Power generation prediction method, device and medium based on hydrology-hydrodynamics-dispatching coupling

Abstract

The present invention discloses a method, device, and medium for predicting power generation based on hydrological-hydrodynamic-dispatching coupling. The method comprises: obtaining basic geographic information data, underlying surface data, underwater topography data, hydrometeorological data, and reservoir data within a region; constructing a distributed water cycle model for the basin to simulate and verify regional hydrological processes; constructing a power generation simulation and prediction model for reservoir hydrodynamic-reservoir scheduling coupling to simulate and verify the reservoir hydrodynamic processes and power generation; using the basin distributed water cycle model to predict reservoir inflow under different emission scenarios based on future climate change and different future emission scenarios; and predicting hydroelectric power generation under different future emission scenarios based on the basin hydrological-reservoir hydrodynamic-reservoir scheduling coupling model. This invention can provide effective scientific support for the planning, management, and decision-making of future hydropower energy supply.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrology, hydrodynamics and hydroelectric power generation, and in particular to a method, device and medium for predicting power generation based on hydrology-hydrodynamics-scheduling coupling. Background Art

[0002] Hydropower is a clean, renewable, green energy source characterized by flexible scheduling and low production costs. Its share in China's power mix has been growing annually, playing a vital role in national economic development. However, with the increasing impact of climate change and extreme hydrometeorological events, power supply faces significant challenges. In recent years, many countries have experienced varying degrees of power shortages. Therefore, research on simulation and prediction of hydropower generation under the influence of climate change is urgently needed. This is of great significance for energy supply risk analysis and can provide scientific support for clean energy production in river basins.

[0003] According to a literature review, traditional hydropower generation simulation and prediction is primarily based on the water balance of reservoir capacity at each time period, with the goal of maximizing power generation benefits or comprehensive benefits, focusing on the optimal operation of reservoirs. Given a fixed installed capacity and power generation efficiency, the key to hydropower generation depends on the inflow and head difference. This is closely related to the hydrological and hydrodynamic processes in the reservoir area under the influence of reservoir operation. This is particularly true for narrow and long channel reservoirs, where the simulation of these hydrological and hydrodynamic processes directly affects the accuracy of the hydropower generation flow and head difference. However, current power generation prediction models are mostly based on the water balance of the static relationship between water level and storage capacity, lacking an accurate consideration of the impact of reservoir hydrodynamic processes on power generation simulation. Furthermore, under the dual influence of future climate change and human activities, changes in the spatiotemporal pattern of the water cycle in the basin bring significant uncertainty to the prediction of reservoir inflow. Currently, power generation predictions that comprehensively consider the impact of future climate change and human activities remain insufficient.

[0004] Based on this, it is necessary to further consider the impact of future climate change and human activities, combine the relationship between reservoir operation and reservoir area hydrology and hydrodynamics, and improve the design of reservoir power generation simulation and prediction methods. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a method, device and medium for power generation prediction based on hydrological-hydrodynamic-dispatching coupling. On the basis of establishing a hydrological-hydrodynamic-reservoir scheduling coupling model, the uncertainty of the water circulation system under the influence of future climate change and human activities is fully considered to achieve power generation prediction.

[0006] Therefore, the technical solution of the present invention is:

[0007] According to a first aspect of the present invention, a method for predicting power generation based on hydrological-hydrodynamic-dispatching coupling is provided, the method comprising:

[0008] Obtain basic geographic information data, underlying surface data, underwater topography data, hydrological and meteorological data, and reservoir data within the region;

[0009] Construct a distributed water cycle model for the basin to simulate and verify regional hydrological processes;

[0010] Construct a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation, and simulate and verify the reservoir hydrodynamic process and power generation;

[0011] Based on future climate change and different emission scenarios, the basin-wide distributed water cycle model is used to predict reservoir inflows under different emission scenarios;

[0012] Based on the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model, the hydropower generation under different future emission scenarios is predicted; wherein, the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model includes a basin distributed water cycle model and a reservoir hydrodynamics-reservoir scheduling coupled power generation simulation prediction model.

[0013] Furthermore, the basic geographic information data includes digital elevation model ground terrain data and hydrological and meteorological station vector data; the underlying surface data includes current land use, soil type data and future predicted land use data; the underwater terrain data is large-section measurement data or underwater terrain point cloud data; the hydrological and meteorological data includes a series of water level and flow observation data from hydrological stations in the region, a series of meteorological observation data from meteorological stations, and future different scenario forecast data output by climate models; the reservoir data includes characteristic parameters and scheduling procedures of reservoir projects in the region.

