An intelligent management system for water conservancy and hydropower resources

Abstract

The present application relates to the technical field of resource management, comprising an intelligent water conservancy and hydropower resource management system, which comprises an inflow rate monitoring module, a reservoir capacity dynamic prediction module, a time-sharing water release adjustment module, a unit output optimization module and a power production scheduling module. In the present application, by monitoring the reservoir water level data in real time, the inflow rate variation characteristics are calculated, the flow variation trend is identified, the reservoir capacity variation rate is calculated, the future reservoir capacity trend is predicted, and the influence of the inflow rate on the reservoir capacity variation is analyzed. Combined with the downstream water demand and power load data, the water release scheme is dynamically adjusted according to the actual flow deviation. By calculating the minimum stable operating power and power adjustment range of the generator set, the unit output is optimized in combination with the water release adjustment data, so that the power generation efficiency and water resource scheduling are coordinated and unified. Through multi-level data calculation, efficient linkage of water quantity management, power scheduling and water release optimization is realized, and the water conservancy and hydropower resource scheduling is more refined and intelligent.

Description

Technical Field

[0001] The present invention relates to the technical field of resource management, and in particular to an intelligent management system for water conservancy and hydropower resources. Background Art

[0002] The field of resource management technology encompasses the intelligent, refined scheduling and optimized allocation of natural resources and energy sources such as water, electricity, and gas to improve resource utilization efficiency and reduce waste. Core elements include resource monitoring, data collection, status analysis, demand forecasting, and optimized scheduling. By building an information management platform, this enables real-time resource perception, data integration, and processing to provide scientific and rational management solutions. This technology is widely used in scenarios such as power dispatching, water conservancy projects, water supply systems, and renewable energy management. Relying on information technology, automatic control technologies, and decision support systems, it ensures rational and secure resource allocation.

[0003] The intelligent water and hydropower resource management system utilizes automated sensing, data analysis, and decision-making optimization to intelligently dispatch and manage water conservancy projects and hydropower resources. This system utilizes sensors to acquire real-time data such as water levels and flows, focusing on technical matters such as hydrological monitoring, reservoir scheduling, and hydropower station operation management. This system then integrates hydrological models and scheduling rules to formulate water resource allocation and scheduling plans. Furthermore, through water demand forecasting and power load analysis, it optimizes hydropower production and distribution strategies to improve hydropower station operational efficiency and ensure the rational dispatch of water resources.

[0004] Existing reservoir and hydropower station resource scheduling processes face limitations. Reservoir water level monitoring data acquisition methods are relatively simple, making it difficult to accurately capture dynamic changes in inflow rates. This results in insufficient water volume trend analysis, impacting scheduling accuracy. Reservoir capacity forecasts primarily rely on static data and fail to fully incorporate inflow rate characteristics for dynamic calculations, limiting forecast accuracy and impacting the rationality of reservoir capacity regulation. Water release regulation uses fixed rules for flow distribution and lacks a dynamic adjustment mechanism for release flow deviations, potentially leading to uneven water resource utilization and downstream water shortages. Generator unit output adjustment methods are relatively simple and fail to fully integrate reservoir regulation needs for fine-grained optimization, limiting unit operating efficiency. Power production scheduling relies on preset load plans and lacks real-time optimization tools for matching supply and demand, resulting in uneven power resource allocation and impacting power system stability. These issues lead to insufficient flexibility in water resource scheduling, limited optimization of power production, and compromised the overall utilization efficiency of hydropower resources. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent management system for water resources and hydropower.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: An intelligent management system for water resources and hydropower resources includes:

[0007] The inflow rate monitoring module collects reservoir water level monitoring data and inflow flow data, calculates the flow change rate at consecutive time points, analyzes the change amplitude of the inflow rate, analyzes the difference between the current inflow rate change and the historical change, and obtains the inflow rate change characteristic data;

[0008] The reservoir capacity dynamic prediction module calculates the current reservoir capacity change rate based on the inflow rate change characteristic data and combines the historical reservoir capacity data, analyzes the reservoir capacity change trend in the future time period, and obtains the reservoir capacity change prediction result;

[0009] The time-sharing water release regulation module calculates the reservoir regulation capacity limit based on the reservoir capacity change prediction result, collects water demand data and power load demand data, determines the optimal water release flow distribution, calculates the deviation between the current water release flow and the optimal water release flow, dynamically adjusts the water release flow, and obtains the time-sharing water release flow adjustment record;

[0010] The unit output optimization module obtains the minimum stable operating power of the current generator set based on the time-sharing water discharge flow adjustment record, calculates the acceptable power adjustment range of the computer group, compares it with the minimum stable operating power of the unit, calculates the power output after adjustment of the computer group, and obtains the optimal output adjustment result of the generator set.

[0011] As a further solution of the present invention, the inflow rate change characteristic data includes flow change rate, inflow fluctuation amplitude, historical comparison deviation data, and abnormal fluctuation identification results; the reservoir capacity change prediction results include future capacity change trend analysis results, capacity change rate, inflow influencing factors, and prediction error evaluation results; the time-sharing water release flow adjustment records include water release flow optimization plans, flow adjustment deviation data, adjustment capacity limitation impact, and time-sharing water release distribution records; the optimal output adjustment results of the generator set include the adjusted power data of the unit, the minimum stable operation comparison results, the power adjustment amplitude, and the output optimization range.

[0012] As a further solution of the present invention, the inflow rate monitoring module includes:

[0013] The water level acquisition submodule obtains real-time water level data collected by the reservoir water level sensor, filters out abnormal data and performs linear interpolation to fill in missing data, calls water level data at consecutive time points to calculate water level change values, determines water level change trends based on water level change values, and obtains water level change trend data;

[0014] The flow calculation submodule calls the historical water level-flow data of the reservoir based on the water level change trend data, calculates the inflow at the corresponding time point, calculates the flow change amount by combining the inflow at consecutive time points, and obtains the flow change rate data;

[0015] The rate analysis submodule is based on the flow change rate data and the historical inflow rate data of the reservoir, using the formula:

[0016] ;

[0017] Calculate the comprehensive deviation of the current inflow rate change , get the inflow rate change characteristic data, where, Represents the inflow flow at the current moment, Represents the water level value at the current moment, Represents the number of data points in the current time window, Represents the number of data points in the historical time window, Represents the total traffic within the historical time window, Represents the total water level within the historical time window.

[0018] As a further solution of the present invention, the storage capacity dynamic prediction module includes:

[0019] The inflow rate analysis submodule receives the reservoir inflow rate change characteristic data, extracts the inflow rate time series, calculates the inflow rate increments in different time intervals, screens the inflow rate mutation points, and determines the inflow rate change trend in different time periods by comparing the interval distribution of the inflow rate change rate, thereby obtaining an inflow rate change trend analysis record;

[0020] The reservoir capacity change calculation submodule calculates the reservoir capacity change rate for each time period based on the inflow rate change trend analysis record and the historical reservoir capacity data using the formula:

[0021] ;

[0022] Calculate the storage capacity change rate , calculate the cumulative storage capacity change in each time period and obtain storage capacity change rate data, where, Representative The inflow rate for each time period, Representative The outflow rate in each time period, Representative The reservoir area in each time period, represents the time interval, represents the total number of time periods;

[0023] The future trend assessment submodule calculates the storage capacity change in the future time period based on the storage capacity change rate data and the inflow rate change trend analysis records, establishes a storage capacity change trend time series, compares the historical storage capacity change pattern, determines the storage capacity change direction, and obtains the reservoir capacity change prediction result.

