A short-term peak regulation and scheduling collaborative optimization method for cascade hydropower stations

Abstract

The application discloses a kind of short-term peak shaving scheduling coordination optimization methods of cascade hydropower station group, and the application relates to the technical field of hydropower scheduling, based on the correlation curve of the power generation of each hydropower unit and water consumption rate is constructed by cloud historical operation data, the optimal operation interval is locked by curve analysis, the dual optimization of unit efficiency and water consumption economy is realized;According to the real-time power generation demand of power grid, combined with the current power state of unit and the storage capacity condition, the unit starting strategy of water consumption rate priority is adopted, and the power difference is intelligently distributed by introducing the curve slope inverse proportion normalization algorithm, to ensure that the water resource consumption is minimized while meeting the load demand, the dynamic fluctuation curve of storage capacity is constructed in real time during scheduling process, the safety threshold monitoring mechanism is set, the deviation of actual water output and theoretical value of unit and abnormal fluctuation of reservoir capacity are double early warning, form the closed-loop control system of prediction, optimization, monitoring and alarm, effectively improve the peak shaving response speed and operation safety of cascade hydropower station group.

Description

Technical Field

[0001] This invention belongs to the field of hydropower dispatching technology, specifically, it relates to a collaborative optimization method for short-term peak-shaving dispatching of a cascade hydropower station group. Background Technology

[0002] Due to their significant advantages of multi-reservoir joint operation and cascade utilization of hydropower, cascade hydropower station groups have become important infrastructure for ensuring regional power supply and promoting the consumption of new energy sources.

[0003] Existing technologies for handling short-term peak-shaving scheduling of cascade hydropower stations often rely on fixed water consumption rate models or empirical formulas, treating the water consumption rate of hydropower units as a constant or having only a simple linear relationship with power generation. This fails to fully utilize the correlation between water consumption rate and power generation, and cannot accurately reflect the true efficiency characteristics of the units under different operating conditions. Consequently, scheduling decisions struggle to pinpoint the truly optimal operating range, leading to water waste or insufficient peak-shaving capacity. When allocating power among multiple units, existing technologies often employ relatively crude strategies such as equal capacity or proportional allocation, lacking refined consideration of the optimal water output range and water consumption rate differences for individual units. This prevents the full exploitation of the units' regulation potential, hindering the achievement of optimal overall peak-shaving benefits. Furthermore, existing scheduling methods typically focus on pre-control planning while neglecting real-time monitoring and feedback of dynamic changes in hydropower station water storage during control execution. This makes it difficult to promptly detect abnormal water storage exceeding limits due to prediction deviations, execution errors, or fluctuations in inflow, failing to provide operators with intuitive early warning information. Consequently, the safety and robustness of the scheduling process are insufficient.

[0004] To address the aforementioned issues, this invention proposes a collaborative optimization method for short-term peak-shaving scheduling of cascade hydropower station groups. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a collaborative optimization method for short-term peak-shaving scheduling of cascade hydropower station groups, solving the problems of low peak-shaving accuracy, low water resource utilization efficiency, and delayed safety early warning in existing technologies for short-term peak-shaving scheduling of cascade hydropower station groups.

[0006] The objective of this invention can be achieved through the following technical solutions: A collaborative optimization method for short-term peak-shaving scheduling of a cascade hydropower station group, the method comprising: Step 1: Use the cloud to obtain the set of operating data associated with the hydropower units in each cascade hydropower station under different historical operating conditions, determine the water consumption rate corresponding to different power generation states, construct the correlation curve between water consumption rate and power generation, lock the optimal correlation curve, and generate the optimal water output range. Step 2: Determine the power generation demand, initial water storage, and current power generation capacity of each hydropower unit in the next short-term regulation of the current cascade hydropower station. Combine the optimal power generation capacity range and optimal water output range of each hydropower unit in the current cascade hydropower station, and perform optimization regulation operations on each hydropower unit respectively. Step 3: After the optimization and control operation is completed, construct the water storage fluctuation curve of the current cascade hydropower station in the short term of control, monitor abnormal situations, and generate abnormal alarm signals.

[0007] As a further aspect of the present invention, the specific method for determining the water consumption rate corresponding to different power generation states in step one is as follows: Obtain all hydropower stations in the cascade hydropower station group, arrange them in order from upstream to downstream, and denote them as the hydropower station sequence Q1, Q2, ..., Qj, where j is the total number of hydropower stations; Let Qi be any cascade hydropower station, where 1≤i≤j; Obtain all hydropower units in hydropower station Qi and randomly arrange them as q1, q2, ..., qm, where m is the total number of hydropower units; Based on cloud-based extraction of arbitrary hydropower unit qn, with the current time as the endpoint, the set of operating data in the past historical period, including power generation and water consumption rate, where the historical period is a preset time period, 1≤n≤m; Remove duplicate values ​​from all power generation outputs, sort them in ascending order, and denote them as the power generation output sequence P1, P2, ..., Po, where o is the total number of different power generation outputs; Traverse the power generation sequence, extract the water consumption rate associated with the corresponding power generation, and perform an averaging operation to obtain the average water consumption rate. Sort the o average water consumption rates according to the power generation sequence, and denote them as the water consumption rate sequence W1, W2, ..., Wo; Similarly, the water consumption rate corresponding to different power generation states of all hydropower units in all hydropower stations is locked.