[0014] Furthermore, the basin distributed water cycle model includes sub-basins and water conservancy project storage nodes. The construction of the basin distributed water cycle model simulates and verifies the regional hydrological process, specifically including:

[0015] Based on the ground terrain data of the digital elevation model, the sub-basin is divided, and the dynamic hydrological response units of the sub-basin are divided according to the underlying soil type and land use change characteristics;

[0016] Based on the spatial distribution data of meteorological stations, meteorological data are interpolated to each sub-basin to establish a historical meteorological driving dataset for the study area;

[0017] Establish water conservancy project storage nodes based on the impact of human activities and reservoir engineering operations on the water cycle;

[0018] Based on the historical meteorological data driving the basin distributed water cycle model, the water cycle process affected by land use and reservoir scheduling was simulated and verified, and the efficiency coefficient, relative error and correlation coefficient were used as model verification evaluation indicators.

[0019] Furthermore, the efficiency coefficient, relative error and correlation coefficient are calculated using the following formula:

[0020]

[0021] Where, NSE, PBIAS, and R1 are efficiency coefficient, relative error, and correlation coefficient, respectively. m,i ,Q s,i are the measured flow rate and the simulated flow rate, respectively. are the measured average flow and the simulated average flow, respectively; i and n1 are the data sequence number and the total number of data, respectively.

[0022] Furthermore, a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation is constructed to simulate and verify the reservoir hydrodynamic process and power generation. Specifically, the model includes:

[0023] Based on the underwater topography data of the reservoir, the reservoir area is divided into several sections, and a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation is constructed. The upper boundary conditions and interval conditions of the power generation simulation and prediction model are the flow boundaries of surface runoff, subsurface flow, and groundwater runoff simulated by the basin water cycle model. The lower boundary condition of the power generation simulation and prediction model is the water level boundary simulated by the reservoir operation model. The initial conditions are determined based on steady flow calculations.

[0024] Based on the power generation simulation prediction model, the reservoir hydrodynamics-reservoir scheduling coupling process is solved through iterative calculation to calculate the reservoir power generation.

[0025] Furthermore, based on the power generation simulation prediction model, the reservoir power generation is calculated by solving the reservoir area hydrodynamic-reservoir operation coupling process through iterative calculation, specifically including:

[0026] The reservoir hydrodynamics is calculated using the following formula:

[0027]

[0028] Where A is the cross-sectional area, t is the time, Q is the flow rate, x is the flow rate, q is the interval unit flow rate, g is the acceleration of gravity, B is the water surface width, Z is the water level, n is the roughness, and R is the wetted perimeter. The four-point implicit difference scheme of Pressman is used to discretize the equation (4), and the three-level solution method is used to solve the equation (4);

[0029] The reservoir discharge through the gate is calculated using the following formula:

[0030]

[0031] Where Q d is the discharge flow, C d is the discharge coefficient, a, b are the gate opening and gate net width, h0 is the water level in front of the dam;

[0032] The power generation of the hydropower station is calculated using the following formula:

[0033] P=ρ·g·H·Q W ·η·t (6)

[0034] Where P is the power generation, ρ is the density of water, g is the acceleration of gravity, H is the effective water head, Q is w is the turbine flow rate, η is the power generation efficiency, and t is the time.

[0035] Furthermore, based on future climate change and different emission scenarios, the basin-wide distributed water cycle model is used to predict reservoir inflows under different emission scenarios, specifically including:

[0036] Correct the precipitation, temperature, wind speed, humidity, and radiation data output by the climate model to obtain long-term climate prediction data for different future emission scenarios;

[0037] Obtain soil utilization prediction data under different future emission scenarios;

[0038] The long series of climate prediction data and soil utilization prediction data of different future emission scenarios are used to drive the distributed water cycle model of the basin, simulate the future water cycle change process of the basin, and predict the reservoir inflow under different scenarios.

[0039] Furthermore, based on the coupled model of basin hydrology, reservoir hydrodynamics, and reservoir operation, the hydropower generation under different future emission scenarios is predicted, including:

[0040] The daily runoff process data simulated by the distributed water cycle model of the watershed is used as the boundary condition of the power generation simulation and prediction model of the reservoir area hydrodynamics-reservoir operation coupling. A model coupler is established based on the data scale conversion interface to construct the watershed hydrology-reservoir area hydrodynamics-reservoir operation coupling model.

[0041] The predicted reservoir inflow under different emission scenarios is input into the model coupler to drive the reservoir area hydrodynamic-reservoir scheduling coupled power generation simulation prediction model to predict the hydropower generation under different future scenarios.

