Meteorological-downscaling-coupled dispatching method for power generation and consumption of hydro-wind-solar system

Abstract

The present invention belongs to the field of multi-energy complementary coordinated dispatching. Disclosed is a meteorological-downscaling-coupled dispatching method for the power generation and consumption of a hydro-wind-solar system. The method comprises: using a support vector machine regression algorithm to identify different hydrometeorological variable data, and establishing a statistical relationship between observation data and a meteorological factor to implement high-resolution spatial downscaling; using a wind-solar power-generation empirical formula to calculate a wind-solar output process; and introducing a series of time-series peak regulation modes to determine a regulated peak and a hydro-wind-solar power consumption linkage equation, thereby avoiding overestimation of energy consumption caused by neglecting climate change impacts and short-term power generation rules. By means of the analysis of engineering examples consisting of Yunnan Lancang River Basin and surrounding wind-solar power stations thereof, the result shows that the present invention can effectively reduce hydrometeorological downscaling errors, and more accurately describe hydro-wind-solar energy power generation rules by means of a hydro-wind-solar power consumption linkage equation, thereby making the dispatching result show better accuracy and reliability.

Description

A method for power generation and consumption scheduling of hydro-wind-solar systems coupled with meteorological downscaling Technical Field

[0001] This invention belongs to the field of multi-energy complementary coordinated scheduling, and relates to a method for scheduling power generation and consumption of hydro-wind-solar systems coupled with meteorological downscaling. Background Technology

[0002] With the rapid transformation of my country's energy structure towards green and low-carbon development, the installed capacity of new energy sources, primarily wind and solar power, is increasing. This makes the operation of the power system increasingly susceptible to weather conditions, especially since clean energy generation from hydropower, wind, and solar power is heavily dependent on changes in meteorological factors. In this context, a detailed assessment of the impact of complex climate and meteorological conditions on the coordinated scheduling of multi-energy complementarity and the absorption of new energy sources has become crucial and necessary.

[0003] Currently, using downscaling techniques to obtain high-resolution meteorological conditions is an important way to accurately reflect future climate change and improve data accuracy. The two main meteorological data downscaling techniques are dynamic downscaling and statistical downscaling. Dynamic downscaling starts from the physical level to obtain complete climate variables with spatial continuity, but its complex model structure and massive computational load limit its accuracy. Statistical downscaling, on the other hand, achieves downscaling by establishing a statistical relationship between observed data and the data to be downscaled. Its principle is simple and intuitive, and its computational load is small, and it has been widely used (Wang S, Zhu J, Huang G, et al. Assessment of climate change impacts on energy capacity planning in Ontario, Canada using high-resolution regional climate model[J]. Journal of Cleaner Production, 2020, 274:123026.). Among them, machine learning algorithms have advantages such as high accuracy, good applicability, and low susceptibility to sudden changes, and are widely used in large-scale hydrological simulation statistical downscaling. Moreover, they can obtain information from high-dimensional climate variables without prior analysis of local hydrological processes in specific regions (Hu Baojian, Li Wei, Chen Chuanfa, et al. Improving the quality of GPM satellite remote sensing precipitation using spatial random forest method [J]. Journal of Remote Sensing, 2024, 28(02):414-425.). In terms of new energy consumption assessment, the impact of climate change on clean energy systems is usually assessed on a monthly or ten-day scale, assuming that the model inputs, such as inflow runoff and new energy output, remain unchanged during this period (Zhang Y, Cheng C, Yang T, et al. Assessment of climate change impacts on the hydro-wind-solar energy supply system [J]. Renewable and Sustainable Energy Reviews, 2022, 162:112480.). However, the output of new energy has strong intermittency, volatility, and unpredictability within a day. These characteristics make it impossible to accurately reflect short-term energy consumption patterns, leading to inaccurate assessment results that are difficult to apply.

[0004] To address the aforementioned issues, this invention proposes a hydro-wind-solar power generation and consumption scheduling method coupled with meteorological downscaling, and applies it to an engineering example consisting of a large river basin and its surrounding wind and solar power stations. The results show that this invention can effectively reduce hydro-meteorological downscaling errors, and the linkage equation for hydro-wind-solar power consumption can more accurately describe the power generation patterns of hydro-wind-solar energy, resulting in better accuracy and reliability of the scheduling results.

[0005] Summary of the Invention

[0006] This invention primarily addresses the problem of power generation and consumption scheduling technology for hydro-wind-solar systems coupled with meteorological downscaling. The aim is to improve the accuracy and reliability of consumption scheduling results by refining the assessment of the impact of complex climate and meteorological conditions on the coordinated scheduling of multi-energy complementarity and new energy consumption.

