Optimized dispatching method for cascade water, wind, light and storage battery complementary power generation system

Abstract

The invention relates to an optimal scheduling method for a cascade water, wind, light and storage battery complementary power generation system, and the method comprises the steps: determining an optimal scheduling target function of the cascade water, wind, light and storage battery complementary power generation system based on an optimization target of the power generation economic scheduling cost of the cascade water, wind, light and storage battery complementary power generation system; determining constraint conditions of the cascade water-wind-light-storage complementary power generation system based on hydraulic connection and electric connection between the cascade hydropower station and the hybrid pumped storage power station; and constructing an optimal scheduling model of the cascade water-wind-light-storage-battery complementary power generation system, solving the optimal scheduling model of the cascade water-wind-light-storage-battery complementary power generation system, and generating an optimal scheduling strategy of the cascade water-wind-light-storage-battery complementary power generation system. Therefore, the problems that in the related technology, due to the fact that collaborative optimization design of wind and light uncertainty, cascade hydropower station hydraulic connection and hybrid pumped storage power station coupling relation cannot be considered, the combined regulation potential of cascade hydropower, wind and light storage cannot be fully played, and operation risks caused by high-proportion new energy grid connection are difficult to effectively deal with are solved.

Description

Technical Field

[0001] This application relates to the field of multi-energy complementary optimal dispatching technology for power systems, and in particular to an optimal dispatching method for a cascade hydro-wind-solar-storage complementary power generation system. Background Technology

[0002] Extreme weather events can cause significant fluctuations in the output of new energy sources, exhibiting stronger intermittency and volatility. Hydropower units, with their rapid ramp-up rates, are an ideal power source for handling the randomness and volatility of wind and solar power. While there is considerable research on the coordinated and optimized operation of renewable energy sources such as wind and solar power with hydropower, there is a lack of research on the optimal scheduling of cascade hydropower-wind-solar hybrid pumped-storage complementary power generation systems considering the uncertainties of wind and solar power. Traditional power system scheduling models are no longer adequate for the optimal scheduling of multi-energy power systems containing a high proportion of renewable energy and hybrid pumped-storage systems.

[0003] In related technologies, stochastic optimization or scenario analysis methods are used to characterize the uncertainty of wind and solar power output. A complementary optimization scheduling model of water-wind-solar-storage is constructed and solved by algorithms such as double-layer nested optimization framework and mixed integer linear programming to achieve load allocation and power output coordination among power sources.

[0004] However, the relevant technologies, which focus on the complementary scheduling of single hydropower and wind and solar power, cannot take into account the uncertainties of wind and solar power, the hydraulic connection of cascade hydropower stations and the synergistic optimization design of the coupling relationship of mixed pumped storage power stations. As a result, the joint regulation potential of cascade hydropower and pumped storage cannot be fully utilized, it is difficult to effectively cope with the operational risks brought about by the high proportion of new energy grid connection, and it is impossible to guarantee the improvement of clean energy consumption capacity and the economic and stable operation of the power grid. Therefore, it is urgent to improve. Summary of the Invention

[0005] This application provides an optimized scheduling method for a cascade hydropower-wind-solar-storage complementary power generation system to solve the problems in related technologies. Because the focus is on the complementary scheduling of single hydropower and wind and solar power, it is impossible to take into account the uncertainty of wind and solar power, the hydraulic connection of cascade hydropower stations and the coupling relationship of mixed pumped storage power stations. This results in the inability to fully utilize the joint regulation potential of cascade hydropower and pumped storage, and it is difficult to effectively cope with the operational risks brought about by the high proportion of new energy grid connection.

[0006] The first aspect of this application provides an optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system, comprising the following steps: determining an optimized scheduling objective function for the cascade hydro-wind-solar-storage complementary power generation system based on the optimization objective of the economic scheduling cost of the power generation of the cascade hydro-wind-solar-storage complementary power generation system; determining the constraints of the cascade hydro-wind-solar-storage complementary power generation system based on the hydraulic and electrical connections between the cascade hydropower stations and the mixed pumped storage power stations; constructing an optimized scheduling model for the cascade hydro-wind-solar-storage complementary power generation system based on the optimized scheduling objective function and the constraints, and solving the optimized scheduling model to generate an optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0007] Through the aforementioned technical means, the embodiments of this application can take minimizing the economic dispatch cost of power generation as the objective, and consider the hydraulic and electrical connections between cascade hydropower stations and hybrid pumped storage power stations to determine the objective function and constraints. An optimal dispatch model for the cascade hydropower-wind-solar-storage complementary power generation system is constructed, and the optimal dispatch strategy is obtained by solving the optimal dispatch model. This provides a specific optimal dispatch method for the complementary operation of the cascade hydropower-wind-solar-storage system, making the model more closely match the actual operating characteristics of the system, possessing effectiveness, efficiency, and rationality. It achieves the optimal decision-making in terms of global economics, thereby significantly improving the level of clean energy consumption and ensuring the economic operation of the power grid while ensuring grid security.

[0008] Optionally, in one embodiment of this application, the expression of the optimization scheduling objective function is: , in, For total cost, for Time-of-use system electricity purchase cost The operating cost of the cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, The operating cost of the hybrid pumped storage power station.

[0009] Through the above-mentioned technical means, the embodiments of this application can determine the optimal scheduling objective function of the cascade hydro-wind-solar-storage complementary power generation system. The optimization objective is to minimize the economic scheduling cost of the cascade hydro-wind-solar-pumped-storage complementary power generation system. This achieves a comprehensive and refined consideration of the scheduling economy, avoids the overall economic imbalance caused by optimizing a single cost dimension, and can accurately select the economically optimal scheduling scheme, thereby improving the economic rationality of system operation.

[0010] Optionally, in one embodiment of this application, the step of solving the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system to generate an optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system includes: setting the size of the whale population and generating initial position information of individual whales to obtain an initial whale pod; calculating the objective function value of the initial position information of all whales in the initial whale pod according to the objective function of the cascade hydro-wind-solar-storage complementary optimal scheduling model, and sorting them based on the objective function value to select a leader; based on the position of the leader, introducing the movement of calves toward the leader, and based on the predation and predator escape behavior during whale migration, having the leader explore new territory, and updating the position information of all whales in the whale pod based on the movement and the new territory to obtain updated position information of all whales; based on the updated position information of all whales, sorting the whale pod and reselecting a leader, obtaining an optimal leader after multiple iterations, and obtaining an optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system based on the position information of the optimal leader.

[0011] Through the above-mentioned technical means, the embodiments of this application can use the whale migration algorithm to solve the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system. By simulating the behavior of whale populations for iterative optimization, it has both global exploration and local development capabilities. The juvenile whale approach mechanism strengthens the convergence of the population to the optimal solution, and the leader's domain exploration ability improves the global optimization range. It can quickly and accurately find the optimal scheduling scheme, which is suitable for the multi-variable and strongly coupled optimization requirements of the cascade hydro-wind-solar-storage system.

[0012] Optionally, in one embodiment of this application, before setting the size of the whale population and generating the initial location information of individual whales, the process includes: setting the output power of the hydropower station and the hybrid pumped storage power station as an individual whale, setting the upper and lower limits of the output of the hydropower station and the hybrid pumped storage power station; and determining the movement range of the whale population based on the upper and lower limits of the output of the hydropower station and the hybrid pumped storage power station.