[0042] According to a second aspect of the present invention, there is provided a power generation prediction device based on hydrology-hydrodynamics-scheduling coupling, the device comprising:

[0043] A data acquisition unit is configured to acquire basic geographic information data, underlying surface data, underwater topography data, hydrological and meteorological data, and reservoir data in the region;

[0044] The first model building unit is configured to build a basin-distributed water cycle model to simulate and verify regional hydrological processes;

[0045] The second model building unit is configured to build a reservoir area hydrodynamic-reservoir operation coupled power generation simulation prediction model to simulate and verify the reservoir area hydrodynamic process and power generation;

[0046] The first prediction unit is configured to predict the reservoir inflow under different emission scenarios based on future climate change and different emission scenarios using the basin distributed water cycle model;

[0047] The second prediction unit is configured to predict the hydropower generation under different future emission scenarios based on the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model; wherein the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model includes a basin distributed water cycle model and a reservoir hydrodynamics-reservoir scheduling coupled power generation simulation prediction model.

[0048] According to a third aspect of the present invention, a non-transitory computer-readable storage medium storing instructions is provided. When the instructions are executed by a processor, the power generation prediction method based on hydrology-hydrodynamics-scheduling coupling as described above is executed.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention fully considers the regulation of hydrodynamic factors affecting hydropower generation by the interaction between the dam scheduling process of large river-type reservoirs and the hydrological and hydrodynamic effects of the river and reservoir. Compared with the current water balance calculation method based on the static relationship between water level and reservoir capacity, it can provide a more scientific and accurate model basis for the simulation and prediction of power generation under different hydrological processes.

[0051] 2. This invention fully considers the uncertainty of how changes in the water cycle pattern will affect reservoir inflow and thus hydropower generation under the combined influence of future climate change and human activities, and can provide effective scientific support for the planning, management and decision-making of future hydropower energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In the drawings, which are not necessarily drawn to scale, the same reference numerals may describe similar components in different views. The same reference numerals with letter suffixes or different letter suffixes may represent different instances of similar components. The accompanying drawings generally illustrate various embodiments by way of example and not limitation, and together with the description and claims, serve to illustrate the embodiments of the invention. Where appropriate, the same reference numerals are used throughout the drawings to refer to the same or similar parts. Such embodiments are illustrative and are not intended to be exhaustive or exclusive of the embodiments of the present apparatus or method.

[0053] Figure 1 The figure is a flow chart of a method for predicting power generation based on hydrological-hydrodynamic-scheduling coupling according to an embodiment of the present invention.

[0054] Figure 2 3. A comparison chart of daily runoff simulation and measurement at Zhutuo Station in the study area according to an embodiment of the present invention.

[0055] Figure 3 This is a comparison diagram of the simulated and measured hydrodynamic process and power generation in the Three Gorges Reservoir area, a study area, according to an embodiment of the present invention.

[0056] Figure 4 This is a diagram showing changes in the inflow rate of the Three Gorges Reservoir compared to the current situation under different future scenarios in the study area according to an embodiment of the present invention.

[0057] Figure 5 This is a graph showing the change in power generation of the Three Gorges Reservoir compared to the current situation under different future scenarios in the study area according to an embodiment of the present invention.

[0058] Figure 6 3D relationship diagram of power generation, water level and flow of the Three Gorges Reservoir in the study area according to an embodiment of the present invention.

[0059] Figure 7 2 is a structural diagram of a power generation prediction device based on hydrology-hydrodynamics-scheduling coupling according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following examples are merely intended to better illustrate the present invention, but the present invention is not limited to the examples set forth herein. Therefore, non-essential improvements and adjustments to the embodiments made by those skilled in the art based on the above-described invention and applied to other embodiments are still within the scope of the present invention.

[0061] The present invention will now be further described with reference to the accompanying drawings.

[0062] The embodiment of the present invention provides a method for predicting power generation based on hydrology-hydrodynamics-scheduling coupling, the process of which is as follows: Figure 1As shown, the simulation method includes the following steps S1 to S5, which are described in detail below.

[0063] S1. Obtain basic geographic information data, underlying surface data, underwater topography data, hydrological and meteorological data, and reservoir data in the area.

[0064] It should be noted that the region described herein refers to the geographical area for which power generation prediction is to be performed. In order to fully illustrate the present invention, this embodiment uses the Three Gorges Reservoir in the upper reaches of the Yangtze River as the research area for power generation prediction.

[0065] In one embodiment, the basic geographic information data collected and organized in the study area include digital elevation model ground terrain data and hydrological and meteorological station vector data; the underlying surface data include current land use, soil type data and future predicted land use data; the underwater terrain data are large-section measurement data or underwater terrain point cloud data; the hydrological and meteorological data include a series of water level and flow observation data from the main hydrological stations in the study area, a series of meteorological observation data from the meteorological stations, and future different scenario prediction data output by the climate model; the reservoir data include the characteristic parameters and scheduling procedures of the main reservoir projects in the study area.

