A method and device for managing tail water level of a hydropower station, an electronic device and a storage medium

Abstract

The application provides a hydropower station tailwater level management method and device, electronic equipment and storage medium, relates to the technical field of water conservancy and hydropower engineering operation optimization and design, and the method comprises the following steps: acquiring multi-source data of a hydropower station; taking ecological data of the hydropower station in the multi-source data as a water power constraint condition or a boundary parameter, inputting the ecological data into a tailwater channel water power characteristic dynamic model, and calculating tailwater level data; constructing a constraint condition, and inputting a tailwater channel management scheme in the multi-source data into a multi-objective collaborative optimization decision engine; wherein the multi-objective collaborative optimization decision engine adopts a double-layer nested optimization architecture, takes discrete tailwater channel management schemes and continuous dynamic tailwater level control sequences as coupled decision variables, takes maximum comprehensive benefit as a target function, iteratively optimizes under the constraint condition, and generates a tailwater level management scheme. The application realizes multi-element collaborative iterative optimization, and solves the problems of single data, non-combination of ecological models and fragmented optimization elements in the prior art.

Description

Technical Field

[0001] This invention relates to the field of water conservancy and hydropower engineering operation optimization and design technology, and more specifically, to a method, device, electronic equipment and storage medium for controlling the tailrace water level of a hydropower station. Background Technology

[0002] The tailwater level of a hydropower station refers to the water level in the downstream river channel at the outlet of the hydropower station's generating units. It is a core parameter that determines the effective head of the power station. Even slight rises and falls in the tailwater level can significantly affect the head and power generation. Moreover, the tailwater level is dynamically affected by multiple factors such as river topography, inflow, downstream cascade power station scheduling, and river siltation / dredging. It is crucial to the power generation efficiency and safe operation of riverbed hydropower stations.

[0003] In related technologies, to obtain a larger effective head, tailrace channel dredging and excavation are often used to artificially lower the tailrace level through one-time engineering measures. However, this method has three major flaws. First, it adopts a static and fragmented design paradigm, determining a fixed tailrace level and a single channel management scheme based on the design flood or multi-year average flow, ignoring the dynamic evolution of factors such as hydrological conditions, downstream cascade scheduling, electricity market, and ecological requirements during the decades-long operation of the power station. The initial investment cannot adapt to the complex operating environment in the later stages. Second, the optimization objective is isolated and singular, focusing only on the economic comparison between minimizing the amount of excavation work and maximizing static power generation benefits, without considering the hydraulic coupling effect of downstream cascades, the difference in marginal benefits of real-time electricity prices, and the rigid constraints of ecological baseflow protection. Third, the channel management scheme is irreversible and completely separated from the tailrace level control strategy during operation, failing to release the potential value of the initial engineering investment. Even if existing technologies achieve dynamic prediction and control of tailwater level, they only take flood control safety as the single objective and optimize the single variable of discharge flow. This is a single-point optimization at the operation level and does not solve the problem of coupling optimization between river management schemes and dynamic operation strategies. There is no technology that can use discrete engineering management schemes and continuous operation control strategies as coupling variables to maximize the comprehensive benefits of the power station throughout its entire life cycle under multiple constraints. Summary of the Invention

[0004] The present invention aims to solve at least one of the above-mentioned problems.

[0005] To address the aforementioned problems, this invention provides a method, apparatus, electronic device, and storage medium for controlling the tailrace water level of a hydropower station.

[0006] In a first aspect, the present invention provides a method for controlling the tailrace level of a hydropower station, comprising: Acquire multi-source data from hydropower stations; The hydropower station ecological data from the multi-source data is used as hydraulic constraints or boundary parameters and input into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace water level data. Constraints are constructed, and the tailwater channel treatment schemes from the multi-source data are input into a multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints, with the objective function of maximizing comprehensive benefits, to generate tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

[0007] Optionally, constraints are constructed, including: Constraints are constructed based on the tailwater level data, the hydropower station operating parameters from the multi-source data, and the electricity cost data.

