Reservoir scheduling method based on double-layer coordination and multiple targets and related device

Abstract

The invention discloses a reservoir scheduling method and a related device based on double-layer coordination multiple objectives, a reservoir scheduling model based on double-layer coordination multiple objectives is constructed under the condition that reservoir safe operation constraints are met, the reservoir scheduling model based on double-layer coordination multiple objectives comprises a first-level optimization objective and a second-level optimization objective, and the second-level optimization objective comprises a first-level optimization objective and a second-level optimization objective. The first-level optimization target takes the maximum peak clipping flow and the minimum reservoir dam break risk as targets, and the second-level optimization target takes the maximum ecological benefit and the maximum reservoir economic benefit as targets; and solving the reservoir scheduling model based on double-layer coordination and multiple objectives, firstly solving a primary optimization objective, then solving a secondary optimization objective based on a solving result of the primary optimization objective, and feeding back a solving result of the secondary optimization objective to the primary optimization objective for dynamic optimization. Performing loop iteration between the first-level optimization target and the second-level optimization target by taking respective target functions as targets, and when each target reaches balance, stopping iteration to obtain a reservoir scheduling scheme; and carrying out reservoir scheduling according to the reservoir scheduling scheme. According to the invention, the problem of man-made subjectivity in weight distribution can be avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-objective reservoir optimization scheduling, and in particular relates to a reservoir scheduling method based on double-layer coordination and multi-objective scheduling and related devices. Background Art

[0002] In recent years, due to global warming and increased human activity, extreme rainfall events have become more frequent in many regions. The resulting floods and droughts are becoming increasingly widespread, extreme, abnormal, and uncertain. As a crucial component of flood control systems, reservoirs play a vital role in regional development and flood prevention. Reservoir water regulation is a crucial non-engineering measure for disaster prevention and mitigation, impacting both the protected areas and the safety of the reservoirs themselves. Appropriate flood control regulation schemes can significantly improve the construction efficiency of reservoir projects and maximize their economic benefits.

[0003] Numerous water conservancy researchers have been dedicated to optimizing reservoir flood control capabilities and scheduling rules. Currently, commonly used reservoir flood control scheduling rules include procedural scheduling and optimized scheduling. Procedural scheduling is simple and easy to use, but it suffers from delayed response, strong limitations, and a lack of scientific basis, making it difficult to meet the complex tasks of multi-objective reservoirs and cope with extremely changing environments. Optimized scheduling, on the other hand, comprehensively considers and formulates appropriate scheduling plans by setting a series of objectives and constraints. This results in scheduling models that are high-dimensional, dynamic, nonlinear, and random. Traditional optimized scheduling models have drawbacks when solving for objectives of different priorities. Specifically, when solving conflicting objectives, weights must be assigned to prioritize different objectives, but this weight assignment is subject to human subjectivity. Summary of the Invention

[0004] In response to the problems existing in the prior art, the present invention provides a reservoir scheduling method and related devices based on double-layer coordinated multi-objectives, which can avoid the problem of human subjectivity in weight allocation.

[0005] In order to solve the above technical problems, the present invention is implemented through the following technical solutions:

[0006] According to a first aspect of the present invention, a reservoir scheduling method based on double-layer coordination and multiple objectives is provided, comprising:

[0007] Under the constraints of reservoir safety operation, a reservoir operation model based on two-layer coordinated multi-objectives is constructed. The reservoir operation model based on two-layer coordinated multi-objectives includes a primary optimization objective and a secondary optimization objective. The primary optimization objective includes maximizing peak flow and minimizing reservoir dam breach risk, and the secondary optimization objective includes maximizing ecological benefits and maximizing reservoir economic benefits.

[0008] Solve the reservoir operation model based on double-layer coordinated multi-objectives, first solve the primary optimization objective, then solve the secondary optimization objective based on the solution of the primary optimization objective, and feed the solution of the secondary optimization objective back to the primary optimization objective for dynamic optimization, and iterate the primary optimization objective and the secondary optimization objective with their respective objective functions as targets. When the objectives are balanced, stop the iteration and obtain the reservoir operation plan;

[0009] Reservoir operation is performed according to the reservoir operation plan.