[0024] As a further solution of the present invention, the time-sharing water release adjustment module includes:

[0025] The water demand statistics submodule collects water demand data downstream of the reservoir based on the predicted results of the reservoir capacity change, extracts water demand in different time periods, calculates the change range of water demand in each time period, and analyzes the change trend of water demand;

[0026] The power load calculation submodule obtains power load demand data, calculates the hydropower generation power under different water discharge rates of the reservoir, analyzes the power load demand curve, calculates the power load demand change rate in each period, and obtains the power load demand change rate data;

[0027] The water discharge flow adjustment submodule combines the water demand change trend and the power load demand change rate data to calculate the deviation between the current water discharge flow and the optimal water discharge flow, using the formula:

[0028] ;

[0029] Calculate the water discharge flow adjustment value , and adjust the reservoir water release flow to obtain dynamic water release flow data, among which, Representative Water demand during the period, Representative The power load demand of the time period is converted into flow, Represents the current water discharge flow, represents the time interval, represents the adjustment factor, represents the total number of time periods;

[0030] The regulating reservoir capacity constraint judgment submodule receives the dynamic water release flow data, combines the regulating reservoir capacity limit, determines whether the water release flow adjustment is subject to the regulating reservoir capacity constraint, calculates the water release adjustment range, adjusts the water release flow to a feasible range, and obtains the time-sharing water release flow adjustment record.

[0031] As a further solution of the present invention, the unit output optimization module includes:

[0032] The stable power acquisition submodule obtains the minimum stable operating power of the generator set, calculates the generating power of the generator set under different water discharge flow rates based on the time-sharing water discharge flow adjustment record, selects the time period that meets the minimum stable operating power, and obtains the stable operating power range;

[0033] The power adjustment range calculation submodule calculates the acceptable power adjustment range of the computer group based on the stable operating power range, calculates the unit output adjustment range, and compares it with the minimum stable operating power using the formula:

[0034] ;

[0035] Power adjustment for computer groups , obtain the power adjustment amplitude data, where, Representative The optimal power output for the time period, Representative The current unit power of the time period, Represents the minimum stable operating power of the unit, represents the time interval, represents the adjustment coefficient, represents the total number of time periods;

[0036] The optimal output adjustment submodule adjusts the unit output based on the power adjustment amplitude data and the stable operation power range, calculates the adjusted power output of the computer unit, determines whether the adjusted power meets the stable operation requirements, and obtains the optimal output adjustment result of the generator set.

[0037] As a further solution of the present invention, the system further includes a power production scheduling module;

[0038] The power production scheduling module analyzes the current power supply and demand balance based on the optimal output adjustment results of the generator set and the power load demand data, calculates the power production adjustment range, dynamically adjusts the power generation process, and obtains the water conservancy and hydropower resource scheduling optimization plan;

[0039] The water conservancy and hydropower resource scheduling optimization plan includes the adjustment range of power production, supply and demand balance assessment results, power generation dynamic adjustment strategy, and load matching optimization results.

[0040] As a further solution of the present invention, the power production scheduling module includes:

[0041] The generator set output adjustment submodule receives the optimal output adjustment result of the generator set, calculates the current output load rate of the generator set and the grid load demand adaptation ratio in combination with the real-time load demand data of the power grid, determines the deviation between the adaptation ratio and the power production load rate threshold, selects the unit numbers whose deviation exceeds the load rate deviation control benchmark, adjusts the output target level of the units that exceed the load rate deviation control benchmark, and obtains the unit output rate adjustment result;

[0042] The power supply and demand balance analysis submodule calculates the total output of all generating units and the total real-time load demand of the power grid based on the unit output rate adjustment result, determines the real-time deviation rate between the total output and the total demand, and compares the deviation rate with the power supply and demand balance threshold, compares the supply and demand balance threshold, and outputs the power supply and demand deviation rate range;

[0043] The hydropower resource scheduling optimization submodule adopts the formula based on the power supply and demand deviation rate interval and the unit output rate adjustment result:

[0044] ;

[0045] Calculate the dynamic load adaptability of hydropower units , according to the calculation results, the real-time output adjustment target of the hydropower unit is selected to obtain the optimization plan for water conservancy and hydropower resource scheduling, among which, Representative The real-time load rate of each hydropower unit, Representative The target load rate of each hydropower unit is Representative Reservoir regulation capacity parameters of each hydropower unit, Representative The target load rate of each hydropower unit is Representative The output variation of each hydropower unit is Represents the number of hydropower units currently participating in the dynamic adaptation analysis of load rate, Represents the number of hydropower units currently participating in the regulation capacity assessment.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, by real-time monitoring of reservoir water level data, calculating the characteristics of inflow rate changes, and comparing them with historical data, it is possible to accurately identify the flow change trend, improve the perception of the dynamic water volume of the reservoir, calculate the storage capacity change rate based on the inflow rate characteristics, predict the future storage capacity trend, and analyze the impact of the inflow rate on storage capacity changes, so that the storage capacity prediction is more in line with the actual hydrological conditions. Combined with downstream water demand and power load data, the storage capacity limit is calculated and adjusted, the optimal water release flow is determined, and the water release plan is dynamically adjusted according to the actual flow deviation, thereby optimizing the time sequence allocation of water resources and improving the quality of life. High water supply guarantee capability and hydropower utilization rate, by calculating the minimum stable operating power and power adjustment range of the generator set, combining the water release adjustment data to optimize the unit output, so that the power generation efficiency and water resource scheduling are coordinated and unified, based on the load demand data to conduct supply and demand balance analysis, calculate the adjustment range of power production, optimize the power supply strategy, improve the scheduling accuracy, make the power production more in line with dynamic demand, through multi-level data calculation, realize the efficient linkage of water management, power scheduling and water release optimization, improve the overall utilization efficiency of hydropower resources, and make the water conservancy and hydropower resource scheduling more refined and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a system flow chart of the present invention;

[0049] Figure 2 This is a flow chart of the inflow rate monitoring module of the present invention;

[0050] Figure 3 This is a flow chart of the dynamic prediction module of storage capacity of the present invention;

[0051] Figure 4 This is a flow chart of the time-sharing water release regulation module of the present invention;

[0052] Figure 5 This is a flow chart of the unit output optimization module of the present invention;

[0053] Figure 6 This is a flow chart of the power production scheduling module of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] See also Figure 1 , an intelligent management system for water conservancy and hydropower resources includes:

[0057] The inflow rate monitoring module collects reservoir water level monitoring data through the water level sensor, counts the inflow flow data, calculates the flow change rate at consecutive time points, analyzes the change amplitude of the inflow rate, and analyzes the difference between the current inflow rate change and the historical change in combination with the reservoir's historical inflow rate data to obtain the inflow rate change characteristic data;

[0058] The reservoir capacity dynamic prediction module calculates the current reservoir capacity change rate based on the inflow rate change characteristic data and combines it with the reservoir's historical storage capacity data. It analyzes the reservoir capacity change trend in the future time period, reveals the impact of inflow rate changes on storage capacity changes, and obtains the reservoir capacity change prediction results.

