A reservoir scheduling method and system based on hydrological simulation

Abstract

This invention discloses a reservoir scheduling method and system based on hydrological simulation, belonging to the field of reservoir optimization scheduling methods. The method comprises: constructing a hydrological environment simulation model based on hydrological environment data and a pre-set soil and water conservation model; obtaining initial watershed runoff data based on the hydrological environment simulation model; obtaining initial watershed drought characteristics based on the initial watershed runoff data and a pre-set drought assessment model; constructing a reservoir balance model based on reservoir characteristic information; constructing a multi-objective reservoir scheduling model based on the initial watershed runoff data, initial watershed drought characteristics, and the reservoir balance model; and solving the multi-objective reservoir scheduling model to obtain an optimized reservoir scheduling scheme, thereby achieving multi-objective reservoir scheduling. Therefore, by implementing this invention, the limitations of existing reservoir scheduling optimization schemes in practical applications are solved. By constructing a model, flexible scheduling and real-time control of multi-objective reservoirs are achieved, avoiding hydrological drought problems.

Description

Technical Field

[0001] This 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 Technology

[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 global warming. Currently, the development and utilization of new energy technologies have become an effective way to address current energy needs and combat global warming. New energy technologies typically include solar thermal power generation, photovoltaic power generation, hydropower, wind power, tidal power generation, wave energy, and geothermal power generation. Solar thermal power generation converts solar energy into mechanical energy through a heating medium, is suitable for high-radiation areas, and can be combined with thermal storage systems to achieve continuous and regulated power generation. Photovoltaic power generation directly converts solar energy into electricity through photovoltaic cells, producing no noise or pollution, and is suitable for both distributed and centralized power generation; its cost continues to decrease with technological advancements. Hydropower 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, including onshore and offshore wind power, with offshore wind power showing great development potential. Tidal power generation uses the energy generated by tidal rises and falls to drive power generation devices, featuring high predictability and renewable resources. Wave energy generation captures ocean wave energy and converts it into electrical or mechanical energy, making it suitable for areas rich in wave resources. Geothermal power generation converts underground high-temperature magma or hot water resources into heat or electricity, and is especially suitable for volcanic areas and regions with frequent geothermal activity.

[0003] Furthermore, drought control can be achieved through various technologies, including drought monitoring, water-saving irrigation, soil and water conservation, groundwater regulation, rainwater harvesting and reuse, and artificial rainfall. Among these, meteorological early 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-sprinkler irrigation improve water use efficiency, but their effect is limited in areas with limited water resources; soil and water conservation enhances water conservation through vegetation restoration and soil improvement, but its effects are slow and require investment; groundwater regulation can provide emergency water sources, but over-extraction may cause problems; rainwater harvesting and reuse can alleviate water shortages in areas with high rainfall, but its effect is limited in areas with low rainfall; artificial rainfall technology can alleviate drought in the short term, but its effects are unstable. In contrast, reservoir management is flexible and controllable, effectively ensuring agricultural irrigation, water supply, and power generation needs, and alleviating downstream hydrological drought, thus playing a crucial role in drought control.

[0004] Most existing technologies are limited in application due to high construction and operation costs and limited functionality. Although hydropower can serve multiple functions such as flood control, irrigation, and water supply, there is a lack of research on technologies that utilize simulation-optimization coupled models for optimized scheduling of water conservancy projects to simultaneously maximize water production capacity and control drought. Furthermore, most current drought mitigation technologies are primarily passively adaptive, failing to achieve proactive regulation and dynamic scheduling of water resources at the watershed scale. Research on reservoir drought scheduling largely relies on empirical or statistical methods, generally lacking physically grounded distributed hydrological simulations, making it difficult to systematically reveal the spatial effects and drought response mechanisms of scheduling measures at the watershed scale. Therefore, optimizing water conservancy project scheduling to simultaneously optimize hydropower generation and control drought is an effective way to address current energy demand issues and cope with drought, and is of great significance for promoting sustainable and high-quality economic and social development. How to utilize hydrological simulation-optimization algorithms for optimized scheduling of water conservancy projects to simultaneously maximize water production capacity and control drought is the key technical problem this project aims to solve. Summary of the Invention

[0005] This invention provides a reservoir scheduling method and system based on hydrological simulation. This method can solve the problem that the reservoir scheduling scheme is limited in practical application due to the high construction and operation and maintenance costs and the single function of the existing technology. By constructing a multi-objective scheduling model and calling the optimal reservoir scheduling scheme, flexible scheduling and real-time control of reservoirs under various reservoirs can be realized, and hydrological drought problems can be avoided.

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

[0007] Acquire hydrological environmental data, and construct a hydrological environmental simulation model based on the hydrological environmental data and a pre-set soil and water conservation model;

[0008] Initial watershed runoff data were obtained based on a hydrological environment simulation model.

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

[0010] Obtain reservoir characteristic information for all reservoirs within the watershed, and construct a reservoir balance model for the watershed based on the reservoir characteristic information;

[0011] A multi-objective reservoir scheduling model was constructed based on initial watershed runoff data, initial watershed drought characteristics, and reservoir balance model.

[0012] Solve the multi-objective reservoir scheduling model to obtain the reservoir scheduling optimization scheme, so as to realize the scheduling of multi-objective reservoirs.

[0013] The multi-objective reservoir scheduling method proposed in this invention constructs a hydrological environment simulation model by acquiring relevant hydrological environmental data for hydrological prediction, obtains the required watershed runoff data, and then predicts drought conditions based on the predicted watershed runoff data. Combined with a reservoir balance model, it constructs a multi-objective reservoir scheduling model that can obtain the required reservoir scheduling optimization scheme. This achieves scientific and feasible multi-objective reservoir scheduling based on actual hydrological environment predictions and relevant reservoir characteristic data. Using a soil and water conservation model as the connecting hub for drought assessment and reservoir scheduling optimization, a systematic and highly operable overall solution is formed. This solves the problem that existing technologies, due to high construction and maintenance costs and limited functionality, restrict the practical application of reservoir scheduling schemes. By constructing a multi-objective scheduling model and calling the optimal reservoir scheduling scheme, flexible scheduling and real-time control of multi-objective reservoirs are achieved, avoiding hydrological drought problems.

