Reservoir group medium and long term power generation optimization scheduling method based on gallery iteration dynamic optimization

Abstract

The invention discloses a reservoir group medium and long term power generation optimization scheduling method based on gallery iteration dynamic optimization, which comprises the following steps of: firstly, calibrating convex hull plane parameters constrained by output of cascade reservoirs through a plane convex hull linear approximation method, constructing a scheduling model, and solving to obtain an initial solution (including reservoir capacity and output flow in each time period); taking the initial solution as a benchmark, and constructing an output corridor of each time period through reservoir capacity and warehouse-out flow scale parameters; taking the gallery as a feasible region, re-calibrating independent convex hull plane parameters in each time period, updating the model, and solving to obtain a current iterative solution; if the target function value change of the current solution and the previous solution is smaller than a threshold value, convergence is performed, and an optimal solution is output to guide scheduling; otherwise, updating the scale parameters, repeatedly constructing the galleries, calibrating the parameters, modeling and solving until convergence. According to the method, a convex hull plane is updated through dynamic calibration to approach an optimal solution, linear errors are corrected, coordinated utilization of water resources can be optimized, and operation decisions of cascade hydropower stations are guided.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary technical field of operations research and long-term scheduling of cascade reservoirs. More specifically, it proposes a long-term power generation optimization scheduling method for reservoir groups based on corridor iterative dynamic optimization, which guides the scheduling and operation of cascade reservoirs. Background Technology

[0002] Hydropower, as the world's most technologically mature and largest-scale clean energy source, plays a central role in addressing climate change and promoting the clean transformation of the power system. The joint optimal scheduling of cascade reservoir groups can leverage excellent reservoir capacity regulation and compensation capabilities, maximizing comprehensive benefits such as flood control and power generation by optimizing the spatiotemporal allocation of water resources, and enhancing the safety and stability of the power system by utilizing the rapid response characteristics of hydropower units. The medium- and long-term optimal scheduling of cascade reservoir power generation is a core issue for the efficient utilization of water resources and the economical operation of the power system, and its core lies in accurately solving the complex hydropower output function (HOF). The HOF is essentially a highly nonlinear function of reservoir capacity and outflow. To address the challenges of solving large-scale, multi-period optimization problems, linear programming (LP) models are widely used due to their mature algorithms and high solution efficiency. However, LP models require both the objective function and constraints to be linear; therefore, the nonlinear HOF must be linearized. Currently, the planar convex hull linear approximation method is a commonly used approach, which approximates the HOF by constructing a convex hull plane covering discrete points within the feasible region of water level and flow. While this method significantly reduces model complexity, it inevitably introduces linearization error. This linearization error leads to a systematic deviation between the scheduling scheme obtained from the LP model and the optimal solution of the original nonlinear problem. This can result in a mismatch between the scheduling results and the actual situation, directly impacting the scientific validity and economic efficiency of scheduling decisions. Therefore, effectively controlling or reducing HOF linearization error within a high-efficiency LP framework is a key bottleneck in improving the accuracy of cascade reservoir optimal scheduling.

[0003] The planar convex hull linear approximation method faces significant limitations. Theoretically, increasing the mesh resolution can reduce linearization errors. However, high-resolution meshes drastically increase the number of convex hull planes, leading to a geometrical increase in the size of variables and constraints in the LP model, consuming substantial computational resources and significantly reducing solution efficiency. This is particularly problematic for large-scale cascade reservoirs or long-term optimization problems, where the computational cost may become prohibitive. More critically, existing methods suffer from accuracy bottlenecks. As mesh resolution increases to a certain level, the errors generated by fitting the HOF using the planar convex hull method tend to converge and no longer decrease significantly. This means that simply increasing the mesh density without limit cannot completely eliminate errors, especially when the nonlinear characteristics of the HOF are particularly pronounced in specific local regions. Uniform mesh partitioning cannot accurately capture these local details, leading to the accumulation of errors in local regions and affecting the global optimal solution. Therefore, the inherent linearization error of the current planar convex hull linear approximation method in handling HOF has become a major obstacle to obtaining high-precision optimal solutions to the original problem using the LP model. An innovative method is urgently needed that can effectively correct local errors and improve overall approximation accuracy without significantly increasing computational complexity.