[0059] The time-sharing water release regulation module calculates the water demand data and power load demand data of the downstream reservoir based on the prediction results of reservoir storage capacity changes, calculates the reservoir regulation capacity limit, determines the optimal water release flow distribution in the future period, calculates the deviation between the current water release flow and the optimal water release flow, and dynamically adjusts the water release flow according to the deviation. It also determines the constraint effect of the reservoir regulation capacity limit on the water release adjustment range and obtains the time-sharing water release flow adjustment record;

[0060] The unit output optimization module obtains the minimum stable operating power of the current generator set based on the time-sharing water discharge flow adjustment record, calculates the acceptable power adjustment range of the unit, calculates the unit output adjustment range, and compares it with the minimum stable operating power of the unit. The power output after the unit adjustment is calculated to obtain the optimal output adjustment result of the generator set;

[0061] The power production scheduling module analyzes the current power supply and demand balance based on the optimal output adjustment results of the generator set and combines the power load demand data, calculates the power production adjustment range, dynamically adjusts the power generation process, and obtains the water conservancy and hydropower resource scheduling optimization plan.

[0062] The inflow rate change characteristic data include flow change rate, inflow fluctuation amplitude, historical comparison deviation data, and abnormal fluctuation identification results. The reservoir capacity change prediction results include future capacity change trend analysis results, capacity change rate, inflow influencing factors, and prediction error evaluation results. The time-sharing water release flow adjustment records include water release flow optimization plan, flow adjustment deviation data, adjustment capacity limitation impact, and time-sharing water release distribution records. The optimal output adjustment results of the generator set include the adjusted power data of the unit, the minimum stable operation comparison results, the power adjustment amplitude, and the output optimization range. The water conservancy and hydropower resource scheduling optimization plan includes the power production adjustment amplitude, supply and demand balance evaluation results, power generation dynamic adjustment strategy, and load matching optimization results.

[0063] See also Figure 2 , the inflow rate monitoring module includes:

[0064] The water level acquisition submodule obtains real-time water level data collected by the reservoir water level sensor, filters out abnormal data and performs linear interpolation to fill in missing data, calls water level data at consecutive time points to calculate water level change values, determines water level change trends based on water level change values, and obtains water level change trend data;

[0065] Acquire real-time water level data collected by reservoir water level sensors. Water level sensors are placed at different monitoring points in the reservoir. Data from each monitoring point is recorded every 10 minutes and sent to the data processing center through the wireless data transmission module. When screening abnormal data, determine whether the data exceeds the historical water level change range (usually If the data at a certain time point is missing, the data before and after the time point will be used for linear interpolation to complete the missing data. The calculation method is:

[0066] ;

[0067] Assume that the water level data at a certain moment is , the calculated change value is as follows:

[0068] ;

[0069] Extract the water level change trend based on the water level change value and calculate the water level growth rate within the time period:

[0070] ;

[0071] Assuming the time period is 40 minutes, then:

[0072] ;

[0073] Adjustment content: The water level growth rate needs to be compared with the average rate of change during the normal inflow period of the reservoir. Under normal circumstances, according to the monitoring data of 50 precipitation cycles in the past five years, the average rate of water level rise in the reservoir under stable inflow conditions is between 0.008m / min and 0.010m / min, which mainly depends on the confluence time of the basin, the water storage area of ​​the reservoir and the stability of the inflow flow. Therefore, when A rate exceeding 0.010 m / min usually means that the reservoir is affected by additional inflow water sources in a short period of time, such as a surge in upstream precipitation or an increase in storage and regulation.

[0074] The flow calculation submodule uses the historical water level-flow data of the reservoir based on the water level change trend data to calculate the inflow at the corresponding time point, and calculates the flow change amount by combining the inflow at consecutive time points to obtain the flow change rate data;

[0075] Based on the water level change trend data, the historical water level-flow relationship data of the reservoir is called. This relationship is usually formed into an empirical formula based on long-term observation data, for example:

[0076] ;

[0077] in, is the reservoir characteristic constant, represents the reservoir outflow index, Represents the water level in the absence of inflow. The parameters of this formula are set based on the physical characteristics of the reservoir itself, where Obtained by fitting the reservoir outlet section shape, reservoir regulation capacity and flow test data, Reflects the nonlinear outflow characteristics of the reservoir, which is related to the reservoir's water storage-outflow relationship. It represents the dead water level of the reservoir, that is, the lowest water level maintained by the reservoir when there is no external flow entering. This value is determined by the reservoir design documents and revised based on many years of monitoring data.

[0078] The specific setting process is as follows: For this reservoir, historical hydrological monitoring data shows that under different water level conditions, the flow and The fitting relationship between them conforms to the power function form, that is, Through the regression analysis of the average daily flow data of the reservoir and the corresponding water level in the past 20 years, the fitting curve was obtained and the least squares method was used to estimate the and The optimal value of makes the error of the fitting curve minimum. Under the hydrological conditions of this reservoir, the statistical The value range is 2.1-2.5, and the final selection is As the optimal value, Reflects the sensitivity of water level changes to flow. Its value is usually between 1.3-1.7. After regression calculation, the optimal value is determined. .also, Represents the dead water level of the reservoir. The reservoir operation manual stipulates that the water level is 10m and has been verified in many storage tests. .

[0079] Assuming the current water level ,calculate:

[0080] ;

[0081] Call the inflow flow at consecutive time points to calculate the flow change. Suppose the inflow flow at the previous time point is 8.7 , the current flow is 9.1 , the flow change is:

[0082] ;

[0083] Establish the flow rate of change:

[0084] ;

[0085] Assuming the time period is 40 minutes, then:

[0086] ;

[0087] Adjustment content: The reference range of flow rate change rate is obtained by analyzing the daily average flow change data during the reservoir operation period over the past ten years. Historical data show that in the absence of heavy rainfall, the daily average flow change rate of the reservoir is usually between 0.002m³ / s / min and 0.005m³ / s / min. The change is affected by the reservoir basin area, water collection time and scheduling operation mode. The current calculated value of 0.01m³ / s / min is significantly higher than this range, which usually means an abnormal increase in inflow water volume in a short period of time. Analysis shows that when A flow rate greater than 0.008 m³ / s / min is often associated with short-term heavy rainfall or flood discharge from upstream reservoirs. Therefore, it is necessary to compare it with the precipitation data from upstream hydrological stations to confirm the source of the increased flow.

[0088] The rate analysis submodule is based on the flow change rate data and the historical inflow rate data of the reservoir, using the formula:

[0089] ;

[0090] Calculate the comprehensive deviation of the current inflow rate change , get the inflow rate change characteristic data, where, Represents the inflow flow at the current moment, Represents the water level value at the current moment, Represents the number of data points in the current time window, Represents the number of data points in the historical time window, Represents the total traffic within the historical time window, Represents the total water level within the historical time window;

[0091] Based on the flow change rate data, the historical inflow rate data of the reservoir is called to calculate the deviation between the current inflow rate change and the historical inflow rate change using the formula:

[0092]

[0093] Set the current time window , historical time window , the traffic volume during the current time:

[0094] ;

[0095] Water level at current time:

[0096] m;

[0097] Traffic volume in historical time:

[0098] ;

[0099] Water levels in historical time:

[0100] m;

[0101] calculate:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] Bring it into calculation:

[0107] ;

[0108] calculate:

[0109] ;

[0110] Adjustment content: Inflow rate change characteristic data The benchmark range is set based on the daily inflow change statistics of the reservoir over the past 20 years. Historical data show that under stable meteorological conditions (no sudden increase in precipitation, no storage and flood discharge), the reservoir inflow rate characteristic value is usually less than 0.3. This value is affected by factors such as reservoir volume, basin topography and vegetation coverage. If it is greater than 0.3, it means that the current flow change rate deviates from the normal range, and there may be sudden rise in water level, upstream flood discharge or sudden change in precipitation. It is much higher than the benchmark value, indicating that the current inflow rate fluctuates significantly compared with historical data, and further water situation analysis may be needed to determine whether reservoir scheduling is necessary.

