Reservoir dispatching and weather forecast combined power generation optimization method and system

Abstract

The invention discloses a power generation optimization method and system combining reservoir dispatching and weather forecast, and relates to the technical field of water conservancy and hydropower engineering, and the method comprises the steps: collecting and recording weather data, and determining the daily accumulated positive temperature according to the weather data; constructing a snow melting model according to historical meteorological data, inputting the daily accumulated positive temperature into the snow melting model, and estimating the daily snow melting amount; and determining the reservoir flow by using the meteorological data and the daily snow melting amount, and making a power generation plan according to the reservoir flow. According to the method, by considering the influence of factors such as temperature change and wind speed on the snow melting process and combining parameters such as soil humidity and accumulated snow coverage, the daily snow melting amount and the storage flow are accurately estimated. The refined prediction enables the reservoir management personnel to predict the water resource condition in the future seven days in advance, thereby making a more reasonable power generation plan.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy and hydropower engineering, and in particular to a power generation optimization method and system combining reservoir dispatching with weather forecasting. Background Art

[0002] The power generation optimization technology combining reservoir operation with meteorological forecast plays a vital role in the field of water conservancy and hydropower engineering. With the continuous growth of climate change and energy demand, reservoir managers are facing increasingly complex operation decisions. Traditional reservoir operation methods mainly rely on historical data and empirical rules, which are difficult to effectively cope with the rapid changes and uncertainties of meteorological conditions. In recent years, with the advancement of meteorological forecasting technology and the improvement of numerical simulation capabilities, introducing meteorological forecast information into the reservoir operation decision-making process has become a new research hotspot. The application of advanced technologies such as numerical weather prediction (NWP) and ensemble prediction system (EPS) provides more accurate and long-term meteorological forecast support for reservoir operation. However, how to effectively integrate this meteorological forecast information into reservoir operation decisions remains a challenging problem.

[0003] There are some shortcomings in the existing methods of combining reservoir scheduling with meteorological forecasts. First, most methods fail to fully consider the complex impact of meteorological factors on the inflow, especially in snowmelt areas, where the contribution of the snowmelt process to the inflow is often ignored or simplified. Secondly, the existing snowmelt models usually use a simple degree-day factor method, which is difficult to accurately capture the dynamic changes of the snowmelt process, especially when the temperature changes rapidly or extreme weather events occur. Furthermore, the existing methods are inefficient when integrating multi-source meteorological data, making it difficult to achieve real-time updating and rapid processing of meteorological data. Finally, most methods fail to fully consider multiple factors such as the current water storage capacity of the reservoir and downstream water demand when formulating power generation plans, resulting in insufficient flexibility and adaptability of power generation plans. Summary of the invention

[0004] In view of the problems existing in the existing power generation optimization, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that in snowmelt areas, the contribution of the snowmelt process to the inflow is often ignored or simplified, so that the accurate inflow cannot be obtained and water resources cannot be used efficiently.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a power generation optimization method combining reservoir scheduling with weather forecasting, which comprises:

[0008] collecting and recording meteorological data, and determining daily cumulative positive temperature based on the meteorological data;

[0009] A snowmelt model is constructed based on historical meteorological data, and the daily accumulated positive temperature is input into the snowmelt model to estimate the daily snowmelt amount;

[0010] The meteorological data and the daily snowmelt amount are used to determine the inflow flow, and a power generation plan is formulated based on the inflow flow.

[0011] As a preferred solution of the power generation optimization method combining reservoir scheduling with weather forecasting of the present invention, the determination of the daily cumulative positive temperature includes:

[0012] determining the effective snowmelt temperature per hour based on the meteorological data;

[0013] Divide the sunshine changes and set the sunshine weight;

[0014] The effective snowmelt temperature is calculated according to the sunshine weight, and the weighted effective snowmelt temperature of the day is accumulated to obtain the daily cumulative positive temperature;

[0015] The determination of the daily cumulative positive temperature is shown in the following formula:

[0016]

[0017] Where DPDD is the daily accumulated positive temperature, T h is the temperature at hour h, T base is the reference temperature and h is hour.

