A robustness evaluation method and system of a water and wind complementary system

Abstract

This invention discloses a robustness assessment method and system for a hydro-wind-solar hybrid system, relating to the intersection of renewable energy utilization and reservoir scheduling. The method includes: generating a set of uncertainty scenarios for multiple hydro-wind-solar resource sequences using a multivariate stochastic simulation method, based on thresholds for changes in the average values ​​of runoff and wind / solar output time series; constructing a medium- to long-term stochastic optimization scheduling model for hydro-wind-solar hybrid systems and solving the model using stochastic dynamic programming; extracting medium- to long-term scheduling rules for hydro-wind-solar hybrid systems using the model based on historical data of runoff, wind, and solar output; simulating the operation of the hydro-wind-solar hybrid system under the uncertainty scenario sets of multiple hydro-wind-solar resource sequences according to the scheduling rules and obtaining scheduling results; calculating the robustness index of the scheduling results and analyzing the sensitivity using a global sensitivity analysis method. This invention can effectively and comprehensively assess the robustness of hydro-wind-solar hybrid systems.

Description

Technical Field

[0001] This invention relates to the intersection of renewable energy utilization and reservoir operation, and particularly to a robustness assessment method and system for a hydro-wind-solar hybrid system. Background Technology

[0002] To address global climate change and energy shortages, the vigorous development of clean energy sources, such as hydropower, wind power, and solar power, has become a consensus. Existing technologies have studied medium- to long-term scheduling rules for hydropower-wind-solar complementary systems based on historical conditions and existing hydropower-wind-solar resource availability. However, hydropower, wind power, and solar power are significantly affected by meteorological factors such as precipitation, wind speed, temperature, and radiation, making it difficult to effectively assess the robustness of these systems under future climate change conditions. Robustness refers to the ability of a system to maintain its performance unchanged under external disturbances.

[0003] In existing technologies, assessments of hydro-wind-solar hybrid systems, which bundle non-dispatchable energy sources such as wind and solar power with traditional adjustable hydropower, often focus on the system's economics and reliability. The lack of assessment methods for the system's robustness under climate change results in poor applicability of the operating and scheduling rules for hydro-wind-solar hybrid systems to future climate change. Summary of the Invention

[0004] This invention provides a robustness assessment method and system for a hydro-wind-solar hybrid system, which can solve the problem that the existing technology has an incomplete assessment of the ability of hydro-wind-solar hybrid systems to cope with future climate change.

[0005] This invention provides a robustness assessment method for a hydro-wind-solar hybrid system, comprising the following steps: simulating runoff time series and wind / solar output time series in the hydro-wind-solar hybrid system under future conditions, and obtaining the threshold for changes in the average values ​​of the runoff time series and wind / solar output time series; the runoff time series is the water resource component used to record changes in river flow over time, and the wind / solar output time series is the wind / solar resource component used to record changes in the output power of wind and solar power generation over time; based on the threshold for changes in the average values ​​of the runoff time series and wind / solar output time series, using a multivariate stochastic simulation method to generate multiple uncertainty scenario sets for the hydro-wind-solar resource series, the uncertainty scenario sets for the hydro-wind-solar resource series being used to describe groups of different runoff time series and wind / solar output time series. Under suitable conditions, a medium- to long-term stochastic optimization scheduling model for hydro-wind-solar complementary systems is constructed, with the objective functions being the maximum total power generation and the maximum total power generation guarantee rate, and constraints including water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity. Historical data on runoff, wind, and solar power output are substituted into the model, and stochastic dynamic programming is used to optimize the model, yielding medium- to long-term scheduling rules. Based on these rules, the medium- to long-term scheduling process of the hydro-wind-solar complementary system is simulated under multiple uncertainty scenarios involving hydro-wind-solar resource sequences, and scheduling results are obtained. Based on the scheduling results, robustness indicators of the hydro-wind-solar complementary system are obtained, and sensitivity is analyzed using global sensitivity analysis based on these robustness indicators to complete the evaluation.

[0006] Furthermore, the method of generating a set of uncertain scenarios for multiple water, wind, and solar resource sequences using a multivariate stochastic simulation method specifically includes: generating a set of uncertain scenarios for multiple water, wind, and solar resource sequences using a joint probability model and a Latin hypercube random sampling method.

[0007] Furthermore, the specific steps of using the maximization of total power generation and the maximization of total power generation guarantee rate as objective functions include:

[0008] Objective function 1: Maximize total power generation

[0009]

[0010] Objective function 2: Maximize the total power generation guarantee rate

[0011]

[0012] Where E represents the total power generation of the hydro-wind-solar hybrid system, and T represents the total dispatch period; This represents the total output of the hydro-wind-solar hybrid system during time period t; This represents the power output of the m-th cascade hydropower station during time period t; and These represent the output of photovoltaic and wind power respectively during time period t; f represents the power output of wind and solar power curtailment during period t; EC (·) represents the obtained curtailment rate response function; ΔT represents the dispatch period length; F represents the complementary system generation guarantee rate; This indicates the total guaranteed output of the complementary system; This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the scheduling period, where h represents ... and M represents ...

[0013] Furthermore, the acquisition of medium- and long-term scheduling rules for hydro-wind-solar hybrid systems specifically includes: solving the medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid systems based on the stochastic dynamic programming method, characterizing the uncertainty of random variables through probability distribution, and using it as input to optimize the medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid systems to obtain uncertain medium- and long-term scheduling rules for hydro-wind-solar hybrid systems.

[0014] Furthermore, the specific steps for obtaining the robustness index of the hydro-wind-solar hybrid system include:

[0015] The robustness indicators of the scheduling results are analyzed, and the robustness indicators include:

[0016] The improved regret index Rm is calculated as follows:

[0017]

[0018] Where, F(x) i,j D represents the i-th basic performance index value of the hydro-wind-solar hybrid system under the j-th uncertainty scenario; i,j Represents F(x) i,j and The deviation between Let x represent the target value at baseline, and D represent the time series. i D represents the performance deviation value of the i percentile. i,95 D represents the performance deviation value at the 95th percentile. i,90 This represents the performance deviation value at the 90th percentile.

