Reservoir scheduling method and system based on hydrological simulation

Abstract

The invention discloses a reservoir scheduling method and system based on hydrological simulation, and belongs to the field of reservoir optimal scheduling method.The method comprises the steps that a hydrological environment simulation model is built based on hydrological environment data and a preset water and soil conservation model; obtaining initial drainage basin runoff data based on the hydrological environment simulation model; based on the initial drainage basin runoff data and a preset drought evaluation model, obtaining initial drainage basin drought characteristics; constructing a reservoir balance model based on the reservoir feature information; constructing a multi-target reservoir scheduling model based on the initial drainage basin runoff data, the initial drainage basin drought characteristics and a reservoir balance model; and solving the multi-target reservoir scheduling model to obtain a reservoir scheduling optimization scheme so as to realize the scheduling of the multi-target reservoir. Therefore, the problem that in the prior art, a reservoir dispatching optimization scheme is limited to a certain extent in practical application is solved, flexible dispatching and real-time regulation and control of the multi-target reservoir are achieved by building the model, and the hydrological drought problem is avoided.

Description

Technical Field

[0001] The present invention relates to the field of reservoir optimization scheduling methods, and in particular to a reservoir scheduling method and system based on hydrological simulation. Background Art

[0002] The use of traditional energy sources not only leads to greenhouse gas emissions, environmental pollution, and ecological damage, but also exacerbates energy depletion and climate change. Currently, the development and utilization of new energy technologies has become an effective way to address current energy needs and combat global warming. New energy technologies generally include solar thermal power generation, photovoltaic power generation, hydropower, wind power, tidal power generation, wave power, and geothermal power generation. Solar thermal power generation converts a heated medium into mechanical energy and is suitable for areas with high radiation levels. It can be combined with thermal storage systems to achieve continuous and adjustable power generation. Photovoltaic power generation directly converts solar energy into electricity through photovoltaic cells. This is noiseless and pollution-free, suitable for both distributed and centralized power generation, and its cost continues to decline with technological advancement. Hydropower generation utilizes the potential and kinetic energy of water flow to generate electricity and is one of the largest and most technologically mature forms of clean energy. Wind power generation converts the kinetic energy of air flow into electricity, encompassing both onshore and offshore wind power, with significant potential for offshore wind power development. Tidal power generation utilizes the energy generated by tidal fluctuations to drive power generation devices, offering high predictability and renewable resources. Wave power generation captures the energy of ocean waves and converts it into electrical or mechanical energy, making it suitable for areas with abundant wave resources. Geothermal power generation converts underground high-temperature magma or hot water resources into thermal energy or electrical energy, and is particularly suitable for volcanic areas and areas with frequent geothermal activity.

[0003] Drought control can be achieved through a variety of technologies, including drought monitoring, water-saving irrigation, soil and water conservation, groundwater regulation, rainwater harvesting and reuse, and cloud seeding. Meteorological warning and drought monitoring utilize meteorological data and remote sensing technology to predict drought risk, but their accuracy is limited. Water-saving irrigation technologies such as drip irrigation and micro-sprinklers improve water use efficiency, but their effectiveness is limited in areas with limited water resources. Soil and water conservation enhances water conservation through vegetation restoration and soil improvement, but its effectiveness is slow and requires investment. Groundwater regulation can provide emergency water resources, but over-exploitation can cause problems. Rainwater harvesting and reuse can alleviate water shortages in areas with high rainfall, but their effectiveness is limited in areas with low rainfall. Cloud seeding can alleviate droughts in the short term, but its effectiveness is inconsistent. In contrast, reservoir operation offers flexibility and controllability, effectively ensuring agricultural irrigation, water supply, and power generation needs, and alleviating downstream hydrological droughts. Therefore, it is crucial for drought control.

[0004] Most existing technologies are limited in their application due to shortcomings such as high construction and maintenance costs and limited functionality. Although hydropower generation can simultaneously serve flood control, irrigation, and water supply functions, research attempting to optimize the scheduling of water conservancy projects using coupled simulation and optimization models to simultaneously maximize water conservancy capacity and manage droughts is currently rare. Furthermore, most current drought mitigation technologies rely primarily on passive adaptation, making it difficult to achieve active regulation and dynamic scheduling of water resources at the basin scale. Research on reservoir drought scheduling often relies on empirical or statistical methods and generally lacks physically based distributed hydrological simulation, making it difficult to systematically reveal the spatial effects of scheduling measures and drought response mechanisms at the basin scale. Therefore, optimizing water conservancy project scheduling while simultaneously optimizing hydropower generation and managing droughts is an effective approach to addressing current energy demand and coping with droughts, and is of great significance for promoting sustainable and high-quality economic and social development. The key technical challenge addressed in this project is how to utilize hydrological simulation-optimization algorithms to optimize the scheduling of water conservancy projects while simultaneously maximizing water conservancy capacity and managing droughts. Summary of the Invention

[0005] The present invention provides a reservoir scheduling method and system based on hydrological simulation. This method can solve the problem that reservoir scheduling schemes are subject to certain limitations in practical applications due to the shortcomings of the existing technology such as high construction and operation and maintenance costs and single functions. By constructing a multi-objective scheduling model to call the optimal reservoir scheduling scheme, flexible scheduling and real-time regulation of reservoirs under various reservoirs can be achieved, thus avoiding hydrological drought problems.

[0006] The present invention provides a reservoir scheduling method based on hydrological simulation, comprising:

[0007] Acquire hydrological environment data to build a hydrological environment simulation model based on the hydrological environment data and a preset soil and water conservation model;

[0008] Obtain initial basin runoff data based on the hydrological environment simulation model;

[0009] Based on the initial watershed runoff data and the preset drought assessment model, the initial watershed drought characteristics are obtained;

[0010] Obtaining reservoir characteristic information corresponding to all reservoirs in the basin, so as to construct a reservoir balance model corresponding to the basin based on the reservoir characteristic information;

[0011] A multi-objective reservoir operation model is constructed based on initial basin runoff data, initial basin drought characteristics and reservoir balance model;

[0012] Solve the multi-objective reservoir operation model and obtain the reservoir operation optimization plan to achieve multi-objective reservoir operation.

[0013] The multi-objective reservoir scheduling method proposed in the present invention obtains corresponding hydrological environment data to construct a hydrological environment simulation model to perform hydrological forecasting, obtains the required basin runoff data, and then predicts drought conditions based on the predicted basin runoff data. In combination with the reservoir balance model, a multi-objective reservoir scheduling model that can obtain the required reservoir scheduling optimization plan is constructed, thereby realizing scientific and feasible multi-objective reservoir scheduling based on the actual hydrological environment prediction and reservoir-related characteristic data; using the soil and water conservation model as the connecting hub for drought assessment and reservoir scheduling optimization, a systematic and highly operational overall solution is formed, which solves the problem that the reservoir scheduling plan is subject to certain limitations in actual application due to the shortcomings of the existing technology such as high construction and operation and maintenance costs and single functions. By constructing a multi-objective scheduling model to call the optimal reservoir scheduling plan, flexible scheduling and real-time regulation of multi-objective reservoirs are achieved, avoiding the problem of hydrological drought.