[0004] To address the aforementioned key shortcomings, this invention proposes an error correction optimization method based on the planar convex hull linear approximation method. The planar convex hull method can more accurately describe the nonlinear characteristics within a local, small-scale power output region. Based on this, this invention creatively introduces a "divide and conquer" strategy: first, the overall feasible region of hydropower output is subdivided into multiple small-scale local power output sub-regions. Then, fine-grained meshing and convex hull plane parameter calibration are performed independently within each local sub-region. This regional processing avoids interference with global calibration and achieves local refinement. Mesh partitioning within a single small region results in a higher density of discrete points compared to partitioning the entire global region with the same density, thus capturing local nonlinear characteristics more precisely and finding a set of optimal convex hull planes that approximate the power output of that small region. Through this independent regional optimization approximation, the deviation of the original global approximation method in local high-error regions can be significantly corrected. The implementation of this invention enables the cascade reservoir optimization scheduling scheme obtained by the LP model to be closer to the optimal solution of the original nonlinear problem, effectively improving the solution accuracy and practicality of the LP model in complex hydropower optimization scheduling problems. This method can promote the further development and improvement of LP in the field of reservoir optimization scheduling. More importantly, applying this method to actual cascade reservoir scheduling models can provide more accurate guidance for hydropower station operation decisions, optimize the coordinated utilization of water resources, and improve the comprehensive benefits of hydropower systems. Summary of the Invention

[0005] The problem this invention aims to solve is that the linear model for long-term power generation optimization scheduling of cascade reservoirs, constructed based on the planar convex hull linear approximation method, produces linear errors with the actual scheduling problem, leading to a mismatch between the scheduling results and the actual situation, thus affecting the scientific nature and economic benefits of scheduling decisions.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A medium- to long-term power generation optimization scheduling method for reservoir groups based on corridor iterative dynamic optimization includes:

[0008] S1. Based on the linear approximation method of planar convex hull, the convex hull plane parameters of the cascade reservoir output constraints are calibrated, and a medium- and long-term optimization scheduling model of the cascade reservoir is constructed based on the convex hull plane parameters.

[0009] S2. Solve the long-term optimization scheduling model of the cascade reservoirs to obtain the initial solution, which includes the reservoir capacity and outflow decision values ​​for each time period.

[0010] S3. Based on the reservoir capacity and outflow decision values ​​of each reservoir at each time period, construct the time period output corridor corresponding to each reservoir at each time period through the reservoir capacity scale parameter and the outflow scale parameter.

[0011] S4. Taking the power output corridor of each reservoir at each time period as the feasible region, the independent convex hull plane parameters of each reservoir at each time period are obtained by calibration based on the planar convex hull linear approximation method and used as the updated time period convex hull plane parameters.

[0012] S5. Construct a new long-term optimization scheduling model for cascade reservoirs using the updated time-period convex hull plane parameters and solve it to obtain the current iterative solution;

[0013] S6. Determine whether the relative change of the objective function value between the current iterative solution and the previous iterative solution or the initial solution is less than a set threshold. If it is less than the set threshold, it is considered converged, and the current iterative solution is output as the optimal solution for the long-term optimal scheduling of cascade reservoirs, which is used to guide the scheduling decisions of each cascade reservoir in each time period. If it is greater than or equal to the set threshold, update the reservoir capacity scale parameters and outflow scale parameters of each reservoir, return to step S3, reconstruct the output corridor of each reservoir in each time period, calibrate the solution and update the independent convex hull plane parameters of each reservoir in each time period, construct a new long-term optimal scheduling model for cascade reservoirs and solve it until the objective function value of the solution converges iteratively, and output the optimal solution and the optimal objective function value.

[0014] Furthermore, the long-term optimal scheduling model for the cascade reservoirs considers the entire cascade reservoir system, with a one-year scheduling cycle and a ten-day period as the optimization time period. The objective function is to maximize the power generation of the entire cascade reservoir system and maximize the minimum power output during each period. The objective function is as follows:

[0015] ;

[0016] in, and These represent the hydropower station / reservoir and the time period number, respectively. and These represent the total number of hydropower stations / reservoirs and the total number of time periods, respectively. This represents the minimum output of the cascade reservoirs during a given period. It is a relatively large weight value assigned to the minimum output during the time period; For hydroelectric power station During the period The effort required at that time; The duration of a period is measured in hours;

[0017] The constraints include cascade reservoir water balance constraints, reservoir capacity and outflow constraints, minimum output constraints for different time periods, and reservoir output constraints.

