power station decommissioning and upgrading, tapping untapped potential, and adapting to climate change and ecological protection needs.

[0004] Existing hydropower resource assessment technologies have significant shortcomings: While the virtual hydropower station model constructed by Gernaat et al. uses high-resolution data, it fails to consider the impact of installation density, head utilization efficiency, and efficiency curves of different turbine types on the assessment results; the assessment method of Oak Ridge National Laboratory (ORNL) is only applicable to the United States, failing to quantify global hourly and kilometer-level hydropower technology potential at the spatial grid scale, and neglecting the influence of turbine efficiency curves; Kaunda et al.'s small-scale hydropower potential assessment technology also fails to quantify hourly and kilometer-level hydropower potential at the spatial grid scale. Traditional assessment models generally suffer from low spatiotemporal resolution and insufficient consideration of turbine technology characteristics, resulting in inaccurate assessment results that are difficult to meet the needs of precise energy planning.

[0005] Therefore, this invention proposes a high-precision dynamic calculation method and related device for the technical potential of runoff-type hydropower resources. Summary of the Invention

[0006] This invention provides a high-precision dynamic calculation method and related device for the technical potential of runoff-based hydropower resources, in order to solve the aforementioned technical problems.

[0007] This invention provides a high-precision dynamic calculation method for the technical potential of runoff-based hydropower resources, including: Step 1: Hydrological analysis is performed using ERA 5-hour runoff data with a spatial resolution of 0.25 degrees and digital elevation model data with a resolution of 1 km. The cumulative flow area of ​​the 30 arcsecond grid is obtained, and the water flow rate through the turbine at time t is calculated. Step 2: Based on the water flow through the turbine at time t, and combined with the flow with an annual exceedance probability of 90%, determine the effective water flow on an hourly scale, and then combine with the flow with an annual exceedance probability of 30% to calculate the rated flow of the turbine, thus obtaining the relative flow of the hydropower station. Step 3: Extract the river network and match it with the Strahler 5-10 level river network data in HydroRIVERS_v10. Calculate the riverbed elevation and net head of the river section. Use the hydraulic geometry model to correct the impact of flow fluctuations on the tailwater level and determine the rated head and the corrected head utilization efficiency. Step 4: Select at least one of impulse turbines, mixed-flow turbines, and axial-flow turbines according to the head range. Calculate the rated comprehensive efficiency based on the turbine peak efficiency, power generation efficiency, transformer loss, and pipeline transportation loss. Combine this with the relative flow rate of the hydropower station to obtain the actual comprehensive efficiency through an interpolation algorithm. Step 5: Calculate the river width based on the annual maximum flow, determine the number of turbines to be installed by combining the turbine runner diameter, minimum edge distance and lateral spacing, calculate the installation density based on the rated power, number of turbines, longitudinal spacing and river width, and then obtain the installed capacity potential by combining the land utilization rate. Step 6: Based on the installation density, the relative flow rate through the turbine, the corrected head utilization efficiency, the actual comprehensive efficiency, the land utilization rate, and the time interval, calculate the actual hydropower potential of grid i at time t; Step 7: Based on the actual hydropower potential, installed capacity potential, and time interval at time t, obtain the hydropower capacity factor at time t.

[0008] Preferably, the hydrological analysis includes depression filling, flow direction analysis, river obstacle neighborhood analysis, flow direction correction, cumulative flow weight determination, cumulative flow grid calculation, and cumulative flow area calculation. When determining the cumulative flow weight, the distribution of reservoirs, river discharge points, and river obstacles are comprehensively considered, and the proportion of river flow at river obstacles is set.

[0009] Preferably, the river network is extracted and matched with the Strahler 5-10 level river network data in HydroRIVERS_v10, and the riverbed elevation and net head of the river section are calculated, including: Using the StreamLink tool, input the corrected flow direction and cumulative flow grid, and output a river network chain that uniquely identifies the river segment with a spatial resolution of 0.25 degrees; River network data that matches HydroRIVERS_v10 was selected based on the extracted river network; Using the river network raster as a mask file, extract the DEM values ​​of the river network at a resolution of 1km, where the DEM values ​​after eliminating local depressions are selected. The extreme values ​​of hydrological conditions for each river ID are extracted by using regional statistical tools. The extreme value of elevation is the maximum value, which represents the upstream water level elevation of the river segment. The extreme value of elevation is the minimum value, which represents the downstream tailwater level elevation, representing the downstream water level of the river after the hydropower station generates electricity. The net head of each river segment is calculated. The difference between the maximum and minimum values ​​is used as the net head using a grid calculator. Neighborhood analysis is then used to aggregate the maximum head of 1 km to 25 km.

[0010] Preferably, the cumulative flow area of ​​the 30 arcsecond grid is obtained, and the water flow rate through the turbine at time t is calculated, including: Calculate the cumulative flow area of ​​a 30 arcsecond grid. Where Aacc refers to the cumulative grid size, and R1 refers to the Earth's average radius, typically taken as 6.371 × 10⁻⁶. 6 m, Refers to the longitude spacing of the grid. Refers to the grid latitudinal spacing. The latitude of the grid center point; Calculate the water flow rate through the turbine at time t. R2 refers to the runoff depth at time t; Refers to a time interval.

[0011] The high-precision dynamic calculation method for the technical potential of runoff hydropower resources according to claim 1 is characterized by using a hydraulic geometric model to correct the influence of flow fluctuations on the tailrace level, and determining the rated head and the corrected head utilization efficiency, including: Calculate the river depth in grid i. ,in, , The adjustment factors are set to 0.23 and 0.37 respectively. Let be the water flow rate at time t in river segment r; Determine the water head: ,in, The net head after i-grid integration with flow fluctuation correction. Design head for the r-section of the river; Calculate the rated head ,in, The maximum river depth in grid i throughout the year; Determine the head utilization efficiency after i-grid correction .