[0007] Technical solution of the present invention:

[0008] A method for power generation and consumption scheduling of hydro-wind-solar systems coupled with meteorological downscaling includes the following steps:

[0009] (1) Spatial downscaling of hydrological and meteorological variable data was performed using support vector machine regression (SVR) to accurately reflect the impact of climate change on the power output of hydropower, wind power and solar power stations.

[0010] (1.1) Select hydrological and meteorological variables as the original dataset: use time-by-time precipitation, evaporation, surface air temperature, and soil moisture content (0-35cm) to map the time-by-time changes in inflow to hydropower stations, use time-by-time 10m wind speed to predict time-by-time near-surface wind speed at wind power and photovoltaic power stations, and use time-by-time surface shortwave radiation and surface air temperature to predict time-by-time solar panel radiation received by photovoltaic power stations and ambient temperature.

[0011] (1.2) Division of the original dataset of hydrological and meteorological variables: The original data were divided into training set, validation set and test set in a 6:2:2 ratio according to the time order;

[0012] (1.3) Assume the training set is {(x i ,y i )}, i∈[1,N), where x i These are large-scale hydrometeorological variable data from different atmospheric circulation models (GCM), y i The actual data corresponds to the time period, and N is the size of the dataset; the linear regression decision surface function of SVR is represented by equation (1), where ω is the weight vector and b is the bias; a nonlinear transformation function is applied. Mapping the input space to a high-dimensional feature space:

[0013] (1.4) Establish an insensitive loss function for the allowable prediction error ε of hydro-meteorological variables:

[0014] (1.5) With the goal of minimizing the structural risk of prediction error of hydrological and meteorological variables, the prediction error minimization problem is transformed into an equivalent quadratic convex programming problem under constraint (4) using the Python-sklearn program module;

[0015] In the formula: ξ i and ξ is the positive relaxation factor for the prediction error. i This indicates the degree of relaxation when the predicted value is higher than the actual value. This indicates the degree of relaxation when the predicted value is lower than the actual value, and C is the prediction error regularization penalty coefficient.

[0016] (2) Let p i,t Let r be the predicted value of hydrological and meteorological variables for power station i at time period t; i,t The actual observed values ​​of hydrological and meteorological variables for power station i at time period t; r represents the maximum value of the actual observed hydro-meteorological variables at time period t for power station i; i,t Let be the minimum value of the actual observed hydro-meteorological variables at time period t for power station i; the difference between the observed and predicted time series of hydro-meteorological variables on the test set is characterized by the normalized root mean square error (NRMSE) and the relative square error (RSE) to evaluate the prediction performance. The smaller the index value, the better the model effect; the specific calculation formula is as follows:

[0017] In the formula: p i,t Let r be the predicted value of hydrological and meteorological variables for power station i at time period t; i,t The actual observed values ​​of hydrological and meteorological variables for power station i at time period t; r represents the maximum value of the actual observed hydro-meteorological variables at time period t for power station i; i,t This represents the minimum value of the actual observed hydro-meteorological variables at time t for power station i.

[0018] (3) Input the hydrological and meteorological variables at the power station, construct empirical formulas for wind power generation and photovoltaic power generation, import downscaled data of future hydrological and meteorological variables from an Excel file using the Python programming language, and use the Python-math library to solve the downscaled data again to obtain the change process of wind and solar power output; the specific formulas are as follows:

[0019] Empirical formula for wind power generation:

[0020] In the formula: Let z be the wind speed at the turbine height of the wind power station, in m / s; These are the cut-in wind speed and cut-out wind speed of the wind turbine, respectively, in m / s; Z is the near-surface wind speed at the location of the wind power station, in m / s; Z0 is the surface roughness length, taken as 0.0002m. This refers to the power output rate of a wind power station (i.e., the ratio of power output to installed capacity). These are the coefficients of the power generation function; For wind power station n w Installed capacity; △t m The number of hours in month m;

[0021] Empirical formula for photovoltaic power generation:

[0022] In the formula: For photovoltaic power station n pv The output rate; The performance ratio of solar panels; For photovoltaic power station n pv Earth's surface radiation, W·m 2 rsds STC Earth's surface radiation (rsds) at standard atmospheric pressure (101.325 kPa) STC =1000W*m 2 ); γ is taken as -0.005℃ -1 , are coefficients calculated using empirical formulas; The temperature of a solar cell is affected by a combination of temperature, radiation, and wind speed. STC The ambient air temperature (Tas) under standard atmospheric pressure (101.325 kPa) STC =25℃); For photovoltaic power station n pv The installed capacity;

