System for automatically limiting abandoned water in forecasting and dispatching process of cascade hydropower station

Abstract

The invention provides an automatic water abandoning limiting system for a cascade hydropower station forecasting and dispatching process. The system comprises a cascade hydropower station forecasting and dispatching module, a scene switching module and a water abandoning limiting module. The cascade hydropower station forecast scheduling module comprises a cascade hydropower station long-term optimization scheduling module, a cascade hydropower station medium-term optimization scheduling module, a cascade hydropower station short-term optimization scheduling module, a cascade hydropower station in-plant economic operation module, a scheduling model heuristic optimization algorithm module and a monthly water inflow prediction module. A daily inflow prediction module and an hourly inflow prediction module; and the water discarding limiting module comprises a flood peak before-coming full release module and a water level over-limit full release module. In cascade hydropower station forecast scheduling, automatic full-water-release scene day identification and automatic full-water-release process implementation are realized through a scene switching and water abandoning limiting module, the risk of generating a large amount of water abandoning is reduced, and manual blind operation is avoided; in addition, the main process can be automatically regressed on a non-full day, and short-term optimization scheduling is not affected.

Description

Technical Field

[0001] The invention relates to the technical field of hydropower generation, and in particular to a system for automatically limiting water abandonment in a forecast dispatching process of a cascade hydropower station. Background Art

[0002] In the era of big data, by fully mining the characteristics of data such as hydrological and meteorological data, unit operation data, and cascade hydropower station design data, we can effectively describe the complex unit characteristics and spatiotemporal correlations, predict the water inflow and power generation at future times, and provide appropriate optimization scheduling strategies to make full use of the inflow runoff and maximize the power generation and power generation revenue of cascade hydropower stations.

[0003] The forecast dispatch of cascade hydropower stations is based on the prediction of water inflow, and is extended from the two aspects of control and feedback. The calculation results of the upper-level longer-time-scale dispatch model control the dispatch boundary of the lower-level shorter-time-scale dispatch model, and the actual operation process of the lower-level dispatch model will be fed back to the upper-level dispatch model to roll over and update the remaining period dispatch plan. The control and feedback of the upper and lower models are intertwined and carried out layer by layer, which can effectively realize the real-time rolling correction of the dispatch decision of the cascade hydropower station and improve the reliability of power generation dispatch. However, while the forecast dispatch of cascade hydropower stations increases power generation and power generation revenue, it often significantly raises the water level of the reservoir, which makes the reservoir at risk of large-scale water abandonment during the flood season. Therefore, it is necessary to provide an effective water abandonment restriction technology for the forecast dispatch of cascade hydropower stations.

[0004] In the past, during the flood season, staff often only set a single flood season limit water level and artificially implemented full power generation of hydropower stations in advance to keep the reservoir water level below the flood season limit water level for a long time. Once the reservoir water level reaches the flood season limit water level, the gates are immediately opened to abandon water. Now, with the maturity of water inflow forecasting technology, reliable forecasts of daily water inflows in the next few days or even a month have become possible. By embedding automatic scene switching and automatic power station full power generation functions based on daily water inflow forecasts in the automatic execution of cascade hydropower station forecast scheduling, it is possible to intelligently identify whether the day is a full power generation scene day. If full power generation is required to limit water abandonment, the hydropower station will be fully launched autonomously to avoid the blindness in the process of manual identification and manual full power generation. Summary of the invention

[0005] The object of the present invention is to provide a system for automatically limiting water abandonment during the forecast dispatch process of cascade hydropower stations in response to the above-mentioned problems.

[0006] To achieve the above object, the present invention adopts the following technical solution:

[0007] A system for automatically limiting water abandonment during the forecast dispatching process of a cascade hydropower station, comprising a forecast dispatching module for a cascade hydropower station, a scene switching module and a water abandonment limitation module;

[0008] The cascade hydropower station forecast dispatch module includes: a cascade hydropower station long-term optimization dispatch module, a cascade hydropower station medium-term optimization dispatch module, a cascade hydropower station short-term optimization dispatch module, a cascade hydropower station in-plant economic operation module, a dispatch model heuristic optimization algorithm module, a monthly water inflow forecast module, a daily water inflow forecast module and an hourly water inflow forecast module;

[0009] The scenario switching module obtains the daily water volume forecast value and the water level at the end of the first day of the mid-term scheduling remaining period from the cascade hydropower station forecast scheduling module, and obtains the full-fire status bit from the water abandonment restriction module to determine whether the hydropower station needs to be fully fired, and determines the scenario value, and then sends the scenario value to the cascade hydropower station forecast scheduling module and the water abandonment restriction module for scenario judgment and process switching in the operation process;

[0010] The water abandonment restriction module includes a full discharge module before the flood peak and a full discharge module when the water level exceeds the limit.

[0011] While adopting the above technical solutions, the present invention may also adopt or combine the following technical solutions:

[0012] As a preferred technical solution of the present invention: the scene switching module is executed once a day and includes the following operation steps:

[0013] S201: Determine whether the time is a new day, if yes, proceed to the next step, if no, continue to determine the time;

[0014] S202: Check if the full-send status bit is 1? If yes, keep the original scene value unchanged and go to S206; if no, go to the next step;

[0015] S203: Determine whether the reservoir's initial water level and final water level are both less than the warning water level limit. ? If yes, proceed to the next step. If no, full discharge is required to limit the reservoir from abandoning water. Set the scenario value to scenario 3 and go to S206. The reservoir’s initial water level of the day is obtained through actual measurement. The reservoir’s final water level of the day = the final water level of the first day of the mid-term scheduling remaining period. The warning water level limit The calculation formula is:

[0016]

[0017] In the formula, f Z-V is the functional expression of the water level-reservoir capacity relationship curve, n s0 is the total number of power stations that directly draw water from reservoirs for power generation, q i,max represents the maximum water flow of power station i that directly draws water from the reservoir for power generation, q s is the ecological flow of the reservoir, The confidence level α is obtained based on the historical average daily water flow statistics of the reservoir. IThe upper limit of the confidence interval of the reservoir day The probability is lower than α I , α I Generally, 95% or 97.5% is taken;

[0018] S204: According to the designed inflow flood process line and the daily water volume forecast value, the flood peak arrival date is determined, and the flood peak process start date is calculated. The basis for determining the flood peak arrival date is: the daily water volume forecast value on the forecast day ≥ the total inflow flood volume on the day of the peak value of the designed inflow flood process line * scaling factor? If yes, the forecast day is the flood peak arrival date; if no, the forecast day is not the flood peak arrival date, and the scaling factor is less than or equal to 1;