[0111] Table 1.1: Examples of monitoring data;

[0112] ;

[0113] As shown in Table 1.1, the table lists the water level and flow data during the monitoring period, which are used to calculate the flow change rate and inflow rate change characteristic data.

[0114] See also Figure 3 , the reservoir capacity dynamic prediction module includes:

[0115] The inflow rate analysis submodule receives the reservoir inflow rate change characteristic data, extracts the inflow rate time series, calculates the inflow rate increments in different time intervals, screens the inflow rate mutation points, and determines the inflow rate change trend in different time periods by comparing the interval distribution of the inflow rate change rate, thereby obtaining the inflow rate change trend analysis record;

[0116] To obtain the characteristic data of reservoir inflow rate changes, it is necessary to extract the original data set from the flow monitoring data at different time nodes. For example, the average daily inflow rate of a reservoir can be obtained by using the data records of the monitoring station. Assuming that the daily inflow rate of a reservoir in a certain week is 150m³ / s, 165m³ / s, 180m³ / s, 200m³ / s, 195m³ / s, 175m³ / s, and 160m³ / s, a time series can be formed. When processing time series, the inflow rate increments in different time intervals are calculated using The calculation results show that the daily increments are 15m³ / s, 15m³ / s, 20m³ / s, -5m³ / s, -20m³ / s, and -15m³ / s, respectively. When screening the inflow rate mutation point, a mutation threshold is set for the daily increment. For example, if the threshold is set to 18m³ / s, mutation points exceeding the threshold are detected on the 3rd and 4th days. By comparing the interval distribution of the inflow rate change rate, for example, the intervals are [0, 50]m³ / s, (50, 100]m³ / s, and (100, 150]m³ / s, the increments in each time period are compared. The interval distribution of the amount is used to judge the changing trend of the inflow rate in different time periods. If it is detected that the growth trend of the inflow rate subsequently slows down, the changing trend of the inflow rate is obtained. This trend reflects the fluctuation of the inflow rate of the reservoir in different time periods and can be used to calculate the subsequent storage capacity change rate. Specifically, when the inflow rate increments in multiple consecutive time periods exceed the positive threshold, it means that the inflow rate is increasing, otherwise it is a decreasing trend. In the case of drastic increases or decreases in a short period of time, the trend can be adjusted in combination with the outflow rate to ensure the accuracy of the prediction.

[0117] The reservoir capacity change calculation submodule calculates the reservoir capacity change rate for each time period based on the inflow rate change trend analysis record and the historical reservoir capacity data using the formula:

[0118] ;

[0119] Calculate the storage capacity change rate , calculate the cumulative storage capacity change in each time period and obtain storage capacity change rate data, where, Representative The inflow rate for each time period, Representative The outflow rate in each time period, Representative The reservoir area in each time period, represents the time interval, represents the total number of time periods;

[0120] Based on the analysis record of the change trend of inflow rate, the historical storage capacity data of the reservoir is called. The historical storage capacity data can be obtained from the water level monitoring system. For example, the storage capacity of the reservoir at different time points is 230 million m³, 235 million m³, 245 million m³, 260 million m³, and 258 million m³ respectively. When calculating the storage capacity change rate of each time period, the The calculations yielded 50 million m³, 100 million m³, 150 million m³, and -20 million m³, and the formula was used to calculate the change in reservoir capacity corresponding to the unit inflow rate.

[0121] For example, in a certain period of time, the inflow rate , outflow rate Reservoir area m², time interval (one day), calculated:

[0122] ;

[0123] The cumulative reservoir capacity change amount of all time periods is calculated in sequence to obtain reservoir capacity change rate data, which represents the change of reservoir capacity per unit time and can be used for future trend prediction. In the long-term change trend analysis of reservoir capacity, if the reservoir capacity change rate is positive and the value is large for a long time, it means that the reservoir capacity is continuously increasing, and it may be necessary to adjust the outflow. Conversely, it may be necessary to increase the water replenishment or reduce the discharge. If the rate value is close to zero, it means that the reservoir capacity is stable. This parameter can be used as an important indicator for subsequent trend evaluation.

[0124] The future trend evaluation submodule calculates the reservoir capacity change amount of the future time period based on the reservoir capacity change rate data and the inflow rate change trend analysis record, establishes a reservoir capacity change trend time series, compares the historical reservoir capacity change pattern, judges the reservoir capacity change direction, and obtains the reservoir capacity change prediction result;

[0125] The reservoir capacity change rate data is called, combined with the inflow rate change trend analysis record, to calculate the reservoir capacity change amount of the future time period. It is assumed that the recent daily reservoir capacity change amounts are 0.05 billion m³, 0.1 billion m³, 0.15 billion m³, and -0.02 billion m³. The reservoir capacity change amount of the next five days is predicted by trend fitting. A regression model for calculating future trends is used based on historical change trends. The historical reservoir capacity change pattern is compared to judge the reservoir capacity change direction. When judging whether the reservoir capacity is increasing, a reservoir capacity growth judgment threshold of 0.05 billion m³ is set. The threshold is set based on the daily reservoir capacity fluctuation range of the reservoir. It is assumed that the average daily change amount of the reservoir capacity in the past 30 days is 0.04 billion m³, and the maximum change amount is 0.12 billion m³. Therefore, 0.05 billion m³ is taken as the growth determination threshold to ensure that the trend is not misjudged during short fluctuations and to accurately reflect the growth trend during continuous changes. For example, if the predicted reservoir capacity change amount of the next three days is positive and greater than 0.05 billion m³, it can be judged that the reservoir capacity is increasing. The future reservoir capacity change trend is obtained, which can be used for reservoir scheduling planning. If the trend is increasing and exceeds the safe reservoir capacity range, a flood discharge plan needs to be developed in advance. If the trend is decreasing and close to the minimum operating reservoir capacity, water replenishment or adjustment measures need to be taken to ensure the stability of water resource allocation.

[0126] Please refer to Figure 4 The time-sharing water release regulation module includes:

[0127] The water demand statistics submodule is based on the reservoir capacity change prediction result to statistically analyze the water demand data downstream of the reservoir, extract the water demand amount at different time periods, calculate the change amplitude of water demand at each time period, and analyze the water demand change trend.

[0128] To obtain water demand data downstream of the reservoir, it is necessary to first determine the various water users downstream, including agriculture, industry, and domestic water use. According to statistical data, agricultural irrigation water usually accounts for 40%-60% of the total water consumption, industrial water accounts for 20%-30%, and domestic water accounts for 15%-25%. Collect historical statistical data and monitor the flow changes at the current water intake point through flow meters. For example, the average monthly agricultural irrigation water consumption in a certain river basin is 12 million cubic meters, industrial water consumption is 6 million cubic meters, and domestic water consumption is 3 million cubic meters. On this basis, establish a water demand time series, incorporate daily, weekly, and quarterly change trends into the analysis, and calculate the water demand for each period in combination with seasonal factors. Calculate the quantity. For example, during the peak irrigation water use period in summer, the daily water intake can reach 500,000 cubic meters, while in winter it may be only 100,000 cubic meters. Calculate the change in water demand in each period and use the sliding average method to calculate the average demand for three consecutive days to reduce the impact of single-day data fluctuations. For example, in a certain period of time, the water consumption for three days is measured to be 350,000, 400,000, and 380,000 cubic meters respectively. The calculated average water consumption is (35+40+38) / 3=376,700 cubic meters. Analyze the trend of water demand changes to determine whether the demand is stable or increasing. For example, if the agricultural water use of a reservoir has increased by 3% each year for five consecutive years, it is predicted that the growth trend will continue in the next five years, and finally the trend of water demand changes is obtained.

