Method for evaluating influence of wind and light absorption on operation of hydropower station in consideration of climate change conditions

Abstract

The invention discloses a method for evaluating the influence of wind and light absorption on the operation of a hydropower station by considering climate change conditions, which belongs to the field of reservoir dispatching in hydroelectric power generation, and comprises the following steps: carrying out short-term simulation dispatching on a water-wind-light complementary system, and extracting power abandoning results under different hydropower outputs as a wind and light power abandoning loss function with short-term dispatching characteristics; constructing a target function based on the wind-solar power abandoning loss function, and constructing a medium-and-long-term optimization scheduling model of the water-wind-solar complementary system coupled with short-term scheduling characteristics; analyzing the meteorological data to obtain a climate sudden change year and an extreme weather typical year, and according to the medium-and-long-term optimization scheduling model of the water-wind-light complementary system coupled with the short-term scheduling characteristics, calculating to obtain reservoir water level processes and hydropower station output conditions before and after the climate sudden change year and in the extreme weather typical year; and according to the reservoir water level process before and after the climate sudden change year and the extreme weather typical year and the output condition of the hydropower station, evaluating the influence of wind and light absorption on the operation of the hydropower station.

Description

Technical Field

[0001] This invention belongs to the field of hydropower reservoir scheduling technology, specifically a method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions. Background Technology

[0002] Driven by the "dual carbon" goals, the installed capacity of renewable energy is expanding rapidly. However, the strong volatility and intermittency of wind and solar power limit their grid connection and absorption, resulting in significant curtailment issues. In contrast, hydropower combines rapid response peak-shaving characteristics with reservoir storage advantages, effectively mitigating fluctuations in renewable energy output. Therefore, it is urgent to construct a hydro-wind-solar synergistic system, breaking through the spatial and temporal regulation bottlenecks of single energy sources through multi-energy coupling and bundled transmission, thereby achieving efficient absorption of clean energy and improved system flexibility.

[0003] However, current research focuses mainly on the operation mechanism and capacity configuration optimization of hydro-solar complementarity, with insufficient analysis of the regulation efficiency of wind power participating in multi-energy synergy and its dynamic coupling relationship with hydropower station scheduling parameters. Furthermore, it lacks multi-scenario adaptive analysis on the impact mechanism of wind and solar integration on hydropower output characteristics and reservoir capacity scheduling modes. In particular, it lacks comparative verification of the reliability of hydro-wind-solar systems under extreme weather events and climate change scenarios, indicating that hydro-wind-solar systems are not resilient enough to extreme climate and climate change. Summary of the Invention

[0004] The purpose of this invention is to address the fact that existing research has not adequately considered the impact of wind and solar integration on hydropower station operation under climate change and extreme weather conditions. This invention proposes an assessment method for the impact of wind and solar integration on hydropower station operation that takes into account climate change conditions. By comparing and analyzing the differences in hydropower dispatching and operation strategies before and after climate change abrupt changes and in typical years of extreme weather, and with and without wind and solar integration, this invention reveals the specific impact mechanism of wind and solar integration on hydropower dispatching and operation, providing a scientific basis and technical support for the optimized design and operation management of multi-energy complementary systems of water, wind and solar.

[0005] To address the aforementioned technical problems, this invention provides a method for assessing the impact of wind and solar power integration on hydropower station operation, taking into account climate change conditions, including: S1. Conduct short-term simulation scheduling of the hydro-wind-solar hybrid system, and extract the curtailment results under different hydropower outputs into a wind and solar curtailment loss function with short-term scheduling characteristics. S2. Based on the wind and solar curtailment loss function, construct an objective function and build a long-term optimal scheduling model for the hydro-wind-solar complementary system that couples short-term scheduling characteristics. S3. Analyze meteorological data to obtain the year of climate change and the typical year of extreme weather. Based on the long-term optimization scheduling model of the water-wind-solar complementary system with coupled short-term scheduling characteristics, calculate the reservoir water level process and hydropower station output before and after the year of climate change and the typical year of extreme weather. S4. Based on the reservoir water level process and hydropower station output before and after the climate change year and in typical years of extreme weather, conduct an assessment of the impact of wind and solar power consumption on the operation of the hydropower station.

[0006] Preferably, S1 includes: collecting historical power output data from wind and solar power plants as wind and solar power output sequences, and historical power output data from hydropower plants as the initial total load curve of the system; using the actual hydropower output as the initial total load, and combining different wind and solar power outputs, simulating the daily power generation planning and real-time scheduling process of the complementary system; calculating the sum of hydropower and wind and solar power outputs to obtain the theoretical total system output; if it exceeds the system transmission capacity, cutting off the excess wind and solar power, i.e., wind and solar power curtailment, to obtain the actual grid-connected power; sorting and grouping the grid-connected hydropower output, and calculating the average actual grid-connected hydropower output within each group. and the corresponding wind and solar curtailment rates , denoted as sample point .

[0007] Preferably, the average on-grid output of hydropower within each group is [the following]. Corresponding wind and solar curtailment rates Specifically, it is expressed as follows: ; In the formula: For wind and solar power curtailment rates; Wasted electricity due to wind and solar power; This represents the actual power generation from wind and solar power.

[0008] As a preferred method, the wind and solar power curtailment loss function is obtained by fitting multiple sample points. According to hydropower output Substitute into the wind and solar curtailment loss function The calculated hydropower output of the wind and solar power station is... The rate of power curtailment at that time.