[0008] As a further aspect of the present invention, in step one, the specific method for constructing the correlation curve between water consumption rate and power generation is as follows: Construct a two-dimensional coordinate system XY with power generation as the horizontal axis and water consumption rate as the vertical axis; Take the power generation sequence P1, P2, ..., Po and the water consumption rate sequence W1, W2, ..., Wo, align and combine them according to the sequence number to construct o sets of coordinates: (P1, W1), (P2, W2), ..., (Po, Wo); O sets of coordinates were plotted in the two-dimensional coordinate system XY to obtain o data points, and the correlation curve S1 between water consumption rate and power generation was obtained by fitting the curve.

[0009] As a further aspect of the present invention, the specific method for locking the optimal correlation curve and generating the optimal effluent range in step one is as follows: Obtain the known stable operating power range [Pmin, Pmax] of the hydropower unit qn; Construct straight lines passing through scales Pmin and Pmax on the horizontal axis of the two-dimensional coordinate system, denoted as the first straight line L1 and the second straight line L2. The portion of the correlation curve S1 within the interval between L1 and L2 is denoted as the stable correlation curve S2. Construct a straight line perpendicular to the vertical axis and parallel to the horizontal axis, denoted as the third line L3; Let L3 start from the 0 mark on the vertical axis and gradually climb upwards until it intersects with the stable correlation curve S2. This intersection point is denoted as the optimal correlation intersection point G. Discard the portion of the stable correlation curve S2 located to the left of the optimal correlation intersection point G, let L3 rise by a preset water consumption rate value, stop, and lock the portion of the stable correlation curve S2 located below L3, which is denoted as the optimal correlation curve S3. Extract the scale interval on the horizontal axis corresponding to the optimal correlation curve S3, and denote it as the optimal power generation interval [P_g1,P_g2]. Extract the scale interval on the vertical axis corresponding to the optimal correlation curve S3, and denote it as the optimal water consumption rate interval [W_g1,W_g2]. The optimal power generation in the optimal power generation range [P_g1, P_g2] is multiplied by the corresponding optimal water consumption rate in the optimal water consumption rate range [W_g1, W_g2] to calculate the optimal water output. The minimum and maximum water output are taken and denoted as C_g1 and C_g2 respectively. They are combined to form the optimal water output range [C_g1, C_g2] of the hydropower unit qn.

[0010] Similarly, by iterating through all the hydropower units in all hydropower stations, the optimal water output range for each hydropower unit is generated.

[0011] As a further aspect of the present invention, in step two, the power generation demand signal for the next short-term regulation sent by the power grid is determined in real time by any hydropower station Qi, wherein the short-term regulation is a time period preset by the operator, and the power generation demand in the power generation demand signal is specifically the demanded power generation P_need. Determine the available water storage capacity of hydropower station Qi at time t1 before the start of the next short-term regulation, and denot it as the initial water storage capacity B_t1. Based on the optimal power generation range and optimal water output range of all hydropower units q1, q2, ..., qm in the hydropower station Qi, optimize the control operation.

[0012] As a further aspect of the present invention, the specific method for performing optimized control operations on each hydropower unit in step two is as follows: Take the required power generation P_need and the water consumption rate and power generation of each of the m hydropower units q1,q2,...,qm at the optimal correlation intersection point; Sort q1, q2, ..., qm in ascending order according to the water consumption rate at the optimal correlation intersection point, and denote it as the optimal water-consuming generator sequence q1', q2', ..., qm'; Arrange the water consumption rate and power generation of the optimal water-consuming power units in sequence to obtain the optimal water consumption rate sequence W1',W2',...,Wm' and the optimal power generation sequence P1',P2',...,Pm'. Starting from the start of operation of hydropower unit q1', set the power generation of q1' to P1', let the total power generation P_all=0+P1', calculate the theoretical water output of hydropower unit q1' C1'=P1'×W1'; Similarly, set the power generation capacity of subsequent hydropower units, update the total power generation capacity P_all, and calculate the water output of each hydropower unit; If the total number of activated hydropower units is less than or equal to m, and P_all is greater than or equal to P_need, then activation is stopped and an optimized control operation completion signal is generated. If the total number of hydropower units in operation is m, and P_all < P_need, then the differential power generation P_ce is calculated using P_need - P_all = P_ce. Extract the optimal power generation range of m hydropower units, and extract the average slope of the optimal correlation curve corresponding to each of the m optimal power generation ranges, and denote the corresponding q1',q2',...,qm' as the slope sequence K1',K2',...,Km'; Normalize K1',K2',...,Km' to obtain the normalized slope sequence RK1',RK2',...,RKm', where RK1'+RK2'+...+RKm'=1; Construct an inversely proportional normalized sequence FRK1',FRK2',...,FRKm' of the normalized slope sequence, where FRK1'+FRK2'+...+FRKm'=1, and FRK1' is inversely proportional to RK1', and the rest are similar; The differential power generation P_ce is divided according to the numerical proportion in the inverse normalized sequence, and then rounded up to the nearest integer to obtain the power allocation sequence P_ce1', P_ce2', ..., P_cem'. The generating power in the power allocation sequence is sequentially allocated to m hydropower units q1',q2',...,qm' to complete the optimized control operation.