[0129] The power load calculation submodule obtains power load demand data, calculates the hydropower generation power under different water discharge rates of the reservoir, analyzes the power load demand curve, calculates the power load demand change rate in each period, and obtains the power load demand change rate data;

[0130] To obtain the power load demand data, it is necessary to first confirm the operating mode of the hydropower station. The power generation capacity depends on the discharge flow rate and head height of the reservoir. The power generation capacity can be calculated using the formula Calculate, where is the water discharge rate (unit: cubic meters per second), is the water head height (unit: meter), is the unit efficiency. For example, if the reservoir head height is 50 meters and the unit efficiency is 85%, if the discharge flow is 100 cubic meters per second, the power generation capacity is Kilowatts, further analyze the power load demand curve. Based on the daily load change data of the power grid, for example, the load demand during the peak period (18:00-22:00) is 40% higher than the load demand during the night off period (00:00-06:00), calculate the power load demand change rate for each period. For example, if the average load of a city is 2000MW and the peak demand increases to 2800MW, the load change rate is calculated as (2800-2000) / 2000×100%=40%, and the power load demand change rate data is obtained.

[0131] The water discharge flow adjustment submodule combines the water demand change trend and power load demand change rate data to calculate the deviation between the current water discharge flow and the optimal water discharge flow, using the formula:

[0132] ;

[0133] Calculate the water discharge flow adjustment value , and adjust the reservoir water release flow to obtain dynamic water release flow data, among which, Representative Water demand during the period, Representative The power load demand of the time period is converted into flow, Represents the current water discharge flow, represents the time interval, represents the adjustment factor, represents the total number of time periods;

[0134] Based on the water demand change trend and power load demand change rate data, the reservoir regulation capacity limit is calculated, and the safe storage capacity range is set to a minimum capacity of 50 million cubic meters and a maximum capacity of 80 million cubic meters. On this basis, the deviation between the current water release flow and the optimal water release flow is calculated, and the calculation is performed in combination with the formula.

[0135] Assuming agricultural water demand The flow rate converted from power load demand is 350,000 cubic meters per hour. The current water discharge flow is 200,000 cubic meters per hour. 500,000 cubic meters per hour, time interval For 1 hour, the adjustment factor The setting of is based on the current storage capacity ratio of the reservoir, the short-term inflow expected deviation and the response sensitivity of the downstream emergency water demand. The higher the storage capacity ratio of the reservoir, the lower the adjustment factor should be to reduce unnecessary water volume fluctuations. The larger the short-term inflow expected deviation is, the higher the adjustment factor should be to avoid overshoot due to short-term errors in the reservoir. In addition, when there is a sudden increase in short-term water demand in the downstream, such as an increase of more than 30% in industrial water withdrawal or more than 50% in agricultural irrigation due to weather changes, the adjustment factor should be increased to ensure the flexibility of reservoir scheduling. In this embodiment, the adjustment factor is set The value is 1.1, which is calculated based on the current reservoir capacity of 85%, a short-term inflow error of 5%, and a daily growth rate of downstream water demand of 8%. The calculation method is:

[0136] ;

[0137] Among them, the current storage capacity of the reservoir Ten thousand cubic meters, maximum storage capacity Ten thousand cubic meters, short-term inflow error , the daily growth rate of downstream water demand , substitute into the calculation:

[0138] ;

[0139] The final calculation is:

[0140] ;

[0141] The calculation results show that the current release rate deviates by 60,250 cubic meters per hour from the optimal release rate, necessitating adjustments in subsequent periods to ensure a balance between water supply and demand. Specifically, the current release rate is slightly lower than demand (350,000 + 200,000 = 550,000 cubic meters per hour), indicating that the reservoir needs to increase its release rate appropriately to replenish the insufficient water supply and respond to future growth in water demand. If this adjustment value exceeds the reservoir's regulating capacity, the release plan will need to be further optimized to avoid the risk of overshoot or water shortages. Ultimately, this value will be used to revise the release strategy, making reservoir scheduling more precise and stable, ensuring that downstream water and power generation needs are fully met.

[0142] The regulating reservoir capacity constraint judgment submodule receives dynamic water release flow data, combines the regulating reservoir capacity limit, determines whether the water release flow adjustment is subject to the regulating reservoir capacity constraint, calculates the water release adjustment range, adjusts the water release flow to the feasible range, and obtains the time-sharing water release flow adjustment record;

[0143] Based on the dynamic water release flow data and the regulation reservoir capacity limit, determine whether the water release flow adjustment is constrained by the regulation reservoir capacity and calculate the release adjustment range. For example, if the current reservoir capacity is 70 million cubic meters, if the dynamic water release flow is adjusted to 600,000 cubic meters / hour, the water release in the next 10 hours will be 6 million cubic meters. Calculate the estimated reservoir capacity after 10 hours:

[0144] ;

[0145] The storage capacity is still within the safe storage capacity range, so water release adjustment is allowed. Otherwise, the water release volume needs to be further restricted to make it consistent with the regulation storage capacity range, and finally the time-sharing water release flow adjustment record is obtained.

[0146] See also Figure 5 , the unit output optimization module includes:

[0147] The stable power acquisition submodule obtains the minimum stable operating power of the generator set, calculates the generating power of the generator set under different water discharge flow rates based on the time-sharing water discharge flow adjustment record, and selects the time period that meets the minimum stable operating power to obtain the stable operating power range;

[0148] To obtain the minimum stable operating power of a generator set, it is necessary to first analyze the operating characteristics of the unit. Different types of hydro-generator sets have different requirements for minimum stable operating power. For example, the minimum stable output of a conventional Francis turbine is usually between 30% and 40% of the rated power, while an axial-flow turbine may require more than 50% of the rated power to operate stably. The time-sharing discharge flow adjustment record is called to count the unit output at different discharge flow rates. The discharge flow changes are recorded at 10-minute intervals and the generated power during the corresponding period is calculated. Assuming that the rated power of a turbine is 100MW and the minimum stable power threshold is set to 35MW, it is necessary to filter out all periods where the unit output is greater than 35MW from the discharge flow data. In this process, the actual generated power at different flow rates is calculated based on the turbine efficiency curve. If the discharge flow in a certain period is 150m³ / s, the turbine efficiency is 85%, and the head is 50m, the generated power is calculated as follows:

[0149] ;

[0150] ;

[0151] During this period, the unit output is greater than 35MW, which meets the requirements for stable operation. This period is included in the stable operation power range. The output of multiple time periods is calculated using the same method, and finally the time range that meets the requirements is screened out, as shown in Table 4.1.

[0152] Table 4.1 Unit stable operating power screening table:

[0153] ;

[0154] As shown in Table 4.1, all time periods that meet the stable operating power requirements of the unit are screened out, and finally the stable operating power range is obtained.