[0019] S205: Determine (the current day is the flood peak process start date) or (the current day is the tth day in reverse order before the flood peak process start date) f +1 day, t f ≥0, and the reservoir's initial water level on the day ≥ the t-th day in reverse order before the start of the flood peak process f The water level limit at the beginning of the day )? Yes, then full discharge is required to limit the reservoir from discarding water, set the scenario value to scenario 2, and execute the next step; No, full discharge is not required, set the scenario value to scenario 1, and execute the next step. The starting date of the flood peak process = the flood peak arrival date - (the peak date of the designed inflow flood process line - the initial date of the designed inflow flood process line), the starting date of the flood peak process is the tth day in reverse order before the starting date of the flood peak process f The water level limit at the beginning of the day The calculation formula is:

[0020]

[0021] Where, T p is the duration of the flood peak process, that is, the duration of the designed flood process line entering the reservoir. is the average water flow rate of the reservoir calculated based on the designed flood process line, t f Generally no more than 15 days;

[0022] S206: Outputting scenario values ​​to the forecasting and dispatching module and the water abandonment restriction module of the cascade hydropower station;

[0023] S207: Determine whether to end, if yes, end the process, if not, go to S201.

[0024] As a preferred technical solution of the present invention: the water disposal restriction module is executed once a day and includes the following operation steps:

[0025] S301: Initialize full delivery status bit = 0, remaining full delivery days = 0;

[0026] S302: Determine whether the time is a new day, if yes, proceed to the next step, if no, continue to determine the time;

[0027] S303: Determine whether the scene value of the day is equal to scene 2, if yes, go to S312, if no, go to the next step;

[0028] S304: Determine whether the scene value of the day is equal to scene 3, if yes, execute the next step, if no, go to S309;

[0029] S305: Determine whether the full delivery status bit is equal to 0. If yes, set the full delivery status bit to 1, set the remaining full delivery days to 1, and execute the next step. If no, keep the full delivery status bit = 1, keep the remaining full delivery days unchanged, and execute the next step.

[0030] S306: Execute the water level over-limit full-send module;

[0031] S307: remaining full payment days = remaining full payment days - 1;

[0032] S308: Determine whether the remaining full payment days are less than or equal to 0. If yes, proceed to the next step. If no, go to S310.

[0033] S309: Set the full-send status bit to 0;

[0034] S310: Output the full transmission status bit to the scene switching module;

[0035] S311: Determine whether to end, if yes, end the process, if no, go to S302;

[0036] S312: Determine whether the full delivery status bit is equal to 0. If yes, set the full delivery status bit to 1 and set the remaining full delivery days = T p + (peak process start date - current date), and execute the next step; otherwise, keep the full-sending status bit = 1, keep the remaining full-sending days unchanged, and execute the next step;

[0037] S313: Execute the full-send module before the flood peak arrives, and go to S307.

[0038] As a preferred technical solution of the present invention: the execution methods of the full-generation module before the flood peak and the full-generation module when the water level exceeds the limit are both to forcibly set the output of all units in the power station that directly draws water from the reservoir to generate electricity = the maximum output of the units.

[0039] As a preferred technical solution of the present invention: the operation of the forecast dispatching module of the cascade hydropower station includes the following steps:

[0040] S1: Initialize the target final water level of the 12th month of the kth year and use it as the final water level boundary constraint of the long-term optimal scheduling module of the cascade hydropower stations, k = current year, l = 1;

[0041] S2: Execute the long-term optimization dispatching module of cascade hydropower stations, and call the dispatching model heuristic optimization algorithm module for optimization, to obtain the corrected optimal long-term dispatching remaining period reference flow, and the end-of-month water levels of all months within the long-term dispatching remaining period, that is, the end-of-month water levels of reservoirs from the first month of the kth year to the 12th month of the kth year;

[0042] S3: The water level at the end of the first month of the long-term scheduling remaining period, that is, the reservoir water level at the end of the month of the kth year, is used as the final water level boundary constraint of the mid-term optimization scheduling module of the cascade hydropower stations;

[0043] S4: Initialize m=1;

[0044] S5: Execute the mid-term optimization scheduling module of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module for optimization, and obtain the corrected optimal mid-term scheduling remaining period reference flow, as well as all end-of-day water levels within the mid-term scheduling remaining period, that is, the end-of-day water levels of the reservoir from the mth day of the lth month of the kth year to the last day of the lth month of the kth year;

[0045] S6: The final water level of the first day of the remaining period of the mid-term optimal dispatch, i.e., the final water level of the reservoir on the mth day of the lth month of the kth year, is used as the final water level boundary constraint of the short-term optimal dispatch module of the cascade hydropower stations;

[0046] S7: Determine whether the scenario value of the day is scenario 1. If yes, execute the short-term optimization scheduling module of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module for optimization, and obtain the corrected optimal daily reference flow, daily reference flow plan, daily output plan, and all hourly water levels within the day, that is, the hourly water levels of the reservoir from 1:00 on the mth day of the lth month of the kth year to 24:00 on the mth day of the lth month of the kth year, and execute the next step. If no, go to S11;

[0047] S8: Initialize n=1;

[0048] S9: Determine whether to adopt the "electricity determines water" method. If yes, execute the in-plant economic operation module of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module for optimization, carry out the in-plant economic operation of the cascade hydropower station "electricity determines water", and obtain the optimal unit output. If no, execute the in-plant economic operation module of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module for optimization, carry out the in-plant economic operation of the cascade hydropower station "water determines electricity", and obtain the optimal unit reference flow;

[0049] S10: Check if n≥24? If yes, proceed to the next step; if no, go to S14;

[0050] S11: Check if m ≥ the total number of days in the first month. If yes, proceed to the next step; if no, go to S15;

[0051] S12: Check if l≥12? If yes, proceed to the next step; if no, go to S16;

[0052] S13: Determine whether to end, if yes, end the process, if not, go to S17;

[0053] S14: Determine whether the time is a new hour. If yes, n=n+1 and go to S9. If no, continue to determine the time.

[0054] S15: Determine whether the time is a new day, if yes, m=m+1 and go to S5, if no, continue to determine the time;

[0055] S16: Determine whether the time is a new month, if yes, then l=l+1 and go to S2, if no, continue to determine the time;

[0056] S17: Determine whether the time is a new year, if yes, go to S1, if no, continue to determine the time.