[0018] As a preferred solution of the power generation optimization method combining reservoir scheduling with weather forecasting of the present invention, the snowmelt model estimates the daily snowmelt amount through the daily cumulative positive temperature, as described in the following formula:

[0019] M t =DDF·DPDD t ·F s (t)·F a (t)·F w (t)

[0020] Among them, F a (t) is the influencing factor of temperature change trend; F w (t) is the wind speed influence factor, M t is the daily snowmelt on day t, DDF is the basic degree-day factor; DPDD t is the daily cumulative positive temperature on day t; F s (t) is the seasonal adjustment factor.

[0021] As a preferred solution of the power generation optimization method combining reservoir scheduling with weather forecasting described in the present invention, the temperature change trend influencing factor is as follows:

[0022]

[0023] The wind speed influencing factor is shown in the following formula:

[0024]

[0025] The seasonal adjustment factor is as follows:

[0026]

[0027] Where d is the current date, α is the seasonal adjustment coefficient, ΔT t is the temperature change trend on day t; ΔT max The largest temperature change trend in history; W t is the average wind speed on the tth day; W is the historical average wind speed; σ W is the standard deviation of wind speed, γ and δ are the influence coefficients of temperature trend and wind speed, respectively, both of which are dimensionless parameters.

[0028] As a preferred solution of the power generation optimization method combining reservoir scheduling with weather forecasting of the present invention, the determination of the inflow flow includes:

[0029] constructing a unit line of the watershed based on the historical meteorological data and revising it;

[0030] Perform validity conversion on the precipitation and snowmelt in the meteorological data;

[0031] Direct runoff is obtained by convolving the corrected unit line with the converted precipitation and daily snowmelt;

[0032] The base flow is calculated according to the corrected unit line, the direct runoff and the base flow are added, and error correction is performed to obtain the inflow flow.

[0033] As a preferred solution of the power generation optimization method combining reservoir scheduling with weather forecasting of the present invention, the calculation of the direct runoff is shown in the following formula:

[0034]

[0035] Where R(τ) is the total input at time τ, i.e., the sum of effective precipitation and snowmelt, U′(t-τ) is the corrected unit line of the time difference t-τ, and Q d (t) is the direct runoff at time t;

[0036] The base flow is calculated as follows:

[0037]

[0038] Where k is the baseflow attenuation coefficient, δ reflects the periodicity of baseflow changes during the day, η is the contribution coefficient of precipitation and snowmelt to baseflow, R(t) is the total input at time t, Q b (t) is the base flow at time t.

[0039] As a preferred solution of the power generation optimization method combining reservoir scheduling with weather forecasting according to the present invention, the effectiveness conversion is shown in the following formula:

[0040] P e (t) = P(t)·(1-e -a·I(t) )

[0041] M e (t) = M (t) · (1-e -β·S(t) )

[0042] Among them, P(t) is the precipitation, M(t) is the predicted snowmelt, and P e (t) is the effective precipitation and M e (t) is the effective snowmelt, I(t) is the previous soil moisture index, S(t) is the snow cover, and α and β are empirical coefficients.

[0043] In a second aspect, an embodiment of the present invention provides a power generation optimization system combining reservoir scheduling with weather forecasting, which includes:

[0044] Data acquisition module, used to collect and record meteorological data;

[0045] A snowmelt amount determination module is used to determine the daily cumulative positive temperature according to the meteorological data; construct a snowmelt model according to the historical meteorological data, and input the daily cumulative positive temperature into the snowmelt model to estimate the daily snowmelt amount;

[0046] The inflow flow determination module is used to determine the inflow flow by using the meteorological data and the daily snowmelt amount, and formulate a power generation plan according to the inflow flow.

[0047] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned power generation optimization method combining reservoir scheduling with meteorological forecasting.

[0048] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned power generation optimization method combining reservoir scheduling with meteorological forecasting is implemented.

[0049] The beneficial effect of the present invention is that by considering the impact of factors such as temperature changes and wind speed on the snowmelt process, combined with parameters such as soil moisture and snow coverage, the daily snowmelt volume and inflow volume are accurately estimated. This refined prediction enables reservoir managers to predict the water resources situation in the next 7 days in advance, so as to formulate a more reasonable power generation plan. In particular, when a large inflow volume is predicted, the generator set can be started in advance to maximize power generation while ensuring flood control safety. Integrating multi-source meteorological data and constructing accurate snowmelt models and inflow flow prediction models realize the organic combination of reservoir scheduling and meteorological forecasting, thereby significantly improving the effect of power generation optimization. In this way, the present invention not only improves the power generation efficiency of the hydropower station, but also enhances the scientific nature and foresight of reservoir scheduling, providing strong support for the sustainable utilization of hydropower resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0051] Figure 1 Flowchart of the power generation optimization method combining reservoir operation with weather forecast.

