Multi-time-scale nested hybrid pumped storage power station scheduling method and device

Abstract

The invention belongs to the technical field of hybrid pumped storage power station dispatching, and particularly discloses a multi-time-scale nested hybrid pumped storage power station dispatching method and device, and the method comprises the steps: constructing a multi-objective function with the maximum power station income and the minimum power grid residual load peak-valley difference as the objective; based on the multi-objective function, sequentially carrying out optimal scheduling on the hybrid pumped storage power station according to a plurality of time scales from large to small to obtain a system model; converting the system model into a mixed integer linear programming model; and solving the mixed integer linear programming model to obtain a scheduling result. According to the method, the economic benefit of power station operation and the load regulation capability of a power grid can be remarkably improved, the complexity of nonlinear model solution is effectively reduced, and the scheduling efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application belongs to the technical field of hybrid pumped-storage power station scheduling, and more specifically, relates to a hybrid pumped-storage power station scheduling method and device with multi-time-scale nesting. Background Art

[0002] Hybrid pumped-storage power stations, built by expanding or renovating existing cascade hydropower stations, offer advantages such as a short construction period, low costs, and abundant site resources. Furthermore, they can both pump water for energy storage and utilize natural runoff for power generation, making their operation more flexible and complex. However, the addition of hybrid pumped-storage power stations strengthens the hydraulic connections between upstream and downstream cascade hydropower stations, forming a more complex multi-energy joint scheduling system. This makes it difficult for traditional single-timescale scheduling strategies to meet the actual operational needs of hybrid pumped-storage power stations, resulting in lower economic benefits, lower load regulation capabilities, and lower scheduling efficiency for the power grid, hindering the safe and stable operation of the grid. Summary of the Invention

[0003] In response to the defects of the existing technology, the purpose of this application is to provide a hybrid pumped-storage power station scheduling method and device with multi-time scale nesting, aiming to solve the problem that the traditional single-time scale scheduling strategy is difficult to meet the actual needs of the operation of the hybrid pumped-storage power station, resulting in low economic benefits of the power station operation, load regulation capacity of the power grid and scheduling efficiency, which is not conducive to the safe and stable operation of the power grid.

[0004] To achieve the above objectives, in a first aspect, the present application provides a multi-time-scale nested hybrid pumped-storage power station scheduling method, comprising: Construct a multi-objective function with the goals of maximizing power plant revenue and minimizing the peak-to-valley difference of grid residual load; Based on the multi-objective function, the hybrid pumped storage power station is optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model; Converting the system model into a mixed integer linear programming model; Solve the mixed integer linear programming model to obtain a scheduling result.

[0005] Under the premise of maximizing the benefits of the power station and minimizing the peak-to-valley difference of the residual load of the power grid, this application adopts a layer-by-layer nested multi-time scale optimization framework to finely optimize the scheduling of hybrid pumped storage power stations, significantly improving the economic benefits of power station operation and the load regulation capacity of the power grid; through advanced piecewise linearization and mixed integer linear programming techniques, the complexity of solving nonlinear models is effectively reduced, and the scheduling efficiency and accuracy are improved.

[0006] According to a multi-time-scale nested hybrid pumped-storage power station scheduling method provided by the present application, based on the multi-objective function, the hybrid pumped-storage power station is optimized and scheduled in sequence according to multiple time scales from large to small to obtain a system model, including: Based on the multi-objective function, the hybrid pumped storage power station is optimized and dispatched in turn at the scale of ten days within a year, the scale of day within ten days, and the scale of hour within a day.

[0007] This application adopts a multi-time scale optimization framework that is nested layer by layer, which can accurately realize the optimization scheduling at the ten-day scale within the year, the daily scale within the ten-day, and the hourly scale within the day, significantly improving the economic benefits of power station operation and the load regulation capacity of the power grid.

[0008] According to a multi-time-scale nested hybrid pumped-storage power station scheduling method provided by the present application, based on the multi-objective function, the hybrid pumped-storage power station is optimized and scheduled in sequence according to multiple time scales from large to small to obtain a system model, including: Based on the preset priority of each objective function in the multi-objective function, the hybrid pumped storage power station is hierarchically optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model.

