Optimal dispatching method and system for step-by-step release of flood control storage capacity of cascade reservoirs

Abstract

The application discloses a kind of cascade reservoir flood control storage capacity gradual release optimization scheduling method and the system for realizing this method, this method includes: first, the objective function and constraint condition of cascade reservoir flood control storage capacity gradual release optimization scheduling are constructed, and the initial state of the water level process of cascade reservoir flood control storage capacity gradual release is given;Then, based on the step-by-step optimization method as the framework, the multi-stage cascade reservoir flood control storage capacity gradual release optimization scheduling problem is decomposed into multiple two-stage sub-problems, and the gradient method is embedded in each sub-problem calculation process, and the initial state is updated by gradient depth search;Finally, the global optimal solution is approached by iteration optimization one by one, and the cascade reservoir flood control storage capacity gradual release optimization scheduling process considering optimal benefit is output.The application reduces the calculation complexity, improves the local depth search capability, avoids the dimension disaster problem, and is suitable for large-scale cascade reservoir group optimization scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir group optimization scheduling, and in particular to a method and system for optimizing scheduling of cascade reservoirs for gradually releasing flood control storage capacity. Background Art

[0002] To promote benefits and eliminate harm, store floodwaters to supplement drought relief, and regulate runoff, reservoir projects have been successively completed and put into operation. Through the implementation of scientific and precise scheduling of reservoir clusters, they have provided important support for protecting people's lives and property and promoting the stable and healthy development of the economy and society. However, most reservoirs currently share storage capacity for both flood control and water conservation. Single reservoirs are used for multiple development tasks, including flood control, power generation, water supply, shipping, and ecological conservation. This makes it difficult to coordinate the joint flood control and water storage and release of reservoir clusters. It is necessary to coordinate flood control and water conservation to fully realize the comprehensive benefits of reservoir clusters.

[0003] As reservoir clusters continue to expand, their ability to regulate floods is increasing. Furthermore, with advancements in hydrological forecasting technology and a clearer understanding of flood patterns, it's crucial to gradually release flood control capacity from reservoir clusters to promote the effective utilization of flood resources while ensuring flood control safety. In other words, given the significant improvement in the basin's overall flood control capacity following the operation of large-scale reservoir clusters, while ensuring flood control safety, optimized control schemes for cascade reservoir operating water levels during flood season can be developed based on flood season water inflow patterns. This will create conditions for effectively utilizing flood resources during the flood season, improving end-of-flood water storage capacity, and ultimately maximizing the overall benefits of cascade reservoirs. Therefore, optimizing the gradual release of flood control capacity from cascade reservoirs during the flood season, taking into account the changing patterns of floods during the flood season, is of great practical significance.

[0004] In essence, the gradual release of flood control capacity from cascade reservoirs also controls the operating water levels of these reservoirs during flood season, making it a key means of coordinating flood control with public benefits. To optimize public benefits while ensuring flood control safety, it is necessary to conduct research on the optimal scheduling of the gradual release of flood control capacity from cascade reservoirs. Mathematically, this optimal scheduling of the gradual release of flood control capacity from cascade reservoirs is similar to the optimal scheduling of cascade power generation and flood control, characterized by multi-stage, high-dimensional, strongly constrained, and nonlinear characteristics.

[0005] The stepwise optimization approach (POA), an improvement on traditional dynamic programming, decomposes a multi-stage optimization problem into several two-stage subproblems. Each calculation then iterates and optimizes only the current stage, continuing step by step until a final solution is output. POA has been widely used in fields such as hydropower scheduling and flood control. However, as the scale of the system increases, POA still suffers from poor search capabilities and long computation times, making it difficult to meet the requirements for accurate and effective optimization problem solving.

[0006] The traditional gradient method is a common method for solving nonlinear optimization problems. It gradually approaches the optimal solution along the effective direction, but the prerequisite is that the objective function is differentiable within the solution range. However, since the characteristic curves used in the optimal operation of a reservoir group are mostly measured point sets, the constraints and objective functions are nonlinear and difficult to directly differentiate. To this end, based on the gradient method, related studies have proposed a discrete gradient descent method for the optimal operation of a reservoir group (Zhao Zhipeng, Liao Shengli, Cheng Chuntian, et al. Discrete gradient stepwise optimization algorithm for the medium- and long-term optimal operation of cascade hydropower stations [J]. Journal of Hydraulic Engineering, 2018, 49(10): 1243-1253.). However, each iterative calculation of this method requires the calculation of discrete gradient values, which makes the calculation process very complicated. In addition, the search step size is not easy to determine. Whether the step size is too large or too small, it will affect the calculation accuracy.

[0007] Therefore, the applicant considered proposing a gradient-like stepwise optimization method to solve the problem of easy local convergence when POA faces the problem of optimal scheduling of cascade reservoir flood control storage capacity release. Summary of the Invention

[0008] In order to overcome the shortcomings of the above-mentioned technology, the present invention provides a method and system for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs, which solves the problems of local convergence and dimensionality curse faced by POA when solving the optimization scheduling problem of the release of flood control storage capacity of cascade reservoirs. It has the advantages of high computational efficiency and high solution accuracy.

