40 results about "Reservoir optimization" patented technology

The invention discloses a cascade reservoir optimization regulation and control method based on nitrogen and phosphorus circulation of a water body, and relates to the technical field of water resource management optimization. Multi-dimensional data of a gradient reservoir is collected, and a time-space aligned multi-modal data set is constructed; according to a biogeochemical process of nitrogen and phosphorus circulation, a physical equation of the nitrogen and phosphorus circulation is deduced, and a physical constraint loss item is designed and is subjected to weighted combination with data driving loss to form a mixed loss function. According to the method, the physical constraint neural network model is constructed, and the physical equation of nitrogen and phosphorus circulation is embedded into the neural network loss function, so that the strong fitting capability of the data driving model is utilized, and the model output is ensured to accord with the biogeochemical law through the physical constraint; the key parameters of the physical equation are calibrated through the Bayesian optimization algorithm, the prediction precision of the model on the nitrogen and phosphorus migration and transformation process is further improved, the dependence on big data is effectively reduced, and a reliable prediction result can still be provided especially under the condition that the temporal-spatial resolution of data is low.

The invention belongs to the field of reservoir optimization intelligent scheduling, and particularly provides a reservoir multi-objective optimization intelligent scheduling method based on deep reinforcement learning and a deterministic strategy gradient algorithm, which comprises the following steps: collecting original hydrological data and reservoir characteristic curves of a target reservoir; establishing a multi-objective optimization scheduling model including an objective function and constraint conditions; mapping the multi-objective optimization scheduling model into a Markov decision process, and establishing a corresponding reinforcement learning environment; a deep reinforcement learning deterministic strategy gradient algorithm is selected to perform interactive training with the environment, and a reservoir multi-objective optimization intelligent scheduling model is established; and performing multi-target optimization intelligent scheduling on the reservoir through the reservoir multi-target optimization intelligent scheduling model. The method solves the problems that in the prior art, a deep reinforcement learning method generally adopts a fixed weight to design an excitation function, so that the flexibility of a learned scheduling strategy is insufficient, and real-time dynamic coordination optimization of each reservoir scheduling target is difficult to realize according to an environment state.

The invention discloses a deep and large reservoir ecological scheduling method, system and equipment based on a data-driven model of a coupling physical mechanism, and belongs to the technical field of water resource management and environmental protection. Firstly, a physical water temperature model is constructed based on measured data, diversified water temperature change scenes are generated, and a deep learning model constrained by a physical mechanism is constructed. Secondly, carrying out sensitivity analysis to identify key influence factors for driving water temperature change, constructing a reservoir optimization scheduling model, coupling a deep learning model constrained by a physical mechanism, and deducing a scheduling rule set on the premise of meeting a water temperature target; and finally, carrying out multi-index optimization analysis. The invention further provides a deep and large reservoir ecological scheduling system and electronic equipment, and the deep and large reservoir ecological scheduling method is realized. According to the method, the power generation scheduling rule set of the deep and large reservoir can be scientifically deduced, the optimal scheduling scheme with both ecological benefits and economic benefits is screened out by introducing the multi-index optimization method, and overall balance of ecological requirements and power generation benefits is achieved.

The invention relates to the technical field of reservoir flood control dispatching, and discloses a dam hydraulic opening and closing control method, device and equipment and a storage medium. The method comprises the steps that multi-source hydrological data, weather forecast information and unit operation states of a current reservoir are collected; inputting the multi-source hydrological data and the weather forecast information into a preset hydrological prediction model, and outputting a hydrological prediction result; based on the hydrological prediction result and the real-time reservoir area water level and the unit operation state in the multi-source hydrological data, a gate opening control instruction is generated through a reservoir optimization scheduling model; and a hydraulic opening and closing system is adjusted according to the gate opening control instruction, and the current reservoir flood discharge gate opening is controlled. According to the method, data such as upstream incoming water, reservoir area water level and water consumption are utilized, weather forecast information is combined, a hydrological prediction model is constructed, a reservoir dispatching scheme is optimized according to a hydrological model prediction result, the reservoir area water level and unit operation conditions, and on the premise that flood prevention safety is guaranteed, reservoir dispatching is optimized, the reservoir area water level is automatically adjusted, and power generation benefits are improved.