[0023] In the formula: the values ​​of the coefficients For photovoltaic power station n pv The ambient temperature at the location, in °C; For photovoltaic power station n pv Surface wind speed at location, m / s;

[0024] (4) Using the hydrological and meteorological variables at each power station as characteristic inputs, a piecewise linear fitting method is used to construct the linkage equation for the absorption of hydropower, wind power, and solar power, thereby realizing the extraction of the complementary absorption relationship between hydropower, wind power, and solar power. The specific expression is as follows:

[0025] In the formula: Let f(x) be the linkage function for the absorption of hydropower, wind power, and solar power, representing the load in month m when the load is... The quantitative relationship between hydropower generation and wind and solar power consumption in this scenario is investigated. Using the Gurobi solver as the modeling platform, the nonlinear model is transformed into a mixed-integer linear programming problem using Python to determine the impact of hydropower generation on the scale of wind and solar power consumption. The specific impact includes four main stages:

[0026] Phase 1: Insufficient hydropower regulation capacity limits the absorption of wind and solar power. However, with the increase in hydropower output and the enhanced flexibility of hydropower, the proportion of wind and solar power absorption is on the rise.

[0027] Phase 2: Hydropower regulation capacity can completely smooth out fluctuations in wind and solar power generation, responding to the peak-shaving needs of the receiving-end power grid. Therefore, wind and solar resources can be fully absorbed by the receiving-end power grid.

[0028] Phase 3: Channel capacity limits the combined power output of hydropower, wind power, and solar power. Consequently, the proportion of wind and solar power consumption decreases as hydropower output increases.

[0029] Phase 4: Hydropower output continues to increase until it exceeds the channel capacity limit, at which point wind and solar power generation will be unable to be absorbed.

[0030] The beneficial effects of this invention are as follows: Compared with a single monthly-scale scheduling method, the scheduling method that couples meteorological downscaling and energy consumption characteristics can effectively reduce the inaccuracy of energy consumption assessment, correlate the impact of climate change on clean energy power generation, and exhibit better reliability, practicality, and reduced scheduling risks. Meteorological downscaling essentially refers to the refined prediction of hydrological and meteorological variables at each power station. By obtaining the required high-precision data through downscaling, the impact on clean energy power generation can be determined, thus providing a detailed description of the fluctuations in wind and solar power output. At the same time, by introducing a linkage equation between hydropower output and wind and solar power consumption, the influence relationship between hydropower output and wind and solar power consumption is established, avoiding the overestimation of new energy consumption by the traditional single monthly-scale scheduling method under the influence of climate change, and improving the accuracy of consumption analysis. Attached Figure Description

[0031] Figure 1 is a diagram of the overall solution framework of the method of the present invention;

[0032] Figure 2 is a schematic diagram of the support vector machine regression algorithm;

[0033] Figure 3. Schematic diagram of error relaxation in the support vector machine regression algorithm;

[0034] Figure 4 is a schematic diagram of the energy consumption pattern of water, wind and solar power;

[0035] Figure 5 is a schematic diagram showing the changes in power generation and consumption of the hydro-wind-solar hybrid system. Detailed Implementation

[0036] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings and technical solutions.

[0037] Generally speaking, downscaling meteorological data from clean energy systems such as hydropower, wind power, and solar power can more accurately reflect the impact of climate change on power generation. To establish the statistical relationship between observed data and data to be downscaled, hydrometeorological variables were selected: time-period precipitation, evaporation, surface air temperature, and soil moisture content (0-35cm) were used to map time-period changes in inflow to hydropower stations; time-period 10m wind speed was used to predict time-period near-surface wind speed at wind and solar power stations; and time-period surface shortwave radiation and surface air temperature were used to predict time-period solar panel radiation received by solar power stations and ambient temperature.

[0038] The historical data is then partitioned into training, validation, and test sets in a 6:2:2 ratio according to chronological order. The training set is used to fit the model, the validation set is used for hyperparameter tuning, and the test set is used to evaluate the performance of the trained model.