[0009] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0010] A method for optimizing the scheduling of gradually releasing flood control storage capacity of cascade reservoirs comprises the following steps:

[0011] 1) Collect basic data on flood season operation of cascade reservoirs and joint operation plan of cascade reservoirs;

[0012] 2) according to the joint scheduling plan for cascade reservoirs, obtaining the releasable flood control storage capacity of the cascade reservoirs at each stage as it changes with flood characteristics; based on the basic information and the releasable flood control storage capacity, constructing the objective function and constraints for the optimal scheduling of the gradual release of the flood control storage capacity of the cascade reservoirs, and setting calculation parameters; the calculation parameters include the number of reservoirs N, the number of scheduling stages T, the maximum number of iterations M, and the maximum number of class gradient searches K;

[0013] 3) Using artificial experience decision-making or conventional methods to obtain the initial state of the water level process of the cascade reservoir flood control storage capacity gradually released to meet the constraints And calculate the discrete step length of each reservoir at each stage Δ=(Δ i,j ) N×T , the calculation formula is as follows:

[0014]

[0015] Where, and Δ i,j They represent the initial state of reservoir i at stage j; and Z i,j Respectively represent the upper and lower limits of the water level of reservoir i at stage j; i = 1, 2, ..., N; j = 1, 2, ..., T;

[0016] 4) Initialize the number of iterations m = 1;

[0017] 5) performing a gradient-like stepwise optimization method;

[0018] 6) Let m = m + 1. If m > M, go to step 7); otherwise, go to step 5) and continue to perform gradient-like depth search.

[0019] 7) Stop the calculation and output the final optimal trajectory state, that is, obtain the optimized scheduling process of gradually releasing the flood control storage capacity of the cascade reservoirs with the optimal benefits.

[0020] Preferably, in step 1), the basic data include characteristic parameters of each reservoir, water level and storage capacity curve, downstream water level and flow relationship curve, power station unit output characteristic curve, and reservoir water inflow process.

[0021] Preferably, in step 2), the objective function is:

[0022]

[0023] Where: F is the total power generation during the gradual release of the flood control storage capacity of the cascade reservoirs; N is the number of reservoirs; i is the reservoir number, and i = 1, 2, ..., N; T is the number of stages in the scheduling period during which the flood control storage capacity of the cascade reservoirs is gradually released; j is the stage number, and j = 1, 2, ..., T; A i is the output coefficient of reservoir i; Q i,j is the power generation flow of reservoir i in stage j, m 3 / s;H i,j is the average power generation head of reservoir i in stage j after deducting the head loss, m; Δt is the stage duration, h.

[0024] Preferably, in step 2), the constraints include:

[0025] (1) Water balance constraints of each reservoir: V i,j =V i,j-1 +(I i,j-1 -O i,j-1 )×Δt,

[0026] (2) Hydraulic connection constraints of each reservoir: I i,j =Oi-1,j +R i,j ,

[0027] (3) Water level constraints of each reservoir:

[0028] (4) Constraints on outflow from each reservoir: O i,j =Q i,j +S i,j ,

[0029] (5) Constraints on discharge capacity of each reservoir: i,j ≤O i,max (Z i,j ),

[0030] (6) Output constraints of each reservoir: P min i,j ≤A i Q i,j ·H i,j ≤P max i,j ,

[0031] (7) Cascade reservoirs can release flood control storage capacity constraints:

[0032] (8) Constraints on total output of cascade reservoirs:

[0033] (9) Non-negative constraint: all variables are non-negative;

[0034] Where V i,j is the storage capacity of reservoir i at the end of stage j, m 3 ;I i,j is the inflow of reservoir i in stage j, m 3 / s;O i,j is the outflow of reservoir i in stage j, m 3 / s; and are the minimum outflow and maximum outflow of reservoir i in stage j, m 3 / s;S i,j is the water discharge of reservoir i at stage j, m 3 / s;R i,j is the interval flow of reservoir i in stage j, m 3 / s;Z i,j is the water level before the dam of reservoir i at stage j, m; and are the lowest and highest water levels in front of the dam of reservoir i at stage j, m; O i,max (Z i,j ) is the water level Z corresponding to reservoir i at stage j i,j Maximum discharge capacity, m 3 / s;P min i,j and P max i,j are the minimum and maximum output of reservoir i in stage j, kW; V i 0 is the storage capacity corresponding to the flood control limit water level of reservoir i, m 3 SV j is the total releasable flood control storage capacity of the cascade reservoirs at stage j, m 3 NP j is the minimum total output of the cascade reservoir in stage j, kW.

[0035] Preferably, the step 5) comprises the following steps:

[0036] 5.1) Decompose the optimal scheduling problem of gradually releasing flood control reservoir capacity into T-1 two-stage problems;

[0037] 5.2) For each two-stage problem, perform gradient-like depth search in turn.