The invention relates to the technical field of reservoir optimization scheduling, in particular to a dynamic multi-objective optimization control system and method for the water level of a reservoir in a flood season. The method comprises the following steps: acquiring real-time monitoring data of a reservoir; extracting current water level state information according to the real-time monitoring data of the reservoir; establishing a reservoir water balance basic model based on the current water level state information; analyzing pipeline water supply flow change characteristics according to the reservoir water balance basic model; identifying response lag time of pipeline fluid inertia according to pipeline water supply flow change characteristics; predicting a water supply line pressure fluctuation risk based on the response lag time; and determining the water level regulation and control advance according to the water supply pipeline pressure fluctuation risk and the response lag time. According to the method, the multi-objective optimization model is adopted, the flood control safety margin and the water supply guarantee degree can be balanced, and it is ensured that the reservoir can cope with the flood risk in the flood season and can stably meet the water supply requirement.

The invention discloses a cascade reservoir scheduling method and system based on an artificial bee colony algorithm, and the method comprises the following steps: the system collects multi-source hydrological data in real time through a hydrological perception and preprocessing module, and introduces a large language model to carry out the semantic judgment and anomaly labeling of an abnormal hydrological time sequence; inputting the processed high-quality data into a reservoir model construction module, establishing a cascade reservoir optimal scheduling system, and setting corresponding boundary conditions and operation constraints in combination with reservoir scheduling regulations; the scheduling optimization module receives model input, adopts a variable structure taking a water level as a core to construct an optimization individual, and completes population initialization, disturbance generation and fitness evaluation based on a potential solution guide mechanism in an improved artificial bee colony algorithm; the system transmits the scheduling sequence optimized and output by the scheduling optimization module into an LLM intelligent auxiliary module; and the intelligent text interpretation generated by the LLM and the scheduling optimization solution enter a result evaluation and visualization module together. According to the scheduling method and system, a high-quality and physically feasible scheduling scheme can be output within reasonable calculation time.

This invention provides a method for linearizing the power output function of a hydropower station based on a simplified constraint strategy. The method includes: equally dividing the feasible region of the power output function into rectangular grids; defining a corresponding plane for each rectangular grid; constructing a quadratic programming model for calibrating the plane cluster parameters that approximate the power output surface of the hydropower station based on the characteristic parameters of the planes and the simplified plane set corresponding to the rectangular grids generated using the simplified constraint strategy; solving the quadratic programming model to obtain a set of plane equation parameters that approximate the optimal power output of the hydropower station; and in a reservoir optimization scheduling model where the scheduling objective includes maximizing the power output or power generation of the hydropower station within the scheduling period, using the parameters of the solved plane equations to construct a linearized expression for the hydropower output function in the reservoir optimization scheduling model. This invention avoids introducing integer variables, reduces the size of the quadratic programming model while ensuring the accuracy of fitting the hydropower station's power output, and greatly improves the solution efficiency.

The invention discloses a reservoir optimization scheduling method based on water conservancy runoff forecast, and the method comprises the steps: 1, collecting the rainfall, evaporation, soil humidity and other data of a drainage basin, eliminating the dimensional difference through normalization processing, constructing a multi-source database, and providing input for a data-driven model; 2, inputting multi-source data to the LSTM-BiLSTM-EnKF hybrid model, generating short-term runoff forecast, and converting a discrete forecast result into a continuous reservoir flow process through a cubic spline interpolation algorithm, and 3, inputting the continuous runoff process into a dynamic optimization model, and solving a minimum abandoned water scheduling scheme; and 4, dynamic flood control water level control and in-plant optimization operation strategy. The LSTM-BiLSTM-EnKF hybrid model is adopted to replace a traditional physical model, the problems that parameter calibration is complex and the data requirement is high are solved, the hybrid model is beneficial to reducing the forecasting error rate through multi-source data fusion and a dynamic feedback mechanism, and then the forecasting precision is improved.