[0018] Furthermore, the water balance constraint of the cascade reservoirs is as follows:

[0019] ;

[0020] in, In order to be with the reservoir A collection of directly connected upstream reservoirs; This refers to the sequence number of the reservoirs in the upstream reservoir group; For reservoir exist Real-time storage capacity; For reservoir During the period Inbound flow at that time; For reservoir During the period Outbound flow rate at that time;

[0021] The constraints on reservoir capacity and outflow are as follows:

[0022] ;

[0023] in, Because the scheduling period is long; and For reservoir During the period The upper and lower limits of the warehouse capacity at that time and For reservoir During the period The upper and lower limits of outbound flow at that time;

[0024] The minimum output constraint for the specified time period is:

[0025] ;

[0026] The reservoir output constraint is expressed as a set of linearized inequalities related to the reservoir's average storage capacity and outflow during each time period, specifically:

[0027] ;

[0028] in, Hydropower stations During the period The upper and lower limits of output at that time For the corresponding reservoir During the period The convex hull plane parameters of the output constraint at that time. , The index of the convex hull plane. This represents the total number of convex hull planes. For reservoir During the period Average storage capacity at that time.

[0029] Furthermore, the convex hull plane parameters of the reservoir output constraint are related to the output characteristics of each reservoir and are obtained by fitting and calibration using the planar convex hull linear approximation method.

[0030] Furthermore, the decision variables of the long-term optimal scheduling model for the cascade reservoirs include the reservoir capacity of each cascade reservoir at each time point. Outbound flow at different times Using a mathematical solver and linear programming, the long-term optimal scheduling model of cascade reservoirs was solved, yielding the initial solution. The form is a time series of average reservoir capacity and outflow for each stage of the reservoirs, where the initial solution is the decision for each time period, expressed as follows: ,in For reservoir During the period The specific value of the decision variable at time, i.e., the storage capacity at time. Outbound flow during different time periods Composition, objective function value .

[0031] Furthermore, the time-period output corridor refers to the feasible output region of the hydropower station in each time period, which is composed of the upper and lower limits of reservoir capacity and the upper and lower limits of outflow for that time period. The specific construction method for constructing the time-period output corridor corresponding to each reservoir in each time period includes:

[0032] Based on the initial solution Decision-making for each cascade reservoir in China at different times It forms a small-scale power output area in its neighborhood. This is updated to the feasible output domain for each time period, obtaining the upper and lower limits of the storage capacity and outflow of the output area for that time period, respectively expressed as:

[0033] ;

[0034] in, Reservoirs Time period Upper and lower limits of reservoir capacity in small-scale power output areas; Reservoirs Dead water level corresponds to dead reservoir capacity and time. The reservoir capacity corresponding to the highest water level limit; Reservoirs Time period Upper and lower limits of outflow volume in small-scale power output areas; Reservoirs Minimum outbound flow rate and time period Maximum allowable outbound flow rate; Reservoirs The reservoir capacity scale parameter is used to construct the range of variation of the upper and lower limits of the reservoir capacity within the feasible region of output over a given period. For reservoir The outflow flow rate scale parameter is used to construct the upper and lower limits of the outflow flow rate variation range within the feasible region of output capacity over a given period. Let be the number of iterations, when =0 represents the initial value of the storage capacity and outflow flow scale parameters.

[0035] Furthermore, the independent convex hull plane parameters of each reservoir at each time period refer to the convex hull plane parameters used by each reservoir to construct the output constraints for each time period. The solution method is to use the constructed output corridors of each reservoir at each time period as the feasible region, i.e., the upper and lower limits of reservoir capacity and outflow, as follows:

[0036]

[0037] Using the planar convex hull linear approximation method, the independent convex hull planar parameters used to construct output constraints for each stage of the reservoir at each time period are obtained through calibration. , and , ,in The index of the convex hull plane. This represents the total number of convex hull planes.