[0066] This example takes the Three Gorges Reservoir in the upper reaches of the Yangtze River as an example. Basic geographic information data in the study area are collected and organized, including 30m resolution digital elevation model data, location vector data of 180 meteorological stations and 7 hydrological stations; underlying surface data include current 1km resolution land use and 1km resolution soil type from 1980 to 2020, and 1km resolution land use data under different future emission scenarios, including low emission SSP1-2.6, medium emission SSP2-4.5, medium-high emission SSP3-7.0, and high emission SSP5-8.5; underwater topography data The data consists of 397 large-section measurement data; the hydrological and meteorological data include water level or flow observation data from seven hydrological stations including Zhutuo, Beibei, Wulong, Cuntan, Qingxichang, Wanxian, and Miaohe from their establishment to 2018; meteorological observation data such as precipitation, temperature, wind speed, humidity, and sunshine time from 180 meteorological stations from 1960 to 2018; and predicted precipitation, temperature, wind speed, humidity, radiation, and other data under different future scenarios output by climate models; reservoir data mainly include characteristic water levels, characteristic storage capacities, or dispatching procedures for the Three Gorges Reservoir and 23 major controlled reservoirs in the upper reaches of the Yangtze River.

[0067] S2. Construct a distributed water cycle model for the watershed to simulate and verify the regional hydrological process.

[0068] In one embodiment, the process of constructing a watershed distributed water cycle model and simulating and verifying regional hydrological processes includes the following steps:

[0069] S201. Divide the sub-basin based on the digital elevation model ground terrain data, consider the underlying soil type and land use change characteristics, and divide the basin into dynamic hydrological response units.

[0070] S202. Based on the spatial distribution data of meteorological stations, the meteorological data are interpolated to each sub-basin using methods such as Thiessen polygons to establish a historical meteorological driving dataset for the study area.

[0071] S203. Consider the impact of human activities and reservoir engineering scheduling in the basin on the water cycle and establish water conservancy engineering storage nodes.

[0072] S204. Based on the historical meteorological data, a distributed time-varying gain water cycle model is driven to simulate and verify the water cycle process affected by climate change and human activities, including land use and reservoir scheduling. The model parameters are calibrated using an optimization algorithm, and the efficiency coefficient, relative error and correlation coefficient are used as model verification evaluation indicators.

[0073] The calculation formulas for the efficiency coefficient, relative error, and correlation coefficient are as follows:

[0074]

[0075]

[0076] Where, NSE, PBIAS, and R1 are efficiency coefficient, relative error, and correlation coefficient, respectively. m,i ,Q s,i are the measured flow rate and the simulated flow rate, respectively. are the measured average flow and the simulated average flow, respectively; i and n1 are the data sequence number and the total number of data, respectively.

[0077] This example uses the upper reaches of the Yangtze River as an example to construct a distributed time-varying gain water cycle model for the study area, which is divided into 161 sub-basins, 1900 hydrological response units, and 23 reservoir engineering nodes. Based on historical hydrological and meteorological data, the water cycle of the upper reaches of the Yangtze River is simulated and verified. The simulation results of the main stations are shown in Table 1. The comparison of the simulated and measured flow of the typical station Zhutuo Station is shown in Figure 2 .

[0078] Table 1 Daily runoff simulation results at major stations

[0079]

[0080] S3. Construct a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir scheduling to simulate and verify the reservoir hydrodynamic process and power generation.

[0081] Based on the underwater topography data of the reservoir, the reservoir area is divided into several sections, and a power generation simulation prediction model of the reservoir area hydrodynamics-reservoir operation coupling is constructed. The upper boundary conditions and interval conditions of the model are the flow boundaries of surface runoff, subsurface flow, and underground runoff simulated by the basin water cycle model. The lower boundary condition of the model is the water level boundary simulated by the reservoir operation model. The initial conditions are determined according to the steady flow calculation. The reservoir area hydrodynamics-reservoir operation coupling process is solved by iterative calculation. First, it is assumed that the water level in front of the reservoir dam is z s , the water level and flow rate Q in the reservoir area can be obtained using the hydrodynamic model s process, and calculate the discharge flow Q according to the reservoir operation model d , when the calculated flow error is less than the operating error e, that is, |Q s -Q d When |≤e, the iteration is terminated, and the reservoir hydrodynamic-reservoir operation coupling process that meets the convergence conditions is obtained, based on which the reservoir power generation is calculated.

[0082] The calculation formula for reservoir hydrodynamics is as follows:

[0083]

[0084] Where A is the cross-sectional area, t is time, Q is the flow rate, x is the flow rate, q is the interval unit flow rate, g is the acceleration of gravity, B is the water surface width, Z is the water level, n is the roughness, and R is the wetted perimeter. The flow equation is discretized using Pressman's four-point implicit difference scheme, and equation (4) can be solved using a three-level solution.