[0010] In a possible implementation of the first aspect, the goal of maximizing peak flow reduction and minimizing the risk of reservoir dam failure is specifically as follows:

[0011]

[0012] Where: W1 is the peak shaving flow; Q 上游,t is the upstream inflow flow in each period during the reservoir operation cycle; Q 下泄，t is the discharge flow in each period during the reservoir operation cycle; T is the total number of periods; Δt is the time interval between periods; W2 is the reservoir dam break risk; U(L t ) is the first indicator function; Z t is the water level at each moment during the reservoir operation cycle; Z 防洪 To prevent high water levels in reservoirs for flood control.

[0013] In a possible implementation of the first aspect, the goal of maximizing ecological benefits and reservoir economic benefits is specifically as follows:

[0014]

[0015] Where W3 is the ecological benefit, which means that the flow rate of the river section is greater than the ecological water demand for the longest time to reduce the impact of flow fluctuations on the ecosystem; N is the total number of sections; n is the section number; F(G t ) is the second indicator function; Q nt represents the flow rate at the nth section in the tth period; Q ng represents the ecological water demand of the nth section; W4 is the economic benefit of the reservoir, i is the number of water users; T is the total number of time periods; I is the total number of water users; a, b, c and d are the prices of agricultural irrigation, urban water supply, industrial water supply and other water use in the reservoir respectively; Q 灌溉(i,t) , Q 供水(i,t) , Q 工业(i,t) and Q 其它(i,t) are the water supply for agricultural irrigation, urban water supply, industrial production and other purposes during period t; m is the unit price of hydropower grid connection, η is the turbine efficiency of the hydropower unit; h t is the net water head at the corresponding moment; Q e,tis the flow rate flowing through the turbine at the corresponding moment; Δt is the time interval.

[0016] In a possible implementation of the first aspect, the reservoir safety operation constraints include water balance constraints, reservoir total storage capacity constraints, water level constraints, outflow constraints, water supply flow constraints and reservoir power generation flow constraints.

[0017] In a possible implementation of the first aspect, the water balance constraint is specifically:

[0018] Q s,t =Q 灌溉(i,t) +Q 供水(i,t) +Q 工业(i,t) +Q 其它(i,t)

[0019]

[0020] Where Q s,t is the total water intake of each water user during period t; Q t H is the inflow flow rate of the upstream inflow control section of the reservoir at time t; t is the precipitation per unit time at time t; S t is the water surface area of ​​the reservoir at time t; V 蒸发 and V 渗漏 are the evaporation and leakage losses of the reservoir in the time period T; Q e,t and Q 泄洪,t are the water consumption for power generation and the reservoir discharge at time t respectively.

[0021] In a possible implementation of the first aspect, the total reservoir capacity constraint is specifically:

[0022]

[0023] Where V min V is the minimum storage capacity allowed by the reservoir; max V is the maximum storage capacity allowed by the reservoir; 初 is the initial water volume of the reservoir. In a possible implementation of the first aspect, the water level constraint is specifically:

[0024] Z min <Z t <Z max

[0025] Where Z t is the water level at each moment during the reservoir operation cycle; Z min and Z max are the minimum and maximum water levels allowed by the reservoir during the scheduling period.

[0026] In a possible implementation of the first aspect, the outbound flow constraint is specifically:

[0027] 0<Q 下泄，t <Q 下泄,max

[0028] Where Q 下泄,max The maximum discharge flow allowed by the reservoir;

[0029] The water supply flow constraint is specifically:

[0030] 0<Q s，t <Q max,t

[0031] Where Q max,t The maximum flow rate allowed at the reservoir intake;

[0032] The reservoir power generation flow constraint is specifically:

[0033] 0<Q e,t <Q e,max

[0034] Where Q e,max The upper limit of the flow rate allowed to flow through the turbine.

