Wind-light-water pumping and storage scheduling optimization method based on hydraulic coupling and wind-light typical output

Abstract

The invention belongs to the field of power optimization dispatching, and particularly relates to a wind-light-water pumping and storage dispatching optimization method based on hydraulic coupling and wind-light typical output, which comprises the following steps: constructing a hydraulic connection matrix, and establishing output models of a hydropower station and a pumped storage power station; modeling is carried out on wind power and photovoltaic power generation typical output and a pumped storage power station, a water power output typical scene is divided, a wind-light-water pumping and storage combined output model of multi-objective optimization considering hydraulic coupling is established, and the optimization objectives are that the power generation benefit is maximum and the output stability of the wind-light-water pumping and storage combined system is best; performing normalization processing on a target function of the joint output model; and solving the wind-light-water pumping and storage combined output model by adopting an improved grey wolf algorithm to obtain an optimal scheduling scheme of the wind-light-water pumping and storage combined system, so that the stability of the power system is improved, and the power generation benefit is maximized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power optimization and dispatching, and in particular relates to a wind-solar-water pumped storage dispatching optimization method based on hydraulic coupling and typical wind-solar output. Background Art

[0002] The coordinated operation of complementary power generation systems is playing an increasingly significant role in promoting the decarbonization of power systems. Therefore, studying the coordinated operation mechanisms of complementary power generation systems, optimizing scheduling strategies, and improving the system's overall performance can accelerate the green transformation of the power generation industry. The power generation process of a hydropower station is closely related to the station's hydraulic conditions. The power output of hydropower stations in the same basin is influenced by the spatial coupling characteristics of the hydropower station cluster within the basin's topological network. Therefore, to improve the power transmission capacity of a combined wind, solar, and hydropower pumped-storage system, it is necessary to study the optimal scheduling of wind, solar, and hydropower combined systems with hydraulic connections.

[0003] Patent publication number CN118071540A uses a deep extreme learning machine to predict the short-term power of renewable energy sources, determining the predicted power, grid connection bid, and market electricity price for a specific timeframe. An optimal scheduling model is then established to balance renewable energy utilization with the economic benefits of the power grid. This model is solved using an improved sparrow algorithm to obtain the predicted output power of wind, solar, and pumped-storage power. This strategy effectively guides the optimal operation of renewable energy sources and is highly practical, successfully addressing the grid accommodation challenges posed by the large-scale integration of renewable energy. Patent publication number CN109245169B discloses a combined wind, solar, and pumped-storage scheduling method designed to optimize system operating costs. This method first establishes an economically optimal objective function and corresponding system constraints, then solves for the optimal solution set for optimal operation. This method provides a new approach to solving wind, solar, and pumped-storage scheduling problems and enhances the grid's ability to accommodate renewable energy. However, none of these patents consider the hydraulic connections between hydropower stations in a river basin, which affects the accuracy of the optimized scheduling strategy for wind, solar, and pumped-storage power generation. Summary of the Invention

[0004] The purpose of the present invention is to address the above-mentioned problems and provide a wind-solar-water pumping-storage scheduling optimization method based on hydraulic coupling and typical wind-solar output, construct a hydraulic connection matrix, model the typical output of wind power, photovoltaic power generation and pumped-storage power stations, divide typical hydropower output scenarios, establish a wind-solar-water pumping-storage combined output model with multi-objective optimization considering hydraulic coupling, solve and obtain the optimal scheduling plan for the wind-solar-water pumping-storage combined system, improve the stability of the power system, and maximize the power generation efficiency.

[0005] In order to achieve the above object, the technical solution provided by the present invention is: The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output includes the following steps: Step 1: Construct a hydraulic connection matrix and establish output models for hydropower stations and pumped storage power stations; Step 2: Generate a typical wind power output curve using the Weibull distribution model. Generate a typical photovoltaic output curve based on photovoltaic output characteristics using the normal distribution model, and divide the typical hydropower output scenarios. Step 3: Based on the typical output of wind, solar and hydropower, a wind, solar and hydropower pumped storage combined output model considering hydraulic coupling is established. The optimization objectives are to maximize the power generation efficiency and optimize the output stability of the wind, solar and hydropower pumped storage combined system. Step 4: normalizing the objective function of the joint output model; Step 5: Use the improved grey wolf algorithm to solve the wind, solar, hydropower and pumped storage combined output model to obtain the optimal solution, that is, the optimal scheduling plan for the wind power, photovoltaic, hydropower and pumped storage combined system, and optimize the scheduling of the combined system.