[0014] Furthermore, based on the initial watershed runoff data and the preset drought assessment model, the initial watershed drought characteristics are obtained, including:

[0015] Obtain drought assessment data based on initial watershed runoff data and a preset drought assessment model;

[0016] Based on the drought assessment data, the average duration of drought, the number of drought events and the maximum severity of drought are obtained. The calculation process satisfies the following formula:

[0017]

[0018] D s,max =max{v s,i |i∈[1,D n ]}

[0019] Where D d,mean represents the average duration of drought, D n represents the number of drought events, D s,max Indicates the maximum degree of drought, v d,i represents the duration of a single drought event, v s,i Indicates the severity of a single drought event;

[0020] The average drought duration, number of drought events and maximum drought severity were used as initial drought characteristics.

[0021] In the above scheme, drought assessment calculations are performed on the relevant initial watershed runoff data through a preset drought assessment model to obtain the water drought conditions under the corresponding time scenario, providing scientific reference data for subsequent reservoir scheduling optimization, so as to achieve drought control, reduce the impact of drought on agriculture, ecology, and water resources supply, and ensure residents' water safety and economic development.

[0022] Furthermore, reservoir characteristic information corresponding to all reservoirs in the basin is obtained to construct a reservoir balance model corresponding to the basin based on the reservoir characteristic information, including:

[0023] Obtain the reservoir characteristic information corresponding to all reservoirs in the basin, and obtain the power generation flow data based on the reservoir characteristic information and the preset reservoir balance conditions. The calculation process satisfies the following formula:

[0024]

[0025] Where t represents month, S t represents the reservoir capacity at the beginning of the month t, S t+1 represents the reservoir capacity at the end of month t, I t represents the inflow in month t, represents the power generation flow data for hydropower generation in month t, represents the fixed water outflow in month t, E t Indicates the evaporation loss in month t, L t represents the leakage loss in month t, represents the overflow flow in month t;

[0026] Reservoir constraint variables are obtained based on reservoir characteristic information, and the calculation process satisfies the following formula:

[0027] H i,t =α×S i,t β

[0028] Where H i,t represents the reservoir balance constraint variable, α and β represent the reservoir experience coefficients, S i,t Represents reservoir characteristic information;

[0029] Obtaining hydropower generation data based on power generation flow data;

[0030] A reservoir balance model is constructed based on hydropower generation data and reservoir constraint variables.

[0031] Furthermore, obtaining hydropower generation data based on power generation flow data includes:

[0032] Obtain monthly average effective head based on reservoir characteristic information;

[0033] Obtain monthly average power generation flow data based on power generation flow data;

[0034] The hydropower generation data is obtained based on the monthly average effective head, monthly average power generation flow data and reservoir power generation formula. The calculation process satisfies the following formula:

[0035]

[0036] Where F represents hydropower data, g represents gravity acceleration, η represents power generation efficiency coefficient, i represents all reservoirs, Q m Represents the monthly average power generation flow data, N m represents the monthly average effective head, Y m Represents the total number of seconds in the mth month.

[0037] In the above scheme, by combining the relevant parameters, operation, policy and other characteristic information of all reservoirs, and combining the water balance requirements in actual reservoir operation, a reservoir balance model is designed and constructed to fully reflect the reservoir's scheduling constraints and power generation capacity, and to build a complete and comprehensive reservoir balance model for the optimization simulation of reservoir scheduling.

[0038] Furthermore, a multi-objective reservoir operation model is constructed based on the initial basin runoff data, initial basin drought characteristics and reservoir balance model, including:

[0039] Obtain reservoir decision variables based on initial basin runoff data and initial basin drought characteristics;

[0040] Obtaining reservoir power generation function based on reservoir decision variables and reservoir balance model;

[0041] A multi-objective reservoir operation model is constructed based on reservoir decision variables, reservoir power generation function, reservoir balance model, preset drought constraints and preset reservoir constraints, as shown in the following formula:

[0042] Maxf(x)

[0043] S t (x,y)=0

[0044] S d (x,y)=0

[0045] S s (x,y)=0

[0046] Where x represents the reservoir decision variable, y represents the season, f(x) represents the reservoir power generation function, S t Represents the preset reservoir balance model, S d represents the preset drought constraint condition, S s Indicates the preset reservoir constraint conditions, 0 means the constraint conditions are met;

[0047] The preset reservoir constraints include:

[0048] f xmin (y)≤x≤f xmax (y)

[0049] f lmin (y)≤L(x)≤f lmax (y)

[0050] f vmin (y)≤V(x)≤f vmax (y)

[0051] Where: L(x) represents the reservoir water level, V(x) represents the reservoir capacity, f xmax (y) represents the maximum reservoir discharge, f lmax (y) represents the maximum reservoir water level, f vmax (y) represents the maximum reservoir volume, f xmin (y) represents the minimum reservoir discharge, f lmin (y) represents the minimum reservoir water level, f vmin (y) represents the minimum reservoir volume;

[0052] The preset drought constraints include:

[0053] D opt ≤D base

[0054] Where: D opt represents the drought characteristics after inputting the reservoir decision variables, D base Represents the baseline of drought characteristics.

[0055] In the above scheme, the hydrological environment simulation model, drought assessment, reservoir balance model and related constraints are combined to construct a systematic and highly operational multi-objective scheduling model based on hydrological simulation, drought assessment, hydropower generation and reservoir balance. The model can optimize the reservoir scheduling scheme based on drought control and power generation maximization, and can be flexibly applied in multiple river basins and under different reservoir conditions, thus realizing perfect river basin water conservancy management. It has strong versatility and promotion value.

[0056] Furthermore, a multi-objective reservoir scheduling model is solved to obtain a reservoir scheduling optimization plan to achieve the scheduling of the multi-objective reservoir, including: solving based on a genetic algorithm and the multi-objective reservoir scheduling model to obtain a target reservoir scheduling plan; obtaining a fitness score based on the target reservoir scheduling plan; when the fitness score meets a preset termination condition, using the target reservoir scheduling plan as the reservoir scheduling optimization plan to achieve the scheduling of the multi-objective reservoir.

[0057] In the above scheme, a genetic algorithm is used to solve the multi-objective reservoir operation model to obtain an optimized operation scheme, and a fitness score is performed to determine whether the optimized scheme is feasible, thereby ensuring the correctness and feasibility of the final result.

[0058] Furthermore, a solution is performed based on a genetic algorithm and a multi-objective reservoir scheduling model to obtain a target reservoir scheduling scheme, including: obtaining a number of current reservoir decision variables based on initial basin runoff data and initial basin drought characteristics; inputting the number of current reservoir decision variables into the multi-objective reservoir scheduling model to execute the solution steps: obtaining a number of reservoir scheduling schemes based on a number of current reservoir decision variables, a reservoir balance model and preset reservoir constraints; obtaining a number of corresponding scheduling runoff data based on the number of reservoir scheduling schemes and the initial basin runoff data, to obtain a number of corresponding scheduling basin drought characteristics based on the number of scheduling runoff data; obtaining a number of corresponding power generation data based on the reservoir balance model; and obtaining a target reservoir scheduling scheme based on a number of scheduling basin drought characteristics, corresponding power generation data, preset power generation constraints and preset drought constraints.