[0012] Preferably, the calculation of the installation density for different types of water turbines includes: in, This represents the number of different types of water turbines installed; This represents the installation density of different water turbines; This represents the installed capacity potential of water turbines; Represents the width of the river; The runner diameter represents the diameter of different water turbines; Minimum edge distance; Represents horizontal spacing; Represents the vertical spacing; The rated power represents different water turbines; ρ refers to the mass density of fresh water; g refers to the acceleration due to gravity. This refers to the rated flow rate of the water turbine; The rated overall efficiency of the water turbine; The i-grid refers to the land utilization rate suitable for hydropower development; Hrated is the rated head. The annual maximum flow rate is indicated; u and z are empirical coefficients, respectively. and 0.75; This indicates that the minimum width is limited to 10m; This represents the percentage of the ecological protection zone area within grid i; For grid i, the seismic risk level coefficient is... , The weighting coefficient has values ​​of 0.6, 0, and 4, determined based on the priority of ecological and geological constraints in hydropower development.

[0013] Preferably, the actual comprehensive efficiency is obtained by combining the relative flow of the hydropower station with an interpolation algorithm, including: The five-dimensional measured data of efficiency, relative flow rate, head, water temperature, and operating time of impulse, mixed-flow, and axial-flow turbines are standardized and preprocessed, and the preprocessed data are transformed into five-dimensional feature vectors. The row hash value corresponding to the five-dimensional feature vector is calculated based on the first adaptive hash function, and the column hash value corresponding to the five-dimensional feature vector is calculated based on the second adaptive hash function to obtain the hash value group of each five-dimensional feature vector; Based on the hash value group, a two-dimensional hash table corresponding to different turbine types is constructed. The row index corresponds to the turbine type and the running time interval, and the column index corresponds to the water temperature interval and the flow rate interval. The table stores the original measured data corresponding to the five-dimensional feature vector. Based on the associated data of the same turbine type in the two-dimensional hash table, the coupling fluctuation slope of the fused runtime is calculated. and calculate The second derivative The flow rate is segmented using the abrupt change in the absolute value of the second derivative as a threshold, wherein... This represents the fluctuation value of the turbine's efficiency. This represents the percentage of relative traffic fluctuation. This represents the fluctuation value of the water head; Water temperature correction factor; This is a runtime decay correction factor; The relative flow rate of the hydropower station; The head adaptation range and efficiency-temperature-duration coupling correction coefficient corresponding to each flow segment are calculated. Interpolation algorithm identifiers and second derivative segmented thresholds are organized into a parameter matrix queue. After adding turbine batch version numbers and timestamps to the queue, they are continuously written into the efficiency curve segmented feature library. The storage structure of the matrix queue corresponds to the weighted index logic of the two-dimensional hash table. The relative flow rate of the current hydropower station is range-matched to obtain the flow segment, and the head adaptation range and efficiency-temperature-duration coupling correction coefficient corresponding to the flow segment are retrieved. and interpolation algorithm identifier; The matching interpolation function is determined from the identifier-function lookup table based on the interpolation algorithm identifier, and then the interpolation function, the head adaptation range, and the efficiency-water temperature-duration coupling correction coefficient are used as the basis for the result. The actual overall efficiency is obtained by adjusting the rated overall efficiency.

[0014] Preferably, after obtaining the hydroelectric capacity factor at time t, the process further includes: Based on the hydropower capacity factor, real-time head, and water temperature sequences at time t over a continuous period, the operating condition coupled time-series fluctuation coefficient is calculated. ,in, The average capacity factor over the time period. Let t be the head fluctuation value at time t; The average head fluctuation over the period; The water temperature fluctuation value at time t; This represents the average water temperature fluctuation over a given period. Let be the water capacity factor at time t; according to With rated capacity factor The deviation, combined with real-time operating parameters and Dynamically adjust turbine operating parameters: like Retrieve the appropriate efficiency-water temperature-duration coupling correction coefficient from the efficiency curve segmentation feature library. and head correction factor and combined Calculate the guide vane opening adjustment range ; like Efficiency attenuation coefficient based on over-rated flow segmentation Calculate the maximum relative flow rate ; Integrate the capacity factor at time t and the adjusted operating parameters to generate and output an evaluation report.

[0015] This invention provides a high-precision dynamic calculation device for the technical potential of runoff-based hydropower resources, comprising: The water flow calculation module is used to perform hydrological analysis using ERA5 hourly runoff data with a spatial resolution of 0.25 degrees and digital elevation model data with a resolution of 1 km, to obtain the cumulative flow area of ​​the 30 arcsecond grid and calculate the water flow through the turbine at time t. The relative calculation module is used to determine the hourly effective water flow based on the water flow passing through the turbine at time t, combined with the flow with an annual exceedance probability of 90%, and then calculate the turbine's rated flow based on the flow with an annual exceedance probability of 30%, thus obtaining the relative flow of the hydropower station. The efficiency determination module is used to extract the river network and match the Strahler 5-10 level river network data in HydroRIVERS_v10, calculate the riverbed elevation and net head of the river section, use the hydraulic geometry model to correct the impact of flow fluctuations on the tailwater level, and determine the rated head and the corrected head utilization efficiency. The actual adjustment module is used to select at least one of impulse turbines, mixed-flow turbines, and axial-flow turbines according to the head range, calculate the rated comprehensive efficiency based on the turbine peak efficiency, power generation efficiency, transformer loss and pipeline transportation loss, and obtain the actual comprehensive efficiency by interpolation algorithm in combination with the relative flow of the hydropower station. The module for determining installed capacity potential is used to calculate the river width based on the annual maximum flow, determine the number of turbines to be installed by combining the turbine runner diameter, minimum edge distance and lateral spacing, calculate the installation density based on rated power, number of installations, longitudinal spacing and river width, and then obtain the installed capacity potential by combining land utilization rate. The actual potential calculation module is used to calculate the actual hydropower potential of grid i at time t based on the installation density, the relative flow through the turbine, the corrected head utilization efficiency, the actual comprehensive efficiency, the land utilization rate, and the time interval. The capacity factor determination module is used to obtain the hydropower capacity factor at time t based on the actual hydropower potential, installed capacity potential, and time interval at time t.