[0013] Through the above-mentioned technical means, the embodiments of this application can clarify the physical meaning of an individual whale as the power plant output power process before the initialization of the whale pod, and define the population movement range based on the upper and lower limits of the power plant output. This achieves a precise mapping between the optimization variables and the actual operating characteristics of the power plant. The constraint of the population movement range avoids the exploration of the invalid solution space, greatly improves the effectiveness of the initial population of the algorithm and the convergence efficiency of subsequent iterations, and ensures that the power plant output power in the final scheduling strategy meets the equipment safe operation threshold, thus guaranteeing the engineering feasibility of the scheduling scheme from the source.

[0014] Optionally, in one embodiment of this application, the step of obtaining an optimal leader after multiple iterations and obtaining an optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system based on the location information of the optimal leader includes: if, during the iteration process, it is determined that the objective function value of the leader has not been updated within a preset fixed number of iterations, the iteration process is terminated, and the leader at the time of iteration termination is taken as the optimal leader; the optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system is obtained based on the location information of the optimal leader; if a preset number of iterations is reached, the iteration process is terminated to determine the optimal leader, and the location information of the optimal leader is output to determine the optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0015] Through the above technical means, the embodiments of this application can set a dual iteration termination mechanism where the objective function value has not been updated for a preset fixed number of iterations and has reached a preset termination iteration number. This can dynamically balance the solution accuracy and efficiency. When the objective function value tends to be stable, the iteration is terminated in advance to avoid the waste of computing resources and time loss caused by invalid iterations. The preset termination number provides a clear time boundary for the solution process, ensuring the reliability and timeliness of the optimization scheduling model solution process.

[0016] A second aspect of this application provides an optimized scheduling device for a cascade hydro-wind-solar-storage complementary power generation system, comprising: a first determining module, configured to determine an optimized scheduling objective function for the cascade hydro-wind-solar-storage complementary power generation system based on an optimization objective of the economic scheduling cost of the power generation of the cascade hydro-wind-solar-storage complementary power generation system; a second determining module, configured to determine constraints on the cascade hydro-wind-solar-storage complementary power generation system based on the hydraulic and electrical connections between the cascade hydropower stations and the hybrid pumped storage power stations; and an optimization module, configured to construct an optimized scheduling model for the cascade hydro-wind-solar-storage complementary power generation system based on the optimized scheduling objective function and the constraints, and solve the optimized scheduling model to generate an optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0017] Through the aforementioned technical means, this application embodiment aims to minimize the economic dispatch cost of power generation, and considers the hydraulic and electrical connections between cascade hydropower stations and hybrid pumped storage power stations. It determines the objective function and constraints, constructs an optimal dispatch model for a cascade hydropower-wind-solar-storage complementary power generation system, and solves the optimal dispatch model to obtain an optimized dispatch strategy. This provides a specific optimized dispatch method for the complementary operation of the cascade hydropower-wind-solar-storage system, making the model more closely aligned with the actual operating characteristics of the system, possessing effectiveness, efficiency, and rationality. It achieves globally optimal economic decision-making, thereby significantly improving the level of clean energy consumption and ensuring the economic operation of the power grid while guaranteeing grid security.

[0018] Optionally, in one embodiment of this application, the expression of the optimization scheduling objective function is: , in, For total cost, for Time-of-use system electricity purchase cost The operating cost of the cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, The operating cost of the hybrid pumped storage power station.

[0019] Through the above-mentioned technical means, the embodiments of this application can determine the optimal scheduling objective function of the cascade hydro-wind-solar-storage complementary power generation system. The optimization objective is to minimize the economic scheduling cost of the cascade hydro-wind-solar-pumped-storage complementary power generation system. This achieves a comprehensive and refined consideration of the scheduling economy, avoids the overall economic imbalance caused by optimizing a single cost dimension, and can accurately select the economically optimal scheduling scheme, thereby improving the economic rationality of system operation.

[0020] Optionally, in one embodiment of this application, the optimization module includes: an initialization unit, configured to set the size of the whale population and generate initial position information of individual whales to obtain an initial whale pod; a sorting unit, configured to calculate the objective function value of the initial position information of all whales in the initial whale pod according to the objective function of the cascade hydro-wind-solar-storage complementary optimization scheduling model, and sort them based on the objective function value to select a leader; an update unit, configured to introduce movement of calves toward the leader based on the position of the leader, and based on the predation and predator escape behavior of whales during migration, have the leader explore new areas, and update the position information of all whales in the whale pod based on the movement and the new areas to obtain the updated position information of all whales; and an optimization scheduling unit, configured to sort the whale pod and reselect a leader based on the updated position information of all whales, and obtain the optimal leader after multiple iterations, so as to obtain the optimized scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system based on the position information of the optimal leader.

[0021] Through the above-mentioned technical means, the embodiments of this application can use the whale migration algorithm to solve the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system. By simulating the behavior of whale populations for iterative optimization, it has both global exploration and local development capabilities. The juvenile whale approach mechanism strengthens the convergence of the population to the optimal solution, and the leader's domain exploration ability improves the global optimization range. It can quickly and accurately find the optimal scheduling scheme, which is suitable for the multi-variable and strongly coupled optimization requirements of the cascade hydro-wind-solar-storage system.

[0022] Optionally, in one embodiment of this application, it includes: a first setting unit, configured to set the output power process of the hydropower station and the hybrid pumped storage power station to that of an individual whale before setting the size of the whale population and generating the initial position information of individual whales; and to set the upper and lower limits of the output of the hydropower station and the hybrid pumped storage power station; and a second setting unit, configured to determine the movement range of the whale population based on the upper and lower limits of the output of the hydropower station and the hybrid pumped storage power station before setting the size of the whale population and generating the initial position information of individual whales.

[0023] Through the above-mentioned technical means, the embodiments of this application can clarify the physical meaning of an individual whale as the power plant output power process before the initialization of the whale pod, and define the population movement range based on the upper and lower limits of the power plant output. This achieves a precise mapping between the optimization variables and the actual operating characteristics of the power plant. The constraint of the population movement range avoids the exploration of the invalid solution space, greatly improves the effectiveness of the initial population of the algorithm and the convergence efficiency of subsequent iterations, and ensures that the power plant output power in the final scheduling strategy meets the equipment safe operation threshold, thus guaranteeing the engineering feasibility of the scheduling scheme from the source.

[0024] Optionally, in one embodiment of this application, the optimization scheduling unit includes: a first termination unit, configured to terminate the iteration process if, during the iteration process, it is determined that the objective function value of the leader has not been updated within a preset fixed number of iterations, and to take the leader at the time of iteration termination as the optimal leader, and to obtain the optimization scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system based on the location information of the optimal leader; and a second termination unit, configured to terminate the iteration process if a preset number of termination iterations is reached, to determine the optimal leader, and to output the location information of the optimal leader and the optimization scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system.

[0025] Through the above technical means, the embodiments of this application can set a dual iteration termination mechanism where the objective function value has not been updated for a preset fixed number of iterations and has reached a preset termination iteration number. This can dynamically balance the solution accuracy and efficiency. When the objective function value tends to be stable, the iteration is terminated in advance to avoid the waste of computing resources and time loss caused by invalid iterations. The preset termination number provides a clear time boundary for the solution process, ensuring the reliability and timeliness of the optimization scheduling model solution process.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system.