[0059] In the above scheme, based on the genetic algorithm, the initial basin runoff data and initial basin drought characteristics obtained by the hydrological environment simulation model are combined to preliminarily call the optimal several current reservoir decision variables, and then solve them based on them. First, a reservoir scheduling scheme is generated based on the current reservoir decision variables and the reservoir balance model, so as to combine the corresponding data with the initial basin runoff data of the hydrological simulation model, solve the corresponding hydrological data, and solve the drought characteristics, and then calculate the variables that meet the reservoir operation and hydrological process, drought constraints, etc., and combine the maximization of reservoir power generation to obtain a solution that meets the requirements, and use it as the required target reservoir scheduling scheme, and use hydrological simulation and optimization algorithms to optimize the scheduling of water conservancy projects, thereby achieving maximum water conservancy production capacity on the basis of drought control.

[0060] Furthermore, after obtaining the fitness score based on the target reservoir scheduling plan, it also includes: when the fitness score does not meet the preset termination conditions, obtaining a new generation of reservoir decision variables based on the genetic algorithm and the current reservoir decision variables; inputting the new generation of reservoir decision variables into the multi-objective reservoir scheduling model to re-execute the solution steps to obtain a new generation of target reservoir scheduling plan.

[0061] In the above scheme, according to the calculation of fitness score, when the solution does not meet the preset termination conditions, the genetic algorithm is used to form new decision variables through selection, crossover and mutation to re-solve the problem, thereby realizing iterative optimization of the solution process until an approximately optimal reservoir operation optimization scheme is obtained, thereby ensuring the reliability of the solution results of the multi-objective reservoir operation model.

[0062] The present invention provides a reservoir scheduling method based on hydrological simulation. First, a hydrological environment simulation model is constructed based on the hydrological environment characteristics of different river basins. Then, based on the concept of optimizing production capacity and drought control, the integrated application of drought assessment technology and water conservancy project scheduling is emphasized. The SWAT model (soil and water conservation model) is used as the hub connecting drought assessment and reservoir scheduling optimization. The method combines the hydrological environment simulation model, drought assessment and reservoir balance model, and combines the corresponding reservoir production capacity calculation and evaluation, drought constraints, reservoir constraints and other links to realize a multi-objective reservoir scheduling method for hydropower generation and drought control that considers hydrological simulation. The method simultaneously realizes the maximization of water conservancy production capacity and drought control functions, which helps to improve the utilization efficiency of water resources in the river basin; it can regulate downstream flow in real time, reduce the frequency, duration and intensity of drought, so as to effectively alleviate the hydrological drought problem in the river basin, reduce the impact of hydrological drought on agriculture, ecology and water resources supply, and ensure the water safety and economic development of downstream residents; at the same time, the method of model simulation construction has the advantages of being easy to implement, low cost, high efficiency and flexible and wide application range.

[0063] The present invention provides a reservoir operation system based on hydrological simulation, which implements the above-mentioned reservoir operation method based on hydrological simulation, including a hydrological simulation module, a drought assessment module, a reservoir balance module, a model construction module and a reservoir operation module, wherein:

[0064] The hydrological simulation module is used to obtain hydrological environment data, to build a hydrological environment simulation model based on the hydrological environment data and a preset soil and water conservation model; and to obtain initial watershed runoff data based on the hydrological environment simulation model;

[0065] The drought assessment module is used to obtain the initial watershed drought characteristics based on the initial watershed runoff data and the preset drought assessment model;

[0066] The reservoir balance module is used to obtain the reservoir characteristic information corresponding to all reservoirs in the basin, so as to construct the reservoir balance model corresponding to the basin based on the reservoir characteristic information;

[0067] The model building module is used to build a multi-objective reservoir operation model based on the initial basin runoff data, initial basin drought characteristics and reservoir balance model;

[0068] The reservoir operation module is used to solve the multi-objective reservoir operation model and obtain the reservoir operation optimization plan to achieve multi-objective reservoir operation.

[0069] The present invention provides a reservoir scheduling system based on hydrological simulation. The system obtains corresponding hydrological environment data through a hydrological simulation module to construct a hydrological environment simulation model to perform hydrological prediction and obtain required basin runoff data. Then, the drought situation is predicted according to the predicted basin runoff data through a drought assessment module. Then, the model construction module is combined with the reservoir balance model of the reservoir balance module to construct a multi-objective reservoir scheduling model that can obtain the required reservoir scheduling optimization plan, and the model is solved based on the reservoir scheduling module, thereby realizing scientific and feasible multi-objective reservoir scheduling based on the actual hydrological environment prediction and reservoir-related characteristic data; using the soil and water conservation model as the connecting hub between drought assessment and reservoir scheduling optimization, a systematic and highly operational overall solution is formed, which solves the problem that the reservoir scheduling plan is subject to certain limitations in actual application due to the shortcomings of the existing technology such as high construction and operation and maintenance costs and single functions. By constructing a multi-objective scheduling model to call the optimal reservoir scheduling plan, flexible scheduling and real-time regulation of multi-objective reservoirs are achieved, avoiding the problem of hydrological drought. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0071] Figure 1 Schematic diagram of a reservoir scheduling method based on hydrological simulation provided in this embodiment;

[0072] Figure 2 This is a schematic diagram of a reservoir scheduling method based on a legacy algorithm and hydrological simulation provided by this embodiment;

[0073] Figure 3 This is a schematic diagram of a technical route for achieving production capacity optimization and drought control based on hydrological environment simulation provided by an embodiment. DETAILED DESCRIPTION

[0074] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0076] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0077] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0078] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0079] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0080] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0081] Example 1:

[0082] In order to solve the problem that the reservoir scheduling scheme is subject to certain limitations in practical application due to the shortcomings of the existing technology such as high construction and operation and maintenance costs and single functions, a multi-objective scheduling model is constructed to call the optimal reservoir scheduling scheme, thereby realizing flexible scheduling and real-time regulation of reservoirs under various reservoirs to avoid hydrological drought problems. An embodiment of the present invention provides a reservoir scheduling method based on hydrological simulation. Please participate in Figure 1 ,include:

[0083] S1. Acquire hydrological environment data to construct a hydrological environment simulation model based on the hydrological environment data and a preset soil and water conservation model;

[0084] S2. Obtaining initial basin runoff data based on the hydrological environment simulation model;

[0085] S3. Obtaining initial watershed drought characteristics based on initial watershed runoff data and a preset drought assessment model;

[0086] S4. Obtaining reservoir characteristic information corresponding to all reservoirs in the watershed, and constructing a reservoir balance model corresponding to the watershed based on the reservoir characteristic information;

[0087] S5. Construct a multi-objective reservoir operation model based on initial basin runoff data, initial basin drought characteristics and reservoir balance model;

[0088] S6. Solve the multi-objective reservoir operation model and obtain the reservoir operation optimization plan to achieve multi-objective reservoir operation.