[0008] Optionally, the objective function includes a life-cycle cost objective function, a power generation revenue objective function, and a comprehensive benefit objective function, and the multi-objective collaborative optimization decision engine includes an outer optimization module and an inner optimization module based on the double-layer nested optimization architecture; The outer optimization module is used to enumerate or search the tailrace channel treatment schemes, and calculate the full life cycle cost of each scheme based on the constraints. The inner optimization module is used to solve the power generation revenue objective function based on the constraints of the tailrace channel treatment scheme, and obtain the dynamic tailrace level control sequence scheme and the corresponding power generation revenue. The outer optimization module is also used to solve the comprehensive benefit objective function based on the total life cycle cost and the power generation revenue to obtain the initial implementation plan.

[0009] Optionally, the tailrace channel hydraulic characteristic dynamic model is based on HEC-RAS to build a one-dimensional and / or simplified two-dimensional hydrodynamic model, and embeds a dynamic correction module for the Manning coefficient.

[0010] Optionally, the tailrace water level control method of the hydropower station further includes: The initial implementation plan shall be executed during the non-operational phase of the hydropower station. The dynamic tailwater level control sequence scheme is executed during the operation phase of the hydropower station.

[0011] Optionally, the hydropower station ecological data includes the operating water level and scheduling plan of the downstream cascade power stations, real-time monitoring data of river ecological flow and ecological scheduling requirements, water level-flow relationship curve of the downstream river section, and periodic river topographic scanning data; The hydropower station's operating parameters include the unit efficiency curve and reservoir water level operating parameters; The electricity cost data includes day-ahead and real-time electricity price signals from the electricity market, as well as project budgets.

[0012] Optionally, the tailrace water level control method of the hydropower station further includes: The tailwater level treatment plan is visualized.

[0013] Secondly, the present invention provides a tailrace water level control device for a hydropower station, comprising: The acquisition module is used to acquire multi-source data from hydropower stations; The hydrodynamic module is used to input the hydropower station ecological data from the multi-source data as hydraulic constraints or boundary parameters into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace level data. The decision module is used to construct constraints and input the tailwater channel treatment schemes from the multi-source data into the multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints with the objective function of maximizing comprehensive benefits, generating tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

[0014] Thirdly, the present invention provides an electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the tailrace water level control method for hydropower stations as described in the first aspect when executing the computer program.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the tailrace water level control method for hydropower stations as described in the first aspect.

[0016] The beneficial effects of the hydropower station tailrace water level control method, device, electronic equipment, and storage medium of the present invention are: By acquiring multi-source data from hydropower stations, a comprehensive data foundation is established for tailrace level calculation and optimization decisions, avoiding the shortcomings of existing technologies that rely on single data sources and cannot adapt to dynamic decision-making needs. Furthermore, the hydropower station's ecological data from the multi-source data is input as hydraulic constraints or boundary parameters into the tailrace channel's hydraulic characteristic dynamic model to calculate the tailrace level data. This addresses the issues of existing technologies failing to incorporate ecological data into hydraulic model constraints and the disconnect between tailrace level calculations and rigid ecological requirements, ensuring that the tailrace level calculation results conform to ecological constraints and possess physical rationality. Finally, a constraint condition including downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints is constructed, and the tailrace channel management scheme is input into a multi-objective optimization architecture employing a double-layer nested optimization framework. The collaborative optimization decision engine uses discrete tailraceway management schemes and continuous dynamic tailrace level control sequences as coupled decision variables. It iteratively optimizes the tailrace level management scheme by maximizing the comprehensive benefits of the weighted difference between the power generation revenue and the management project cost over the entire life cycle. This generates a tailrace level management scheme that includes both the initial implementation scheme and the dynamic tailrace level control sequence scheme. It overcomes the bottlenecks of existing technologies where management schemes and operational strategies are isolated, fail to achieve coupled optimization, and have a single optimization objective. It achieves collaborative optimization of discrete and continuous decision variables through a two-layer nested architecture, balances power generation revenue and project costs with the comprehensive benefits over the entire life cycle as the core, and satisfies multiple key constraints, thus realizing the integrated collaborative generation of tailrace level management schemes and dynamic control strategies. Attached Figure Description

[0017] Figure 1 A schematic flowchart of a hydropower station tailrace level control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of the tailrace water level control device for a hydropower station provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0019] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0020] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0021] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0022] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0023] like Figure 1 As shown in the figure, an embodiment of the present invention provides a method for controlling the tailrace water level of a hydropower station, comprising: Acquire multi-source data from hydropower stations.