[0013] As a further aspect of the present invention, in step two, the remaining j-1 hydropower stations are processed synchronously based on the processing method of hydropower station Qi, and the optimized control operation of each hydropower station is completed. In the next short period of control, the actual water output of any hydropower unit is monitored in real time using a flow meter, and the difference rate is calculated with the theoretical water output of the hydropower unit. If the difference rate exceeds the difference rate threshold preset by the operator, an abnormal alarm notification for the hydropower unit is generated.

[0014] As a further aspect of the present invention, the specific method for constructing the water storage fluctuation curve of the current cascade hydropower station in the short term, monitoring abnormal situations, and generating abnormal alarm signals in step three is as follows: Determine the total number of moments within the short-term control period, denoted as T; After optimizing and controlling the operation of the hydropower station Qi, the real-time water storage volume within the hydropower station Qi is obtained at T time points, and the water storage volume sequence B_t1, B_t2, ..., B_tT is generated. Construct a two-dimensional coordinate system XY1 with the timeline as the horizontal axis and the water storage volume as the vertical axis; The water storage sequence B_t1, B_t2, ..., B_tT is plotted as data points in a two-dimensional coordinate system XY1 to obtain T data points. The water storage fluctuation curve SBt is obtained by curve fitting and displayed to the operator. The minimum and maximum safe water storage thresholds of the hydropower station Qi are obtained. The water storage value in the water storage fluctuation curve SBt is verified. When SBt is lower than the minimum safe water storage threshold or higher than the maximum safe water storage threshold, an abnormal alarm signal is generated to notify the operator.

[0015] The beneficial effects of this invention are: (1) By constructing the correlation curve between water consumption rate and power generation and locking the optimal range, this invention can accurately match the high-efficiency operating area of ​​hydropower units under different operating conditions, significantly reduce the water consumption rate per unit of power generation, thereby improving water resource utilization efficiency and overall power generation benefits; the control process combines real-time power generation demand and water storage status to perform collaborative optimization on each unit, so that the cascade hydropower station group can maintain the optimal water output allocation while meeting peak shaving requirements; by constructing the water storage fluctuation curve and implementing abnormal monitoring, deviations or risks in operation can be detected in time and alarms can be generated, ensuring the safety and reliability of the operation of the cascade hydropower station and realizing efficient and stable short-term peak shaving scheduling collaborative optimization. (2) This invention transforms the unit operation optimization problem into a quantifiable and operable precise interval decision through data analysis and geometric modeling. First, the operating data within the historical period is extracted and averaged to eliminate the interference caused by instantaneous fluctuations or abnormal operating conditions. A water consumption rate and power generation correlation curve that closely matches the actual operating characteristics is constructed for each hydropower unit to ensure the authenticity of the basic model. On this basis, a stable operating interval constraint and dynamic optimization mechanism are further introduced. By gradually climbing and intercepting the straight line in the coordinate system, the optimal correlation curve with the lowest water consumption rate and its corresponding power generation and water output interval are locked. The abstract efficiency maximization target is concretized into the control of a specific power interval to minimize the unit water consumption. All units are traversed to generate their own optimal water output intervals to realize the overall joint optimization scheduling of the cascade hydropower station group. (3) This invention improves the response accuracy and operational economy of hydropower stations to grid dispatch instructions through mathematical modeling and priority ranking. It introduces water consumption rate as the core ranking indicator, arranging hydropower units in descending order of efficiency and prioritizing the use of high-efficiency units. This reduces water consumption per unit of power generation from the source, achieving the goal of water conservation and efficiency improvement. Secondly, it creatively introduces an inverse proportional normalization allocation mechanism based on the slope of the optimal correlation curve. This proportionally allocates the differential power, allowing the originally less efficient units to bear less additional load increments, effectively avoiding water waste and operational losses caused by forcibly increasing the output of inefficient units, and ensuring the reliability of the control strategy and the long-term safe and stable operation of the power station. (4) This invention transforms abstract data into an intuitive visual presentation and constructs a closed-loop automated early warning mechanism. It fits discrete real-time water storage data points into a continuous water storage fluctuation curve and displays it to the operators in a graphical manner, making the trend of water storage change clear at a glance. This enhances the operators' global perception of the short-term control process. On this basis, by introducing minimum and maximum safe water storage thresholds, the values ​​in the curve are dynamically verified, realizing the transformation from passive recording to active monitoring, and enhancing the operational safety and risk response capabilities of cascade hydropower stations in short-term control. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a schematic diagram of the system described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 3 of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, this application provides a collaborative optimization method for short-term peak-shaving scheduling of a cascade hydropower station group; As an embodiment 1 of this application, it specifically includes: Step 1: Use the cloud to obtain the set of operating data associated with the hydropower units in each cascade hydropower station under different historical operating conditions, determine the water consumption rate corresponding to different power generation states, construct the correlation curve between water consumption rate and power generation, lock the optimal correlation curve, and generate the optimal water output range. Step 2: Determine the power generation demand, initial water storage, and current power generation capacity of each hydropower unit in the next short-term regulation of the current cascade hydropower station. Combine the optimal power generation capacity range and optimal water output range of each hydropower unit in the current cascade hydropower station, and perform optimization regulation operations on each hydropower unit respectively. Step 3: After the optimization and control operation is completed, construct the water storage fluctuation curve of the current cascade hydropower station in the short term of control, monitor abnormal situations, and generate abnormal alarm signals.