[0009] This application introduces a multi-objective hierarchical optimization strategy, which flexibly takes into account the priorities of different objectives and achieves the best balance between power station revenue and peak-to-valley regulation of the power grid. Through the hierarchical optimization method, it ensures that the optimization results of high-priority objective functions are as optimal as possible, while further improving the optimization effects of low-priority objective functions on this basis.

[0010] According to a multi-time-scale nested hybrid pumped-storage power station scheduling method provided by the present application, based on the multi-objective function, the hybrid pumped-storage power station is optimized and scheduled in sequence according to multiple time scales from large to small to obtain a system model, including: Based on the multi-objective function and preset constraints, the hybrid pumped storage power station is optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model.

[0011] According to a multi-time-scale nested hybrid pumped-storage power station scheduling method provided by the present application, converting the system model into a mixed integer linear programming model includes: The system model is linearized by a single variable function, linearized by a two variable function, and linearized by mutually exclusive power station operation to obtain the mixed integer linear programming model.

[0012] According to a multi-time-scale nested hybrid pumped-storage power station scheduling method provided by the present application, solving the mixed integer linear programming model to obtain a scheduling result includes: The mixed integer linear programming model is solved using a mixed integer linear programming algorithm through the Gurobi solver.

[0013] In a second aspect, the present application provides a hybrid pumped storage power station scheduling device with multiple time scales nested, comprising: A construction module is used to construct a multi-objective function with the goals of maximizing the power plant's profit and minimizing the peak-to-valley difference of the grid's residual load; An optimization scheduling module is used to optimize the scheduling of the hybrid pumped storage power station in sequence according to multiple time scales from large to small based on the multi-objective function to obtain a system model; A conversion module, configured to convert the system model into a mixed integer linear programming model; The solution module is used to solve the mixed integer linear programming model to obtain a scheduling result.

[0014] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the multi-time-scale nested hybrid pumped-storage power station scheduling method described in the first aspect or any possible implementation of the first aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the multi-time-scale nested hybrid pumped-storage power station scheduling method described in the first aspect or any possible implementation of the first aspect.

[0016] In a fifth aspect, the present application provides a computer program product, which, when running on a processor, enables the processor to execute the multi-time-scale nested hybrid pumped-storage power station scheduling method described in the first aspect or any possible implementation of the first aspect.

[0017] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0018] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the existing technologies: Under the premise of maximizing the benefits of the power station and minimizing the peak-to-valley difference of the residual load of the power grid, this application adopts a layer-by-layer nested multi-time scale optimization framework to finely optimize the scheduling of hybrid pumped storage power stations, significantly improving the economic benefits of power station operation and the load regulation capacity of the power grid; through advanced piecewise linearization and mixed integer linear programming techniques, the complexity of solving nonlinear models is effectively reduced, and the scheduling efficiency and accuracy are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 This is one of the flow charts of the multi-time-scale nested hybrid pumped-storage power station scheduling method provided in the embodiment of the present application; Figure 2 This is the second flow chart of the multi-time-scale nested hybrid pumped-storage power station scheduling method provided in the embodiment of the present application; Figure 3 1 is a schematic structural diagram of a hybrid pumped-storage power station scheduling device with multiple time scales and nesting according to an embodiment of the present application; Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0022] The term "and / or" as used herein describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. The symbol " / " as used herein indicates that the related objects are in an "or" relationship, for example, A / B means either A or B.

[0023] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0024] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more, for example, multiple processing units means two or more processing units, etc.; multiple elements means two or more elements, etc.

[0025] First, let’s introduce the following contents: With the rapid development of new energy sources such as wind power and photovoltaics, the randomness, volatility and intermittency of their output have significantly increased the uncertainty of the net load of the power grid, posing huge challenges to the safe and stable operation of the power system. Especially under the high penetration rate of new energy, improving the flexible adjustment capability of the power grid has become an urgent problem to be solved.