[0009] Preferably, S2 includes: constructing an objective function based on the wind and solar curtailment function, and constructing a long-term optimal scheduling model for the water-wind-solar complementary system that couples short-term scheduling characteristics, with the goal of maximizing the final power generation of the water-wind-solar complementary system while satisfying various constraints.

[0010] Preferably, the objective function is specifically expressed as: ; In the formula: For the power generation of the hydro-wind-solar hybrid system; This represents the total number of hours in the calculation period; Let t be the output of the hydropower station during time period t; The power output of relevant wind and solar power stations in the basin during time period t; This indicates that the power output of the wind and solar power station is... The curtailment rate at that time was obtained based on a short-term simulation model; The calculation period is long.

[0011] Preferably, the constraints are specifically expressed as follows: Water balance equation for a hydroelectric power station: ; In the formula: , They represent hydroelectric power stations. The water storage volume at the beginning and end of the time period is a state variable; Indicates the first Natural inflow into the reservoir during a given period; Indicates the first The average outflow from the reservoir over a given period is the decision variable. Indicates the first The evaporation and seepage flow rates of the reservoir during a given period can be taken as a fixed value; Water storage restrictions for hydroelectric power stations: ; In the formula: This indicates the minimum allowable water storage capacity of a hydropower station, generally corresponding to dead storage capacity; This indicates the maximum water storage capacity allowed for power generation in the reservoir. During the flood season, the reservoir capacity corresponding to the flood limit water level can be used, while during the non-flood season, the reservoir capacity corresponding to the normal high water level can be used. Hydropower station outflow restrictions: ; In the formula: This indicates the lower limit of the water release capacity of a hydropower station, which is generally given by the requirements of downstream irrigation, water supply, or navigation. This indicates the upper limit of water release from a hydropower station, which is generally given based on downstream flood control requirements and the maximum discharge capacity of the reservoir.

[0012] Hydropower station output limitations: ; In the formula: This indicates the lower limit of the hydropower station's output. This indicates the upper limit of the hydropower station's output; it is determined by comprehensively considering factors such as the rated output of the unit, the capacity under obstruction, the vibration zone, and peak-shaving requirements.

[0013] Reservoir boundary conditions: , ; In the formula: Indicates the initial reservoir water level during the scheduling period; Indicates the reservoir water level at the end of the scheduling period; This indicates the reservoir water level during the first period of the calculation. This indicates the reservoir's water level during the last period of the calculation.

[0014] Preferably, in step S3, the specific steps for analyzing meteorological data to obtain years of climate change and typical years of extreme weather are as follows: Sa, using the inflow of hydropower stations, the actual output of wind power stations, and the actual output of photovoltaic power stations as weather index factors for climate change analysis, standardizes the multi-year measured data of each index, determines the weight of each index through principal component analysis (PCA), and adds the corresponding weights of each index to form a comprehensive weather index. ; In the formula: These are the standardized values ​​of the measured data for the corresponding indicators. These are the original values ​​of the measured data for this type of indicator; This represents the average of the measured data for this type of indicator. The standard deviation of the measured data for this type of indicator; Sb, based on the aforementioned comprehensive weather index Construct annual series samples in chronological order Where n represents the total number of years of measured data, forming a continuous time series for climate change detection; Sc. Three methods were used in parallel to detect years of abrupt climate change: Mann-Kendall method: When the UF-UB statistical curves intersect within the critical line corresponding to the significance level of 0.05 and cross the critical line, record the year of the intersection. Sliding T-test: Set a 5-year sliding window. If the two subsequences... The test value satisfies (in If the mutation is identified, then it is determined to be a mutation. Yamamoto method: when signal-to-noise ratio Furthermore, a sudden change is defined as the difference between the means of adjacent time periods exceeding twice the standard deviation. Ultimately, only years detected by at least two methods will be adopted as climate change years. If the change years detected by the three methods are inconsistent, the year with the average of the three results will be selected as the climate change year. Sd. Frequency analysis was performed on the inflow data of hydropower stations and the output data of wind and solar power stations over many years. Extremely low water inflow (inflow ≤ 10% quantile of historical series), extremely high water inflow (inflow ≥ 90% quantile of historical series), extremely low wind and solar power output (actual output of wind and solar power ≤ 10% quantile of historical series), and extremely high wind and solar power output (actual output of wind and solar power ≥ 90% quantile of historical series) were selected as typical years of extreme weather.

[0015] Preferably, the comprehensive weather index is specifically expressed as follows: ; In the formula: For the first Comprehensive weather factors for the year; , , The weights for hydropower station inflow, wind power station actual output, and photovoltaic power station actual output are respectively. For the first Annual hydropower station inflow; For the first Actual power generation of the wind power station in the year; For the first Actual power generation of the photovoltaic power station in that year.

[0016] As a preferred approach, relevant meteorological data from the years before and after climate change and typical years of extreme weather are input into the long-term optimization scheduling model of the water-wind-solar complementary system coupled with short-term scheduling characteristics. Discrete differential dynamic programming is used for optimization to obtain the reservoir water level process and hydropower station output from the years before and after climate change and typical years of extreme weather.

[0017] Preferably, in step S4, the hydropower station is optimized and analyzed in two stages before and after wind and solar power integration: Before wind and solar power integration, optimization calculations are performed based on the hydropower station's operating parameters and the wind and solar inputs of the long-term optimization scheduling model of the hydro-wind-solar complementary system are set to 0. At this time, wind and solar power outputs are not included in the scheduling decision, and a baseline hydropower scheduling scheme is generated; After wind and solar power integration, the real-time output data of the hydropower station and the wind and solar power station are synchronously input into the model constructed by the long-term optimization scheduling model of the hydro-wind-solar complementary system with coupled short-term scheduling characteristics, and a hydropower optimized scheduling scheme including the influence of wind and solar power is generated. The two scheduling schemes were compared and analyzed. Based on the monthly water level of the reservoir before and after the wind and solar power integration, the monthly fluctuation range, and the rate of change of the monthly power output of the hydropower station, the impact of wind and solar power integration on the operation of the hydropower station was evaluated.