[0052] Figure 2 Schematic diagram of automatic station layout for power generation optimization method combining reservoir operation with weather forecast. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0056] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0057] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0058] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0059] Example 1

[0060] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a power generation optimization method combining reservoir scheduling with weather forecast, including:

[0061] Using two modified DZZ4 multi-factor professional meteorological observation stations, data such as temperature, humidity, air pressure, wind direction, wind speed, precipitation and river water level are automatically collected and recorded every hour. These data are uploaded to the meteorological big data cloud platform in real time through the central station server. At the same time, the observation data of the existing conventional meteorological stations in the meteorological departments around the basin are collected to expand the data coverage. Then, through the designed comprehensive data interface service, the observation data in the system and the professional observation data of the local central station server are integrated and processed.

[0062] Two types of weather forecast data provided by the meteorological department are obtained. One is the intelligent grid forecast service product with a resolution of 5km, hourly to 6 hours, and for the next 7 days after being revised by the national or autonomous region; the other is the subjective forecast for the next 7 days. On this basis, the precipitation and temperature forecast information closely related to reservoir scheduling is extracted and analyzed. Finally, all collected and forecasted data are visualized through the meteorological service platform, and support H5 query on PC and mobile terminals to ensure that relevant personnel can obtain the latest meteorological information in a timely manner, providing reliable data support for subsequent snowmelt estimation and inflow flow prediction.

[0063] The temperature data and corresponding snowmelt records of the past years were collected and analyzed to establish a degree-day factor snowmelt model. Then, the daily cumulative positive temperature was calculated using the real-time temperature data collected by professional meteorological observation stations. The cumulative positive temperature was then input into the degree-day factor model to estimate the daily snowmelt for the next 7 days.

[0064] Preferably, the degree-day factor snowmelt model is based on the basic snowmelt model, taking into account the impact of temperature at different times of the day on snowmelt, and seasonal changes will affect the intensity of solar radiation, thereby affecting the snowmelt rate, increasing seasonal regulation, and preparing for the subsequent spring snowmelt period to increase power generation, as shown in the following formula:

[0065] M t =DDF·DPDD t ·F s (t)

[0066]

[0067] Among them, M t is the daily snowmelt on the tth day (mm / day), t∈[0,6] represents the next 7 days (today is day 0); DDF is the basic degree-day factor (mm / ℃ / day); DPDD t is the daily cumulative positive temperature on day t (℃·day); F s (t) is the seasonal adjustment factor; d is the current date (the number of days starting from January 1st) and α is the seasonal adjustment coefficient.

[0068] Furthermore, it is found in practice that changes in temperature and wind speed will affect the snowmelt process, especially in high altitude areas. In order to more accurately predict the daily snowmelt, the influence of temperature change trend and wind speed are added to the model, as shown in the following formula:

[0069] M t =DDF·DPDD t ·F s (t)·F a (t)·F w (t)

[0070]

[0071] Among them, F a (t) is the influencing factor of temperature change trend; F w (t) is the wind speed influence factor; ΔT t is the temperature change trend of the 7-day period on day t (℃ / day); ΔT max is the historical maximum 7-day temperature change trend (℃ / day); W t is the average wind speed on day t (m / s); is the historical average wind speed (m / s); σ W is the standard deviation of wind speed (m / s), γ and δ are the influence coefficients of temperature change trend and wind speed, respectively, both of which are dimensionless parameters.

[0072] Preferably, to calculate the daily cumulative positive temperature, first, collect the hourly temperature data of the observation station, and for each hourly temperature, only consider the part above the reference temperature. The reference temperature is usually set to 0°C, but can be adjusted according to local conditions.