[0020] Example 2 This embodiment, based on Embodiment 1, further discloses a method for determining the optimal operating condition range of cascade hydropower station units, specifically including the following: First, it needs to be clarified that cascade hydropower station groups are mostly built on rivers with high and low elevation differences, including several hydropower stations. We need to obtain the cascade hydropower station group that the operators have determined, extract all hydropower stations, and arrange all hydropower stations in order from upstream to downstream to obtain the hydropower station sequence Q1, Q2, ..., Qj, where j is the total number of hydropower stations. Then, extract any one of the cascade hydropower stations from the hydropower station sequence Q1, Q2, ..., Qj and denote it as Qi, where i is the counting index with a value range of 1 ≤ i ≤ j. As shown below, perform example processing on hydropower station Qi, and perform the same synchronous processing on the remaining hydropower stations in the same way as hydropower station Qi. First, obtain all the hydropower units in the hydropower station Qi, count the total number, and denote it as m. Then, randomly arrange the m hydropower units and represent them as: q1, q2, ..., qm; Extract any one of the m hydropower units qn from the pre-built cloud and its associated cloud database, where n is the counting index with a value range of 1 ≤ n ≤ m; The following is an example of processing qn for hydropower units; the other hydropower units are processed synchronously: Using the current time as the endpoint, obtain the set of operating data of hydropower unit qn over the past historical period. The set of operating data includes the power generation and water consumption rate of hydropower unit qn. The historical period is a time period preset by the operator, and its time span needs to be determined in combination with actual needs.

[0021] Extract all power generation data from the operating dataset of hydropower unit qn, remove duplicate values, and sort them in ascending order to obtain the power generation sequence P1, P2, ..., Po associated with hydropower unit qn (essentially the historical power generation sequence of hydropower unit qn). Here, o represents the total number of different power generation values. For example, if the power generation of hydropower unit qn is set to 33KW, 55KW, and 33KW in the historical period, then o=2. Iterate through the power generation sequence P1, P2, ..., Po of the hydropower unit qn. Starting from the first power generation P1, obtain all water consumption rates of the hydropower unit qn when it operates at power generation P1 within the historical period. Average all water consumption rates to obtain an average water consumption rate. This average water consumption rate is used as the water consumption rate associated with the hydropower unit qn under the power generation P1 condition. Similarly, process the water consumption rates (i.e., average water consumption rates) corresponding to the hydropower unit qn under different power generation conditions, and arrange the resulting o average water consumption rates in the order of the power generation sequence P1, P2, ..., Po. These are denoted as the water consumption rate sequence W1, W2, ..., Wo.

[0022] As shown above, the water consumption rate corresponding to the hydropower unit qn under different power generation states was determined. Based on this method, the water consumption rate corresponding to the hydropower units in all hydropower stations under different power generation states was determined, and the water consumption rate sequence associated with all hydropower units was determined.

[0023] Next, we will still use the hydropower unit qn as an example. We will use a two-dimensional coordinate system with power generation as the horizontal axis and water consumption rate as the vertical axis, denoted as XY. Based on the above, extract the determined power generation sequence P1, P2, ..., Po and the water consumption rate sequence W1, W2, ..., Wo, align and combine them according to the sequence number to construct o sets of coordinates, where the horizontal axis is the power generation and the vertical axis is the water consumption rate. The o sets of coordinates are specifically represented as: (P1, W1), (P2, W2), ..., (Po, Wo).