[0035] According to a second aspect of the present invention, a reservoir scheduling device based on double-layer coordination and multiple objectives is provided, comprising:

[0036] A construction module is used to construct a reservoir operation model based on a double-layer coordinated multi-objective under the constraints of satisfying the safe operation of the reservoir. The reservoir operation model based on the double-layer coordinated multi-objective includes a primary optimization objective and a secondary optimization objective. The primary optimization objective includes maximizing peak flow and minimizing the risk of reservoir dam failure, and the secondary optimization objective includes maximizing ecological benefits and maximizing reservoir economic benefits.

[0037] A solution module is used to solve the reservoir operation model based on the two-layer coordinated multi-objective, first solve the primary optimization objective, then solve the secondary optimization objective based on the solution result of the primary optimization objective, and feed the solution result of the secondary optimization objective back to the primary optimization objective for dynamic optimization. The primary optimization objective and the secondary optimization objective are iterated cyclically with their respective objective functions as targets. When the objectives are balanced, the iteration is stopped to obtain a reservoir operation plan;

[0038] The dispatching module is used to perform reservoir dispatching according to the reservoir dispatching plan.

[0039] According to a third aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for reservoir scheduling based on double-layer coordinated multi-objectives is implemented.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] The present invention provides a reservoir scheduling method based on a two-layer coordinated multi-objective approach. This method addresses the issue of multiple objectives with different priorities by constructing a two-layer coordinated multi-objective reservoir scheduling model. The method first solves the primary optimization objective, then solves the secondary optimization objective based on the solution of the primary optimization objective, and implements a cyclic iteration between the primary and secondary optimization objectives. This avoids the strong subjectivity and difficulty of weight assignment in weight allocation, eliminates the adverse effects of subjective weighting on scheduling results, and makes the scheduling plan more reasonable. The primary optimization objective of the present invention clearly includes maximizing peak flow and minimizing the risk of reservoir dam failure. Prioritizing the primary optimization objective during the model solution process ensures that the reservoir's flood control safety requirements are met first during the scheduling plan formulation process, effectively improving the reservoir's flood control capacity. The present invention not only focuses on reservoir flood control safety, but also maximizes the ecological and economic benefits of the reservoir through the secondary optimization objective based on the primary optimization objective. Through cyclic iteration and dynamic optimization between the primary and secondary optimization objectives, while prioritizing flood control tasks, ecological and economic factors are fully considered, enabling the scheduling plan to maximize the comprehensive benefits of the reservoir while ensuring safety.

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a flow chart of a reservoir scheduling method based on double-layer coordination and multiple objectives of the present invention. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] like Figure 1 As shown, the embodiment of the present invention provides a reservoir scheduling method based on double-layer coordination and multiple objectives, which specifically includes the following steps:

[0047] Step 1: Under the constraints of reservoir safety operation, a reservoir scheduling model based on double-layer coordinated multi-objectives is constructed. The reservoir scheduling model based on double-layer coordinated multi-objectives includes a primary optimization objective and a secondary optimization objective. The primary optimization objective includes maximizing peak flow and minimizing reservoir dam break risk, and the secondary optimization objective includes maximizing ecological benefits and maximizing reservoir economic benefits.

[0048] Specifically, before building a reservoir operation model, it is necessary to first comprehensively sort out and determine the constraints that must be met for the safe operation of the reservoir. By setting the constraints, a safety margin is provided for model construction. In this implementation, the constraints include but are not limited to:

[0049] Water balance constraints ensure that the water volume of the reservoir is balanced during the operation process, avoiding operation errors caused by water volume calculation errors. The specific water balance constraints are:

[0050] Q s,t =Q 灌溉(i,t) +Q 供水(i,t) +Q 工业(i,t) +Q 其它(i,t)

[0051]

[0052] Where Q s,t is the total water intake of each water user during period t, Q t is the inflow flow rate at the upstream water control section of the reservoir at time t, m3 / s; H t is the precipitation per unit time at time t, m / m 2 *s;S t is the water surface area of ​​the reservoir at time t, m 2 ; V 蒸发 and V 渗漏 are the evaporation and leakage losses of the reservoir in the time period T, m 3 ;Q e,t and Q 泄洪,t are respectively the water consumption for power generation and the flood discharge of the reservoir at time t, m3 / s.