[0026] As a mature and efficient power source, pumped-storage power stations offer outstanding advantages such as rapid response, large energy storage capacity, and strong regulation capabilities. They are widely used to smooth fluctuations in renewable energy sources and regulate power grid peaks. However, the further development of traditional pumped-storage power stations is limited by long construction periods, large initial investments, and difficult site selection. To address these issues, utilizing the reservoir resources of existing cascade hydropower stations and integrating and transforming them into hybrid pumped-storage power stations is becoming an important approach to improving power system flexibility and peak-shaving capabilities.

[0027] Compared to traditional pure pumped-storage power plants, hybrid pumped-storage power plants rely on the expansion or renovation of existing cascade hydropower stations. They offer advantages such as shorter construction periods, lower costs, and abundant site resources. Furthermore, they can both pump water for energy storage and utilize natural runoff for power generation, making their operation more flexible and complex. However, the addition of hybrid pumped-storage power plants strengthens the hydraulic connections between upstream and downstream cascade hydropower stations, forming a more complex multi-energy joint scheduling system. This makes it difficult for traditional single-timescale scheduling strategies to meet the actual operational needs of hybrid pumped-storage power plants. There is an urgent need to develop joint scheduling and operation models that consider multiple timescales to more effectively leverage the regulation capabilities of pumped-storage power plants and improve the overall operational efficiency of the system.

[0028] Currently, most research focuses on optimizing the scheduling of cascade hydropower stations or traditional pumped-storage power stations, while research on the multi-timescale joint scheduling of hybrid pumped-storage power stations is still in its infancy. Therefore, research on multi-timescale scheduling and operation models for hybrid pumped-storage power stations is not only of great theoretical value, but also of practical significance for improving the capacity to absorb renewable energy, ensuring the safe and stable operation of the power grid, and promoting the transformation and upgrading of the energy structure.

[0029] Next, combine Figure 1-Figure 2 The multi-time-scale nested hybrid pumped-storage power station scheduling method provided in the embodiments of the present application is introduced.

[0030] Figure 1 This is one of the flow charts of the hybrid pumped storage power station scheduling method with multiple time scale nesting provided in the embodiment of the present application, such as Figure 1 As shown, the method includes the following steps: Step 100: construct a multi-objective function with the goals of maximizing the power station profit and minimizing the peak-to-valley difference of the grid residual load; Considering the benefits of the hybrid pumped storage power station and its peak-shaving capacity for the power grid, a multi-objective function is established to maximize the benefits and minimize the peak-to-valley difference of the power grid's residual load. The objective function is as follows:

[0031]

[0032]

[0033] Where: For the revenue of the power station; For the i conventional power plants t Always make an effort, For the i pumped storage power stations t Generating power at all times, for t Pumped storage power stations consume electricity at all times. for t On-grid electricity price at all times, for t Water and electricity prices at all times, is the calculation period step, is the peak-to-valley difference of the grid's residual load, T is the total number of scheduling time periods, N is the total number of conventional cascade power stations, for t The remaining load of the period, for t The grid load during the period.

[0034] Step 110 , based on the multi-objective function, optimize the hybrid pumped storage power station according to multiple time scales from large to small to obtain a system model; The multi-objective function is to ensure the maximization of power station revenue and the minimization of the peak-to-valley difference of the residual load of the power grid during the scheduling process. In order to obtain the scheduling and operation mode of hybrid pumped-storage power stations at different scales, a model framework is constructed with multiple time scales nested layer by layer from large to small scheduling periods and time periods to optimize the scheduling of hybrid pumped-storage power stations. During the scheduling process, the time period of the previous layer is the scheduling period of the next layer, and the decision results of the previous layer provide boundary conditions for the next layer.

[0035] Optionally, the multiple time scales from large to small may be years, months, days, or ten days, months, days, etc.

[0036] The system model includes a model framework with multiple time scales nested layer by layer, as well as multi-objective functions.

[0037] Step 120, converting the system model into a mixed integer linear programming model; Alternatively, the nonlinear system model can be transformed into a mixed integer linear programming model through piecewise linearization, quadrilateral-based gridding techniques with Special Ordered Set of Type 2 (SOS2) constraints, or adding 0-1 variables.

