Cascade reservoir medium-and-long-term power generation optimization scheduling method considering forced water abandoning and related device

Abstract

The invention discloses a cascade reservoir medium-and-long-term power generation optimal scheduling method considering forced water abandoning and a related device. The method comprises the following steps: constructing an optimal scheduling model considering forced water abandoning based on requirements and reservoir basic data; utilizing a plane convex hull linear approximation method to calibrate plane convex hull parameters of the hydroelectric output constraint, and constructing a hydroelectric output function linear expression; based on the hydroelectric output function linear expression, integer variable linearization forced water abandoning constraint is introduced, and a mixed integer linear programming model is formed; the model is solved through a mathematical solver, the warehouse-out flow and the warehouse capacity value of each time period are obtained, and a scheduling decision is guided. According to the method, the water abandoning constraint is fitted through planar convex hull approximation, the mixed integer linear programming model is constructed in combination with integer variable linearization processing, and model and method support is provided for accurately describing a scheduling problem, meeting actual requirements and improving the water energy utilization rate.

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 method and related devices for optimizing the scheduling of long-term power generation in cascade reservoirs that takes into account forced water abandonment, providing guidance for the scheduling and operation of cascade reservoirs. Background Technology

[0002] Hydropower, as the world's largest-scale clean energy source, plays a strategic role in promoting the clean transformation of the power system. Long-term optimal scheduling of reservoirs, a core technology for achieving this goal, requires the coordination of multiple objectives, including flood control and power generation, at monthly and annual scales. Maximizing the utilization of hydropower resources is essentially a high-dimensional, strongly nonlinear mathematical optimization problem. The planar convex hull linear approximation method, as an efficient optimization solution for non-convex nonlinear problems, significantly reduces the difficulty of solving large-scale, multi-period reservoir scheduling models by constructing a series of linearly constrained envelopes of the original nonlinear problem, and has broad application prospects. For the long-term power generation optimization scheduling problem of cascade reservoirs, this method transforms the complex non-convex nonlinear problem into an iterative linear programming problem by linearizing key nonlinear factors such as the reservoir's storage-water level relationship, tailrace water level-flow relationship, and power generation efficiency curve. It demonstrates advantages in computational efficiency and solution feasibility, making it an effective tool for handling the optimal scheduling of large-scale reservoir systems.

[0003] However, existing reservoir long-term power generation optimization scheduling models based on the planar convex hull linear approximation method have a significant, unresolved flaw: they fail to consider the crucial factor of water abandonment in actual reservoir scheduling, particularly neglecting the key operational constraint of "forced water abandonment" and its inherent physical principles. In actual reservoir operation, "forced water abandonment" follows strict physical laws: only when the reservoir's water level exceeds its set limit level (such as the flood control limit level) at the end of a specific period must the excess inflow exceeding the maximum power generation capacity of the generating units during that period be discharged through spillways or other discharge facilities. However, existing models, lacking explicit expression and effective coupling of this core constraint, frequently exhibit "non-physical water abandonment" phenomena in their optimization results, which do not match actual scheduling conditions. That is, the model may "actively" or "unreasonably" suggest water abandonment when the limit level has not been reached, or fail to accurately constrain the relationship between the amount of water abandoned and the available flood discharge capacity when the limit level is reached, violating the scheduling principle of "cherishing water like gold." Such scheduling schemes, which are not compatible with engineering practice, not only fail to accurately depict the physical nature and complexity of reservoir scheduling problems, but also, because they cannot adapt to environmental changes and the randomness of water inflow, they ultimately severely weaken the practical value and decision support capabilities of the optimization model. The resulting scheduling schemes are often infeasible in engineering practice or lead to potential water energy waste and safety risks. Summary of the Invention

[0004] The problem this invention aims to solve is that existing reservoir long-term power generation optimization scheduling models based on the planar convex hull linear approximation method do not consider the water abandonment factor of cascade reservoirs. Furthermore, due to the lack of constraints on water abandonment, the scheduling results obtained from solving the optimization scheduling model produce unreasonable water abandonment phenomena, which do not match the actual scheduling situation and affect the safety and economy of reservoir scheduling.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment, comprising:

[0007] Based on demand and basic data of reservoirs and hydropower stations, a cascade reservoir optimization scheduling model is constructed to consider forced water abandonment.