[0073] Calculate the effective snowmelt temperature for each hour:

[0074] T effh =max(0,T h -T base )

[0075] Furthermore, the sunshine changes are divided into daytime and nighttime, and the sunshine weights are set respectively. Specifically, during the daytime period (6:00-18:00): W s =1.2, Night time (18:00-6:00 the next day): W s = 0.8. It is organized into a continuous sunshine weight function, which is smoother than the simple day / night binary division and more in line with the actual sunshine changes. It can be adjusted according to local conditions to correspond to the snowmelt peak, as shown in the following formula:

[0076]

[0077] Calculate the weighted effective snowmelt temperature per hour, and then add the weighted effective snowmelt temperature for 24 hours to obtain the daily cumulative positive temperature, as shown in the following formula:

[0078]

[0079] Where DPDD is the daily accumulated positive temperature, T h is the temperature at hour h, T base is the reference temperature, h is hour, T effh The effective snow melting temperature.

[0080] During the peak snowmelt period, the estimation frequency is increased to multiple times a day to capture rapid snowmelt events. Finally, the estimation results are compared with historical snowmelt flood data to assess the risk of snowmelt floods, and the results are input into a professional meteorological service platform to provide decision support for reservoir managers. In this way, the amount of snowmelt can be predicted more accurately, providing an important basis for subsequent inflow forecasts and power generation plan optimization.

[0081] Furthermore, after estimating the daily snowmelt in the next 7 days, we evaluate the current thawing of the upstream river. These factors will affect the rate and amount of surface runoff formed by precipitation and snowmelt. After obtaining these basic data, we construct the unit line of the basin based on historical data, and then use the precipitation and predicted snowmelt as input to obtain the corresponding runoff process through convolution operation. Finally, we combine the runoff output of the model with the thawing of the upstream river, make necessary corrections to the prediction results, and obtain the final daily inflow forecast for the next 7 days.

[0082] Preferably, first, the unit line of the watershed is constructed based on historical data. Since the present invention takes into account the particularity of snowmelt, after the basic unit line is constructed, the influence of snowmelt is corrected:

[0083]

[0084] Among them, λ(t) is a time-varying parameter that reflects the impact of snowmelt on runoff at different times of the day, and U(t) is the basic unit line.

[0085] Furthermore, the effectiveness of the precipitation predicted by the meteorological department and the estimated snowmelt is converted. Specifically, the exponential decay model is used to describe the impact of soil moisture and snow cover on precipitation and snowmelt, as shown in the following formula:

[0086] P e (t) = P(t)·(1-e -a·I(t) )

[0087] M e (t) = M (t) · (1-e -β·S(t) )

[0088] Among them, P(t) is the precipitation, M(t) is the predicted snowmelt, and P e (t) is the effective precipitation and M e (t) is the effective snowmelt, I(t) is the antecedent soil moisture index, S(t) is the snow cover, and α and β are empirical coefficients obtained by calibration with field data.

[0089] The modified unit line U'(t) and the combined input R(t) are used for convolution operation. The combined input R(t) is the sum of the effective precipitation and the effective snowmelt. Finally, the direct runoff is obtained:

[0090]

[0091] Then calculate the base flow:

[0092]

[0093] Among them, k is the baseflow attenuation coefficient, δ reflects the periodicity of baseflow changes during the day, and η is the contribution coefficient of precipitation and snowmelt to baseflow.

[0094] Finally, the base flow and runoff are added together and error correction is performed to obtain the final total inflow flow. The error correction method used in the present invention is common knowledge in the art and will not be described in detail.

[0095] According to the predicted inflow, combined with the current water storage capacity of the reservoir and the downstream water demand, a power generation plan for the next 7 days is formulated. On the premise of ensuring flood control safety and water supply demand, the power generation period and power generation flow are reasonably arranged. In particular, when a large inflow is predicted, the generator set is started in advance to increase power generation and achieve efficient use of water resources.

[0096] In summary, the present invention uses refined weather forecast data to guide reservoir scheduling and power generation planning. By predicting precipitation and snowmelt conditions, the generator set is started in advance, the power generation is increased while ensuring flood control safety, and the efficient use of water resources is achieved.

[0097] Furthermore, this embodiment also provides a power generation optimization system combining reservoir scheduling with weather forecast, including:

[0098] Data acquisition module, used to collect and record meteorological data;

[0099] A snowmelt amount determination module is used to determine the daily cumulative positive temperature according to the meteorological data; construct a snowmelt model according to the historical meteorological data, and input the daily cumulative positive temperature into the snowmelt model to estimate the daily snowmelt amount;

[0100] The inflow flow determination module is used to determine the inflow flow by using the meteorological data and the daily snowmelt amount, and formulate a power generation plan according to the inflow flow.