[0016] Compared with the prior art, the beneficial effects of this application are as follows: For the first time, this study comprehensively considers the impact of different turbine types' installation density, head utilization efficiency, and efficiency curves on hydropower resource potential, thus addressing the shortcomings of previous run-of-river hydropower resource assessment models that did not take into account the technical characteristics of different turbine types.

[0017] By using hourly high spatial resolution ERA5 data, ecological protection zones, and earthquake risk levels, the potential of runoff hydropower resources is quantified at the spatial grid scale, solving the problem of low spatiotemporal resolution in previous runoff hydropower resource potential assessment technologies.

[0018] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of a high-precision dynamic calculation method for the technical potential of runoff-based hydropower resources in an embodiment of the present invention; Figure 2 This is a structural diagram of a high-precision dynamic calculation device for the technical potential of runoff-based hydropower resources in an embodiment of the present invention; Figure 3 The diagram shows the efficiency curves of different types of water turbines in the embodiments of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] This invention provides a high-precision dynamic calculation method for the technical potential of runoff-based hydropower resources, such as... Figure 1 As shown, it includes: Step 1: Hydrological analysis is performed using ERA 5-hour runoff data with a spatial resolution of 0.25 degrees and digital elevation model data with a resolution of 1 km. The cumulative flow area of ​​the 30 arcsecond grid is obtained, and the water flow rate through the turbine at time t is calculated. Step 2: Based on the water flow through the turbine at time t, and combined with the flow with an annual exceedance probability of 90%, determine the effective water flow on an hourly scale, and then combine with the flow with an annual exceedance probability of 30% to calculate the rated flow of the turbine, thus obtaining the relative flow of the hydropower station. Step 3: Extract the river network and match it with the Strahler 5-10 level river network data in HydroRIVERS_v10. Calculate the riverbed elevation and net head of the river section. Use the hydraulic geometry model to correct the impact of flow fluctuations on the tailwater level and determine the rated head and the corrected head utilization efficiency. Step 4: Select at least one of impulse turbines, mixed-flow turbines, and axial-flow turbines according to the head range. Calculate the rated comprehensive efficiency based on the turbine peak efficiency, power generation efficiency, transformer loss, and pipeline transportation loss. Combine this with the relative flow rate of the hydropower station to obtain the actual comprehensive efficiency through an interpolation algorithm. Step 5: Calculate the river width based on the annual maximum flow, determine the number of turbines to be installed by combining the turbine runner diameter, minimum edge distance and lateral spacing, calculate the installation density based on the rated power, number of turbines, longitudinal spacing and river width, and then obtain the installed capacity potential by combining the land utilization rate. Step 6: Based on the installation density, the relative flow rate through the turbine, the corrected head utilization efficiency, the actual comprehensive efficiency, the land utilization rate, and the time interval, calculate the actual hydropower potential of grid i at time t; Step 7: Based on the actual hydropower potential, installed capacity potential, and time interval at time t, obtain the hydropower capacity factor at time t.

[0023] Preferably, the hydrological analysis includes depression filling, flow direction analysis, river obstacle neighborhood analysis, flow direction correction, cumulative flow weight determination, cumulative flow grid calculation, and cumulative flow area calculation. When determining the cumulative flow weight, the distribution of reservoirs, river discharge points, and river obstacles are comprehensively considered, and the proportion of river flow at river obstacles is set.

[0024] Preferably, the cumulative flow area of ​​the 30 arcsecond grid is obtained, and the water flow rate through the turbine at time t is calculated, including: Calculate the cumulative flow area of ​​a 30 arcsecond grid. Where Aacc refers to the cumulative grid size, and R1 refers to the Earth's average radius, typically taken as 6.371 × 10⁻⁶. 6 m, Refers to the longitude spacing of the grid. Refers to the grid latitudinal spacing. The latitude of the grid center point; Calculate the water flow rate through the turbine at time t. Where R2 refers to the runoff depth at time t; Δt refers to the time interval.

[0025] The high-precision dynamic calculation method for the technical potential of runoff hydropower resources according to claim 1 is characterized by using a hydraulic geometric model to correct the influence of flow fluctuations on the tailrace level, and determining the rated head and the corrected head utilization efficiency, including: Calculate the river depth in grid i. ,in, , The adjustment factors are set to 0.23 and 0.37 respectively. Let be the water flow rate at time t in river segment r; Determine the water head: ,in, The net head after i-grid integration with flow fluctuation correction. Design head for the r-section of the river; Calculate the rated head ,in, The maximum river depth in grid i throughout the year; Determine the head utilization efficiency after i-grid correction .

[0026] Preferably, the calculation of the installation density for different types of water turbines includes: in, This represents the number of different types of water turbines installed; This represents the installation density of different water turbines; This represents the installed capacity potential of water turbines; Represents the width of the river; The runner diameter represents the diameter of different water turbines; Minimum edge distance; Represents horizontal spacing; Represents the vertical spacing; The rated power represents different water turbines; ρ refers to the mass density of fresh water; g refers to the acceleration due to gravity. This refers to the rated flow rate of the water turbine; The rated overall efficiency of the water turbine; The i-grid refers to the land utilization rate suitable for hydropower development; Hrated is the rated head. The annual maximum flow rate is indicated; u and z are empirical coefficients, respectively. and 0.75; This indicates that the minimum width is limited to 10m; This represents the percentage of the ecological protection zone area within grid i; For grid i, the seismic risk level coefficient is... , The weighting coefficient has values ​​of 0.6, 0, and 4, determined based on the priority of ecological and geological constraints in hydropower development.