[0057] The present invention provides a system for automatically limiting water abandonment in the forecast and dispatch process of cascade hydropower stations, which has the following beneficial effects: in the forecast and dispatch process of cascade hydropower stations, two types of full-discharge scenario days, namely full-discharge before the flood peak and full-discharge when the water level exceeds the limit, are comprehensively considered, and automatic full-discharge scenario day identification and automatic full-discharge process implementation are realized through a scenario switching module and a water abandonment limitation module, thereby effectively reducing the risk of large-scale water abandonment in the forecast and dispatch process of cascade hydropower stations, and avoiding the blindness existing in artificial scenario day identification and artificial full-discharge processes; in addition, it can automatically switch back to the main process of the forecast and dispatch process of the cascade hydropower stations on non-full-discharge scenario days, without affecting the short-term optimization dispatch process of the day. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is a schematic diagram of the automatic water abandonment restriction system in the forecast dispatching process of the cascade hydropower station provided by the present invention.

[0059] Figure 2 This is the operation flow chart of the scene switching module.

[0060] Figure 3 Operation flow chart of the water rejection limit module.

[0061] Figure 4 Operation flow chart of the forecast and dispatch module of cascade hydropower stations.

[0062] Figure 5 This is a schematic diagram of the hydraulic connections of the Xiecunyuan cascade hydropower stations.

[0063] Figure 6 This is the water flow curve of Xiecunyuan Secondary Reservoir.

[0064] Figure 7This is the flood discharge curve of Xiecunyuan Secondary Reservoir before and after optimization.

[0065] Figure 8 This is the water level curve of Xiecunyuan Secondary Reservoir before and after optimization.

[0066] Fig. 9 This is the total output curve of Xiecunyuan Secondary Hydropower Station before and after optimization.

[0067] Fig.10 This is the total output curve of Xiecunyuan Level 3 Hydropower Station before and after optimization.

[0068] In the figure: 1- forecast and dispatch module of cascade hydropower stations; 2- scene switching module; 3- water abandonment restriction module; 11- long-term optimization dispatch module of cascade hydropower stations; 12- medium-term optimization dispatch module of cascade hydropower stations; 13- short-term optimization dispatch module of cascade hydropower stations; 14- in-plant economic operation module of cascade hydropower stations; 15- heuristic optimization algorithm module of dispatch model; 16- monthly water inflow forecast module; 17- daily water inflow forecast module; 18- hourly water inflow forecast module; 31- full discharge module before flood peak; 32- full discharge module when water level exceeds limit. DETAILED DESCRIPTION

[0069] The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.

[0070] like Figure 1 As shown, a system for automatically limiting water abandonment in the forecast dispatch process of a cascade hydropower station includes a forecast dispatch module 1 for a cascade hydropower station, a scene switching module 2 and a water abandonment limitation module 3;

[0071] The cascade hydropower station forecast dispatch module includes: a cascade hydropower station long-term optimization dispatch module 11, a cascade hydropower station medium-term optimization dispatch module 12, a cascade hydropower station short-term optimization dispatch module 13, a cascade hydropower station in-plant economic operation module 14, a dispatch model heuristic optimization algorithm module 15, a monthly water inflow forecast module 16, a daily water inflow forecast module 17 and an hourly water inflow forecast module 18;

[0072] The scene switching module 2 obtains the daily water volume forecast value and the water level at the end of the first day of the mid-term scheduling remaining period from the cascade hydropower station forecast scheduling module 1, and obtains the full-fire status bit from the water abandonment restriction module 3 to determine whether the hydropower station needs to be fully fired, and determines the scene value, and then sends the scene value to the cascade hydropower station forecast scheduling module 1 and the water abandonment restriction module 3 for scene judgment and process switching in the operation process;

[0073] The water abandonment restriction module 3 comprises: a full discharge module 31 before the flood peak and a full discharge module 32 when the water level exceeds the limit.

[0074] The data interaction relationship between modules is as follows:

[0075] The long-term optimization scheduling module 11 of the cascade hydropower station obtains the monthly water volume forecast value from the monthly water volume forecast module 16 as an input condition, calculates the long-term scheduling remaining period reference flow population correction value according to the long-term scheduling remaining period reference flow population original value, and then calculates the long-term scheduling remaining period objective function value and inputs it into the scheduling model heuristic optimization algorithm module 15. The scheduling model heuristic optimization algorithm module 15 iteratively updates the new long-term scheduling remaining period reference flow population original value, and returns it to the long-term optimization scheduling module 11 of the cascade hydropower station for a new round of calculation, iterates repeatedly until the maximum number of iterations, and returns the final generation of the population optimal solution to the long-term optimization scheduling module 11 of the cascade hydropower station.

[0076] The medium-term optimization scheduling module 12 of the cascade hydropower station obtains the daily water volume forecast value from the daily water volume forecast module 17 as an input condition, obtains the water level at the end of the first month of the long-term scheduling remaining period from the long-term optimization scheduling module 11 of the cascade hydropower station as the boundary constraint of the final water level, calculates the correction value of the medium-term scheduling remaining period reference flow population according to the original value of the medium-term scheduling remaining period reference flow population, and then calculates the medium-term scheduling remaining period objective function value and inputs it into the scheduling model heuristic optimization algorithm module 15. The scheduling model heuristic optimization algorithm module 15 iteratively updates the new original value of the medium-term scheduling remaining period reference flow population, and returns it to the medium-term optimization scheduling module 12 of the cascade hydropower station for a new round of calculation, iterates repeatedly until the maximum number of iterations, and sends the optimal solution of the final generation of population to the medium-term optimization scheduling module 12 of the cascade hydropower station.

[0077] The short-term optimization scheduling module 13 of the cascade hydropower station obtains the hourly water inflow forecast value from the hourly water inflow forecast module 18 as an input condition, obtains the final water level of the first day of the mid-term scheduling remaining period from the mid-term optimization scheduling module 12 of the cascade hydropower station as the final water level boundary constraint, calculates the daily reference flow population correction value according to the original value of the daily reference flow population, and then calculates the daily objective function value and inputs it into the scheduling model heuristic optimization algorithm module 15. The scheduling model heuristic optimization algorithm module 15 iteratively updates the new original value of the daily reference flow population, and returns it to the short-term optimization scheduling module 13 of the cascade hydropower station for a new round of calculation, iterates repeatedly until the maximum number of iterations, and sends the optimal solution of the final generation of population to the short-term optimization scheduling module 13 of the cascade hydropower station.