[0018] Preferably, the monthly water level change rate of the reservoir is specifically expressed as follows: ; The rate of change of the water level fluctuation range is specifically expressed as follows: ; The rate of change in power output of the hydropower station is specifically expressed as follows: ; In the formula: , , These are the month-end water level change rate of the reservoir, the water level fluctuation range change rate, and the hydropower station output change rate, respectively. , These are the month-end water levels of the reservoir before and after the wind and solar power integration; , These represent the maximum and minimum monthly water levels of the reservoir before the integration of wind and solar power. , These represent the maximum and minimum monthly water levels of the reservoir after the integration of wind and solar power. , These figures represent the monthly power output of the hydropower station before and after the integration of wind and solar power.

[0019] The beneficial effects of this invention are: 1. This invention addresses the issue that existing research has not adequately addressed the impact of wind and solar integration on hydropower station operation under climate change and extreme weather conditions. It proposes a method for assessing the impact of wind and solar integration on hydropower station operation that considers climate change conditions. By comparing and analyzing the differences in hydropower dispatching and operation strategies before and after climate change abrupt changes, in typical years of extreme weather, and with and without wind and solar integration, this invention reveals the specific impact mechanism of wind and solar integration on hydropower dispatching and operation, providing a scientific basis and technical support for the optimized design and operation management of multi-energy complementary systems (hydropower, wind, and solar).

[0020] 2. This study, under climate change and extreme weather scenarios, constructs a sophisticated assessment model and collects and processes detailed meteorological, wind and solar power generation, and hydropower station operation data. It then analyzes in depth the specific impacts of changes in wind and solar power absorption on hydropower station power generation, water level regulation, and equipment operation and maintenance under different extreme scenarios. The results show that fluctuations in wind and solar power absorption not only affect the power generation stability and economic benefits of hydropower stations but also pose new challenges to the overall stability of the power system. Based on these findings, researchers propose a method for assessing the impact of wind and solar power absorption on hydropower station operation that considers climate change conditions.

[0021] 3. This scheme comprehensively selects hydropower station inflow and actual wind and solar power output to construct a comprehensive weather index. It uses three statistical verification methods to detect climate abrupt change points and identifies typical years of various types of extreme weather through frequency ranking analysis. By integrating multi-dimensional information and employing multi-method cross-validation, it improves the accuracy and comprehensiveness of identifying key high-risk climate scenarios (such as the new normal after climate abrupt changes and extreme drought and flood events). This allows the scheme's assessment methodology to focus on climate conditions that threaten the operation of hydropower, wind, and solar power systems, and to deeply analyze the impact of wind and solar power absorption fluctuations on hydropower operation under these scenarios. This provides a reference for the power system to formulate more targeted and resilient climate change risk response strategies. Attached Figure Description

[0022] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the multi-year average inflow process before and after climate change in an embodiment of the present invention; Figure 3 The following is a schematic diagram of the multi-year average wind and solar power output before and after climate change in an embodiment of the present invention: (a) before climate change; (b) after climate change. Figure 4 The following is a diagram illustrating the power output process of the complementary system in a typical year of extreme weather in an embodiment of the present invention: (a) water, wind, and solar power are all scarce; (b) water is scarce, wind and solar power are abundant; (c) water is abundant, wind and solar power are scarce; (d) water, wind, and solar power are all abundant. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] Example 1: As Figure 1 Figure 4 shows the assessment method for the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions, including: S1. Perform short-term simulation scheduling of the hydro-wind-solar hybrid system, and extract the curtailment results under different hydropower outputs into a wind and solar curtailment loss function with short-term scheduling characteristics.

[0025] Specifically, historical power output data from wind and solar power plants are collected as wind and solar power output sequences, while historical power output data from hydropower plants are used as the initial total load curve of the system. Using actual hydropower output as the initial total load, and combining different wind and solar power outputs, the system simulates the daily generation planning and real-time scheduling process of the complementary system. The theoretical total system output is calculated by summing hydropower and wind / solar power outputs. If the output exceeds the system's transmission capacity, the excess wind and solar power is discarded, i.e., wind and solar power curtailment, to obtain the actual grid-connected power. The hydropower grid-connected output is then sorted and grouped, and the average actual grid-connected hydropower output within each group is calculated. and the corresponding wind and solar curtailment rates , denoted as sample point .

[0026] Among them, the average actual grid-connected power output of hydropower in each group Corresponding wind and solar curtailment rates Specifically, it is expressed as follows: ; In the formula: For wind and solar power curtailment rates; Wasted electricity due to wind and solar power; This represents the actual power generation from wind and solar power.

[0027] The wind and solar power curtailment loss function is obtained by fitting multiple sample points. According to hydropower output Substitute into the wind and solar curtailment loss function The calculated hydropower output of the wind and solar power station is... The rate of power curtailment at that time.

[0028] S2. Based on the wind and solar curtailment loss function, construct the objective function and build a long-term optimal scheduling model for the hydro-wind-solar complementary system that couples short-term scheduling characteristics.