[0039] Suppose the training set is {(x i ,y i )}, i∈[1,N), where x i These are large-scale hydrometeorological variable data from different atmospheric circulation models (GCM), y i The actual data corresponds to the time period, and N is the size of the dataset; Equation (14) represents the linear regression decision surface function of SVR, where ω is the weight vector and b is the bias; a nonlinear transformation function is applied. Mapping the input space to a high-dimensional feature space:

[0040] Subsequently, a loss function that allows for prediction error ε to be insensitive to hydro-meteorological variables is introduced:

[0041] As shown in Figure 2, with the goal of minimizing the structural risk of prediction error, the prediction error minimization problem is transformed into an equivalent quadratic convex programming problem under constraint (17) using the Python-sklearn program module:

[0042] In the formula: ξ i and ξ is the positive relaxation factor for the prediction error. i This indicates the degree of relaxation when the predicted value is higher than the actual value, while This represents the degree of relaxation when the predicted value is lower than the actual value, where C is the prediction error penalty and N is the sample size.

[0043] To measure the accuracy of predictions based on the combination of different downscaling techniques and meteorological data sources, the normalized root mean square error (NRMSE) and relative squared error (RSE) are used to characterize the differences between observed and predicted time series of hydrometeorological variables on the test set, thus reflecting the effectiveness of downscaling techniques.

[0044] In the formula p i,t Let r be the predicted value of hydrological and meteorological variables for power station i at time period t; i,t The actual observed values ​​of hydrological and meteorological variables for power station i at time period t; r represents the maximum value of the actual observed hydro-meteorological variables at time period t for power station i; i,t Let be the minimum value of the actual observed hydrological and meteorological variables of power station i at time period t.

[0045] Using empirical formulas for wind and solar power generation, downscaled data of future hydrological and meteorological variables were imported from an Excel file using the Python programming language. The Python-math library was then used to further calculate the downscaled data to obtain the changes in wind and solar power output. Wind power generation can be converted using empirical formulas. Furthermore, the approximation of the wind power curve using a polynomial power curve is reasonable, with the hourly wind speed at hub height being the independent variable and increasing approximately logarithmically with height. The empirical formula for wind power generation is as follows:

[0046] In the formula: Let z be the wind speed at the turbine height of the wind power station, in m / s; These are the cut-in wind speed and cut-out wind speed of the wind turbine, respectively, in m / s; Z represents the near-surface wind speed at the location of the wind power station, in m / s; Z0 represents the surface roughness length, 0.0002 m. This refers to the power output rate of a wind power station (i.e., the ratio of power output to installed capacity). These are the coefficients of the power generation function; For wind power station n w Installed capacity; △t m The number of hours in month m.

[0047] Photovoltaic power plants generate electricity through solar cells, and their power output can be expressed as an empirical function with ambient temperature and solar radiation as independent variables. The empirical formula for photovoltaic power generation is as follows:

[0048] In the formula: For photovoltaic power station n pv The output rate; The performance ratio of solar panels; For photovoltaic power station n pv Earth's surface radiation, W·m 2 rsds STC Earth's surface radiation (rsds) at standard atmospheric pressure (101.325 kPa) STC =1000W*m 2 ); γ is taken as -0.005℃ -1 , are coefficients calculated using empirical formulas; The temperature of a solar cell is affected by a combination of temperature, radiation, and wind speed. STC The ambient air temperature (Tas) under standard atmospheric pressure (101.325 kPa) STC =25℃); For photovoltaic power station n pv The installed capacity.

[0049] In the formula: the values ​​of the coefficients For photovoltaic power station n pv The ambient temperature at the location, in °C; For photovoltaic power station n pv The surface wind speed at that location is in m / s.

[0050] As shown in Figure 4, by utilizing the spatiotemporal complementary absorption characteristics between power sources, the absorption patterns of water, wind, and solar power can be extracted, which can help improve the accuracy of scheduling simulation in assessing energy absorption under climate change.

[0051] A linkage equation for the absorption of hydropower, wind power, and solar power was constructed. The hydrological and meteorological variables at each power station were used as characteristic inputs. A piecewise linear fitting method was adopted to determine the linkage equation for the absorption of hydropower, wind power, and solar power. The Gurobi solver was used as the modeling platform. The above nonlinear model was transformed into a mixed integer linear programming problem using Python language to solve the problem and determine the influence relationship between the amount of hydropower generation and the scale of wind and solar power absorption.

[0052] In summary, the equation for the linkage between hydropower, wind power, and solar power consumption is as follows:

[0053] In the formula: Let f(x) be the linkage function for the absorption of hydropower, wind power, and solar power, representing the load in month m when the load is... Quantitative relationship between hydropower, wind power, solar power and electricity consumption in the scenario.