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

[0015] Drought assessment data were obtained based on initial watershed runoff data and a pre-set drought assessment model.

[0016] Based on 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] In the formula, D d,mean D represents the average duration of drought. n D represents the number of drought events. s,max Indicates the maximum degree of drought, v d,i Indicates the duration of a single drought event, v s,i Indicates the severity of a single drought event;

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

[0021] In the above scheme, a drought assessment model is used to perform drought assessment calculations on the relevant initial watershed runoff data to obtain the drought situation of the water area under the corresponding time scenario. This provides scientific reference data for subsequent reservoir scheduling optimization, so as to achieve drought control, reduce the impact of drought on agriculture, ecology and water resource supply, and ensure the safety of residents' water use and economic development.

[0022] Furthermore, reservoir characteristic information for all reservoirs within the watershed is obtained, and a reservoir balance model for the watershed is constructed based on this characteristic information, including:

[0023] Obtain reservoir characteristic information for all reservoirs within the basin, and based on this information and pre-set reservoir balance conditions, obtain power generation flow data. The calculation process satisfies the following formula:

[0024]

[0025] In the formula, t represents the month, and S t S represents the reservoir capacity at the beginning of month t. t+1 I represents the reservoir capacity at the end of month t. t This represents the inbound flow in month t. This represents the power generation flow data used for hydropower generation in month t. E represents the fixed water outflow in month t. t L represents the evaporation loss in month t. t This represents the leakage loss in month t. This represents the overflow discharge in month t;

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

[0027] H i,t =α×S i,t β

[0028] In the formula, H i,t S represents the reservoir balance constraint variable, α and β represent the reservoir empirical coefficients, and S i,t Indicates reservoir characteristic information;

[0029] Hydropower generation data is obtained based on power generation flow data;

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

[0031] Furthermore, hydropower generation data is obtained based on power generation flow data, including:

[0032] Monthly average effective head is obtained based on reservoir characteristic information;

[0033] Monthly average power generation flow data is obtained based on power generation flow data;

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

[0035]

[0036] In the formula, F represents hydropower generation data, g represents gravitational acceleration, η represents the power generation efficiency coefficient, i represents all reservoirs, and Q m N represents the average monthly power generation flow data. m Y represents the average monthly effective head. m This represents the total number of seconds in month m.

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

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

[0039] Reservoir decision variables are obtained based on initial watershed runoff data and initial watershed drought characteristics;

[0040] The reservoir power generation function is obtained based on reservoir decision variables and reservoir balance model;

[0041] A multi-objective reservoir scheduling 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 equation:

[0042] Maxf(x)

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

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

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

[0046] In the formula, x represents the reservoir decision variable, y represents the season, f(x) represents the reservoir power generation function, and S t S represents the preset reservoir balance model. d S represents the preset drought constraint. s This indicates the preset reservoir constraints; 0 indicates that the constraints 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] In the formula: L(x) represents the reservoir water level, V(x) represents the reservoir capacity, and 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] Preset drought constraints include:

[0053] D opt ≤D base

[0054] In the formula: D opt D represents the drought characteristics after inputting reservoir decision variables. base This represents the baseline for drought characteristics.

[0055] The above scheme combines hydrological environment simulation models, drought assessments, reservoir balance models, and related constraints to construct a systematic and highly operable multi-objective scheduling model based on hydrological simulation, drought assessment, hydropower generation, and reservoir balance. It optimizes reservoir scheduling schemes based on drought control and power generation maximization. It can be flexibly applied in multiple watersheds and under different reservoir conditions, achieving comprehensive watershed water conservancy management and possessing strong versatility and promotional value.

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

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

[0058] Furthermore, a solution is obtained based on a genetic algorithm and a multi-objective reservoir scheduling model to acquire the target reservoir scheduling scheme. This includes: acquiring several current reservoir decision variables based on initial watershed runoff data and initial watershed drought characteristics; inputting these current reservoir decision variables into the multi-objective reservoir scheduling model to execute the following steps: acquiring several reservoir scheduling schemes based on the current reservoir decision variables, the reservoir balance model, and preset reservoir constraints; acquiring several corresponding scheduling runoff data based on the several reservoir scheduling schemes and initial watershed runoff data, and then acquiring several corresponding watershed drought characteristics based on the scheduling runoff data; acquiring several corresponding power generation data based on the reservoir balance model; and finally, acquiring the target reservoir scheduling scheme based on the several watershed 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 watershed runoff data and initial watershed drought characteristics obtained from the hydrological environment simulation model are used to initially call several optimal current reservoir decision variables, and then solve them. First, a reservoir scheduling scheme is generated based on the current reservoir decision variables and the reservoir balance model. The corresponding data are combined with the initial watershed runoff data from the hydrological simulation model to solve the corresponding hydrological data and drought characteristics. Then, variables that meet the reservoir operation, hydrological process, and drought constraints are calculated. Combined with maximizing reservoir power generation, a solution that meets the requirements is obtained, which is used as the target reservoir scheduling scheme. Hydrological simulation and optimization algorithms are used to optimize the scheduling of water conservancy projects, and water conservancy capacity is maximized on the basis of drought control.

[0060] Furthermore, after obtaining the fitness score based on the target reservoir scheduling scheme, the process also includes: when the fitness score does not meet the preset termination condition, 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 and obtain a new generation of target reservoir scheduling scheme.