[0029] Specifically, S2 includes: Based on the wind and solar curtailment function, an objective function is constructed. Under the condition of satisfying various constraints, a long-term optimal scheduling model for the hydro-wind-solar complementary system is constructed with the goal of maximizing the final power generation of the hydro-wind-solar complementary system and coupled with short-term scheduling characteristics.

[0030] As a preferred option, the objective function is specifically expressed as follows: ; In the formula: For the power generation of the hydro-wind-solar hybrid system; This represents the total number of hours in the calculation period; Let t be the output of the hydropower station during time period t; The power output of relevant wind and solar power stations in the basin during time period t; This indicates that the power output of the wind and solar power station is... The curtailment rate at that time was obtained based on a short-term simulation model; The calculation period is long.

[0031] The specific constraints are as follows: Water balance equation for a hydroelectric power station: ; In the formula: , They represent hydroelectric power stations. The water storage volume at the beginning and end of the time period is a state variable; Indicates the first Natural inflow into the reservoir during a given period; Indicates the first The average outflow from the reservoir over a given period is the decision variable. Indicates the first The evaporation and seepage flow rates of the reservoir during a given period can be taken as a fixed value; Water storage restrictions for hydroelectric power stations: ; In the formula: This indicates the minimum allowable water storage capacity of a hydropower station, generally corresponding to dead storage capacity; This indicates the maximum water storage capacity allowed for power generation in the reservoir. During the flood season, the reservoir capacity corresponding to the flood limit water level can be used, while during the non-flood season, the reservoir capacity corresponding to the normal high water level can be used. Hydropower station outflow restrictions: ; In the formula: This indicates the lower limit of the water release capacity of a hydropower station, which is generally given by the requirements of downstream irrigation, water supply, or navigation. This indicates the upper limit of water release from a hydropower station, which is generally given based on downstream flood control requirements and the maximum discharge capacity of the reservoir.

[0032] Hydropower station output limitations: ; In the formula: This indicates the lower limit of the hydropower station's output. This indicates the upper limit of the hydropower station's output; it is determined by comprehensively considering factors such as the rated output of the unit, the capacity under obstruction, the vibration zone, and peak-shaving requirements.

[0033] Reservoir boundary conditions: , ; In the formula: Indicates the initial reservoir water level during the scheduling period; Indicates the reservoir water level at the end of the scheduling period; This indicates the reservoir water level during the first period of the calculation. This indicates the reservoir's water level during the last period of the calculation.

[0034] S3. Analyze meteorological data to obtain the year of climate change and the typical year of extreme weather. Based on the long-term optimization scheduling model of the water-wind-solar complementary system with coupled short-term scheduling characteristics, calculate the reservoir water level process and hydropower station output before and after the year of climate change and the typical year of extreme weather.

[0035] Specifically, in S3, the steps for analyzing meteorological data to obtain years of climate change and typical years of extreme weather are as follows: S31. The inflow of hydropower stations, the actual output of wind power stations, and the actual output of photovoltaic power stations are used as weather index factors for climate change analysis. The weight of each index is determined by principal component analysis. After assigning corresponding weights to each index, they are added together to form a comprehensive weather index. S32. Based on the comprehensive weather index, construct a weather sample sequence in yearly order as the comprehensive weather index sequence; S33. The climate abrupt change year of the comprehensive weather index sequence is detected by the Mann-Kendall method, the sliding T test and the Yamamoto method. The year detected by at least two methods is finally adopted as the climate abrupt change year. If the abrupt change year detected by the three methods is inconsistent, the year with the average of the three results is selected as the climate abrupt change year. S34. Frequency analysis was performed on the inflow data of hydropower stations and the output data of wind and solar power stations over many years, and the years with extremely low water inflow, extremely high water inflow, extremely low wind and solar power, and extremely high wind and solar power were selected as typical years of extreme weather.

[0036] Specifically, it can be done through the following steps: Sa, using the inflow of hydropower stations, the actual output of wind power stations, and the actual output of photovoltaic power stations as weather index factors for climate change analysis, standardizes the multi-year measured data of each index, determines the weight of each index through principal component analysis (PCA), and adds the corresponding weights of each index to form a comprehensive weather index. ; In the formula: These are the standardized values ​​of the measured data for the corresponding indicators. These are the original values ​​of the measured data for this type of indicator; This represents the average of the measured data for this type of indicator. The standard deviation of the measured data for this type of indicator; The comprehensive weather index is specifically expressed as follows: ; In the formula: For the first Comprehensive weather factors for the year; , , The weights for hydropower station inflow, wind power station actual output, and photovoltaic power station actual output are respectively. For the first Annual hydropower station inflow; For the first Actual power generation of the wind power station in the year; For the first Actual power generation of the photovoltaic power station in that year.

[0037] Sb, based on comprehensive weather indicators Construct annual series samples in chronological order Where n represents the total number of years of measured data, forming a continuous time series for climate change detection; Sc. Three methods were used in parallel to detect years of abrupt climate change: Mann-Kendall method: When the UF-UB statistical curves intersect within the critical line corresponding to the significance level of 0.05 and cross the critical line, record the year of the intersection. Sliding T-test: Set a 5-year sliding window. If the two subsequences... The test value satisfies (in If the mutation is identified, then it is determined to be a mutation. Yamamoto method: when signal-to-noise ratio Furthermore, a sudden change is defined as the difference between the means of adjacent time periods exceeding twice the standard deviation. Ultimately, only years detected by at least two methods will be adopted as climate change years. If the change years detected by the three methods are inconsistent, the year with the average of the three results will be selected as the climate change year. Sd. Frequency analysis was performed on the inflow data of hydropower stations and the output data of wind and solar power stations over many years. Extremely low water inflow (inflow ≤ 10% quantile of historical series), extremely high water inflow (inflow ≥ 90% quantile of historical series), extremely low wind and solar power output (actual output of wind and solar power ≤ 10% quantile of historical series), and extremely high wind and solar power output (actual output of wind and solar power ≥ 90% quantile of historical series) were selected as typical years of extreme weather.