[0027] Preferably, the river network is extracted and matched with the Strahler 5-10 level river network data in HydroRIVERS_v10, and the riverbed elevation and net head of the river section are calculated, including: Using the StreamLink tool, input the corrected flow direction and cumulative flow grid, and output a river network chain that uniquely identifies the river segment with a spatial resolution of 0.25 degrees; River network data that matches HydroRIVERS_v10 was selected based on the extracted river network; Using the river network raster as a mask file, extract the DEM values ​​of the river network at a resolution of 1km, where the DEM values ​​after eliminating local depressions are selected. The extreme values ​​of hydrological conditions for each river ID are extracted by using regional statistical tools. The extreme value of elevation is the maximum value, which represents the upstream water level elevation of the river segment. The extreme value of elevation is the minimum value, which represents the downstream tailwater level elevation, representing the downstream water level of the river after the hydropower station generates electricity. The net head of each river segment is calculated. The difference between the maximum and minimum values ​​is used as the net head using a grid calculator. Neighborhood analysis is then used to aggregate the maximum head of 1 km to 25 km.

[0028] In this embodiment, depressions are filled to eliminate local depressions and ensure the continuity of water flow; flow direction analysis is performed; the river obstacle influence zone is analyzed (neighborhood analysis and focus statistics are used to create a 3×3 grid); flow direction is corrected (the cascade point is captured to ensure that the river outlet is located on the flow path, and the flow direction at the reservoir and the river cascade point is corrected); cumulative flow weights are applied (considering the reservoir distribution, the river cascade point, and the river obstacles, assuming that the river flow at the river obstacle accounts for 80%); cumulative flow grids are calculated based on the corrected flow and cumulative flow weights; and cumulative flow area is considered (considering the changes in grid area at different latitudes).

[0029] In this embodiment, for run-of-river hydropower stations, in order to ensure the minimum ecological water consumption downstream of the river, the annual exceedance probability of 90% (Q90) flow rate is calculated, and based on this, the effective water flow rate on an hourly scale is calculated. The calculation formula is as follows: Qt is the effective flow rate (m3 / s) that ensures minimum ecological water use downstream; Qt is the water flow rate (m3 / s) passing through the turbine at time t; Q90 is the flow rate (m3 / s) with an annual exceedance probability of 90%. In the formula, Qrated refers to the rated flow rate of the turbine (m³ / s); Q30 refers to the flow rate with an annual exceedance probability of 30% based on the effective flow rate that meets the minimum ecological water use requirements downstream; and Q90 is the flow rate value with an annual exceedance probability of 90% (m³ / s). The relative flow rate of a hydropower station refers to the ratio of the effective flow rate to the rated flow rate. The specific calculation formula is as follows: .

[0030] In this embodiment, the formula for calculating the head of purified water is as follows: In the formula, Hr refers to the design net head of the r-th river segment; Zup, r is the upstream water level elevation of the r-th river segment, which is equal to the dam height; Zdown, r is the downstream tailwater level elevation of the r-th river segment.

[0031] In this embodiment, c The coefficients were 0.23 and d was 0.37, determined by 264 sets of synchronously measured flow, river depth, and river width data from the Columbia River Basin in the United States.

[0032] In this embodiment, the mainstream turbine types are shown in Table 1: Table 1 Characteristics of Mainstream Hydropower Turbine Types Different types of water turbines have different relative efficiencies. When the turbine flow rate deviates from the optimal value, the efficiency curves of different types of water turbines show significant differences, such as... Figure 3 As shown. This invention selects three types of turbines: impulse (bucket type), mixed flow, and axial flow. Due to the lack of curves for the axial flow turbine, this invention does not consider this turbine for the time being.

[0033] In this embodiment, the specific calculation process for the rated comprehensive efficiency of the water turbine of the present invention is as follows: ; In the formula, The rated overall efficiency of the water turbine; This refers to the peak efficiency of the water turbine. For power generation efficiency, it is typically 97–99%; Transformer losses typically range from 1% to 3%. The losses due to equipment such as pipeline transportation are typically 1-4%.

[0034] In this embodiment, the minimum edge distance is generally 0.5 times the runner diameter; the lateral spacing is generally 3 to 5 times the runner diameter; Sy represents the longitudinal spacing, which is generally between 10km and 20km to avoid tailwater fluctuations affecting the efficiency of downstream power plants and to reserve a buffer zone for fish migration and wetland protection.

[0035] In this embodiment, the formula for calculating the hydropower potential of the present invention is as follows: ,in, The actual hydropower potential (kWh) of grid i at time t. This represents the installation density (W / m2) of different water turbines. The relative flow rate through the turbine at time t refers to the flow rate of grid i. Refers to the head utilization efficiency after i-grid correction; This refers to the actual overall efficiency of hydropower in the i-grid. The land utilization rate suitable for hydropower development in grid i is determined by taking into account the constraints of natural geographical conditions.

[0036] In this embodiment, the hydropower capacity factor (CF), also known as the load factor, refers to the ratio of the average actual power generation of a hydropower station operating continuously for a certain period at the grid scale to its rated power generation, characterizing the average level of hydropower operation during that period. The capacity factor is influenced by factors such as hydrological conditions of the hydropower station (e.g., river flow), design parameters (e.g., exceedance probability level, head utilization efficiency, and turbine efficiency), and operational characteristics (e.g., dispatching methods). The calculation formula for the hourly hydropower capacity factor is as follows: In the formula, Represents the hydroelectric capacity factor at time t; t represents the hydropower potential at time t (kWh); CP represents the average installed hydropower capacity potential of a given area (kW).

[0037] The beneficial effects of the above technical solution are: by using hourly high spatial resolution ERA5 data, ecological protection area data, and earthquake risk level data, and by comprehensively considering the impact of different turbine types' installation density, head utilization efficiency, and efficiency curves on hydropower resource potential, the potential of run-of-river hydropower resources can be quantified at the spatial grid scale, thus solving the shortcomings of previous run-of-river hydropower resource assessment models that did not consider the technical characteristics of different turbine types and had low spatiotemporal resolution.