[0024] In the constructed two-dimensional coordinate system XY, the constructed set of o coordinates is plotted, and o data points are finally obtained. The o data points are fitted with a curve to obtain a complete curve characterizing the relationship between water consumption rate and power generation, which is denoted as the correlation curve S1.

[0025] To obtain the stable operating power range [Pmin, Pmax] of the hydropower unit qn, it should be noted that the stable operating power is generally the rated operating power range defined by the performance of the hydropower unit itself, which can be obtained from the equipment nameplate or the manufacturer, and therefore is considered a known value. Next, mark the maximum value Pmax and the minimum value Pmin in the stable operating power range [Pmin, Pmax] on the horizontal axis of the two-dimensional coordinate system XY, and construct two straight lines through these two scales, passing through the associated curve S1. The two straight lines are recorded as the first straight line L1 and the second straight line L2, respectively. Extract the portion of the correlation curve S1 located between the first straight line L1 and the second straight line L2, and denote it as the stable correlation curve S2. Then construct a straight line perpendicular to the vertical axis and parallel to the horizontal axis, denoted as the third line L3; The third straight line L3 is modulated starting from the 0 mark on the vertical axis and gradually climbing upwards (in the positive direction of the vertical axis). The first intersection point of the third straight line L3 and the stable correlation curve S2 is then marked as the optimal correlation intersection point G. The stable correlation curve S2 is divided according to the optimal correlation intersection point G, resulting in the left and right parts located at the optimal correlation intersection point G. The left part of the stable correlation curve S2 located at the optimal correlation intersection point G is discarded, and the third straight line L3 is made to climb upward to the rated scale, where the rated scale is the water consumption rate value preset by the operator (e.g., 5% of the minimum water consumption rate), and the third straight line L3 is stopped at this scale. At this point, the portion of the stable correlation curve S2 located below L3 (starting from the optimal correlation intersection point G) is determined and denoted as the optimal correlation curve S3; On the horizontal axis of the two-dimensional coordinate system XY, the scale interval corresponding to the optimal correlation curve S3 is determined, and all power generation values ​​within this scale interval are extracted. The minimum and maximum values ​​are locked to form the optimal power generation interval [P_g1,P_g2]. Similarly, on the vertical axis of the two-dimensional coordinate system XY, the scale interval corresponding to the optimal correlation curve S3 is determined, and all water consumption rate values ​​within this scale interval are extracted, and the minimum and maximum values ​​are locked to form the optimal water consumption rate interval [W_g1, W_g2].

[0026] Then, the optimal power generation in the optimal power generation range [P_g1, P_g2] is multiplied by its corresponding optimal water consumption rate in the optimal water consumption rate range [W_g1, W_g2] to calculate the optimal water output. It should be noted that when multiplying the optimal water consumption rate and power generation, the units need to be unified. For example, if the power unit is KW and the water consumption rate unit is m³ / kWh, they can be multiplied directly. If the power unit is MW, they need to be multiplied by 1000 to unify the units. Extract the calculated minimum and maximum water output, denoted as C_g1 and C_g2 respectively, and combine them to form the optimal water output range [C_g1, C_g2] for hydropower unit qn. Then, in the same way and synchronously, traverse all hydropower units in all hydropower stations to generate the optimal water output range for each hydropower unit.

[0027] Example 3 This embodiment further discloses a method for joint optimization and control of multiple hydropower units based on embodiment 2, such as... Figure 2 As shown, it specifically includes the following: During the actual operation of a hydropower station, it will receive a power generation demand signal from the power grid. The power generation demand in the power generation demand signal is specifically the demand for power generation. This solution uses a short-term regulation period as a cycle for regulation, where the short-term regulation period is a time period preset by the operator. Extract any hydropower station Qi, perform example processing, and lock the demand power P_need in the next short-term control demand signal received by the hydropower station; Retrieve all hydroelectric generating units in hydropower station Qi: q1,q2,...,qm; The available water storage capacity of all hydropower units at time t1 before the start of the next short-term regulation is determined and marked as the initial water storage capacity B_t1. The initial water storage capacity B_t1 can be obtained directly from the water level gauge of the hydropower station Qi, or it can be obtained according to the measurement method equipped by the hydropower station Qi itself. This is the part covered by the existing technology, so it will not be elaborated on in this solution.