[0029] This application's embodiments aim to minimize the economic dispatch cost of power generation, considering the hydraulic and electrical connections between cascade hydropower stations and mixed pumped storage power stations. By determining the objective function and constraints, an optimal dispatch model for a cascade hydropower-wind-solar-storage complementary power generation system is constructed. Solving this model yields an optimized dispatch strategy, providing a specific optimized dispatch method for the complementary operation of the cascade hydropower-wind-solar-storage system. This makes the model more closely reflect the actual operating characteristics of the system, possessing effectiveness, efficiency, and rationality, achieving globally optimal economic decision-making. Furthermore, while ensuring grid security, it significantly improves the level of clean energy consumption and guarantees the economic operation of the grid. This solves the problems in related technologies where the focus on complementary dispatch of single hydropower and wind / solar power fails to consider the uncertainties of wind and solar power, the hydraulic connections of cascade hydropower stations, and the synergistic optimization design of the coupling relationship between mixed pumped storage power stations. This results in the inability to fully utilize the joint regulation potential of cascade hydropower and pumped storage, and difficulty in effectively addressing the operational risks brought about by a high proportion of new energy grid connection.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system provided according to an embodiment of this application; Figure 2 This is a flowchart of an optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system according to an embodiment of this application; Figure 3 This is a power balance diagram of a cascade hydropower-wind-solar hybrid storage system according to an embodiment of this application; Figure 4 This is a comparison chart of wind and solar power output and energy consumption according to an embodiment of this application; Figure 5This is a diagram showing the real-time number of operating hydroelectric power units and mixed-storage power station units according to an embodiment of this application; Figure 6 This is a convergence curve diagram for different algorithms provided according to one embodiment of this application; Figure 7 This is a schematic diagram of the structure of an optimized dispatching device for a cascade hydro-wind-solar-storage complementary power generation system according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Optimization and dispatching device for cascade hydro-wind-solar-storage complementary power generation system; 100-First determination module, 200-Second determination module, 300-Optimization module; 801-Memory, 802-Processor, 803-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following describes the optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system according to an embodiment of this application, with reference to the accompanying drawings. In response to the aforementioned related technologies, which focus on the complementary scheduling of single hydropower and wind / solar power, and fail to consider the uncertainties of wind and solar power, the hydraulic connections of cascade hydropower stations, and the synergistic optimization design of the coupling relationship between mixed pumped storage power stations, the combined regulation potential of cascade hydropower and pumped storage cannot be fully utilized, and the operational risks brought about by the high proportion of new energy grid connection are difficult to effectively address. This application provides an optimized scheduling method for a cascade hydropower-wind-solar-storage complementary power generation system. In this method, the minimum economic scheduling cost of power generation is taken as the objective, and the hydraulic and electrical connections between cascade hydropower stations and mixed pumped storage power stations are considered. The objective function and constraints are determined, and an optimized scheduling model for the cascade hydropower-wind-solar-storage complementary power generation system is constructed. Solving the optimized scheduling model yields an optimized scheduling strategy, thus providing a specific optimized scheduling method for the complementary operation of the cascade hydropower-wind-solar-storage system. This makes the model more consistent with the actual operating characteristics of the system, possessing effectiveness, efficiency, and rationality, and achieving the optimal decision-making in terms of global economics. Consequently, under the premise of ensuring grid security, the level of clean energy consumption is significantly improved, and the economic operation of the grid is guaranteed. This solves the problems in related technologies, such as the inability to fully utilize the joint regulation potential of cascade hydropower and pumped storage due to the focus on the complementary scheduling of single hydropower and wind and solar power, the inability to take into account the uncertainty of wind and solar power, the hydraulic connection of cascade hydropower stations and the coupling relationship of mixed pumped storage power stations, and the difficulty in effectively coping with the operational risks brought about by the high proportion of new energy grid connection.

[0035] Specifically, Figure 1 This is a flowchart illustrating an optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system provided in an embodiment of this application.

[0036] like Figure 1 As shown, the optimized scheduling method for this cascade hydro-wind-solar-storage complementary power generation system includes the following steps: In step S101, the optimization scheduling objective function of the cascade hydro-wind-solar-storage complementary power generation system is determined based on the optimization objective of the power generation economic dispatch cost of the cascade hydro-wind-solar-storage complementary power generation system.

[0037] It is understood that the economic dispatch cost of the cascade hydro-wind-solar-storage complementary power generation system in this application embodiment may include the system power purchase cost, the operating cost of the cascade hydropower station, the operating cost of the photovoltaic power station, the operating cost of the wind power station, and the operating cost of the hybrid pumped storage power station.

[0038] In practical implementation, the embodiments of this application can determine the optimal scheduling objective function of the cascade hydro-wind-solar-storage complementary power generation system based on the optimization objective of the economic dispatch cost of the power generation system. Through historical dispatch data statistics and on-site parameter collection, the cost components of each unit of the cascade hydro-wind-solar-storage system are clarified, including system power purchase cost, cascade hydropower station operating cost, photovoltaic power station operating cost, wind power station operating cost, and hybrid pumped storage power station operating cost. Based on the correspondence between cost elements and dispatch periods, linear weighting or nonlinear fitting techniques can be used to transform the objective of minimizing the economic dispatch cost of power generation into a mathematical function containing each cost item, thus completing the determination of the optimal scheduling objective function.

[0039] This application embodiment can determine the optimal scheduling objective function of the cascade hydro-wind-solar-storage complementary power generation system based on the optimization objective of the power generation economic dispatch cost of the cascade hydro-wind-solar-storage complementary power generation system. This avoids economic losses caused by objective ambiguity during the dispatch process. At the same time, the quantified function form provides a standardized evaluation basis for subsequent model solving, helps to select the economically optimal dispatch direction, and lays the foundation for improving the economic efficiency of system operation.

[0040] Optionally, in one embodiment of this application, the expression for the optimization scheduling objective function is: , in, For total cost, for Time-of-use system electricity purchase cost For the operating costs of cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, This refers to the operating costs of a hybrid pumped storage power station.

[0041] For example, the expressions for all costs in the embodiments of this application are as follows: , , , , , in, for Time-of-use system electricity purchase cost For the operating costs of cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, The operating cost of a hybrid pumped storage power station; for Electricity purchase status during specific time periods; The number of cascade hydropower stations; The number of photovoltaic power plants; The number of photovoltaic power plants; The number of hybrid pumped storage power stations; This is the electricity purchase cost coefficient; This refers to the power generation cost coefficient of cascade hydropower stations; This represents the power generation cost coefficient of a photovoltaic power plant. This is the power generation cost coefficient for wind power plants; This is the pumping cost coefficient for the reversible unit of a mixed-use power station. This represents the power generation cost coefficient for reversible units in a hybrid power station. This represents the power generation cost coefficient for conventional units in a hybrid power station. refer to Time period Power purchase capacity under the specified conditions; for Time period Dispatch and output of cascade hydropower stations; for Time period Dispatch output of photovoltaic power plants at all levels; for Time period Dispatch and output of wind power stations at all levels; , , These are the amounts of abandoned hydropower, solar power, and wind power, respectively. for Time period Average pumping power of reversible units in a mixed-storage power station; for Time period Average power generation of reversible units in a mixed-use power station; for Time period Average output of conventional units in a hybrid power station; , , These are the penalty coefficients for water, solar, and wind abandonment, respectively. , They are respectively Time period The state variables of a reversible unit in a hybrid power station, when and This indicates that the reversible unit is in pumping mode. and The time indicates that the reversible unit is in the power generation state, and the time is 0 when it is in the shutdown state; They are respectively Time period The state variables of a conventional unit in a hybrid power station, when A value of 0 indicates that the unit is in a generating state, while a value of 0 indicates that it is in a shutdown state.

[0042] Furthermore, the expression for the optimization scheduling objective function is as follows: , in, For total cost, for Time-of-use system electricity purchase cost For the operating costs of cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, This refers to the operating costs of a hybrid pumped storage power station.