[0024] Specifically, this embodiment uses a multi-source data access and fusion module to perform multi-source data acquisition operations for hydropower stations. This module combines real-time acquisition with periodic updates to uniformly access, clean, fuse, and standardize various types of data required for decision-making throughout the entire life cycle of the hydropower station. It integrates and collects multi-dimensional and multi-time-series data to form a standardized dataset that can be directly accessed. This provides complete, accurate, and real-time basic data support for subsequent hydraulic characteristic simulation and optimization decisions. The data acquisition process covers the entire operation cycle of the power station and can adapt to dynamically changing external environments and operating conditions.

[0025] The hydropower station ecological data from the multi-source data is used as hydraulic constraints or boundary parameters and input into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace water level data.

[0026] Specifically, in this embodiment, the hydropower station ecological data from the acquired multi-source data is used as hydraulic constraints or boundary parameters and input into a pre-constructed tailrace channel hydraulic characteristic dynamic model. This model is based on hydrodynamic numerical calculation and simulates the flow characteristics and water level response of the tailrace channel based on the input ecological data. The tailrace water level values ​​under different operating conditions are solved through hydraulic calculation to generate continuous and accurate tailrace water level data. The tailrace water level data can reflect the correspondence between channel morphology, inflow conditions and water level, and provide core hydraulic calculation results for the subsequent optimization engine. These results may include real-time tailrace water level, backwater level, and flow parameters, and can serve as the constraint basis for optimization decisions and the basis for head calculation.

[0027] Constraints are constructed, and the tailwater channel treatment schemes from the multi-source data are input into a multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints, with the objective function of maximizing comprehensive benefits, to generate tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

[0028] Specifically, firstly, constraints are constructed, including at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. Downstream cascade backwater level constraints refer to the allowable backwater level of downstream cascade power stations; ecological flow constraints refer to the water level constraints for ecological flow discharge (such as the upper limit of the water level corresponding to the minimum discharge flow); and unit safety head constraints refer to the head constraints for safe and stable operation of the units (such as minimum and maximum head limits). Next, the tailrace channel treatment plan is input into a multi-objective collaborative optimization decision engine. The engine uses discrete tailrace channel treatment plans and continuous operation control strategies (dynamic tailrace level control sequences) as coupled decision variables. Through multi-level iterative optimization calculations, collaborative optimization is carried out. During the iteration process, physical constraints, ecological constraints, and engineering constraints are simultaneously verified. Finally, a complete tailrace level treatment plan is generated, including an initial one-time engineering implementation plan and a full-cycle dynamic tailrace level control sequence plan, achieving integrated coupled output of the treatment plan and operation strategy. The initial implementation plan refers to physically altering key parameters such as the cross-sectional shape, bottom elevation, bottom width, and slope of the tailrace channel through one-time engineering measures such as river excavation, dredging, cross-section reshaping, and bottom slope trimming. This fundamentally reshapes the river's water level-discharge relationship curve, laying the hydraulic foundation for a low tailrace level. The dynamic tailrace level control sequence plan refers to adjusting the downstream flow by regulating the unit output, floodgate opening, and sediment flushing gate opening during the hydropower station's operation phase. Based on the hydraulic characteristics of the treated river, the tailrace level is dynamically controlled in real-time to a sequence target value to maximize benefits. The comprehensive benefit is calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailrace channel treatment plan refers to pre-set discrete treatment schemes used by a target collaborative optimization decision engine to find the optimal tailrace level treatment scheme, which may include: Option 1: Perform only routine dredging to maintain the current river channel, with an initial investment of 2 million yuan; Option 2: Excavation length 1.3km, bottom slope 1.5‰, bottom width 80m, initial investment 12 million yuan; Option 3: Excavation length 2.0km, bottom slope 1.0‰, bottom width 100m, initial investment 25 million yuan.

