Wind-light-water storage multi-energy complementation and intelligent scheduling system and method based on large energy storage pump

Abstract

The invention discloses a wind-light-water-storage multi-energy complementation and intelligent dispatching system and method based on an energy storage large pump, and the system comprises a power generation module, a wind power station and a photovoltaic power station which are used for providing intermittent and unstable electric quantity output, a cascade reservoir power station which generates power through the water level difference of a reservoir, and an energy storage module which is connected with the wind-light-water-storage multi-energy complementation and intelligent dispatching module. The pumping module is used for pumping water from a downstream reservoir to an upstream reservoir by using electric quantity which is not merged into a power grid when wind power or photovoltaic power generation is excessive; the scheduling module is used for carrying out overall optimization scheduling on the wind-light-water storage system by utilizing an intelligent scheduling system according to weather forecast and load prediction information; and the monitoring and control module is used for monitoring the running state of the system in real time, controlling energy distribution among wind power generation, photovoltaic power generation, an energy storage pump station and a cascade reservoir power station, and optimizing wind, light and water storage multi-energy complementary scheduling. Stable large-scale grid connection of photovoltaic power generation and wind power generation is ensured, electric power waste is remarkably reduced, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a wind, solar, water and storage multi-energy complementarity and intelligent dispatching system and method based on a large energy storage pump. Background Art

[0002] The development and utilization of renewable energy such as wind power and solar energy has become an important way to promote the transformation of energy structure and promote green and low-carbon development. However, the intermittent, volatile and unstable nature of these renewable energy sources has brought unprecedented challenges to the safe and stable operation of the power grid. Although traditional pumped storage technology can alleviate the instability of renewable energy power generation to a certain extent, its response speed and regulation capabilities are stretched in the face of rapid changes in renewable energy power generation, and cannot fully meet the needs of modern power grids for flexibility and stability. Summary of the invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the present invention proposes a wind, solar, water storage multi-energy complementary and intelligent scheduling system based on a large energy storage pump to solve the problems of large limitations of existing technical means and weak power grid's ability to absorb wind and solar power.

[0005] Another object of the present invention is to propose a scheduling method based on a particle swarm optimization algorithm for a wind, solar, water storage multi-energy complementary and intelligent scheduling system based on a large energy storage pump.

[0006] To achieve the above objectives, the present invention proposes, on one hand, a wind, solar, water storage multi-energy complementary and intelligent dispatching system based on a large energy storage pump, comprising:

[0007] The power generation module includes a wind power station, a photovoltaic power station and a cascade reservoir power station; among them, the wind power station and the photovoltaic power station are used to provide intermittent and unstable power output, and the cascade reservoir power station uses the water level difference of the reservoir to generate electricity and provide hydroelectric power for the power grid;

[0008] Energy storage modules are used to pump water from downstream reservoirs to upstream reservoirs when there is excess wind or photovoltaic power generation, thereby forming a high-capacity cascade hydropower storage system.

[0009] The dispatching module uses the intelligent dispatching system to optimize the overall dispatching of the wind, solar and water storage systems based on weather forecast and load forecast information;

[0010] The monitoring and control module is used to monitor the system operation status in real time and control the energy distribution between wind power generation, photovoltaic power generation, energy storage pumping stations and cascade reservoir power stations, realize dynamic adjustment of output, and optimize the complementary scheduling of wind, solar, water and storage multiple energy sources.

[0011] The wind, solar, water and storage multi-energy complementary and intelligent dispatching system based on the energy storage large pump in the embodiment of the present invention may also have the following additional technical features:

[0012] In one embodiment of the present invention, the energy storage module is a large pump energy storage system composed of a centrifugal pump with a high lift and a large flow rate.

[0013] In one embodiment of the present invention, the energy storage pump station adopts a high-lift, large-flow centrifugal pump, which is a high-performance centrifugal pump with variable speed under all operating conditions; through the coordinated work of the energy storage pump station, when wind energy and photovoltaic power generation are in excess, the system will store excess energy in the cascade reservoirs; when electricity demand increases, the control system will dispatch the cascade reservoirs to generate electricity.