[0008] The planar convex hull parameters of the hydropower output constraints for constructing the cascade reservoir optimization scheduling model are obtained by calibrating using the planar convex hull linear approximation method. A linear expression of the hydropower output function is then constructed based on the planar convex hull parameters of the hydropower output constraints.

[0009] Based on the linear expression of the hydropower output function, the cascade reservoir optimization scheduling model considering forced water abandonment is transformed from a nonlinear model into a mixed integer linear programming model by introducing integer variables.

[0010] The mixed-integer linear programming model is solved using a mathematical solver to obtain the outflow and reservoir capacity values ​​of the cascade reservoirs at each time period that satisfy the forced water release requirement. This information is then used to guide the scheduling decisions of each cascade reservoir at each time period.

[0011] As a preferred embodiment of the long-term power generation optimization scheduling method for cascade reservoirs considering forced water abandonment as described in this invention, wherein:

[0012] The cascade reservoir optimization scheduling model has an annual cycle and a ten-day period as its objective function, aiming to maximize the power generation of the entire cascade and the minimum power output during each period. The objective function is as follows:

[0013]

[0014] 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. Minimum output for each stage of the process; 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 time period is represented by hours; the constraints of the objective function include cascade reservoir water balance constraints, cascade reservoir capacity constraints, upper and lower limits of cascade reservoir outflow constraints, minimum output constraints of cascade reservoir group during the time period, hydropower output constraints, forced water abandonment constraints of cascade reservoirs, and hydropower station power abandonment constraints.

[0015] 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:

[0016]

[0017] 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;

[0018] The constraints include cascade reservoir water balance constraints, reservoir capacity and outflow constraints, minimum output constraints for different time periods, and reservoir output constraints.

[0019] As a preferred embodiment of the long-term power generation optimization scheduling method for cascade reservoirs considering forced water abandonment as described in this invention, the water balance constraint of the cascade reservoirs is:

[0020]

[0021] in, and These represent the hydropower station / reservoir and the time period number, respectively. For a long period of time; 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;

[0022] The reservoir capacity constraints of the cascade reservoirs are as follows:

[0023]

[0024] in, Reservoirs During the period The upper and lower limits of warehouse capacity at that time;

[0025] The upper and lower limits of the outflow from the cascade reservoirs are as follows:

[0026]

[0027] in, Reservoirs During the period The upper and lower limits of outbound flow at that time;

[0028] The minimum output constraint for the cascade reservoir group during a given time period is:

[0029]

[0030] The hydropower output constraint is expressed as follows:

[0031]

[0032]

[0033]

[0034] in, Hydropower stations During the period The upper and lower limits of output at any time; For the corresponding reservoir During the period The convex hull plane parameters are determined with the maximum output of the hydropower station as the constraint. The index of the convex hull plane. This represents the total number of convex hull planes. For reservoir During the period Average warehouse capacity at that time;

[0035] The forced water release constraint of the cascade reservoirs is as follows:

[0036]

[0037] in For reservoir Time period The discharge force corresponding to the discarded water, For reservoir time Right now The reservoir capacity corresponding to the restricted water level at the end of the time period;

[0038] The aforementioned hydropower station curtailment constraint is:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] in The variable introduced is used to control the magnitude of the discharge force; For the corresponding reservoir During the period The convex hull plane parameters are calibrated with the upper limit of power output determined by the reservoir outflow as a constraint. This represents the index of the corresponding convex hull plane. This represents the total number of corresponding convex hull planes; These are 0-1 auxiliary variables introduced to represent the hydroelectric power station. During the period Convex hull plane fitted under the influence of outflow rate The function status is as follows: if the value is 1, it means that the convex hull plane has a function in the power output of the power station during this period; otherwise, the convex hull plane has no function. It is a sufficiently large positive number, representing the error deviation of the plane with the largest error under the convex hull plane of all outbound flow.