[0006] Furthermore, in step 1, the hydropower station type vector for: ; In the formula express The type of the watershed network node, Elements The value is 1 or 0. Representation node That is i Each hydropower station is a hydropower station node with storage capacity. Representation node It is a node of run-of-river hydropower station; Watershed Correlation Matrix for: ; In the formula for The direction of water flow at each hydropower station; for The element value of Representation node and There is no hydraulic connection. Indicates the direction of water flow as a node Flow Node , Indicates the direction of water flow as a node Flow Node .

[0007] Preferably, in step 1, the output model of the small hydropower station is: ; In the formula For hydropower stations exist Power generation at the moment; Indicates hydropower station in the basin power generation efficiency; is the acceleration due to gravity; and For hydropower stations in the basin exist The net water head and power flow at the moment; t represents the time step; The generating and pumping power of the pumped storage power station is: ; In the formula 、 They are The power generation flow and pumping flow of the pumped storage power station at all times; It represents the efficiency of converting water energy into electricity in a pumped storage power station. Indicates the efficiency of the pumped storage power station in converting electrical energy into hydropower; is the net water head height of the pumped storage power station; 、 They are The generating capacity and pumping capacity of the pumped-storage power station at all times.

[0008] Preferably, in step 2, a Weibull distribution model is used to describe the wind speed uncertainty problem, and the distribution function is: ; In the formula is the wind speed, in units of ; is the probability density function of wind speed; is the shape parameter; is the scale parameter; 、 Respectively represent the mean and standard deviation of the sample; is the gamma function.

[0009] The calculation formula for photovoltaic power considering the fluctuation characteristics is: ; In the formula is the rated power of the photovoltaic power source; is the power temperature coefficient of the photovoltaic panel, take ; is the operating temperature of the PV modules; Indicates latitude Photovoltaic construction area moon sky Hourly mean solar irradiance on the surface at ; Photovoltaic output obeys the log-normal distribution, and the expression of the probability distribution function is: ; In the formula for Solar radiation intensity during the period; ; In the formula 、 They are Mean and variance of the lognormal random variable over the time period.

[0010] Preferably, in step 3, the objective function of the joint output model includes: ; ; In the formula 、 represent the first and second objective functions respectively; for The electricity price at the time; The power generation capacity of pumped storage; For hydropower stations exist Power generation at the moment; is the number of hydropower stations; For wind farms Power generation at the moment; For photovoltaic power stations Power generation at the moment; for The electricity purchase price of pumped storage at all times; the power required to pump water for pumped storage; is the time window length; T Indicates the total number of time periods; For wind, solar and water pumped storage combined system Total output at any given moment; is the average value of the total output process of the combined output system.

[0011] Preferably, the constraints of the joint output model include: (1) Output power constraints of wind power stations, photovoltaic power stations, hydropower stations, and pumped storage power stations: ; In the formula is the upper limit of the output power of the photovoltaic power station; is the upper limit of the output power of the wind power station; is the maximum power generation capacity of the hydropower station; is the minimum power generation capacity of the hydropower station; 、 are the minimum and maximum power generation capacities of the pumped storage power station, and are the minimum and maximum pumped storage power of the pumped storage power station respectively; (2) Spatial coupling characteristics of hydroelectric current domain network: ; In the formula for Time basin network node Hydraulic correlation flow between other nodes; For hydropower stations exist Power generation flow at the moment; For hydropower stations exist The abandoned water flow at the moment; (3) Dynamic balance of reservoir capacity of hydropower stations in the basin: ; In the formula For hydropower stations exist Natural water flow at any given time; Indicates hydropower station exist Storage capacity at a given moment; (4) Upper and lower limits of hydropower station storage capacity: ; In the formula 、 Node The upper and lower limits of the reservoir capacity flow.