[0054] For single-month-scale scheduling, the introduction of the linkage equation between hydropower, wind power, and solar power consumption means establishing the correlation between hydropower generation and the scale of wind and solar power consumption. This avoids overestimation of new energy consumption under the influence of climate change in single-month-scale scheduling and improves the rationality of scheduling analysis results.

[0055] This paper takes the LXW and LNZD giant multi-year regulating cascade hydropower stations and surrounding wind and solar power stations on the main stream of a super-large river basin as an example for application verification, using hourly wind and solar power output and runoff data for the whole year of 2023 as samples. The power grid load process, wind and solar power output process, and historical data used in this invention are all based on the actual operating data of the power grid and power stations.

[0056] The calculation results are shown in Tables 1 to 4. Compared with other downscaling machine learning algorithms, the Support Vector Machine Regression (SVR) algorithm consistently performs best in predicting future hydrometeorological variables, regardless of the GCM model output data used. Furthermore, FGOALS-g3-SVR, MRI-ESM2-0-SVR, MIROC6-SVR, and CanESM5-SVR show the best performance in predicting solar radiation, ambient temperature, surface wind speed, and runoff, respectively. This demonstrates that different GCMs exhibit specific accuracy and reliability in predicting different hydrometeorological variables. Therefore, combining SVR with the outputs of different GCMs in the prediction model can provide more accurate information about climate change.

[0057] When the peak-shaving depth remains constant, the annual variation in wind and solar power absorption aligns with the natural trends of these resources. Furthermore, in the complementary absorption characteristics across different months, the absorption of wind and solar power increases with the increase in hydropower generation. However, a sustained increase in hydropower generation will conversely suppress the absorption of wind and solar power. This is because excessive or insufficient hydropower generation weakens the hydropower regulation capacity, resulting in the hydropower system being unable to provide sufficient flexibility to absorb intermittent wind and solar power.

[0058] To verify the accuracy of the proposed method in assessing the energy absorption of hydropower, wind power, and solar power, two complementary operation modes were constructed. Considering the current actual peak-shaving demand, the simulation results of the hydropower-wind power-solar power independent operation model and the two complementary operation models were compared. The traditional long-term dispatch model without nesting the short-term complementary absorption characteristic curve of the base energy is called complementary operation mode 1, or simply mode 1; the long-term dispatch model with nested short-term complementary absorption characteristic curves is called complementary operation mode 2, or simply mode 2. The results of the two modes are shown in Figure 5. The power generation level of mode 1 is generally higher than that of mode 2. However, under the SSP119 climate change path, the total energy absorption of mode 2 accounts for only 72% of that of mode 1, and the average energy absorption ratio under different climate changes is about 79%. This means that under the traditional single-month scale framework, if the short-term peak-shaving requirements of the grid are ignored when assessing future energy generation in the basin considering the impact of climate change, the expected benefits may not be achieved. Therefore, ignoring the short-term complementary characteristics of the hydropower-wind power-solar power complementary system under such a framework will lead to overly optimistic absorption assessment results, and this result may be amplified under the coupled effects of different climate changes.

[0059] Through comparative analysis of different algorithms and schemes, it was verified that the proposed method for power generation and consumption scheduling of hydro-wind-solar systems coupled with meteorological downscaling can be applied to a single monthly scale framework, and the results are highly accurate and applicable, which can effectively assess the level of clean energy consumption under the influence of climate and meteorological changes.

[0060] Table 1. Prediction of solar radiation using different GCM model output data and downscaling techniques.

[0061] Table 2. Prediction of ambient temperature using different GCM model output data and combinations of downscaling techniques.

[0062] Table 3. Prediction of surface wind speed using different GCM model output data and downscaling techniques.

[0063] Table 4. Prediction of runoff using different GCM model output data and downscaling techniques.