[0038] This invention provides a high-precision dynamic calculation method for the technical potential of runoff-based hydropower resources. It combines the relative flow of the hydropower station with an interpolation algorithm to obtain the actual comprehensive efficiency, including: The five-dimensional measured data of efficiency, relative flow rate, head, water temperature, and operating time of impulse, mixed-flow, and axial-flow turbines are standardized and preprocessed, and the preprocessed data are transformed into five-dimensional feature vectors. The row hash value corresponding to the five-dimensional feature vector is calculated based on the first adaptive hash function, and the column hash value corresponding to the five-dimensional feature vector is calculated based on the second adaptive hash function to obtain the hash value group of each five-dimensional feature vector; Based on the hash value group, a two-dimensional hash table corresponding to different turbine types is constructed. The row index corresponds to the turbine type and the running time interval, and the column index corresponds to the water temperature interval and the flow rate interval. The table stores the original measured data corresponding to the five-dimensional feature vector. Based on the associated data of the same turbine type in the two-dimensional hash table, the coupling fluctuation slope of the fused runtime is calculated. and calculate The second derivative The flow rate is segmented using the abrupt change in the absolute value of the second derivative as a threshold, wherein... This represents the fluctuation value of the turbine's efficiency. This represents the percentage of relative traffic fluctuation. This represents the fluctuation value of the water head; Water temperature correction factor; This is a runtime decay correction factor; The relative flow rate of the hydropower station; The head adaptation range and efficiency-temperature-duration coupling correction coefficient corresponding to each flow segment are calculated. Interpolation algorithm identifiers and second derivative segmented thresholds are organized into a parameter matrix queue. After adding turbine batch version numbers and timestamps to the queue, they are continuously written into the efficiency curve segmented feature library. The storage structure of the matrix queue corresponds to the weighted index logic of the two-dimensional hash table. The relative flow rate of the current hydropower station is range-matched to obtain the flow segment, and the head adaptation range and efficiency-temperature-duration coupling correction coefficient corresponding to the flow segment are retrieved. and interpolation algorithm identifier; The matching interpolation function is determined from the identifier-function lookup table based on the interpolation algorithm identifier, and then the interpolation function, the head adaptation range, and the efficiency-water temperature-duration coupling correction coefficient are used as the basis for the result. The actual overall efficiency is obtained by adjusting the rated overall efficiency.

[0039] In this embodiment, the impulse turbine is a type of turbine that relies on the impact force of water flow to do work, and is suitable for high head scenarios, such as when the head of a hydropower station in a mountainous area reaches 800m, this type of turbine is selected; the mixed-flow turbine utilizes both water flow pressure and impact force, and is suitable for medium head scenarios, such as when the head of a certain watershed is 300m; the axial-flow turbine relies on the axial thrust of water flow to do work, and is suitable for low head scenarios, such as when the head of a plain river is 50m, this type of turbine is selected.

[0040] Five-dimensional measured data includes actual measurements of efficiency, relative flow rate, head, water temperature, and operating time. For example, for a mixed-flow turbine, the measured efficiency at a certain moment is 95%, relative flow rate is 90%, head is 200m, water temperature is 22℃, and operating time is 2 years. This set of data is five-dimensional measured data. Standardization preprocessing is a method of mapping data from different dimensions to the same interval (such as [0,1]). The min-max method is used. Taking head as an example, if the head range of a turbine is 50-300m, the standardized result of the measured head of 200m is (200-50) / (300-50)=0.6.

[0041] In this embodiment, the five-dimensional feature vector is an ordered combination of standardized five-dimensional data. For example, the standardized data combination of the above-mentioned mixed-flow turbine is [0.95, 0.9, 0.6, 0.5, 0.4], and this array is the five-dimensional feature vector.

[0042] In this embodiment, the adaptive hash function is a hash function that adjusts the calculation logic according to the characteristics of the input data. The first adaptive hash function focuses on matching the turbine type and operating time dimension, while the second focuses on the water temperature and flow rate dimension. After weighting the corresponding dimension data in the five-dimensional feature vector, the hash value is calculated using the MD5 algorithm. The row hash value is the calculation result of the first adaptive hash function and is used to identify the turbine type + operating time range. For example, the row hash value of a certain impulse turbine with an operating time of 1 year is "a1b2c3". The column hash value is the calculation result of the second adaptive hash function and identifies the water temperature range + flow rate range. For example, the column hash value corresponding to a water temperature of 20℃ and a relative flow rate of 80% is "d4e5f6". The hash value group is a combination of the row and column hash values. For example, "a1b2c3-d4e5f6" is the hash value group of this five-dimensional feature vector.

[0043] In this embodiment, the two-dimensional hash table is a hash structure that organizes data by row and column indexes. It is implemented through a database table. The row index column stores the turbine type and the operating time range, the column index column stores the water temperature range and the flow rate range, and the data column stores the raw measured data. For example, the row index is for impact turbine + 0-1 years, and the column index is for 20-25℃ + 80-90%. The corresponding table cells store the five-dimensional measured raw data under this operating condition.

[0044] In this embodiment, the flow segment is a relative flow interval divided according to the abrupt change of the second derivative. For example, when the absolute value of the second derivative changes abruptly from 0.01 to 0.03, the relative flow is divided into two segments: "≤80%" and ">80%".

[0045] In this embodiment, Where T is the measured water temperature and 20 is the standard water temperature; Where t0 is the runtime, for example, 2 years.

[0046] In this embodiment, the head adaptation range is the effective head range corresponding to a certain flow segment, for example, the head adaptation range of a certain flow segment is 180-220m.

[0047] In this embodiment, the efficiency-water temperature-duration coupling correction coefficient is obtained and stored based on measured data and can be directly called. For example, with an efficiency of 90%, a water temperature of 22℃, and a running time of 2 years, the coefficient is 0.98. The interpolation algorithm identifier is a symbol that identifies the interpolation method; for example, "Q2" represents quadratic polynomial interpolation. The parameter matrix queue is a matrix sequence that organizes parameters according to flow segment. For example, a row in a queue stores "80-90% flow segment, 180-220m head, 0.98, Q2, 0.03". In this embodiment, the turbine batch version number is the turbine production batch number, for example, "B202305".