[0029] In this embodiment, by acquiring multi-source data from the hydropower station, a comprehensive data foundation is laid for tailrace level calculation and optimization decision-making, avoiding the shortcomings of existing technologies that rely on a single data source and cannot adapt to dynamic decision-making needs. Then, the hydropower station's ecological data from the multi-source data is input as hydraulic constraints or boundary parameters into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace level data. This solves the problems of existing technologies failing to incorporate ecological data into hydraulic model constraints and the disconnect between tailrace level calculation and rigid ecological requirements, ensuring that the tailrace level calculation results conform to ecological constraints and possess physical rationality. Finally, a constraint condition including downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints is constructed, and the tailrace channel treatment scheme is input into a double-layer nested optimization architecture. The multi-objective collaborative optimization decision engine uses discrete tailraceway management schemes and continuous dynamic tailrace level control sequences as coupled decision variables. It iteratively optimizes based on maximizing the comprehensive benefits of the weighted difference between the power generation revenue and the management project cost over the entire life cycle, generating a tailrace level management scheme that includes both the initial implementation scheme and the dynamic tailrace level control sequence scheme. This overcomes the bottlenecks of existing technologies where management schemes and operational strategies are isolated, fail to achieve coupled optimization, and have a single optimization objective. It achieves collaborative optimization of discrete and continuous decision variables through a double-layer nested architecture, balances power generation revenue and project costs with the comprehensive benefits over the entire life cycle as the core, and satisfies multiple key constraints, realizing the integrated collaborative generation of tailrace level management schemes and dynamic control strategies.

[0030] Optionally, the construction constraints include: Constraints are constructed based on the tailwater level data, the hydropower station operating parameters from the multi-source data, and the electricity cost data.

[0031] Specifically, this embodiment constructs comprehensive constraints covering hydraulic safety, ecological protection, unit stability, and engineering economics based on generated tailrace level data, acquired hydropower station operating parameters, and electricity cost data, combined with hydropower station physical operation rules, ecological management requirements, downstream cascade dispatching specifications, and engineering investment limits. These constraints may also include tailrace channel hydraulic characteristic constraints, specifically ensuring the Froude number (Fr) at the control section is within a preset range and that there are no harmful flow patterns (such as backflow or crossflow). Furthermore, constraints may include engineering investment budget constraints. The alternative tailrace channel treatment schemes from the multi-source data are input into a multi-objective collaborative optimization decision engine. Under the rigid constraints described above, the engine iteratively solves the preset objective function, eliminating invalid schemes that do not meet the constraints through dual screening of constraint verification and benefit calculation. Finally, it outputs a tailrace level treatment scheme that balances compliance, feasibility, and optimal benefits. These constraints are applied throughout the entire optimization process to ensure that the decision results conform to engineering realities and policy requirements.

[0032] Optionally, the objective function includes a life-cycle cost objective function, a power generation revenue objective function, and a comprehensive benefit objective function, and the multi-objective collaborative optimization decision engine includes an outer optimization module and an inner optimization module based on the double-layer nested optimization architecture; The outer optimization module is used to enumerate or search the tailrace channel treatment schemes, and calculate the full life cycle cost of each scheme based on the constraints. The inner optimization module is used to solve the power generation revenue objective function based on the constraints of the tailrace channel treatment scheme, and obtain the dynamic tailrace level control sequence scheme and the corresponding power generation revenue. The outer optimization module is also used to solve the comprehensive benefit objective function based on the total life cycle cost and the power generation revenue to obtain the initial implementation plan.