[0053] The total storage capacity constraint of the reservoir ensures that the storage capacity of the reservoir is within a safe range and prevents the reservoir from being empty or over-stored. The total storage capacity constraint of the reservoir is specifically:

[0054]

[0055] Where V min is the minimum storage capacity allowed by the reservoir, m 3 ; V max is the maximum storage capacity allowed by the reservoir, m 3 ; V 初 is the initial water volume of the reservoir, m 3 .

[0056] Water level constraints ensure that the reservoir water level is within a reasonable range and guarantee the safe operation of the reservoir dam. The specific water level constraints are:

[0057] Z min <Z t <Z max

[0058] Where Z t is the water level at each moment during the reservoir operation cycle, m; Z min and Z max are the minimum and maximum water levels allowed by the reservoir during the scheduling period, m.

[0059] Outflow constraints limit the upper limit of the reservoir's discharge flow to avoid excessive flood control pressure on downstream areas. The specific outflow constraints are:

[0060] 0<Q 下泄，t <Q 下泄,max

[0061] Where Q 下泄,max The maximum discharge flow allowed by the reservoir.

[0062] Water supply flow constraints ensure that the reservoir water supply flow is within a reasonable range to meet the water needs of water users while avoiding adverse effects on reservoir operation. The specific water supply flow constraints are:

[0063] 0<Q s，t <Q max,t

[0064] Where Q max,t The maximum flow rate allowed at the reservoir intake;

[0065] Reservoir power generation flow constraints, through which the power generation flow is ensured to be within a reasonable range, guaranteeing the safe and stable operation of the hydropower units. The specific constraints for reservoir power generation flow are:

[0066] 0<Q e,t <Q e,max

[0067] Where Q e,max The upper limit of the flow rate allowed to flow through the turbine.

[0068] It's important to note that determining the constraints necessary for a reservoir's safe operation requires collecting and processing basic data such as the target reservoir's water level, storage capacity, and water surface area. For reservoirs with complete data, this data is collated to identify the corresponding storage capacity and water surface area at different water levels. For areas where basic data is lacking, a multi-layer perceptron (MLP) neural network model can learn complex nonlinear relationships. Using the reservoir's water level as input and its storage capacity and water surface area as output, a neural network is trained to fit the mapping between these data, thereby predicting the missing water level, storage capacity, and water surface area.

[0069] After determining the above constraints, set the first-level optimization objectives and the second-level optimization objectives.

[0070] Specifically, the primary optimization goal is flood control, and its objective function mainly includes two parts: (1) maximizing peak flow reduction to maximize the safety of downstream protection objects; (2) minimizing the risk of reservoir dam failure, that is, minimizing the total duration of the water level above the flood control high water level at each moment during the reservoir operation cycle to maximize the safety of the reservoir dam. With the goals of maximizing peak flow reduction and minimizing the risk of reservoir dam failure, the specific objective function is as follows:

[0071]

[0072] Where: W1 is the peak shaving flow; Q 上游,t is the upstream inflow flow in each period during the reservoir operation cycle, m 3 / s;Q 下泄，t is the discharge flow in each period during the reservoir operation cycle, m 3 / s; T is the total number of time periods; Δt is the time interval between time periods, s; W2 is the risk of reservoir dam failure; U(L t ) is the first indicator function; Z t is the water level at each moment during the reservoir operation cycle, m; Z 防洪 The high water level of the reservoir for flood control, m.