[0045] Target constraint method

[0046] As a preferred embodiment of the long-term power generation optimization scheduling method for cascade reservoirs considering forced water abandonment as described in this invention, wherein: the convex hull plane parameters calibrated with the maximum output of the hydropower station as a constraint ( The parameters of the convex hull are related to the power output characteristics of each hydropower station. The maximum power output of the hydropower station under given outflow and reservoir capacity decision variables is fitted using the planar convex hull linear approximation method to calibrate the convex hull plane parameters. A detailed calibration calculation method is as follows:

[0047] The objective function of the calibration method is to minimize the square of the sum of the actual output errors fitted at the three corner points and one center point of all triangular mesh regions defined in the feasible region, which can be expressed as:

[0048]

[0049] In the formula and These represent the weight values ​​at the center point and the other corner points of the triangular mesh, respectively. and Triangular mesh China's reservoir During the period Four representative points The fitting of the actual output force includes both negative and positive errors, and both are positive values.

[0050] Its constraints include triangular mesh representative point constraints, hydropower output function constraints, reservoir capacity outflow interval constraints, actual reservoir capacity and outflow constraints, fitting deviation constraints, global triangular mesh effect constraints, forced intersection constraints, and error non-negativity constraints.

[0051] The triangular mesh representative point constraint of the calibration method is as follows:

[0052]

[0053] in Triangular mesh China's reservoir During the period The selected number One representative point, Triangular mesh China's reservoir During the period The selected number The storage capacity of each representative point Triangular mesh China's reservoir During the period The selected number Outbound flow at representative points;

[0054] The hydropower output function constraint of the calibration method is:

[0055]

[0056] in For reservoir Hydropower output function, For reservoir During the period Average storage capacity For reservoir During the period Outbound flow For reservoir During the period water head, For reservoir Maximum power generation flow rate For the reservoir Water consumption rate;

[0057] The constraint on the storage capacity outflow interval range in the calibration method is as follows:

[0058]

[0059]

[0060]

[0061] The reservoir capacity V and the outflow Q are divided into NV and NQ intervals respectively, with the upper and lower limits of the allowable range. During the period The storage capacity and outflow values ​​for each segment are as follows: and , and Reservoirs During the period The upper and lower limits of storage capacity, and Reservoirs During the period The upper and lower limits of outbound flow. This represents the total number of triangular grids.

[0062] The actual storage capacity and outflow constraints of the calibration method are as follows:

[0063] ,

[0064] ,

[0065] The feasible region for hydropower output is defined by the sequence number […]. Storage capacity interval and The outflow intervals are divided into several rectangular grids. For reservoir During the period In serial number The actual storage capacity For reservoir During the period In serial number The actual outbound flow rate;

[0066] The fitting deviation constraint of the calibration method is:

[0067]

[0068] in The parameters of the convex hull plane are calibrated with the maximum output of the hydropower station as the constraint. The fitting error is determined by the difference between the output value calculated by the four representative points in each triangular mesh at the corresponding plane to be calibrated and the actual calculated output value.

[0069] The calibration method uses a global triangular mesh for comparison.

[0070]

[0071] In the formula The triangular mesh number is used as the constraint to ensure that the plane corresponding to each triangular mesh is effective at the midpoint of the region. That is, the function value of the corresponding plane at the midpoint of this triangular mesh should be less than or equal to the function value of any other plane at the same point. Note the decision variables... The value of is not specified to have a range of values;

[0072] The mandatory intersection constraint of the calibration method is:

[0073]

[0074]

[0075] In the formula These are all triangular mesh numbers. The forced intersection constraint ensures that two adjacent active planes share a common edge on the side shared by their respective triangular meshes. A set of triangular grid points sharing a common intersection point;

[0076] The error non-negativity constraint of the calibration method is:

[0077]