[0078] The in-plant economic operation module 14 of the cascade hydropower station obtains the daily output plan and the daily reference flow plan from the short-term optimization scheduling module 13 of the cascade hydropower station as input conditions, calculates the in-plant economic operation objective function value according to the unit output population or the unit reference flow population, and inputs it into the scheduling model heuristic optimization algorithm module 15. The scheduling model heuristic optimization algorithm module 15 iteratively updates the new unit output population or the unit reference flow population, and returns it to the in-plant economic operation module 14 of the cascade hydropower station for a new round of calculation, iterates repeatedly until the maximum number of iterations, and sends the optimal solution of the final generation of population to the in-plant economic operation module 14 of the cascade hydropower station.

[0079] The scene switching module 2 obtains the daily water inflow forecast value and the water level at the end of the first day of the medium-term scheduling remaining period from the cascade hydropower station forecast scheduling module 1, and obtains the full-power status bit from the water abandonment restriction module 3 to determine whether the hydropower station needs to be fully powered, and determines the scene value, and then sends the scene value to the cascade hydropower station forecast scheduling module 1 and the water abandonment restriction module 3 for scene judgment and process switching in the operation process.

[0080] like Figure 2 As shown, the scene switching module is executed once a day at the beginning of the day and includes the following operation steps:

[0081] S201: Determine whether the time is a new day, if yes, proceed to the next step, if no, continue to determine the time;

[0082] S202: Check if the full-send status bit is 1? If yes, keep the original scene value unchanged and go to S206; if no, go to the next step;

[0083] S203: Determine whether the reservoir's initial water level and final water level are both less than the warning water level limit. ? If yes, proceed to the next step. If no, full discharge is required to limit the reservoir from abandoning water. Set the scenario value to scenario 3 and go to S206. The reservoir’s initial water level of the day is obtained through actual measurement. The reservoir’s final water level of the day = the final water level of the first day of the mid-term scheduling remaining period. The warning water level limit The calculation formula is:

[0084]

[0085] In the formula, f Z-V is the functional expression of the water level-reservoir capacity relationship curve, n s0 is the total number of power stations that directly draw water from reservoirs for power generation, q i,max represents the maximum water flow of power station i that directly draws water from the reservoir for power generation, q s is the ecological flow of the reservoir, The confidence level α is obtained based on the historical average daily water flow statistics of the reservoir. I The upper bound of the confidence interval of the reservoir The probability is lower than α I , α I Generally, 95% or 97.5% is taken;

[0086] S204: According to the designed inflow flood process line and the daily inflow water forecast value, the flood peak arrival date is determined, and the starting date of the flood peak process is estimated. The basis for determining the flood peak arrival date is: the daily inflow water forecast value on the forecast day ≥ the total inflow flood volume on the day of the peak value of the designed inflow flood process line * scaling factor? If yes, the forecast day is the flood peak arrival date; if no, the forecast day is not the flood peak arrival date, and the scaling factor is less than or equal to 1 (theoretically, the scaling factor should be equal to 1. Considering that the daily inflow water forecast on the flood peak arrival date often has a large negative deviation, a scaling factor less than 1 can be used to improve the sensitivity of the flood peak arrival date determination);

[0087] S205: Determine (the current day is the flood peak process start date) or (the current day is the tth day in reverse order before the flood peak process start date) f +1 day, t f ≥0, and the reservoir's initial water level on the day ≥ the t-th day in reverse order before the start of the flood peak process f The water level limit at the beginning of the day )? Yes, then full discharge is required to limit the reservoir from discarding water, set the scenario value to scenario 2, and proceed to the next step. No, full discharge is not required, set the scenario value to scenario 1, and proceed to the next step. The starting date of the flood peak process = the day of the flood peak arrival - (the peak date of the designed inflow flood process line - the initial date of the designed inflow flood process line), the day before the start of the flood peak process is the tth day in reverse order f The water level limit at the beginning of the day The calculation formula is:

[0088]

[0089] Where, T p is the duration of the flood peak process, that is, the duration of the designed flood process line entering the reservoir, I p is the average water flow rate of the reservoir calculated based on the designed flood process line, t f Generally no more than 15 days;

[0090] S206: Outputting scenario values ​​to the forecasting and dispatching module and the water abandonment restriction module of the cascade hydropower station;

[0091] S207: Determine whether to end, if yes, end the process, if not, go to S201.

[0092] like Figure 3 As shown, the water disposal restriction module is executed once a day at the beginning of the day and includes the following operation steps:

[0093] S301: Initialize full delivery status bit = 0, remaining full delivery days = 0;

[0094] S302: Determine whether the time is a new day, if yes, proceed to the next step, if no, continue to determine the time;

[0095] S303: Determine whether the scene value of the day is equal to scene 2, if yes, go to S312, if no, go to the next step;

[0096] S304: Determine whether the scene value of the day is equal to scene 3, if yes, execute the next step, if no, go to S309;

[0097] S305: Determine whether the full delivery status bit is equal to 0. If yes, set the full delivery status bit to 1, set the remaining full delivery days to 1, and execute the next step. If no, keep the full delivery status bit = 1, keep the remaining full delivery days unchanged, and execute the next step.

[0098] S306: Execute the water level over-limit full-send module;

[0099] S307: remaining full payment days = remaining full payment days - 1;

[0100] S308: Determine whether the remaining full payment days are less than or equal to 0. If yes, proceed to the next step. If no, go to S310.

[0101] S309: Set the full-send status bit to 0;

[0102] S310: Output the full transmission status bit to the scene switching module;

[0103] S311: Determine whether to end, if yes, end the process, if no, go to S302;

[0104] S312: Determine whether the full delivery status bit is equal to 0. If yes, set the full delivery status bit to 1 and set the remaining full delivery days = T p + (peak process start date - current date), and execute the next step; otherwise, keep the full-sending status bit = 1, keep the remaining full-sending days unchanged, and execute the next step;

[0105] S313: Execute the full-send module before the flood peak arrives, and go to S307.

[0106] The execution methods of the full-generation module before the flood peak and the full-generation module when the water level exceeds the limit are both to forcibly set the output of all units in the power station that directly draws water from the reservoir for power generation = the maximum output of the units.