[0019] Satisfaction index S1, the formula is:

[0020]

[0021] Among them, Λ j Let Λ represent the j-th uncertainty scenario of the hydro-wind-solar hybrid system. j A value of Λ indicates that the performance index meets the requirements for this uncertain scenario; otherwise, Λ j =0 indicates that the performance indicators of this uncertainty scenario do not meet the requirements, N represents the sequence length, and J represents the total number of uncertainty scenarios;

[0022] Satisfaction index S2, the formula is:

[0023]

[0024] in, F(x) represents the number of uncertain scenarios in which the basic requirements of the hydro-wind-solar hybrid system are not compromised. j Let represent the performance index of the hydro-wind-solar hybrid system under the j-th uncertainty scenario, r represent the probability of meeting the requirements, α represent the maximum uncertainty range of a certain performance index that the system can accept without being lower than the threshold, and j∈(α) means that uncertainty scenario j belongs to the range α.

[0025] Furthermore, the step of analyzing sensitivity using a global sensitivity analysis method based on robustness indices includes: using the wind-solar installed capacity ratio and total installed capacity as sensitive factors for the long-term stochastic optimization scheduling model of the hydro-wind-solar hybrid system; using the robustness index as the output of the model; statistically analyzing the variance of the robustness index under the uncertainty scenario set; and analyzing the sensitivity of the robustness of the hydro-wind-solar hybrid system to the wind-solar installed capacity ratio and total installed capacity.

[0026] Furthermore, the specific steps for simulating the runoff time series and wind and solar power output time series in the water-wind-solar complementary system under future conditions include: acquiring global-scale model data from the Sixth Coupled Model Intercomparison Project (CMIP6); selecting daily-scale data as variables for precipitation, temperature, relative humidity, solar radiation, and wind speed under 12 climate change models and 4 carbon emission scenarios; and simulating the runoff time series and wind and solar power output time series in the water-wind-solar complementary system under future conditions using hydrological models and wind and solar resource assessment models, respectively.

[0027] This invention provides a robustness evaluation system for a hydro-wind-solar hybrid system, comprising:

[0028] The time series simulation module is used to simulate the runoff time series and wind and solar power output time series in a water-wind-solar hybrid system under future conditions, and to obtain the threshold for changes in the average values ​​of the runoff time series and wind and solar power output time series; the runoff time series is the water resource part used to record the changes in river flow over time, and the wind and solar power output time series is the wind and solar resource part used to record the changes in the output power of wind power generation and solar power generation over time.

[0029] An uncertainty scenario set generation module is used to generate multiple uncertainty scenario sets of water, wind and solar resource sequences using a multivariate stochastic simulation method based on the threshold of changes in the average values ​​of runoff time series and wind and solar output time series. The uncertainty scenario sets of water, wind and solar resource sequences are used for combinations of different runoff time series and wind and solar output time series.

[0030] The scheduling rule acquisition module is used to construct a medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid power generation with the objective functions of maximizing total power generation and maximizing total power generation guarantee rate, and with constraints such as water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity. Historical data on runoff, wind, and solar power output are substituted into the medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid power generation and the model is optimized using stochastic dynamic programming to obtain the medium- and long-term scheduling rules for hydro-wind-solar hybrid power generation.

[0031] The scheduling evaluation module is used to simulate the medium- and long-term scheduling process of the water-wind-solar complementary system under uncertain scenario sets of multiple water-wind-solar resource sequences according to the medium- and long-term scheduling rules, and obtain the scheduling results; obtain the robustness index of the water-wind-solar complementary system based on the scheduling results, and analyze the sensitivity using the global sensitivity analysis method based on the robustness index to complete the evaluation.

[0032] This invention provides a robustness evaluation method and system for a hydro-wind-solar hybrid system, which has the following advantages compared with the prior art:

[0033] Based on the threshold for changes in the average values ​​of runoff time series and wind and solar power output time series, a multivariate stochastic simulation method is used to generate multiple uncertainty scenario sets for hydro-wind-solar resource sequences. With the objective functions of maximizing total power generation and maximizing total power generation guarantee rate, and constraints including water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity, a medium- to long-term stochastic optimization scheduling model for hydro-wind-solar complementary systems is constructed. Historical scheduling results of the hydro-wind-solar complementary system are substituted into the medium- to long-term stochastic optimization scheduling model, and the model is optimized using stochastic dynamic programming to obtain the medium- to long-term scheduling rules. Based on these rules, the medium- to long-term scheduling of the hydro-wind-solar complementary system is simulated under multiple uncertainty scenario sets for hydro-wind-solar resource sequences, and the scheduling results are obtained. Based on the scheduling results, robustness indicators of the hydro-wind-solar complementary system are obtained, and sensitivity is analyzed using a global sensitivity analysis method based on these robustness indicators to complete the evaluation.

[0034] The process involves constructing a long-term stochastic optimization scheduling model for water-wind-solar hybrid systems to obtain long-term scheduling rules. Based on these rules, the system is simulated under uncertain scenarios involving multiple water, wind, and solar resource sequences to obtain scheduling results. Since these uncertain scenarios describe the utilization of water and wind / solar resources under different future conditions, the obtained scheduling results represent the future state of the system. Robustness indices are generated for the scheduling results, and then global sensitivity analysis is used to analyze the sensitivity based on these indices, effectively assessing the robustness of the water-wind-solar hybrid system. Attached Figure Description

[0035] Figure 1 A technical roadmap for a robustness evaluation method for a water-wind-solar hybrid system provided by an embodiment of the present invention;

[0036] Figure 2 This invention provides a robustness evaluation method for a water-wind-solar hybrid system, including a set of uncertainties related to water, wind, and solar resources.

[0037] Figure 3 The objective function of the long-term stochastic optimization scheduling model for water-wind-solar hybrid systems provided in this embodiment of the invention;

[0038] Figure 4 A schematic diagram of robustness indicators provided in this embodiment of the invention. Detailed Implementation

[0039] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0040] See Figures 1-4 This invention provides a robustness evaluation method for a hydro-wind-solar hybrid system, comprising the following steps:

[0041] Step 1: Obtain global-scale model data from the Sixth Coupled Model Intercomparison Project (CMIP6), select daily-scale data for variables such as precipitation, temperature, relative humidity, solar radiation, and wind speed under 12 climate change models and 4 carbon emission scenarios; simulate the runoff time series and wind and solar power output time series of the water-wind-solar hybrid system under future conditions, and determine the threshold for changes in the average values ​​of the runoff time series and wind and solar power output time series; the runoff time series is the water resources component used to record the changes in river flow over time, and the wind and solar power output time series is the wind and solar resources component used to record the changes in the output power of wind power generation and solar power generation over time.