[0078] As a preferred embodiment of the long-term power generation optimization scheduling method for cascade reservoirs considering forced water abandonment described in this invention, wherein: the convex hull plane parameters calibrated with the upper limit of power output determined by the reservoir outflow as a constraint ( The output characteristics of each hydropower station are related to its own characteristics. The convex hull plane parameters are calibrated by fitting the possible output values ​​of the hydropower station under given outflow and reservoir capacity decision variables using the planar convex hull linear approximation method. The detailed process of the calibration calculation method is as follows:

[0079] The objective function of the calibration method is to minimize the square of the sum of the actual output errors fitted at the three corner points and one center point of all triangular mesh regions defined in the feasible region, which can be expressed as:

[0080]

[0081] In the formula and These represent the weight values ​​at the center point and the other corner points of the triangular mesh, respectively. and Triangular mesh China's reservoir During the period Four representative points The fitting of the actual output force includes both negative and positive errors, and both are positive values.

[0082] Its constraints include triangular mesh representative point constraints, hydropower output function constraints, reservoir capacity outflow interval constraints, actual reservoir capacity and outflow constraints, fitting deviation constraints, global triangular mesh effect constraints, forced intersection constraints, and error non-negativity constraints.

[0083] The triangular mesh representative point constraint of the calibration method is as follows:

[0084]

[0085] in Triangular mesh China's reservoir During the period The selected number One representative point, Triangular mesh China's reservoir During the period The selected number The storage capacity of each representative point Triangular mesh China's reservoir During the period The selected number Outbound flow at representative points;

[0086] The hydropower output function constraint of the calibration method is:

[0087]

[0088] in For reservoir Hydropower output function, For reservoir During the period Average storage capacity For reservoir During the period Outbound flow For reservoir During the period water head, For the reservoir Water consumption rate;

[0089] The constraint on the storage capacity outflow interval range in the calibration method is as follows:

[0090]

[0091]

[0092]

[0093] The reservoir capacity V and the outflow Q are divided into NV and NQ intervals respectively, with the upper and lower limits of the allowable range. During the period The storage capacity and outflow values ​​for each segment are as follows: and , and Reservoirs During the period The upper and lower limits of storage capacity, and Reservoirs During the period The upper and lower limits of outbound flow. This represents the total number of triangular grids.

[0094] The actual storage capacity and outflow constraints of the calibration method are as follows:

[0095] ,

[0096] ,

[0097] The feasible region for hydropower output is defined by the sequence number […]. Storage capacity interval and The outflow intervals are divided into several rectangular grids. For reservoir During the period In serial number The actual storage capacity For reservoir During the period In serial number The actual outbound flow rate;

[0098] The fitting deviation constraint of the calibration method is:

[0099]

[0100] in The convex hull plane parameters are calibrated with the upper limit of power output determined by the outflow of the reservoir as a constraint. The fitting error is determined by the difference between the power output value calculated by the four representative points in each triangular grid at the corresponding plane to be calibrated and the actual calculated power output value.

[0101] The calibration method uses a global triangular mesh for comparison.

[0102]

[0103] In the formula The triangular mesh number is used as the constraint to ensure that the plane corresponding to each triangular mesh is effective at the midpoint of the region. That is, the function value of the corresponding plane at the midpoint of this triangular mesh should be less than or equal to the function value of any other plane at the same point. Note the decision variables... The value of is not specified to have a range of values;

[0104] The mandatory intersection constraint of the calibration method is:

[0105]

[0106]

[0107] In the formula These are all triangular mesh numbers. The forced intersection constraint ensures that two adjacent active planes share a common edge on the side shared by their respective triangular meshes. A set of triangular grid points sharing a common intersection point;

[0108] The error non-negativity constraint of the calibration method is:

[0109]

[0110] As a preferred embodiment of the long-term optimization scheduling algorithm for cascade reservoirs based on corridor iterative dynamic optimization described in this invention, the step of constructing a new long-term optimization scheduling model for cascade reservoirs and solving it to obtain the current iterative solution specifically includes: linearizing the forced water release constraints of cascade reservoirs by introducing integer variables, specifically expressed as follows:

[0111]

[0112]

[0113]

[0114] Among them The introduced 0-1 auxiliary variable represents the reservoir. exist Whether water can be released during a given period; a value of 0 indicates that the reservoir... exist If the water level does not reach the upper limit at the end of the time period, no water can be released; when the value is 1, it means that the water level has reached the upper limit at the end of the time period, and no more water will be stored during this time period, and water can be released. A sufficiently large positive number represents the maximum regulating storage capacity of the reservoir. A sufficiently large positive number represents the maximum water discharge capacity that the reservoir can potentially generate (in actual calculations). and Normal settings about).

[0115] As a preferred embodiment of the long-term power generation optimization scheduling method for cascade reservoirs considering forced water abandonment as described in this invention, the mixed integer linear programming model is solved using the Gurobi or Cplex mathematical solver.

[0116] 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.

[0117] 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.

[0118] The beneficial effects of this invention are:

[0119] The proposed method for optimizing the long-term power generation scheduling of cascade reservoirs, considering forced water abandonment, fully takes into account this key factor. Based on the analysis of the relationship between hydropower station outflow, maximum power generation reference flow, and water abandonment, a new output relationship considering water abandonment is constructed. Furthermore, by introducing integer variables to linearize the forced water abandonment constraint, the original problem is transformed into a mixed-integer linear programming model. This model can constrain the timing of water abandonment to meet the actual reservoir scheduling principles, ensuring that water abandonment only occurs when the reservoir has no storage capacity. Moreover, even if water abandonment occurs at the beginning of the flood season, the objective can still be maximized. However, reservoir water abandonment only occurs during the period when the water level reaches the limit level, which is more consistent with actual scheduling conditions and can more scientifically guide reservoir scheduling and operation. Attached Figure Description

[0120] 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:

[0121] Figure 1 This is an algorithm flowchart for a long-term power generation optimization scheduling method for cascade reservoirs that considers forced water abandonment, as described in Example 1.

[0122] Figure 2 This is a reservoir hydraulic connection diagram for a long-term power generation optimization scheduling method for cascade reservoirs considering forced water abandonment, as described in Example 1.

[0123] Figure 3 This is a schematic diagram illustrating the generation of forced water abandonment in a long-term power generation optimization scheduling method for cascade reservoirs that considers forced water abandonment, as described in Example 1.

[0124] Figure 4 This is an example of optimizing the storage and release process of cascade reservoirs during a high-water year when considering the constraint of forced water release, as described in Example 1, a method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water release.

[0125] Figure 5 This is an example of optimizing the storage and release process of cascade reservoirs in a high-water year without considering the constraint of forced water release, as described in Example 1, a method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water release. Detailed Implementation

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Example 1

[0130] Reference Figure 1 This embodiment provides a method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment, including the following steps:

[0131] Step 1: Based on the demand and basic data of reservoirs and hydropower stations, construct an optimized scheduling model for cascade reservoirs that considers forced water release;

[0132] Step 2: Use the planar convex hull linear approximation method to calibrate the planar convex hull parameters of the hydropower output constraints for constructing the cascade reservoir optimization scheduling model, and construct a linear expression for the hydropower output function based on the planar convex hull parameters of the hydropower output constraints;

[0133] Step 3: Based on the linear expression of the hydropower output function, the cascade reservoir optimization scheduling model considering forced water abandonment is transformed from a nonlinear model into a mixed integer linear programming model by introducing integer variables;

[0134] Step 4: Use a mathematical solver to solve the mixed integer linear programming model to obtain the outflow and reservoir capacity values ​​of the cascade reservoirs at each time period that satisfy the forced water release requirement. This information is used to guide the scheduling decisions of each cascade reservoir at each time period.

[0135] The method steps described below will be detailed in the section on the construction of a long-term optimal scheduling model for cascade reservoirs that consider forced water release and the method for solving the optimal objective.