[0014] To achieve the above purpose, the present invention proposes a scheduling method based on a particle swarm optimization algorithm for a wind, solar, water storage multi-energy complementary and intelligent scheduling system based on a large energy storage pump, comprising:

[0015] S101, initialize the particle swarm; randomly generate a group of particles, each individual corresponds to a scheduling plan, and each particle contains the current power generation, fitness, optimal position, and global optimal position;

[0016] S102, calculate fitness; for each particle, calculate fitness based on the multi-objective function of minimizing output volatility and minimizing cost; fitness is the minimum value of the inner layer objective and the outer layer objective with different weights:

[0017] Fitness=minZ=min[λZ1+(1-λ)Z2]

[0018] S103, updating the particle position and velocity; updating the velocity and position of each particle according to the following formula:

[0019] v i =w·v i +c1·r1·(pbest i -x i )+c2·r2·(gbest-x i )

[0020] x i =x i +v i

[0021] In the formula, v i is the speed of the particle; w is the inertia weight, which controls the degree of influence of particle flight; c1, c2 are learning factors; r1, r2 are random numbers between [0, 1]; pbest i is the best position of the particle; gbest is the global best position; x iis the current position of the particle;

[0022] S104, check the constraints; ensure that the updated particle position meets all constraints; if the constraints are violated, reduce the fitness through the penalty function or adjust the particle to the allowed range;

[0023] S105, update the best solution; for each particle, if the current fitness is better than its historical best fitness, update its best position; if the current fitness is better than the global optimal fitness, update the global best position; the judgment of goodness and badness is based on non-dominated sorting and congestion;

[0024] S106, determine the stop condition; set the stop condition, if the stop condition is met, output the optimal scheduling plan; otherwise, return to S103.

[0025] The wind, solar, water and storage multi-energy complementarity and intelligent scheduling system and method based on the energy storage large pump of the embodiment of the present invention forms a multi-energy complementary energy supply system by combining multiple renewable energy sources such as wind energy, solar energy, and water energy with an efficient energy storage pump station. At the same time, with the help of advanced intelligent scheduling algorithms, the system can monitor and analyze the changing trends of new energy power generation in real time, accurately predict electricity demand, and realize real-time matching and intelligent scheduling of energy storage equipment and new energy power generation. This can not only effectively improve the utilization rate of new energy and reduce energy waste, but also enhance the flexibility and stability of the power grid and ensure the safe and stable operation of the power system.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 This is a structural schematic diagram of a wind, solar, water storage multi-energy complementary and intelligent dispatching system based on a large energy storage pump provided by the present invention;

[0029] Figure 2 This is a flow chart of the wind, solar, water and storage multi-energy complementarity and intelligent scheduling strategy based on the particle swarm optimization algorithm provided by the present invention;

[0030] Figure 3 This is a schematic diagram of the intelligent scheduling results of the wind, solar, water and storage multi-energy complementary system after optimized scheduling provided by the present invention. DETAILED DESCRIPTION

[0031] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] The following describes, with reference to the accompanying drawings, a wind, solar, and hydroelectric storage multi-energy complementarity and intelligent scheduling system and method based on a large energy storage pump according to an embodiment of the present invention.

[0034] Figure 1 is a structural schematic diagram of a wind-solar-water-storage multi-energy complementary and intelligent dispatching system based on a large energy storage pump according to an embodiment of the present invention, such as Figure 1 As shown, including:

[0035] The power generation module includes a wind power station, a photovoltaic power station and a cascade reservoir power station; among them, the wind power station and the photovoltaic power station are used to provide intermittent and unstable power output, and the cascade reservoir power station uses the water level difference of the reservoir to generate electricity and provide hydroelectric power for the power grid;

[0036] Energy storage modules are used to pump water from downstream reservoirs to upstream reservoirs when there is excess wind or photovoltaic power generation, thereby forming a high-capacity cascade hydropower storage system.

[0037] The dispatching module uses the intelligent dispatching system to optimize the overall dispatching of the wind, solar and water storage systems based on weather forecast and load forecast information;

[0038] The monitoring and control module is used to monitor the system operation status in real time and control the energy distribution between wind power generation, photovoltaic power generation, energy storage pumping stations and cascade reservoir power stations, realize dynamic adjustment of output, and optimize the complementary scheduling of wind, solar, water and storage multiple energy sources.

[0039] It is understandable that the power generation module includes wind power stations, photovoltaic power stations and cascade reservoir power stations. Wind power stations and photovoltaic power stations provide intermittent and unstable power output, and cascade reservoir power stations use the water level difference of the reservoir to generate electricity, provide stable hydropower for the power grid, and alleviate the fluctuation of wind and solar power generation.

[0040] Specifically, when wind speed or light intensity changes, wind and photovoltaic power generation are updated in real time to ensure that the amount of wind and photovoltaic power received by the power grid is consistent with actual meteorological conditions.