[0048] In this embodiment, the efficiency curve segment feature library is a database that stores parameters for each flow segment. It is built using a relational database and stores the parameter matrix queues in separate tables according to the turbine type.

[0049] In this embodiment, interval matching determines the traffic segment to which the current relative traffic belongs. For example, when the current relative traffic is 85%, it matches the traffic segment of 80-90%, and then retrieves the corresponding parameters from the feature library.

[0050] In this embodiment, the identifier-function lookup table is a mapping table between interpolation algorithm identifiers and corresponding functions. For example, "Q2" corresponds to a quadratic polynomial interpolation function, and the mapping relationship is stored using key-value pairs.

[0051] In this embodiment, the interpolation function is a function of computational efficiency, for example, the quadratic polynomial interpolation function is... , where a, b, and c are obtained by fitting the feature library data.

[0052] In this embodiment, the rated comprehensive efficiency is the comprehensive efficiency of the turbine under rated operating conditions. For example, the rated comprehensive efficiency of a mixed-flow turbine is 96%. The actual comprehensive efficiency is the efficiency adjusted by an interpolation function and a correction coefficient. For example, if the interpolation function calculates an efficiency of 94%, multiplied by... After that, the actual overall efficiency was 92.12%.

[0053] The beneficial effects of the above technical solution are as follows: by standardizing five-dimensional data, constructing an adaptive hash table, and segmenting the second derivative of the coupled slope, the actual comprehensive efficiency under different turbine types and operating conditions can be accurately calculated, solving the problem that traditional methods do not fully consider the coupling effect of multiple operating conditions, and controlling the efficiency calculation error within 2%; at the same time, by associating the parameter matrix queue with the feature library, the efficiency of data retrieval and updating is improved, providing more accurate and efficient technical support for the dynamic calculation of runoff hydropower resource potential.

[0054] This invention provides a high-precision dynamic calculation method for the technical potential of runoff-based hydropower resources. After obtaining the hydropower capacity factor at time t, the method further includes: Based on the hydropower capacity factor, real-time head, and water temperature sequences at time t over a continuous period, the operating condition coupled time-series fluctuation coefficient is calculated. ,in, The average capacity factor over the time period. Let t be the head fluctuation value at time t; The average head fluctuation over the period; The water temperature fluctuation value at time t; This represents the average water temperature fluctuation over a given period. Let be the water capacity factor at time t; according to With rated capacity factor The deviation, combined with real-time operating parameters and Dynamically adjust turbine operating parameters: like Retrieve the appropriate efficiency-water temperature-duration coupling correction coefficient from the efficiency curve segmentation feature library. and head correction factor and combined Calculate the guide vane opening adjustment range ; like Efficiency attenuation coefficient based on over-rated flow segmentation Calculate the maximum relative flow rate ; Integrate the capacity factor at time t and the adjusted operating parameters to generate and output an evaluation report.

[0055] In this embodiment, a continuous time period refers to a time interval of at least 30 days; Real-time head is the difference in water level between the upstream and downstream of the turbine at time t. For example, if the upstream water level is 250m and the downstream water level is 120m at a certain time, the real-time head is 130m. Water temperature sequence is the set of water temperatures at each time t within a continuous period. For example, the water temperature at 12 o'clock every day for 30 days is 20℃, 21℃, etc. These data form the water temperature sequence.

[0056] In this embodiment, the rated capacity factor is the capacity factor under the design operating conditions of the turbine. For example, the rated capacity factor of a certain mixed-flow turbine is 0.6. The real-time operating parameters are the head, water temperature and other data at the current time t, for example, the real-time head is 125m and the water temperature is 22℃.

[0057] In this embodiment, the head correction factor is a factor that reflects the deviation between the real-time head and the rated head. For example, if the rated head is 130m and the real-time head is 125m, the head correction factor is 0.996.

[0058] In this embodiment, the efficiency attenuation coefficient of the over-rated flow segment is the efficiency attenuation coefficient under the over-rated flow, such as a value of 0.02.

[0059] In this embodiment, the adjusted operating parameters refer to data such as the adjusted guide vane opening and maximum relative flow rate. The evaluation report is a document that includes capacity factor fluctuations and operating recommendations. The data is organized into a visual document using office software and then output.

[0060] The beneficial effects of the above technical solution are as follows: by calculating the time-series fluctuation coefficient coupled with the operating conditions, the turbine operating parameters are dynamically adjusted in combination with the fluctuations of capacity factor, head, and water temperature. This optimizes the guide vane opening to improve efficiency under low capacity factor conditions and limits the flow to avoid equipment overload under over-rated conditions. The final evaluation report can support precise hydropower scheduling, improve turbine operating efficiency by about 10%, and reduce the risk of equipment overload.

[0061] This invention provides a high-precision dynamic calculation device for the technical potential of runoff-based hydropower resources, such as... Figure 2 As shown, it includes: The water flow calculation module is used to perform hydrological analysis using ERA5 hourly runoff data with a spatial resolution of 0.25 degrees and digital elevation model data with a resolution of 1 km, to obtain the cumulative flow area of ​​the 30 arcsecond grid and calculate the water flow through the turbine at time t. The relative calculation module is used to determine the hourly effective water flow based on the water flow passing through the turbine at time t, combined with the flow with an annual exceedance probability of 90%, and then calculate the turbine's rated flow based on the flow with an annual exceedance probability of 30%, thus obtaining the relative flow of the hydropower station. The efficiency determination module is used to extract the river network and match the Strahler 5-10 level river network data in HydroRIVERS_v10, calculate the riverbed elevation and net head of the river section, use the hydraulic geometry model to correct the impact of flow fluctuations on the tailwater level, and determine the rated head and the corrected head utilization efficiency. The actual adjustment module is used to select at least one of impulse turbines, mixed-flow turbines, and axial-flow turbines according to the head range, calculate the rated comprehensive efficiency based on the turbine peak efficiency, power generation efficiency, transformer loss and pipeline transportation loss, and obtain the actual comprehensive efficiency by interpolation algorithm in combination with the relative flow of the hydropower station. The module for determining installed capacity potential is used to calculate the river width based on the annual maximum flow, determine the number of turbines to be installed by combining the turbine runner diameter, minimum edge distance and lateral spacing, calculate the installation density based on rated power, number of installations, longitudinal spacing and river width, and then obtain the installed capacity potential by combining land utilization rate. The actual potential calculation module is used to calculate the actual hydropower potential of grid i at time t based on the installation density, the relative flow through the turbine, the corrected head utilization efficiency, the actual comprehensive efficiency, the land utilization rate, and the time interval. The capacity factor determination module is used to obtain the hydropower capacity factor at time t based on the actual hydropower potential, installed capacity potential, and time interval at time t.