[0033] Specifically, in this embodiment, the objective functions of the multi-objective collaborative optimization decision engine are divided into three categories: the full life cycle cost objective function, the power generation revenue objective function, and the comprehensive benefit objective function. The comprehensive benefit objective function is F=Max(α·E-β·C), where E is the total discounted power generation revenue over the entire life cycle, which is calculated by integrating and accumulating time-periods with the real-time head of the generating unit (upstream water level - dynamic tailwater level) as the core by embedding a dynamic electricity market price model; C is the full life cycle cost of the tailwater channel treatment project, including initial project investment, long-term maintenance costs, and secondary dredging costs; α and β are weight coefficients that can be flexibly adjusted according to policy guidance and project needs. The engine adopts a two-layer nested optimization architecture. The outer optimization module is equipped with an evolutionary algorithm, which receives and enumerates or searches for alternative tailrace channel treatment schemes, and solves the life-cycle cost objective function under constraints to obtain the life-cycle cost corresponding to each scheme. The inner optimization module is equipped with a rolling time-domain optimization algorithm and a sequential quadratic programming algorithm. For the same treatment scheme, it solves the power generation revenue objective function under constraints, generates a continuous dynamic tailrace level control sequence scheme on an hourly / dayly basis, and calculates the corresponding life-cycle power generation revenue. Among them, the sequential quadratic programming (SQP) algorithm transforms the original nonlinear programming problem into a series of quadratic programming subproblems. In each iteration, it solves a quadratic programming problem to approximate the optimal solution of the original problem. It can efficiently handle continuous optimization problems under nonlinear constraints and has the advantages of fast convergence speed and high accuracy. The outer optimization module then substitutes the power generation revenue output by the inner layer and the full life cycle cost calculated by itself into the comprehensive benefit objective function to obtain the comprehensive benefit value of each scheme. The governance scheme corresponding to the maximum comprehensive benefit is selected as the initial implementation scheme. The inner and outer layers achieve deep coupling optimization between discrete governance schemes and continuous operation sequences through iterative interaction. The outer layer uses the comprehensive benefit as the fitness function to guide the evolution of the scheme, and finally converges to the global optimal solution.

[0034] Optionally, the tailrace channel hydraulic characteristic dynamic model is based on HEC-RAS to build a one-dimensional and / or simplified two-dimensional hydrodynamic model, and embeds a dynamic correction module for the Manning coefficient.

[0035] Specifically, in this embodiment, the dynamic model of the tailrace channel's hydraulic characteristics is built using HEC-RAS software as its core. Based on the topography, hydrology, and geometric characteristics of the tailrace channel, a one-dimensional hydrodynamic model and / or a simplified two-dimensional hydrodynamic model are flexibly constructed. At the same time, a dynamic correction module for the Manning coefficient is embedded within the model. After the model is built, the Manning coefficient can be dynamically calibrated, corrected, and updated based on real-time collected river topography scanning data, riverbed roughness monitoring data, and flow velocity data. This eliminates calculation errors caused by river scouring and deposition and topographic changes, accurately simulating the water level response relationship of "Z=f(Q,Si)" (Z is the tailrace water level, Q is the inflow flow, and Si is the treatment plan). Simultaneously, it completes physical feasibility verification such as backwater effect simulation, Froude number calculation, and harmful flow regime judgment. This provides real-time and accurate tailrace water level calculation data for the multi-objective collaborative optimization decision engine. The model can be updated periodically based on new topographic data to adapt to the long-term evolution characteristics of the river channel.

[0036] Optionally, the tailrace water level control method of the hydropower station further includes: The initial implementation plan shall be executed during the non-operational phase of the hydropower station. The dynamic tailwater level control sequence scheme is executed during the operation phase of the hydropower station.

[0037] Specifically, this embodiment divides the tailrace level treatment plan into stages according to the construction and operation phases of the hydropower station. During the non-operation phase of the hydropower station (design phase, construction phase, and technical renovation phase), the initial implementation plan of the tailrace level treatment plan is implemented. Through one-time engineering measures such as river channel excavation, dredging, cross-section reshaping, and bottom slope adjustment, key parameters such as the cross-sectional shape, bottom elevation, bottom width, and slope of the tailrace channel are physically changed, fundamentally reshaping the river water level-discharge relationship curve and laying the hydraulic foundation for a low tailrace level. During the operation phase of the hydropower station, the dynamic tailrace level control sequence plan is continuously implemented. By adjusting the unit output, the opening of the floodgate, and the opening of the flushing gate, the downstream flow is changed. Based on the hydraulic characteristics of the treated river channel, the tailrace level is dynamically controlled in real time to the sequence target value. During the operation phase, a rolling time-domain optimization algorithm is used for short-term closed-loop control. At the same time, river topographic data is collected regularly to update the hydraulic model to achieve adaptive optimization, forming a full-cycle collaborative treatment model of "one-time foundation laying of the project + fine-tuning of operation".

[0038] Optionally, the hydropower station ecological data includes the operating water level and scheduling plan of the downstream cascade power stations, real-time monitoring data of river ecological flow and ecological scheduling requirements, water level-flow relationship curve of the downstream river section, and periodic river topographic scanning data; The hydropower station's operating parameters include the unit efficiency curve and reservoir water level operating parameters; The electricity cost data includes day-ahead and real-time electricity price signals from the electricity market, as well as project budgets.