[0089] The multi-objective reservoir scheduling method proposed in this embodiment obtains corresponding hydrological environment data to construct a hydrological environment simulation model to perform hydrological forecasting, obtain the required basin runoff data, and then predict drought conditions based on the predicted basin runoff data. In combination with the reservoir balance model, a multi-objective reservoir scheduling model that can obtain the required reservoir scheduling optimization plan is constructed, thereby realizing scientific and feasible multi-objective reservoir scheduling based on the actual hydrological environment prediction and reservoir-related characteristic data; using the soil and water conservation model as the connecting hub for drought assessment and reservoir scheduling optimization, a systematic and highly operational overall solution is formed, which solves the problem that the reservoir scheduling plan is subject to certain limitations in actual application due to the shortcomings of the existing technology such as high construction and operation and maintenance costs and single functions. By constructing a multi-objective scheduling model to call the optimal reservoir scheduling plan, flexible scheduling and real-time regulation of multi-objective reservoirs are achieved, avoiding the problem of hydrological drought.

[0090] In practice, before constructing a hydrological environment simulation model, a systematic survey of basic data, including topography, land use, soil properties, and meteorology, is required. This provides the necessary foundational information for constructing a hydrological environment simulation model tailored to the basin. This foundation allows the simulation of the basin's hydrological environment using the soil and water conservation model (SWAT). The SWAT model is a physically based hydrological model widely used in water resources management, watershed pollution control, and ecological protection. The hydrological environment simulation model constructed using this model can simulate the dynamics of the basin's hydrological cycle, including precipitation, evaporation, runoff, soil moisture, and groundwater. By simulating the impacts of different land uses, meteorological conditions, and management measures, the interactions between various hydrological processes within the basin can be assessed, providing a scientific basis for water resources management and environmental protection. Furthermore, the SWAT model can be coupled with other models (such as reservoir balance models and drought assessment models) to comprehensively analyze the basin's hydrological environment and support decision-makers in optimizing resource allocation, irrigation management, flood control, and drought relief.

[0091] Optionally, step S3 includes:

[0092] Obtain drought assessment data based on initial watershed runoff data and a preset drought assessment model;

[0093] Based on the drought assessment data, the average duration of drought, the number of drought events and the maximum severity of drought are obtained. The calculation process satisfies the following formula:

[0094]

[0095] D s,max =max{v s,i |i∈[1,D n ]}

[0096] Where D d,mean represents the average duration of drought, D n represents the number of drought events, D s,max Indicates the maximum degree of drought, v d,i represents the duration of a single drought event, v s,i Indicates the severity of a single drought event;

[0097] The average drought duration, number of drought events and maximum drought severity were used as initial drought characteristics.

[0098] In practice, the SRI is often used as a core indicator for assessing hydrological drought. By standardizing watershed runoff data, the SRI effectively quantifies the severity of hydrological drought. Changes in the standardized runoff distribution can reveal seasonal fluctuations in water volume within a watershed, as well as the onset and development of drought. Therefore, variations in the SRI value can help distinguish different levels of hydrological drought, making it a common tool for analyzing and monitoring drought phenomena. When conducting drought assessments, initial watershed runoff data derived from hydrological environment simulation models is used as input for calculating the SRI. Specifically, high-precision runoff data is obtained by simulating hydrological processes such as precipitation, soil moisture, and surface runoff within the watershed. After normalization, this data is converted into an SRI value for further analysis of hydrological drought conditions. Different SRI value ranges are used to categorize drought severity. For example, an SRI value below -2 is generally defined as extreme drought, while an SRI value between -0.5 and -1 corresponds to mild to moderate drought.

[0099] In the specific implementation process, run theory can also be introduced to quantify the characteristics of drought, and the impact of drought conditions can be quantitatively evaluated by considering the length and intensity of the drought period: by tracking the periods in which the runoff sequence is continuously below the drought threshold, the duration and severity of the drought event can be defined, and the period when the drought index is continuously below a specific critical value (ε) can be determined. d ) is measured by the length of time that the SRI is below ε, and the drought severity (v s ) is calculated by the sum of the deficits during this period, and all sub-basins that want to be tested in the basin can be evaluated, where v d and v s The calculation is as follows:

[0100] v d =t e -t s +1

[0101]

[0102] Where, t s represents the drought onset time, t e represents the end time of drought, Ψ t represents the drought index value at time t, v d represents the duration of drought, v s represents the drought severity, and ε represents a specific critical value.

[0103] Optionally, step S4 includes:

[0104] Obtain the reservoir characteristic information corresponding to all reservoirs in the basin, and obtain the power generation flow data based on the reservoir characteristic information and the preset reservoir balance conditions. The calculation process satisfies the following formula:

[0105]

[0106] Where t represents month, S t represents the reservoir capacity at the beginning of the month t, S t+1 represents the reservoir capacity at the end of month t, I t represents the inflow in month t, represents the power generation flow data for hydropower generation in month t, represents the fixed water outflow in month t, E t Indicates the evaporation loss in month t, L t represents the leakage loss in month t, represents the overflow flow in month t;

[0107] Reservoir constraint variables are obtained based on reservoir characteristic information, and the calculation process satisfies the following formula:

[0108] H i,t =α×S i,t β

[0109] Where H i,t represents the reservoir balance constraint variable, α and β represent the reservoir experience coefficients, S i,t Represents reservoir characteristic information;

[0110] Obtaining hydropower generation data based on power generation flow data;

[0111] A reservoir balance model is constructed based on hydropower generation data and reservoir constraint variables.

[0112] Optionally, hydropower generation data is obtained based on power generation flow data, including:

[0113] Obtain monthly average effective head based on reservoir characteristic information;

[0114] Obtain monthly average power generation flow data based on power generation flow data;

[0115] The hydropower generation data is obtained based on the monthly average effective head, monthly average power generation flow data and reservoir power generation formula. The calculation process satisfies the following formula:

[0116]

[0117] Where F represents hydropower data, g represents gravity acceleration, η represents power generation efficiency coefficient, i represents all reservoirs, Q m Represents the monthly average power generation flow data, N mrepresents the monthly average effective head, Y m Represents the total number of seconds in the mth month.

[0118] During implementation, the reservoir balance model was constructed based on a detailed, systematic survey and collection of reservoir characteristic information for all reservoirs in the relevant basin. This information covered key aspects such as the reservoir's basic conditions, geographic features, operating modes, and seasonal flood control and water supply policies. Based on this reservoir characteristic information, combined with the water balance requirements of actual reservoir operations, and taking into account the maximum and minimum reservoir capacities, water level ranges, and power generation conditions, the reservoir balance model was designed to fully reflect the reservoir's scheduling constraints and power generation capabilities.