[0038] Step 130: Solve the mixed integer linear programming model to obtain a scheduling result.

[0039] The present application provides a hybrid pumped-storage power station scheduling method with nested multiple time scales. Under the premise of maximizing the power station revenue and minimizing the peak-to-valley difference of the residual load of the power grid, a layer-by-layer nested multiple time scale optimization framework is adopted to finely optimize the scheduling of the hybrid pumped-storage power station, significantly improving the economic benefits of the power station operation and the load regulation capacity of the power grid; through advanced piecewise linearization and mixed integer linear programming technology, the complexity of solving nonlinear models is effectively reduced, and the scheduling efficiency and accuracy are improved.

[0040] In some embodiments, step 110 specifically includes: Based on the multi-objective function, the hybrid pumped storage power station is optimized at the scale of ten days within a year, the scale of day within ten days, and the scale of hour within a day.

[0041] Figure 2 This is the second flow chart of the hybrid pumped storage power station scheduling method with multiple time scale nesting provided in the embodiment of the present application, such as Figure 2 As shown, preferably, in order to obtain the scheduling operation mode of the hybrid pumped storage power station at different scales, a model framework is constructed in which the scheduling period and time period are nested layer by layer from large to small, and the optimization scheduling is carried out in sequence at the ten-day scale within the year, the day scale within the ten-day scale, and the hour scale within the day. The time period of the upper layer is the scheduling period of the lower layer, and the decision result of the upper layer provides the boundary condition for the lower layer. Finally, the operating condition of the hybrid pumped storage power station for 8760 hours throughout the year is obtained.

[0042] The scales for the ten-day period within the year are as follows:

[0043]

[0044] After the ten-day-scale scheduling is completed within the year, the initial and final water levels of each ten-day period will be obtained, namely: , are the starting water level and ending water level of the tth decade respectively.

[0045] The daily scale within ten days is as follows:

[0046]

[0047] In the daily optimization calculation within a ten-day period, the water level boundary conditions for each day of the t-th decade are provided by the initial and final water levels of the t-th decade calculated at the previous level (within-year ten-day scale), namely:

[0048] Where, 、 are the initial water level on the first day of the t-th decade and the final water level on the last day respectively; is the number of days in the tth decade.

[0049] The intraday hour scale is as follows:

[0050]

[0051] In the intra-day hourly scale optimization, the boundary conditions for each hour on the dth day are determined by the initial and final water levels of the day after optimization at the previous level (intra-decade daily scale):

[0052] Where, 、 are the initial water level at the first hour of day d and the final water level at the 24th hour, respectively.

[0053] In some embodiments, step 110 specifically includes: Based on the preset priority of each objective function in the multi-objective function, the hybrid pumped storage power station is hierarchically optimized and dispatched according to multiple time scales from large to small to obtain a system model.

[0054] like Figure 2 As shown, this application uses the multi-objective optimization mechanism provided by the Gurobi optimizer to solve the problem. When there are multiple objective functions, Gurobi provides a hierarchical optimization strategy, that is, optimization is performed step by step according to the priority of the objective function specified by the user.

[0055] Specifically, for the two objective functions 、 If their priorities are different, assume that the priorities are 、 , where the larger the number, the higher the priority. Gurobi will optimize as follows: 1a. First solve the highest priority objective function , get its optimal solution (or approximate optimal solution) :

[0056] 2a. Then, without significantly reducing the optimization quality of the first objective function (allowing a certain tolerance ), for the second-level objective function To optimize:

[0057] Through the above hierarchical optimization method, the optimization results of high-priority objective functions are ensured to be as optimal as possible, while the optimization effects of low-priority objective functions are further improved on this basis.

[0058] In some embodiments, step 110 specifically includes: Based on multi-objective functions and preset constraints, the hybrid pumped storage power station is optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model.

[0059] The preset constraints are used to limit the dispatch results of the hybrid pumped storage power station.

[0060] Optionally, the preset constraints can be flexibly configured according to different actual situations.