[0012] Furthermore, the normalized objective function is: ; In the formula is the normalized value of the objective function, 、 represent the first and second objective functions respectively, The output power of wind, solar, water and pumped storage combined power is The output fluctuation stability when the wind, solar, and water pumping storage systems are operated jointly; 、 They are 、 The weight value is set according to different solution requirements.

[0013] The wind-solar-water pumped storage scheduling optimization system of the above method includes the following modules: Wind power output module: used to calculate the output power of the wind power station; Photovoltaic output module: used to calculate the output power of the photovoltaic power station; Hydropower output module: used to calculate the output power of the hydropower station; Pumped storage module: used to calculate the power generation and pumped storage power of the pumped storage power station; Optimal scheduling scheme module: used to establish a wind-solar-hydro-pumped-storage combined output model considering hydraulic coupling, calling the wind power output module, photovoltaic output module, hydropower output module and pumped storage module to obtain the optimal solution, that is, the optimal scheduling scheme for the wind power, photovoltaic, hydropower and pumped storage combined system.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1) Based on the characteristics of the combined output of wind, solar, and hydropower, this paper establishes a multi-objective wind, solar, and hydropower pumped storage combined scheduling optimization model. The optimization objectives are to maximize the power generation efficiency and output stability of the wind, solar, and hydropower pumped storage combined system. The optimal scheduling scheme obtained by solving the model guides the system scheduling operation, improves the stability of the power grid, reduces the impact of renewable energy power generation on power quality, and maximizes the power generation efficiency.

[0015] 2) The present invention improves the accuracy of hydropower output prediction by constructing a spatial coupling relationship matrix of hydropower stations and considering the complete basin topology relationship of small hydropower stations in the basin, thus solving the problem of joint output among multiple small hydropower stations in the basin.

[0016] 3) In a multi-objective wind, solar, and pumped-storage combined optimization model, the two-objective problem is converted into a single-objective problem for solution, thus reducing the difficulty of the solution. The Grey Wolf Algorithm (GWA) adaptively adjusts the convergence factor when solving the optimization problem. As the number of iterations increases, the algorithm gradually approaches the optimal solution, demonstrating good robustness and stability, thus reducing the difficulty of the solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 Schematic diagram of a wind-solar-water pumped storage scheduling optimization method according to an embodiment of the present invention.

[0019] Figure 2 This is a network topology diagram of a small hydropower station according to an embodiment of the present invention.

[0020] Figure 3 This is a flowchart of the improved grey wolf algorithm according to an embodiment of the present invention.

[0021] Figure 4 This is a hydropower station output diagram without considering the spatial coupling of the hydropower stations according to an embodiment of the present invention.

[0022] Figure 5This is a hydropower station output diagram considering the spatial coupling of hydropower stations according to an embodiment of the present invention.

[0023] Figure 6 This is a load power comparison diagram before and after scheduling optimization according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] like Figure 1 As shown in the figure, the wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output includes: Step 1: Construct a hydraulic connection matrix and establish output models of small hydropower stations and pumped storage power stations.

[0025] Hydropower station and basin topology diagram Figure 2 As shown, the hydraulic connection matrix of the hydropower station is as follows: Hydropower station types vector for: ; In the formula express The type of the watershed network node, Elements The value is 1 or 0. Representation node That is i Each hydropower station is a hydropower station node with storage capacity. Representation node It is a node of run-of-river hydropower station; Watershed Correlation Matrix for: ; In the formula for The direction of water flow at each hydropower station; for The element value of Representation node and There is no hydraulic connection. Indicates the direction of water flow as a node Flow Node , Indicates the direction of water flow as a node Flow Node .

[0026] The output model of a small hydropower station is: ; In the formula For hydropower stations exist Power generation at the moment; Indicates hydropower station in the basin power generation efficiency; is the acceleration due to gravity; and For hydropower stations in the basin exist Net water head and power flow at the moment; represents the time step; The generating and pumping power of the pumped storage power station is: ; In the formula 、 They are The power generation flow and pumping flow of the pumped storage power station at all times; It represents the efficiency of converting water energy into electricity in a pumped storage power station. Indicates the efficiency of the pumped storage power station in converting electrical energy into hydropower; is the net water head height of the pumped storage power station; 、 They are The generating capacity and pumping capacity of the pumped-storage power station at all times.