[0107] like Figure 4 As shown in FIG. 1 , the operation of the forecast dispatch module of the cascade hydropower station includes the following steps:

[0108] S1: Initialize the target final water level of the 12th month of the kth year and use it as the final water level boundary constraint of the long-term optimal dispatching module 101 of the cascade hydropower station, k = current year, l = 1;

[0109] S2: Execute the long-term optimization dispatching module 101 of the cascade hydropower station, and call the dispatching model heuristic optimization algorithm module 105 for optimization, and obtain the corrected optimal long-term dispatching remaining period reference flow, and the end-of-month water levels of all months within the long-term dispatching remaining period, that is, the end-of-month water levels of the reservoir from the first month of the kth year to the 12th month of the kth year;

[0110] S3: The water level at the end of the first month of the long-term scheduling remaining period, i.e., the reservoir water level at the end of the month of the kth year, is used as the final water level boundary constraint of the cascade hydropower station mid-term optimization scheduling module 102;

[0111] S4: Initialize m=1;

[0112] S5: Execute the mid-term optimization scheduling module 102 of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module 105 for optimization, and obtain the corrected optimal mid-term scheduling remaining period reference flow, and all end-of-day water levels within the mid-term scheduling remaining period, that is, the end-of-day water levels of the reservoir from the mth day of the lth month of the kth year to the last day of the lth month of the kth year;

[0113] S6: The final water level of the first day of the remaining period of the mid-term optimal dispatch, i.e., the final water level of the reservoir on the mth day of the lth month of the kth year, is used as the final water level boundary constraint of the short-term optimal dispatch module 103 of the cascade hydropower station;

[0114] S7: Determine whether the scenario value of the day is scenario 1. If yes, execute the short-term optimization scheduling module 103 of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module 105 for optimization, and obtain the corrected optimal daily reference flow, daily reference flow plan, daily output plan, and all hourly water levels within the day, that is, the hourly water levels of the reservoir from 1:00 on the mth day of the lth month of the kth year to 24:00 on the mth day of the lth month of the kth year, and execute the next step. If no, go to S11;

[0115] S8: Initialize n=1;

[0116] S9: Determine whether to adopt the "power-to-water" method. If yes, execute the in-plant economic operation module 104 of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module 105 for optimization, carry out the in-plant economic operation of the cascade hydropower station "power-to-water", and obtain the optimal unit output. If no, execute the in-plant economic operation module 104 of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module 105 for optimization, carry out the in-plant economic operation of the cascade hydropower station "water-to-power", and obtain the optimal unit reference flow;

[0117] S10: Check if n≥24? If yes, proceed to the next step; if no, go to S14;

[0118] S11: Check if m ≥ the total number of days in the first month. If yes, proceed to the next step; if no, go to S15;

[0119] S12: Check if l≥12? If yes, proceed to the next step; if no, go to S16;

[0120] S13: Determine whether to end, if yes, end the process, if not, go to S17;

[0121] S14: Determine whether the time is a new hour. If yes, n=n+1 and go to S9. If no, continue to determine the time.

[0122] S15: Determine whether the time is a new day, if yes, m=m+1 and go to S5, if no, continue to determine the time;

[0123] S16: Determine whether the time is a new month, if yes, then l=l+1 and go to S2, if no, continue to determine the time;

[0124] S17: Determine whether the time is a new year, if yes, go to S1, if no, continue to determine the time.

[0125] Daily reference flow plan = optimal daily reference flow after correction, daily output plan = power station comprehensive output coefficient * optimal daily reference flow after correction * head.

[0126] The long-term optimization scheduling module of cascade hydropower stations takes one year as the scheduling cycle and one month as the scheduling period. It optimizes the power station reference flow with the goal of maximizing the total power generation and minimizing the total amount of abandoned water. It is executed once at the beginning of each month. The long-term optimization scheduling module of cascade hydropower stations contains the objective function, constraints, input conditions and decision variables for the long-term scheduling residual period.

[0127] Long-term scheduling remaining period objective function:

[0128]

[0129] Where, T y is the remaining period of long-term scheduling, that is, from the current month to the 12th month, Δt y is the length of the long-term scheduling period, i.e., 1 month, n s and n re are the total number of cascade hydropower stations and reservoirs, respectively, i t , are the total power output and power station reference flow of the i-th power station in the t-th period, For the kth re The discharge flow of a reservoir in period t.

[0130] Constraints include equality constraints and inequality constraints. Equality constraints include: water balance constraints, water level-reservoir capacity relationship curve, water level-flow relationship curve, initial water level boundary constraints and final water level boundary constraints; inequality constraints include: water level upper and lower limit constraints, water head upper and lower limit constraints, power station reference flow upper and lower limit constraints and power station total output upper and lower limit constraints. The initial water level boundary constraint is the actual measured initial water level of the month, and the final water level boundary constraint is the target final water level of the 12th month.

[0131] The input condition is the predicted value of monthly water inflow.

[0132] The decision variable is the long-term scheduling remaining period reference flow Long-term scheduling remaining period reference flow It is a column vector composed of the monthly reference flow of all cascade hydropower stations within the remaining period of long-term scheduling, and its expression is as follows:

[0133]

[0134] The mid-term optimization scheduling module of cascade hydropower stations takes one month as the scheduling cycle and one day as the scheduling period. It optimizes the power station reference flow with the goal of maximizing the total power generation and minimizing the total amount of abandoned water, and is executed once at the beginning of each day. The mid-term optimization scheduling module of cascade hydropower stations contains the objective function, constraints, input conditions and decision variables for the mid-term scheduling residual period.

[0135] The objective function of the remaining period of mid-term scheduling is:

[0136]

[0137] Where, T m is the remaining period of mid-term scheduling, that is, from the current day to the end of the month, Δt m It is the length of the medium-term scheduling period, i.e. 1 day.

[0138] The difference between the constraint conditions and the long-term optimization scheduling module of cascade hydropower stations is that the initial water level boundary constraint of the medium-term optimization scheduling module of cascade hydropower stations is the actual measured initial water level on the day, and the final water level boundary constraint is the water level at the end of the first month of the long-term scheduling remaining period.

[0139] The input condition is the predicted value of daily water inflow.

[0140] The decision variable is the reference flow rate of the remaining period of mid-term scheduling Medium-term scheduling remaining period reference flow It is a column vector composed of the daily reference flow of all cascade hydropower stations within the remaining period of medium-term scheduling, and its expression is as follows:

[0141]

[0142] The short-term optimization scheduling module of cascade hydropower stations takes 1 day as the scheduling cycle and 1 hour as the scheduling period. It optimizes the power station reference flow with the goal of maximizing the total power generation revenue and minimizing the total amount of abandoned water. It is executed once a day. The short-term optimization scheduling module of cascade hydropower stations contains short-term scheduling objective functions, constraints, input conditions and decision variables.

[0143] Short-term scheduling objective function:

[0144]

[0145] Where, T d is the short-term scheduling period, i.e. 1 day, Δt d is the duration of the short-term scheduling period, i.e. 1 hour, c t is the electricity price during period t.