[0136] like Figure 2 The hydraulic connection diagram shown considers four cascade reservoirs, with a one-year cycle and a ten-day period. The objective function is to maximize the power generation of the entire cascade and the minimum power output during each period. The following optimal scheduling model for the cascade reservoirs, considering forced water release, is established:

[0137] (1) Objective function:

[0138]

[0139] 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. Minimum output for each stage of the process; 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 of time is expressed in hours.

[0140] (2) Constraints:

[0141] The water balance constraints for cascade reservoirs are:

[0142]

[0143] in, and These represent the hydropower station / reservoir and the time period number, respectively. For a long period of time; 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;

[0144] The capacity constraints of the cascade reservoirs are:

[0145]

[0146] in, Reservoirs During the period The upper and lower limits of warehouse capacity at that time;

[0147] The upper and lower limits of outflow from cascade reservoirs are as follows:

[0148]

[0149] in, Reservoirs During the period The upper and lower limits of outbound flow at that time;

[0150] The minimum output constraint for a cascade reservoir group during a given time period is:

[0151]

[0152] Hydropower output constraints are expressed as follows:

[0153]

[0154]

[0155]

[0156] in, Hydropower stations During the period The upper and lower limits of output at any time; For the corresponding reservoir During the period The convex hull plane parameters are determined with the maximum output of the hydropower station as the constraint. The index of the convex hull plane. This represents the total number of convex hull planes. For reservoir During the period Average warehouse capacity at that time;

[0157] Conditions for forced water abandonment include Figure 3 As shown, the forced water release constraint of the cascade reservoirs is:

[0158]

[0159] in For reservoir Time period The discharge force corresponding to the discarded water, For reservoir time Right now The reservoir capacity corresponding to the restricted water level at the end of the time period;

[0160] The power curtailment constraint of the hydropower station is:

[0161]

[0162]

[0163]

[0164]

[0165]

[0166] in The variable introduced is used to control the magnitude of the discharge force; For the corresponding reservoir During the period The convex hull plane parameters are calibrated with the upper limit of power output determined by the reservoir outflow as a constraint. This represents the index of the corresponding convex hull plane. This represents the total number of corresponding convex hull planes; These are 0-1 auxiliary variables introduced to represent the hydroelectric power station. During the period Convex hull plane fitted under the influence of outflow rate The function status is as follows: if the value is 1, it means that the convex hull plane has a function in the power output of the power station during this period; otherwise, the convex hull plane has no function. It is a sufficiently large positive number, representing the error deviation of the plane with the largest error under the convex hull plane of all outbound flow.

[0167] Convex hull plane parameters constrained by the maximum output of the hydropower station ( The parameters of the convex hull are related to the power output characteristics of each hydropower station. The maximum power output value of the hydropower station under the given outflow and reservoir capacity decision variables is fitted by the planar convex hull linear approximation method to calibrate the convex hull plane parameters.

[0168] The convex hull plane parameters calibrated with the upper limit of power output determined by the reservoir outflow as a constraint. The convex hull parameters are calibrated by fitting the possible output values ​​of the hydropower station under given outflow and reservoir capacity decision variables using the planar convex hull linear approximation method, which is related to the output characteristics of each hydropower station.

[0169] By introducing integer variables, the forced water release constraint of cascade reservoirs is linearized, specifically expressed as follows:

[0170]

[0171]

[0172]

[0173] Among them The introduced 0-1 auxiliary variable represents the reservoir. exist Whether water can be released during a given period; a value of 0 indicates that the reservoir... exist If the water level does not reach the upper limit at the end of the time period, no water can be released; when the value is 1, it means that the water level has reached the upper limit at the end of the time period, and no more water will be stored during this time period, and water can be released. A sufficiently large positive number represents the maximum regulating storage capacity of the reservoir. A sufficiently large positive number represents the maximum water discharge capacity that the reservoir can potentially generate (in actual calculations). and Normal settings about).

[0174] The optimal scheduling model for cascade reservoirs under forced water release is a mixed integer linear programming model containing integer variables and linear constraints, which can be solved using mathematical solvers such as Gurobi and Cplex.