[0038] Furthermore, the construction of a new long-term optimal scheduling model for cascade reservoirs and the solution to obtain the current iterative solution specifically includes:

[0039] The new medium- and long-term optimal scheduling model for cascade reservoirs is based on the original model, but updates the constraints on reservoir capacity and outflow, as well as the reservoir output constraints. The updated constraints on reservoir capacity and outflow are as follows:

[0040] ;

[0041] The updated linear expression for reservoir output is:

[0042] ;

[0043] Using a mathematical solver, a linear programming method is employed to solve the new long-term optimal scheduling model for cascade reservoirs, yielding iterative solutions. and objective function value .

[0044] Furthermore, the relative change in the objective function value between the current iterative solution and the previous iterative solution or the initial solution is calculated as follows:

[0045] .

[0046] Furthermore, updating the reservoir capacity and outflow rate parameters of each reservoir involves dividing the current capacity and outflow rate parameters by a fixed scaling factor to obtain the updated capacity and outflow rate parameters. The specific calculation formula is as follows:

[0047] ;

[0048] ;

[0049] This is the scaling factor for the control parameters of the corridor storage capacity and outflow, and is a constant greater than 1.

[0050] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the method described above.

[0051] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the method described above.

[0052] The beneficial effects of this invention are:

[0053] The proposed method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization has convergence and can more accurately simulate the characteristics of the nonlinear hydropower output function of the original problem. Furthermore, the use of scaling factors during the iterative update of corridor control parameters ensures that the algorithm can quickly converge to the optimal solution of the optimization problem. The proposed method can effectively correct the linear error generated by the planar convex hull linear approximation method for approximating the hydropower output function, reducing the linear error to zero. The optimized scheduling scheme for cascade reservoirs is closer to the optimal solution of the original problem. Applying this method to the reservoir optimization scheduling model makes the scheduling results more consistent with the actual situation, effectively ensuring the scientific nature and economic benefits of cascade reservoir scheduling decisions. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0055] Figure 1 This is a flowchart of a medium-to-long-term power generation optimization scheduling method for a reservoir group based on corridor iterative dynamic optimization in Example 1.

[0056] Figure 2 This is a reservoir hydraulic connection diagram for a medium- and long-term power generation optimization scheduling method for a reservoir group based on corridor iterative dynamic optimization in Example 1.

[0057] Figure 3 This is a diagram showing the time-period output corridor construction for a long-term power generation optimization scheduling method for a reservoir group based on corridor iterative dynamic optimization in Example 1.

[0058] Figure 4 This is an iterative update diagram of the reservoir capacity scale parameter and the outflow flow scale parameter of a medium- and long-term power generation optimization scheduling method for a reservoir group based on corridor iterative dynamic optimization in Example 1.

[0059] Figure 5 This is a flowchart of the time-by-time output corridor adjustment process for a reservoir group medium- and long-term power generation optimization scheduling method based on corridor iterative dynamic optimization in Example 1. Detailed Implementation

[0060] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0061] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0063] Example 1

[0064] Reference Figure 1 This embodiment provides a medium- and long-term power generation optimization scheduling method for a reservoir group based on corridor iterative dynamic optimization, including the following steps:

[0065] Step 1: Calibrate the convex hull plane parameters of the cascade reservoir output constraints based on the planar convex hull linear approximation method. A long-term optimization scheduling model for cascade reservoirs is constructed based on the convex hull plane parameters.

[0066] Step 2: Solve the long-term optimal scheduling model of the cascade reservoirs to obtain the initial solution. The initial solution includes the reservoir capacity decision values ​​for each time period. and outbound flow decision value ;

[0067] Step 3: Based on the reservoir capacity and outflow decision values ​​for each time period in the initial solution, and using the reservoir capacity scale parameter... and outbound flow rate parameters Construct time-period output corridors for each reservoir at each time period. ;

[0068] Step 4: Taking the power output corridor of each reservoir at each time period as the feasible region, the independent convex hull plane parameters of each reservoir at each time period are calibrated based on the planar convex hull linear approximation method. As updated time-period convex hull plane parameters;

[0069] Step 5: Construct a new long-term optimization scheduling model for cascade reservoirs using the updated time-period convex hull plane parameters and solve it to obtain the current iterative solution. ;