[0146] The difference between the constraint conditions and the long-term optimal scheduling module of cascade hydropower stations is that the initial water level boundary constraint of the short-term optimal scheduling module of cascade hydropower stations is the actual measured initial water level on the day, and the final water level boundary constraint is the final water level on the first day of the medium-term scheduling residual period.

[0147] The input condition is the hourly water inflow forecast value.

[0148] The decision variable is the daily citation flow Daily citation flow It is a column vector composed of the hourly reference flow of all power stations in the cascade hydropower station within a day, and the expression is as follows:

[0149]

[0150] The in-plant economic operation module of cascade hydropower stations adopts the optimization method of "determining water consumption by electricity" or "determining electricity by water", with the goal of minimizing the total water consumption or maximizing the total power generation of cascade hydropower stations in the current period, and is executed once every hour. The in-plant economic operation module of cascade hydropower stations contains the in-plant economic operation objective function, constraints, input conditions and decision variables.

[0151] If the “power-to-water” method is adopted, the economic operation objective function of the plant is:

[0152]

[0153] In the formula, q ij , P ij are the unit reference flow and unit output of the jth unit in the i-th power station, Δt is the duration of the current period, i.e., 1 hour, n u is the total number of units in the power station.

[0154] If the “water-based electricity” approach is adopted, the economic operation objective function of the plant is:

[0155]

[0156] The constraints include equality constraints and inequality constraints. Equality constraints include: water balance constraints, water level-reservoir capacity relationship curve, water level-flow relationship curve, and unit efficiency model; inequality constraints include: water level upper and lower limit constraints, water head upper and lower limit constraints, unit reference flow upper and lower limit constraints, unit output upper and lower limit constraints, and unit vibration zone constraints.

[0157] The input conditions are the measured water inflow in the current period, the daily output plan and the daily reference flow plan. For the in-plant economic operation of the cascade hydropower station that "determines water by electricity", the unit output allocation optimization is carried out according to the planned total output of the power station in the current period in the daily output plan, so that the total output of the power station = the planned total output of the power station in the current period, and the total water consumption of the cascade hydropower station in the current period is the lowest; for the in-plant economic operation of the cascade hydropower station that "determines electricity by water", the unit reference flow allocation optimization is carried out according to the planned reference flow of the power station in the current period in the daily reference flow plan, so that the total power generation of the cascade hydropower station in the current period is the largest when the power station reference flow = the planned reference flow of the power station in the current period.

[0158] If the "electricity determines water" method is adopted, the decision variable is the unit output; if the "water determines electricity" method is adopted, the decision variable is the unit reference flow.

[0159] Since the process of generating the original value of the long-term scheduling remaining period reference flow population, the original value of the medium-term scheduling remaining period reference flow population, and the original value of the daily reference flow population by the scheduling model heuristic optimization algorithm module is random, when the long-term, medium-term, and short-term optimization scheduling limits the final water level boundary constraint, the above original values ​​are difficult to accurately deduce the final water level boundary constraint while satisfying the water balance constraint. This means that the original value of the long-term scheduling remaining period reference flow population, the original value of the medium-term scheduling remaining period reference flow population, and the original value of the daily reference flow population given by the scheduling model heuristic optimization algorithm module cannot simultaneously satisfy the water balance constraint and the final water level boundary constraint. Therefore, it is necessary to carry out reference flow correction based on the final water level boundary constraint so that the corrected reference flow can simultaneously satisfy the water balance constraint and the final water level boundary constraint. In addition, in the long-, medium- and short-term optimization scheduling process, the optimal solution of the final generation of population output by the heuristic optimization algorithm module of the scheduling model is also obtained by searching the original values ​​of the reference flow population of each generation. Therefore, it is also necessary to obtain the corrected optimal long-term scheduling remaining period reference flow, the corrected optimal medium-term scheduling remaining period reference flow and the corrected optimal daily reference flow through correction.

[0160] The correction value of the reference flow population for the long-term scheduling remaining period, the correction value of the reference flow population for the medium-term scheduling remaining period and the correction value of the daily reference flow population are obtained by correcting the reference flow of the original value of the reference flow population for the long-term scheduling remaining period, the original value of the reference flow population for the medium-term scheduling remaining period and the original value of the daily reference flow population respectively.

[0161] The corrected optimal long-term scheduling remaining period reference flow, the corrected optimal medium-term scheduling remaining period reference flow and the corrected optimal short-term scheduling remaining period reference flow are all obtained by correcting the reference flow of the optimal solution of the population in the final generation.

[0162] Specifically, the automatic water abandonment restriction system in the forecast dispatching process of the above-mentioned cascade hydropower station is implemented in the following ways:

[0163] Based on the simulation calculation of Xiecunyuan cascade hydropower station, the hydraulic connection diagram of Xiecunyuan cascade hydropower station is as follows Figure 5 As shown in the figure, the Xiecunyuan Second Level Hydropower Station draws water directly from the Xiecunyuan Second Level Reservoir to generate electricity, while the tailwater of the Xiecunyuan Second Level Hydropower Station and the discharge flow and ecological flow of the Xiecunyuan Second Level Reservoir are intercepted and stored by the weir and then introduced into the Xiecunyuan Third Level Hydropower Station as the reference flow of the Xiecunyuan Third Level Hydropower Station. The installed capacity of the Xiecunyuan Second Level Hydropower Station and the Xiecunyuan Third Level Hydropower Station are 2*8.67MW and 2*2MW respectively, and the rated heads are 219.24m and 48.289m respectively. The power generation price is 0.57 yuan for peak power (peak power period: 7:00 to 11:00 and 13:00 to 23:00) and 0.228 yuan for valley power (valley power period: 11:00 to 13:00, 23:00 to 7:00 the next day). Let α I =97.5%, at this time, the warning water level limit of Xiecunyuan Secondary Reservoir for According to the design reservoir flood process line of 50 / 10 / 5-year rainstorm, calculate the reverse order of the t-th day before the start of the flood peak process. f The water level limit at the beginning of the day As shown in Table 1, the starting date of the flood peak process is set to one day before the flood peak.

[0164] Table 1 Relationship table

[0165]

[0166] The economic operation module of the cascade hydropower station is implemented in a "water-based electricity" manner. The heuristic optimization algorithm module of the scheduling model is based on the iteration and optimization of the adaptive fuzzy particle swarm algorithm (AFPSO algorithm for short). For details of the algorithm, please refer to the document "Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization" (Author: Dong Liu et al., Journal Name: Energies, Volume Number: 2019, Issue Number: 12, Article Number: 3903).