[0175] Example 2

[0176] The second embodiment of the present invention differs from the first embodiment in that it further includes:

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Example 3

[0182] The third embodiment of the present invention differs from the first two embodiments in that:

[0183] right Figure 4 and Figure 5 The comparison of the storage and release process of the four cascade reservoirs in the high-water year with and without considering the forced water release constraint shows that the inflow is high throughout the year, especially during the flood season. The upstream reservoir 1 cannot regulate more water volume, so water release will also occur during the flood season. Figure 5 The study did not consider the constraints of forced water release. Reservoirs 1, 2, and 3 all released water during the flood season, and the periods of water release occurred during the process of water level rise in the reservoirs. Figure 4 The results show that when forced water release constraints are considered, reservoirs 1, 2, and 3 also release water, and the water release only occurs when the reservoir water level reaches the maximum limit level. This demonstrates that the forced water release constraints play their due role and effectively control the timing of water release, which is in line with the actual reservoir scheduling and operation principles.

[0184] Therefore, the mixed-integer linear programming model for long-term optimal scheduling of cascade reservoirs considering forced water release constraints proposed in this invention is reasonable. The forced water release constraint after linearization is effective, constraining the timing of water release from the reservoir to meet the actual reservoir scheduling principles. Water can only be released when the reservoir water level reaches the limit level at the end of the period. Furthermore, even if water release occurs at the beginning of the flood season, the objective can still be maximized. However, reservoir water release will only occur during the period when the water level reaches the limit level, which is more in line with actual scheduling conditions.

[0185] This invention patent aims to provide an innovative solution: based on the framework of the planar convex hull linear approximation method, it constructs and solves a long-term optimal scheduling model for cascade reservoirs that incorporates precise "forced water release constraints." The core of this patent lies in its in-depth study of the physical mechanism of forced water release (i.e., water release occurring when the water level exceeds the limit and the generating units are unable to absorb it), and the design of a constraint linearization method. This method accurately expresses the originally complex, state-dependent nonlinear constraint (coupling of water level limits and discharge capacity) as a linear or piecewise linear form, seamlessly integrating it into the iterative optimization process of planar convex hull linear approximation. Through this technological breakthrough, the patented method can strictly constrain the model to allow water release only during periods when the water level reaches or exceeds the limit, and only when the generating units are operating at full capacity and still cannot absorb the excess flow, thus completely eliminating the phenomenon of "non-physical water release." This ensures that the optimization results are more in line with the actual scheduling principles of engineering projects, resulting in more significant technical benefits. Even in scenarios where reservoirs face water release (such as at the beginning of the flood season), the model can maximize objectives (such as power generation) while satisfying physical constraints. The timing and magnitude of water release are strictly limited to a range consistent with engineering logic (i.e., only occurring when the reservoir is full and the generating capacity is exhausted), significantly improving the reliability and engineering applicability of the scheduling scheme. The implementation of this patent will provide more scientific and reliable medium- and long-term optimization scheduling decision support for cascade hydropower station groups, effectively improving the efficiency of water resource utilization and the operational benefits of hydropower stations.

[0186] 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 method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment, characterized in that, include: Based on demand and basic data of reservoirs and hydropower stations, a cascade reservoir optimization scheduling model is constructed to consider forced water abandonment. The planar convex hull parameters of the hydropower output constraints for constructing the cascade reservoir optimization scheduling model are obtained by calibrating using the planar convex hull linear approximation method. A linear expression of the hydropower output function is then constructed based on the planar convex hull parameters of the hydropower output constraints. Based on the linear expression of the hydropower output function, the cascade reservoir optimization scheduling model considering forced water abandonment is transformed from a nonlinear model into a mixed integer linear programming model by introducing integer variables. The mixed-integer linear programming model is solved using a mathematical solver to obtain the outflow and reservoir capacity values ​​of the cascade reservoirs at each time period that satisfy the forced water release requirement. This information is then used to guide the scheduling decisions of each cascade reservoir at each time period.