[0042] Step 2: Based on the threshold, a multivariate stochastic simulation method is used to generate multiple uncertainty scenario sets for water, wind, and solar resource sequences. These uncertainty scenario sets describe the combinations of different runoff time series and wind and solar power output time series. Using the maximum total power generation and maximum total power generation guarantee rate as the objective functions, and with constraints such as water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity, a medium- to long-term stochastic optimization scheduling model for water-wind-solar complementarity is constructed. Historical data on runoff, wind, and solar power output are substituted into the medium- to long-term stochastic optimization scheduling model for water-wind-solar complementarity, and a stochastic dynamic programming method is used to optimize the model to obtain the medium- to long-term scheduling rules for water-wind-solar complementarity.

[0043] Step 3: Based on the medium- and long-term scheduling rules for water-wind-solar hybrid systems, simulate the medium- and long-term scheduling of the water-wind-solar hybrid system under uncertain scenario sets of multiple water-wind-solar resource sequences, and obtain the scheduling results; obtain the robustness index of the water-wind-solar hybrid system based on the scheduling results, and analyze the sensitivity using the global sensitivity analysis method based on the robustness index to complete the evaluation.

[0044] I. Based on global-scale model data from CM IP6, combined with a two-parameter monthly water balance model and watershed soil moisture content, and using water balance as the fundamental principle, runoff simulation of the watershed is conducted through generalized or empirical methods. Temperature and solar radiation data under climate change conditions are substituted into the photovoltaic output calculation model to simulate and generate photovoltaic output sequences under different future carbon emission scenarios and different GCM modes. Wind speed data under climate change conditions are substituted into the wind power output calculation model to simulate and generate wind power output sequences under different future carbon emission scenarios and different GCM modes. Based on the runoff, wind power, and photovoltaic output sequences under future scenarios, the threshold values ​​for changes in water, wind, and solar resources are determined.

[0045] II. Based on the above threshold changes in water, wind, and solar resources, and using a joint probability model and Latin hypercube random sampling method, uncertainties in wind and solar power output sequences are generated. Scenario combinations are as follows: Figure 2 As shown.

[0046] III. Constructing a Medium- to Long-Term Stochastic Optimization Scheduling Model for Hydro-Wind-Solar Hybrid Systems. The primary task of a hydro-wind-solar hybrid system is power supply; therefore, maximizing power generation is selected as the first objective of the medium- to long-term optimization scheduling model. Simultaneously, under uncertain conditions, wind and solar power exhibit strong stochasticity, which can easily impact the power supply reliability of the hybrid system; therefore, the total power generation guarantee rate is selected as the second objective.

[0047] Objective 1: Maximize total power generation

[0048]

[0049] Objective 2: Maximize the total power generation guarantee rate

[0050]

[0051] In the formula: E represents the total power generation of the hydro-wind-solar hybrid system; T represents the total dispatch period; This represents the total output of the complementary system during time period t; This represents the power output of the m-th cascade hydropower station during time period t; and These represent the output of photovoltaic and wind power respectively during time period t; f represents the power output of wind and solar power curtailment during period t; EC (·) represents the curtailment rate response function; ΔT represents the dispatch period length; F represents the complementary system generation guarantee rate; This indicates the total guaranteed output of the complementary system; This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the scheduling period.

[0052] In this study, a penalty function is used to process the second objective function, transforming the two-objective optimization problem into a single-objective problem. The processed objective function can be expressed as:

[0053]

[0054] In the formula: The penalty function; α represents the penalty coefficient, which is usually a very large positive number. In this study, α = 10. 8 .

[0055] IV. Based on historical runoff and wind / solar power output sequences, medium- to long-term scheduling rules are generated. Stochastic dynamic programming generates corresponding scheduling decisions for each combination of state variables in adjacent time periods, rather than a single scheduling line. The decision variable (final reservoir capacity) can be expressed as a function of the state variables (reservoir capacity, inflow, solar and wind power output):

[0056]

[0057] In the formula: Z t+ 1 and Z t Indicates the water level at the end of time period t and the water level at the beginning of time period t; I t This represents the reservoir runoff input during time period t; and These represent the photovoltaic and wind power output during time period t, respectively.

[0058] V. Select a basic performance indicator system for constructing robustness evaluation indicators, including

[0059] (1) Water and electricity (HP):

[0060]

[0061] In the formula: Let t represent the output of the m-th cascade hydropower station during time period t; M represents the number of cascade hydropower stations; T represents the total number of time periods; and ΔT represents the length of the time period.

[0062] (2) Total power generation (TP):

[0063]

[0064] In the formula: and These represent the photovoltaic and wind power outputs during time period t, respectively. This represents the power output of wind and solar power curtailment during time period t.

[0065] (3) Water rejection rate (SWR):

[0066]

[0067] In the formula: Q represents the power generation flow of the m-th cascade reservoir during time period t; m,t This represents the discharge flow of the m-th cascade reservoir during time period t.

[0068] (4) Curtailment Rate (CER) of wind and solar power:

[0069]

[0070] In the formula: and These represent the photovoltaic and wind power output during time period t, respectively.

[0071] (5) Total Generation Guarantee Rate (TGR):

[0072]

[0073] In the formula: This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the scheduling period.

[0074] VI. Construct multiple robustness evaluation indicators, including:

[0075] (1) Improved regret index Rm

[0076] The traditional regret index R is typically used to characterize the degree to which a system deviates from its historical baseline state under uncertain disturbances. It is defined as the extent to which the performance index, under uncertain conditions, with the largest deviation meets the requirements (maintaining above 95% of the baseline level), and is expressed as:

[0077]

[0078] In the formula: F(x) i,jD represents the i-th basic performance index value of the complementary system under the j-th uncertainty scenario; i,j Represents F(x) i,j and The deviation between; R is between 0 and 1, the closer to 0, the stronger the system robustness; Let x represent the target value at baseline, and D represent the time series. i D represents the performance deviation value of the i percentile. i,95 D represents the performance deviation value at the 95th percentile. i,90 This represents the performance deviation value at the 90th percentile.