[0028] Next, based on the optimal power generation range and optimal water output range of all hydropower units q1, q2, ..., qm in the hydropower station Qi determined in Example 2, optimization and control operations are performed as follows: First, obtain the required power generation capacity P_need for backup; Then, extract the water consumption rate and corresponding power generation of each of the m hydropower units q1,q2,...,qm at the optimal correlation intersection point; The m hydropower units q1,q2,...,qm are sorted in ascending order according to the water consumption rate at the optimal correlation intersection point. The lower the water consumption rate, the higher the sorting position. Finally, the optimal hydropower unit sequence q1',q2',...,qm' is obtained. Then, according to the sorting order of the optimal water consumption hydropower unit sequence q1',q2',...,qm', the water consumption rate and corresponding power generation of each hydropower unit at the optimal correlation intersection point are sorted to obtain the optimal water consumption rate sequence W1',W2',...,Wm' and the optimal power generation sequence P1',P2',...,Pm'; Next, starting with the first hydropower unit, start hydropower unit q1' and set the power generation of hydropower unit q1' to P1'. Calculate the total power generation P_all using P_all=0+P1', where the initial value of the total power generation P_all is 0. Simultaneously calculate the theoretical water output C1'=P1'×W1' of hydropower unit q1'. Similarly, start the hydropower unit q2' and set the power generation of the hydropower unit q2' to P2', and use P_all=0+P1'+P2' to calculate the total power generation P_all; Similarly, set the power generation capacity of subsequent hydropower units, update the total power generation capacity P_all, and calculate the water output of each hydropower unit; During the process of activating the hydropower units, the total number of activated hydropower units is monitored in real time. If the total number of hydropower units is ≤ m and P_all ≥ P_need, the activation operation is stopped, and an optimized control operation completion signal is generated at this moment and sent back to the power grid terminal. If the total number of activated hydropower units is m, and P_all < P_need, then the current differential power generation P_ce is calculated by using P_need - P_all = P_ce. Next, extract the optimal power generation sequence q1',q2',...,qm', and lock the optimal power generation range of m power generation units to obtain m optimal power generation ranges. Further lock the optimal correlation curves corresponding to each of the m optimal power generation ranges to obtain m optimal correlation curves. Calculate the average slope of each of the m optimal correlation curves and arrange them in the order of the corresponding optimal power generation sequence q1',q2',...,qm' to obtain the slope sequence K1',K2',...,Km'. Normalize the slope sequence K1',K2',...,Km' to obtain the normalized slope sequence RK1',RK2',...,RKm' corresponding to the slope sequence K1',K2',...,Km', where RK1'+RK2'+...+RKm'=1; Then, construct the inverse normalized sequence FRK1',FRK2',...,FRKm' of the normalized slope sequence RK1',RK2',...,FRKm', where FRK1'+FRK2'+...+FRKm'=1, and FRK1' is inversely proportional to RK1', and the rest are similar; The specific method for constructing the inverse proportional normalized sequence FRK1',FRK2',...,FRKm' is as follows: Extract the normalized slope sequence RK1', RK2', ..., RKm', subtract each normalized slope from 1 to obtain m values, arrange them into a sequence in the original order, and then normalize the sequence, which is denoted as the inverse normalized sequence FRK1', FRK2', ..., FRKm'; Based on the proportion of all values ​​in the inverse normalized sequence FRK1',FRK2',...,FRKm', the differential power generation P_ce is segmented, and the values ​​after proportional segmentation are rounded up to retain integers as redundancy. Finally, the power allocation sequence P_ce1',P_ce2',...,P_cem' of the corresponding optimal hydropower unit sequence q1',q2',...,qm' is obtained. The m generating capacities in the power allocation sequence P_ce1', P_ce2', ..., P_cem' are sequentially allocated to m hydropower units q1', q2', ..., qm'. Based on the already allocated power of the m hydropower units q1', q2', ..., qm', a power increase operation is performed to complete the optimized control operation.

[0029] As described above, the same and synchronous processing is applied to the remaining j-1 hydropower stations in the same way as the processing of hydropower station Qi, thus completing the optimized control operation of all hydropower stations. Within the next short period of regulation required by the power grid, the actual water output of all hydropower units in all hydropower stations is monitored in real time using flow meters and recorded in real time. This data is then compared with the theoretical water output calculated based on the actual water consumption rate and actual power generation of the hydropower units, and the difference rate is calculated. If the difference rate calculated for any hydropower unit exceeds the difference rate threshold preset by the operator, an abnormal alarm notification for that hydropower unit is generated. The difference rate threshold needs to be determined in conjunction with the actual needs of the corresponding hydropower unit.

[0030] Example 4 This embodiment further discloses a method for monitoring water storage curves and triggering threshold alarms based on embodiment 3, specifically including the following: As described in Example 3, the optimized control operation of all hydropower stations has been completed. After entering the control period and starting the power generation operation, it is also necessary to monitor the actual water storage of each hydropower station in real time to avoid overspending or excessive water volume causing hidden problems. First, determine the total number of moments in the short-term regulation period and denote it as T; After the hydropower station Qi performs optimization operations, the real-time water storage of the hydropower station Qi is obtained at T time points within a short period of control, and arranged in chronological order to obtain the water storage sequence B_t1, B_t2, ..., B_tT. It should be noted that the water storage sequence B_t1, B_t2, ..., B_tT is gradually improved over time.