[0167] The main inequality constraint settings for the Xiecunyuan cascade hydropower station are shown in Table 2.

[0168] The optimization effects of the Xiecunyuan cascade hydropower station under the three forecast and dispatch systems are investigated. The first one is a forecast and dispatch system that does not consider scenarios 2 and 3, that is, the forecast and dispatch system does not contain a water abandonment restriction module 3; the second one is a forecast and dispatch system that only considers scenario 2, that is, the forecast and dispatch system contains a water abandonment restriction module 3, but the water abandonment restriction module 3 only contains a full-discharge module 31 before the flood peak arrives; the third one is a forecast and dispatch system that considers scenarios 2 and 3, that is, this embodiment.

[0169] Table 2 Parameter setting table of inequality constraints of Xiecunyuan cascade hydropower station

[0170]

[0171] The forecast dispatch optimization results of the Xiecunyuan cascade hydropower station are shown in Table 3. The results show that the first forecast dispatch system, the second forecast dispatch system and this embodiment can effectively improve the annual power generation income of the Xiecunyuan cascade hydropower station. The annual flood discharge of the Xiecunyuan cascade hydropower station: the first forecast dispatch system> the second forecast dispatch system> this embodiment≈before optimization. It can be seen that this embodiment can effectively improve the annual power generation income of the Xiecunyuan cascade hydropower station while effectively reducing the risk of large-scale water abandonment in the reservoir during the power generation optimization process, so that the annual flood discharge of the Xiecunyuan cascade hydropower station is reduced to the level before optimization.

[0172] Table 3 Optimization results of this embodiment of Xiecunyuan cascade hydropower station

[0173]

[0174] Draw as Figure 6-10The inflow flow curve of the Xiecunyuan Secondary Reservoir from May 15, 2020 to June 15, 2020, the flood discharge flow curve of the Xiecunyuan Secondary Reservoir before and after optimization, the water level curve of the Xiecunyuan Secondary Reservoir before and after optimization, the total output curve of the Xiecunyuan Secondary Hydropower Station before and after optimization, and the total output curve of the Xiecunyuan Thirdary Hydropower Station before and after optimization are shown to intuitively demonstrate the ability of this embodiment in reducing reservoir water abandonment. Figure 6-10 It can be seen that the Xiecunyuan Secondary Reservoir experienced a flood peak caused by a rainstorm that occurs once every five years on May 30, followed by two smaller flood peaks on June 5 and June 10; the first forecast and dispatch system, the second forecast and dispatch system, and the power generation optimization process of this embodiment can all raise the water level of the Xiecunyuan Secondary Reservoir, and reasonably allocate the daily output of the Xiecunyuan Secondary Hydropower Station and the Xiecunyuan Third Hydropower Station according to the time period electricity price; and the second forecast and dispatch system and this embodiment can both fully generate the Xiecunyuan Secondary Hydropower Station before the arrival of the flood peak of the rainstorm that occurs once every five years (i.e., early May 29) to vacate the reservoir capacity until early June 4, thereby avoiding a large amount of flood discharge under the flood peak of the rainstorm that occurs once every five years; due to the high head after the flood peak fills the reservoir capacity Therefore, this embodiment can continue to generate full power after June 4 until the beginning of June 12, thereby avoiding the large-scale flood discharge caused by the small flood peak on June 10; because the flood peak on June 5 is relatively large and is close to the flood peak of the once-in-five-years rainstorm, therefore, although this embodiment still maintains the full power state of the Xiecunyuan Secondary Hydropower Station after the flood peak of the once-in-five-years rainstorm, there is still not enough time to free up sufficient storage capacity for the flood peak, resulting in a certain amount of abandoned water, which can be basically the same as before the optimization.

[0175] The above-mentioned specific implementation methods are used to explain the present invention and are only preferred embodiments of the present invention, rather than limiting the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A system for automatically limiting water abandonment during forecast dispatching of cascade hydropower stations, characterized by: It includes cascade hydropower station forecast dispatch module, scene switching module and water abandonment restriction module; The cascade hydropower station forecast dispatch module includes: a cascade hydropower station long-term optimization dispatch module, a cascade hydropower station medium-term optimization dispatch module, a cascade hydropower station short-term optimization dispatch module, a cascade hydropower station in-plant economic operation module, a dispatch model heuristic optimization algorithm module, a monthly water inflow forecast module, a daily water inflow forecast module and an hourly water inflow forecast module; The scenario switching module obtains the daily water volume forecast value and the water level at the end of the first day of the mid-term scheduling remaining period from the cascade hydropower station forecast scheduling module, and obtains the full-fire status bit from the water abandonment restriction module to determine whether the hydropower station needs to be fully fired, and determines the scenario value, and then sends the scenario value to the cascade hydropower station forecast scheduling module and the water abandonment restriction module for scenario judgment and process switching in the operation process; The water abandonment restriction module includes: a full discharge module before the flood peak and a full discharge module when the water level exceeds the limit.

2. The automatic water abandonment restriction system in the forecast dispatching process of cascade hydropower stations according to claim 1 is characterized by: The scene switching module is executed once a day and includes the following steps: S201: Determine whether the time is a new day, if yes, proceed to the next step, if no, continue to determine the time; S202: Check if the full-send status bit is 1? If yes, keep the original scene value unchanged and go to S206; if no, go to the next step; S203: Determine whether the reservoir's initial water level and final water level are both less than the warning water level limit. If yes, execute the next step. If no, full discharge is required to limit the reservoir from abandoning water. Set the scenario value to scenario 3 and go to S206. The reservoir’s initial water level of the day is obtained through actual measurement. The reservoir’s final water level of the day = the final water level of the first day of the mid-term scheduling remaining period. The warning water level limit The calculation formula is: In the formula, f Z-V is the functional expression of the water level-reservoir capacity relationship curve, n s0 is the total number of power stations that directly draw water from reservoirs for power generation, q i,max represents the maximum water flow of power station i that directly draws water from the reservoir for power generation, q s is the ecological flow of the reservoir, The confidence level α is obtained based on the historical average daily water flow statistics of the reservoir. I The upper limit of the confidence interval, that is, the average daily water flow of the reservoir> The probability is lower than α I , α I Generally, 95% or 97.5% is taken; S204: According to the designed inflow flood process line and the daily water volume forecast value, the flood peak arrival date is determined, and the flood peak process start date is calculated. The basis for determining the flood peak arrival date is: the daily water volume forecast value on the forecast day ≥ the total inflow flood volume on the day of the peak value of the designed inflow flood process line * scaling factor? If yes, the forecast day is the flood peak arrival date; if no, the forecast day is not the flood peak arrival date, and the scaling factor is less than or equal to 1; S205: Determine (the current day is the flood peak process start date) or (the current day is the tth day in reverse order before the flood peak process start date) f +1 day, t f ≥0, and the reservoir's initial water level on the day ≥ the t-th day in reverse order before the start of the flood peak process f The water level limit at the beginning of the day )? Yes, then full discharge is required to limit the reservoir from discarding water, set the scenario value to scenario 2, and execute the next step; No, full discharge is not required, set the scenario value to scenario 1, and execute the next step. The starting date of the flood peak process = the flood peak arrival date - (the peak date of the designed inflow flood process line - the initial date of the designed inflow flood process line), the starting date of the flood peak process is the tth day in reverse order before the starting date of the flood peak process f The water level limit at the beginning of the day The calculation formula is: Where, T p is the duration of the flood peak process, that is, the duration of the designed flood process line entering the reservoir. is the average water flow rate of the reservoir calculated based on the designed flood process line, t f Generally no more than 15 days; S206: Outputting scenario values ​​to the forecasting and dispatching module and the water abandonment restriction module of the cascade hydropower station; S207: Determine whether to end, if yes, end the process, if not, go to S201.