[0155] The power adjustment range calculation submodule calculates the acceptable power adjustment range of the computer group based on the stable operating power range, calculates the unit output adjustment range, and compares it with the minimum stable operating power using the formula:

[0156] ;

[0157] Power adjustment for computer groups , obtain the power adjustment amplitude data, where, Representative The optimal power output for the time period, Representative Current unit power for the time period, Represents the minimum stable operating power of the unit, represents the time interval, represents the adjustment coefficient, represents the total number of time periods;

[0158] To calculate the acceptable power adjustment range for the unit based on the stable operating power range, we first need to obtain the deviation between the unit's current power and the target optimal power, calculate the unit's output adjustment range for each time period, and compare it with the minimum stable operating power to calculate the adjustment amount. Assuming that the unit's target optimal power is between 40MW and 70MW, if the current output is 30MW in a certain period, its power needs to be adjusted to above 40MW. Substitute the data for calculation:

[0159] ;

[0160] ;

[0161] ;

[0162] ;

[0163] The power adjustment range is calculated and compared with the adjustment capability of the unit to determine the adjustment range. The setting of the value depends on the response speed of the unit load adjustment and the type of unit. If the turbine is below 30% of the rated power, the response speed is slow, so a larger value should be set. value, and when the power is close to the rated power, the unit regulation is relatively stable. A smaller value can be taken. In this embodiment, set Based on the analysis of turbine operation stability, assuming that the unit's current output is within 50% of the rated power, historical statistics show that within this range, the unit's adjustment response time is about 10 seconds and the adjustment rate is about 1.5MW / s. Therefore, the calculation is If the adjustment rate is increased to 2.0MW / s, then It should be adjusted to 1.2 to ensure the accuracy of the adjustment calculation.

[0164] The final calculated power adjustment range is 33.9MW, meaning the unit can adjust its output within this range, ensuring the adjusted power remains within a reasonable range. This result demonstrates that the unit can adjust its power up or down within the current load range, and the adjustment rate is consistent with the unit's load response characteristics, ensuring continued stable operation after adjustment.

[0165] The optimal output adjustment submodule adjusts the unit output based on the power adjustment amplitude data and the stable operating power range, calculates the adjusted power output of the unit, determines whether the adjusted power meets the stable operation requirements, and obtains the optimal output adjustment result of the generator set;

[0166] According to the power adjustment amplitude data, combined with the stable operating power range, the unit output is adjusted to ensure that the adjusted power meets the stable operation requirements. If the current output in a certain period is 30MW and the adjusted target power is 60MW, the adjustment step is calculated and adjusted every 5 minutes, using the linear adjustment method:

[0167] ;

[0168] Where N is the number of adjustment steps. If N is set to 6, then:

[0169] ;

[0170] After the first adjustment step, the power increased to 35 MW. The next adjustment step was then executed until the target power was reached, ultimately resulting in the optimal output adjustment for the generator set. This result demonstrates that the generator set can smoothly increase power within a reasonable timeframe, following the set adjustment step size, ensuring that the power output matches the load demand while remaining within the generator set's load adjustment capability. In practical applications, this adjustment scheme can be used to regulate grid load fluctuations, ensuring the generator set's adequate responsiveness to load changes and improving power system stability.

[0171] See also Figure 6 , the power production scheduling module includes:

[0172] The generator output adjustment submodule receives the optimal output adjustment result of the generator set, combines it with the real-time load demand data of the power grid, calculates the current output load rate of the generator set and the grid load demand adaptation ratio, determines the deviation between the adaptation ratio and the power production load rate threshold, selects the unit numbers whose deviation exceeds the load rate deviation control benchmark, adjusts the output target level of the units that exceed the load rate deviation control benchmark, and obtains the unit output rate adjustment result;

[0173] Based on the current output level of the generator set, the operating parameters of the power generation equipment, and the real-time load demand data of the power grid, the current output data of all generator sets and the real-time operating status parameters of the power generation equipment are obtained, including the current generating power of each unit, the current active output of the unit, the voltage level, the reactive power output, the equipment temperature, the operating status of the cooling system, etc. Combined with the load demand data on the grid side, the total load demand of the power grid in the current period, the regional distributed load demand and the instantaneous load value of the key load nodes are obtained. In the process of obtaining the load demand data of the power grid, the load data stream is updated in real time according to the refresh frequency of 500ms for the load collection points, and the average load level and load of each load collection point are calculated. The fluctuation amplitude and fluctuation period are combined with the generator output data on the power generation side and the load demand data on the load side to calculate the current output load rate of each generator set. The output load rate is calculated as the ratio of the current active output of the generator set to the maximum active output of the generator set, and the output load rate of each unit is counted one by one to form the load rate distribution array of the current unit group. For example, if the current active output of unit 1 is 400MW and the maximum output is 500MW, the load rate is 0.8, and the statistical array is [0.8, ...]. By cumulatively calculating each unit, the average and maximum load rates of the output load rates of all generator sets are obtained, and the real-time load demand data of the power grid is called to obtain the total current load demand of the power grid. The total load demand and the average of the current output load rate are used to calculate the grid load demand adaptation ratio. The adaptation ratio is calculated as the ratio of the current total load demand of the grid to the total maximum output of the generator set. The adaptation ratio reflects the current grid load demand ratio. The load rate offset control benchmark value is called. The benchmark value is obtained based on the historical load fluctuation characteristics. The statistical method is to use the load data of the past 24 hours as the basis, eliminate the abnormal load mutation data, calculate the average load demand change rate and the upper and lower limits of the change rate, and form the upper and lower limit range of the load rate offset control benchmark value. For example, if the upper and lower limit range of the load rate offset benchmark is 0.75-0.85, then when the current adaptation ratio is lower than 0.75 or higher than 0.85, All of them are judged as load adaptation abnormalities. The unit numbers and corresponding output parameters of the units whose load rate deviation exceeds the deviation control benchmark interval are screened one by one, and a list of the exceeded unit output deviation is recorded. For each unit in the exceeded list, the target output level is reset. The target output level is calculated jointly with the current load demand adaptation ratio and the operating status parameters of the offset unit. In the process of resetting the target output level, the inhibitory effect of the unit operating temperature and the cooling system load rate on the target output is taken into account. The inhibitory effect is based on the temperature threshold. When it exceeds 85°C, the target output decreases by 5%, and when it exceeds 90°C, the target output decreases by 10%. A target output adjustment list is formed, and the unit output rate adjustment result is generated.

[0174] Table 5.1 Generator set real-time output and target output adjustment table:

[0175] ;

[0176] As shown in Table 5.1, the current output and target output of each generator set are reset based on the load adaptation ratio and the offset control benchmark.

[0177] The power supply and demand balance analysis submodule calculates the total output of all generating units and the total real-time load demand of the power grid based on the unit output rate adjustment results, determines the real-time deviation rate between the total output and the total demand, and compares the deviation rate with the power supply and demand balance threshold. After comparing the supply and demand balance threshold, it outputs the power supply and demand deviation rate range;

[0178] The adjusted unit output rate results and the real-time load demand data of the power grid are called to obtain the adjusted target output data of each generator unit. These are compared one by one with the demand values ​​of key load nodes in the real-time load demand data of the power grid. For each key load node, the total output of the corresponding generator unit is counted to form a node output list. The node output list records the actual output, target output, load demand, and deviation of each load node. The deviation is calculated as the difference between the target output and the actual output. The total output of all nodes is counted to obtain the target output of the adjusted generator group. The total load demand of all nodes in the load demand data is counted to form the current total power production and total load demand. The real-time supply and demand deviation rate is calculated based on the total data. The supply and demand deviation rate is calculated as the ratio of the target output of the generator group to the total load demand of the power grid. If the deviation rate is less than 0.95, it is determined to be insufficient supply and demand. If the deviation rate is greater than 1.05, it is determined to be excessive supply and demand, forming the power supply and demand deviation rate range.