[0031] Next, construct a two-dimensional coordinate system XY1 with the timeline as the horizontal axis and the water storage volume as the vertical axis; Extract the water storage sequence B_t1, B_t2, ..., B_tT of the hydropower station Qi determined above, and plot the T water storage values ​​in the form of data points in the two-dimensional coordinate system XY1 to obtain T data points. Perform a curve fitting operation on the T data points to obtain the water storage fluctuation curve SBt, and display the water storage fluctuation curve SBt to the operator. The minimum safe water storage threshold and the maximum safe water storage threshold are then obtained by the operator based on the actual situation of the hydropower station Qi. The water storage value in the water storage fluctuation curve SBt is continuously verified using the minimum safe water storage threshold and the maximum safe water storage threshold. When the water storage fluctuation curve SBt is lower than the minimum safe water storage threshold or higher than the maximum safe water storage threshold, an abnormal alarm signal is generated to notify the operator of the abnormal water storage.

[0032] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0033] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0034] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A collaborative optimization method for short-term peak-shaving scheduling of a cascade hydropower station group, characterized in that, The method includes: Step 1: Use the cloud to obtain the set of operating data associated with the hydropower units in each cascade hydropower station under different historical operating conditions, determine the water consumption rate corresponding to different power generation states, construct the correlation curve between water consumption rate and power generation, lock the optimal correlation curve, and generate the optimal water output range. Step 2: Determine the power generation demand, initial water storage, and current power generation capacity of each hydropower unit in the next short-term regulation of the current cascade hydropower station. Combine the optimal power generation capacity range and optimal water output range of each hydropower unit in the current cascade hydropower station, and perform optimization regulation operations on each hydropower unit respectively. Step 3: After the optimization and control operation is completed, construct the water storage fluctuation curve of the current cascade hydropower station in the short term of control, monitor abnormal situations, and generate abnormal alarm signals.

2. The method according to claim 1, characterized in that, In step one, the specific method for determining the water consumption rate corresponding to different power generation states is as follows: Obtain all hydropower stations in the cascade hydropower station group, arrange them in order from upstream to downstream, and denote them as the hydropower station sequence Q1, Q2, ..., Qj, where j is the total number of hydropower stations; Let Qi be any cascade hydropower station, where 1≤i≤j; Obtain all hydropower units in hydropower station Qi and randomly arrange them as q1, q2, ..., qm, where m is the total number of hydropower units; Based on cloud-based extraction of arbitrary hydropower unit qn, with the current time as the endpoint, the set of operating data in the past historical period, including power generation and water consumption rate, where the historical period is a preset time period, 1≤n≤m; Remove duplicate values ​​from all power generation outputs, sort them in ascending order, and denote them as the power generation output sequence P1, P2, ..., Po, where o is the total number of different power generation outputs; Traverse the power generation sequence, extract the water consumption rate associated with the corresponding power generation, and perform an averaging operation to obtain the average water consumption rate. Sort the o average water consumption rates according to the power generation sequence, and denote them as the water consumption rate sequence W1, W2, ..., Wo; Similarly, the water consumption rate corresponding to different power generation states of all hydropower units in all hydropower stations is locked.

3. The method according to claim 2, characterized in that, In step one, the specific method for constructing the correlation curve between water consumption rate and power generation is as follows: Construct a two-dimensional coordinate system XY with power generation as the horizontal axis and water consumption rate as the vertical axis; Take the power generation sequence P1, P2, ..., Po and the water consumption rate sequence W1, W2, ..., Wo, align and combine them according to the sequence number to construct o sets of coordinates: (P1, W1), (P2, W2), ..., (Po, Wo); O sets of coordinates were plotted in the two-dimensional coordinate system XY to obtain o data points, and the correlation curve S1 between water consumption rate and power generation was obtained by fitting the curve.

4. The method according to claim 3, characterized in that, In step one, the specific method for locking the optimal correlation curve and generating the optimal effluent range is as follows: Obtain the known stable operating power range [Pmin, Pmax] of the hydropower unit qn; Construct straight lines passing through scales Pmin and Pmax on the horizontal axis of the two-dimensional coordinate system, denoted as the first straight line L1 and the second straight line L2. The portion of the correlation curve S1 within the interval between L1 and L2 is denoted as the stable correlation curve S2. Construct a straight line perpendicular to the vertical axis and parallel to the horizontal axis, denoted as the third line L3; Let L3 start from the 0 mark on the vertical axis and gradually climb upwards until it intersects with the stable correlation curve S2. This intersection point is denoted as the optimal correlation intersection point G. Discard the portion of the stable correlation curve S2 located to the left of the optimal correlation intersection point G, let L3 rise by a preset water consumption rate value, stop, and lock the portion of the stable correlation curve S2 located below L3, which is denoted as the optimal correlation curve S3. Extract the scale interval on the horizontal axis corresponding to the optimal correlation curve S3, and denote it as the optimal power generation interval [P_g1,P_g2]. Extract the scale interval on the vertical axis corresponding to the optimal correlation curve S3, and denote it as the optimal water consumption rate interval [W_g1,W_g2]. The optimal power generation in the optimal power generation range [P_g1, P_g2] is multiplied by the optimal water consumption rate in the optimal water consumption rate range [W_g1, W_g2] to calculate the optimal water output. The minimum and maximum water output are taken and denoted as C_g1 and C_g2 respectively. They are combined to form the optimal water output range [C_g1, C_g2] of the hydropower unit qn. Similarly, by iterating through all the hydropower units in all hydropower stations, the optimal water output range for each hydropower unit is generated.