[0119] In practice, the reservoir discharge in the reservoir balance model is typically set as the reservoir decision variable. Reservoir discharge, along with the reservoir water level, determines the reservoir's power generation capacity. Specifically, changes in discharge directly affect the reservoir water level, which in turn affects discharge and power generation potential. To achieve optimal reservoir operation and scheduling, an optimization algorithm can be used to iteratively adjust the discharge decisions of multiple reservoirs. During the optimization process, the discharges of multiple reservoirs form a set, with different sets representing different decision-making options, or scheduling strategies, ultimately impacting the basin's overall hydrological environment and reservoir capacity.

[0120] In practice, the monthly reservoir water balance controls the storage-release dynamics of the reservoir system. Fixed water outflow is generally considered to be directly extracted from the reservoir, not involved in hydropower generation, and used to supply water for agriculture, industry, and domestic use. This can be determined based on surveys in different study areas. Evaporation and seepage losses can be calculated in two ways: one is to estimate them as a certain proportion of the reservoir capacity, and the other is based on hydrological model simulation. The latter can be achieved by constructing a SWAT model under natural conditions (natural model) and a SWAT model that includes point source input but excludes the sub-basin where the reservoir is located and its upstream sub-basin (point source model). The natural model simulates the reservoir outflow process, including evaporation and seepage losses, based on factors such as water body area. The outlet flow of the reservoir sub-basin is extracted as a point source input and transmitted to the point source model as the reservoir inflow. The point source model performs reservoir water balance calculations based on this inflow without considering evaporation and seepage.

[0121] In the specific implementation process, the reservoir water level is one of the constraint variables of the model. α and β are empirical coefficients related to reservoir characteristics. They can be obtained by fitting based on long-term series observation data of historical water level and storage capacity through methods such as nonlinear regression.

[0122] In the specific implementation process, in order to evaluate the annual hydropower generation of the reservoir, a physical estimation method based on monthly average data is usually adopted, as shown in the following formula:

[0123]

[0124] Among them E year is the annual power generation (kilowatt-hours, kWh), i is a different reservoir, in the Dongjiang River Basin refers to the three main reservoirs Fengshuba, Xinfengjiang, and Baipenzhu. η is the power generation efficiency coefficient, Q m Monthly average hydropower outflow (m 3 / s), H m is the monthly average water head (m) used to represent the effective water head, Y m is the total number of seconds in month m. The constant 9.81 represents the acceleration due to gravity (m / s 2 ), 3.6×10 in the denominator 6 is the unit conversion factor used to convert energy units from joules (J) to kilowatt-hours (kWh).

[0125] Optionally, step S5 includes:

[0126] Obtain reservoir decision variables based on initial basin runoff data and initial basin drought characteristics;

[0127] Obtaining reservoir power generation function based on reservoir decision variables and reservoir balance model;

[0128] A multi-objective reservoir operation model is constructed based on reservoir decision variables, reservoir power generation function, reservoir balance model, preset drought constraints and preset reservoir constraints, as shown in the following formula:

[0129] Maxf(x)

[0130] S t (x,y)=0

[0131] S d (x,y)=0

[0132] S s (x,y)=0

[0133] Where x represents the reservoir decision variable, y represents the season, f(x) represents the reservoir power generation function, S t Represents the preset reservoir balance model, S d represents the preset drought constraint condition, S s Indicates the preset reservoir constraint conditions, 0 means the constraint conditions are met;

[0134] The preset reservoir constraints include:

[0135] f xmin (y)≤x≤f xmax (y)

[0136] flmin (y)≤L(x)≤f lmax (y)

[0137] f vmin (y)≤V(x)≤f vmax (y)

[0138] Where: L(x) represents the reservoir water level, V(x) represents the reservoir capacity, f xmax (y) represents the maximum reservoir discharge, f lmax (y) represents the maximum reservoir water level, f vmax (y) represents the maximum reservoir volume, f xmin (y) represents the minimum reservoir discharge, f lmin (y) represents the minimum reservoir water level, f vmin (y) represents the minimum reservoir volume;

[0139] The preset drought constraints include:

[0140] D opt ≤D base

[0141] Where: D opt represents the drought characteristics after inputting the reservoir decision variables, D base Represents the baseline of drought characteristics.

[0142] In the specific implementation process, the drought characteristic baseline represents the drought situation without reservoir operation. In the reservoir constraint conditions, L(x) and V(x) are the reservoir water level and capacity, respectively, which are related functions of the reservoir discharge. The reservoir has flood control standards, and the standards are different in different seasons of the year. The reservoir constraint conditions are reflected in the monthly reservoir discharge constraint, monthly reservoir water level constraint, monthly reservoir capacity constraint, etc. xmax (y), f lmax (y), f vmax (y) represents the maximum reservoir discharge, maximum reservoir water level and maximum reservoir volume in different seasons, and the corresponding f xmin (y), f lmin (y), f vmin (y) represent the minimum value respectively.

[0143] In the specific implementation process, drought assessment is carried out based on drought characteristics, calculated by SRI and run theory. This embodiment is based on hydrological model simulation and can evaluate all sub-basins in the basin that need to be tested. Taking the basin outlet as an example, the total outlet runoff sequence of the basin is read, SRI is calculated, and three drought characteristics are further calculated based on run theory: the number of drought events (D n ), the average duration of these events (D d,mean ) and the maximum severity (D s,maxThe duration of a single drought event (v d,i ) and severity (v s,i ) is used to calculate these indicators, thus providing a comprehensive overview of drought characteristics within a specified timeframe. For a specific study period, the drought characteristics calculated from the baseline and the basin outlet runoff series after reservoir operation meet the following constraints:

[0144] D opt [s(x)]≤D base [s(x)]

[0145] D opt [s(x)] and D base [s(x)] represents the drought characteristics of the baseline and after reservoir operation, including the number of drought events (D n ), the average duration of these events (D d,mean ) and the maximum severity (D s,max ), where s(x) represents the runoff at the basin outlet simulated by the hydrological environment simulation model.

[0146] Optionally, step S6 includes: solving based on a genetic algorithm and a multi-objective reservoir scheduling model to obtain a target reservoir scheduling plan; obtaining a fitness score based on the target reservoir scheduling plan; when the fitness score meets a preset termination condition, using the target reservoir scheduling plan as a reservoir scheduling optimization plan to achieve scheduling of multi-objective reservoirs.

[0147] During the specific implementation process, a genetic algorithm is used to solve the multi-objective reservoir operation model to obtain an optimized operation plan, and a fitness score is performed to determine whether the optimized plan is feasible, ensuring the correctness and feasibility of the final result.

[0148] Optionally, a solution is performed based on a genetic algorithm and a multi-objective reservoir scheduling model to obtain a target reservoir scheduling plan, including: obtaining a number of current reservoir decision variables based on initial basin runoff data and initial basin drought characteristics; inputting a number of current reservoir decision variables into the multi-objective reservoir scheduling model to execute the solution steps: obtaining a number of reservoir scheduling plans based on a number of current reservoir decision variables, a reservoir balance model and preset reservoir constraints; obtaining a number of corresponding scheduling runoff data based on a number of reservoir scheduling plans and initial basin runoff data, to obtain a number of corresponding scheduling basin drought characteristics based on the number of scheduling runoff data; obtaining a number of corresponding power generation data based on the reservoir balance model; and obtaining a target reservoir scheduling plan based on a number of scheduling basin drought characteristics, corresponding power generation data, preset power generation constraints and preset drought constraints.