[0041] Energy storage module: A large pump energy storage system composed of high-lift, large-flow centrifugal pumps. When there is excess wind or photovoltaic power generation, the energy storage module uses the electricity that is not connected to the grid to pump water from the downstream reservoir to the upstream reservoir, forming a high-capacity cascade water energy storage system.

[0042] Specifically, when load demand increases or wind and solar power generation is insufficient, the large pump system can release stored water energy, supplement grid demand through cascade reservoir power generation, and achieve efficient storage and scheduling of electricity.

[0043] Dispatching module: Using the intelligent dispatching system, the overall optimization dispatching of the wind, solar and water storage system is carried out according to weather forecast, load forecast and other information. By adjusting the output of each power station, the system can achieve stable power supply under the conditions of wind and solar power generation fluctuations. The system dispatching gives priority to the use of wind and solar power and stores the excess.

[0044] Monitoring and control module: real-time monitoring of system operation status to ensure the coordinated operation of the modules. This module controls the energy distribution between wind power generation, photovoltaic power generation, energy storage pumping stations and cascade reservoir power stations, realizes dynamic adjustment of output, and optimizes the complementary dispatch of wind, photovoltaic, water and storage.

[0045] Specifically, through the coordinated work of the energy storage pump station, when there is excess wind and photovoltaic power generation, the system can store excess energy in the cascade reservoirs. When the demand for electricity increases, the control system will dispatch the cascade reservoirs to generate electricity to meet the load demand of the power grid.

[0046] According to the wind, photovoltaic, hydroelectric and water storage multi-energy complementary and intelligent dispatching system based on energy storage large pumps according to the embodiments of the present invention, the system connects wind power stations, photovoltaic power stations, large pump energy storage systems, cascade reservoir power stations and control systems to the power grid through power lines. The system uses excess wind power and photovoltaic power to pump water from downstream reservoirs to upstream through large pumps to achieve large-capacity cascade water energy storage. On the premise of ensuring stable power supply to the system, the system can absorb excess electricity in the wind, photovoltaic, hydroelectric and water storage multi-energy systems to form a green, efficient and flexible multi-energy complementary system to ensure safe and stable operation of the power grid and avoid waste of resources. In addition, the system can also absorb excess capacity of wind and solar power generation while meeting the output requirements of the multi-energy system, effectively reducing the phenomenon of wind and solar power abandonment.

[0047] In order to implement the above embodiment, Figure 2 As shown, this embodiment also provides a scheduling method based on a particle swarm optimization algorithm for a wind, solar, water storage multi-energy complementary and intelligent scheduling system based on a large energy storage pump, including:

[0048] S101, initialize the particle swarm. A group of particles (solutions) are randomly generated, and each individual corresponds to a possible scheduling solution. Each particle contains the current power generation (wind power, photovoltaic, hydropower, energy storage, etc.), fitness (i.e., objective function value), optimal position (its own historical best solution), and global optimal position (the group's historical best solution).

[0049] S102, calculate fitness. For each particle, the fitness is calculated based on the multi-objective function of minimizing output volatility and minimizing cost. The fitness is the comprehensive value of the weighted calculation of each objective, or the quality is judged by non-dominated sorting. The fitness is the minimum value of the inner layer objective and the outer layer objective with different weights:

[0050] Fitness=minZ=min[λZ1+(1-λ)Z2]

[0051] S103, updating particle positions and velocities. The velocity and position of each particle are updated according to the following formula:

[0052] v i =w·v i +c1·r1·(pbest i -x i )+c2·r2·(gbest-x i )

[0053] x i =x i +v i

[0054] In the formula, v i is the speed of the particle; w is the inertia weight, which controls the degree of influence of the particle flight; c1, c2 are learning factors, usually between [0, 2]; r1, r2 are random numbers, between [0, 1]; pbest i is the best position of the particle; gbest is the global best position; x i is the current position of the particle.

[0055] S104, check constraints. Ensure that the updated particle position meets all constraints: power generation capacity constraint, energy storage capacity constraint and power balance constraint. If the constraint is violated, reduce the fitness through the penalty function or adjust the particle to within the allowable range.

[0056] S105, update the best solution. Update the best solution. For each particle, if the current fitness is better than its historical best fitness, update its best position. If the current fitness is better than the global best fitness, update the global best position. The judgment of merit is based on non-dominated sorting and congestion.