[0085] The formula for the reservoir discharge through the gate is as follows:

[0086]

[0087] Where Q d is the discharge flow, C d is the discharge coefficient, a, b are the gate opening and the gate net width, g is the acceleration of gravity, and h0 is the water level in front of the dam.

[0088] The formula for calculating the power generation of a hydropower station is as follows:

[0089] P=ρ·g·H·Q W ·η·t (6)

[0090] Where P is the power generation, ρ is the density of water, g is the acceleration of gravity, H is the effective water head, Q is w is the turbine flow rate, η is the power generation efficiency, and t is the time.

[0091] This example uses the Three Gorges Reservoir in the upper reaches of the Yangtze River as an example. A one-dimensional hydrodynamic model of the Three Gorges Reservoir area, covering a total area of ​​760 km from Zhutuo to the Three Gorges Dam, is established. The model is divided into 397 sections for hydrodynamic simulation. The model is coupled with the reservoir operation model constructed by the Three Gorges Reservoir Operation Regulations to achieve a hydropower generation simulation prediction coupled with hydrodynamics and reservoir operation. The simulation results of the hydrodynamic process and power generation in the Three Gorges Reservoir area are shown in Figure 2. Figure 3 shown.

[0092] S4. Based on future climate change and different future emission scenarios, the basin distributed water cycle model is used to predict reservoir inflow under different emission scenarios.

[0093] Specifically, different future emission scenarios are considered, including low-emission SSP1-2.6, medium-emission SSP2-4.5, medium-high-emission SSP3-7.0, and high-emission SSP5-8.5. Precipitation, temperature, wind speed, humidity, radiation, and other data output by the climate model are bias-corrected to generate long-term climate forecast data for different future emission scenarios. Based on different future emission scenarios, land use changes related to human activities at low, medium, medium-high, and high emission levels are selected. The basin-wide distributed time-varying gain water cycle model established in step 2 is driven by climate and land use forecast data under different future emission scenarios to simulate future water cycle changes in the basin and predict reservoir inflows under different scenarios.

[0094] In this example, four future scenarios are selected: low emission SSP1-2.6, medium emission SSP2-4.5, medium-high emission SSP3-7.0, and high emission SSP5-8.5. Future climate prediction data are based on the climate prediction results output by five global climate models and are bias-corrected, as shown in Table 2. The corresponding prediction data under different land use emission scenarios are driven by the distributed time-varying gain water cycle model of the upper Yangtze River basin constructed in step 2 to simulate the spatiotemporal evolution characteristics of the water cycle in the upper Yangtze River under future climate change and human activities. The annual changes in the inflow to the Three Gorges Reservoir in 2020s, 2030s, and 2040s compared to the current situation are predicted. Figure 4 shown.

[0095] S5. Predict hydropower generation under different future emission scenarios based on a basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model; wherein the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model includes a basin distributed water cycle model and a reservoir hydrodynamics-reservoir scheduling coupled power generation simulation prediction model.

[0096] Specifically, daily runoff data simulated by the basin-wide distributed time-varying gain water cycle model constructed in S2 was used as boundary conditions for the reservoir hydrodynamics-reservoir operation model in S3. A model coupler was established based on the data scaling interface to construct a coupled model of basin hydrology, reservoir hydrodynamics, and reservoir operation. The future Three Gorges Reservoir inflow and interval runoff (including surface runoff, subsurface flow, and groundwater runoff) predicted by S4 were input into the coupler to drive the reservoir hydrodynamics-reservoir operation coupling model. This model then predicted hydropower generation under different future scenarios and analyzed the range and uncertainty of future hydropower generation. Based on the simulation and prediction results, a three-dimensional relationship diagram of reservoir power generation, water level, and flow was constructed, providing a reference tool for reservoir power generation operation.

[0097] In this example, based on the distributed time-varying gain water cycle model of the upper Yangtze River basin, the impact of future climate change and human activities is considered. The inflow flow of the Three Gorges Reservoir into Zhutuo, Beibei, and Wulong, as well as the runoff in the Three Gorges Reservoir area, are predicted under different future emission scenarios. A data scale conversion interface model coupler is established to drive the Three Gorges Reservoir hydrodynamic-reservoir operation coupling model. The hydropower generation and its uncertainty of the Three Gorges Reservoir are predicted under different future scenarios. Figure 5 Based on the model simulation prediction results, a three-dimensional relationship diagram of the Three Gorges Reservoir power generation, water level and flow is drawn, see Figure 6 shown.