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

[0062] This invention provides a reservoir scheduling method based on hydrological simulation. First, a hydrological environment simulation model is constructed to address the hydrological characteristics of different watersheds. Then, based on the concepts of optimizing production capacity and drought control, it emphasizes the integrated application of drought assessment technology and water conservancy project scheduling. Using the SWAT model (soil and water conservation model) as the hub connecting drought assessment and reservoir scheduling optimization, it combines the hydrological environment simulation model, drought assessment, and reservoir balance model, along with corresponding reservoir production capacity calculation and assessment, drought constraints, and reservoir constraints, to achieve a multi-objective reservoir scheduling method that considers hydrological simulation for hydropower generation and drought control. This method simultaneously maximizes water conservancy production capacity and controls drought, helping to improve the efficiency of water resource utilization in the watershed. It can regulate downstream flow in real time, reducing the frequency, duration, and intensity of droughts to effectively alleviate hydrological drought problems in the watershed, reduce the impact of hydrological drought on agriculture, ecology, and water resource supply, and ensure the water security of downstream residents and economic development. Furthermore, the model simulation construction method has the advantages of being easy to implement, low-cost, highly efficient, and having a flexible and wide range of applications.

[0063] This invention provides a reservoir scheduling system based on hydrological simulation, implementing the aforementioned reservoir scheduling method based on hydrological simulation. The system includes a hydrological simulation module, a drought assessment module, a reservoir balance module, a model building module, and a reservoir scheduling module, wherein:

[0064] The hydrological simulation module is used to acquire hydrological environmental data, construct a hydrological environmental simulation model based on the hydrological environmental data and a preset soil and water conservation model, and acquire initial watershed runoff data based on the hydrological environmental 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 reservoir characteristic information for all reservoirs in the basin, so as to construct a reservoir balance model for the basin based on the reservoir characteristic information.

[0067] The model building module is used to construct a multi-objective reservoir scheduling model based on initial watershed runoff data, initial watershed drought characteristics, and reservoir balance model.

[0068] The reservoir scheduling module is used to solve the multi-objective reservoir scheduling model and obtain the reservoir scheduling optimization scheme to realize the scheduling of multi-objective reservoirs.

[0069] This invention provides a reservoir scheduling system based on hydrological simulation. The system uses a hydrological simulation module to acquire relevant hydrological environmental data and construct a hydrological environment simulation model for hydrological prediction. It obtains the required watershed runoff data and then uses a drought assessment module to predict drought conditions based on the predicted watershed runoff data. Next, a model building module, combined with a reservoir balance module, constructs a multi-objective reservoir scheduling model that yields the desired reservoir scheduling optimization scheme. This model is then solved using the reservoir scheduling module, enabling scientifically feasible multi-objective reservoir scheduling based on actual hydrological environment predictions and relevant reservoir characteristic data. Using a soil and water conservation model as the connecting hub between drought assessment and reservoir scheduling optimization, a systematic and highly operable overall solution is formed. This addresses the limitations of existing technologies, such as high construction and maintenance costs and limited functionality, which restrict the practical application of reservoir scheduling schemes. By constructing a multi-objective scheduling model and calling the optimal reservoir scheduling scheme, flexible scheduling and real-time control of multi-objective reservoirs are achieved, avoiding hydrological drought problems. Attached Figure Description

[0070] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0071] Figure 1 This is a 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 hydrological simulation using a heritage algorithm, provided in 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 in the embodiment. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort 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 one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0076] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

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

[0078] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0079] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0080] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0081] Example 1:

[0082] To address the limitations of existing reservoir scheduling schemes in practical applications due to high construction and maintenance costs and limited functionality, this invention proposes a reservoir scheduling method based on hydrological simulation. This method utilizes a multi-objective scheduling model to invoke the optimal reservoir scheduling scheme, enabling flexible scheduling and real-time control of various reservoirs and preventing hydrological droughts. Figure 1 ,include:

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

[0084] S2. Obtain initial watershed runoff data based on hydrological environment simulation models;

[0085] S3. Based on the initial watershed runoff data and the preset drought assessment model, obtain the initial watershed drought characteristics;

[0086] S4. Obtain reservoir characteristic information for all reservoirs in the basin, and construct a reservoir balance model for the basin based on the reservoir characteristic information.

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

[0088] S6. Solve the multi-objective reservoir scheduling model to obtain the reservoir scheduling optimization scheme, so as to realize the scheduling of multi-objective reservoirs.

[0089] The multi-objective reservoir scheduling method proposed in this embodiment constructs a hydrological environment simulation model by acquiring relevant hydrological environmental data for hydrological prediction, obtains the required watershed runoff data, and then predicts drought conditions based on the predicted watershed runoff data. Combined with a reservoir balance model, it constructs a multi-objective reservoir scheduling model that can obtain the required reservoir scheduling optimization scheme. This achieves scientific and feasible multi-objective reservoir scheduling based on actual hydrological environment predictions and relevant reservoir characteristic data. Using a soil and water conservation model as the connecting hub for drought assessment and reservoir scheduling optimization, a systematic and highly operable overall solution is formed. This solves the problem that existing technologies, due to high construction and maintenance costs and limited functionality, restrict the practical application of reservoir scheduling schemes. By constructing a multi-objective scheduling model and calling the optimal reservoir scheduling scheme, flexible scheduling and real-time control of multi-objective reservoirs are achieved, avoiding hydrological drought problems.