[0057] S106, determine the stopping condition. Set the stopping condition (such as reaching the maximum number of iterations or the fitness change is less than the set threshold). If the stopping condition is met, output the optimal scheduling solution; otherwise, return to S103.

[0058] Specifically, an optimization scheduling model is established for the above-mentioned wind, solar, water and storage multi-energy complementarity and intelligent scheduling system, such as Figure 2 As shown, it specifically includes the inner and outer optimization objective functions and constraints:

[0059] The inner model objective is to reduce the volatility of the total system output to ensure the stability of the power grid. The optimization objective is to minimize the output volatility of the wind-solar-water-storage multi-energy complementary system after the equivalent load is adjusted by the hydropower station and the energy storage pump station, and establish the optimization objective function, which is expressed as follows:

[0060]

[0061] P total (t) = P wind (t)+P solar (t)+P hydro (t)-P pump (t)

[0062]

[0063] Where Z1 is the volatility of the multi-energy complementary system; t is the scheduling time; T is a scheduling period, which is 24; P total (t) is the output of the multi-energy complementary system in the tth dispatching period; is the average output power of the multi-energy complementary system in one cycle, that is, the average output power within the day; P wind (t) is the wind power generation power in period t; P solar (t) is the photovoltaic power generation power in period t; P hydro (t) is the hydropower generation power in period t; P pump (t) is the large pump energy storage power during period t.

[0064] The outer model objective is to improve economic benefits by minimizing the system's operation and maintenance costs. Taking the system's economic operation as the optimization goal, the wind, solar, water and storage multi-energy complementary system operation cost optimization objective function is established, and its expression is:

[0065]

[0066] Where Z2 is the total operating cost of the multi-energy complementary system; C wind is the unit wind power generation cost; C solar is the unit photovoltaic power generation cost; C hydro is the unit hydropower generation cost; Cpump is the unit energy storage cost.

[0067] The constraints mainly include power generation capacity constraints, energy storage capacity constraints and power balance constraints.

[0068] The power generation capacity constraints include wind power generation constraints, photovoltaic power generation constraints, and hydropower processing constraints. The constraint expressions are:

[0069] 0≤P wind (t)≤P wind,max

[0070] 0≤P solar (t)≤P solar,max

[0071] 0≤P hydro (t)≤P hydro,max

[0072] Where P wind,max is the maximum power of wind power generation; P solar,max is the maximum power of photovoltaic power generation; P hydro,max The maximum power of hydroelectric power.

[0073] Energy storage capacity constraints ensure that the energy state of the energy storage system based on large pump energy storage is within a reasonable range in each time period; based on the forecast of new energy output and load at the previous moment, the initial state of the energy storage pump station is determined, and according to the new energy output and load at time t, the actual state of the energy storage pump station is updated, the day-ahead operation strategy is tracked, and the compensation and allocation ratio are adjusted according to the deviation. The energy storage capacity constraint expression is:

[0074] 0≤P pump (t)≤P pump,max

[0075] E storage (t) = E storage (t-1)+η pump ·P pump (t)

[0076] E storage,min ≤E storage (t)≤E storage,max

[0077] In the formula, E storage (t) is the capacity of the cascade reservoir in the tth period; E storage (t-1) is the capacity of the cascade reservoir in the t-1th period; η pump is the efficiency of the energy storage pump; E storage,min is the minimum storage capacity of the cascade reservoirs; E storage,max It is the maximum storage capacity of the cascade reservoirs.

[0078] The power balance constraint ensures that in each time period t, the sum of all power generation and consumption resources (wind power, photovoltaic power, hydropower and energy storage pump power consumption) must be equal to the power load demand in that time period. The calculation formula is:

[0079] P wind (t)+P solar (t)+P hydro (t)+P pump (t) = P load (t)

[0080] Where P load (t) is the total load demand during period t.

[0081] The wind, solar, water and storage multi-energy complementarity and intelligent scheduling strategy designed based on the particle swarm optimization algorithm is illustrated by taking the cascade hydropower station in a certain river basin as an example. The operating power parameters of the energy storage pump station are determined by giving the hourly output of wind power and photovoltaic power stations in the river basin on different days. By accurately setting the power generation power of wind power and photovoltaic power stations in the river basin at each hour of the day, the present invention optimizes the working power parameters of the energy storage pump station. The natural resource overview of the river basin is as follows: the total installed capacity of hydropower reaches 1280MW, wind power is 400MW, photovoltaic power stations are 850MW, and the installed capacity of large-scale energy storage water pump stations is 300MW.