[0079] The regret index R above indicates that the greater the deviation of the system's performance indicators from the baseline state under uncertain scenarios, the worse its robustness. However, for some performance indicators of hydro-wind-solar hybrid systems, such as power generation, the greater the deviation when the value is lower than the baseline state, the worse the robustness; but when the value is higher than the baseline state, the greater the deviation, the better the robustness. Therefore, the traditional regret index R is not entirely applicable to this study and needs to be improved. The improved regret index Rm is shown in the following formula. The improved regret index Rm indicates that the greater the deviation of the complementary system's performance indicators from the baseline state, the worse its robustness. For some indicators where smaller values ​​are better (such as curtailment rate), the index value needs to be negative, transforming it into a target where larger values ​​are better.

[0080]

[0081] (2) Satisfaction index S1

[0082] The satisfaction index S1 is defined as the degree to which the most important performance indicator of a complementary system can meet needs (maintaining above 95% of the baseline level) under conditions of uncertainty. The expression for S1 is as follows:

[0083]

[0084] In the formula: Λ j =1 indicates that the performance index of the complementary system can meet the requirements under the j-th uncertainty scenario, and vice versa. s,j =0 indicates that the demand is not met; N represents the sequence length, and J represents the total number of uncertain scenarios. For the satisfaction index S1, the most important step is to determine the most important performance index of the complementary system. The main function of the hydro-wind-solar complementary system is power generation and supply; ensuring the "quality" and "quantity" of power supply is paramount. Therefore, this study selects the total power generation index and the total power generation guarantee rate index to calculate the satisfaction index S1.

[0085] (3) Satisfaction index S2

[0086] The satisfaction index S2 is defined as the degree to which the minimum requirements of system performance are not violated under conditions of uncertainty. The expression for the satisfaction index S2 is as follows:

[0087]

[0088] In the formula: F(x) represents the number of uncertain scenarios in which the system's minimum requirements are not violated. j Let represent the performance index of the hydro-wind-solar hybrid system under the j-th uncertainty scenario, r represent the probability of meeting the demand, α represent the maximum uncertainty range within which a certain performance index of the system can be accepted without falling below a threshold, and j∈(α) indicate that uncertainty scenario j belongs to the range α. This study selects the power generation guarantee rate index to calculate the satisfaction index S2. A power generation guarantee rate greater than 90% of the baseline state under uncertainty scenarios indicates that the system's minimum demand is not compromised, i.e., r * The value is given as 0.9.

[0089] This invention provides a robustness evaluation system for a hydro-wind-solar hybrid system, comprising:

[0090] The time series simulation module is used to simulate the runoff time series and wind and solar power output time series in a water-wind-solar hybrid system under future conditions, and to obtain the threshold for changes in the average values ​​of the runoff time series and wind and solar power output time series; the runoff time series is the water resource part used to record the changes in river flow over time, and the wind and solar power output time series is the wind and solar resource part used to record the changes in the output power of wind power generation and solar power generation over time.

[0091] The uncertainty scenario set generation module is used to generate uncertainty scenario sets for multiple water, wind and solar resource sequences based on the threshold of changes in the average values ​​of runoff time series and wind and solar power output time series, using a multivariate stochastic simulation method. The uncertainty scenario sets of water, wind and solar resource sequences are used to describe combinations of different runoff time series and wind and solar power output time series.

[0092] The scheduling rule acquisition module is used to construct a medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid systems, with the objective functions of maximizing total power generation and maximizing total power generation guarantee rate, and with constraints such as water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity. The historical scheduling results of the hydro-wind-solar hybrid system are substituted into the medium- and long-term stochastic optimization scheduling model and the model is optimized using stochastic dynamic programming to obtain the medium- and long-term scheduling rules for hydro-wind-solar hybrid systems.

[0093] The scheduling evaluation module is used to simulate the medium- and long-term scheduling process of the water-wind-solar complementary system under uncertain scenario sets of multiple water-wind-solar resource sequences according to the medium- and long-term scheduling rules, and obtain the scheduling results; obtain the robustness index of the water-wind-solar complementary system based on the scheduling results, and analyze the sensitivity using the global sensitivity analysis method based on the robustness index to complete the evaluation.

[0094] A specific example is as follows:

[0095] 1. A multi-objective cuckoo algorithm was used to optimize the parameters of a two-parameter monthly water balance model. The objective functions were to maximize the Nash efficiency coefficient (NSE), the multi-year average relative error (RE), and the root mean square error (RMSE) between measured and simulated runoff during the model calibration period (1961-1998). The model validation period was from 1999 to 2014. Global-scale model data from CMIP6 were selected, including four emission scenarios (SSP1-2.6 (low-carbon emission scenario, radiative forcing of 2.6 W / m²)). 2 SSP2-4.5 (medium carbon emission scenario, radiative forcing of 4.5 W / m²) 2 SSP3-7.0 (medium-to-high carbon emission scenario, radiative forcing of 7.0 W / m²) 2 ) and SSP5-8.5 (high carbon emission scenario, radiative forcing of 8.5 W / m) 2 Precipitation and evaporation data under the given conditions are substituted into the optimized two-parameter monthly water balance model. Combined with the watershed soil moisture content, and based on the water balance principle, the watershed runoff is simulated using generalized or empirical methods.

[0096] By substituting temperature and solar radiation data under climate change conditions into the photovoltaic power output calculation model, the photovoltaic power output sequences under different future carbon emission scenarios and different GCM modes are simulated and generated.

[0097] By substituting wind speed data under climate change conditions into the wind power output calculation model, wind power output sequences under different future carbon emission scenarios and different GCM modes are simulated and generated.

[0098] 2. Based on the joint probability model and the Latin hypercube random sampling method, uncertainty scenarios for wind power and solar power output sequences are generated. The historical runoff sequence is represented as X, and the wind power / solar power output sequence is represented as Y. The uncertainty changes in sequences X and Y are represented as a% and b%, respectively. The change thresholds a and b are obtained from the previous step. The specific steps are as follows:

[0099] (1) Scaling the historical runoff and wind / photovoltaic series by the same factor can be expressed as:

[0100] X' = ​​X*(1-a%); Y' = Y*(1-b%) where X' and Y' represent the scaled runoff and wind / solar power output sequences, respectively.