[0098] The embodiment of the present invention provides a power generation prediction device based on hydrology-hydrodynamics-scheduling coupling, such as Figure 7 As shown, the device includes:

[0099] The data acquisition unit 801 is configured to acquire basic geographic information data, underlying surface data, underwater terrain data, hydrological and meteorological data, and reservoir data in the region;

[0100] The first model building unit 802 is configured to build a basin-distributed water cycle model to simulate and verify regional hydrological processes;

[0101] The second model building unit 803 is configured to build a reservoir hydrodynamic-reservoir scheduling coupled power generation simulation prediction model to simulate and verify the reservoir hydrodynamic process and power generation;

[0102] The first prediction unit 804 is configured to predict the reservoir inflow under different emission scenarios based on future climate change and different emission scenarios using the basin distributed water cycle model;

[0103] The second prediction unit 805 is configured to predict the hydropower generation under different future emission scenarios based on the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model; wherein the basin hydrology-reservoir hydrodynamics-reservoir scheduling coupling model includes a basin distributed water cycle model and a reservoir hydrodynamics-reservoir scheduling coupled power generation simulation prediction model.

[0104] In some embodiments, the basic geographic information data includes digital elevation model ground terrain data and hydrological and meteorological station vector data; the underlying surface data includes current land use, soil type data and future predicted land use data; the underwater terrain data is large-section measurement data or underwater terrain point cloud data; the hydrological and meteorological data includes a series of water level and flow observation data from hydrological stations in the region, a series of meteorological observation data from meteorological stations, and future different scenario forecast data output by climate models; the reservoir data includes characteristic parameters and scheduling procedures of reservoir projects in the region.

[0105] In some embodiments, the first model building unit is further configured to:

[0106] Based on the ground terrain data of the digital elevation model, the sub-basin is divided, and the dynamic hydrological response units of the sub-basin are divided according to the underlying soil type and land use change characteristics;

[0107] Based on the spatial distribution data of meteorological stations, meteorological data are interpolated to each sub-basin to establish a historical meteorological driving dataset for the study area;

[0108] Establish water conservancy project storage nodes based on the impact of human activities and reservoir engineering operations on the water cycle;

[0109] Based on the historical meteorological data driving the basin distributed water cycle model, the water cycle process affected by land use and reservoir scheduling was simulated and verified, and the efficiency coefficient, relative error and correlation coefficient were used as model verification evaluation indicators.

[0110] In some embodiments, the first model building unit is further configured to calculate the efficiency coefficient, relative error and correlation coefficient using the following formula:

[0111]

[0112] Where, NSE, PBIAS, and R1 are efficiency coefficient, relative error, and correlation coefficient, respectively. m,i ,Q s,i are the measured flow rate and the simulated flow rate, respectively. are the measured average flow and the simulated average flow, respectively; i and n1 are the data sequence number and the total number of data, respectively.

[0113] In some embodiments, the second model building unit is further configured to:

[0114] Based on the underwater topography data of the reservoir, the reservoir area is divided into several sections, and a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation is constructed. The upper boundary conditions and interval conditions of the power generation simulation and prediction model are the flow boundaries of surface runoff, subsurface flow, and groundwater runoff simulated by the basin water cycle model. The lower boundary condition of the power generation simulation and prediction model is the water level boundary simulated by the reservoir operation model. The initial conditions are determined based on steady flow calculations.

[0115] Based on the power generation simulation prediction model, the reservoir hydrodynamics-reservoir scheduling coupling process is solved through iterative calculation to calculate the reservoir power generation.

[0116] In some embodiments, the second model building unit is further configured to:

[0117] The reservoir hydrodynamics is calculated using the following formula:

[0118]

[0119] Where A is the cross-sectional area, t is the time, Q is the flow rate, x is the flow rate, q is the interval unit flow rate, g is the acceleration of gravity, B is the water surface width, Z is the water level, n is the roughness, and R is the wetted perimeter. The four-point implicit difference scheme of Pressman is used to discretize the equation (4), and the three-level solution method is used to solve the equation (4);

[0120] The reservoir discharge through the gate is calculated using the following formula:

[0121]

[0122] Where Q d is the discharge flow, C d is the discharge coefficient, a, b are the gate opening and gate net width, h0 is the water level in front of the dam;

[0123] The power generation of the hydropower station is calculated using the following formula:

[0124] P=ρ·g·H·Q W ·η·t (6)

[0125] Where P is the power generation, ρ is the density of water, g is the acceleration of gravity, H is the effective water head, Q is w is the turbine flow rate, η is the power generation efficiency, and t is the time.

[0126] In some embodiments, the first prediction unit is further configured to:

[0127] Correct the precipitation, temperature, wind speed, humidity, and radiation data output by the climate model to obtain long-term climate prediction data for different future emission scenarios;

[0128] Obtain soil utilization prediction data under different future emission scenarios;

[0129] The long series of climate prediction data and soil utilization prediction data of different future emission scenarios are used to drive the distributed water cycle model of the basin, simulate the future water cycle change process of the basin, and predict the reservoir inflow under different scenarios.