3. The automatic water abandonment restriction system in the forecast dispatching process of cascade hydropower stations according to claim 1 is characterized by: The water disposal restriction module is executed once a day and includes the following operation steps: S301: Initialize full delivery status bit = 0, remaining full delivery days = 0; S302: Determine whether the time is a new day, if yes, proceed to the next step, if no, continue to determine the time; S303: Determine whether the scene value of the day is equal to scene 2, if yes, go to S312, if no, go to the next step; S304: Determine whether the scene value of the day is equal to scene 3, if yes, execute the next step, if no, go to S309; S305: Determine whether the full delivery status bit is equal to 0. If yes, set the full delivery status bit to 1, set the remaining full delivery days to 1, and execute the next step. If no, keep the full delivery status bit = 1, keep the remaining full delivery days unchanged, and execute the next step. S306: Execute the water level over-limit full-send module; S307: remaining full payment days = remaining full payment days - 1; S308: Determine whether the remaining full payment days are less than or equal to 0. If yes, proceed to the next step. If no, go to S310. S309: Set the full-send status bit to 0; S310: Output the full transmission status bit to the scene switching module; S311: Determine whether to end, if yes, end the process, if no, go to S302; S312: Determine whether the full delivery status bit is equal to 0. If yes, set the full delivery status bit to 1 and set the remaining full delivery days = T p + (peak process start date - current date), and execute the next step; otherwise, keep the full-sending status bit = 1, keep the remaining full-sending days unchanged, and execute the next step; S313: Execute the full-send module before the flood peak arrives, and go to S307.

4. The automatic water abandonment restriction system in the forecast dispatching process of cascade hydropower stations according to claim 3 is characterized by: The execution methods of the full-generation module before the flood peak and the full-generation module when the water level exceeds the limit are both to forcibly set the output of all units in the power station that directly draws water from the reservoir to generate electricity = the maximum output of the units.

5. The automatic water abandonment restriction system in the forecast dispatching process of cascade hydropower stations according to claim 1 is characterized by: The operation of the forecast dispatching module of the cascade hydropower station includes the following steps: S1: Initialize the target final water level of the 12th month of the kth year and use it as the final water level boundary constraint of the long-term optimal scheduling module of the cascade hydropower stations, k = current year, l = 1; S2: Execute the long-term optimization dispatching module of cascade hydropower stations, and call the dispatching model heuristic optimization algorithm module for optimization, to obtain the corrected optimal long-term dispatching remaining period reference flow, and the end-of-month water levels of all months within the long-term dispatching remaining period, that is, the end-of-month water levels of reservoirs from the first month of the kth year to the 12th month of the kth year; S3: The water level at the end of the first month of the long-term scheduling remaining period, that is, the reservoir water level at the end of the month of the kth year, is used as the final water level boundary constraint of the mid-term optimization scheduling module of the cascade hydropower stations; S4: Initialize m=1; S5: Execute the mid-term optimization scheduling module of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module for optimization, and obtain the corrected optimal mid-term scheduling remaining period reference flow, as well as all end-of-day water levels within the mid-term scheduling remaining period, that is, the end-of-day water levels of the reservoir from the mth day of the lth month of the kth year to the last day of the lth month of the kth year; S6: The final water level of the first day of the remaining period of the mid-term optimal dispatch, i.e., the final water level of the reservoir on the mth day of the lth month of the kth year, is used as the final water level boundary constraint of the short-term optimal dispatch module of the cascade hydropower stations; S7: Determine whether the scenario value of the day is scenario 1. If yes, execute the short-term optimization scheduling module of the cascade hydropower station, and call the scheduling model heuristic optimization algorithm module for optimization, and obtain the corrected optimal daily reference flow, daily reference flow plan, daily output plan, and all hourly water levels within the day, that is, the hourly water levels of the reservoir from 1:00 on the mth day of the lth month of the kth year to 24:00 on the mth day of the lth month of the kth year, and execute the next step. If no, go to S11; S8: Initialize n=1; S9: Determine whether to adopt the "power-to-water" method. If yes, execute the in-plant economic operation module of the cascade hydropower station, and call the dispatch model heuristic optimization algorithm module for optimization, carry out the in-plant economic operation of the cascade hydropower station "power-to-water", and obtain the optimal unit output. If no, execute the in-plant economic operation module of the cascade hydropower station, and call the dispatch model heuristic optimization algorithm module for optimization, carry out the in-plant economic operation of the cascade hydropower station "water-to-power", and obtain the optimal unit reference flow; S10: Check if n≥24? If yes, proceed to the next step; if no, go to S14; S11: Check if m ≥ the total number of days in the first month. If yes, proceed to the next step; if no, go to S15; S12: Check if l≥12? If yes, proceed to the next step; if no, go to S16; S13: Determine whether to end, if yes, end the process, if not, go to S17; S14: Determine whether the time is a new hour. If yes, n=n+1 and go to S9. If no, continue to determine the time. S15: Determine whether the time is a new day, if yes, m=m+1 and go to S5, if no, continue to determine the time; S16: Determine whether the time is a new month, if yes, then l=l+1 and go to S2, if no, continue to determine the time; S17: Determine whether the time is a new year, if yes, go to S1, if no, continue to determine the time.

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