[0179] Table 5.2 Output and demand statistics of key nodes:

[0180] ;

[0181] As shown in Table 5.2, the output, demand and deviation of key load nodes serve as the basic data for power supply and demand balance analysis.

[0182] The hydropower resource scheduling optimization submodule is based on the power supply and demand deviation rate interval and the unit output rate adjustment results, using the formula:

[0183] ;

[0184] Calculate the dynamic load adaptability of hydropower units , according to the calculation results, the real-time output adjustment target of the hydropower unit is selected to obtain the optimization plan for water conservancy and hydropower resource scheduling, among which, Representative The real-time load rate of each hydropower unit, Representative The target load rate of each hydropower unit is Representative Reservoir regulation capacity parameters of each hydropower unit, Representative The target load rate of each hydropower unit is Representative The output variation of each hydropower unit is Represents the number of hydropower units currently participating in the dynamic adaptation analysis of load rate, Represents the number of hydropower units currently participating in the regulation capacity assessment;

[0185] Call the power supply and demand deviation rate interval and the unit output rate adjustment results. Based on the upper and lower limit deviation rate value range in the power supply and demand deviation rate interval, the difference between the current real-time load rate and the target load rate of each hydropower unit, combined with the output change amplitude of each hydropower unit and the real-time load rate change trend, obtain the output change target of each hydropower unit in the current period, call the reservoir regulation capacity parameters, analyze the current storage capacity, inflow flow, outflow flow, unit output and water level dynamic change parameters of each hydropower unit one by one, determine the reservoir adjustable space of each unit, and obtain the The current water head height. In the process of obtaining the water head height, the water inlet and tail water level sensor data of each hydropower unit collected in the early stage are combined. After deducting the tail water level data from the water inlet water level data, the real-time effective water head height of each unit is calculated. If the effective water head height is lower than the minimum design water head of the unit, it is determined that the unit currently does not have the ability to dynamically adjust the load rate. If the effective water head height is higher than the maximum allowable water head of the unit, it is determined that the current reservoir of the unit has an overshoot risk and is not included in this round of dynamic adaptation analysis of the load rate. By screening the unit numbers that meet the adaptation conditions, the unit is selected. The dynamic adaptability calculation link of the load rate of the water-inlet generating unit is based on the real-time load rate and target load rate of the adaptable unit. The unit-by-unit calculation method is used to obtain the real-time load rate deviation of each unit, and the square sum of the load rate deviations of all the adaptable units is summed to obtain the square sum of the load rate deviations of all the units. Combined with the reservoir regulation capacity parameters of the adaptable unit, the reservoir regulation capacity parameters of each unit are accumulated one by one to obtain the total regulation capacity of the adaptable unit. In the process of obtaining the total regulation capacity, the effective storage capacity and inflow flow of each unit reservoir monitored in the early stage are called The water level drop rate data corresponding to the output change is used to calculate the adjustment capacity parameters one by one, and the dynamic load adaptability of the current hydropower unit is calculated comprehensively according to the weighted output change amplitude and target load rate of the unit. The calculated dynamic load adaptability of the hydropower unit is compared with the preset load rate dynamic adaptability benchmark value. If the adaptability is higher than the benchmark value, the current load adjustment plan is judged to be reasonable. If it is lower than the benchmark value, the current load adjustment plan is judged to be insufficient and the output target of the hydropower unit needs to be readjusted, and finally the water conservancy and hydropower resource scheduling optimization plan is obtained.

[0186] Table 5.3 Real-time operating parameters of hydropower units:

[0187] ;

[0188] Table 5.3 lists the real-time operating parameters of some hydropower units. As shown in Table 1, the real-time load rate, target load rate, inflow rate, outflow rate, water level and effective head of each unit are used as basic data for subsequent dynamic adaptability calculations.

[0189] :Read the real-time load rate of each unit directly according to the table, for example, the load rate of unit 1 is 76.4%;

[0190] :According to the target load rate in the table, the load of unit 1 80%;

[0191] :By dynamically calculating the real-time storage capacity, inflow and outflow of the reservoir, according to:

[0192] ;

[0193] The real-time effective storage capacity of unit 1 is 62 million m³, the minimum operating storage capacity is 58 million m³, the inflow flow is 620 m³ / s, and the outflow flow is 605 m³ / s. Then:

[0194] ;

[0195] : Calculated based on target load rate and real-time load rate:

[0196] ;

[0197] Unit 1 :

[0198] ;

[0199] Enter the formula to calculate:

[0200] Sum of squares of load factor deviations:

[0201] ;

[0202] ;

[0203] Total regulating capacity:

[0204] ;

[0205] Load factor weighted changes:

[0206] ;

[0207] ;

[0208] Fitness calculation:

[0209] ;

[0210] ;

[0211] Assume that the dynamic adaptability benchmark value of the load rate is 0.04 (the basis for setting the dynamic adaptability benchmark value of 0.04 for the load rate is determined by combining the hourly load regulation deviation level of each major hydropower station in the basin with the corresponding reservoir regulation capacity parameters. Specifically, the hourly load rate deviation and regulation capacity data of a typical hydropower station for 120 consecutive days are calculated hourly and synchronously to obtain the dynamic adaptability of the load rate in each period. Based on the dynamic adaptability sequence, the dynamic adaptability value of the load rate corresponding to the 95% quantile is calculated as the control benchmark for the dynamic adaptability of the overall load rate in the basin. At the same time, the benchmark value As parameters such as the reservoir capacity ratio, maximum output to minimum output ratio, and maximum load rate fluctuation amplitude of different hydropower stations fluctuate synchronously, if the reservoir capacity ratio is higher than 3.5, the benchmark value of the dynamic adaptability of the load rate increases by 0.005 to 0.01; if the maximum output to minimum output ratio exceeds 2.5, the benchmark value decreases by 0.003 to 0.005; if the maximum load rate fluctuation amplitude is higher than 15%, the benchmark value further decreases by 0.005 to 0.007). Comparison shows that the dynamic load adaptability of the current hydropower units is lower than the benchmark value, and it is necessary to readjust the output targets of some units and update the scheduling optimization plan.