[0043] The embodiments of this application can determine the optimal scheduling objective function of a cascade hydro-wind-solar-storage complementary power generation system. The optimization objective is to minimize the economic scheduling cost of the cascade hydro-wind-solar-pumped-storage complementary power generation system. This achieves a comprehensive and refined consideration of scheduling economy, avoids overall economic imbalance caused by optimization of a single cost dimension, and can accurately select the economically optimal scheduling scheme, thereby improving the economic rationality of system operation.

[0044] In step S102, the constraints of the cascade hydropower-wind-solar-storage complementary power generation system are determined based on the hydraulic and electrical connections between the cascade hydropower stations and the hybrid pumped storage power stations.

[0045] It is understood that, in the embodiments of this application, hydraulic connection can be understood as the relationship in terms of water distribution, water flow propagation, and water level coupling; electrical connection can be understood as the mutual constraints in terms of power balance, voltage stability, and ramp rate coordination; the constraints can include conventional power generation constraints and hybrid pumped storage constraints. Conventional power generation constraints can include power balance constraints, water balance constraints, inflow constraints, ecological flow constraints, power generation flow constraints, reservoir capacity constraints, reservoir capacity boundary constraints, water level-reservoir capacity relationship constraints, tailrace water level-discharge flow relationship constraints, conventional unit output constraints, unit ramp rate constraints, head constraints, conventional unit output characteristic constraints, and wind and solar power grid connection constraints. Hybrid pumped storage constraints can include hybrid pumped storage power station water balance constraints, pumping flow constraints, pumping power constraints, power generation constraints, pumping and power generation mutual exclusion constraints, pumping power characteristic constraints, and reserve constraints.

[0046] For example, embodiments of this application can determine the operational constraints of a cascade hydropower-wind-solar-storage complementary power generation system based on the hydraulic and electrical connections between the cascade hydropower station and the hybrid pumped storage power station, including conventional power generation constraints and hybrid pumped storage constraints.

[0047] Specifically, conventional power generation constraints may include power balance constraints, water balance constraints, inflow constraints, ecological flow constraints, power generation flow constraints, reservoir capacity constraints, reservoir capacity boundary constraints, water level-reservoir capacity relationship constraints, tailwater level-discharge relationship constraints, conventional unit output constraints, unit ramping constraints, head constraints, conventional unit output characteristics constraints, and wind and solar power grid connection constraints.

[0048] The expression for the power balance constraint is: , In the formula, For the system Load power at different times and states.

[0049] The expression for the water balance constraint is: , , In the formula, and Hydropower stations The reservoir capacity at the beginning and end of the upstream reservoir's operation period, in 100 million kilowatts. ; and Hydropower stations The reservoir capacity at the beginning and end of the downstream reservoir's operation period, in 100 million kilowatts. ; for Average inbound flow rate over a given time period ; and They are respectively Periodic hydroelectric power station Power generation flow and water wastage, in units of ; The time intervals for each period are expressed in units of 1 / 2. .

[0050] The expression for the inbound flow constraint is: , In the formula, For the hydroelectric power station Total water inflow during the period, in units of ; for Periodic hydroelectric power station The interval of natural inflow, in units of ; It is with the hydroelectric power station A collection of upstream reservoirs with direct hydraulic connections; The upstream reservoir's water release flow rate, in units of ; The water flow delay time from the upstream hydropower station to the downstream hydropower station, in units of .

[0051] The expression for ecological flow constraints is: , In the formula, For hydroelectric power station The ecological flow lower limit requirement, in units of ; For hydroelectric power station The ecological flow limit requirement, in units of .

[0052] The expression for the power generation flow constraint is: , In the formula, , Hydropower stations The minimum and maximum power generation flow rates of the hydro turbine, in units of .

[0053] The expression for the storage capacity constraint is: , In the formula, , Hydropower stations Dead storage capacity and normal storage level corresponding to storage capacity, in 100 million liters. .

[0054] The expression for the storage capacity boundary constraint is: , In the formula, and These represent the reservoir capacity at the beginning and end of the operation period, in billions of units. ; , These represent the reservoir capacity boundaries at the beginning and end of the operation period, respectively, in billions of tons. .

[0055] The expression for the water level-reservoir capacity relationship constraint is: , In the formula, This is the water level-reservoir capacity relationship curve. For hydroelectric power station The upstream water level, in units of .

[0056] The expression for the tailrace level-discharge ratio constraint is: , In the formula, The curve showing the relationship between tailwater level and discharge rate. For hydroelectric power station The tailwater level, in units of .

[0057] The expression for the output constraint of a conventional unit is: , In the formula, , Hydropower stations The upper and lower limits of the generating capacity of the unit, in units of ; For hydroelectric power station The unit in State variables for a given time period .when When it is in power generation state, when It is currently in a shutdown state.

[0058] The expression for the unit ramp-up constraint is: , In the formula, For hydroelectric power station of The ramp-up limit for the No. 1 unit refers to the permissible fluctuation range of the unit within a unit of time.

[0059] The expression for the head constraint is: , In the formula, The unit is the hydroelectric head used for power generation. ; Head loss, unit: ; The expression for the output characteristic constraint of a conventional unit is: , In the formula, This is the output coefficient.

[0060] The expression for the grid connection power constraint of wind and solar power is: , , In the formula, For the scene Photovoltaic power output ; For the scene Wind power output, in units .

[0061] Furthermore, the constraints of the hybrid pumped storage system can include water balance constraints, pumping flow constraints, pumping power constraints, power generation constraints, pumping and power generation mutual exclusion constraints, pumping power characteristic constraints, and reserve constraints.

[0062] The expression for the water balance constraint of a hybrid pumped storage power station is: , , In the formula, and These are hybrid power stations The reservoir capacity at the beginning and end of the upstream reservoir's operation period, in 100 million kilowatts. ; and These are hybrid power stations The reservoir capacity at the beginning and end of the downstream reservoir's operation period, in 100 million kilowatts. ; for Average inbound flow rate over a given time period, in units of ; for The amount of water pumped from the downstream reservoir to the upstream reservoir during a given period, in units of... ; , They are respectively Time-of-use hybrid power station Reversible unit power generation flow and conventional unit power generation flow, in units of ; for Time-of-use hybrid power station Water volume, in units ; The time intervals for each period are expressed in units of 1 / 2. .

[0063] The expression for the pumping flow constraint is: , In the formula, for The amount of water pumped from the downstream reservoir to the upstream reservoir during a given period, in units of... ; , These are the upper and lower limits for pumping flow rate.

[0064] The expression for the pumping power constraint is: , In the formula, For mixed-use power stations Reversible units Pumping power from downstream reservoir to upstream reservoir during a given period, in units of ; , These are the upper and lower limits of pumping power.

[0065] The expression for the power generation constraint is: , In the formula, For mixed-use power stations Reversible units Power generation per period, in units ; , These are the upper and lower limits of the generating capacity of reversible generator units.

[0066] , In the formula, For mixed-use power stations conventional units Power generation per time period, in units ; , These are the upper and lower limits of the power generation capacity of conventional generating units.

[0067] The expression for the mutual exclusion constraint of pumped hydro power generation is: , In the formula, , These refer to the pumping power and generating power of the reversible unit, respectively, indicating that the reversible unit cannot pump water and generate electricity simultaneously at the same time.

[0068] The expression for the pumping power characteristic constraint is: , In the formula, This is the output coefficient.