The invention belongs to the technical field of water resource optimal scheduling, and discloses a reservoir optimal scheduling method based on hybrid swarm intelligent optimization, which comprises the following steps: S1, collecting data required by reservoir historical inflow, a reservoir capacity curve, a water supply demand and a scheduling constraint condition; s2, setting a constraint condition and a scheduling target, and establishing a reservoir scheduling optimization model; s3, constructing a candidate solution group on the basis of combining reservoir scheduling decision variables, performing initial fitness calculation by utilizing an objective function of the reservoir scheduling optimization model, introducing a hybrid group intelligent optimization algorithm, performing multi-objective optimization scheduling calculation on the reservoir scheduling optimization model, and performing iterative updating; s4, completing all set iterations, and outputting a reservoir optimization scheduling scheme meeting constraint conditions; according to the method, the premature convergence problem of a traditional swarm intelligence optimization algorithm is effectively avoided, the convergence speed and the solution stability are improved, and the method is suitable for optimization solution of single-library and multi-library systems in single-target or multi-target scheduling problems of water supply, flood control, power generation and the like.

The invention relates to a reservoir optimization scheduling system and scheduling method for generating multiple objectives based on AI, the reservoir optimization scheduling system comprises a data input module, a multi-objective optimization module, a constraint processing module, a result output module and a visualization module, the data input module is configured to obtain basic data of a reservoir; the multi-objective optimization module is configured to construct an optimization model based on a particle swarm optimization algorithm, minimize the maximum discharged flow, minimize the flood exceeding storage capacity and minimize the discharged flow change rate as objective functions, and search the optimal discharged flow process through particle iteration; the constraint processing module is configured to perform constraint check on the discharged flow and the water level of the reservoir and apply punishment to solutions violating constraints so as to ensure the feasibility of the scheduling scheme; the result output module is configured to output the optimized discharge flow process, the optimized water level process and the optimized flood limit exceeding storage capacity process; the visualization module is configured to draw a water level-flow process chart and optimize a target convergence curve.

The invention discloses equipment and a method for predicting, controlling and dispatching the scouring and silting amount of a downstream river channel of a dam, which are taken into account the influence of a flood process on the scouring and silting amount of the river channel and comprehensively consider the influence of water and sediment elements and flood characteristics on the scouring and silting amount of the downstream river channel of the dam. Water surface gradient, flow, peak pattern coefficient, jacking coefficient, sediment content and sediment grading elements are incorporated to predict the erosion and deposition amount of the downstream river channel of the dam, a calculation formula structure of the erosion and deposition amount of the downstream river channel of the dam and a matched calculation method are constructed, a corresponding table of the erosion and deposition amount of the downstream river channel of the dam is compiled according to the method, and reverse derivation can be achieved through table query. When the scouring and silting amount of the downstream river channel of the dam is controlled to be a fixed value, key dispatching parameters such as reservoir outlet flow and peak pattern coefficient required to be regulated and controlled by an upstream reservoir; a reservoir management department can formulate a scientific reservoir optimization scheduling scheme through the method, technical guarantee is provided for river regime stability and bank slope protection of a downstream river channel of a dam, flood control standard improvement and shipping safety maintenance are effectively supported, and a decision basis is provided for exerting comprehensive benefits of the reservoir.