[0070] Step 6: Determine whether the relative change in the objective function value between the current iterative solution and the previous iterative solution or the initial solution is less than a set threshold. If the value is less than the set threshold, it is considered converged, and the current iterative solution is output as the optimal solution for the long-term optimal scheduling of cascade reservoirs, which is used to guide the scheduling decisions of each cascade reservoir in each time period. If the value is greater than or equal to the set threshold, the reservoir capacity scale parameters and outflow scale parameters of each reservoir are updated, and the process returns to step 3. The output corridors of each reservoir in each time period are reconstructed, the solutions are calibrated and the independent convex hull plane parameters of each reservoir in each time period are updated, a new long-term optimal scheduling model for cascade reservoirs is constructed and solved, until the objective function value of the solution converges iteratively, and the optimal solution and the optimal objective function value are output.

[0071] like Figure 2 The hydraulic connection diagram of the reservoirs shown considers four cascade reservoirs, with a one-year scheduling cycle and a ten-day period as an optimization period. The objective functions are to maximize the total power generation of the entire cascade reservoir system and maximize the minimum power output during each period. The following long-term optimal scheduling model for the cascade reservoirs is established:

[0072] (1) Objective function:

[0073] ;

[0074] in, and These represent the hydropower station / reservoir and the time period number, respectively. and These represent the total number of hydropower stations / reservoirs and the total number of time periods, respectively. This represents the minimum output of the cascade reservoirs during a given period. It is a relatively large weight value assigned to the minimum output during the time period; For hydroelectric power station During the period The effort required at that time; The duration is measured in hours.

[0075] (2) Constraints:

[0076] Water balance constraints:

[0077] ;

[0078] in, In order to be with the reservoir A collection of directly connected upstream reservoirs; This refers to the sequence number of the reservoirs in the upstream reservoir group; For reservoir exist Real-time storage capacity; For reservoir During the period Inbound flow at that time; For reservoir During the period Outbound flow rate at that time;

[0079] Reservoir capacity and outflow constraints:

[0080] ;

[0081] in, Because the scheduling period is long; and For reservoir During the period The upper and lower limits of the warehouse capacity at that time and For reservoir During the period The upper and lower limits of outbound flow at that time;

[0082] Minimum output constraint for a given time period:

[0083] ;

[0084] Reservoir output constraints:

[0085] ;

[0086] in, Hydropower stations During the period The upper and lower limits of output at that time For the corresponding reservoir During the period The convex hull plane parameters of the output constraint at that time. The index of the convex hull plane. This represents the total number of convex hull planes. For reservoir During the period Average storage capacity at that time.

[0087] The decision variables in the long-term optimal scheduling model of cascade reservoirs include the reservoir capacity of each cascade reservoir at each time point. Outbound flow at different times Using a mathematical solver and linear programming, the long-term optimal scheduling model of cascade reservoirs was solved, yielding the initial solution. The form is a time series of average reservoir capacity and outflow for each stage of the reservoirs, where the initial solution is the decision for each time period, expressed as follows: ,in For reservoir During the period The specific value of the decision variable at time, i.e., the storage capacity at time. Outbound flow during different time periods Composition, objective function value .

[0088] The time-period output corridor is the feasible output region of a hydropower station in each time period, consisting of the upper and lower limits of reservoir capacity and the upper and lower limits of outflow for that time period. The construction of the time-period output corridor for each reservoir in each time period is as follows: Figure 3 As shown, the specific construction methods include:

[0089] Based on the initial solution Decision-making for each cascade reservoir in China at different times It forms a small-scale power output area in its neighborhood. This is updated to the feasible output domain for each time period, obtaining the upper and lower limits of the storage capacity and outflow of the output area for that time period, respectively expressed as:

[0090] ;

[0091] in, Reservoirs Time period Upper and lower limits of reservoir capacity in small-scale power output areas; Reservoirs Dead water level corresponds to dead reservoir capacity and time. The reservoir capacity corresponding to the highest water level limit; Reservoirs Time period Upper and lower limits of outflow volume in small-scale power output areas; Reservoirs Minimum outbound flow rate and time period Maximum allowable outbound flow rate; Reservoirs The reservoir capacity scale parameter is used to construct the range of variation of the upper and lower limits of the reservoir capacity within the feasible region of output over a given period. For reservoir The outflow flow rate scale parameter is used to construct the upper and lower limits of the outflow flow rate variation range within the feasible region of output capacity over a given period. Let be the number of iterations, when =0 represents the initial value of the storage capacity and outflow flow scale parameters.