[0101] (2) Marginal probability distribution fitting:

[0102] Eight theoretical probability distribution functions were selected to fit the probability distributions of X' and Y' sequences respectively. The AIC information criterion was used to quantitatively describe the differences between the theoretical and empirical probability distributions of different types. The optimal theoretical probability distribution type and parameters of X' and Y' sequences were selected as their marginal probability distributions.

[0103] (3) Construction of joint probability distribution

[0104] The joint probability distribution functions of the X' and Y' sequences are constructed based on three types of Archimedes Copula functions. The optimal joint probability distribution function type and parameters are obtained by using the squared Euclidean distance method, that is, by calculating the squared Euclidean distance d between the theoretical Copula and the empirical Copula. The smaller d is, the better the corresponding Copula type.

[0105] 3. A medium- to long-term stochastic optimization scheduling model for hydro-wind-solar hybrid systems considering multi-source uncertainties is constructed. The primary task of the hydro-wind-solar hybrid system is power supply; therefore, maximizing power generation is selected as the first objective of the medium- to long-term optimization scheduling model. Simultaneously, under uncertain conditions, wind and solar power exhibit strong stochasticity, which can easily affect the power supply reliability of the hybrid system; therefore, the total power generation guarantee rate is selected as the second objective.

[0106] Objective 1: Maximize total power generation

[0107]

[0108] Objective 2: Maximize the total power generation guarantee rate

[0109]

[0110] In the formula: E represents the total power generation of the hydro-wind-solar hybrid system; T represents the total dispatch period; This represents the total output of the complementary system during time period t; This represents the power output of the m-th cascade hydropower station during time period t; and These represent the output of photovoltaic and wind power respectively during time period t; f represents the power output of wind and solar power curtailment during period t; EC (·) represents the obtained curtailment rate response function; ΔT represents the dispatch period length; F represents the complementary system generation guarantee rate; This indicates the total guaranteed output of the complementary system; This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the scheduling period.

[0111] In this study, a penalty function is used to process the second objective function, transforming the two-objective optimization problem into a single-objective problem. The processed objective function can be expressed as:

[0112]

[0113] In the formula: The penalty function; α represents the penalty coefficient, which is usually a very large positive number. In this study, α = 10. 8 .

[0114] The model needs to meet the following constraints:

[0115] (1) Water balance constraint:

[0116] V m,t+1 =V m,t +3600(I m,t -Q m,t -EI m,t )ΔT

[0117] (2) Storage capacity constraints:

[0118]

[0119] (3) Reservoir discharge constraints:

[0120]

[0121] (4) Hydropower output constraints:

[0122]

[0123] (5) Conveying channel capacity constraints:

[0124]

[0125] In the formula: V m,t and V m,t+1 Let I represent the reservoir capacity of the m-th cascade reservoir at time t and time t+1, respectively; m,t Q m,t and EI m,t These represent the inflow, outflow, and evaporation / seepage loss of the m-th cascade reservoir at time t. and These are the minimum and maximum reservoir capacities of the m-th cascade reservoir during time period t, respectively. and These are the minimum and maximum discharge flows of the m-th cascade reservoir, respectively. and These are the lower and upper limits of the output of the m-th cascade hydropower station during time period t, respectively. The capacity of the transmission channel for the multi-energy complementary system.

[0126] 4. A medium- to long-term optimal scheduling model for water, wind, and solar power is solved using stochastic dynamic programming. Medium- to long-term scheduling rules are generated based on historical runoff and wind and solar power output sequences. These rules are then simulated under a set of uncertain scenarios related to water, wind, and solar power to verify the system's robustness. Stochastic dynamic programming is an explicit stochastic optimization method that aims to characterize the uncertainty of random variables through probability distribution patterns and use these patterns as input to an optimal scheduling model to obtain uncertain medium- to long-term scheduling rules.

[0127] The specific explanation is as follows:

[0128] The reservoir water level is used as the state variable in the medium- to long-term scheduling model to describe the system state. In addition, the state variables include other stochastic inputs: reservoir inflow, photovoltaic (PV) and wind power output. A Markov chain-based probability transition matrix is ​​used to consider the transition probability between two adjacent scheduling periods, obtained by calculating the number of intervals in historical data divided by discrete eigenvalues. Finally, stochastic dynamic programming generates corresponding scheduling decisions for each combination of state variables in adjacent periods, rather than a single scheduling line. The decision variable (final reservoir capacity) can be expressed as a function of the state variables (reservoir capacity, inflow, PV and wind power output).

[0129]

[0130] In the formula: Z t+1 and Z t Indicates the water level at the end of time period t and the water level at the beginning of time period t; I t This represents the reservoir runoff input during time period t; and These represent the photovoltaic and wind power output during time period t, respectively.

[0131] Assuming that runoff and solar / wind power output are correlated, the recurrence equation for complementary optimization considering correlated inflow and solar / wind power output in a stochastic dynamic programming approach can be expressed as:

[0132]

[0133] In the formula: f t (·) and f t+1 (·) represent the expected power generation of the hydro-wind-solar hybrid system from time period t and time period t+1 to the end of the time period, respectively; B t (·) represents the system power generation during time period t; S t,i S represents the i-th discrete eigenvalue of the state variable during time period t; t+1,j p(S) represents the j-th discrete eigenvalue of the state variable during time period t+1;t+1,j |S t,i ) represents the transition probability of the state variable from the i-th discrete state to the j-th discrete state from time period i to time period i+1.

[0134] Since only the Cihaxia Reservoir in this study possesses seasonal regulation capabilities, its water level is used as the state variable in the medium- to long-term scheduling model to describe the system state. In addition, the state variables include other stochastic inputs: reservoir inflow, photovoltaic (PV) and wind power output. In the stochastic dynamic programming method, probability distribution fitting and representative probability interval extraction are used to discretize these state variables. Furthermore, a probability transition matrix based on a Markov chain is employed to consider the transition probability between two adjacent scheduling periods, obtained by calculating the number of intervals in historical data divided by discrete eigenvalues. Finally, stochastic dynamic programming generates corresponding scheduling decisions for each combination of state variables in adjacent periods, rather than a single scheduling line. The decision variable (final reservoir capacity) can be expressed as a function of the state variables (reservoir capacity, inflow, PV, and wind power output):

[0135]

[0136] In the formula: Z t+1 and Z t Indicates the water level at the end of time period t and the water level at the beginning of time period t; I t This represents the reservoir runoff input during time period t; and These represent the photovoltaic and wind power output during time period t, respectively.