[0082] like Figure 3 As shown in the figure, under the scheduling strategy, only a small amount of power wasted occurred during the sunny period of the flood season, and the power was fully utilized in other time periods. For a 300MW large-scale energy storage water pump station, the power wasted rate on a typical day was accurately controlled within 0.9%.

[0083] The embodiment of the present invention combines multiple renewable energy sources such as wind energy, solar energy, and hydropower with efficient energy storage pump stations to form a multi-energy complementary energy supply system. At the same time, with the help of advanced intelligent scheduling algorithms, the system can monitor and analyze the changing trends of new energy power generation in real time, accurately predict electricity demand, and achieve real-time matching and intelligent scheduling of energy storage equipment and new energy power generation. This can not only effectively improve the utilization rate of new energy and reduce energy waste, but also enhance the flexibility and stability of the power grid and ensure the safe and stable operation of the power system.

[0084] In particular, the energy storage pump station utilizes the upper and lower reservoirs of the existing cascade hydropower stations, without interfering with the operation and storage capacity of the existing reservoirs; the construction scale of the energy storage pump station is mainly determined by the surplus water energy storage capacity that can be utilized in the upper or lower reservoir. The energy storage pump station adopts a high-lift, large-flow centrifugal pump, which is a high-performance centrifugal pump with variable speed under all working conditions.

[0085] In summary, the present invention, with its unique advantages, has broad application prospects and important strategic significance in promoting the transformation of energy structure and promoting green and low-carbon development.

[0086] The embodiment of the present invention innovatively uses a high-lift, large-flow energy storage pump to convert underutilized wind energy and light energy into pumped storage energy, thereby realizing efficient, large-scale storage and utilization of wind and solar power. The energy storage pump station is composed of a variable-speed water pump and an electric motor, and a high-capacity cascade water energy storage mechanism is constructed by connecting the water transmission line of the upper reservoir and the lower reservoir. In addition, the present invention performs intelligent scheduling optimization of the wind, light, and water storage multi-energy complementary system based on the particle swarm optimization algorithm, which effectively reduces the output volatility and improves the overall economic benefits. Through a highly integrated control system, the system can dynamically respond to fluctuations in wind, light, and water resources, flexibly adjust the output power, ensure the stable and large-scale grid connection of photovoltaic and wind power generation, significantly reduce electricity waste, reduce operating costs, enhance grid stability, and provide strong support for promoting green, low-carbon, and sustainable energy strategies.

[0087] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0088] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A wind, solar, water storage multi-energy complementary and intelligent dispatching system based on a large energy storage pump, characterized in that: include: The power generation module includes a wind power station, a photovoltaic power station and a cascade reservoir power station; among them, the wind power station and the photovoltaic power station are used to provide intermittent and unstable power output, and the cascade reservoir power station uses the water level difference of the reservoir to generate electricity and provide hydroelectric power for the power grid; Energy storage modules are used to pump water from downstream reservoirs to upstream reservoirs when there is excess wind or photovoltaic power generation, using the electricity that is not connected to the grid to form a high-capacity cascade hydropower storage system; The dispatching module uses the intelligent dispatching system to optimize the overall dispatching of the wind, solar and water storage systems based on weather forecast and load forecast information; The monitoring and control module is used to monitor the system operation status in real time and control the energy distribution between wind power generation, photovoltaic power generation, energy storage pumping stations and cascade reservoir power stations, realize dynamic adjustment of output, and optimize the complementary scheduling of wind, solar, water and storage multiple energy sources.

2. The system according to claim 1, characterized in that The energy storage module is a large pump energy storage system composed of a centrifugal pump with high lift and large flow.

3. The system according to claim 1, characterized in that The energy storage pump station adopts a high-lift, large-flow centrifugal pump, which is a high-performance centrifugal pump with variable speed under all working conditions. Through the coordinated work of the energy storage pump station, when wind energy and photovoltaic power generation are in excess, the system will store excess energy in the cascade reservoirs; when electricity demand increases, the control system will dispatch the cascade reservoirs to generate electricity.