[0092] The independent convex hull plane parameters for each reservoir at each time period refer to the convex hull plane parameters used to construct the output constraints for each reservoir at each time period. The solution method is to use the constructed output corridors of each reservoir at each time period as the feasible region, i.e., the upper and lower limits of reservoir capacity and outflow, as follows:

[0093] ;

[0094] Using the planar convex hull linear approximation method, the independent convex hull planar parameters used to construct output constraints for each stage of the reservoir at each time period are obtained through calibration. , and .

[0095] Specifically, the planar convex hull linear approximation method proposed in patent ZL 202411002058.X can be used to establish a quadratic programming model:

[0096] Its objective function is:

[0097] ;

[0098] in, and These are the four corner grid points in the rectangular grid. The fitting of the actual output force includes both negative and positive errors, all of which are positive. Rectangular grid within the feasible region for hydropower station output The The weight coefficients for each grid point are specifically set as follows:

[0099]

[0100] Its constraints include fitting deviation constraints, effective plane constraints, and deviation non-negativity constraints;

[0101] The fitting deviation constraint is:

[0102]

[0103] in, and Grid within the feasible region for hydropower station output The The force values ​​at each grid point are located at the actual force output surface and on the corresponding plane.

[0104] The functional plane constraint is:

[0105]

[0106] in The feasible area for power output of hydropower stations differs from that of planar regions. Any rectangular grid number;

[0107] The non-negativity constraint of the deviation is:

[0108]

[0109] Rectangular mesh in this model The corresponding plane equation is:

[0110]

[0111] in, Grid within the feasible region for hydropower station output any point inside The values ​​that can be taken on the corresponding plane, For grid Parameters corresponding to the plane;

[0112] Solving the constructed quadratic programming model yields a set of parameters for a plane equation that approximates the optimal output of the hydropower station, which is a set of parameters with... Parameters of a plane equation ,in That is, the convex hull plane parameters of the reservoir output constraint that needs to be calibrated.

[0113] The new medium- and long-term optimal scheduling model for cascade reservoirs is based on the original model, but updates the constraints on reservoir capacity and outflow, as well as the reservoir output constraints. The updated constraints on reservoir capacity and outflow are as follows:

[0114] ;

[0115] The updated linear expression for reservoir output is:

[0116]

[0117] Using a mathematical solver, a linear programming method is employed to solve the new long-term optimal scheduling model for cascade reservoirs, yielding iterative solutions. and objective function value .

[0118] The relative change in the objective function value between the current iteration solution and the previous iteration solution or the initial solution is calculated as follows:

[0119]

[0120] Updating the reservoir capacity and outflow rate parameters for each reservoir involves dividing the current capacity and outflow rate parameters by a fixed scaling factor to obtain the updated parameters. Figure 4 As shown, the specific calculation formula is as follows:

[0121] ;

[0122] ;

[0123] This is the scaling factor for the control parameters of the corridor storage capacity and outflow, i.e., a constant greater than 1.

[0124] The complete process of adjusting the output corridor of each reservoir at each time period is as follows: Figure 5 As shown in the flowchart.

[0125] Example 2

[0126] The second embodiment of the present invention differs from the first embodiment in that it further includes:

[0127] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0129] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0130] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0131] Example 3

[0132] The third embodiment of the present invention differs from the first two embodiments in that:

[0133] right Figure 3 In the long-term optimization scheduling problem of the cascade reservoirs consisting of four cascade reservoirs, a more accurate piecewise linear approximation method (Method 1) and a planar convex hull linear approximation method (ZL 202411002058.X) combined with the method described in this invention for linear error elimination (Method 2) were compared in single-year optimization scheduling under different typical inflow conditions. The results are shown in Table 1. The results show that applying Method 2 proposed in this invention for single-year optimization scheduling of the cascade reservoirs significantly reduces the average value of the decadal output error for all four reservoirs under three typical hydrological year types: wet year, normal year, and dry year. The error approaches zero in wet year and equals zero in normal and dry years. Compared with Method 1, Method 2 shows a significant advantage in reducing the decadal output error. Thanks to the higher solution accuracy, the total annual power generation of the cascade reservoirs using Method 2 is significantly higher than that using Method 1.