[0149] In practice, reservoir operation simulation optimization models are typically solved using a genetic algorithm (GA). The GA continuously invokes the basin hydrological environment simulation model to search for the globally optimal decision variables that achieve the optimal objective function value while satisfying constraints on reservoir operation and hydrological processes. The genetic algorithm's solution process begins with population initialization, generating a set of chromosomes (i.e., several current reservoir decision variables) within the bounds of the optimal decision variable x. These chromosomes represent different reservoir operation scenarios, including variables such as the monthly outflow time series for each reservoir. Each chromosome is input into the reservoir power generation formula in the reservoir balance model to calculate its corresponding reservoir power generation. Simultaneously, the solver first invokes the hydrological environment simulation model to simulate the upstream runoff process of the reservoir, representing the initial basin runoff data. Based on the operation scenario defined by the chromosome, the reservoir model then calculates the downstream outflow sequence, which is then re-input into the hydrological model to simulate the downstream hydrological process of the entire basin, thus generating the operation runoff data. The resulting scheduling runoff data is used to calculate scheduling drought characteristics and determine whether various reservoir operation and reservoir balance constraints, as well as drought constraints, are met. Within this solution framework, nonlinear constraints are integrated into the corresponding objective function by introducing Lagrange multipliers and penalty functions. This allows for a fitness score for each solution to determine whether the current scheduling solution that meets the constraints is the desired solution. If not, the GA algorithm then iteratively updates the population through standard selection, crossover, and mutation operations until a near-optimal reservoir scheduling solution is obtained.

[0150] Optionally, after obtaining the fitness score based on the target reservoir scheduling plan, it also includes: when the fitness score does not meet the preset termination conditions, obtaining a new generation of reservoir decision variables based on the genetic algorithm and the current reservoir decision variables; inputting the new generation of reservoir decision variables into the multi-objective reservoir scheduling model to re-execute the solution steps to obtain a new generation of target reservoir scheduling plan.

[0151] During the specific implementation process, according to the calculation of fitness score, when the solution does not meet the preset termination conditions, the genetic algorithm is used to form new decision variables through selection, crossover and mutation to re-solve the problem, thereby realizing iterative optimization of the solution process until an approximately optimal reservoir scheduling optimization solution is obtained, thereby ensuring the reliability of the solution results of the multi-objective reservoir scheduling model.

[0152] In its implementation, this example proposes a simulation-based drought optimization model for a multi-reservoir system. This model organically combines hydrological simulation, drought assessment, and a heuristic optimization algorithm to achieve the dual goals of "improving reservoir power generation efficiency" and "mitigating hydrological drought risks." Using the Dongjiang River Basin as a case study, the study first uses the SWAT model to simulate long-term runoff processes under different scheduling schemes. The SRI is then calculated based on the simulation results. In combination with operational theory, drought characteristics such as frequency, duration, and maximum intensity are extracted as constraints for subsequent optimization. Finally, a genetic algorithm (GA) is used to search for the optimal reservoir outflow strategy, aiming to maximize annual power generation without exacerbating drought conditions.

[0153] This embodiment provides a reservoir operation method based on hydrological simulation. First, a hydrological environment simulation model is constructed based on the hydrological environment characteristics of different river basins. Then, based on the concept of optimizing production capacity and drought control, it emphasizes the integrated application of drought assessment technology and water conservancy project operation. The SWAT model (soil and water conservation model) is used as the hub connecting drought assessment and reservoir operation optimization. The method combines the hydrological environment simulation model, drought assessment, and reservoir balance model, and combines the corresponding reservoir production capacity calculation and evaluation, drought constraints, and reservoir constraints to realize a multi-objective reservoir operation method for hydropower generation and drought control that considers hydrological simulation. This method simultaneously achieves water conservancy production capacity maximization and drought control functions, helping to improve the utilization efficiency of water resources in the river basin; it can regulate downstream flow in real time, reduce the frequency, duration, and intensity of droughts, effectively alleviate hydrological drought problems in the river basin, reduce the impact of hydrological drought on agriculture, ecology, and water resources supply, and ensure water security and economic development for downstream residents. At the same time, the model simulation construction method has the advantages of being easy to implement, low cost, high efficiency, and flexible and wide application range; it improves the simulation performance, water conservancy production capacity optimization performance, and drought control performance of the study area.

[0154] Example 2:

[0155] See also Figure 2 This embodiment provides a reservoir operation method based on a genetic algorithm and hydrological simulation, including:

[0156] start;

[0157] S21, generating population based on genetic algorithm;

[0158] S22, obtaining a reservoir operation plan corresponding to the population;

[0159] S23, inputting the reservoir operation plan into the reservoir balance model, and combining it with the hydrological environment simulation model to obtain the corresponding downstream runoff data;

[0160] S24, performing drought characteristic analysis based on corresponding downstream runoff data to obtain corresponding drought characteristic data;

[0161] S25. Obtaining a corresponding dispatching plan based on the corresponding drought characteristic data, drought constraints, reservoir balance constraints, reservoir dispatching plan, and preset power generation conditions;

[0162] S26. Obtaining a fitness score based on the corresponding scheduling plan;

[0163] S27, determine whether the fitness score meets the termination condition: if it does, then end; if not, go to step S28;

[0164] S28, perform gene manipulation and return to step S21;

[0165] Finish.

[0166] In a specific implementation process, the population generated in step S21 includes a first-generation population generated randomly and a next-generation population generated by genetic manipulation.

[0167] During the specific implementation process, the reservoir scheduling optimization method proposed in this embodiment is highly effective. Through actual investigation and data analysis of basin reservoirs, it accurately simulates the operating status of reservoirs, has strong adaptability, and can quickly respond to seasonal drought changes, with good timeliness. Compared with traditional methods, it can not only improve the power generation capacity of reservoirs, but also regulate downstream flow in real time, reduce the frequency, duration and intensity of droughts, and effectively alleviate the hydrological drought problem in the basin. Based on the current demand for new energy development and the important position of the basin in regulating water resources in surrounding areas, this method can achieve the dual goals of optimizing reservoir power generation and controlling hydrological drought, and has significant effectiveness.