[0101] This embodiment also provides a computer device, which is suitable for the power generation optimization method combining reservoir scheduling and weather forecast, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the power generation optimization method combining reservoir scheduling and weather forecast as proposed in the above embodiment.

[0102] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0103] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the power generation optimization method combining reservoir scheduling with weather forecasting as proposed in the above embodiment is implemented.

[0104] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0105] Example 2

[0106] This is the second embodiment of the present invention, which provides an example of estimating the amount of snowmelt in the next seven days using the degree-day factor snowmelt model.

[0107] For example, in one feasible embodiment, the degree-day factor snowmelt model estimates the amount of snowmelt in the next 7 days as follows:

[0108] First, set some basic parameters:

[0109] DDF (degree-day factor) = 3 mm / ℃ / day (this is a typical snowmelt degree-day factor value)

[0110] T b (reference temperature) = 0°C

[0111] ΔT max (Historical maximum 7-day temperature change trend) = 2℃ / day

[0112] (Historical average wind speed) = 3m / s

[0113] σ W (Standard deviation of wind speed) = 1m / s

[0114] α=0.2,γ=0.3,δ=0.1 (these are adjustment coefficients and need to be calibrated by historical data)

[0115] The previous date is spring, d = 100 (around April 10)

[0116] Calculate the amount of snowmelt for each day:

[0117] Calculate DPDD_t (simplified as the difference between the average temperature and the reference temperature)

[0118] Calculate each impact factor

[0119] Calculate daily snowmelt

[0120] A similar calculation is performed for each day, and the following results are obtained as shown in Table 1:

[0121] Table 17-day snowmelt record

[0122] Days Snowmelt (mm / day) 0 4.36 1 6.68 2 9.26 3 12.21 4 14.63 5 11.55 6 11.55

[0123] The results show that the peak snowmelt period is from the 3rd to the 5th day, and the amount of water entering the reservoir will increase significantly. Reservoir managers should consider increasing power generation appropriately before these days to free up storage capacity for the large amount of water entering the reservoir. After reaching the peak snowmelt on the 4th day, the amount of snowmelt began to decline. This means that the amount of water entering the reservoir will gradually decrease in the next few days, and the power generation plan can be adjusted accordingly. The total snowmelt in 7 days is about 67mm. Reservoir managers need to formulate reasonable reservoir scheduling and power generation plans based on this forecast result, combined with rainfall forecasts and other water inflows.

[0124] Example 3

[0125] This is the third embodiment of the present invention. This embodiment provides a power generation optimization method that combines reservoir scheduling with weather forecasting. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0126] First, two modified DZZ4 multi-factor professional meteorological observation stations were installed upstream of the reservoir, named Station A and Station B. These two observation stations automatically collect and record data such as temperature, humidity, air pressure, wind direction, wind speed, precipitation and river water level every hour. At the same time, observation data from five surrounding conventional meteorological stations were collected to expand the data coverage. All data are uploaded to the meteorological big data cloud platform in real time through the central station server.

[0127] Next, the observation data in the system and the professional observation data of the local central station server are fused through the designed comprehensive data interface service. In the data fusion process, the data assimilation technology based on Kalman filtering is used to improve the accuracy and consistency of the data.

[0128] At the same time, two types of weather forecast data were obtained from the National Meteorological Center: one is the intelligent grid forecast service product with a resolution of 5km, hourly to 6 hours, and for the next 7 days; the other is the subjective forecast for the next 7 days. The precipitation and temperature forecast information closely related to reservoir operation was extracted and analyzed.

[0129] In order to establish an accurate degree-day factor snowmelt model, the temperature data and corresponding snowmelt records of the region in the past 10 years were collected and analyzed. Through multiple regression analysis, the basic degree-day factor (DDF) was determined to be 3.2 mm / ℃ / day, and the seasonal adjustment coefficient α was 0.015.

[0130] During the experiment, the cumulative positive temperature was calculated every hour, and the influence of sunshine changes was taken into account. The sunshine weight for the daytime period (6:00-18:00) was set to 1.2, and that for the nighttime period (18:00-6:00 the next day) was set to 0.8. In order to more accurately describe the sunshine changes, a continuous sunshine weight function was used: Ws(h) = 1 + 0.2*sin(π*(h-6) / 12), where h is the number of hours in the 24-hour system.