The application discloses an ecological regulation method for relieving the retention of the biogenic substance of a deep and large reservoir, and belongs to the technical field of water resource management and environmental protection technology. The steps are as follows: first, collecting characteristic parameters and multiple types of hydrological and water quality monitoring data of the deep and large reservoir; second, constructing a three-dimensional hydrodynamic-water quality model of the deep and large reservoir; third, based on the three-dimensional hydrodynamic-water quality model of the deep and large reservoir, combining a long series of multiple inputs and multiple output sequences, a data-driven proxy model is constructed; fourth, constructing a reservoir optimization regulation model to deduce suitable regulation rules for decision makers. The application improves the hydrodynamic condition by discharge to reduce the retention of the biogenic substance, and realizes the activation and transportation by using the water temperature stratification condition of the reservoir area, so that the effective biogenic substance is transported to the downstream while the power generation capacity is ensured, thereby realizing the 'quantity, energy and quality comprehensive regulation and control' considering the power generation, ecological flow and biogenic substance, and providing technical support for the comprehensive management of optimizing the utilization of water resources and ecological environment protection of the basin.

The application discloses a kind of arid region reservoir water and sand prediction and coordinated regulation potential evaluation method and system, belong to water resources, silt treatment and reservoir optimization scheduling technical field.The technical problems of traditional model prediction accuracy insufficient caused by arid region hydrology data scarcity, water and sand prediction process is fragmented and reservoir desilting and silt reduction and water storage benefit difficult to be synergistically optimized, its gist is that: long short-term memory network is used to predict future runoff sequence by fusing attention mechanism, and the sediment concentration is calculated by combining the water and sand relationship formula;By setting the critical sediment concentration threshold, the high sediment concentration period is identified and the available water for dredging is calculated;At the same time, the flood resource potential is evaluated by combining reservoir capacity and water demand constraints;Finally, the coordinated scheduling scheme is generated by comparing the two potentials.The application realizes the integrated accurate prediction of water and sand process in arid region and the simultaneous quantitative evaluation of regulation potential, and provides intelligent decision support for multi-objective optimization scheduling of reservoir.

This invention relates to the field of water resource optimization scheduling technology in water conservancy and hydropower engineering, specifically to a reservoir optimization scheduling method and related products based on intertemporal sensitivity sparse repair. The method involves constructing and solving implicit equations for power generation and water volume, establishing an evaluation system and calculating intertemporal sensitivity to obtain a set of key time periods. Then, sparse repair is implemented on the initial power generation plan within this set, and the repair operation is embedded in an iterative optimization framework. This invention avoids the use of empirical estimation or coarse trial calculations in traditional reservoir scheduling, enhances the water resource safety net of reservoirs, suppresses the oscillation phenomenon of traditional intelligent evolutionary algorithms, and maintains the scheduling intent and temporal consistency of the initial predetermined plan on the power grid side.

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

A deep reservoir ecological scheduling method, system and device based on a data-driven model of a coupled physical mechanism belong to the technical field of water resources management and environmental protection. First, a physical water temperature model is constructed based on measured data, diversified water temperature change scenarios are generated, and a deep learning model with physical mechanism constraints is constructed. Second, sensitivity analysis is carried out to identify key influencing factors driving water temperature change, a reservoir optimization scheduling model is constructed, and a deep learning model with physical mechanism constraints is coupled to derive a set of scheduling rules under the premise of meeting water temperature targets. Finally, multi-index optimization analysis is carried out. A deep reservoir ecological scheduling system and electronic device are also provided to implement the above deep reservoir ecological scheduling method. The present application can scientifically derive a set of power generation scheduling rules for deep reservoirs, filter out the optimal scheduling scheme with both ecological and economic benefits by introducing a multi-index optimization method, and achieve the overall balance of ecological demand and power generation benefit.

Disclosed is a method for improving nitrogen water quality of a dammed river based on reservoir operation, including following steps: selecting a dammed river to be tested, determining a study area in a riparian zone downstream of a dam, obtaining historical long-sequence water level data of the study area, and analyzing duration and amplitude variation ranges of a historical water level; setting a plurality of groups of flood waves according to the duration and amplitude variation ranges; establishing a riparian zone nitrogen transport and transformation numerical model to simulate a denitrification level of the riparian zone under different flood wave conditions; obtaining an A / T ratio value of one flood wave corresponding to a maximum value of denitrification capacity of the riparian zone according to results of all cases; and taking the obtained A / T ratio value as a threshold of a reservoir discharge hydrograph to achieve reservoir optimization operation.