[0090] In the specific implementation process, before constructing a hydrological environment simulation model, a systematic survey of basic data such as topography, land use, soil characteristics, and meteorology of the corresponding watershed is necessary. This provides the necessary foundational information for constructing a suitable hydrological environment simulation model for the watershed. Based on this, the SWAT model for soil and water conservation is used to simulate the watershed's hydrological environment. The SWAT model is a physics-based hydrological model widely used in water resource management, watershed pollution control, and ecological protection. The hydrological environment simulation model constructed using this model can simulate the hydrological cycle processes of the watershed, including the dynamic changes of precipitation, evaporation, runoff, soil moisture, and groundwater. By simulating the impact of different land uses, meteorological conditions, and management measures, the interactions of various hydrological processes within the watershed can be assessed, providing a scientific basis for water resource 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 watershed's hydrological environment, supporting decision-makers in optimizing resource allocation, irrigation management, flood control, and drought relief.

[0091] Optionally, step S3 includes:

[0092] Drought assessment data were obtained based on initial watershed runoff data and a pre-set drought assessment model.

[0093] Based on 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] In the formula, D d,mean D represents the average duration of drought. n D represents the number of drought events. s,max Indicates the maximum degree of drought, v d,i Indicates the duration of a single drought event, v s,i Indicates the severity of a single drought event;

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

[0098] In practical implementation, the SRI (Self-Regulating Ratio) is typically used as the core indicator for assessing hydrological drought. Through standardized processing of watershed runoff data, the SRI can effectively quantify the severity of hydrological drought. Changes in standardized runoff distribution can reveal seasonal fluctuations in water volume within the watershed and the occurrence and development of drought. Therefore, changes in SRI values ​​can help distinguish different levels of hydrological drought, making it a commonly used tool for analyzing and monitoring drought phenomena. When conducting drought assessments, initial watershed runoff data obtained based on hydrological environmental simulation models are used as input data for calculating the SRI. Specifically, high-precision runoff data is obtained by simulating hydrological processes such as precipitation, soil moisture, and surface runoff in the watershed. This data is then standardized and converted into SRI values ​​for further analysis of hydrological drought conditions. Different SRI value ranges are used to classify different levels of drought. For example, an SRI value below -2 is usually defined as extreme drought, while an SRI value between -0.5 and -1 corresponds to mild to moderate drought.

[0099] In practical implementation, run theory can be introduced to quantify the characteristics of drought. By considering the length and intensity of drought periods, the impact of drought conditions can be quantitatively assessed: the duration and severity of drought events are defined by tracking periods in the runoff sequence that are continuously below the drought threshold, and the periods during which the drought index remains below a specific critical value (ε) are determined, along with the drought duration (v). d Drought intensity (v) is measured by the length of time the SRI is below ε. s The deficit during this period is used to calculate and can be used to assess all the sub-basins that need to be detected within the watershed, where v d and v s The calculation is as follows:

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

[0101]

[0102] In the formula, t s Indicates the start time of the drought, t e Indicates the end time of the drought, Ψ t v represents the drought index value at time t. d Indicates the duration of drought, v s ε represents the degree of drought, and ε represents a specific critical value.

[0103] Optionally, step S4 includes:

[0104] Obtain reservoir characteristic information for all reservoirs within the basin, and based on this information and pre-set reservoir balance conditions, obtain power generation flow data. The calculation process satisfies the following formula:

[0105]

[0106] In the formula, t represents the month, and S t S represents the reservoir capacity at the beginning of month t. t+1 I represents the reservoir capacity at the end of month t. t This represents the inbound flow in month t. This represents the power generation flow data used for hydropower generation in month t. E represents the fixed water outflow in month t. t L represents the evaporation loss in month t. t This represents the leakage loss in month t. This represents the overflow discharge in month t;

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

[0108] H i,t =α×S i,t β

[0109] In the formula, H i,t S represents the reservoir balance constraint variable, α and β represent the reservoir empirical coefficients, and S i,t Indicates reservoir characteristic information;

[0110] Hydropower generation data is obtained 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 data can be obtained based on power generation flow data, including:

[0113] Monthly average effective head is obtained based on reservoir characteristic information;

[0114] Monthly average power generation flow data is obtained based on power generation flow data;

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

[0116]

[0117] In the formula, F represents hydropower generation data, g represents gravitational acceleration, η represents the power generation efficiency coefficient, i represents all reservoirs, and Q m N represents the average monthly power generation flow data. mY represents the average monthly effective head. m This represents the total number of seconds in month m.

[0118] In practical implementation, the construction of the reservoir balance model is based on a detailed and systematic survey and acquisition of reservoir characteristic information for all reservoirs in the relevant watershed. This information covers key aspects such as the reservoir's basic conditions, geographical features, operational modes, and seasonal flood control and water supply policies. Based on this reservoir characteristic information, combined with the actual water balance requirements during reservoir operation, and considering the reservoir's maximum and minimum capacity, water level range, and power generation, a reservoir balance model is designed to comprehensively reflect the reservoir's scheduling constraints and power generation capacity.

[0119] In practical implementation, the reservoir discharge rate in the reservoir balance model is usually set as the reservoir decision variable. The reservoir discharge rate and the reservoir water level work together to determine the reservoir's power generation. Specifically, changes in discharge rate directly affect the water level in the reservoir, while the water level, in turn, affects the discharge rate and power generation potential. To achieve optimal reservoir operation and scheduling, iterative optimization algorithms can be used to adjust the discharge decisions of multiple reservoirs. During the optimization process, the discharge rates of multiple reservoirs will form a set, with different sets representing different decision schemes, i.e., scheduling strategies, which ultimately affect the overall hydrological environment of the basin and the reservoir's capacity.