[0039] Specifically, in this embodiment, the hydropower station's ecological data, operating parameters, and electricity cost data are all accurately acquired through a multi-source data access and fusion module. The hydropower station's ecological data specifically includes: real-time operating water levels and medium-to-long-term scheduling plans for downstream cascade power stations, real-time monitoring values ​​of river ecological flow and the rigid ecological scheduling requirements issued by the competent authorities, measured water level-flow relationship curves for the downstream river section, and periodically scanned river topography data. The hydropower station's operating parameters specifically include: unit efficiency characteristic curves and real-time operating parameters of the power station's reservoir water level. The electricity cost data specifically includes: day-ahead electricity market trading price signals, real-time trading price signals, and the total investment budget for the tailrace river management project. These three types of data cover all dimensions of hydrology, ecology, scheduling, electricity market, engineering, and unit operation. The data is updated in real-time and calibrated periodically, providing comprehensive, complete, and dynamic data support for the dynamic model calculation of the tailrace river's hydraulic characteristics and the iteration of the multi-objective collaborative optimization decision engine. Furthermore, the data can be obtained through the downstream cascade power station scheduling system, the electricity market trading system, ecological monitoring and management departments, and the river topography monitoring system.

[0040] Optionally, the tailrace water level control method of the hydropower station further includes: The tailwater level treatment plan is visualized.

[0041] Specifically, this embodiment uses a scheme simulation and visualization output module to provide a full-dimensional visualization of the tailwater level treatment scheme. The module transforms the optimal solution output by the optimization engine into intuitive visualization results. The specific display content includes: detailed engineering parameters of the initial implementation scheme, time-series curves of the dynamic tailwater level control sequence scheme, expected power generation gain curves of different treatment schemes, a cost-benefit comparison analysis chart of the entire life cycle, an impact assessment report of the treatment scheme on the downstream ecological environment and cascade power stations, and a bar chart comparing the comprehensive benefits of each scheme. The visualization results are presented through a human-computer interaction interface, which simultaneously supports the export of data reports and the issuance of control commands. It intuitively displays the core content of the scheme, expected benefits, and execution requirements, making it convenient for engineering designers and operation and scheduling personnel to view, analyze, make decisions, and implement the scheme.