The invention discloses a continental facies shale reservoir compressibility evaluation model weight calculation method, and belongs to the technical field of hydraulic fracturing. By fusing geological modeling and fracturing simulation results and introducing weight calculation methods such as an analytic hierarchy process and a grey correlation method, the weight of each index is scientifically determined, the degree of dependence on field monitoring data is reduced, meanwhile, quantitative correlation with actual productivity is established, contribution of three types of capability indexes to the productivity is quantified, and the method is suitable for mass production. And finally, a compressibility comprehensive evaluation model with engineering guidance value is determined, the fracturing transformation potential of the reservoir can be represented more accurately, and a scientific theoretical basis is provided for reservoir optimization and fracturing design.

The present invention belongs to the field of reservoir optimization and intelligent scheduling, and specifically provides a reservoir multi-objective optimization and intelligent scheduling method based on deep reinforcement learning and a deterministic policy gradient algorithm. The method includes collecting original hydrological data and reservoir characteristic curves of the target reservoir; establishing a multi-objective optimization and scheduling model including objective functions and constraints; mapping the multi-objective optimization and scheduling model into a Markov decision process and establishing a corresponding reinforcement learning environment; selecting a deep reinforcement learning deterministic policy gradient algorithm for interactive training with the environment to build a reservoir multi-objective optimization and intelligent scheduling model; and performing multi-objective optimization and intelligent scheduling of the reservoir using the reservoir multi-objective optimization and intelligent scheduling model. This method solves the problem in the prior art that deep reinforcement learning methods generally use fixed weights to design incentive functions, resulting in insufficient flexibility in the learned scheduling strategy and difficulty in achieving real-time dynamic coordinated optimization of the scheduling objectives of each reservoir according to the environmental state.

The invention relates to the technical field of reservoir scheduling, and discloses a reservoir optimization scheduling method and device based on an improved piebald algorithm, and the method comprises the steps: constructing a reservoir optimization scheduling model; wherein the reservoir optimal scheduling model takes the water level of the hydropower station in each time period within the reservoir scheduling period as a decision variable; randomly generating an initialized population; wherein individuals in the initialized population represent the water level of the hydropower station in each time period within the reservoir dispatching deadline; iteratively updating the position of each individual in the initialized population by simulating the inhabiting and hovering behaviors, the diving behavior and the change of predation efficiency of the emerald; and when the iteratively updated population satisfies an iteration stop condition, determining an optimal reservoir scheduling scheme based on the iteratively updated population. According to the method, the problems of algorithm prematurity and local optimum can be effectively avoided, and the optimization efficiency of the reservoir scheduling scheme is improved.

The invention discloses a long and short term coupling scheduling method for a hybrid pumped storage power station, and belongs to the technical field of power station scheduling. According to the method, based on an existing medium-and-long-term reservoir optimization scheduling model, a long-and-short-term coupling scheduling model considering medium-and-long-term constraints and short-term operation characteristics of the hybrid pumped storage power station at the same time is constructed, a typical day is selected every month, and medium-and-long-term monthly scale constraints and short-term typical day constraints are incorporated into the model; the model comprises an objective function, medium and long term monthly scale constraints, short term typical day constraints and long and short term coupling constraints, nonlinear constraints are linearized and converted into a mixed integer linear programming problem, and the problem is solved by a gurobi solver. The method solves the problem that a traditional model is difficult to accurately describe a'water pumping-power generation 'mode, avoids the deviation of scheduling schemes of different time scales, guides the efficient operation of the hydropower station, improves the power generation benefit, and can be widely applied to the scheduling of the hybrid pumped storage power station.