[0061] In one embodiment of the present application, the following constraints are set: 1b. Conventional power plant operation constraints:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Where: 、 are the upper and lower limits of the output of the i-th conventional power station at time t; is the power generation status of the i-th conventional power station at time t; 、 are the upper and lower limits of power generation flow of the i-th conventional power station at time t; is the generating head of the i-th conventional power station at time t, m; is the upper reservoir water level of the i-th conventional power station at time t, is the water level behind the dam of the i-th conventional power station at time t, is the ramp limit of the i-th conventional power station. 、 、 is the natural water inflow, abandoned water volume and power generation flow of the i-th conventional power station at time t, is the storage capacity of the i-th conventional power station at time t, 、 are the initial and final water levels of the i-th power station, 、 are the upper and lower limits of the water level of the i-th power station at time t, 、 are the pumping and power generation flows of the pumped storage power station at time t, respectively.

[0073] 2b. Pumped Storage Power Station Operation Constraints:

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] Where: 、 、 、 are the upper and lower limits of the power output and pumping power consumption of the pumped storage power station at time t, 、 are the power generation and pumping state variables of the pumped storage power station at time t, 、 、 、 are the upper and lower limits of the power generation flow and pumping flow of the pumped storage power station at time t, The first conventional power station generates power at time t. is the pumping head of the pumped storage power station at time t.

[0082] In some embodiments, step 120 specifically includes: The system model is linearized by univariate function, bivariate function and mutually exclusive linearization of power station operation to obtain a mixed integer linear programming model.

[0083] The system model is transformed into a mixed-integer linear programming model using linearization techniques.

[0084] like Figure 2 As shown in Figure 2, the system model can be transformed into a mixed integer linear programming model through univariate function linearization, binary function linearization, and power station operation mutually exclusive linearization, where: The linearization of a univariate function into piecewise linearity includes:

[0085]

[0086]

[0087]

[0088] Where, is the indicator variable of the cth storage capacity interval of reservoir i in period t, which is an integer from 0 to 1 and is used to judge the storage capacity The interval in which you are located; is the storage capacity value of reservoir i in the cth storage capacity interval during period t, is the right endpoint of the c-th storage capacity interval of reservoir i.

[0089] The water level of the reservoir at any time can be calculated by the following formula:

[0090] Where, is the right endpoint of the c-th dam front water level interval of reservoir i.

[0091] The linearization of binary functions is based on quadrilateral gridding technology and SOS2 constraints, including:

[0092]

[0093]

[0094] Where: 、 are the discrete water head and power generation flow, R and S are the grid interval numbers of the net water head and power generation flow directions, respectively; P is the output value of the corresponding grid point; is the weight coefficient of the grid point; 、 Represents the sum of the corresponding weights of all grid points in column r and row s, and then 、 Apply SOS2 constraints.

[0095] The power plant operation mutually exclusive linearization adds 0-1 variables, including:

[0096]

[0097] Where, is the power generation status of the first conventional power station at time t, is the pumping state variable of the pumped storage power station at time t, is the power generation state variable of the pumped storage power station at time t.

[0098] In some embodiments, step 130 specifically includes: The mixed integer linear programming model is solved using the Gurobi solver and the mixed integer linear programming algorithm.

[0099] like Figure 2 As shown, the Gurobi solver is called and the mixed integer linear programming algorithm is used to solve the mixed integer linear programming model to obtain the operating status of the hybrid pumped storage power station for 8760 hours.

[0100] Figure 3 is a structural diagram of a hybrid pumped storage power station scheduling device with multiple time scales nested provided in an embodiment of the present application, such as Figure 3 As shown, the system includes a construction module 310, an optimization scheduling module 320, a conversion module 330 and a solution module 340, wherein: A construction module 310 is used to construct a multi-objective function with the objectives of maximizing the power station profit and minimizing the peak-to-valley difference of the grid residual load; An optimization scheduling module 320 is used to optimize the scheduling of the hybrid pumped storage power station based on a multi-objective function and multiple time scales from large to small to obtain a system model; A conversion module 330 is used to convert the system model into a mixed integer linear programming model; The solving module 340 is used to solve the mixed integer linear programming model to obtain a scheduling result.