[0212] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent management system for water resources and hydropower, characterized in that: The system comprises: The inflow rate monitoring module collects reservoir water level monitoring data and inflow flow data, calculates the flow change rate at consecutive time points, analyzes the change amplitude of the inflow rate, analyzes the difference between the current inflow rate change and the historical change, and obtains the inflow rate change characteristic data; The reservoir capacity dynamic prediction module calculates the current reservoir capacity change rate based on the inflow rate change characteristic data and combines the historical reservoir capacity data, analyzes the reservoir capacity change trend in the future time period, and obtains the reservoir capacity change prediction result; The time-sharing water release regulation module calculates the reservoir regulation capacity limit based on the reservoir capacity change prediction result, collects water demand data and power load demand data, determines the optimal water release flow distribution, calculates the deviation between the current water release flow and the optimal water release flow, dynamically adjusts the water release flow, and obtains the time-sharing water release flow adjustment record; The unit output optimization module obtains the minimum stable operating power of the current generator set based on the time-sharing water discharge flow adjustment record, calculates the acceptable power adjustment range of the unit, compares it with the minimum stable operating power of the unit, calculates the power output after adjustment, and obtains the optimal output adjustment result of the generator set; The unit output optimization module includes: The stable power acquisition submodule obtains the minimum stable operating power of the generator set, calculates the generating power of the generator set under different water discharge flow rates based on the time-sharing water discharge flow adjustment record, selects the time period that meets the minimum stable operating power, and obtains the stable operating power range; The power adjustment range calculation submodule calculates the acceptable power adjustment range of the computer group based on the stable operating power range, calculates the unit output adjustment range, and compares it with the minimum stable operating power using the formula: ； Power adjustment for computer groups , obtain the power adjustment amplitude data, where, Representative The optimal power output for the time period, Representative Current unit power for the time period, Represents the minimum stable operating power of the unit, represents the time interval, represents the adjustment coefficient, represents the total number of time periods; The optimal output adjustment submodule adjusts the unit output based on the power adjustment amplitude data and the stable operation power range, calculates the adjusted power output of the computer unit, determines whether the adjusted power meets the stable operation requirements, and obtains the optimal output adjustment result of the generator set.

2. The intelligent management system for water resources and hydropower according to claim 1, characterized in that: The inflow rate change characteristic data includes flow change rate, inflow fluctuation amplitude, historical comparison deviation data, and abnormal fluctuation identification results. The reservoir capacity change prediction results include future capacity change trend analysis results, capacity change rate, inflow influencing factors, and prediction error evaluation results. The time-sharing water release flow adjustment records include water release flow optimization plans, flow adjustment deviation data, adjustment capacity limitation impact, and time-sharing water release distribution records. The optimal output adjustment results of the generator set include the adjusted power data of the unit, the minimum stable operation comparison results, the power adjustment amplitude, and the output optimization range.

3. The intelligent management system for water resources and hydropower according to claim 1, characterized in that: The inflow rate monitoring module includes: The water level acquisition submodule obtains real-time water level data collected by the reservoir water level sensor, filters out abnormal data and performs linear interpolation to fill in missing data, calls water level data at consecutive time points to calculate water level change values, determines water level change trends based on water level change values, and obtains water level change trend data; The flow calculation submodule uses the historical water level-flow data of the reservoir based on the water level change trend data to calculate the inflow at the corresponding time point, and calculates the flow change amount by combining the inflow at consecutive time points to obtain the flow change rate data; The rate analysis submodule is based on the flow change rate data and the historical inflow rate data of the reservoir, using the formula: ； Calculate the comprehensive deviation of the current inflow rate change , get the inflow rate change characteristic data, where, Represents the inflow flow at the current moment, Represents the water level value at the current moment, Represents the number of data points in the current time window, Represents the number of data points in the historical time window, Represents the total traffic in the historical time window, Represents the total water level within the historical time window.

4. The intelligent management system for water resources and hydropower resources according to claim 1, characterized in that: The storage capacity dynamic prediction module includes: The inflow rate analysis submodule receives the reservoir inflow rate change characteristic data, extracts the inflow rate time series, calculates the inflow rate increments in different time intervals, screens the inflow rate mutation points, and determines the inflow rate change trend in different time periods by comparing the interval distribution of the inflow rate change rate, thereby obtaining an inflow rate change trend analysis record; The reservoir capacity change calculation submodule calculates the reservoir capacity change rate for each time period based on the inflow rate change trend analysis record and the historical reservoir capacity data using the formula: ； Calculate the storage capacity change rate , calculate the cumulative storage capacity change in each time period and obtain storage capacity change rate data, where, Representative The inflow rate for each time period, Representative The outflow rate in each time period, Representative The reservoir area in each time period, represents the time interval, represents the total number of time periods; The future trend assessment submodule calculates the storage capacity change in the future time period based on the storage capacity change rate data and the inflow rate change trend analysis record, establishes a storage capacity change trend time series, compares the historical storage capacity change pattern, determines the direction of storage capacity change, and obtains the reservoir capacity change prediction result.

5. The intelligent management system for water resources and hydropower according to claim 1, characterized in that: The time-sharing water release adjustment module includes: The water demand statistics submodule collects water demand data downstream of the reservoir based on the predicted results of the reservoir capacity change, extracts water demand in different time periods, calculates the change range of water demand in each time period, and analyzes the change trend of water demand; The power load calculation submodule obtains power load demand data, calculates the hydropower generation power under different water discharge rates of the reservoir, analyzes the power load demand curve, calculates the power load demand change rate in each period, and obtains the power load demand change rate data; The water discharge flow adjustment submodule combines the water demand change trend and the power load demand change rate data to calculate the deviation between the current water discharge flow and the optimal water discharge flow, using the formula: ； Calculate the water discharge flow adjustment value , and adjust the reservoir water release flow to obtain dynamic water release flow data, among which, Representative Water demand during the period, Representative The power load demand of the time period is converted into flow, Represents the current water discharge flow, represents the time interval, represents the adjustment factor, represents the total number of time periods; The regulating reservoir capacity constraint judgment submodule receives the dynamic water release flow data, combines the regulating reservoir capacity limit, determines whether the water release flow adjustment is subject to the regulating reservoir capacity constraint, calculates the water release adjustment range, adjusts the water release flow to a feasible range, and obtains the time-sharing water release flow adjustment record.

6. The intelligent management system for water resources and hydropower resources according to claim 1, characterized in that: The system also includes a power production scheduling module; The power production scheduling module analyzes the current power supply and demand balance based on the optimal output adjustment results of the generator set and the power load demand data, calculates the power production adjustment range, dynamically adjusts the power generation process, and obtains the water conservancy and hydropower resource scheduling optimization plan; The water conservancy and hydropower resource scheduling optimization plan includes the adjustment range of power production, supply and demand balance assessment results, power generation dynamic adjustment strategy, and load matching optimization results.

7. The intelligent management system for water resources and hydropower according to claim 6, characterized in that: The power production scheduling module includes: The generator set output adjustment submodule receives the optimal output adjustment result of the generator set, calculates the current output load rate of the generator set and the grid load demand adaptation ratio in combination with the real-time load demand data of the power grid, determines the deviation between the adaptation ratio and the power production load rate threshold, selects the unit numbers whose deviation exceeds the load rate deviation control benchmark, adjusts the output target level of the units that exceed the load rate deviation control benchmark, and obtains the unit output rate adjustment result; The power supply and demand balance analysis submodule calculates the total output of all generating units and the total real-time load demand of the power grid based on the unit output rate adjustment result, determines the real-time deviation rate between the total output and the total demand, and compares the deviation rate with the power supply and demand balance threshold, compares the supply and demand balance threshold, and outputs the power supply and demand deviation rate range; The hydropower resource scheduling optimization submodule adopts the formula based on the power supply and demand deviation rate interval and the unit output rate adjustment result: ； Calculate the dynamic load adaptability of hydropower units , according to the calculation results, the real-time output adjustment target of the hydropower unit is selected to obtain the optimization plan for water conservancy and hydropower resource scheduling, among which, Representative The real-time load rate of each hydropower unit, Representative The target load rate of each hydropower unit is Representative Reservoir regulation capacity parameters of each hydropower unit, Representative The target load rate of each hydropower unit is Representative The output variation of each hydropower unit is Represents the number of hydropower units currently participating in the dynamic adaptation analysis of load rate, Represents the number of hydropower units currently participating in the regulation capacity assessment.

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