[0120] In practical implementation, the monthly reservoir water balance controls the water storage-discharge dynamics of the reservoir system. The fixed water outflow is generally considered to be directly extracted from the reservoir, not used for hydropower generation, and used to supply agricultural, industrial, and domestic water, etc., and can be obtained through surveys based on different study areas. Evaporation loss and seepage loss can be calculated in two ways: one is to estimate it according to a certain proportion of the reservoir capacity, and the other is to simulate it based on a hydrological model. 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 does not include the sub-basin where the reservoir is located and its upstream sub-basins (point source model). The natural model can simulate the reservoir outflow process including evaporation and seepage loss based on factors such as water area, and extract the outlet flow of the sub-basin where the reservoir is located as the point source input and transfer it to the point source model as the reservoir inflow. The point source model then 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 the characteristics of the reservoir, which can be obtained by fitting the data through nonlinear regression and other methods based on long-term observation data of historical water level and reservoir capacity.

[0122] In practical implementation, to assess the annual hydropower generation of a reservoir, a physical estimation method based on monthly average data is typically used, as shown in the following formula:

[0123]

[0124] Where E year This represents the total annual power generation (kWh), where i represents different reservoirs; in the Dongjiang River basin, this refers to the three main control reservoirs: Fengshuba, Xinfengjiang, and Baipenzhu. η is the power generation efficiency coefficient, Q. m Average monthly hydropower outflow (m³) 3 / s), H m Y is the monthly average head (m) used to represent the effective head. m This represents the total number of seconds in month m. The constant 9.81 represents the acceleration due to gravity (m / s²). 2 ), 3.6 × 10 in the denominator 6 This is a unit conversion factor used to convert energy units from joules (J) to kilowatt-hours (kWh).

[0125] Optionally, step S5 includes:

[0126] Reservoir decision variables are obtained based on initial watershed runoff data and initial watershed drought characteristics;

[0127] The reservoir power generation function is obtained based on reservoir decision variables and reservoir balance model;

[0128] A multi-objective reservoir scheduling 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 equation:

[0129] Maxf(x)

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

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

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

[0133] In the formula, x represents the reservoir decision variable, y represents the season, f(x) represents the reservoir power generation function, and S t S represents the preset reservoir balance model. d S represents the preset drought constraint. s This indicates the preset reservoir constraints; 0 indicates that the constraints 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] In the formula: L(x) represents the reservoir water level, V(x) represents the reservoir capacity, and 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] Preset drought constraints include:

[0140] D opt ≤D base

[0141] In the formula: D opt D represents the drought characteristics after inputting reservoir decision variables. base This represents the baseline for drought characteristics.

[0142] In practical implementation, the drought characteristic baseline represents the drought situation without reservoir regulation. In the reservoir constraints, L(x) and V(x) represent the reservoir water level and capacity, respectively, and are related functions of reservoir discharge. The reservoir has flood control standards, which differ across seasons. The reservoir constraints are reflected in monthly reservoir discharge constraints, monthly reservoir water level constraints, and monthly reservoir capacity constraints, 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) represents the minimum value.

[0143] In practical implementation, drought assessment is based on drought characteristics and calculated using SRI and run theory. This embodiment is based on hydrological model simulation and can assess all sub-basins to be monitored within the watershed. Taking the watershed outlet as an example, the total outlet runoff sequence is read, the SRI is calculated, and further, three drought characteristics are calculated based on run theory: the number of drought events (D...). n ), the average duration of these events (D d,mean ) and maximum severity (D s,maxDuration of a single drought event (v) d,i ) and severity (v s,i These indicators are used to calculate a comprehensive overview of drought characteristics over a specified time period. For a specific study period, the drought characteristics calculated from the baseline drought characteristics and the watershed outlet runoff sequence after reservoir scheduling satisfy 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 at the baseline and after reservoir scheduling, respectively, including the number of drought events (D). n ), the average duration of these events (D d,mean ) and maximum severity (D s,max ), where s(x) represents the runoff at the outlet of the watershed simulated by the hydrological environment simulation model.

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

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

[0148] Optionally, a solution is obtained based on a genetic algorithm and a multi-objective reservoir scheduling model to acquire the target reservoir scheduling scheme. This includes: acquiring several current reservoir decision variables based on initial watershed runoff data and initial watershed drought characteristics; inputting these current reservoir decision variables into the multi-objective reservoir scheduling model to perform the following steps: acquiring several reservoir scheduling schemes based on the current reservoir decision variables, the reservoir balance model, and preset reservoir constraints; acquiring several corresponding scheduling runoff data based on the several reservoir scheduling schemes and initial watershed runoff data, and then acquiring several corresponding watershed drought characteristics based on the scheduling runoff data; acquiring several corresponding power generation data based on the reservoir balance model; and finally, acquiring the target reservoir scheduling scheme based on the several watershed drought characteristics, corresponding power generation data, preset power generation constraints, and preset drought constraints.

[0149] In practical implementation, the simulation optimization model for reservoir scheduling is typically solved using a genetic algorithm (GA). GA continuously calls the watershed hydrological environment simulation model to search for globally optimal decision variables that achieve the optimal objective function value while satisfying constraints related to reservoir operation and hydrological processes. The solution process of this genetic algorithm begins with the population initialization stage, where a set of chromosomes (i.e., several current reservoir decision variables) are generated within the boundary range of the optimal decision variable x. These chromosomes represent different reservoir scheduling schemes, including monthly outflow time-series variables 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 calls the hydrological environment simulation model to simulate the upstream runoff process of the reservoir, i.e., the initial watershed runoff data. Then, based on the scheduling scheme defined by the chromosomes, the reservoir model calculates and generates the downstream outflow sequence, which is then re-input into the hydrological model to simulate the downstream hydrological process of the entire watershed, thus obtaining the scheduled runoff data. The obtained runoff data is used to calculate drought characteristics and determine whether various reservoir operation and balance constraints, as well as drought constraints, are met. Within this solution framework, the Lagrange multiplier method and penalty function method are introduced to integrate nonlinear constraints into the corresponding objective function, thereby achieving a fitness score for each solution set to determine whether the current scheduling scheme 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 scheme is obtained.