[0027] Step 2: Use the Weibull distribution model to generate a typical wind power output curve. Use the Beta distribution model to generate a typical photovoltaic output curve based on the photovoltaic output characteristics, and divide the typical small hydropower output scenarios.

[0028] The Weibull distribution model is used to describe the uncertainty of wind speed, and the distribution function is: ; In the formula is the wind speed, in units of ; is the probability density function of wind speed; is the shape parameter; is the scale parameter; 、 Respectively represent the mean and standard deviation of the sample; is the gamma function.

[0029] The calculation formula for photovoltaic power considering the fluctuation characteristics is: ; In the formula is the rated power of the photovoltaic power source; is the power temperature coefficient of the photovoltaic panel, take ; is the operating temperature of the PV modules; Indicates latitude Photovoltaic construction area moon sky Hourly mean solar irradiance on the surface at ; The photovoltaic output approximately obeys the log-normal distribution, and the expression of the probability distribution function is: ; In the formula for Solar radiation intensity during the period; ; In the formula 、 They are Mean and variance of the lognormal random variable over the time period.

[0030] Step 3: Based on the typical output of wind, solar and hydropower, a wind, solar and hydropower pumping storage combined output model considering hydraulic coupling is established. The optimization objectives are to maximize the power generation efficiency and achieve the best output stability of the wind, solar and hydropower pumping storage combined system.

[0031] The objective function of the joint output model includes: ; ; In the formula 、 represent the first and second objective functions respectively; for The electricity price at the time; The power generation capacity of pumped storage; For hydropower stations exist Power generation at the moment; is the number of hydropower stations; For wind farms Power generation at the moment; For photovoltaic power stations Power generation at the moment; for The purchase price of electricity from pumped storage at all times; the power required to pump water for pumped storage; is the time window length; T Indicates the total number of time periods; For wind, solar and water pumped storage combined system Total output at any given moment; is the average value of the total output process of the combined output system.

[0032] in, .

[0033] The constraints of the joint output model include: Output power constraints for wind power stations, photovoltaic power stations, hydropower stations, and pumped storage power stations: ; In the formula is the upper limit of the output power of the photovoltaic power station; is the upper limit of the output power of the wind power station; is the maximum power generation capacity of the hydropower station; is the minimum power generation capacity of the hydropower station; 、 are the minimum and maximum power generation capacities of the pumped storage power station, and are the minimum and maximum pumped storage power of the pumped storage power station respectively; 、 They are The generating capacity and pumping capacity of the pumped-storage power station at all times.

[0034] Spatial coupling characteristics of hydroelectric current domain network: ; In the formula for Time basin network node Hydraulic correlation flow between other nodes; For hydropower stations exist Power generation flow at the moment; For hydropower stations exist The abandoned water flow at the moment; Dynamic balance of reservoir capacity of hydropower stations in the basin: ; In the formula For hydropower stations exist Natural water flow at any given time; Indicates hydropower station exist Storage capacity at a given moment; Upper and lower limit constraints on hydropower station storage capacity: ; In the formula 、 Node The upper and lower limits of the reservoir capacity flow.

[0035] Step 4: Normalize the objective function of the joint output model.

[0036] The normalized objective function is: ; In the formula is the normalized value of the objective function, 、 represent the first and second objective functions respectively, The output power of wind, solar, water and pumped storage combined power is The output fluctuation stability when the wind, solar, and water pumping storage systems are operated jointly; 、 They are 、 The weight value is set according to different solution requirements.

[0037] Step 5: Use the improved grey wolf algorithm to solve the wind, solar, hydropower and pumped storage combined output model to obtain the optimal solution, that is, the optimal scheduling plan for the wind power, photovoltaic, hydropower and pumped storage combined system, and optimize the scheduling of the combined system.

[0038] The Gray Wolf Algorithm simulates the social hierarchy and hunting behavior of gray wolves in nature, solving optimization problems by simulating the cooperation and competition mechanisms of gray wolves in the process of searching for prey. The algorithm's optimization process includes the wolf grouping, surrounding the prey, and capturing the prey.