[0131] Considering the high altitude characteristics of the region, the influence of temperature change trend and wind speed was incorporated into the model. Through historical data analysis, the temperature change trend influence coefficient γ was determined to be 0.05, and the wind speed influence coefficient δ was determined to be 0.03.

[0132] During the peak snowmelt period (days 3 to 5 of the experiment), the estimation frequency was increased to every 4 hours to capture rapid snowmelt events. At the same time, the opening of the upstream river channel was assessed in real time and incorporated into the inflow prediction model.

[0133] Finally, based on the predicted inflow, combined with the current water storage capacity of the reservoir and downstream water demand, a power generation plan for the next 7 days was formulated. Throughout the process, data visualization was performed through the meteorological service platform, and H5 queries on PC and mobile terminals were supported to ensure that relevant personnel could obtain the latest meteorological information and forecast results in a timely manner.

[0134] The following are some of the key data collected during the experiment, as shown in the table below:

[0135] Table 1 Experimental data table

[0136]

[0137] By analyzing the above experimental data, we can draw the following important conclusions:

[0138] Prediction accuracy: The present invention performs well in predicting daily snowmelt and inflow. The average relative errors between the predicted value and the actual observed value are 2.1% and 2.4%, respectively, which are much lower than the 5-10% error range of the traditional method. This high-precision prediction provides a reliable basis for reservoir scheduling and power generation planning.

[0139] Rapid response capability: During the peak snowmelt period (April 3-5), the rapid snowmelt event was successfully captured by increasing the estimation frequency to once every 4 hours. The prediction accuracy during this period was even slightly higher than that of other periods, with an average relative error of only 1.8%, which fully demonstrated the rapid response capability of the invention to emergencies.

[0140] Considering multiple factors: By introducing the temperature change trend and wind speed influencing factors, the application effect of the present invention in high altitude areas is remarkable. In particular, on April 4 and 5, when the temperature changes greatly and the wind speed increases, the prediction results still remain highly accurate, proving the necessity and effectiveness of considering these factors.

[0141] Power generation optimization: Based on accurate inflow forecasts, the power generation plan was developed to achieve efficient use of water resources. During the experiment, the average daily power generation reached 1,577MWh, an increase of 15.3% over the same period last year. In particular, when large inflows were predicted (such as April 4 and 5), the power generation exceeded 2,000MWh per day by starting the generator sets in advance, making full use of the abundant water resources.

[0142] Seasonal adaptation: By introducing seasonal adjustment factors, the present invention is well adapted to the characteristics of the spring snowmelt period. From the trend of cumulative positive temperature changes, it can be seen that the model accurately captures the acceleration and slowdown of the snowmelt process, providing forward-looking guidance for reservoir management.

[0143] Data fusion advantage: By comprehensively utilizing multi-source data (professional observation stations, conventional meteorological stations and weather forecast products), the present invention significantly improves the spatial coverage and time scale of the forecast. This multi-source data fusion not only improves the forecast accuracy, but also enhances the reliability and stability of the forecast results.

[0144] Real-time and accessibility: Through the visual display and multi-terminal support of the meteorological service platform, relevant personnel are ensured to obtain the latest meteorological information and forecast results in a timely manner. This real-time and high accessibility greatly improves the efficiency and accuracy of decision-making.

[0145] In summary, the present invention has shown significant advantages in snowmelt estimation, reservoir flow prediction and power generation optimization. It not only improves the prediction accuracy, but also enhances the scientificity and foresight of reservoir scheduling, and realizes the efficient use of water resources and the maximization of economic benefits. This comprehensive approach based on multi-source data and multi-factor considerations provides a new solution for the intelligent management of reservoirs in high-altitude areas, and has broad application prospects.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A power generation optimization method combining reservoir scheduling with weather forecast, characterized in that: include, collecting and recording meteorological data, and determining daily cumulative positive temperature based on the meteorological data; A snowmelt model is constructed based on historical meteorological data, and the daily accumulated positive temperature is input into the snowmelt model to estimate the daily snowmelt amount; The meteorological data and the daily snowmelt amount are used to determine the inflow flow, and a power generation plan is formulated based on the inflow flow.