[0150] Optionally, after obtaining the fitness score based on the target reservoir scheduling scheme, the method further includes: if the fitness score does not meet the preset termination condition, 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 and obtain a new generation of target reservoir scheduling scheme.

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

[0152] In practical implementation, this embodiment proposes a simulation-based drought-resistance optimization model for multi-reservoir systems, organically combining hydrological simulation, drought assessment, and heuristic optimization algorithms to achieve the dual objectives of "improving reservoir power generation efficiency" and "mitigating hydrological drought risks." Taking 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; then, based on the simulation results, the SRI (Self-Release Rate) is calculated, and features such as drought frequency, duration, and maximum intensity are extracted using operational theory as subsequent optimization constraints; finally, a genetic algorithm (GA) is employed to search for the optimal reservoir outflow strategy, aiming to maximize total annual power generation without exacerbating drought.

[0153] This embodiment provides a reservoir scheduling method based on hydrological simulation. First, a hydrological environment simulation model is constructed to address the hydrological characteristics of different watersheds. Then, based on the concepts of optimizing production capacity and drought control, it emphasizes the integrated application of drought assessment technology and water conservancy project scheduling. The SWAT model (soil and water conservation model) serves as the hub connecting drought assessment and reservoir scheduling optimization. Combining the hydrological environment simulation model, drought assessment, and reservoir balance model, along with corresponding reservoir production capacity calculation and assessment, drought constraints, and reservoir constraints, a multi-objective reservoir scheduling method considering hydrological simulation for hydropower generation and drought control is achieved. This method simultaneously maximizes water conservancy production capacity and controls drought, helping to improve the efficiency of water resource utilization in the watershed. It can regulate downstream flow in real time, reducing the frequency, duration, and intensity of drought, effectively alleviating hydrological drought problems in the watershed, mitigating the impact of hydrological drought on agriculture, ecology, and water resource supply, and ensuring water security for downstream residents and economic development. Furthermore, the model simulation method is easy to implement, low-cost, highly efficient, and has a flexible and wide range of applications. It improves the simulation performance, water conservancy production capacity optimization performance, and drought control performance for the study area.

[0154] Example 2:

[0155] See Figure 2 This embodiment provides a method for solving reservoir scheduling based on hydrological simulation using a genetic algorithm, including:

[0156] start;

[0157] S21. Generate a population based on a genetic algorithm;

[0158] S22. Obtain the reservoir scheduling plan corresponding to the population;

[0159] S23. Input the reservoir scheduling plan into the reservoir balance model and obtain the corresponding downstream runoff data in conjunction with the hydrological environment simulation model;

[0160] S24. Based on the corresponding downstream runoff data, perform drought characteristic analysis to obtain the corresponding drought characteristic data;

[0161] S25. Obtain the corresponding scheduling scheme based on the corresponding drought characteristic data, drought constraints, reservoir balance constraints, reservoir scheduling scheme and preset power generation conditions;

[0162] S26. Obtain fitness scores based on the corresponding scheduling scheme;

[0163] S27. Determine if the fitness score meets the termination condition: if it does, end the process; if it does not, proceed to step S28.

[0164] S28. Perform the gene manipulation action and return to step S21;

[0165] Finish.

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

[0167] In practical implementation, the reservoir scheduling optimization method proposed in this embodiment is highly effective. Through actual surveys and data analysis of reservoirs in the basin, it accurately simulates the operating status of reservoirs, demonstrating strong adaptability and the ability to quickly respond to seasonal drought changes, thus exhibiting good timeliness. Compared with traditional methods, it not only improves the power generation capacity of reservoirs but also regulates downstream flow in real time, reducing the frequency, duration, and intensity of droughts, effectively alleviating hydrological drought problems in the basin. Based on the current needs of new energy development and the important role of the basin in regulating water resources in surrounding areas, this method can achieve the dual objectives of optimizing reservoir power generation and controlling hydrological drought, demonstrating significant effectiveness.

[0168] Example 3:

[0169] See 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 the optimal scheduling scheme for the capacity optimization and drought control gate pump water conservancy project based on the capacity-drought prediction model, wherein:

[0170] The construction of hydrological environment simulation models, reservoir balance models, and drought assessment models includes: Building a hydrological environment simulation model based on the SWAT model: collecting topographic, meteorological, soil, and reservoir data, and constructing the model and determining parameters; upstream watershed data obtained from the hydrological environment simulation model serves as one of the water balance inputs for the reservoir balance model, while reservoir discharge serves as another input. Water balance affects reservoir capacity and consequently, reservoir water level; reservoir discharge impacts the hydrological environment simulation model by affecting downstream watersheds; and reservoir capacity, such as power generation, is calculated using reservoir water level and discharge; downstream watershed data obtained from the hydrological environment simulation model is used to calculate drought characteristics based on the drought assessment model: obtaining the standardized runoff index based on downstream watershed data, and acquiring drought characteristics based on the standardized runoff index and runs theory.

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

[0172] In its specific implementation, this embodiment, considering the unique river network and reservoir conditions of the Dongjiang River, employs a simulation-optimization coupled model based on the SWAT model. With the goal of maximizing reservoir power generation and drought mitigation as a constraint (drought constraint), it conducts a comprehensive study of multi-reservoir scheduling based on hydrological simulation for hydropower generation and drought control. A genetic algorithm is used to find multi-reservoir control schemes, and the effectiveness of each scheme is evaluated through optimization objectives and constraints, ultimately yielding a near-optimal decision scheme and forming a technical system for capacity optimization and drought control. Addressing the characteristics of the river network system in the Dongjiang River basin of Guangdong Province, and based on the concepts of capacity optimization and drought control, this embodiment emphasizes the integrated application of drought assessment technology and water conservancy project scheduling. It pioneers the use of hydrological simulation-optimization algorithms for optimized water conservancy project scheduling, establishing a multi-objective reservoir scheduling method for hydropower generation and drought control that considers hydrological simulation, enabling simultaneous maximization of water conservancy capacity and drought control functions.