[0069] The positive and negative reserve capacity requirements of the complementary power generation system are provided by cascade hydropower and hybrid pumped storage power stations. The sum of the day-ahead dispatch output of the wind and solar power portion and the day-ahead dispatch output of the cascade hydropower portion is used as the reserve requirement of the complementary system. The positive and negative reserve constraints are as follows: , , In the formula, , The spinning reserve factor for both photovoltaic and wind power outputs is set to 10%. The rotational reserve factor for the output of the cascade hydropower is set at 4%.

[0070] The embodiments of this application can determine the constraints of the cascade hydro-wind-solar-storage complementary power generation system based on the hydraulic and electrical connections between the cascade hydropower stations and the hybrid pumped storage power stations. This allows the constraints to accurately match the actual operating characteristics of the multi-energy system, avoiding the infeasibility of the scheduling strategy due to neglecting the coupling relationship. The hydraulic constraints ensure the rational allocation of water resources and the safe operation of the cascade reservoirs, while the electrical constraints ensure the system's power balance and voltage stability, mitigating the power impact caused by fluctuations in wind and solar power output. The synergy of these two constraints ensures that the subsequently generated scheduling strategy meets economic objectives while possessing sufficient engineering feasibility and safety reliability.

[0071] In step S103, based on the optimization scheduling objective function and constraints, an optimization scheduling model for the cascade hydro-wind-solar-storage complementary power generation system is constructed, and the optimization scheduling model for the cascade hydro-wind-solar-storage complementary power generation system is solved to generate an optimization scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0072] It is understood that the optimized scheduling model of the cascade hydro-wind-solar-storage complementary power generation system in the embodiments of this application can be used to solve the scheduling scheme that minimizes economic costs under the premise of meeting the safety operation constraints.

[0073] For example, embodiments of this application can integrate the optimization scheduling objective function and constraints into a complete mathematical model, clarifying the variable dimensions and constraint boundaries of the model; the optimal scheduling model of the cascade hydropower-wind-solar-storage complementary power generation system can be solved by the whale migration algorithm, iteratively calculating the optimal solution of the model, and then transforming the optimal solution into a concrete scheduling strategy, which may include the outflow and power generation of the cascade hydropower station at each time period, the power output upper limit planning of the wind power station and the photovoltaic power station, the pumping / power generation period and power allocation of the hybrid pumped storage power station, the power purchase of the system at each time period, etc., and the feasibility of the strategy can be verified through simulation.

[0074] The embodiments of this application can integrate objective functions and constraints to construct an optimized scheduling model and solve for scheduling strategies, ensuring the coordinated consideration of economic objectives and safety constraints, guaranteeing the efficient discovery of optimal solutions, adapting to the complex characteristics of cascade hydro-wind-solar-storage complementary power generation systems, effectively coordinating the operation of various energy units, minimizing economic costs while ensuring system safety and stability, and improving the absorption capacity of clean energy such as wind and solar.

[0075] Optionally, in one embodiment of this application, solving the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system to generate an optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system includes: setting the size of the whale population and generating initial position information of individual whales to obtain an initial whale pod; calculating the objective function value of the initial position information of all whales in the initial whale pod according to the objective function of the cascade hydro-wind-solar-storage complementary optimal scheduling model, and sorting them based on the objective function value to select a leader; based on the position of the leader, introducing the movement of calves toward the leader, and based on the predation and escape behaviors of whales during migration, having the leader explore new areas, updating the position information of all whales in the pod based on the movement and new areas, to obtain the updated position information of all whales; based on the updated position information of all whales, sorting the whale pod and re-selecting a leader, obtaining the optimal leader after multiple iterations, and obtaining the optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system based on the position information of the optimal leader.

[0076] It is understood that the whale migration algorithm in this application embodiment can be understood as an innovative bio-metaphorical heuristic optimization algorithm based on the cooperative migration behavior of humpback whales. It utilizes the distinctive characteristics of humpback whale migration, emphasizes collective cooperation and leader-follower dynamics to improve convergence and avoid local optima. It improves the performance of solving complex optimization problems by simulating team dynamics, leadership status, and adaptive migration strategies. The whale population size can be understood as the number of individuals used to search for the optimal solution during the algorithm iteration process. Its size directly affects the solution efficiency and accuracy. The initial position information of individual whales can be understood as a set of scheduling variables corresponding to each individual. Initializing the whale pod is equivalent to constructing a set of randomly distributed potential scheduling schemes. The leader can be understood as the individual with the optimal objective function value (minimum economic cost) in the current whale pod, representing the current optimal scheduling scheme.

[0077] In actual implementation, the embodiments of this application can initialize the population. The random function rand of the whale migration algorithm generates a value that is within a lower bound. To the Upper Realm The initial population within the range represents the migrating whale pod. The location of each migrating whale is represented by the following matrix: , In the formula, It refers to the number of population members; It optimizes the size of the target dimension; 1~ N pop Integers between; 1~ D Integers between; For the first The first whale Position in each dimension; vector The expression is as follows.

[0078] , In the formula, the function from dimensional interval Generate a vector of random numbers using the operator " "" represents the Hadamard product of two vectors, where each element of the resulting vector is obtained by multiplying the corresponding elements of the two original vectors.

[0079] The location of migrating whale pods in the ocean is crucial. Within each pod, individuals with more experience, superior location knowledge, and higher objective function values ​​guide the entire pod, leading other whales to their destination. In whale migration algorithms, parameters... This represents the number of more experienced whales (leaders), composed of members with better, more advantageous positions and higher objective function values. The whale migration algorithm describes the actual location of the entire migrating whale pod at any given time using a single point on the migration path in the ocean. Therefore, [the algorithm]... Set as current The average of the leaders, that is: , Inexperienced whales (juveniles) gravitate towards the nearest member of their species. Assume all population members (whales) are sorted in descending order by their fitness value (or objective function value) and corresponding position (or the whale's experience value): , but Being the best member can be represented as ; He is the worst member.

[0080] Young whales exhibit a tendency to imitate their closest age companions. Therefore, in whale migration algorithms, if the objective function value is considered as age, each inexperienced member... The motion is influenced by the nearest previous member in the sequence (i.e., The value of the objective function is better than the value of the objective function due to the high influence of the objective function. by The impact is expressed as follows in the whale migration algorithm: , Inexperienced whales (calves) are guided by experienced whales (leaders). It is assumed that the current position of the entire migrating whale pod in the ocean is equal to the average position of all the more experienced whales. .if Dot and When the distance between points begins to decrease, it means the entire experienced whale pod is approaching. At this point, the inexperienced whale (the calf) must also begin to move towards the vector. Move in the same direction as given. Finally, the... The equation of motion for a young, inexperienced whale at its new position can be expressed as: , Only when Less than At that time, new location Only then will the current position be replaced. .

[0081] New territories are discovered and explored by more experienced whales (leaders). Whale pods are influenced by Earth's gravitational and magnetic fields during migration, and can travel almost in a straight line between their starting and final destinations. To simulate the random movements of whales around their primary straight-line direction as they search for food or escape predators, the whale migration algorithm uses two random parameter vectors. and Based on the following kinematic equations, the first... The leader whale searches for the appropriate path to the destination as follows: , In the formula, and It is an interval Medium has dimension A vector of random numbers, Represents a position vector. It is a relative direction vector. Similarly, it only applies when... Less than At that time, new location Only then will the current position be replaced. At the end of each iteration of the whale migration algorithm, the migrating whale pods are sorted from best to worst, and all are selected. The best member in the group is chosen as the leader.