[0073] Specifically, the secondary optimization objective mainly considers maximizing the comprehensive benefits of the reservoir. Its objective function mainly includes: (1) maximizing ecological benefits, with the longest duration of the river section flow exceeding the ecological water demand, so as to reduce the impact of flow fluctuations on the ecosystem; (2) the economic benefits of the reservoir mainly come from the power generation and water supply benefits of the hydropower station. With the maximization of ecological benefits and the maximization of reservoir economic benefits as the goals, the specific expression is as follows:

[0074] (1) Ecological benefits

[0075]

[0076] (2) Economic benefits

[0077]

[0078] Where W3 is the ecological benefit, which means that the flow rate of the river section is greater than the ecological water demand for the longest time to reduce the impact of flow fluctuations on the ecosystem; N is the total number of sections; n is the section number; F(G t ) is the second indicator function; Q nt Indicates the flow rate of the nth section in the tth period, m 3 / s;Q ng Indicates the ecological water demand of the nth section, m 3 / s; W4 is the economic benefit of the reservoir, i is the number of water users; T is the total number of time periods; I is the total number of water users; a, b, c and d are the prices of agricultural irrigation, urban water supply, industrial water supply and other water use in the reservoir, respectively, in yuan / m 3 ;Q 灌溉(i,t) , Q 供水(i,t) , Q 工业(i,t) and Q 其它(i,t) are the water supply for agricultural irrigation, urban water supply, industrial production and other purposes during period t; m is the unit price of hydropower grid connection; η is the turbine efficiency of the hydropower unit, which is generally between 0.8 and 0.9; h t is the net water head at the corresponding moment, m; Q e,t is the flow rate flowing through the turbine at the corresponding moment, m 3 / s; Δt is the time interval, s.

[0079] Step 2: Solve the reservoir scheduling model based on double-layer coordinated multi-objectives, first solve the first-level optimization objective, then solve the second-level optimization objective based on the solution of the first-level optimization objective, and feed the solution of the second-level optimization objective back to the first-level optimization objective for dynamic optimization. The first-level optimization objective and the second-level optimization objective are iterated cyclically with their respective objective functions as targets. When the objectives are balanced, the iteration is stopped to obtain the reservoir scheduling plan.

[0080] Specifically, a non-dominated sorting genetic algorithm (NSGA-II) is used to optimize and solve the primary optimization objective. First, the reservoir operation decision variables are encoded to generate an initial population. Then, based on the two objective functions of maximizing peak flow and minimizing the risk of reservoir dam failure, the fitness value of each individual is calculated. The fitness value reflects the degree to which the individual is good or bad at meeting the primary optimization objective. Through genetic operations such as selection, crossover, and mutation, the population is continuously evolved, gradually approaching the optimal solution. During the iterative process, the parameters of the non-dominated sorting genetic algorithm are continuously adjusted to improve the solution efficiency and accuracy. Ultimately, a set of reservoir operation decision variable combinations that meet the primary optimization objective is obtained, which serves as the solution to the primary optimization objective.

[0081] The solution to the primary optimization objective is used as a known condition and fed into the solution of the secondary optimization objective. To solve the secondary optimization objective, Yalmip modeling is used, and the Gurobi solver is called. In MATLAB, using the Yalmip modeling language, the variables, parameters, and objective function for maximizing the reservoir's comprehensive benefits, along with the constraints, are integrated into a complete secondary optimization objective for maximizing both the ecological and economic benefits of the reservoir. This constructed secondary optimization objective function is then passed to the Cplex solver for solution. After the Cplex solver returns the solution, MATLAB is used to analyze the results and extract the optimal scheduling solution's water allocation strategies for each time period, as well as the calculated maximum benefit value. At this point, the constraints of the primary optimization objective must be considered. Specifically, within the scheduling framework defined by the primary optimization objective, a combination of scheduling decision variables that maximizes both the ecological and economic benefits of the reservoir must be sought. For example, after the primary optimization objective determines the reservoir's storage and discharge processes during floods, the secondary optimization objective needs to adjust the reservoir's discharge flow at different times based on this process to meet ecological flow requirements and improve power generation efficiency. Through continuous iterative optimization, the solution of the secondary optimization objective is obtained.