[0134] Table 1. Comparison of annual optimization scheduling results for different linear methods

[0135]

[0136] The method described in this invention has convergence and can more accurately describe the characteristics of the nonlinear hydropower output function of the original problem. It can effectively correct the linear error generated by the approximation of the hydropower output function based on the planar convex hull linear approximation method, reduce the linear error to near zero, and make the cascade reservoir optimal scheduling scheme closer to the optimal solution of the original problem. Applying this method to the reservoir optimal scheduling model makes the scheduling results more consistent with the actual situation, and can effectively ensure the scientificity and economic benefits of cascade reservoir scheduling decisions.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A medium- and long-term power generation optimization scheduling method for a reservoir group based on corridor iterative dynamic optimization, characterized in that, Includes the following steps: S1. Based on the linear approximation method of planar convex hull, the convex hull plane parameters of the cascade reservoir output constraints are calibrated, and a medium- and long-term optimization scheduling model of the cascade reservoir is constructed based on the convex hull plane parameters. S2. Solve the long-term optimization scheduling model of the cascade reservoirs to obtain the initial solution, which includes the reservoir capacity and outflow decision values ​​for each time period. S3. Based on the reservoir capacity and outflow decision values ​​of each reservoir at each time period, construct the time period output corridor corresponding to each reservoir at each time period through the reservoir capacity scale parameter and the outflow scale parameter. S4. Taking the power output corridor of each reservoir at each time period as the feasible region, the independent convex hull plane parameters of each reservoir at each time period are obtained by calibration based on the planar convex hull linear approximation method and used as the updated time period convex hull plane parameters. S5. Construct a new long-term optimization scheduling model for cascade reservoirs using the updated time-period convex hull plane parameters and solve it to obtain the current iterative solution; S6. Determine whether the relative change of the objective function value between the current iterative solution and the previous iterative solution or the initial solution is less than a set threshold. If it is less than the set threshold, it is considered converged, and the current iterative solution is output as the optimal solution for the long-term optimal scheduling of cascade reservoirs, which is used to guide the scheduling decisions of each cascade reservoir in each time period. If it is greater than or equal to the set threshold, update the reservoir capacity scale parameters and outflow scale parameters of each reservoir, return to step S3, reconstruct the output corridor of each reservoir in each time period, calibrate the solution and update the independent convex hull plane parameters of each reservoir in each time period, construct a new long-term optimal scheduling model for cascade reservoirs and solve it until the objective function value of the solution converges iteratively, and output the optimal solution and the optimal objective function value.

2. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 1, characterized in that: The aforementioned medium- and long-term optimal scheduling model for cascade reservoirs considers the entire cascade reservoir system, with a one-year scheduling cycle and a ten-day period as an optimization time period. The objective function is to maximize the total power generation of the entire cascade reservoir system and maximize the minimum power output during each time period. The objective function of this model is as follows: ； in, and These represent the hydropower station / reservoir and the time period number, respectively. and These represent the total number of hydropower stations / reservoirs and the total number of time periods, respectively. This represents the minimum output of the cascade reservoirs during a given period. It is a relatively large weight value assigned to the minimum output during the time period; For hydroelectric power station During the period The effort required at that time; The duration of a period is measured in hours; The constraints include cascade reservoir water balance constraints, reservoir capacity and outflow constraints, minimum output constraints for different time periods, and reservoir output constraints.

3. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 2, characterized in that: The water balance constraint of the cascade reservoirs is: ； in, In order to be with the reservoir A collection of directly connected upstream reservoirs; This refers to the sequence number of the reservoirs in the upstream reservoir group; For reservoir exist Real-time storage capacity; For reservoir During the period Inbound flow at that time; For reservoir During the period Outbound flow rate at that time; The constraints on the reservoir capacity and outflow are as follows: ； in, Because the scheduling period is long; and For reservoir During the period The upper and lower limits of the warehouse capacity at that time and For reservoir During the period The upper and lower limits of outbound flow at that time; The minimum output constraint for the specified time period is: ； The reservoir output constraint is expressed as a set of linearized inequalities related to the reservoir's average storage capacity and outflow during each time period, specifically: ； in, Hydropower stations During the period The upper and lower limits of output at that time For the corresponding reservoir During the period The convex hull plane parameters of the output constraint at that time. , The index of the convex hull plane. This represents the total number of convex hull planes. For reservoir During the period Average storage capacity at that time.

4. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 3, characterized in that: The convex hull plane parameters of the reservoir output constraint are related to the output characteristics of each reservoir and are obtained by fitting and calibration using the planar convex hull linear approximation method.

5. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 1, characterized in that: The decision variables of the long-term optimal scheduling model for the cascade reservoirs include the reservoir capacity of each cascade reservoir at each time point. Outbound flow at different times Using a mathematical solver and linear programming, the long-term optimal scheduling model of cascade reservoirs was solved, yielding the initial solution. The form is a time series of average reservoir capacity and outflow for each stage of the reservoirs, where the initial solution is the decision for each time period, expressed as follows: ,in For reservoir During the period The specific value of the decision variable at time, i.e., the storage capacity at time. Outbound flow during different time periods Composition, objective function value .

6. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 5, characterized in that: The time-period output corridor refers to the feasible output region of the hydropower station in each time period, which is composed of the upper and lower limits of reservoir capacity and the upper and lower limits of outflow for that time period. The specific construction method for constructing the time-period output corridor corresponding to each reservoir in each time period includes: Based on the initial solution Decision-making for each cascade reservoir in China at different times It forms a small-scale power output area in its neighborhood. This is updated to the feasible output domain for each time period, obtaining the upper and lower limits of the storage capacity and outflow of the output area for that time period, respectively expressed as: ； in, Reservoirs Time period Upper and lower limits of reservoir capacity in small-scale power output areas; Reservoirs Dead water level corresponding to dead reservoir capacity and time The reservoir capacity corresponding to the highest water level limit; Reservoirs Time period Upper and lower limits of outflow volume in small-scale power output areas; Reservoirs Minimum outbound flow rate and time period Maximum allowable outbound flow rate; Reservoirs The reservoir capacity scale parameter is used to construct the range of variation of the upper and lower limits of the reservoir capacity within the feasible region of output over a given period. For reservoir The outflow flow rate scale parameter is used to construct the upper and lower limits of the outflow flow rate variation range within the feasible region of output capacity over a given period. Let be the number of iterations, when =0 represents the initial value of the storage capacity and outflow flow scale parameters.

7. A method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 1 or 6, characterized in that: The independent convex hull plane parameters of each reservoir at each time period refer to the convex hull plane parameters used by each reservoir to construct the output constraints at each time period. The solution method is to use the constructed output corridor of each reservoir at each time period as the feasible region, that is, the upper and lower limits of reservoir capacity and outflow, as follows: ； Using the planar convex hull linear approximation method, the independent convex hull planar parameters used to construct output constraints for each stage of the reservoir at each time period are obtained through calibration. , and , ,in The index of the convex hull plane. This represents the total number of convex hull planes.

8. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 7, characterized in that: The construction of a new long-term optimal scheduling model for cascade reservoirs and the solution to obtain the current iterative solution specifically include: The new medium- and long-term optimal scheduling model for cascade reservoirs is based on the original model, but updates the reservoir capacity and outflow constraints as well as the reservoir output constraints. The updated reservoir capacity and outflow constraints are as follows: ； The updated linear expression for reservoir output is: ； Using a mathematical solver, a linear programming method is employed to solve the new long-term optimal scheduling model for cascade reservoirs, yielding iterative solutions. and objective function value .

9. The method for medium- and long-term power generation optimization scheduling of reservoir groups based on corridor iterative dynamic optimization as described in claim 8, characterized in that: The calculation method for the relative change of the objective function value between the current iteration solution and the previous iteration solution or the initial solution is as follows: 。 10. The method for medium- and long-term power generation optimization scheduling of a reservoir group based on corridor iterative dynamic optimization as described in claim 6, characterized in that: The updated reservoir capacity and outflow parameters are obtained by dividing the current capacity and outflow parameters by a fixed scaling factor. The specific calculation formula is as follows: ； ； This is the scaling factor for the control parameters of the corridor storage capacity and outflow, and is a constant greater than 1.

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