[0082] For example, in this application embodiment, the whale migration algorithm can be used to solve the optimal scheduling model of a cascade hydropower-wind-solar hybrid pumped storage system. The output power processes of the hydropower station and the hybrid pumped storage station are considered as the optimization individuals. The upper and lower limits of the output of the hydropower station and the hybrid pumped storage station are set as the movement range of the whale pod, and the maximum number of iterations is used as the termination condition of the model. The variable correspondence between the cascade hydropower-wind-solar hybrid pumped storage system and the whale migration algorithm is as follows: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] Group Optimization Scheduling Scheme No. The output value is expressed as That is, the first Only whales Position on the dimension; The fitness value of the whale is the target value for the optimized scheduling of the cascade hydropower-wind-solar hybrid pumped storage system.

[0083] For example, this application embodiment can solve the cascade hydro-wind-solar-storage complementary optimal scheduling model based on the whale migration algorithm, and obtain the optimal operation scheme according to the following process: Set the whale population size and generate initial position information for individual whales to obtain an initial whale pod. Calculate the objective function value of the position information represented by all whales in the initial whale pod according to the objective function of the cascade hydro-wind-solar-storage complementary optimal scheduling model; and select the whale with the optimal position information as the leader after sorting based on the objective function value. Based on the leader's position, introduce the movement of calves towards the leader, and based on the predator-hunting and predator-escaping behaviors during whale migration, allow the leader to explore new areas. Update the position information of all whales in the pod based on the movement and new areas to obtain the updated position information of all whales; based on the updated position information of all whales, sort the whale pod and reselect the leader. After multiple iterations, obtain the optimal leader, and obtain the optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system based on the position information of the optimal leader.

[0084] The embodiments of this application can use the whale migration algorithm to solve the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system. By simulating the behavior of whale populations, it iterative optimization is carried out, which has both global exploration and local development capabilities. The juvenile whale approach mechanism enhances the convergence of the population to the optimal solution, and the leader's domain exploration ability improves the global optimization range. It can quickly and accurately find the optimal scheduling scheme, which is suitable for the multi-variable and strongly coupled optimization requirements of the cascade hydro-wind-solar-storage system.

[0085] Optionally, in one embodiment of this application, before setting the size of the whale population and generating the initial location information of individual whales, the process includes: setting the output power of the hydroelectric power station and the hybrid pumped storage power station as the individual whales, setting the upper and lower limits of the output of the hydroelectric power station and the hybrid pumped storage power station; and determining the movement range of the whale population based on the upper and lower limits of the output of the hydroelectric power station and the hybrid pumped storage power station.

[0086] It is understood that, in the embodiments of this application, the output power process of hydropower stations and hybrid pumped storage power stations is set as a whale individual, which can be understood as establishing a mapping relationship between optimization variables and algorithm individuals, so that the algorithm search directly corresponds to the core scheduling decision variables, thereby improving the targeting of the solution; the upper and lower limits of output can be understood as the output threshold of each power station set based on the rated parameters of the equipment and the requirements for safe operation; the movement range of the whale population can be understood as the search space boundary of the algorithm, and the invalid search can be avoided by defining the upper and lower limits of output, thereby improving the solution efficiency.

[0087] In practical implementation, the embodiments of this application can set the size of the whale population, and the initial location information of individual whales can be randomly selected and generated within the upper and lower limits of the output of the hydropower station and the mixed pumped storage power station. The movement range of the whale population is determined according to the upper and lower limits of the output of the hydropower station and the mixed pumped storage power station. The location information represented by each individual whale in the whale population represents a possible feasible solution of the cascade hydro-wind-solar-storage complementary optimization scheduling model. The variable correspondence between the cascade hydro-wind-solar-storage complementary system and the whale migration optimization algorithm is as follows: the cascade hydro-wind-solar-storage complementary system... Group Optimization Scheduling Scheme No. The output value is expressed as That is, the first Only whales Position on the dimension; The fitness value of the whale is the target value for the optimized scheduling of the cascade hydropower-wind-solar hybrid pumped storage system.

[0088] This application embodiment can clarify the physical meaning of an individual whale as the power plant output power process before the initialization of the whale pod, and define the pod movement range based on the upper and lower limits of the power plant output. This achieves a precise mapping between the optimization variables and the actual operating characteristics of the power plant. The constraint of the pod movement range avoids the exploration of the invalid solution space, greatly improves the effectiveness of the initial pod of the algorithm and the convergence efficiency of subsequent iterations, and ensures that the power plant output power in the final scheduling strategy meets the equipment safe operation threshold, thus guaranteeing the engineering feasibility of the scheduling scheme from the source.

[0089] Optionally, in one embodiment of this application, after multiple iterations, an optimal leader is obtained, and an optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system is obtained based on the location information of the optimal leader. This includes: if, during the iteration process, it is determined that the objective function value of the leader has not been updated within a preset fixed number of iterations, the iteration process is terminated, and the leader at the time of iteration termination is taken as the optimal leader. The optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system is obtained based on the location information of the optimal leader; if a preset number of iterations is reached, the iteration process is terminated to determine the optimal leader, and the location information of the optimal leader is output as the optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0090] It is understood that the preset fixed number of iterations in this embodiment can be 50, and the preset fixed number of iterations can be set by those skilled in the art according to the actual situation, without any specific restrictions here; the preset termination number of iterations can be 200, and the preset termination number of iterations can be set by those skilled in the art according to the actual situation, without any specific restrictions here.

[0091] In actual implementation, the embodiments of this application can set a fixed number of iterations. When the objective function value of the selected whale leader is not updated within the fixed number of iterations, the leader's position at the last iteration is output. Until the set number of iterations is reached, the position information recorded by the whale leader is output as a cascade hydro-wind-solar-storage complementary optimization scheduling scheme.

[0092] For example, in the algorithm iteration process of this application, the objective function value of the leader in each round is recorded in real time, and the number of consecutive iterations without updates is counted: if the number of consecutive iterations without updates reaches a preset fixed number of iterations, it is determined that the optimal solution has stabilized, the iteration is terminated immediately, and the leader position at the last iteration is output; if the number of iterations reaches a preset number of iterations to terminate, the individual with the optimal objective function value in the iteration process is selected as the optimal leader; finally, the position information of the optimal leader is extracted and transformed into a complete scheduling strategy such as the time-period power output allocation of cascade hydropower stations and mixed pumped storage power stations, the power output coordination scheme of wind power stations and photovoltaic power stations, and the system power purchase plan.

[0093] This application embodiment can set a dual iteration termination mechanism where the objective function value is not updated after a preset fixed number of iterations and the preset termination iteration number is reached. This can dynamically balance the solution accuracy and efficiency. When the objective function value tends to stabilize, the iteration is terminated in advance to avoid the waste of computing resources and time caused by invalid iterations. The preset termination number provides a clear time boundary for the solution process, ensuring the reliability and timeliness of the optimization scheduling model solution process.

[0094] Specifically, it can be combined with Figures 2 to 6 As shown, the working principle of the optimized scheduling method for the cascade hydro-wind-solar-storage complementary power generation system in this application is explained in detail with a specific embodiment.

[0095] like Figure 2 As shown, embodiments of this application may include the following steps: Step S201: The objective function for the optimal scheduling of the cascade hydro-wind-solar-storage complementary power generation system is to minimize the economic scheduling cost of the cascade hydro-wind-solar-storage complementary power generation system.

[0096] Step S202: Considering the hydraulic and electrical connections between cascade hydropower stations and hybrid pumped storage power stations, the constraints of the cascade hydropower-wind-solar-storage complementary power generation system were determined, and an optimal scheduling model for the cascade hydropower-wind-solar-storage complementary power generation system was constructed.

[0097] Step S203: The whale migration algorithm is used to solve the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system, providing a specific optimal scheduling method for the complementary operation of the cascade hydro-wind-solar-storage system.