[0137] This chapter assumes that runoff and photovoltaic / wind power output are correlated. In the stochastic dynamic programming approach, the recursive equation considering the correlated inflow and complementary optimization of photovoltaic / wind power output can be expressed as:

[0138]

[0139] In the formula: f t (·) and f t+1 (·) represent the expected power generation of the hydro-wind-solar hybrid system from time period t and time period t+1 to the end of the time period, respectively; Bt(·) represents the system power generation during time period t; S t,i S represents the i-th discrete eigenvalue of the state variable during time period t; t+1,j p(S) represents the j-th discrete eigenvalue of the state variable during time period t+1; t+1,j |S t,i ) represents the transition probability of the state variable from the i-th discrete state to the j-th discrete state from time period i to time period i+1.

[0140] To verify the effectiveness of the long-term optimal scheduling model for hydropower-wind-solar hybrid power, this chapter also compares it with the individual optimal scheduling method for cascade hydropower stations, which is also solved using stochastic dynamic programming. The detailed comparison of the two scheduling schemes and the detailed expressions of the state transition equations are shown in Table 2, which presents the recursive equations solved by stochastic dynamic programming under the individual hydropower scheduling scheme and the hydropower-wind-solar hybrid scheduling scheme.

[0141] Table 2. Recursive equations solved using stochastic dynamic programming under hydropower standalone and hydro-wind-solar complementary scheduling schemes.

[0142]

[0143] f t (·) and f t+1 (·) represent the expected power generation of the hydro-wind-solar hybrid system from time period t and time period t+1 to the end of the time period, respectively; B t (·) represents the system power generation during time period t; Z t,k Z represents the k-th discrete characteristic water level in time period t; t+1,l I represents the l-th discrete characteristic water level in time period t+1; t,i I represents the i-th characteristic discrete runoff in time period t; t+1,j This represents the j-th characteristic discrete runoff in time period t+1; This represents the d-th characteristic discrete photovoltaic output during time period t; This represents the e-th characteristic discrete photovoltaic output during time period t+1; This represents the r-th characteristic discrete wind power output during time period t; represents the s-th characteristic discrete wind power output in time period t+1; m, n, and o represent the number of characteristic discrete values ​​of runoff, photovoltaic output, and wind power output, respectively. Represents the state variable I during time period t. t,i , Transition to state variable I in time period t+1 t+1,j , The transition probability.

[0144] This chapter samples several characteristic discrete values ​​from theoretical probability distributions to represent the uncertainties in runoff, wind power, and solar power output. That is, a series of characteristic discrete values ​​are used to represent a known continuous probability distribution. Eight theoretical probability distribution types are selected to characterize historical runoff and wind / solar power output sequences, and the optimal probability distribution and corresponding parameters are selected based on the AIC information criterion. Several characteristic discrete values ​​for runoff / wind power output / solar power output are obtained through the following steps: First, all runoff / wind power output / solar power output data within the t-th scheduling period (month) are used to fit the distribution to obtain parameter values. Then, assuming there are m characteristic discrete values, their theoretical cumulative probabilities are 1 / (m+1), 2 / (m+1), ..., m / (m+1). Finally, the characteristic discrete value corresponding to each probability is derived from the probability distribution function.

[0145] 5. Select a basic performance indicator system for constructing robustness evaluation indicators, including:

[0146] (1) Water and electricity (HP):

[0147]

[0148] In the formula: Let t represent the output of the m-th cascade hydropower station during time period t; M represents the number of cascade hydropower stations; T represents the total number of time periods; and ΔT represents the length of the time period.

[0149] (2) Total power generation (TP):

[0150]

[0151] In the formula: and These represent the photovoltaic and wind power outputs during time period t, respectively. This represents the power output of wind and solar power curtailment during time period t.

[0152] (3) Water rejection rate (SWR):

[0153]

[0154] In the formula: Q represents the power generation flow of the m-th cascade reservoir during time period t; m,t This represents the discharge flow of the m-th cascade reservoir during time period t.

[0155] (4) Curtailment Rate (CER) of wind and solar power:

[0156]

[0157] In the formula: and These represent the photovoltaic and wind power output during time period t, respectively.

[0158] (5) Total Generation Guarantee Rate (TGR):

[0159]

[0160] In the formula: This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the scheduling period.

[0161] Based on the above fundamental performance indicators, several robust evaluation indicators were constructed, including:

[0162] (1) Improved regret index Rm

[0163] The traditional regret index R is typically used to characterize the degree to which a system deviates from its historical baseline state under uncertain disturbances. It is defined as the extent to which the performance index, under uncertain conditions, with the largest deviation meets the requirements (maintaining above 95% of the baseline level), and is expressed as:

[0164]

[0165] In the formula: F(x) i,j D represents the i-th basic performance index value of the complementary system under the j-th uncertainty scenario; i,j Represents F(x) i,j and The deviation between; R is between 0 and 1, and the closer it is to 0, the stronger the system's robustness.

[0166] The regret index R above indicates that the greater the deviation of the system's performance indicators from the baseline state under uncertain scenarios, the worse its robustness. However, for some performance indicators of hydro-wind-solar hybrid systems, such as power generation, the greater the deviation when the value is lower than the baseline state, the worse the robustness; but when the value is higher than the baseline state, the greater the deviation, the better the robustness. Therefore, the traditional regret index R is not entirely applicable to this study and needs to be improved. The improved regret index Rm indicates that the greater the deviation of the complementary system's performance indicators from the baseline state, the worse the robustness. For some indicators where smaller values ​​are better (such as curtailment rate), the index value needs to be negative, transforming it into a target where larger values ​​are better.

[0167]

[0168] (2) Satisfaction index S1

[0169] The satisfaction index S1 is defined as the degree to which the most important performance indicator of a complementary system can meet needs (maintaining above 95% of the baseline level) under conditions of uncertainty. The expression for S1 is as follows:

[0170]

[0171] In the formula: Λ j =1 indicates that the performance index of the complementary system can meet the requirements under the j-th uncertainty scenario, and vice versa. s,j =0 indicates that the demand is not met. For the satisfaction index S1, the most important step is to determine the most important performance indicators of the complementary system. The main function of the hydro-wind-solar complementary system is power generation and supply; ensuring the "quality" and "quantity" of power supply is paramount. Therefore, this study selects the total power generation index and the total power generation guarantee rate index to calculate the satisfaction index S1.