Claims

1. A method for coupling weather downscaling, water, wind and light systems power generation and consumption scheduling, characterized in that, Comprising the following steps: (1) Using support vector machine regression to spatially downscale hydro-meteorological variables to accurately reflect the impact of climate change on the output of each power station; (1.1) Selecting hydro-meteorological variables as the original data set: using hourly precipitation, evaporation, surface air temperature, and soil moisture to map the hourly runoff changes of hydropower stations, and using 10 m wind speed to predict the near-surface wind speed at wind and photovoltaic power stations, and using hourly surface shortwave radiation and surface air temperature to predict the solar panel receiving radiation and ambient temperature at photovoltaic power stations; (1.2) Division of the original data set of hydro-meteorological variables: dividing the original data into training set, validation set, and test set in the order of time according to the ratio of 6:2:2; (1.3) Assume that the training set is {(x i ,y i )}, i∈[1,N), where x i is the data of large-scale hydro-meteorological variables of different atmospheric circulation models, y i is the actual data corresponding to the time, and N is the size of the data set; the linear regression decision surface function of SVR is represented by formula (1), where ω is the weight vector, and b is the bias; a nonlinear transformation function mapping the input space to a high dimensional feature space: (1.4) Establishing hydro-meteorological variables allows for prediction error ε insensitive loss functions: (1.5) To minimize the prediction error of hydro-meteorological variables, the Python-sklearn program module is used to convert the prediction error minimization problem into an equivalent quadratic convex programming problem under the constraint (4) for solving; In the formula: ξ i and ξ for the positive relaxation factor of the prediction error i denotes the relaxation degree in case the prediction value is higher than the real value, The relaxation degree when the predicted value is lower than the true value, and C is the regularization penalty coefficient of prediction error; (2) set p i,t is the forecast value of the hydro-meteorological variable for power plant i at time period t; r i,t is the true observed value of the hydro-meteorological variable for power plant i at time period t; maxi(t) = max{xi(t)} for i = 1, 2,..., n; r i,t mini(t) = min{xi(t)} for i = 1, 2,..., n; r where: p i,t is the predicted value of the hydro-meteorological variable at the power plant i at the time period t; r i,t is the true observed value of the hydro-meteorological variable at the power plant i at the time period t; maxi(t) is the maximum value of the hydrometeorological variable observed at the power plant i at the time period t; r i,t mini(t) is the minimum value of the hydrometeorological variable observed at the power plant i at the time period t; (3) Inputting the hydro-meteorological variables at the power station, constructing the empirical formula of wind power generation and photovoltaic power generation, using Python programming language to import the downscaling data of future hydro-meteorological variables from Excel file, and using Python-math library to solve the downscaling data again to obtain the change process of wind and light output rate; The specific formula is as follows: Wind power generation empirical formula: In the formulae: V(z) = 10.0 + 0.5z cut-in and cut-out wind speed of the wind turbine, m / s, respectively; V is the wind speed at the location of the wind farm, m / s; z0is the surface roughness length, taken as 0.0002 m; The output rate of the wind power station is the ratio of the output to the installed capacity. for the power generation function coefficient; For wind power station n w installed capacity; Δt m is the number of hours in m months; Photovoltaic power generation empirical formula: In the formulae: For the output rate of a photovoltaic power plant pv ​ For solar panel performance ratio; For the ground surface radiation of the photovoltaic power station n pv in W m 2 ; rsds STC is the ground surface radiation under standard atmospheric pressure, rsds STC = 1000 W*m 2 ; γ takes -0.005℃ -1 , is the empirical formula calculation coefficient; Tas is the ambient air temperature at standard atmospheric pressure, Tas STC Tas is the ambient air temperature at standard atmospheric pressure, Tas STC = 25°C; For photovoltaic power stations n pv of installed capacity; wherein: the coefficients take the values For the ambient temperature at the site of the photovoltaic power plant n pv in °C; For photovoltaic power station n pv Surface wind speed at the site, m / s; (4) Taking the hydro-meteorological variables at each power station as characteristic inputs, a piecewise linear fitting method is used to construct the water-wind-solar power consumption linkage equation to realize the extraction of the water-wind-solar complementary consumption relationship, and the specific expression is as follows: In the formulae: The water, wind, and landscape power consumption linkage function is represented as Quantitative relationship between water, wind, light, and power consumption under the scenario; Using Gurobi solver as the modeling platform, using Python language to convert the above nonlinear model into a mixed integer linear programming for solving, to determine the influence relationship between hydropower generation capacity and wind and light power consumption scale, including four main stages: Stage 1: The regulation capacity of hydropower is insufficient, limiting the consumption of wind and light; With the increase of hydropower output, the flexibility of hydropower increases, and the proportion of wind and light consumption increases; Stage 2: The regulation capacity of hydropower can completely smooth the fluctuations of wind and light generation, and respond to the peak shaving demand of the receiving end power grid; Therefore, wind and light resources can be completely consumed by the receiving end power grid; Stage 3: The channel capacity limits the bundled output of water, wind, and light; Therefore, the proportion of wind and light consumption decreases with the increase of hydropower output; Stage 4: Hydropower output continues to increase until it breaks through the channel capacity limit, and wind and light generation capacity will not be consumed.

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