[0130] In some embodiments, the second prediction unit is further configured to:

[0131] The daily runoff process data simulated by the distributed water cycle model of the watershed is used as the boundary condition of the power generation simulation and prediction model of the reservoir area hydrodynamics-reservoir operation coupling. A model coupler is established based on the data scale conversion interface to construct the watershed hydrology-reservoir area hydrodynamics-reservoir operation coupling model.

[0132] The predicted reservoir inflow under different emission scenarios is input into the model coupler to drive the reservoir area hydrodynamic-reservoir scheduling coupled power generation simulation prediction model to predict the hydropower generation under different future scenarios.

[0133] It should be noted that the various device structures described in this embodiment belong to the same technical concept as the method described previously, and achieve the same technical effect through the same principle, and will not be repeated here.

[0134] An embodiment of the present invention further provides a non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, executes the power generation prediction method based on hydrology-hydrodynamics-scheduling coupling as described in any of the above embodiments.

[0135] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for predicting power generation based on hydrology-hydrodynamics-scheduling coupling, characterized in that: The method comprises: Obtain basic geographic information data, underlying surface data, underwater topography data, hydrological and meteorological data, and reservoir data within the region; Construct a distributed water cycle model for the basin to simulate and verify regional hydrological processes; Construct a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation, and simulate and verify the reservoir hydrodynamic process and power generation; Based on future climate change and different emission scenarios, the basin-wide distributed water cycle model is used to predict reservoir inflows under different emission scenarios; Based on a coupled model of river basin hydrology, reservoir hydrodynamics, and reservoir operation, predict hydropower generation under different future emission scenarios; wherein the coupled model includes a river basin distributed water cycle model and a coupled reservoir hydrodynamics-reservoir operation power generation simulation and prediction model; A power generation simulation and prediction model for reservoir hydrodynamics and reservoir operation coupling was constructed to simulate and verify the reservoir hydrodynamic process and power generation, specifically including: Based on the underwater topography data of the reservoir, the reservoir area is divided into several sections, and a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation is constructed. The upper boundary conditions and interval conditions of the power generation simulation and prediction model are the flow boundaries of surface runoff, subsurface flow, and groundwater runoff simulated by the basin water cycle model. The lower boundary condition of the power generation simulation and prediction model is the water level boundary simulated by the reservoir operation model. The initial conditions are determined based on steady flow calculations. Based on the power generation simulation prediction model, the reservoir power generation is calculated by solving the reservoir area hydrodynamic-reservoir operation coupling process through iterative calculation; Based on future climate change and different emission scenarios, the basin-wide distributed water cycle model is used to predict reservoir inflows under different emission scenarios, including: Correct the precipitation, temperature, wind speed, humidity, and radiation data output by the climate model to obtain long-term climate prediction data for different future emission scenarios; Obtain soil utilization prediction data under different future emission scenarios; Using a long series of climate prediction data and soil utilization prediction data under different future emission scenarios to drive the basin distributed water cycle model, the basin's future water cycle changes are simulated and reservoir inflows are predicted under different scenarios. Based on the coupled model of basin hydrology, reservoir hydrodynamics and reservoir operation, the hydropower generation under different future emission scenarios is predicted, including: The daily runoff process data simulated by the distributed water cycle model of the watershed is used as the boundary condition of the power generation simulation and prediction model of the reservoir area hydrodynamics-reservoir operation coupling. A model coupler is established based on the data scale conversion interface to construct the watershed hydrology-reservoir area hydrodynamics-reservoir operation coupling model. The predicted reservoir inflow under different emission scenarios is input into the model coupler to drive the reservoir area hydrodynamic-reservoir scheduling coupled power generation simulation prediction model to predict the hydropower generation under different future scenarios.

2. The method according to claim 1, characterized in that The basic geographic information data includes digital elevation model ground terrain data and hydrological and meteorological station vector data; the underlying surface data includes current land use, soil type data and future predicted land use data; the underwater terrain data is large-section measurement data or underwater terrain point cloud data; the hydrological and meteorological data includes a series of water level and flow observation data from hydrological stations in the region, a series of meteorological observation data from meteorological stations, and future different scenario forecast data output by climate models; the reservoir data includes characteristic parameters and scheduling procedures of reservoir projects in the region.