[0172] (3) Satisfaction index S2

[0173] The satisfaction index S2 is defined as the degree to which the minimum requirements of system performance are not violated under conditions of uncertainty. The expression for the satisfaction index S2 is as follows:

[0174]

[0175] In the formula: This represents the number of uncertain scenarios in which the system's minimum demand is not violated. This study selects the power generation guarantee rate as the indicator to calculate the satisfaction index S2. A power generation guarantee rate greater than 90% of the baseline state under uncertain scenarios indicates that the system's minimum demand is not violated, i.e., r... * The value is given as 0.9.

[0176] 6. The influence of different sensitivity factors on the model output is quantified by global sensitivity analysis to assess the robustness of the model output to various sources of uncertainty.

[0177] In global sensitivity analysis, the most commonly used method is variance-based sensitivity analysis, which quantifies the model's sensitivity to factors by measuring the variance of the model's output under uncertain input conditions. Therefore, this chapter uses the wind-solar installed capacity ratio and total installed capacity as model sensitivity factors, and three robustness indicators as model outputs. It statistically analyzes the variance results of each indicator under a set of uncertain scenarios to examine the sensitivity of the robustness of the hydro-wind-solar hybrid system to the wind-solar installed capacity ratio and total installed capacity. The robustness sensitivity of the hydro-wind-solar hybrid system to each factor can be expressed as:

[0178] Sen Rm =std(Rm us ),Sen S1 =std(S1) us ),Sen S2 =std(S2) us )

[0179] In the formula: Sen Rm Sen S1 , and Sen S2This indicates the sensitivity of the hydro-wind-solar hybrid system to uncertainty factors when the robustness of the system is characterized by the indices Rm, S1, and S2, respectively; us represents the uncertainty scenario of the wind-solar installed capacity ratio or total installed capacity factor.

[0180] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A robustness evaluation method for a hydro-wind-solar hybrid system, characterized in that, Includes the following steps: The study simulates the runoff time series and wind and solar power output time series in a water-wind-solar hybrid system under future conditions, and obtains the threshold for changes in the average values ​​of the runoff time series and wind and solar power output time series. The runoff time series is the water resource part used to record the changes in river flow over time, and the wind and solar power output time series is the wind and solar resource part used to record the changes in the output power of wind power generation and solar power generation over time. Based on the threshold of changes in the average values ​​of runoff time series and wind and solar power output time series, a multivariate stochastic simulation method is used to generate multiple uncertainty scenario sets of water, wind and solar resource series. The uncertainty scenario sets of water, wind and solar resource series are used to describe the combination of different runoff time series and wind and solar power output time series. With the objective functions of maximizing total power generation and maximizing the total power generation guarantee rate, and with constraints such as water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity, a medium- and long-term stochastic optimization scheduling model for hydro-wind-solar complementary power generation is constructed. Historical data on runoff, wind, and solar power output are substituted into the medium- and long-term stochastic optimization scheduling model for hydro-wind-solar complementary power generation, and the model is optimized using stochastic dynamic programming to obtain the medium- and long-term scheduling rules for hydro-wind-solar complementary power generation. Based on the medium- and long-term scheduling rules for hydro-wind-solar hybrid systems, the medium- and long-term scheduling process of the hydro-wind-solar hybrid system is simulated under multiple uncertainty scenarios of hydro-wind-solar resource sequences, and the scheduling results are obtained. Based on the scheduling results, the robustness index of the hydro-wind-solar hybrid system is obtained, and the sensitivity is analyzed using the global sensitivity analysis method based on the robustness index to complete the evaluation. The uncertainty scenario set generated using a multivariate stochastic simulation method for multiple water, wind, and solar resource sequences specifically includes: A set of uncertain scenarios for multiple water, wind, and solar resource sequences was generated using a joint probability model and a Latin hypercube random sampling method. The objective functions, which are to maximize total power generation and total power generation guarantee rate, include the following steps: Objective function 1: Maximize total power generation Objective function 2: Maximize the total power generation guarantee rate Where E represents the total power generation of the hydro-wind-solar hybrid system, and T represents the total dispatch period; This represents the total output of the hydro-wind-solar hybrid system during time period t; This represents the power output of the m-th cascade hydropower station during time period t; and These represent the output of photovoltaic and wind power respectively during time period t; f represents the power output of wind and solar power curtailment during time period t; EC (·) represents the obtained curtailment rate response function; ΔT represents the dispatch period length; F represents the complementary system generation guarantee rate; This indicates the total guaranteed output of the complementary system; This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the dispatch period, and M represents the number of hydropower stations. The specific steps for obtaining the robustness index of the hydro-wind-solar hybrid system include: The robustness indicators of the scheduling results are analyzed, and the robustness indicators include: The improved regret index Rm is calculated as follows: Where, F(x) i,j D represents the i-th basic performance index value of the hydro-wind-solar hybrid system under the j-th uncertainty scenario; i,j Represents F(x) i,j and The deviation between Let x represent the target value at baseline, and D represent the time series. i D represents the performance deviation value of the i percentile. i,95 D represents the performance deviation value at the 95th percentile. i,90 This represents the performance deviation value at the 90th percentile. Satisfaction index S1, the formula is: Among them, Λ j Let Λ represent the j-th uncertainty scenario of the hydro-wind-solar hybrid system. j A value of Λ indicates that the performance index meets the requirements for this uncertain scenario; otherwise, Λ j =0 indicates that the performance indicators of this uncertainty scenario do not meet the requirements, N represents the sequence length, and J represents the total number of uncertainty scenarios; Satisfaction index S2, the formula is: in, F(x) represents the number of uncertain scenarios in which the basic requirements of the hydro-wind-solar hybrid system are not compromised. j Let r represent the performance index of the hydro-wind-solar hybrid system under the j-th uncertainty scenario. * Let α represent the probability of meeting the requirements, α represent the maximum uncertainty range that the system can accept for a certain performance index not lower than the threshold, and j∈U(α) means that the uncertainty scenario j belongs to the range α.