[0082] The solution results of the secondary optimization objectives are fed back to the primary optimization objectives to dynamically optimize the primary optimization objectives. This is because the solution results of the secondary optimization objectives may have an impact on the primary optimization objectives. For example, under the premise of meeting ecological and economic benefits, it may be necessary to fine-tune the storage and discharge process of the reservoir, which may affect the peak flow and the risk of reservoir dam failure to a certain extent. By feeding the solution results of the secondary optimization objectives back to the primary optimization objectives, the fitness value is recalculated and a new round of iterative optimization is carried out. The primary optimization objectives and the secondary optimization objectives are iterated cyclically with their respective objective functions as the targets. In each iteration process, adjustments and optimizations are made based on the solution results of the other party, so that the objectives are coordinated and promoted with each other.

[0083] When all objectives are balanced, iterations are terminated, resulting in the Patero optimal solution set (i.e., the reservoir operation plan). It should be noted that achieving balance among all objectives does not mean that each objective has reached its absolute optimal value, but rather that a relatively optimal combination of operation decision variables has been found. For example, if the magnitude of change in each objective function is less than a set threshold over multiple consecutive iterations, the objectives are considered balanced and iterations are terminated. At this point, the resulting combination of reservoir operation decision variables becomes the final reservoir operation plan.

[0084] Step 3: Perform reservoir operation according to the reservoir operation plan.

[0085] In other words, the solved reservoir operation plan is applied to the actual reservoir operation work. The dispatcher performs actual operations through the reservoir's operation facilities based on the decision variables such as reservoir storage capacity and discharge flow in each period determined in the operation plan.

[0086] Through the above implementation methods, this method can comprehensively consider multiple factors such as flood control, ecology, and economy of the reservoir, formulate a reasonable and feasible reservoir scheduling plan, and maximize the comprehensive benefits on the basis of giving priority to meeting the reservoir flood control tasks. Ultimately, the scheduling plan made by the double-layer target scheduling decision meets the benefit requirements of all parties and effectively improves the flood control capacity and comprehensive benefits of the reservoir.

[0087] In other words, in this two-tier coordinated multi-objective reservoir operation method, the primary optimization objective is to meet the primary flood control mission of the reservoir and ensure the safety of the reservoir itself and the reservoir area. Under this primary optimization objective, the goal is to minimize the duration of high reservoir water levels during the flood season to maximize the safety of downstream flood control targets. Given these considerations, the primary optimization objectives are to minimize the duration of water levels exceeding the flood control high level at all times during the reservoir operation cycle and to maximize peak load reduction. While ensuring that the reservoir's basic mission is met, the secondary optimization objectives are power generation, water supply, and ecological benefits to improve the overall benefits of the target reservoir. To maximize its ecological benefits, the flow rate at the downstream river section must exceed the ecological water demand for the longest period of time to minimize the impact of flow fluctuations on the ecosystem. The economic benefits of a reservoir primarily come from the power generation and water supply benefits of the hydropower station. Maximizing the reservoir's comprehensive economic benefits is the goal, aiming to increase the target reservoir's revenue.

[0088] To implement this scheduling method, the present invention first considers the water balance of a multi-objective reservoir. The water balance constraints take into account factors such as upstream water inflow, precipitation, evaporation loss, seepage loss, outflow, and intake flow, ensuring dynamic water balance. The model first satisfies the water balance constraints. Secondly, it comprehensively considers target water characteristics and establishes constraints such as the water supply system intake flow, outflow, hydropower unit generation flow, reservoir water level, and ecological water supply flow. Combining these objectives and comprehensively considering the reservoir's actual constraints and objective functions, a reservoir scheduling model based on two-level coordinated multi-objectives is constructed. The primary optimization objective is primarily optimized using a non-dominated sorting genetic algorithm (NSGA-II), while the secondary optimization objective is modeled using yalmip and solved using gurobi. The water level and peak-shaving flow of the primary optimization objective are assigned to the secondary optimization objective. The water levels and flows of the secondary optimization objectives are then fed back to the primary optimization objective for dynamic optimization. Iterations are performed between the primary and secondary optimization objectives, each targeting its own objective function. When all objectives are balanced, iterations cease, ultimately yielding the Patero optimal solution set.