3. The method according to claim 1, characterized in that The basin distributed water cycle model includes sub-basins and water conservancy project storage nodes. The construction of the basin distributed water cycle model simulates and verifies the regional hydrological process, specifically including: Based on the ground terrain data of the digital elevation model, the sub-basin is divided, and the dynamic hydrological response units of the sub-basin are divided according to the underlying soil type and land use change characteristics; Based on the spatial distribution data of meteorological stations, meteorological data are interpolated to each sub-basin to establish a historical meteorological driving dataset for the study area; Establish water conservancy project storage nodes based on the impact of human activities and reservoir engineering operations on the water cycle; Based on the historical meteorological data driving the basin distributed water cycle model, the water cycle process affected by land use and reservoir scheduling was simulated and verified, and the efficiency coefficient, relative error and correlation coefficient were used as model verification evaluation indicators.

4. The method according to claim 3, wherein The efficiency coefficient, relative error and correlation coefficient are calculated using the following formula: Where, NSE, PBIAS, and R1 are efficiency coefficient, relative error, and correlation coefficient, respectively. m,i ,Q s,i are the measured flow rate and the simulated flow rate, respectively. are the measured average flow and the simulated average flow, respectively; i and n1 are the data sequence number and the total number of data, respectively.

5. The method according to claim 1, wherein Based on the power generation simulation and prediction model, the reservoir power generation is calculated by solving the reservoir area hydrodynamic-reservoir operation coupling process through iterative calculation, specifically including: The reservoir hydrodynamics is calculated using the following formula: Where A is the cross-sectional area, t is the time, Q is the flow rate, x is the flow rate, q is the interval unit flow rate, g is the acceleration of gravity, B is the water surface width, Z is the water level, n is the roughness, and R is the wetted perimeter. The four-point implicit difference scheme of Pressman is used to discretize the equation (4), and the three-level solution method is used to solve the equation (4); The reservoir discharge through the gate is calculated using the following formula: Where Q d is the discharge flow, C d is the discharge coefficient, a, b are the gate opening and gate net width, h0 is the water level in front of the dam; The power generation of the hydropower station is calculated using the following formula: P=ρ·g·H·Q w ·η·t (6) Where P is the power generation, ρ is the density of water, g is the acceleration of gravity, H is the effective water head, Q is w is the turbine flow rate, η is the power generation efficiency, and t is the time.

6. A power generation prediction device based on hydrology-hydrodynamics-scheduling coupling, characterized in that: The device comprises: A data acquisition unit is configured to acquire basic geographic information data, underlying surface data, underwater topography data, hydrological and meteorological data, and reservoir data in the region; The first model building unit is configured to build a basin-distributed water cycle model to simulate and verify regional hydrological processes; The second model building unit is configured to build a reservoir area hydrodynamic-reservoir operation coupled power generation simulation prediction model to simulate and verify the reservoir area hydrodynamic process and power generation; The first prediction unit is configured to predict the reservoir inflow under different emission scenarios based on future climate change and different emission scenarios using the basin distributed water cycle model; The second prediction unit is configured to predict hydropower generation under different future emission scenarios based on a coupled model of river basin hydrology, reservoir hydrodynamics, and reservoir operation; wherein the coupled model of river basin hydrology, reservoir hydrodynamics, and reservoir operation includes a river basin distributed water cycle model and a coupled power generation simulation prediction model of reservoir hydrodynamics and reservoir operation; The second model building unit is further configured to: Based on the underwater topography data of the reservoir, the reservoir area is divided into several sections, and a power generation simulation and prediction model that couples reservoir hydrodynamics and reservoir operation is constructed. The upper boundary conditions and interval conditions of the power generation simulation and prediction model are the flow boundaries of surface runoff, subsurface flow, and groundwater runoff simulated by the basin water cycle model. The lower boundary condition of the power generation simulation and prediction model is the water level boundary simulated by the reservoir operation model. The initial conditions are determined based on steady flow calculations. Based on the power generation simulation prediction model, the reservoir power generation is calculated by solving the reservoir area hydrodynamic-reservoir operation coupling process through iterative calculation; The first prediction unit is further configured to: Correct the precipitation, temperature, wind speed, humidity, and radiation data output by the climate model to obtain long-term climate prediction data for different future emission scenarios; Obtain soil utilization prediction data under different future emission scenarios; Using a long series of climate prediction data and soil utilization prediction data under different future emission scenarios to drive the basin distributed water cycle model, the basin's future water cycle changes are simulated and reservoir inflows are predicted under different scenarios. The second prediction unit is further configured to: The daily runoff process data simulated by the distributed water cycle model of the watershed is used as the boundary condition of the power generation simulation and prediction model of the reservoir area hydrodynamics-reservoir operation coupling. A model coupler is established based on the data scale conversion interface to construct the watershed hydrology-reservoir area hydrodynamics-reservoir operation coupling model. The predicted reservoir inflow under different emission scenarios is input into the model coupler to drive the reservoir area hydrodynamic-reservoir scheduling coupled power generation simulation prediction model to predict the hydropower generation under different future scenarios. 7 . A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, executes the method according to claim 1 .

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