2. The method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 1, characterized in that, The cascade reservoir optimization scheduling model has an annual cycle and a ten-day period as its objective function, aiming to maximize the power generation of the entire cascade and the minimum power output during each period. The objective function 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. Minimum output for each stage of the process; 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 time period is represented by hours; the constraints of the objective function include cascade reservoir water balance constraints, cascade reservoir capacity constraints, upper and lower limits of cascade reservoir outflow constraints, minimum output constraints of cascade reservoir group during the time period, hydropower output constraints, forced water abandonment constraints of cascade reservoirs, and hydropower station power abandonment constraints.

3. The method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 2, characterized in that: The water balance constraint of the cascade reservoirs is: ； in, and These represent the hydropower station / reservoir and the time period number, respectively. For a long period of time; 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 reservoir capacity constraints of the cascade reservoirs are as follows: ； in, Reservoirs During the period The upper and lower limits of warehouse capacity at that time; The upper and lower limits of the outflow from the cascade reservoirs are as follows: ； in, Reservoirs During the period The upper and lower limits of outbound flow at that time; The minimum output constraint for the cascade reservoir group during a given time period is: ； The hydropower output constraint is expressed as follows: ； ； ； in, Hydropower stations During the period The upper and lower limits of output at any time; For the corresponding reservoir During the period The convex hull plane parameters are determined with the maximum output of the hydropower station as the constraint. The index of the convex hull plane. This represents the total number of convex hull planes. For reservoir During the period Average warehouse capacity at that time; The forced water release constraint of the cascade reservoirs is as follows: ； in For reservoir Time period The discharge force corresponding to the discarded water, For reservoir time Right now The reservoir capacity corresponding to the restricted water level at the end of the time period; The aforementioned hydropower station curtailment constraint is: ； ； ； ； ； in The variable introduced is used to control the magnitude of the discharge force; For the corresponding reservoir During the period The convex hull plane parameters are calibrated with the upper limit of power output determined by the reservoir outflow as a constraint. This represents the index of the corresponding convex hull plane. This represents the total number of corresponding convex hull planes; These are 0-1 auxiliary variables introduced to represent the hydroelectric power station. During the period Convex hull plane fitted under the influence of outflow rate The function status is as follows: if the value is 1, it means that the convex hull plane has a function in the power output of the power station during this period; otherwise, the convex hull plane has no function. It is a sufficiently large positive number, representing the error deviation of the plane with the largest error under the convex hull plane of all outbound flow.

4. The method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 3, characterized in that: The convex hull plane parameters calibrated with the maximum output of the hydropower station as a constraint ( The parameters of the convex hull are related to the power output characteristics of each hydropower station. The maximum power output value of the hydropower station under the given outflow and reservoir capacity decision variables is fitted by the planar convex hull linear approximation method to calibrate the convex hull planar parameters.

5. The method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 3, characterized in that: The convex hull plane parameters calibrated under the constraint of the upper limit of output determined by the reservoir outflow ( The convex hull parameters are calibrated by fitting the possible output values ​​of the hydropower station under given outflow and reservoir capacity decision variables, which are related to the output characteristics of each hydropower station.

6. The method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 1, characterized in that: The linearization of the forced water release constraint of the cascade reservoirs by introducing integer variables is specifically expressed as follows: ； ； ； Among them The introduced 0-1 auxiliary variable represents the reservoir. exist Whether water can be released during a given period; a value of 0 indicates that the reservoir... exist If the water level does not reach the upper limit at the end of the time period, no water can be released; when the value is 1, it means that the water level has reached the upper limit at the end of the time period, and no more water will be stored during this time period, and water can be released. A sufficiently large positive number represents the maximum regulating storage capacity of the reservoir. A sufficiently large positive number represents the maximum water discharge capacity that the reservoir can potentially generate.

7. A method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 6, characterized in that: and Set as .

8. The method for optimizing the long-term power generation scheduling of cascade reservoirs considering forced water abandonment as described in claim 1, characterized in that: The mixed-integer linear programming model is solved using the Gurobi or Cplex mathematical solver.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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