[0042] For example, this embodiment takes the Dahejia Hydropower Station, a low-head riverbed hydropower station on the upper reaches of the Yellow River, as the actual application object. This power station is a riverbed hydropower station with a head of less than 30 meters. The Bingling Hydropower Station is located 11 km downstream. All the technical features of the above embodiment are implemented throughout the process. First, core data is obtained through the multi-source data access and fusion module: the daily reservoir water level dispatch plan of Bingling Hydropower Station issued by the State Grid Dispatch Platform, the day-ahead electricity price forecast data of Qinghai-Gansu Regional Power Trading Center, the ecological base flow requirement of 125 m³ / s stipulated by the ecological department, the measured water level-flow relationship data of the tailrace channel in the past 5 years, the river channel LiDAR laser topographic scanning data in 2023, the unit efficiency curve and reservoir water level operation parameters of this power station, and the project investment budget data; the ecological data are input into a one-dimensional hydrodynamic model built based on HEC-RAS, and the model is embedded with Manning A dynamic coefficient correction module simulates and generates tailwater level data; three sets of tailwater channel treatment alternative schemes (i.e., tailwater channel treatment schemes) are defined: S1 basic scheme is only conventional dredging and maintaining the existing channel, with an initial investment of 2 million yuan; S2 recommended scheme is an excavation length of 1.3km, bottom slope of 1.5‰, and bottom width of 80m, with an initial investment of 12 million yuan; S3 enhanced scheme is an excavation length of 2.0km, bottom slope of 1.0‰, and bottom width of 100m, with an initial investment of 25 million yuan; downstream cascade backwater level, ecological flow discharge, unit safety head, and channel Froude number (0) are constructed. The system employs a multi-objective collaborative optimization decision engine with two nested layers, including constraints such as .2-0.8 and project budget. The outer layer uses the NSGA-II evolutionary algorithm for optimization, with comprehensive benefits as the fitness function. The inner layer uses a rolling time-domain optimization (RHO) combined with a sequential quadratic programming (SQP) algorithm, optimizing daily based on 30 years of long-term hydrological data to solve the dynamic tailwater level control sequence scheme Zi(t). Simulation calculations are conducted on typical days: upstream water level 1783.0m, inflow 1000m³ / s, and predicted water level of Bingling Hydropower Station 1748m. With a water level of 0m and a real-time electricity price of 0.85 yuan / kWh, the hydraulic model calculates a tailwater level of 1745.2m without backwater support. The Froude number is verified to be 0.35, meeting the requirements. Simultaneously, avoiding backwater exceedance (upper limit 1748.2m) and ecological constraints, the optimal tailwater level is calculated to be 1747.5m, with a unit output of 57.6MW. After 30 years (10950 days) of daily optimization and accumulation, the total power generation revenue of S2 scheme is 980 million yuan. Calculated using the comprehensive benefit formula F=0.8×E-0.2×C, the comprehensive benefit is 781.6 million yuan. The comprehensive benefit of S1 scheme is 620 million yuan, and S3 scheme is 781.6 million yuan.With an initial investment of 500 million yuan, S2 was selected as the initial implementation plan. During the non-operational phase of the power station, a one-time excavation project using the S2 plan was implemented. During the operational phase, the system automatically acquired the 24-hour water level forecast, time-of-use electricity price, and inflow forecast of the Bingling Power Station at 08:00 daily. A rolling time-domain optimization algorithm with a 24-hour prediction time domain and a 4-hour control time domain was used to generate hourly tailwater level control recommendations for the next 24 hours and distribute them to the central control room. During operation, a dynamic sequence was executed by adjusting unit output and gate opening. During the evening peak electricity price of 1.2 yuan / kWh, priority was given to ensuring full power generation at high head, while during high water levels in the downstream cascade, strict control of the tailwater level was implemented to avoid backwater. The system was set... Every year after the flood season (November), riverbed topography is scanned. When scouring and silting deformation exceeds 20cm, the HEC-RAS model parameters are immediately updated to achieve adaptive optimization. Benefit comparison verification shows that the traditional fixed tailwater level mode yields 580 million yuan in benefits, the optimized-only mode yields 650 million yuan (a 12.1% increase), and the collaborative optimization mode of this invention yields 780 million yuan (a 34.5% increase compared to the traditional mode and a 20.0% increase compared to optimized-only mode). This comprehensively achieves the tailwater level management goals of cascade collaborative scheduling, ecological baseflow protection, and maximizing power generation benefits throughout the entire life cycle, fully validating the feasibility and advancement of all technical solutions in this invention.

[0043] like Figure 2 As shown in the figure, an embodiment of the present invention provides a tailrace water level control device for a hydropower station, comprising: The acquisition module is used to acquire multi-source data from hydropower stations; The hydrodynamic module is used to input the hydropower station ecological data from the multi-source data as hydraulic constraints or boundary parameters into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace level data. The decision module is used to construct constraints and input the tailwater channel treatment schemes from the multi-source data into the multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints with the objective function of maximizing comprehensive benefits, generating tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

[0044] like Figure 3 As shown, an electronic device 300 provided in this embodiment of the invention includes a memory 310 and a processor 320; the memory 310 is used to store a computer program; the processor 320 is used to implement the hydropower station tailwater level control method as described above when the computer program is executed.

[0045] Alternatively, an electronic device 300 includes a memory 310 and a processor 320 coupled to the memory 310; the memory 310 is configured to store a computer program; and the processor 320 is configured to perform the following operations when the computer program is executed: Acquire multi-source data from hydropower stations; The hydropower station ecological data from the multi-source data is used as hydraulic constraints or boundary parameters and input into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace water level data. Constraints are constructed, and the tailwater channel treatment schemes from the multi-source data are input into a multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints, with the objective function of maximizing comprehensive benefits, to generate tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

[0046] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the tailrace water level control method for hydropower stations as described above.