[0038] Relevant meteorological data from years before and after climate change and typical years of extreme weather are input into the long-term optimization scheduling model of the water-wind-solar complementary system coupled with short-term scheduling characteristics, and discrete differential dynamic programming is used for optimization solution.

[0039] The optimization solution of discrete differential dynamic programming here specifically includes: First, assume an initial feasible trajectory and delineate a corridor consisting of finite discrete points in its neighborhood; Next, dynamic programming is used to search for a better trajectory within the corridor. If the new trajectory is significantly better than the old trajectory, the trajectory is updated while maintaining the corridor width; otherwise, the corridor range is reduced. The process is iterated until the corridor width and trajectory change both decrease to the preset accuracy, thus obtaining the optimal solution. This yields the reservoir water level process and hydropower station output before and after climate change and in typical years of extreme weather.

[0040] The Mann-Kendall method, the sliding T-test, and the Yamamoto method are commonly used statistical methods in time series analysis, each with its own unique significance and application scenarios: The Mann-Kendall method is a nonparametric statistical test used to determine the trend direction and significance of time series data. It does not assume that the data follows a specific distribution, therefore it is applicable to various data types, including non-normally distributed data and cases with missing data. The Mann-Kendall method determines the trend of a time series by calculating the S statistic and the standardized Z statistic, and is widely used in climate change research, hydrological analysis, economic data analysis, and environmental monitoring.

[0041] The sliding T-test is a method used to examine whether abrupt changes occur in time series data. It determines whether a change has occurred by comparing the differences in the means of two subsequences. The basic idea of ​​the sliding T-test is to treat the question of whether there is a significant difference between the means of two subsequences in a climate series as a question of whether there is a significant difference between the means of two populations. However, this method requires manually setting the sliding step size, which introduces a degree of subjectivity.

[0042] The Yamamoto method, also known as the signal-to-noise ratio (SNR) method, is a technique used to detect abrupt changes in time series data. It assesses the significance of abrupt changes by calculating the SNR. The basic idea of ​​the Yamamoto method is that if the difference between the mean values ​​of the data before and after a change point is sufficiently large, and this difference exceeds the level of random noise, then that point can be considered a significant change point.

[0043] Using the Mann-Kendall method, the sliding T-test, and the Yamamoto method in parallel to detect abrupt climate change years has many beneficial effects.

[0044] First, improving detection accuracy is crucial. Each of these three methods has its own characteristics: the Mann-Kendall method is suitable for detecting trends and abrupt changes in time series data; the sliding T-test identifies mean abrupt changes by performing a two-sample T-test with a moving window; and the Yamamoto method detects abrupt changes by comparing the variances of preceding and following series. Using these methods in parallel allows for mutual validation and complementarity, thereby improving the accuracy of abrupt change detection.

[0045] Secondly, it enhances the reliability of the results: comprehensive analysis using multiple methods can reduce the potential for misjudgments from a single method. For example, the sliding T-test can verify whether the mutation points detected by the Mann-Kendall method truly exist, while the Yamamoto method can provide evidence of mutation points from another perspective.

[0046] Furthermore, they offer a more comprehensive analytical perspective: each method analyzes climate data from a different angle; the Mann-Kendall method focuses on trend changes, the sliding T-test focuses on mean changes, and the Yamamoto method focuses on variance changes. Combining these methods allows for a more comprehensive understanding of the characteristics and patterns of climate change.

[0047] Finally, it can be adapted to different climate variables: different climate variables (such as temperature, precipitation, etc.) may exhibit different characteristics of change. Using these methods in parallel can better adapt to the detection needs of different climate variables. For example, abrupt temperature changes may be more suitable for detection using the Mann-Kendall method and the sliding T-test, while abrupt precipitation changes may be more suitable for detection using the Yamamoto method.

[0048] S4. Based on the reservoir water level process and hydropower station output before and after climate change years and typical years of extreme weather, conduct an assessment of the impact of wind and solar power consumption on the operation of hydropower stations.

[0049] In S4, the optimization scheduling and analysis of the hydropower station are carried out in two stages before and after wind and solar power integration: Before wind and solar power integration, the optimization calculation is performed based on the hydropower station's operating parameters through a preset model and the wind and solar power input of the model constructed in this patent is set to 0. At this time, the wind and solar power output is not included in the scheduling decision, and a baseline hydropower scheduling scheme is generated; After wind and solar power integration, the real-time output data of the hydropower station and the wind and solar power station are synchronously input into the model constructed by the long-term optimization scheduling model of the water-wind-solar complementary system with coupled short-term scheduling characteristics, and a hydropower optimization scheduling scheme including the influence of wind and solar power is generated.

[0050] The two scheduling schemes were compared and analyzed. Based on the monthly water level of the reservoir before and after the wind and solar power integration, the monthly fluctuation range, and the rate of change of the monthly power output of the hydropower station, the impact of wind and solar power integration on the operation of the hydropower station was evaluated.