The application discloses a weight calculation method of a continental shale reservoir fracturability evaluation model, and belongs to the technical field of hydraulic fracturing. The application scientifically determines the weight of each index by fusing geological modeling and fracturing simulation results, introducing weight calculation methods such as the analytic hierarchy process and the grey correlation method, reduces the dependence degree on field monitoring data, simultaneously, establishes a quantitative correlation with actual productivity, quantifies the contribution of three types of capacity indexes to productivity, and finally determines a fracturability comprehensive evaluation model with engineering guiding value, so that the fracturing reconstruction potential of a reservoir can be more accurately represented, and a scientific theoretical basis can be provided for reservoir optimization and fracturing design.

The invention discloses a reservoir optimization scheduling method based on uniform design, relates to the technical field of water conservancy projects, and aims to solve the problem that an existing reservoir scheduling method may generate an inaccurate or low-efficiency scheduling result when facing a complex hydrological environment and multi-target demands.The reservoir optimization scheduling method comprises the following steps that S1, basic data of a reservoir are collected, and the basic data are stored in a database; comprise historical hydrological data, real-time hydrological data, reservoir characteristic parameters and flood control requirements, abnormal values are removed through data cleaning, data of different dimensions are uniformly converted into a [0, 1] interval by adopting a standardization method, and a high-quality data basis is provided for subsequent analysis; s2, determining the decision variables of the drainage flow, the reservoir level and the scheduling time period according to the drainage scheduling scene in the feasible region of each variable; the method has the advantages of maximizing the utilization efficiency of water resources, improving the scheduling capability of the reservoir and reducing the waste of the water resources through the uniformly designed optimal scheduling method.

The invention relates to a reservoir desilting and desilting method beneficial to long-term maintenance of flood control reservoir capacity. The reservoir desilting and desilting method comprises the following steps: collecting river topography, reservoir area water and sediment, reservoir dispatching modes and reservoir siltation data after reservoir storage and application; finding out a main river reach where the flood control storage capacity is silted; in a main river reach where the flood control reservoir capacity is subjected to silt loss, a main time period when the river reach is subjected to scouring is found from actual measurement data; a typical water and sediment process is selected from the main washing time period; establishing a reservoir one-dimensional non-constant running water and sediment mathematical model with a desilting and desilting simulation function; the built mathematical model is adopted, after the silt in the flood control reservoir capacity is calculated and added into the selected typical water and sediment through desilting measures, the desilted silt in the flood control reservoir capacity is conveyed into the dead reservoir capacity on the lower stream or partially conveyed out of the reservoir through reservoir optimization dispatching and by means of the sediment carrying capacity of water flow. The problem of how to couple reservoir desilting and reservoir dispatching and desilting to guarantee long-term use of the flood control reservoir can be solved.

The invention provides a reservoir group multi-objective optimization system combining forecasting and feedback mechanisms, and relates to the technical field of reservoir optimization scheduling research. The reservoir group multi-objective optimization system combining the forecasting and feedback mechanism comprises deep learning-based flood forecasting, feedback scheduling and multi-objective optimization scheduling, and the deep learning-based flood forecasting comprises the following steps: S1, collecting actually measured rainfall runoff data and geographic data of a research basin; and S2, according to the collected data, a deep learning method is selected to establish a flood forecasting model for researching the drainage basin, the benefits of the reservoir are fully exerted through a flood hydrograph obtained through forecasting, the water level and the storage capacity of the reservoir are judged, a proper reservoir dispatching scheme is selected to predict the interval flood, and the foresight of the interval flood is predicted. The method has a certain guiding effect on multi-target reservoir optimization scheduling, the weights of the targets in the scheduling scheme are properly adjusted according to the forecast result, and the comprehensive benefits of the reservoir can be fully exerted.