[0173] In the specific implementation process, to ensure the effectiveness of reservoir scheduling and drought control, this embodiment uses the SWAT model as the connecting hub between the reservoir and drought assessment models. Under different reservoir scheduling schemes, the hydrological environment of the watershed will change, specifically manifested in the varying inflow upstream of the reservoir. The inflow and outflow of the reservoir, through the water balance model, jointly determine the reservoir's power generation capacity. In this way, the scheduling scheme not only affects the reservoir's power generation but also the downstream flow, thus significantly impacting the downstream hydrological drought situation. Ultimately, by optimizing the reservoir scheduling strategy, it is possible to maximize reservoir power generation while effectively alleviating downstream hydrological drought problems and promoting the sustainable use of water resources.

[0174] Example 4:

[0175] This embodiment provides a reservoir scheduling system based on hydrological simulation, implementing the aforementioned reservoir scheduling method based on hydrological simulation. The system includes a hydrological simulation module, a drought assessment module, a reservoir balance module, a model building module, and a reservoir scheduling module, wherein:

[0176] The hydrological simulation module is used to acquire hydrological environmental data, construct a hydrological environmental simulation model based on the hydrological environmental data and a preset soil and water conservation model, and acquire initial watershed runoff data based on the hydrological environmental 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 reservoir characteristic information for all reservoirs in the basin, so as to construct a reservoir balance model for the basin based on the reservoir characteristic information.

[0179] The model building module is used to construct a multi-objective reservoir scheduling model based on initial watershed runoff data, initial watershed drought characteristics, and reservoir balance model.

[0180] The reservoir scheduling module is used to solve the multi-objective reservoir scheduling model and obtain the reservoir scheduling optimization scheme to realize the scheduling of multi-objective reservoirs.

[0181] This embodiment provides a reservoir scheduling system based on hydrological simulation, actively embodying the concepts of optimizing production capacity and drought control. It offers a complete and systematic solution encompassing multiple components, including hydrological models, drought assessment models, reservoir models, and a production capacity-drought multi-objective optimization model. The hydrological simulation module acquires relevant hydrological environmental data to construct a hydrological environmental simulation model for hydrological prediction, obtaining the necessary watershed runoff data. Then, the drought assessment module predicts drought conditions based on the predicted watershed runoff data. Finally, the model building module, combined with the reservoir balance model from the reservoir balance module, constructs a reservoir scheduling optimization plan. This paper presents a multi-objective reservoir scheduling model and solves the problem based on the reservoir scheduling module. It realizes scientific and feasible multi-objective reservoir scheduling based on actual hydrological environment predictions and relevant reservoir characteristic data. Using the soil and water conservation model as the connecting hub for drought assessment and reservoir scheduling optimization, a systematic and highly operable overall solution is formed. This solves the problem that existing technologies have limitations in practical application due to high construction and operation and maintenance costs and single functions. By constructing a multi-objective scheduling model and calling the optimal reservoir scheduling scheme, flexible scheduling and real-time control of multi-objective reservoirs can be achieved, avoiding hydrological drought problems.

[0182] Example 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-described reservoir scheduling method based on hydrological simulation.

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

[0185] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0186] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0187] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and 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 hydrologic simulation, characterized by, The method comprises the following steps: acquiring hydrological environment data to construct a hydrological environment simulation model based on the hydrological environment data and a preset water and soil conservation model; acquiring initial basin runoff data based on the hydrological environment simulation model; acquiring initial basin drought characteristics based on the initial basin runoff data and a preset drought assessment model; acquiring reservoir characteristic information corresponding to all reservoirs in the basin to construct a reservoir balance model corresponding to the basin based on the reservoir characteristic information; constructing a multi-objective reservoir scheduling model based on the initial basin runoff data, the initial basin drought characteristics and the reservoir balance model; solving the multi-objective reservoir scheduling model to obtain a reservoir scheduling optimization scheme to realize scheduling of the multi-objective reservoirs; wherein the acquiring of the reservoir characteristic information corresponding to all reservoirs in the basin to construct the reservoir balance model corresponding to the basin based on the reservoir characteristic information comprises: acquiring reservoir characteristic information corresponding to all reservoirs in the basin, acquiring power generation flow data based on the reservoir characteristic information and a preset reservoir balance condition, and the calculation process satisfies the following formula: where t represents a month, represents a reservoir capacity at the beginning of the tth month, represents a reservoir capacity at the end of the tth month, represents an inflow to the reservoir in the tth month, represents a power generation flow data for hydroelectric power generation in the tth month, represents a fixed water outflow in the tth month, represents an evaporation loss amount in the tth month, represents a seepage loss amount in the tth month, represents a spillway flow in the tth month, acquiring reservoir constraint variables based on the reservoir characteristic information, and the calculation process satisfies the following formula: In the formula, represents the reservoir balance constraint variable, represents the reservoir experience coefficient, represents the reservoir characteristic information; acquiring hydropower generation data based on the power generation flow data; constructing the reservoir balance model based on the hydropower generation data and the reservoir constraint variables.

2. The reservoir scheduling method based on hydrological simulation of claim 1, wherein, The acquiring of the initial basin drought characteristics based on the initial basin runoff data and the preset drought assessment model comprises: acquiring drought assessment data based on the initial basin runoff data and the preset drought assessment model; acquiring a drought average duration, a drought event frequency and a drought maximum degree based on the drought assessment data, and the calculation process satisfies the following formula: wherein represents the average duration of drought, represents the number of drought events, represents the maximum extent of drought, represents the duration of individual drought events, represents the severity of individual drought events; taking the drought average duration, the drought event frequency and the drought maximum degree as initial drought characteristics.