[0051] As a preferred option, the monthly water level change rate of the reservoir is specifically expressed as follows: ; The rate of change of water level fluctuation range is specifically expressed as follows: ; The rate of change of power output of a hydropower station is specifically expressed as follows: ; In the formula: , , These are the month-end water level change rate of the reservoir, the water level fluctuation range change rate, and the hydropower station output change rate, respectively. , These are the month-end water levels of the reservoir before and after the wind and solar power integration; , These represent the maximum and minimum monthly water levels of the reservoir before the integration of wind and solar power. , These represent the maximum and minimum monthly water levels of the reservoir after the integration of wind and solar power. , These figures represent the monthly power output of the hydropower station before and after the integration of wind and solar power.

[0052] This invention addresses the issue that existing research has not adequately addressed the impact of wind and solar integration on hydropower station operation under climate change and extreme weather conditions. It proposes a method for assessing the impact of wind and solar integration on hydropower station operation, taking into account climate change conditions. By comparing and analyzing the differences in hydropower dispatching and operation strategies before and after climate change abrupt changes, in typical years of extreme weather, and with and without wind and solar integration, the invention reveals the specific impact mechanism of wind and solar integration on hydropower dispatching and operation, providing a scientific basis and technical support for the optimized design and operation management of multi-energy complementary systems (hydropower, wind, and solar).

[0053] Example 2. Using the inflow process of the Wudongde Hydropower Station from 1980 to 2020 (a total of 41 years), the maximum operating water level of the reservoir, and the power output of the wind and solar power stations on the left and right banks of the Wudongde Hydropower Station as initial conditions, the above-mentioned method for assessing the impact of wind and solar power integration considering climate change on the operation of the hydropower station was used to evaluate the optimal scheduling of the Wudongde Hydropower Station in two stages: before and after climate change and under extreme weather scenarios, and before and after wind and solar power integration. The results are consistent with the actual situation, indicating that the method for assessing the impact of wind and solar power integration considering climate change on the operation of the hydropower station provided in this example is reasonable and effective.

[0054] Based on the inflow process of the Wudongde Hydropower Station and the power output of the right bank wind and solar power station, the year of climate change abruptly determined using the Mann-Kendall method and the sliding T-test was 2001. Therefore, 1980–2000 represents the period before climate change, and 2001–2020 represents the period after climate change. The inflow of the Wudongde Hydropower Station and the power output of the right bank wind and solar power station before and after climate change are statistically analyzed. The multi-year average inflow process and total wind and solar power output process for each month are as follows: Figure 1-2 As shown.

[0055] Overall, the complementarity of water, wind and solar resources before and after climate change is as follows: in winter, the inflow into the reservoir decreases by 7% to 9%, but the output of wind and solar power increases by 7% to 10%; in summer, the complementarity weakens and energy decreases simultaneously, such as in July when the inflow of hydropower into the reservoir decreases by 16% while the output of wind and solar power decreases by 3.3%.

[0056] Based on the years of climate change, the following two tables show the multi-year average monthly water level results and change rate, water level fluctuation range and change rate of the reservoir before and after climate change in the Wudongde right bank water-wind-solar complementary system.

[0057] Table 1. Multi-year average monthly water level results and change rate before and after climate change Table 2. Range and rate of change of water level before and after climate change Overall, the average annual water level increased slightly from 965.88 meters to 966.43 meters after climate change. Water levels generally declined from January to March, with the largest drop in March. Water levels rose significantly from May to June, increasing by 0.3531% and 0.3877% in May and June respectively, with a slight increase of 0.58% in April and 0.0664% in September. Water levels remained largely unchanged from July to August and from October to December. The most dramatic fluctuations occurred in March and May to June: the drop in March was far greater than in other months, while the rise in May to June was significant.

[0058] Following climate change, the water level of the Wudongde Hydropower Station reservoir dropped from January to March, with the most significant drop in March, despite a surge in inflow during the same period. The water level rose in May and June, reflecting the reservoir's efforts to store water in advance to cope with reduced summer wind and solar power output in the face of reduced inflow. In January, December, and August, the water level was strictly controlled.

[0059] The following table shows the multi-year average power output of the Wudongde right bank hydro-wind-solar hybrid system before and after climate change, based on the years of climate change.

[0060] Table 3. Multi-year average power output and rate of change of the system before and after climate change. Overall, the average annual output of the Wudongde right bank hydro-wind-solar hybrid system decreased from 4822.80 MW to 4683.53 MW after climate change. However, the output increased during the non-flood season (January-May), with increases of 19.26% and 24.07% in January and February, respectively, and 17.08% in March. During the flood season (June-October), the output decreased, with the largest decrease in July (-11.85%), followed by October (-11.39%). The output decreased by 4.89%-6.55% in November and December, consistent with the trend of decreasing average annual output.

[0061] Following climate change, the proportion of hydropower output increases during winter (January-February) and early spring (March), while wind and solar power output increases slightly, resulting in a significant increase in total output. However, in April-May, the proportion of hydropower output decreases, while wind and solar power output remains stable, slowing the overall growth rate. From June to August, the proportion of hydropower output shrinks sharply, and wind and solar power output declines simultaneously, leading to a significant decrease in total output. From November to December, hydropower output decreases, but the proportion of wind and solar power output increases, indicating a good complementary effect between hydropower, wind power, and solar power.

[0062] In summary, under climate change, the total output of the Wudongde hydro-wind-solar hybrid system increases significantly during the non-flood season, mainly due to the simultaneous increase in hydropower and wind / solar output; while the total output decreases across the board during the flood season, mainly due to the simultaneous decrease in hydropower and wind / solar output; the increase in wind / solar output in winter (November-December) partially compensates for the decrease in hydropower; hydropower output increases in spring (March), but wind / solar output decreases; and the output of hydropower, wind / solar power all decreases in summer (July).