2. The power generation optimization method combining reservoir scheduling with weather forecasting as claimed in claim 1, characterized in that: The determination of the daily cumulative positive temperature includes: determining the effective snowmelt temperature per hour based on the meteorological data; Divide the sunshine changes and set the sunshine weights; The effective snowmelt temperature is calculated according to the sunshine weight, and the weighted effective snowmelt temperature of the day is accumulated to obtain the daily cumulative positive temperature; The determination of the daily cumulative positive temperature is shown in the following formula: Where DPDD is the daily accumulated positive temperature, T h is the temperature at hour h, T base is the reference temperature and h is hour.

3. The power generation optimization method combining reservoir scheduling with weather forecasting as claimed in claim 2 is characterized by: The snowmelt model estimates the daily snowmelt amount through the daily accumulated positive temperature, as described in the following formula: M t =DDF·DPDD t ·F s (t)·F a (t)·F w (t) Among them, F a (t) is the influencing factor of temperature change trend; F w (t) is the wind speed influence factor, M t is the daily snowmelt on day t, DDF is the basic degree-day factor; DPDD t is the daily cumulative positive temperature on day t; F s (t) is the seasonal adjustment factor.

4. The power generation optimization method combining reservoir scheduling with weather forecasting as claimed in claim 3 is characterized by: The influencing factor of the temperature change trend is shown in the following formula: The wind speed influencing factor is shown in the following formula: The seasonal adjustment factor is as follows: Where d is the current date, α is the seasonal adjustment coefficient, ΔT t is the temperature change trend on day t; ΔT max The largest temperature change trend in history; W t is the average wind speed on day t; is the historical average wind speed; W is the standard deviation of wind speed, γ and δ are the influence coefficients of temperature trend and wind speed, respectively, both of which are dimensionless parameters.

5. The power generation optimization method combining reservoir dispatching with weather forecasting as claimed in claim 4 is characterized by: The determination of the inbound flow includes: constructing a unit line of the watershed based on the historical meteorological data and revising it; Perform validity conversion on the precipitation and snowmelt in the meteorological data; Direct runoff is obtained by convolving the corrected unit line with the converted precipitation and daily snowmelt; The base flow is calculated according to the corrected unit line, the direct runoff and the base flow are added, and error correction is performed to obtain the inflow flow.

6. The power generation optimization method combining reservoir scheduling with weather forecasting as claimed in claim 5, characterized in that: The direct runoff is calculated as follows: Where R(τ) is the total input at time τ, i.e., the sum of effective precipitation and snowmelt, U′(t-τ) is the corrected unit line of the time difference t-τ, and Q d (t) is the direct runoff at time t; The base flow is calculated as follows: Where k is the baseflow attenuation coefficient, δ reflects the periodicity of baseflow changes during the day, η is the contribution coefficient of precipitation and snowmelt to baseflow, R(t) is the total input at time t, and Q b (t) is the base flow at time t.

7. The power generation optimization method combining reservoir scheduling with weather forecasting as claimed in claim 6 is characterized by: The validity conversion is shown as follows: P e (t)=P(t)·(1-e -a·I(t) ) M e (t)=M(t)·(1-e -β·S(t) ) Among them, P(t) is the precipitation, M(t) is the predicted snowmelt, and P e (t) is the effective precipitation and M e (t) is the effective snowmelt, I(t) is the previous soil moisture index, S(t) is the snow cover, and α and β are empirical coefficients.

8. A power generation optimization system combining reservoir scheduling and weather forecast, based on the power generation optimization method combining reservoir scheduling and weather forecast according to any one of claims 1 to 7, characterized in that: include, Data acquisition module, used to collect and record meteorological data; A snowmelt amount determination module is used to determine the daily cumulative positive temperature according to the meteorological data; construct a snowmelt model according to the historical meteorological data, and input the daily cumulative positive temperature into the snowmelt model to estimate the daily snowmelt amount; The inflow flow determination module is used to determine the inflow flow by using the meteorological data and the daily snowmelt amount, and formulate a power generation plan according to the inflow flow.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power generation optimization method combining reservoir scheduling with weather forecasting as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power generation optimization method combining reservoir scheduling with weather forecasting as described in any one of claims 1 to 7 are implemented.

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