3. The reservoir scheduling method based on hydrological simulation of claim 1, wherein, The acquiring of the hydropower generation data based on the power generation flow data comprises: acquiring a monthly average effective water head based on the reservoir characteristic information; acquiring monthly average power generation flow data based on the power generation flow data; acquiring hydropower generation data based on the monthly average effective water head, the monthly average power generation flow data and a reservoir power generation formula, and the calculation process satisfies the following formula: wherein, represents the hydropower data, g represents the gravity acceleration, represents the power generation efficiency coefficient, i represents all the reservoirs, represents the monthly average power generation flow data, represents the monthly average effective water head, represents the total seconds of the mth month.

4. The reservoir scheduling method based on hydrological simulation of claim 1, wherein, The constructing of the multi-objective reservoir scheduling model based on the initial basin runoff data, the initial basin drought characteristics and the reservoir balance model comprises: acquiring reservoir decision variables based on the initial basin runoff data and the initial basin drought characteristics; acquiring a reservoir power generation function based on the reservoir decision variables and the reservoir balance model; constructing a multi-objective reservoir scheduling model based on the reservoir decision variables, the reservoir power generation function, the reservoir balance model, a preset drought constraint condition and a preset reservoir constraint condition, as shown in the following formula: wherein, y represents the seasonal, represents the reservoir power generation function, represents the preset reservoir balance model, represents the preset drought constraint condition, represents the preset reservoir constraint condition, 0 represents compliance with the constraint condition; wherein the preset reservoir constraint condition comprises: wherein: represents the reservoir water level, represents the reservoir capacity, represents the maximum reservoir outflow, represents the maximum reservoir water level, represents the maximum reservoir volume, represents the minimum reservoir outflow, represents the minimum reservoir water level, represents the minimum reservoir volume; the preset drought constraint condition comprises: wherein: represents the drought characteristics after inputting the reservoir decision variable, represents the baseline of the drought characteristics.

5. The reservoir scheduling method based on hydrological simulation of claim 1, wherein, The solving of the multi-objective reservoir scheduling model to obtain a reservoir scheduling optimization scheme to realize scheduling of the multi-objective reservoirs comprises: solving based on a genetic algorithm and the multi-objective reservoir scheduling model to obtain a target reservoir scheduling scheme; acquiring an adaptability score based on the target reservoir scheduling scheme; when the adaptability score meets a preset termination condition, taking the target reservoir scheduling scheme as the reservoir scheduling optimization scheme to realize scheduling of the multi-objective reservoirs.

6. The reservoir scheduling method based on hydrological simulation of claim 5, wherein, The genetic algorithm and the multi-objective reservoir scheduling model are used to obtain a target reservoir scheduling scheme, including: Based on the initial basin runoff data and initial basin drought characteristics, a plurality of current reservoir decision variables are obtained; The plurality of current reservoir decision variables are input into the multi-objective reservoir scheduling model to perform the solving step: Based on the plurality of current reservoir decision variables, the reservoir balance model and the preset reservoir constraint condition, a plurality of reservoir scheduling schemes are obtained; Based on the plurality of reservoir scheduling schemes and the initial basin runoff data, a plurality of corresponding scheduling runoff data are obtained to obtain a plurality of corresponding scheduling basin drought characteristics based on the plurality of scheduling runoff data; Based on the reservoir balance model, a plurality of corresponding power generation data are obtained; Based on the plurality of scheduling basin drought characteristics, the corresponding power generation data, the preset power generation constraint condition and the preset drought constraint condition, a target reservoir scheduling scheme is obtained.

7. The reservoir scheduling method based on hydrological simulation of claim 6, wherein, After the fitness score is obtained based on the target reservoir scheduling scheme, 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 of reservoir decision variables is input into the multi-objective reservoir scheduling model to re-execute the solving step and obtain a new generation of target reservoir scheduling scheme.

8. A reservoir scheduling system based on hydrologic simulation, characterized by, A reservoir scheduling method based on hydrological simulation is implemented, including a hydrological simulation module, a drought assessment module, a reservoir balance module, a model construction module and a reservoir scheduling module, wherein: The hydrological simulation module is used to obtain hydrological environment data to construct a hydrological environment simulation model based on the hydrological environment data and a preset water and soil conservation model; and initial basin runoff data is obtained based on the hydrological environment simulation model; The drought assessment module is used to obtain initial basin drought characteristics based on the initial basin 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 to construct a reservoir balance model corresponding to the basin based on the reservoir characteristic information; specifically, reservoir characteristic information corresponding to all reservoirs in the basin is obtained, power generation flow data is obtained based on the reservoir characteristic information and a preset reservoir balance condition, and the calculation process satisfies the following formula: where t represents a month, represents a reservoir capacity at the beginning of the tth month, represents a reservoir capacity at the end of the tth month, represents an inflow to the reservoir in the tth month, represents a power generation flow data for hydroelectric power generation in the tth month, represents a fixed water outflow in the tth month, represents an evaporation loss amount in the tth month, represents a seepage loss amount in the tth month, represents a spillway flow in the tth month, Reservoir constraint variables are obtained based on the reservoir characteristic information, and the calculation process satisfies the following formula: In the formula, represents the reservoir balance constraint variable, represents the reservoir experience coefficient, represents the reservoir characteristic information; Hydraulic power generation data is obtained based on the power generation flow data; and the reservoir balance model is constructed based on the hydraulic power generation data and the reservoir constraint variables; The model construction module is used to construct a multi-objective reservoir scheduling 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 to obtain a reservoir scheduling optimization scheme to realize the scheduling of the multi-objective reservoir.

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