[0063] Based on the quantile selection requirements for typical extreme weather years, typical years of abundant and scarce water, wind, and solar energy were obtained after frequency sorting: water, wind, and solar energy all scarce (2017), water scarce, wind and solar energy abundant (2019), water abundant, wind and solar energy scarce (2000), and water, wind, and solar energy all abundant (2005). The operation of the multi-energy complementary system was then compared and analyzed.

[0064] The following chart shows the annual inflow process for each typical year.

[0065] Table 4. Typical Inflow Processes in Extreme Weather Years The monthly water levels of the reservoirs in the Wudongde right bank hydro-wind-solar hybrid system were compared in typical years of extreme weather events, and the results are shown below.

[0066] Table 5 Comparison of water level results at the end of the month in typical years with extreme weather In June and July, water levels were strictly controlled at 952 meters in all typical years, indicating that water level management prioritizes flood control safety. Significant differences in water levels were observed in typical years from January to May: In years with abundant water and low wind and solar power, the water level rose to 974.89 meters in May, corresponding to a larger inflow and lower wind and solar power output, possibly due to reduced water release to conserve energy. Conversely, in years with abundant water and low wind and solar power, although the inflow was higher in May, the wind and solar power output reached 687.50 MW, and the water level rose slightly to 945.28 meters, reflecting that wind and solar power output alleviated the pressure on hydropower water release. Differences were observed in typical years from August to December: In years with abundant water and low wind and solar power, the water level was lower in August, with an inflow of 9813.59 m³. 3 / s, but the wind and solar power output is only 306.13 MW, requiring continuous water release to balance the power output demand; in years with abundant water, wind, and solar power, both the inflow and the wind and solar power output are relatively high, and the water level recovers to 975 meters. The annual average water level ranking (year with abundant water, wind, and solar power > year with abundant water, wind, and solar power but low water level > year with abundant water, wind, and solar power but low water level > year with low water, wind, and solar power but abundant water level) is strongly correlated with the annual average inflow. Although the annual inflow in years with abundant water, wind, and solar power is lower than that in years with abundant water, wind, and solar power but low water level, the annual average water level is higher, indicating that when the inflow is abundant, the positive effect of wind and solar power output on water storage is greater than the effect of the inflow; the water level is lowest in years with low water, wind, and solar power but abundant water level, indicating that when water is scarce, the increase in wind and solar power output cannot completely compensate for the impact of insufficient inflow on water storage.

[0067] The power output processes of the Wudongde right bank hydro-wind-solar hybrid system were compared under typical extreme weather years, and the results are as follows: Figure 4 As shown.

[0068] It should be noted that the specific interpretation of "water, scenery, and withering" here is as follows: If both extremely low water inflow and extremely low scenic views are met, it can be considered a situation where both water and scenic views are depleted; if both extremely low water inflow and extremely abundant scenic views are met, it can be considered a situation where water is depleted and scenic views are abundant; if both extremely abundant water inflow and extremely low scenic views are met, it can be considered a situation where water is abundant but scenic views are depleted; if both extremely abundant water inflow and extremely abundant scenic views are met, it can be considered a situation where both water and scenic views are abundant, and so on.

[0069] The Wudongde right bank hydro-wind-solar hybrid system has the highest average annual output during years of abundant water and low wind and solar power. During the main flood season, increased hydropower output dominates the system's operation, but lower wind and solar power output leads to some fluctuations in total output during the non-flood season. During years of low water and abundant wind and solar power, the average annual output is the lowest. Hydropower output is limited by insufficient water inflow. Although wind and solar power output can partially compensate, it still cannot offset the reduction in hydropower output. Therefore, the system output fluctuates significantly during the main flood season. In years where hydropower, wind, and solar power are all in a dry season, the system output is lower throughout the year because there is less synchronization between hydropower and wind and solar power. In years where hydropower, wind, and solar power are all in a dry season, both inflow and wind and solar power output are high, but the average annual output is lower than in years where hydropower is abundant and wind and solar power are in a dry season. The main reason may be that flood control constraints during the flood season limit hydropower dispatch and reduce resource coordination efficiency.

[0070] The above-described specific embodiments are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for assessing the impact of wind and solar energy absorption on the operation of hydropower stations considering climate change conditions, characterized in that, Includes the following steps: S1. Conduct short-term simulation scheduling of the hydro-wind-solar hybrid system, and extract the curtailment results under different hydropower outputs into a wind and solar curtailment loss function with short-term scheduling characteristics. S2. Based on the wind and solar curtailment loss function, construct an objective function and build a long-term optimal scheduling model for the hydro-wind-solar complementary system that couples short-term scheduling characteristics. S3. Analyze meteorological data to obtain the year of climate change and the typical year of extreme weather. Based on the long-term optimization scheduling model of the water-wind-solar complementary system with coupled short-term scheduling characteristics, calculate the reservoir water level process and hydropower station output before and after the year of climate change and the typical year of extreme weather. S4. Based on the reservoir water level process and hydropower station output before and after the climate change year and in typical years of extreme weather, conduct an assessment of the impact of wind and solar power consumption on the operation of the hydropower station.

2. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 1, characterized in that, S1 includes: collecting historical power output data from wind and solar power plants as wind and solar power output sequences, and historical power output data from hydropower plants as the initial total load curve of the system; using the actual hydropower output as the initial total load, and combining different wind and solar power outputs, simulating the daily compilation and real-time scheduling process of the complementary system's power generation plan; calculating the sum of hydropower and wind / solar power outputs to obtain the theoretical total system output; if it exceeds the system's transmission capacity, cutting off the excess wind and solar power, i.e., wind and solar power curtailment, to obtain the actual grid-connected power; sorting and grouping the hydropower grid-connected output, and calculating the average actual grid-connected hydropower output within each group. and the corresponding wind and solar curtailment rates , denoted as sample point .

3. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 1, characterized in that, The average actual grid-connected power output of hydropower within each group Corresponding wind and solar curtailment rates Specifically, it is expressed as follows: ； In the formula: For wind and solar power curtailment rates; Wasted electricity due to wind and solar power; This represents the actual power generation from wind and solar power.

4. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 2, characterized in that, The wind and solar power curtailment loss function is obtained by fitting multiple sample points. According to hydropower output Substitute into the wind and solar curtailment loss function The calculated hydropower output of the wind and solar power station is... The rate of power curtailment at that time.

5. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 1, characterized in that, S2 includes: adding the wind and solar power curtailment function to the objective function, and constructing a long-term optimal scheduling model for the hydro-wind-solar complementary system that couples short-term scheduling characteristics, with the goal of maximizing the final power generation of the hydro-wind-solar complementary system while satisfying various constraints.

6. The method for assessing the impact of wind and solar energy integration on hydropower station operation considering climate change conditions as described in claim 5, characterized in that, The objective function is specifically expressed as follows: ； In the formula: For the power generation of the hydro-wind-solar hybrid system; This represents the total number of hours in the calculation period. Let t be the output of the hydropower station during time period t; The power output of relevant wind and solar power stations in the basin during time period t; This indicates that the power output of the wind and solar power station is... The curtailment rate at that time was obtained based on a short-term simulation model; The calculation period is long.

7. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 1, characterized in that, In step S3, the specific steps for analyzing meteorological data to obtain years of climate change and typical years of extreme weather are as follows: S31. The inflow of hydropower stations, the actual output of wind power stations, and the actual output of photovoltaic power stations are used as weather index factors for climate change analysis. The weight of each index is determined by principal component analysis. After assigning corresponding weights to each index, they are added together to form a comprehensive weather index. S32. Based on the comprehensive weather index, construct a weather sample sequence in yearly order as the comprehensive weather index sequence; S33. The climate abrupt change year of the comprehensive weather index sequence is detected by the Mann-Kendall method, the sliding T test and the Yamamoto method. The year detected by at least two methods is finally adopted as the climate abrupt change year. If the abrupt change year detected by the three methods is inconsistent, the year with the average of the three results is selected as the climate abrupt change year. S34. Frequency analysis was performed on the inflow data of hydropower stations and the output data of wind and solar power stations over many years, and the years with extremely low water inflow, extremely high water inflow, extremely low wind and solar power, and extremely high wind and solar power were selected as typical years of extreme weather.

8. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 7, characterized in that, The comprehensive weather index is specifically expressed as follows: ； In the formula: For the first Comprehensive weather factors for the year; , , The weights for hydropower station inflow, wind power station actual output, and photovoltaic power station actual output are respectively. For the first Annual hydropower station inflow; For the first Actual power generation of the wind power station in the year; For the first Actual power generation of the photovoltaic power station in that year.

9. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 1, characterized in that: The relevant meteorological data information before and after climate change and typical years of extreme weather are input into the long-term optimization scheduling model of the water-wind-solar complementary system coupled with short-term scheduling characteristics. Discrete differential dynamic programming is used to optimize the solution and obtain the reservoir water level process and hydropower station output before and after climate change and typical years of extreme weather.

10. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 1, characterized in that, In S4, the hydropower station is optimized and analyzed in two stages before and after wind and solar power integration: Before wind and solar power integration, the wind and solar power inputs of the long-term optimization scheduling model of the hydro-wind-solar complementary system are set to 0 based on the hydropower station's operating parameters, and the wind and solar power outputs are not included in the scheduling decision, generating a baseline hydropower scheduling scheme; After wind and solar power integration, the real-time output data of the hydropower station and the wind and solar power station are synchronously input into the long-term optimization scheduling model of the hydro-wind-solar complementary system with coupled short-term scheduling characteristics, generating a hydropower optimization scheduling scheme that includes the influence of wind and solar power. The two scheduling schemes were compared and analyzed. Based on the monthly water level of the reservoir before and after the wind and solar power connection, the monthly fluctuation range and the rate of change of the monthly power output of the hydropower station, the impact of wind and solar power absorption on the operation of the hydropower station was assessed before and after the climate change and in typical years of extreme weather.

11. The method for assessing the impact of wind and solar energy absorption on hydropower station operation considering climate change conditions as described in claim 10, characterized in that, The rate of change in the reservoir's water level at the end of the month is specifically expressed as follows: ； The rate of change of the water level fluctuation range is specifically expressed as follows: ； The rate of change in power output of the hydropower station is specifically expressed as follows: ； In the formula: , , These are the month-end water level change rate of the reservoir, the water level fluctuation range change rate, and the hydropower station output change rate, respectively. , These are the month-end water levels of the reservoir before and after the wind and solar power integration; , These represent the maximum and minimum monthly water levels of the reservoir before the integration of wind and solar power. , These represent the maximum and minimum monthly water levels of the reservoir after the integration of wind and solar power. , These figures represent the monthly power output of the hydropower station before and after the integration of wind and solar power.

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