4. A scheduling method based on particle swarm optimization algorithm applied to the wind, solar, water storage multi-energy complementary and intelligent scheduling system based on energy storage large pump as claimed in claim 1, characterized in that: include: S101, initialize the particle swarm; randomly generate a group of particles, each individual corresponds to a scheduling plan, and each particle contains the current power generation, fitness, optimal position, and global optimal position; S102, calculate fitness; for each particle, calculate fitness based on the multi-objective function of minimizing output volatility and minimizing cost; fitness is the minimum value of the inner layer objective and the outer layer objective with different weights: Fitness=minZ=min[λZ1+(1-λ)Z2] S103, updating the particle position and velocity; updating the velocity and position of each particle according to the following formula: v i =w·v i +c1·r1·(pbest i -x i )+c2·r2·(gbest-x i ) x i =x i +v i In the formula, v i is the speed of the particle; w is the inertia weight, which controls the degree of influence of particle flight; c1, c2 are learning factors; r1, r2 are random numbers between [0, 1]; pbest i is the best position of the particle; gbest is the global best position; x i is the current position of the particle; S104, check the constraints; ensure that the updated particle position meets all constraints; if the constraints are violated, reduce the fitness through the penalty function or adjust the particle to the allowed range; S105, update the best solution; for each particle, if the current fitness is better than its historical best fitness, update its best position; if the current fitness is better than the global optimal fitness, update the global best position; the judgment of goodness and badness is based on non-dominated sorting and congestion; S106, determine the stop condition; set the stop condition, if the stop condition is met, output the optimal scheduling plan; otherwise, return to S103.

5. The method according to claim 4, characterized in that It also includes the establishment of an optimization scheduling model, including internal and external optimization objective functions and constraints; among them, The inner model objective is to minimize the output volatility of the wind-solar-water-storage multi-energy complementary system after the equivalent load is adjusted by the hydropower station and the energy storage pump station. The optimization objective function is established and the expression is: P total (t)=P wind (t)+P solar (t)+P hydro (t)-P pump (t) Where Z1 is the volatility of the multi-energy complementary system; t is the scheduling time; T is a scheduling cycle; P total (t) is the output of the multi-energy complementary system in the tth dispatching period; is the average output power of the multi-energy complementary system in one cycle, that is, the average output power within the day; P wind (t) is the wind power generation power in period t; P solar (t) is the photovoltaic power generation power in period t; P hydro (t) is the hydropower generation power in period t; P pump (t) is the large pump energy storage power during period t.

6. The method according to claim 5, characterized in that The outer model objective is to optimize the economic operation of the system and establish the objective function for optimizing the operation cost of the wind-solar-water-storage multi-energy complementary system. The expression is: Where Z2 is the total operating cost of the multi-energy complementary system; C wind is the unit wind power generation cost; C solar is the unit photovoltaic power generation cost; C hydro is the unit hydropower generation cost; C pump is the unit energy storage cost.

7. The method according to claim 6, characterized in that The constraints also include: Power generation capacity constraints include wind power generation constraints, photovoltaic power generation constraints, and hydropower processing constraints. The constraint expressions are: 0≤P wind (t)≤P wind,max 0≤P solar (t)≤P solar,max 0≤P hydro (t)≤P hydro,max Where P wind,max is the maximum power of wind power generation; P solar,max is the maximum power of photovoltaic power generation; P hydro,max The maximum power of hydroelectric power.

8. The method according to claim 7, characterized in that The constraints also include: energy storage capacity constraints, which are used to ensure that the energy state of the energy storage system based on large pump energy storage is within a preset range in each time period; based on the new energy output and load forecast at the previous moment, the initial state of the energy storage pump station is determined, and according to the new energy output and load conditions at time t, the actual state of the energy storage pump station is updated, the day-ahead operation strategy is tracked, and the compensation and sharing ratio are adjusted according to the deviation.

9. The method according to claim 8, characterized in that The energy storage capacity constraint expression is: 0≤P pump (t)≤P pump,max AND storage (t)=E storage (t-1)+η pump ·P pump (t) E storage,min ≤E storage (t)≤E storage,max In the formula, E storage (t) is the capacity of the cascade reservoir in the tth period; E storage (t-1) is the capacity of the cascade reservoir in the t-1th period; η pump is the efficiency of the energy storage pump; E storage,min is the minimum storage capacity of the cascade reservoirs; E storage,max It is the maximum storage capacity of the cascade reservoirs.

10. The method according to claim 9, characterized in that The constraints also include: power balance constraints, which are used to ensure that in each time period t, the sum of all power generation and power consumption resources is equal to the power load demand in the time period, and the calculation formula is: P wind (t)+P solar (t)+P hydro (t)+P pump (t)=P load (t) Where P load (t) is the total load demand during period t.

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