2. The robustness evaluation method for a hydro-wind-solar hybrid system as described in claim 1, characterized in that, The acquisition of medium- and long-term scheduling rules for hydro-wind-solar hybrid systems specifically includes: The stochastic dynamic programming method is used to solve the medium- and long-term stochastic optimization scheduling model of water-wind-solar hybrid power generation. The uncertainty of random variables is represented by the probability distribution form and used as the input to optimize the medium- and long-term stochastic optimization scheduling model of water-wind-solar hybrid power generation, so as to obtain the uncertain medium- and long-term scheduling rules of water-wind-solar hybrid power generation.

3. The robustness evaluation method for a hydro-wind-solar hybrid system as described in claim 1, characterized in that, The steps for analyzing sensitivity using a global sensitivity analysis method based on robustness indicators include: Using the wind-solar installed capacity ratio and total installed capacity as sensitive factors in the long-term stochastic optimization scheduling model of hydro-wind-solar hybrid system, and taking the robustness index as the output of the model, the variance results of the robustness index under the uncertainty scenario set are statistically analyzed to analyze the sensitivity of the robustness of the hydro-wind-solar hybrid system to the wind-solar installed capacity ratio and total installed capacity.

4. The robustness evaluation method for a hydro-wind-solar hybrid system as described in claim 1, characterized in that, The specific steps for simulating the runoff time series and wind and solar power output time series of the hydro-wind-solar hybrid system under future conditions include: Obtain global-scale model data for the sixth Coupling Model Intercomparison Project (CMIP6); We selected daily-scale data on precipitation, temperature, relative humidity, solar radiation, and wind speed as variables under 12 climate change models and 4 carbon emission scenarios. Based on daily-scale data, the runoff time series and wind and solar power output time series in the water-wind-solar complementary system under future conditions are simulated using hydrological models and wind-solar resource assessment models, respectively.

5. A robustness evaluation system for a hydro-wind-solar hybrid system, characterized in that, include: The time series simulation module is used to simulate the runoff time series and wind and solar power output time series in a water-wind-solar hybrid system under future conditions, and to obtain the threshold for changes in the average values ​​of the runoff time series and wind and solar power output time series; the runoff time series is the water resource part used to record the changes in river flow over time, and the wind and solar power output time series is the wind and solar resource part used to record the changes in the output power of wind power generation and solar power generation over time. An uncertainty scenario set generation module is used to generate multiple uncertainty scenario sets of water, wind and solar resource sequences using a multivariate stochastic simulation method based on the threshold of changes in the average values ​​of runoff time series and wind and solar output time series. The uncertainty scenario sets of water, wind and solar resource sequences are used to describe the combination of different runoff time series and wind and solar output time series. The scheduling rule acquisition module is used to construct a medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid power generation with the objective functions of maximizing total power generation and maximizing total power generation guarantee rate, and with constraints such as water balance, reservoir capacity, reservoir discharge flow, hydropower output, and transmission channel capacity. Historical data on runoff, wind, and solar power output are substituted into the medium- and long-term stochastic optimization scheduling model for hydro-wind-solar hybrid power generation and the model is optimized using stochastic dynamic programming to obtain the medium- and long-term scheduling rules for hydro-wind-solar hybrid power generation. The scheduling evaluation module is used to simulate the medium- and long-term scheduling process of the water-wind-solar complementary system under uncertain scenario sets of multiple water-wind-solar resource sequences according to the medium- and long-term scheduling rules of water-wind-solar complementary system, and obtain the scheduling results; obtain the robustness index of water-wind-solar complementary system based on the scheduling results, and analyze the sensitivity using the global sensitivity analysis method based on the robustness index to complete the evaluation. The uncertainty scenario set generated using a multivariate stochastic simulation method for multiple water, wind, and solar resource sequences specifically includes: A set of uncertain scenarios for multiple water, wind, and solar resource sequences was generated using a joint probability model and a Latin hypercube random sampling method. The objective functions, which are to maximize total power generation and total power generation guarantee rate, include the following steps: Objective function 1: Maximize total power generation Objective function 2: Maximize the total power generation guarantee rate Where E represents the total power generation of the hydro-wind-solar hybrid system, and T represents the total dispatch period; This represents the total output of the hydro-wind-solar hybrid system during time period t; This represents the power output of the m-th cascade hydropower station during time period t; and These represent the output of photovoltaic and wind power respectively during time period t; f represents the power output of wind and solar power curtailment during time period t; EC (·) represents the obtained curtailment rate response function; ΔT represents the dispatch period length; F represents the complementary system generation guarantee rate; This indicates the total guaranteed output of the complementary system; This indicates the number of periods during which the total output of the hydro-wind-solar hybrid system exceeds the guaranteed output during the dispatch period, and M represents the number of hydropower stations. The specific steps for obtaining the robustness index of the hydro-wind-solar hybrid system include: The robustness indicators of the scheduling results are analyzed, and the robustness indicators include: The improved regret index Rm is calculated as follows: Where, F(x) i,j D represents the i-th basic performance index value of the hydro-wind-solar hybrid system under the j-th uncertainty scenario; i,j Represents F(x) i,j and The deviation between Let x represent the target value at baseline, and D represent the time series. i D represents the performance deviation value of the i percentile. i,95 D represents the performance deviation value at the 95th percentile. i,90 This represents the performance deviation value at the 90th percentile. Satisfaction index S1, the formula is: Among them, Λ j Let Λ represent the j-th uncertainty scenario of the hydro-wind-solar hybrid system. j A value of Λ indicates that the performance index meets the requirements for this uncertain scenario; otherwise, Λ j =0 indicates that the performance indicators of this uncertainty scenario do not meet the requirements, N represents the sequence length, and J represents the total number of uncertainty scenarios; Satisfaction index S2, the formula is: in, F(x) represents the number of uncertain scenarios in which the basic requirements of the hydro-wind-solar hybrid system are not compromised. j Let r represent the performance index of the hydro-wind-solar hybrid system under the j-th uncertainty scenario. * Let α represent the probability of meeting the requirements, α represent the maximum uncertainty range that the system can accept for a certain performance index not lower than the threshold, and j∈U(α) means that the uncertainty scenario j belongs to the range α.

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