[0168] Example 3:

[0169] See also Figure 3 This embodiment provides a technical route for achieving capacity optimization and drought control based on hydrological environment simulation, including: S31, constructing a hydrological environment simulation model, a reservoir balance model, and a drought assessment model; S32, constructing a capacity-drought prediction model based on the hydrological environment simulation model, the reservoir balance model, and the drought assessment model; S33, obtaining an optimal scheduling plan for a capacity-optimized drought control sluice pump water conservancy project based on the capacity-drought prediction model, wherein:

[0170] The construction of the hydrological environment simulation model, reservoir balance model and drought assessment model includes: constructing the hydrological environment simulation model based on the SWAT model: collecting topographic, meteorological, soil and reservoir data to build the model and determine the parameters; the upstream data of the basin obtained by the hydrological environment simulation model is used as one of the water balance inputs of the reservoir balance model, and the reservoir discharge is another input of the water balance. The water balance will affect the reservoir storage capacity and thus the reservoir water level; the reservoir discharge affects the hydrological environment simulation model through its impact on the downstream basin; and the reservoir production capacity, such as power generation, is calculated through the reservoir water level and reservoir discharge; the downstream basin data obtained by the hydrological environment simulation model is used to calculate the drought characteristics based on the drought assessment model: the standardized runoff index is obtained based on the downstream basin data, and the drought characteristics are obtained based on the standardized runoff index and run length theory;

[0171] Constructing a capacity-drought prediction model based on a hydrological environment simulation model, a reservoir balance model, and a drought assessment model includes: constructing a capacity-drought prediction model, and optimizing the capacity-drought prediction model based on an objective function, decision variables, constraints, and a solution technique; wherein the objective function is to calculate the maximum reservoir capacity, the decision variables are the discharge of multiple reservoirs over multiple time periods, the constraints include reservoir balance conditions, drought constraints, and reservoir constraints, and the solution technique is a genetic algorithm.

[0172] In the specific implementation process, this embodiment targets the unique network of river systems and reservoirs of Dongjiang River, and uses the simulation-optimization coupling model of the SWAT model to maximize reservoir power generation and take drought relief as a constraint (drought constraint condition). It conducts a full-process study of multi-reservoir scheduling of hydropower generation-drought regulation based on hydrological simulation, and uses genetic algorithms to find multi-reservoir regulation schemes. By jointly evaluating the scheme effects through optimization goals and constraints, it finally obtains an approximately optimal decision-making scheme, forming a technical system for capacity optimization-drought regulation. In view of the characteristics of the river network system of Dongjiang River Basin in Guangdong Province, based on the concept of optimizing capacity and drought regulation, it emphasizes the comprehensive application of drought assessment technology and water conservancy project scheduling, and innovatively uses hydrological simulation-optimization algorithms to optimize the scheduling of water conservancy projects. It establishes a multi-objective reservoir scheduling method for hydropower generation-drought regulation that takes into account hydrological simulation, which can simultaneously achieve water capacity maximization and drought regulation functions.

[0173] During the specific implementation process, in order to ensure the effectiveness of reservoir scheduling and drought control, this embodiment uses the SWAT model as the connecting hub between the reservoir and the drought assessment model. Under different reservoir scheduling schemes, the hydrological environment of the basin will change, which is specifically manifested in that the inflow upstream of the reservoir will vary depending on the scheduling scheme. The inflow and discharge of the reservoir jointly determine the power generation capacity of the reservoir through the water balance model. In this way, the scheduling plan not only affects the power generation of the reservoir, but also affects the downstream flow, thereby having an important impact on the downstream hydrological drought conditions. Ultimately, by optimizing the reservoir scheduling strategy, it is possible to maximize the power generation of the reservoir while effectively alleviating the downstream hydrological drought problem and promoting the sustainable use of water resources.

[0174] Example 4:

[0175] This embodiment provides a reservoir operation system based on hydrological simulation, which implements the above-mentioned reservoir operation method based on hydrological simulation, including a hydrological simulation module, a drought assessment module, a reservoir balance module, a model construction module and a reservoir operation module, wherein:

[0176] The hydrological simulation module is used to obtain hydrological environment data, to build a hydrological environment simulation model based on the hydrological environment data and a preset soil and water conservation model; and to obtain initial watershed runoff data based on the hydrological environment simulation model;

[0177] The drought assessment module is used to obtain the initial watershed drought characteristics based on the initial watershed runoff data and the preset drought assessment model;

[0178] The reservoir balance module is used to obtain the reservoir characteristic information corresponding to all reservoirs in the basin, so as to construct the reservoir balance model corresponding to the basin based on the reservoir characteristic information;

[0179] The model building module is used to build a multi-objective reservoir operation model based on the initial basin runoff data, initial basin drought characteristics and reservoir balance model;

[0180] The reservoir operation module is used to solve the multi-objective reservoir operation model and obtain the reservoir operation optimization plan to achieve multi-objective reservoir operation.

[0181] The present embodiment provides a reservoir scheduling system based on hydrological simulation, which actively reflects the concept of optimizing production capacity and drought control, and provides a complete set of systematic solutions, covering multiple links such as hydrological model, drought assessment model, reservoir model and production capacity-drought multi-objective optimization model. The corresponding hydrological environment data is obtained through the hydrological simulation module to construct a hydrological environment simulation model to perform hydrological prediction and obtain the required basin runoff data. Then, the drought situation is predicted based on the predicted basin runoff data through the drought assessment module. Then, the model construction module is combined with the reservoir balance model of the reservoir balance module to construct a reservoir scheduling optimization solution. The multi-objective reservoir scheduling model of the scheme is developed and solved based on the reservoir scheduling module, realizing scientific and feasible multi-objective reservoir scheduling based on the actual hydrological environment prediction and reservoir-related characteristic data; using the soil and water conservation model as the connecting hub for drought assessment and reservoir scheduling optimization, a systematic and highly operational overall solution is formed, which solves the problem that the reservoir scheduling scheme is subject to certain limitations in actual application due to the shortcomings of the existing technology such as high construction and operation and maintenance costs and single functions. By constructing a multi-objective scheduling model to call the optimal reservoir scheduling scheme, flexible scheduling and real-time regulation of multi-objective reservoirs are achieved to avoid hydrological drought problems.

[0182] Embodiment 5:

[0183] This embodiment also provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-mentioned reservoir scheduling method based on hydrological simulation.

[0184] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0185] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0186] The processor 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) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0187] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A reservoir operation method based on hydrological simulation, characterized in that: include: Acquire hydrological environment data to build a hydrological environment simulation model based on the hydrological environment data and a preset soil and water conservation model; Acquiring initial watershed runoff data based on the hydrological environment simulation model; Based on the initial watershed runoff data and a preset drought assessment model, obtaining initial watershed drought characteristics; Acquiring reservoir characteristic information corresponding to all reservoirs in the watershed, so as to construct a reservoir balance model corresponding to the watershed based on the reservoir characteristic information; Constructing a multi-objective reservoir operation model based on the initial basin runoff data, the initial basin drought characteristics and the reservoir balance model; Solve the multi-objective reservoir operation model to obtain a reservoir operation optimization plan to achieve multi-objective reservoir operation.