[0089] In another embodiment of the present invention, a reservoir scheduling device based on double-layer coordination and multiple objectives is provided, which is used to implement the above-mentioned reservoir scheduling method based on double-layer coordination and multiple objectives, specifically comprising:

[0090] A construction module is used to construct a reservoir scheduling model based on a double-layer coordinated multi-objective while satisfying the constraints of reservoir safe operation. The reservoir scheduling model based on a double-layer coordinated multi-objective includes a primary optimization objective and a secondary optimization objective. The primary optimization objective includes maximizing peak flow and minimizing the risk of reservoir dam break, and the secondary optimization objective includes maximizing ecological benefits and maximizing reservoir economic benefits.

[0091] The solution module is used to solve the reservoir scheduling model based on double-layer coordinated multi-objectives. The first-level optimization objective is first solved, and then the second-level optimization objective is solved based on the solution result of the first-level optimization objective. The solution result of the second-level optimization objective is fed back to the first-level optimization objective for dynamic optimization. The first-level optimization objective and the second-level optimization objective are cyclically iterated with their respective objective functions as targets. When the objectives are balanced, the iteration is stopped to obtain the reservoir scheduling plan.

[0092] The dispatching module is used to perform reservoir dispatching according to the reservoir dispatching plan.

[0093] All relevant contents of each step involved in the embodiment of the aforementioned reservoir scheduling method based on double-layer coordination and multiple objectives can be referred to the functional description of the functional module corresponding to the reservoir scheduling device based on double-layer coordination and multiple objectives in the embodiment of the present invention, and will not be repeated here. The division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, the functional modules in various embodiments of the present invention can be integrated into one processor, or they can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0094] In another embodiment of the present invention, a computer device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a reservoir scheduling method based on double-layer coordination and multiple objectives.

[0095] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the reservoir scheduling method based on dual-layer coordinated multi-objective in the above embodiment.

[0096] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0100] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0101] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A reservoir operation method based on double-layer coordination and multiple objectives, characterized by: include: Under the constraints of reservoir safety operation, a reservoir operation model based on two-layer coordinated multi-objectives is constructed. The reservoir operation model based on two-layer coordinated multi-objectives includes a primary optimization objective and a secondary optimization objective. The primary optimization objective includes maximizing peak flow and minimizing reservoir dam breach risk, and the secondary optimization objective includes maximizing ecological benefits and maximizing reservoir economic benefits. Solve the reservoir operation model based on double-layer coordinated multi-objectives, first solve the primary optimization objective, then solve the secondary optimization objective based on the solution of the primary optimization objective, and feed the solution of the secondary optimization objective back to the primary optimization objective for dynamic optimization, and iterate the primary optimization objective and the secondary optimization objective with their respective objective functions as targets. When the objectives are balanced, stop the iteration and obtain the reservoir operation plan; Reservoir operation is performed according to the reservoir operation plan.

2. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 1, characterized in that: The stated goal is to maximize peak flow reduction and minimize the risk of reservoir dam failure, specifically: Where: W1 is the peak shaving flow; Q 上游,t is the upstream inflow flow in each period during the reservoir operation cycle; Q 下泄，t is the discharge flow in each period during the reservoir operation cycle; T is the total number of periods; Δt is the time interval between periods; W2 is the reservoir dam break risk; U(Lt) is the first indicator function; Z t is the water level at each moment during the reservoir operation cycle; Z 防洪 To prevent high water levels in reservoirs for flood control.

3. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 2, characterized in that: The above mentioned goal is to maximize ecological benefits and reservoir economic benefits, specifically: Where W3 is the ecological benefit, which means that the flow rate of the river section is greater than the ecological water demand for the longest time to reduce the impact of flow fluctuations on the ecosystem; N is the total number of sections; n is the section number; F(G t ) is the second indicator function; Q nt represents the flow rate at the nth section in the tth period; Q ng represents the ecological water demand of the nth section; W4 is the economic benefit of the reservoir, i is the number of water users; T is the total number of time periods; I is the total number of water users; a, b, c and d are the prices of agricultural irrigation, urban water supply, industrial water supply and other water use in the reservoir respectively; Q 灌溉(i,t) , Q 供水(i,t) , Q 工业(i,t) and Q 其它(i,t) are the water supply for agricultural irrigation, urban water supply, industrial production and other purposes during period t; m is the unit price of hydropower grid connection, η is the turbine efficiency of the hydropower unit; h t is the net water head at the corresponding moment; Q e,t is the flow rate flowing through the turbine at the corresponding moment; Δt is the time interval.

4. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 3, characterized in that: The reservoir safety operation constraints include water balance constraints, reservoir total storage capacity constraints, water level constraints, outflow constraints, water supply flow constraints and reservoir power generation flow constraints.

5. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 4, characterized in that: The water balance constraints are specifically: Q s,t =Q 灌溉(i,t) +Q 供水(i,t) +Q 工业(i,t) +Q 其它(i,t) Where Q s,t is the total water intake of each water user during period t; Q t H is the inflow flow rate of the upstream inflow control section of the reservoir at time t; t is the precipitation per unit time at time t; S t is the water surface area of ​​the reservoir at time t; V 蒸发 and V 渗漏 are the evaporation and leakage losses of the reservoir in the time period T; Q e,t and Q 泄洪,t are the water consumption for power generation and the reservoir discharge at time t respectively.

6. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 5, characterized in that: The total reservoir capacity constraint is specifically: Where V min V is the minimum storage capacity allowed by the reservoir; max V is the maximum storage capacity allowed by the reservoir; 初 is the initial water volume of the reservoir.

7. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 6, characterized in that: The water level constraints are specifically: WITH min <Z t <Z max Where Z t is the water level at each moment during the reservoir operation cycle; Z min and Z max are the minimum and maximum water levels allowed by the reservoir during the scheduling period.

8. A reservoir operation method based on double-layer coordination and multiple objectives according to claim 7, characterized in that: The outbound flow constraints are specifically: 0<Q 下泄，t <Q 下泄,max Where Q 下泄,max The maximum discharge flow allowed by the reservoir; The water supply flow constraint is specifically: 0<Q s，t <Q max,t Where Q max,t The maximum flow rate allowed at the reservoir intake; The reservoir power generation flow constraint is specifically: 0<Q e,t <Q e,max Where Q e,max The upper limit of the flow rate allowed to flow through the turbine.

9. A reservoir dispatching device based on double-layer coordination and multiple objectives, characterized in that: include: A construction module is used to construct a reservoir operation model based on a double-layer coordinated multi-objective under the constraints of satisfying the safe operation of the reservoir. The reservoir operation model based on the double-layer coordinated multi-objective includes a primary optimization objective and a secondary optimization objective. The primary optimization objective includes maximizing peak flow and minimizing the risk of reservoir dam failure, and the secondary optimization objective includes maximizing ecological benefits and maximizing reservoir economic benefits. A solution module is used to solve the reservoir operation model based on the two-layer coordinated multi-objective, first solve the primary optimization objective, then solve the secondary optimization objective based on the solution result of the primary optimization objective, and feed the solution result of the secondary optimization objective back to the primary optimization objective for dynamic optimization. The primary optimization objective and the secondary optimization objective are iterated cyclically with their respective objective functions as targets. When the objectives are balanced, the iteration is stopped to obtain a reservoir operation plan; The dispatching module is used to perform reservoir dispatching according to the reservoir dispatching plan.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the reservoir scheduling method based on double-layer coordination and multiple objectives as described in any one of claims 1 to 8 is implemented.

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