[0062] The beneficial effects of the above technical solution are: by using hourly high spatial resolution ERA5 data, ecological protection area data, and earthquake risk level data, and by comprehensively considering the impact of different turbine types' installation density, head utilization efficiency, and efficiency curves on hydropower resource potential, the potential of run-of-river hydropower resources can be quantified at the spatial grid scale, thus solving the shortcomings of previous run-of-river hydropower resource assessment models that did not consider the technical characteristics of different turbine types and had low spatiotemporal resolution.

[0063] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A high-precision dynamic calculation method for the technical potential of runoff-based hydropower resources, characterized in that, include: Step 1: Hydrological analysis is performed using ERA 5-hour runoff data with a spatial resolution of 0.25 degrees and digital elevation model data with a resolution of 1 km. The cumulative flow area of ​​the 30 arcsecond grid is obtained, and the water flow rate through the turbine at time t is calculated. Step 2: Based on the water flow through the turbine at time t, and combined with the flow with an annual exceedance probability of 90%, determine the effective water flow on an hourly scale, and then combine with the flow with an annual exceedance probability of 30% to calculate the rated flow of the turbine, thus obtaining the relative flow of the hydropower station. Step 3: Extract the river network and match it with the Strahler 5-10 level river network data in HydroRIVERS_v10. Calculate the riverbed elevation and net head of the river section. Use the hydraulic geometry model to correct the impact of flow fluctuations on the tailwater level and determine the rated head and the corrected head utilization efficiency. Step 4: Select at least one of impulse turbines, mixed-flow turbines, and axial-flow turbines according to the head range. Calculate the rated comprehensive efficiency based on the turbine peak efficiency, power generation efficiency, transformer loss, and pipeline transportation loss. Combine this with the relative flow rate of the hydropower station to obtain the actual comprehensive efficiency through an interpolation algorithm. Step 5: Calculate the river width based on the annual maximum flow, determine the number of turbines to be installed by combining the turbine runner diameter, minimum edge distance and lateral spacing, calculate the installation density based on the rated power, number of turbines, longitudinal spacing and river width, and then obtain the installed capacity potential by combining the land utilization rate. Step 6: Based on the installation density, the relative flow rate through the turbine, the corrected head utilization efficiency, the actual comprehensive efficiency, the land utilization rate, and the time interval, calculate the actual hydropower potential of grid i at time t; Step 7: Based on the actual hydropower potential, installed capacity potential, and time interval at time t, obtain the hydropower capacity factor at time t.

2. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, The hydrological analysis includes depression filling, flow direction analysis, river obstacle neighborhood analysis, flow direction correction, cumulative flow weight determination, cumulative flow grid calculation, and cumulative flow area calculation. When determining the cumulative flow weight, the distribution of reservoirs, river discharge points, and river obstacles are comprehensively considered, and the proportion of river flow at river obstacles is set.

3. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, Extract the river network and match it with the Strahler 5-10 level river network data in HydroRIVERS_v10. Calculate the riverbed elevation and net head of the river reach, including: Using the StreamLink tool, input the corrected flow direction and cumulative flow grid, and output a river network chain that uniquely identifies the river segment with a spatial resolution of 0.25 degrees; River network data that matches HydroRIVERS_v10 was selected based on the extracted river network; Using the river network raster as a mask file, extract the DEM values ​​of the river network at a resolution of 1km, where the DEM values ​​after eliminating local depressions are selected. The extreme values ​​of hydrological conditions for each river ID are extracted by using regional statistical tools. The extreme value of elevation is the maximum value, which represents the upstream water level elevation of the river segment. The extreme value of elevation is the minimum value, which represents the downstream tailwater level elevation, representing the downstream water level of the river after the hydropower station generates electricity. The net head of each river segment is calculated. The difference between the maximum and minimum values ​​is used as the net head using a grid calculator. Neighborhood analysis is then used to aggregate the maximum head of 1 km to 25 km.

4. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, Obtain the cumulative flow area of ​​the 30 arcsecond grid and calculate the water flow rate through the turbine at time t, including: Calculate the cumulative flow area of ​​a 30 arcsecond grid. Where Aacc refers to the cumulative grid size, and R1 refers to the Earth's average radius, typically taken as 6.371 × 10⁻⁶. 6 m, Refers to the longitude spacing of the grid. Refers to the grid latitudinal spacing. The latitude of the grid center point; Calculate the water flow rate through the turbine at time t. R2 refers to the runoff depth at time t; Refers to a time interval.

5. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, A hydraulic geometric model is used to correct the impact of flow fluctuations on the tailrace level, and the rated head and corrected head utilization efficiency are determined, including: Calculate the river depth in grid i. ,in, , The adjustment factors are set to 0.23 and 0.37 respectively. Let be the water flow rate at time t in river segment r; Determine the water head: ,in, The net head after i-grid integration with flow fluctuation correction. Design head for the r-section of the river; Calculate the rated head ,in, The maximum river depth in grid i throughout the year; Determine the head utilization efficiency after i-grid correction .

6. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, Calculate the installation density of different turbine types, including: in, This represents the number of different types of water turbines installed; This represents the installation density of different water turbines; This represents the installed capacity potential of water turbines; Represents the width of the river; The runner diameter represents the diameter of different water turbines; Minimum edge distance; Represents horizontal spacing; Represents the vertical spacing; The rated power represents different water turbines; ρ refers to the mass density of fresh water; g refers to the acceleration due to gravity. This refers to the rated flow rate of the water turbine; The rated overall efficiency of the water turbine; The i-grid refers to the land utilization rate suitable for hydropower development; Hrated is the rated head. The annual maximum flow rate is indicated; u and z are empirical coefficients, respectively. and 0.75; This indicates that the minimum width is limited to 10m; This represents the percentage of the ecological protection zone area within grid i; For grid i, the seismic risk level coefficient is... , The weighting coefficient has values ​​of 0.6, 0, and 4, determined based on the priority of ecological and geological constraints in hydropower development.

7. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, The actual overall efficiency is obtained by combining the relative flow rate of the hydropower station through an interpolation algorithm, including: The five-dimensional measured data of efficiency, relative flow rate, head, water temperature, and operating time of impulse, mixed-flow, and axial-flow turbines are standardized and preprocessed, and the preprocessed data are transformed into five-dimensional feature vectors. The row hash value corresponding to the five-dimensional feature vector is calculated based on the first adaptive hash function, and the column hash value corresponding to the five-dimensional feature vector is calculated based on the second adaptive hash function to obtain the hash value group of each five-dimensional feature vector; Based on the hash value group, a two-dimensional hash table corresponding to different turbine types is constructed. The row index corresponds to the turbine type and the running time interval, and the column index corresponds to the water temperature interval and the flow rate interval. The table stores the original measured data corresponding to the five-dimensional feature vector. Based on the associated data of the same turbine type in the two-dimensional hash table, the coupling fluctuation slope of the fused runtime is calculated. and calculate The second derivative The flow rate is segmented using the abrupt change in the absolute value of the second derivative as a threshold, wherein... This represents the fluctuation value of the turbine's efficiency. This represents the percentage of relative traffic fluctuation. This represents the fluctuation value of the water head; Water temperature correction factor; This is a runtime decay correction factor; The relative flow rate of the hydropower station; The head adaptation range and efficiency-temperature-duration coupling correction coefficient corresponding to each flow segment are calculated. Interpolation algorithm identifiers and second derivative segmented thresholds are organized into a parameter matrix queue. After adding turbine batch version numbers and timestamps to the queue, they are continuously written into the efficiency curve segmented feature library. The storage structure of the matrix queue corresponds to the weighted index logic of the two-dimensional hash table. The relative flow rate of the current hydropower station is range-matched to obtain the flow segment, and the head adaptation range and efficiency-temperature-duration coupling correction coefficient corresponding to the flow segment are retrieved. and interpolation algorithm identifier; The matching interpolation function is determined from the identifier-function lookup table based on the interpolation algorithm identifier, and then the interpolation function, the head adaptation range, and the efficiency-water temperature-duration coupling correction coefficient are used as the basis for the result. The actual overall efficiency is obtained by adjusting the rated overall efficiency.

8. The high-precision dynamic calculation method for the technical potential of runoff-type hydropower resources according to claim 1, characterized in that, After obtaining the hydroelectric capacity factor at time t, the following is also included: Based on the hydropower capacity factor, real-time head, and water temperature sequences at time t over a continuous period, the operating condition coupled time-series fluctuation coefficient is calculated. ,in, The average capacity factor over the time period. Let t be the head fluctuation value at time t; The average head fluctuation over the period; The water temperature fluctuation value at time t; This represents the average water temperature fluctuation over a given period. Let be the water capacity factor at time t; according to With rated capacity factor The deviation, combined with real-time operating parameters and Dynamically adjust turbine operating parameters: like Retrieve the appropriate efficiency-water temperature-duration coupling correction coefficient from the efficiency curve segmentation feature library. and head correction factor and combined Calculate the guide vane opening adjustment range ; like Efficiency attenuation coefficient based on over-rated flow segmentation Calculate the maximum relative flow rate ; Integrate the capacity factor at time t and the adjusted operating parameters to generate and output an evaluation report.

9. A high-precision dynamic calculation device for the technical potential of runoff-based hydropower resources, characterized in that, include: The water flow calculation module is used to perform hydrological analysis using ERA5 hourly runoff data with a spatial resolution of 0.25 degrees and digital elevation model data with a resolution of 1 km, to obtain the cumulative flow area of ​​the 30 arcsecond grid and calculate the water flow through the turbine at time t. The relative calculation module is used to determine the hourly effective water flow based on the water flow passing through the turbine at time t, combined with the flow with an annual exceedance probability of 90%, and then calculate the turbine's rated flow based on the flow with an annual exceedance probability of 30%, thus obtaining the relative flow of the hydropower station. The efficiency determination module is used to extract the river network and match the Strahler 5-10 level river network data in HydroRIVERS_v10, calculate the riverbed elevation and net head of the river section, use the hydraulic geometry model to correct the impact of flow fluctuations on the tailwater level, and determine the rated head and the corrected head utilization efficiency. The actual adjustment module is used to select at least one of impulse turbines, mixed-flow turbines, and axial-flow turbines according to the head range, calculate the rated comprehensive efficiency based on the turbine peak efficiency, power generation efficiency, transformer loss and pipeline transportation loss, and obtain the actual comprehensive efficiency by interpolation algorithm in combination with the relative flow of the hydropower station. The module for determining installed capacity potential is used to calculate the river width based on the annual maximum flow, determine the number of turbines to be installed by combining the turbine runner diameter, minimum edge distance and lateral spacing, calculate the installation density based on rated power, number of installations, longitudinal spacing and river width, and then obtain the installed capacity potential by combining land utilization rate. The actual potential calculation module is used to calculate the actual hydropower potential of grid i at time t based on the installation density, the relative flow through the turbine, the corrected head utilization efficiency, the actual comprehensive efficiency, the land utilization rate, and the time interval. The capacity factor determination module is used to obtain the hydropower capacity factor at time t based on the actual hydropower potential, installed capacity potential, and time interval at time t.

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