[0098] Specifically, taking a cascade hydro-wind-solar-storage complementary power generation system in a certain province as an example, with the goal of minimizing operating costs, the upper and lower limits of the output power of the upstream and downstream hydropower stations and the mixed pumped storage power station, as well as the simulated wind and solar power output with a time resolution of 15 minutes, are selected as model inputs. The above-mentioned optimized scheduling method for the cascade hydro-wind-solar-storage complementary power generation system based on the migration algorithm is used to simulate and optimize the scheduling scheme of the cascade hydro-wind-solar-storage complementary power generation system, illustrating the effectiveness, efficiency, and rationality of the embodiments of this application.

[0099] like Figures 3 to 6 As shown, this application's embodiment takes a complementary power generation system consisting of an upstream hydropower station with an installed capacity of 3000MW, a downstream hydropower station with an installed capacity of 300MW, a hybrid pumped storage power station with an installed capacity of 1200MW, a photovoltaic power station with a maximum output of 1000MW, and a wind power station with a maximum output of 192MW as the research object, and conducts an optimization study on its 24-hour power generation dispatch. The upstream hydropower station has a total of 6 units, each with a capacity of 500MW; the downstream hydropower station has a total of 3 units, each with a capacity of 100MW; the hybrid pumped storage power station has a total of 4 reversible units, each with a capacity of 300MW, and shares a reservoir with the upstream hydropower station. Its pumping efficiency is 0.9, and its power generation efficiency is 0.85; the maximum capacity of the upstream reservoir is 10767 million kilowatts; the maximum capacity of the downstream reservoir is 42.29 million kilowatts; the maximum output of the photovoltaic power station is 1000MW, the maximum output of the wind power station is 192MW, and the minimum output of both is 0.

[0100] Specifically, the whale algorithm is used to randomly generate an initial solution set between the maximum and minimum output of the hydropower station and the pumped-storage station. The number of whales in the population is set, and the maximum number of iterations is 200. Since the data time resolution is 15 minutes, the scheduling period is divided into 96 time periods. The optimized power balance diagram is shown below. Figure 2 As shown.

[0101] Depend on Figure 3 It can be seen that wind power maintains a high output throughout the day; photovoltaic power is limited by sunlight conditions and is mainly strong during the day. The period 0-24 corresponds to nighttime to early morning (0:00-6:00), during which wind power output is significant, hydropower provides stable base load output, and the mixed pumped storage power station mainly pumps water to collect surplus nighttime electricity. The period 24-48 corresponds to daytime (6:00-12:00), during which photovoltaic power rapidly climbs to its peak, wind power output decreases, load increases to daytime peak, and the mixed pumped storage power station switches to power generation to supplement the power shortage. The period 48-72 corresponds to daytime (12:00-18:00), load decreases slightly, photovoltaic output gradually declines from its peak, and the mixed pumped storage power station switches to pumping water. The period 72-96 corresponds to nighttime (18:00-24:00), photovoltaic output returns to zero, wind power output increases, load reaches a secondary peak, and hydropower and the mixed pumped storage power station work together to regulate peak load and meet load demand.

[0102] Comparison of wind and solar power output and absorption capacity Figure 4 As shown, in this embodiment, the wind power and photovoltaic power, along with hydropower and pumped storage, can be fully utilized when operating in a complementary manner. Figure 5 The number of hydropower units and pumped storage power station units in real time is maintained at 9. The pumped storage power station can effectively reduce the number of start-ups and shutdowns of hydropower units, avoid frequent start-ups and shutdowns due to fluctuations in wind and solar power output, and improve the operating efficiency and lifespan of hydropower units.

[0103] The impact of using the particle swarm optimization algorithm and the gray wolf optimization algorithm on the economy is shown in Table 1. Table 1 is a comparison table of the running costs under different optimization algorithms.

[0104] Table 1

[0105] Table 1 shows that the whale migration algorithm is the most economical and has the lowest cost when solving the model. The convergence curves of the particle swarm optimization algorithm, gray wolf optimization algorithm, and whale migration algorithm are shown below. Figure 6 As shown. Figure 6 The results show that when the number of iterations is greater than 30, the whale migration algorithm converges faster and has a significantly improved optimization effect than the other two algorithms. It can solve the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system more efficiently, so as to generate the optimal scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system.

[0106] The optimized scheduling method for a cascade hydropower-wind-solar-storage complementary power generation system proposed in this application aims to minimize the economic scheduling cost of power generation. It considers the hydraulic and electrical connections between cascade hydropower stations and mixed pumped-storage power stations, determines the objective function and constraints, constructs an optimized scheduling model for the cascade hydropower-wind-solar-storage complementary power generation system, and solves this model to obtain an optimized scheduling strategy. This provides a specific optimized scheduling method for the complementary operation of the cascade hydropower-wind-solar-storage system, making the model more closely reflect the actual operating characteristics of the system and possessing effectiveness, efficiency, and rationality. It achieves globally optimal economic decision-making, thereby significantly improving the level of clean energy consumption and ensuring the economical operation of the power grid while ensuring grid security. This solves the problem in related technologies where the focus on the complementary scheduling of single hydropower and wind / solar power fails to consider the uncertainties of wind and solar power, the hydraulic connections of cascade hydropower stations, and the synergistic optimization design of the coupling relationship between mixed pumped-storage power stations. This results in the inability to fully utilize the joint regulation potential of cascade hydropower and pumped-storage, and an inability to effectively address the operational risks brought about by a high proportion of new energy grid connection.

[0107] Next, referring to the accompanying drawings, we describe the optimized scheduling device for a cascade hydro-wind-solar-storage complementary power generation system proposed in the embodiments of this application.

[0108] Figure 7This is a schematic diagram of the structure of the optimized scheduling device for a cascade hydro-wind-solar-storage complementary power generation system according to an embodiment of this application.

[0109] like Figure 7 As shown, the optimized scheduling device 10 for the cascade hydro-wind-solar-storage complementary power generation system includes: a first determining module 100, a second determining module 200, and an optimization module 300.

[0110] The first determining module 100 is used to determine the optimization objective function of the cascade hydro-wind-solar-storage complementary power generation system based on the optimization objective of the power generation economic dispatch cost of the cascade hydro-wind-solar-storage complementary power generation system.

[0111] The second determining module 200 is used to determine the constraints of the cascade hydropower-wind-solar-storage complementary power generation system based on the hydraulic and electrical connections between the cascade hydropower stations and the hybrid pumped storage power stations.

[0112] The optimization module 300 is used to construct an optimal scheduling model for a cascade hydro-wind-solar-storage complementary power generation system based on the optimization scheduling objective function and constraints, and to solve the optimal scheduling model for the cascade hydro-wind-solar-storage complementary power generation system to generate an optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0113] Optionally, in one embodiment of this application, the expression for the optimization scheduling objective function is: , in, For total cost, for Time-of-use system electricity purchase cost For the operating costs of cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, This refers to the operating costs of a hybrid pumped storage power station.

[0114] Optionally, in one embodiment of this application, the optimization module 300 includes: an initialization unit, a sorting unit, an update unit, and an optimization scheduling unit.

[0115] The initialization unit is used to set the size of the whale population and generate the initial location information of individual whales to obtain the initial whale population.

[0116] The sorting unit is used to calculate the objective function value of the initial position information of all whales in the whale pod based on the objective function of the cascade water-wind-solar-storage complementary optimization scheduling model, and sort them based on the objective function value to select the leader.