[0101] Based on the method in the above embodiment, Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4As shown, an embodiment of the present application provides an electronic device, which may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call the logic instructions in the memory 430 to execute the multi-time-scale nested hybrid pumped-storage power station scheduling method of the above embodiment.

[0102] In addition, the logic instructions in the aforementioned memory 430 can be implemented in the form of a software functional unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the multi-time-scale nested hybrid pumped-storage power station scheduling method described in various embodiments of the present application.

[0103] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the multi-time-scale nested hybrid pumped-storage power station scheduling method in the above embodiment.

[0104] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the multi-time-scale nested hybrid pumped-storage power station scheduling method in the above embodiment.

[0105] It is understood that the processor in the embodiments of the present application may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0106] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC.

[0107] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, the computer program instructions fully or partially produce the processes or functions described in the embodiments of this application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive (SSD)).

[0108] It will be understood that the various numerical numbers involved in the embodiments of the present application are merely distinctions for the convenience of description and are not intended to limit the scope of the embodiments of the present application.

[0109] It is easy for those skilled in the art to understand that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A hybrid pumped storage power station scheduling method with multiple time scales nested, characterized in that: include: Construct a multi-objective function with the goals of maximizing power plant revenue and minimizing the peak-to-valley difference of grid residual load; Based on the multi-objective function, the hybrid pumped storage power station is optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model; Converting the system model into a mixed integer linear programming model; Solve the mixed integer linear programming model to obtain a scheduling result.

2. The multi-time-scale nested hybrid pumped storage power station scheduling method according to claim 1, characterized in that: The method of optimizing the hybrid pumped storage power station based on the multi-objective function according to multiple time scales from large to small to obtain a system model includes: Based on the multi-objective function, the hybrid pumped storage power station is optimized and dispatched in turn at the scale of ten days within a year, the scale of day within ten days, and the scale of hour within a day.

3. The multi-time-scale nested hybrid pumped storage power station scheduling method according to claim 1, characterized in that: The method of optimizing the hybrid pumped storage power station based on the multi-objective function according to multiple time scales from large to small to obtain a system model includes: Based on the preset priority of each objective function in the multi-objective function, the hybrid pumped storage power station is hierarchically optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model.

4. The multi-time-scale nested hybrid pumped storage power station scheduling method according to any one of claims 1 to 3, characterized in that: The method of optimizing the hybrid pumped storage power station based on the multi-objective function according to multiple time scales from large to small to obtain a system model includes: Based on the multi-objective function and preset constraints, the hybrid pumped storage power station is optimized and dispatched in sequence according to multiple time scales from large to small to obtain a system model.

5. The multi-time-scale nested hybrid pumped storage power station scheduling method according to claim 1, characterized in that: Converting the system model into a mixed integer linear programming model comprises: The system model is linearized by a single variable function, linearized by a two variable function, and linearized by mutually exclusive power station operation to obtain the mixed integer linear programming model.

6. The multi-time-scale nested hybrid pumped storage power station scheduling method according to claim 1, characterized in that: Solving the mixed integer linear programming model to obtain a scheduling result includes: The mixed integer linear programming model is solved using a mixed integer linear programming algorithm through the Gurobi solver.

7. A hybrid pumped storage power station dispatching device with multiple time scales nested, characterized in that: include: A construction module is used to construct a multi-objective function with the goals of maximizing the power plant's profit and minimizing the peak-to-valley difference of the grid's residual load; An optimization scheduling module is used to optimize the scheduling of the hybrid pumped storage power station in sequence according to multiple time scales from large to small based on the multi-objective function to obtain a system model; A conversion module, configured to convert the system model into a mixed integer linear programming model; The solution module is used to solve the mixed integer linear programming model to obtain a scheduling result.

8. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the multi-time-scale nested hybrid pumped-storage power station scheduling method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is enabled to execute the multi-time-scale nested hybrid pumped-storage power station scheduling method according to any one of claims 1 to 6.

10. A computer program product, characterized in that When the computer program product runs on a processor, the processor is enabled to execute the multi-time-scale nested hybrid pumped-storage power station scheduling method according to any one of claims 1 to 6.

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