[0047] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations: Acquire multi-source data from hydropower stations; The hydropower station ecological data from the multi-source data is used as hydraulic constraints or boundary parameters and input into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace water level data. Constraints are constructed, and the tailwater channel treatment schemes from the multi-source data are input into a multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints, with the objective function of maximizing comprehensive benefits, to generate tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

[0048] The present invention will now be described an electronic device 300 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic device 300 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 300 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0049] Electronic device 300 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0051] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for controlling the tailrace water level of a hydropower station, characterized in that, include: Acquire multi-source data from hydropower stations; The hydropower station ecological data from the multi-source data is used as hydraulic constraints or boundary parameters and input into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace water level data. Constraints are constructed, and the tailwater channel treatment schemes from the multi-source data are input into a multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints, with the objective function of maximizing comprehensive benefits, to generate tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

2. The method for controlling the tailrace water level of a hydropower station according to claim 1, characterized in that, The construction constraints include: Constraints are constructed based on the tailwater level data, the hydropower station operating parameters from the multi-source data, and the electricity cost data.

3. The method for controlling the tailrace water level of a hydropower station according to claim 1, characterized in that, The objective function includes a life-cycle cost objective function, a power generation revenue objective function, and a comprehensive benefit objective function. The multi-objective collaborative optimization decision engine includes an outer optimization module and an inner optimization module based on the double-layer nested optimization architecture. The outer optimization module is used to enumerate or search the tailrace channel treatment schemes, and calculate the full life cycle cost of each scheme based on the constraints. The inner optimization module is used to solve the power generation revenue objective function based on the constraints according to the tailrace channel treatment scheme, and obtain the dynamic tailrace level control sequence scheme and the corresponding power generation revenue. The outer optimization module is also used to solve the comprehensive benefit objective function based on the total life cycle cost and the power generation revenue to obtain the initial implementation plan.

4. The method for controlling the tailrace water level of a hydropower station according to claim 1, characterized in that, The dynamic model of the tailrace channel hydraulic characteristics is based on a one-dimensional and / or simplified two-dimensional hydrodynamic model built on HEC-RAS, and incorporates a dynamic correction module for the Manning coefficient.

5. The method for controlling the tailrace water level of a hydropower station according to claim 1, characterized in that, Also includes: The initial implementation plan shall be executed during the non-operational phase of the hydropower station. The dynamic tailwater level control sequence scheme is executed during the operation phase of the hydropower station.

6. The method for controlling the tailrace water level of a hydropower station according to claim 1, characterized in that, The hydropower station's ecological data includes the operating water level and scheduling plan of the downstream cascade power stations, real-time monitoring data of river ecological flow and ecological scheduling requirements, water level-flow relationship curves of the downstream river section, and periodic river topographic scanning data. The hydropower station's operating parameters include the unit efficiency curve and reservoir water level operating parameters; The electricity cost data includes day-ahead and real-time electricity price signals from the electricity market, as well as project budgets.

7. The method for controlling the tailrace water level of a hydropower station according to claim 1, characterized in that, Also includes: The tailwater level treatment plan is visualized.

8. A tailrace water level control device for a hydropower station, characterized in that, include: The acquisition module is used to acquire multi-source data from hydropower stations; The hydrodynamic module is used to input the hydropower station ecological data from the multi-source data as hydraulic constraints or boundary parameters into the tailrace channel hydraulic characteristic dynamic model to calculate the tailrace level data. The decision module is used to construct constraints and input the tailwater channel treatment schemes from the multi-source data into the multi-objective collaborative optimization decision engine. The multi-objective collaborative optimization decision engine adopts a two-layer nested optimization architecture, using discrete tailwater channel treatment schemes and continuous dynamic tailwater level control sequences as coupled decision variables. Iterative optimization is performed under the constraints with the objective function of maximizing comprehensive benefits, generating tailwater level treatment schemes. The constraints include at least downstream cascade backwater level constraints, ecological flow constraints, and unit safety head constraints. The comprehensive benefits are calculated based on the weighted difference between the total life-cycle power generation revenue and the treatment project cost. The tailwater level treatment scheme includes an initial implementation scheme and a dynamic tailwater level control sequence scheme.

9. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the hydropower station tailrace level control method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the hydropower station tailrace level control method as described in any one of claims 1 to 7.

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