[0039] (I) Wolf pack hierarchy; In the social structure of wolves, each member has a clear role. In this system, α wolves play the key role of decision makers, representing the optimal solution sought in the optimization model exploration process; β wolves act as auxiliary decision makers, and their position corresponds to the suboptimal solution in the model; δ wolves follow the guidance of α and β wolves, perform specific tasks, and their role is mapped to the general solution in the model; and at the bottom of the social structure, Wolves are mainly responsible for maintaining balance and harmony within the pack, and their existence symbolizes the set of candidate solutions to the model.

[0040] In the iterative process of the optimization algorithm, The wolves dynamically adjust their position vectors based on the current positions of wolves α, β, and δ. This mechanism ensures efficient exploration of the algorithm within the solution space. Furthermore, if the fitness of wolves in the upper layer (i.e., wolves α, β, or δ) declines, they may be replaced by wolves in the next layer. This process reflects the algorithm's solution updating and competition mechanism. Through this hierarchical role positioning and dynamic updating strategy, the Gray Wolf Optimization Algorithm is able to efficiently search for and approach the optimal solution in complex optimization problems.

[0041] (II) surrounding the prey; In the process of surrounding prey, the gray wolf's surrounding behavior is as follows: ; where represents the distance between the prey and the wolf pack and the updated position of the gray wolf. In addition, Indicates the number of iterations of the current population, and Represent the location of the prey and the location of the gray wolf respectively. A and C is the coefficient vector, and the calculation formula is as follows: ; Where, is the convergence factor, which decreases linearly from 2 to 0 as the number of iterations decreases. and The modulus gets a random number between [0,1].

[0042] (III) hunting prey; exist Wolf, Wolf and Under the hierarchical command of wolves, as a pack executes its predation, its spatial distribution and positional state undergo continuous dynamic adjustment. This dynamic nature is reflected in the fact that pack members flexibly adjust their positions and routes based on the prey's escape direction, terrain characteristics, and the ever-changing internal coordination strategies, until the prey is ultimately achieved. This process can be expressed as follows: ; Where, , , Represents the other individuals in the wolf pack and 、 as well as the distance between them; , , Respectively 、 as well as The wolf's current location; is the current location of the gray wolf; , , Respectively express , as well as The impact of wolves, the positions that other individuals in the population need to adjust, Their final locations were determined.

[0043] like Figure 3 As shown in Figure 2, the improved grey wolf algorithm specifically includes: 1) Determine key parameters such as population size, i.e., the number of gray wolves, the maximum number of iterations, and the upper and lower bounds of the variables; within the upper and lower bounds of the variables, randomly generate the location of the initial gray wolf population; 2) For each individual gray wolf, a fitness value is calculated based on its position, where the fitness value reflects the distance or similarity between the individual gray wolf and the optimal solution; 3) Let A and C be the synergy coefficient vectors used to simulate the attack and encirclement behavior of gray wolves on prey; the value of A is affected by the convergence factor As the number of iterations increases, Decrease linearly from 2 to 0; let C be a random vector; 4) According to 、 and The location information of the gray wolf and the values ​​of A and C are used to update the location of each individual gray wolf. By simulating the social hierarchy and hunting behavior of gray wolves, the location of gray wolves is gradually optimized. 5) If the current number of iterations reaches the preset maximum number of iterations, the algorithm stops running; 6) Output The wolf's position vector is taken as the optimal solution.

[0044] Example: In order to verify the effectiveness of the present invention, a small hydropower station with a total of 64.62MW in a certain basin is selected as an example for analysis. The basin topology is shown in the figure below. Figure 2 As shown. Figure 4 and Figure 5 A direct comparison of the results with and without considering the spatial characteristics of hydropower in the basin reveals that the output of Hydropower Stations 6 and 7, which consider the spatial coupling characteristics of the hydropower stations, has a greater overall power generation benefit during the 4-12 period. The total output of both stations when considering the spatial coupling characteristics of the hydropower stations is greater than when not considering the spatial coupling characteristics of the hydropower stations. This is because the run-of-the-river hydropower station 7 not only considers the natural water inflow, but also the water inflow of the upstream reservoir-type hydropower station 6, thereby increasing the overall power generation of the hydropower station.