[0117] The update unit is used to introduce the movement of calves toward the leader based on the leader's position, and to update the position information of all whales in the pod based on the leader's predator-hunting and predator-escaping behaviors during whale migration, so as to obtain the updated position information of all whales.

[0118] The optimization scheduling unit is used to sort the whale pod and reselect a leader based on the updated location information of all individual whales. After multiple iterations, the optimal leader is obtained, and the optimal scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system is obtained based on the location information of the optimal leader.

[0119] Optionally, in one embodiment of this application, the optimization module 300 further includes: a first setting unit and a second setting unit.

[0120] The first setting unit is used to set the output power of the hydroelectric power station and the hybrid pumped storage power station as an individual whale before setting the size of the whale population and generating the initial location information of individual whales, and to set the upper and lower limits of the output of the hydroelectric power station and the hybrid pumped storage power station.

[0121] The second setting unit is used to determine the range of movement of the whale population based on the upper and lower limits of the output of the hydroelectric power station and the mixed pumped storage power station before setting the size of the whale population and generating the initial location information of individual whales.

[0122] Optionally, in one embodiment of this application, the optimized scheduling unit includes: a first termination unit and a second termination unit.

[0123] The first termination unit is used to terminate the iteration process if the objective function value of the leader is not updated within a preset fixed number of iterations during the iteration process, and to take the leader at the time of iteration stop as the optimal leader, and to obtain the optimal scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system based on the position information of the optimal leader.

[0124] The second termination unit is used to terminate the iteration process when the preset number of termination iterations is reached, so as to determine the optimal leader and output the location information of the optimal leader. This is the optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

[0125] It should be noted that the foregoing explanation of the embodiment of the optimized scheduling method for the cascade hydro-wind-solar-storage complementary power generation system also applies to the optimized scheduling device for the cascade hydro-wind-solar-storage complementary power generation system in this embodiment, and will not be repeated here.

[0126] The optimized scheduling device for a cascade hydropower-wind-solar-storage complementary power generation system proposed in this application aims to minimize the economic scheduling cost of power generation. It considers the hydraulic and electrical connections between cascade hydropower stations and mixed pumped-storage power stations, determines the objective function and constraints, constructs an optimized scheduling model for the cascade hydropower-wind-solar-storage complementary power generation system, and solves the model to obtain an optimized scheduling strategy. This provides a specific optimized scheduling method for the complementary operation of the cascade hydropower-wind-solar-storage system, making the model more closely reflect the actual operating characteristics of the system, possessing effectiveness, efficiency, and rationality. It achieves globally optimal economic decision-making, thereby significantly improving the level of clean energy consumption and ensuring the economic operation of the power grid while ensuring grid security. This solves the problem in related technologies where the focus on the complementary scheduling of single hydropower and wind / solar power fails to consider the uncertainties of wind and solar power, the hydraulic connections of cascade hydropower stations, and the coupling relationship of mixed pumped-storage power stations, resulting in the inability to fully utilize the joint regulation potential of cascade hydropower and pumped-storage, and difficulty in effectively addressing the operational risks brought about by a high proportion of new energy grid connection.

[0127] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0128] When the processor 802 executes the program, it implements the optimized scheduling method for the cascade hydro-wind-solar-storage complementary power generation system provided in the above embodiments.

[0129] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0130] The memory 801 is used to store computer programs that can run on the processor 802.

[0131] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0132] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0133] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0134] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0135] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system.

[0136] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-mentioned optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system.

[0137] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0138] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0139] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0140] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0141] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0142] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0143] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0144] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. An optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system, characterized in that, Includes the following steps: Based on the optimization objective of the economic dispatch cost of the cascade hydro-wind-solar-storage complementary power generation system, the optimization dispatch objective function of the cascade hydro-wind-solar-storage complementary power generation system is determined. Based on the hydraulic and electrical connections between cascade hydropower stations and hybrid pumped storage power stations, the constraints of the cascade hydropower-wind-solar-storage complementary power generation system are determined. Based on the aforementioned optimization scheduling objective function and constraints, an optimization scheduling model for a cascade hydro-wind-solar-storage complementary power generation system is constructed, and the optimization scheduling model is solved to generate an optimization scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

2. The method according to claim 1, characterized in that, The expression for the optimization scheduling objective function is: ， in, For total cost, for Time-of-use system electricity purchase cost The operating cost of the cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, The operating cost of the hybrid pumped storage power station.

3. The method according to claim 1, characterized in that, The process of solving the optimal scheduling model of the cascade hydro-wind-solar-storage complementary power generation system to generate an optimal scheduling strategy for the system includes: Set the size of the whale population and generate the initial location information of individual whales to obtain the initial whale pod; Based on the objective function of the cascade water-wind-solar-storage complementary optimization scheduling model, the objective function value of the initial position information of all individual whales in the initial whale pod is calculated, and the whales are sorted based on the objective function value to select a leader. Based on the position of the leader, the movement of the calves toward the leader is introduced, and based on the predation and escape behaviors of whales during migration, the leader explores new territory. Based on the movement and the new territory, the position information of all individual whales in the pod is updated to obtain the updated position information of all individual whales. Based on the updated location information of all individual whales, the whale pod is sorted and a new leader is selected. After multiple iterations, the optimal leader is obtained, and the optimal scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system is obtained based on the location information of the optimal leader.

4. The method according to claim 3, characterized in that, Before setting the whale population size and generating initial location information for individual whales, the following steps are included: The process of setting the output power of the hydropower station and the hybrid pumped storage power station is likened to an individual whale, and the upper and lower limits of the output of the hydropower station and the hybrid pumped storage power station are set. The range of movement of the whale population is determined based on the upper and lower limits of the output of the hydropower station and the hybrid pumped storage power station.

5. The method according to claim 3, characterized in that, After multiple iterations, an optimal leader is obtained. Based on the location information of the optimal leader, an optimized scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system is derived, including: If, during the iteration process, it is determined that the objective function value of the leader has not been updated within a preset fixed number of iterations, the iteration process is terminated, and the leader at the time of iteration stop is taken as the optimal leader. Based on the position information of the optimal leader, the optimized scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system is obtained. If the preset number of iterations is reached, the iteration process is terminated to determine the optimal leader and output the location information of the optimal leader, which is the optimal scheduling strategy of the cascade hydro-wind-solar-storage complementary power generation system.

6. An optimized scheduling device for a cascade hydro-wind-solar-storage complementary power generation system, characterized in that, include: The first determining module is used to determine the optimization scheduling objective function of the cascade hydro-wind-solar-storage complementary power generation system based on the optimization objective of the power generation economic dispatch cost of the cascade hydro-wind-solar-storage complementary power generation system. The second determining module is used to determine the constraints of the cascade hydropower-wind-solar-storage complementary power generation system based on the hydraulic and electrical connections between the cascade hydropower stations and the hybrid pumped storage power stations. The optimization module is used to construct an optimal scheduling model for the cascade hydro-wind-solar-storage complementary power generation system based on the optimal scheduling objective function and the constraints, and to solve the optimal scheduling model for the cascade hydro-wind-solar-storage complementary power generation system to generate an optimal scheduling strategy for the cascade hydro-wind-solar-storage complementary power generation system.

7. The apparatus according to claim 6, characterized in that, The expression for the optimization scheduling objective function is: ， in, For total cost, for Time-of-use system electricity purchase cost The operating cost of the cascade hydropower stations, For the operating costs of photovoltaic power plants, For the operating costs of wind power plants, The operating cost of the hybrid pumped storage power station.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the optimized scheduling method for a cascade hydro-wind-solar-storage complementary power generation system as described in any one of claims 1-5.

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