2. A reservoir operation method based on hydrological simulation according to claim 1, characterized in that: The obtaining of initial watershed drought characteristics based on the initial watershed runoff data and a preset drought assessment model includes: Acquiring drought assessment data based on the initial watershed runoff data and a preset drought assessment model; The average duration of drought, the number of drought events and the maximum severity of drought are obtained based on the drought assessment data. The calculation process satisfies the following formula: D s,max =max{v s,i |i∈[1,D n ]} Where D d,mean represents the average duration of drought, D n represents the number of drought events, D s,max Indicates the maximum degree of drought, v d,i represents the duration of a single drought event, v s,i Indicates the severity of a single drought event; The average drought duration, number of drought events and maximum drought severity are taken as initial drought characteristics.

3. A reservoir operation method based on hydrological simulation according to claim 1, characterized in that: The obtaining of reservoir characteristic information corresponding to all reservoirs in the watershed to construct a reservoir balance model corresponding to the watershed based on the reservoir characteristic information includes: Obtain reservoir characteristic information corresponding to all reservoirs in the basin, and obtain power generation flow data based on the reservoir characteristic information and preset reservoir balance conditions. The calculation process satisfies the following formula: Where t represents month, S t represents the reservoir capacity at the beginning of the month t, S t+1 represents the reservoir capacity at the end of month t, I t represents the inflow in month t, represents the power generation flow data for hydropower generation in month t, represents the fixed water outflow in month t, E t Indicates the evaporation loss in month t, L t represents the leakage loss in month t, represents the overflow flow in month t; Reservoir constraint variables are obtained based on the reservoir characteristic information, and the calculation process satisfies the following formula: H i,t =α×S i,t β Where H i,t represents the reservoir balance constraint variable, α and β represent the reservoir experience coefficients, S i,t Represents reservoir characteristic information; acquiring hydropower generation data based on the power generation flow data; The reservoir balance model is constructed based on the hydropower generation data and reservoir constraint variables.

4. A reservoir operation method based on hydrological simulation according to claim 3, characterized in that: The obtaining of hydropower generation data based on the power generation flow data includes: Obtaining a monthly average effective water head based on the reservoir characteristic information; Acquire monthly average power generation flow data based on the power generation flow data; Based on the monthly average effective head, monthly average power generation flow data and reservoir power generation formula, the hydropower generation data is obtained, and the calculation process satisfies the following formula: Where F represents hydropower data, g represents gravity acceleration, η represents power generation efficiency coefficient, i represents all reservoirs, Q m Represents the monthly average power generation flow data, N m represents the monthly average effective head, Y m Represents the total number of seconds in the mth month.

5. The reservoir operation method based on hydrological simulation according to claim 1, characterized in that: The multi-objective reservoir operation model is constructed based on the initial watershed runoff data, the initial watershed drought characteristics and the reservoir balance model, including: obtaining reservoir decision variables based on the initial watershed runoff data and initial watershed drought characteristics; Obtaining a reservoir power generation function based on the reservoir decision variables and the reservoir balance model; A multi-objective reservoir operation model is constructed based on the reservoir decision variables, reservoir power generation function, reservoir balance model, preset drought constraint conditions and preset reservoir constraint conditions, as shown in the following formula: Maxf(x) S t (x,y)=0 S d (x,y)=0 S s (x,y)=0 Where x represents the reservoir decision variable, y represents the season, f(x) represents the reservoir power generation function, S t Represents the preset reservoir balance model, S d represents the preset drought constraint condition, S s Indicates the preset reservoir constraint conditions, 0 means the constraint conditions are met; The preset reservoir constraints include: f xmin (y)≤x≤f xmax (y) f lmin (y)≤L(x)≤f lmax (y) f vmin (y)≤V(x)≤f vmax (y) Where: L(x) represents the reservoir water level, V(x) represents the reservoir capacity, f xmax (y) represents the maximum reservoir discharge, f lmax (y) represents the maximum reservoir water level, f vmax (y) represents the maximum reservoir volume, f xmin (y) represents the minimum reservoir discharge, f lmin (y) represents the minimum reservoir water level, f vmin (y) represents the minimum reservoir volume; The preset drought constraint conditions include: D opt ≤D base Where: D opt represents the drought characteristics after inputting the reservoir decision variables, D base Represents the baseline of drought characteristics.

6. A reservoir operation method based on hydrological simulation according to claim 1, characterized in that: The step of solving the multi-objective reservoir operation model to obtain a reservoir operation optimization plan to achieve multi-objective reservoir operation includes: Solving the multi-objective reservoir operation model based on a genetic algorithm to obtain a target reservoir operation plan; Obtaining a fitness score based on the target reservoir operation plan; When the fitness score meets the preset termination condition, the target reservoir scheduling plan is used as the reservoir scheduling optimization plan to achieve the scheduling of multiple target reservoirs.

7. A reservoir operation method based on hydrological simulation according to claim 5, characterized in that: The solving based on the genetic algorithm and the multi-objective reservoir operation model to obtain a target reservoir operation plan includes: obtaining a number of current reservoir decision variables based on the initial watershed runoff data and the initial watershed drought characteristics; Inputting a number of the current reservoir decision variables into the multi-objective reservoir operation model to perform the solution step: Obtaining a plurality of reservoir operation plans based on the plurality of current reservoir decision variables, the reservoir balance model and the preset reservoir constraint conditions; Acquire corresponding multiple scheduling runoff data based on the multiple reservoir scheduling plans and the initial watershed runoff data, and acquire corresponding multiple scheduling watershed drought characteristics based on the multiple scheduling runoff data; Acquiring a number of corresponding power generation data based on the reservoir balance model; A target reservoir scheduling plan is obtained based on several of the scheduling basin drought characteristics, corresponding power generation data, preset power generation constraints and preset drought constraints.

8. A reservoir operation method based on hydrological simulation according to claim 7, characterized in that: After obtaining the fitness score based on the target reservoir scheduling plan, the method further includes: When the fitness score does not meet the preset termination condition, a new generation of reservoir decision variables is obtained based on the genetic algorithm and the current reservoir decision variables; The new generation reservoir decision variables are input into the multi-objective reservoir operation model to re-execute the solution step to obtain a new generation target reservoir operation plan.

9. A reservoir operation system based on hydrological simulation, characterized in that: A reservoir operation method based on hydrological simulation according to any one of claims 1 to 8 is implemented, comprising a hydrological simulation module, a drought assessment module, a reservoir balance module, a model building module and a reservoir operation module, wherein: The hydrological simulation module is used to obtain hydrological environment data, to build a hydrological environment simulation model based on the hydrological environment data and a preset soil and water conservation model; and to obtain initial watershed runoff data based on the hydrological environment simulation model; The drought assessment module is used to obtain initial watershed drought characteristics based on the initial watershed runoff data and a preset drought assessment model; The reservoir balance module is used to obtain reservoir characteristic information corresponding to all reservoirs in the basin, so as to construct a reservoir balance model corresponding to the basin based on the reservoir characteristic information; The model building module is used to build a multi-objective reservoir operation model based on the initial basin runoff data, the initial basin drought characteristics and the reservoir balance model; The reservoir scheduling module is used to solve the multi-objective reservoir scheduling model and obtain a reservoir scheduling optimization plan to achieve multi-objective reservoir scheduling.

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