5. The method according to claim 4, characterized in that, In step two, the power generation demand signal for the next short-term regulation sent by the power grid is determined in real time by any hydropower station Qi. The short-term regulation is a time period preset by the operator, and the power generation demand in the power generation demand signal is specifically the demanded power generation P_need. Determine the available water storage capacity of hydropower station Qi at time t1 before the start of the next short-term regulation, and denot it as the initial water storage capacity B_t1. Based on the optimal power generation range and optimal water output range of all hydropower units q1, q2, ..., qm in the hydropower station Qi, optimize the control operation.

6. The method according to claim 5, characterized in that, In step two, the specific method for performing optimized control operations on each hydropower unit is as follows: Take the required power generation P_need and the water consumption rate and power generation of each of the m hydropower units q1,q2,...,qm at the optimal correlation intersection point; Sort q1, q2, ..., qm in ascending order according to the water consumption rate at the optimal correlation intersection point, and denote it as the optimal water-consuming generator sequence q1', q2', ..., qm'; Arrange the water consumption rate and power generation of the optimal water-consuming power units in sequence to obtain the optimal water consumption rate sequence W1',W2',...,Wm' and the optimal power generation sequence P1',P2',...,Pm'. Starting from the start of operation of hydropower unit q1', set the power generation of q1' to P1', let the total power generation P_all=0+P1', calculate the theoretical water output of hydropower unit q1' C1'=P1'×W1'; Similarly, set the power generation capacity of subsequent hydropower units, update the total power generation capacity P_all, and calculate the water output of each hydropower unit; If the total number of activated hydropower units is ≤ m and P_all ≥ P_need, then activation is stopped and an optimized control operation completion signal is generated. If the total number of hydropower units in operation is m, and P_all < P_need, then the differential power generation P_ce is calculated using P_need - P_all = P_ce. Extract the optimal power generation range of m hydropower units, and extract the average slope of the optimal correlation curve corresponding to each of the m optimal power generation ranges, and denote the corresponding q1',q2',...,qm' as the slope sequence K1',K2',...,Km'; Normalize K1',K2',...,Km' to obtain the normalized slope sequence RK1',RK2',...,RKm', where RK1'+RK2'+...+RKm'=1; Construct an inversely proportional normalized sequence FRK1',FRK2',...,FRKm' of the normalized slope sequence, where FRK1'+FRK2'+...+FRKm'=1, and FRK1' is inversely proportional to RK1', and the rest are similar; The differential power generation P_ce is divided according to the numerical proportion in the inverse normalized sequence, and then rounded up to the nearest integer to obtain the power allocation sequence P_ce1', P_ce2', ..., P_cem'. The generating power in the power allocation sequence is sequentially allocated to m hydropower units q1',q2',...,qm' to complete the optimized control operation.

7. The method according to claim 6, characterized in that, In step two, based on the processing method of hydropower station Qi, the remaining j-1 hydropower stations are processed synchronously to complete the optimized control operation of each hydropower station. In the next short period of control, the actual water output of any hydropower unit is monitored in real time using a flow meter, and the difference rate is calculated with the theoretical water output of the hydropower unit. If the difference rate exceeds the difference rate threshold preset by the operator, an abnormal alarm notification for the hydropower unit is generated.

8. The method according to claim 7, characterized in that, In step three, the specific method for constructing the water storage fluctuation curve of the current cascade hydropower station in the short term, monitoring abnormal situations, and generating abnormal alarm signals is as follows: Determine the total number of moments within the short-term control period, denoted as T; After optimizing and controlling the operation of the hydropower station Qi, the real-time water storage volume within the hydropower station Qi is obtained at T time points, and the water storage volume sequence B_t1, B_t2, ..., B_tT is generated. Construct a two-dimensional coordinate system XY1 with the timeline as the horizontal axis and the water storage volume as the vertical axis; The water storage sequence B_t1, B_t2, ..., B_tT is plotted as data points in a two-dimensional coordinate system XY1 to obtain T data points. The water storage fluctuation curve SBt is obtained by curve fitting and displayed to the operator. The minimum and maximum safe water storage thresholds of the hydropower station Qi are obtained. The water storage value in the water storage fluctuation curve SBt is verified. When SBt is lower than the minimum safe water storage threshold or higher than the maximum safe water storage threshold, an abnormal alarm signal is generated to notify the operator.

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