[0045] Depend on Figure 6 It can be seen that under the pumped storage output and power generation strategy obtained by optimizing the scheduling of the grid load, the energy storage is affected by the peak-valley price difference, and chooses to charge during valley and normal periods and discharge during peak periods. By reducing the peak load, the final electricity consumption curve is smoother.

[0046] The comparison results of the power system volatility of the wind-solar-water pumped-storage combined system provided by the present invention and the wind-solar-water combined system without a pumped-storage power station are shown in Table 1.

[0047] Table 1

[0048] Table 1 shows that after adding the pumped-storage power station, system volatility decreased during the flood, dry, and normal water periods, with reductions of 14.46%, 25.38%, and 15.11% during the flood, dry, and normal water periods, respectively. The greatest reduction occurred during the dry season. Furthermore, system volatility exhibited a decreasing trend across different periods, gradually decreasing from the flood to the normal and then dry seasons. This is primarily due to the relatively abundant water inflow during the flood season, when small hydropower units typically shoulder grid operations. Their peak-shaving capacity is significantly weaker than during the normal and dry seasons, resulting in a relatively weaker regulatory impact on wind and photovoltaic output. During the dry season, however, hydropower reaches its optimal regulatory capacity. Consequently, system volatility is highest during the flood season and lowest during the dry season, demonstrating the superiority and reliability of the present invention in reducing system volatility.

Claims

1. A wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output, characterized by: The method is used to optimize the dispatching of a wind power, photovoltaic, hydropower and pumped storage combined system, comprising the following steps: Step 1: Construct a hydraulic connection matrix and establish output models for hydropower stations and pumped storage power stations; Step 2: Generate a typical wind power output curve using the Weibull distribution model. Generate a typical photovoltaic output curve based on photovoltaic output characteristics using the normal distribution model, and divide the typical hydropower output scenarios. Step 3: Based on the typical output of wind, solar and hydropower, a wind, solar and hydropower pumped storage combined output model considering hydraulic coupling is established. The optimization objectives are to maximize the power generation efficiency and optimize the output stability of the wind, solar and hydropower pumped storage combined system. Step 4: normalizing the objective function of the joint output model; Step 5: Solve the wind, solar, hydropower and pumped storage combined output model to obtain the optimal solution, that is, the optimal scheduling plan for the wind power, photovoltaic, hydropower and pumped storage combined system, and optimize the scheduling of the combined system.

2. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 1 is characterized in that: In step 1, the hydropower station type vector for: ； In the formula express The type of the watershed network node, Elements The value is 1 or 0. Representation node That is i Each hydropower station is a hydropower station node with storage capacity. Representation node It is a node of run-of-river hydropower station; represents the time step; Watershed Correlation Matrix for: ； In the formula for The direction of water flow at each hydropower station; for The element value of Representation node and There is no hydraulic connection. Indicates the direction of water flow as a node Flow Node , Indicates the direction of water flow as a node Flow Node .

3. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 2 is characterized in that: In step 1, the output model of the hydropower station is: ； In the formula For hydropower stations exist Power generation at the moment; Indicates hydropower station in the basin power generation efficiency; is the acceleration due to gravity; and For hydropower stations in the basin exist Net water head and power flow at the moment; represents the time step; The generating and pumping power of the pumped storage power station is: ； ； In the formula 、 They are The power generation flow and pumping flow of the pumped storage power station at all times; It represents the efficiency of converting water energy into electricity in a pumped storage power station. Indicates the efficiency of the pumped storage power station in converting electrical energy into hydropower; is the net water head height of the pumped storage power station; 、 They are The generating capacity and pumping capacity of the pumped-storage power station at all times.

4. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 3 is characterized in that: In step 2, the Weibull distribution model is used to describe the wind speed uncertainty problem, and the distribution function is: ； ； ； In the formula is the wind speed; is the probability density function of wind speed; is the shape parameter; is the scale parameter; 、 Respectively represent the mean and standard deviation of the sample; is the gamma function.

5. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 4 is characterized in that: In step 2, the calculation formula for photovoltaic power considering the fluctuation characteristics is: ； In the formula is the rated power of the photovoltaic power source; is the power temperature coefficient of the photovoltaic panel; is the operating temperature of the PV modules; Indicates latitude Photovoltaic construction area moon sky Hourly mean of surface solar irradiance at time; Photovoltaic output obeys the log-normal distribution, and the expression of the probability distribution function is: ； In the formula for Solar radiation intensity during the period; ； In the formula 、 They are Mean and variance of the lognormal random variable over the time period.

6. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 5 is characterized in that: In step 3, the objective function of the joint output model includes: ； ； ； In the formula 、 represent the first and second objective functions respectively; for The electricity price at the time; The power generation capacity of pumped storage; For hydropower stations exist Power generation at the moment; is the number of hydropower stations; For wind farms Power generation at the moment; For photovoltaic power stations Power generation at the moment; for The electricity purchase price of pumped storage at all times; the power required to pump water for pumped storage; is the time window length; T Indicates the total number of time periods; For wind, solar and water pumped storage combined system Total output at any given moment; is the average value of the total output process of the combined output system.

7. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 6 is characterized in that: In step 3, the constraints of the joint output model include: (1) Output power constraints of wind power stations, photovoltaic power stations, hydropower stations, and pumped storage power stations; (2) Spatial coupling characteristics of hydroelectric current domain network: ； ； In the formula for Time basin network node Hydraulic correlation flow between other nodes; For hydropower stations exist Power generation flow at the moment; For hydropower stations exist The abandoned water flow at the moment; (3) Dynamic balance of reservoir capacity of hydropower stations in the basin: ； In the formula For hydropower stations exist The natural water flow at any given moment; Indicates hydropower station exist Storage capacity at a given moment; (4) Upper and lower limits of hydropower station storage capacity: ； In the formula 、 Node The upper and lower limits of the reservoir capacity flow.

8. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 7 is characterized in that: In step 3, the output power constraints of the wind power station, photovoltaic power station, hydropower station, and pumped storage power station specifically include: ； ； ； ； ； In the formula is the upper limit of the output power of the photovoltaic power station; is the upper limit of the output power of the wind power station; is the maximum power generation capacity of the hydropower station; is the minimum power generation capacity of the hydropower station; 、 are the minimum and maximum power generation capacities of the pumped storage power station, and are the minimum and maximum pumped storage power of the pumped storage power station respectively.

9. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to claim 8 is characterized in that: In step 4, the normalized objective function is: ； In the formula is the normalized value of the objective function, 、 represent the first and second objective functions respectively, is the output power of the wind-solar-water pumped storage combined system, The fluctuation stability of power generation output of wind, solar, hydro and pumped storage combined systems; 、 They are 、 The weight value of .

10. The wind-solar-water pumped storage scheduling optimization method based on hydraulic coupling and typical wind-solar output according to any one of claims 1 to 9, characterized in that: In step 5, the improved grey wolf algorithm is used to solve the wind-solar-water pumped storage combined output model. The improved grey wolf algorithm specifically includes: 1) Determine the population size (i.e., the number of gray wolves), the maximum number of iterations, and the upper and lower bounds of the variables; within the upper and lower bounds of the variables, randomly generate the location of the initial gray wolf population; 2) For each individual gray wolf, a fitness value is calculated based on its position, where the fitness value reflects the distance or similarity between the individual gray wolf and the optimal solution; 3) Let A and C be the synergy coefficient vectors used to simulate the attack and encirclement behavior of gray wolves on prey; the value of A is affected by the convergence factor the impact of; 4) According to 、 and The location information, as well as the values ​​of A and C, update the location of each gray wolf individual, where 、 and They represent the first, second, and third gray wolf individuals with the best fitness, respectively. By simulating the social hierarchy and hunting behavior of gray wolves, the position of gray wolves is gradually optimized. 5) If the current number of iterations reaches the preset maximum number of iterations, the algorithm stops running; 6) Output The position vector of is taken as the optimal solution.

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