The invention relates to the field of oil and gas exploration and development, in particular to a method for rapidly and quantitatively calculating the fracture network volume after low-permeability shale oil volume fracturing by means of recovery data after fracturing. Comprising the following steps: arranging single well production data, and converting wellhead flow pressure into oil layer middle flow pressure; drawing a relation curve of the accumulative actual liquid production capacity and the converted oil layer middle flowing pressure; the regression fracture network controls the slope of a linear decreasing section when the oil layer middle flowing pressure in the flowing period is increased along with the accumulative actual liquid production capacity; calculating a crude oil volume coefficient linear change slope n; and calculating the effective flow fracture network volume. The method is easy and convenient to predict, economical and practical, capable of rapidly calculating the effective fracture network volume of fracturing construction by utilizing measured data including wellhead yield, wellhead pressure and flowing bottomhole pressure which are easily obtained in the initial stage of extraction after fracturing, low in cost, high in universality, wide in application, simple and convenient in quantitative algorithm and high in accuracy, and basic data can be collected daily without large-scale construction. And the productivity of a single well after fracturing can be effectively evaluated, and technical support is provided for reservoir optimization and fracturing effect evaluation.

The invention provides a water-wind-light complementary power generation and flood control scheduling optimization method considering water energy storage, and relates to the field of reservoir optimization scheduling, the method considers a pumped storage power station taking a built reservoir as a lower reservoir or a reversible unit taking the built reservoir as an upper reservoir and a lower reservoir, and constructs a water-wind-light complementary power generation and flood control scheduling optimization model; and solving the water-wind-light complementary power generation and flood control scheduling optimization model by using a reference point-based non-dominated solution sorting genetic algorithm to obtain a three-target Pareto curved surface and a compromise solution, and outputting corresponding water level, flow and power generation process to complete optimization of water-wind-light complementary power generation and flood control scheduling. According to the method, the hydropower dispatching decision problem faced by multi-power-supply cooperative operation and water safety guarantee in a water-energy coupling system under the background that wind-solar new energy high-proportion power generation of a novel electric power system tends to be strong and tends to be frequency of climatic change flood events is solved.

The application discloses a kind of high-efficiency solving methods of cascade reservoir joint power generation optimization scheduling model, comprising: determining the storage capacity of cascade reservoir at the beginning and end of scheduling period, and the inflow of leading reservoir in whole scheduling period, interval inflow between each reservoir, various constraints of cascade reservoir;The storage capacity value of each reservoir is discretized within the upper and lower limits of storage capacity according to the discrete accuracy requirement;In time period t , for all possible storage capacity combinations of cascade reservoir at the beginning of time period, find the corresponding optimal storage capacity combination of cascade reservoir at the end of time period, and store such a correspondence as optimal state transition relationship;Let t = t -1, repeat step 3 until all time periods in scheduling period are traversed;According to the stored optimal state transition relationship, start backtracking from the beginning of the first time period, and obtain the optimal storage capacity process of cascade reservoir in the whole scheduling period;The application can improve the solving efficiency of cascade reservoir optimization scheduling model, while ensuring the quality of solution, so that the calculation result is close to the global optimal solution.

The present application relates to the technical field of reservoir optimization scheduling, and particularly relates to a reservoir flood season water level dynamic multi-objective optimization control system and method. The method comprises the following steps: obtaining real-time monitoring data of the reservoir; extracting current water level state information according to the real-time monitoring data of the reservoir; establishing a reservoir water balance basic model based on the current water level state information; analyzing the pipeline water supply flow variation characteristics according to the reservoir water balance basic model; identifying the response lag time of the pipeline fluid inertia according to the pipeline water supply flow variation characteristics; predicting the water supply pipeline pressure fluctuation risk based on the response lag time; and determining the water level regulation advance according to the water supply pipeline pressure fluctuation risk and the response lag time. The present application adopts a multi-objective optimization model, can balance between the flood control safety margin and the water supply guarantee degree, and ensures that the reservoir can not only cope with the flood risk in the flood season, but also stably meet the water supply demand.

A method for optimizing a wireless sensor network for monitoring hydrogen production from fire flooding involves training a machine learning model to generate an estimate of communication performance of each of a multitude of sensors. The sensors are a component of the wireless sensor network disposed in a sub-surface hydrogen reservoir, with each of the multitude of sensors configured to obtain measurements of environmental variables of the hydrogen reservoir. The method further involves minimizing a cardinality of the multitude of sensors, using the machine learning model.