[0038] Preferably, the step 5.2) comprises the following steps:

[0039] 5.2.1) Initialize the number of class gradient searches k = 1;

[0040] 5.2.2) Status Perform gradient-like depth search to generate gradient-like states in, is the state of the sth two-stage at the mth iteration;

[0041] 5.2.3) Comparing Class Gradient States and current status if Better than The current state is replaced by the gradient-like state, otherwise no processing is done;

[0042] 5.2.4) Let k = k + 1. If k ≤ K, the gradient-like depth search has been completed in the current stage, and go to step 5.2.5). Otherwise, go to step 5.2.2) and continue the gradient-like depth search.

[0043] 5.2.5) Determine whether all two-stage sub-problems have executed gradient-like depth search. If so, go to step 6). Otherwise, go to step 5.2.1) and execute gradient-like depth search on the remaining two-stage sub-problems in sequence.

[0044] Preferably, the gradient-like state The calculation formula is as follows:

[0045]

[0046] Where, α1, α2, ..., α N-1 are N-1 random angles with values ​​in [0,2π]. According to the characteristics of trigonometric functions, the gradient-like equidistant space parameters D1, D2, ..., D N The value of is between -1 and 1, ensuring At the same time, g is the step size control parameter, which is set to gradually decrease as the iteration progresses to gradually shrink the search range of the class gradient.

[0047] Preferably, the calculation formula of the step length control parameter g is: g=1-m / M.

[0048] A cascade reservoir flood control capacity gradual release optimization scheduling system, comprising:

[0049] The initialization module is used to construct the optimization objective function and constraints for the gradual release of the flood control storage capacity of the cascade reservoirs, and generate the water level trajectory of the gradual release of the flood control storage capacity of the cascade reservoirs that meets the constraints based on manual experience decision-making or conventional dynamic programming methods;

[0050] The step-by-step optimization decomposition module is used to decompose the optimization scheduling problem of the gradual release of flood control storage capacity of cascade reservoirs at the current iteration number into multiple two-stage step-by-step optimization sub-problems;

[0051] The gradient-like deep search module performs gradient-like deep search based on the step-by-step optimization decomposition module, including gradient-like parameter generation, gradient-like state generation, state comparison and replacement, and stop condition judgment functions to obtain a better optimization state;

[0052] The output module is used to repeatedly execute the step-by-step optimization decomposition module and the gradient-like deep search module until the preset iteration stop condition is met, thereby obtaining the optimal scheduling result, which serves as the water level optimization process for gradually releasing the flood control storage capacity of the final cascade reservoir.

[0053] A computer device includes a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the above-mentioned cascade reservoir flood control storage capacity gradual release optimization scheduling method.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The method of the present invention has clear principles, simple calculations, and is easy to implement, and realizes the rapid and accurate solution of the optimization scheduling problem of the gradual release of flood control storage capacity of cascade reservoirs; the present invention innovatively introduces the gradient-like method into the solution process of each stage of POA, combines the equidistant space parameters based on the angle pattern and the step control parameters based on the iterative process to generate diverse search information, so as to improve the local search capability of POA and seek the optimization direction of the scheduling trajectory; the method of the present invention performs optimization according to the gradient-like method in the optimization process of each stage, avoids the comprehensive combination of all discrete states of each reservoir at each stage, avoids the problem of dimensionality curse, and improves the accuracy of local deep search. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic flow chart of a method for optimizing the scheduling of a cascade reservoir flood control storage capacity by gradually releasing the storage capacity of the cascade reservoir according to the present invention;

[0057] Figure 2 A comparison diagram of power generation changes in the optimized scheduling of the gradual release of flood control storage capacity of cascade reservoirs in a normal water year using the method of the present invention and the POA method;

[0058] Figure 3 This is a diagram of the water level process of Reservoir A in the optimized scheduling of the gradual release of flood control storage capacity of the cascade reservoirs in a normal water year in an embodiment of the present invention;

[0059] Figure 4 This is a diagram of the water level process of Reservoir B in the optimized scheduling of the gradual release of flood control storage capacity of the cascade reservoirs in a normal water year in an embodiment of the present invention;

[0060] Figure 5 This is a diagram of the water level process of Reservoir C in the optimized scheduling of the gradual release of flood control storage capacity of the cascade reservoirs in a normal water year in an embodiment of the present invention;

[0061] Figure 6 This is a diagram of the water level process of Reservoir D in the optimized scheduling of the gradual release of flood control storage capacity of the cascade reservoirs in a normal water year in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to better explain the present invention, the main contents of the present invention are further illustrated below with reference to the accompanying drawings and specific embodiments, but the contents of the present invention are not limited to the following embodiments.

[0063] like Figure 1 As shown, the present invention provides a method for optimizing the scheduling of gradually releasing the flood control storage capacity of cascade reservoirs, comprising the following steps:

[0064] 1) Collect basic data on flood season operation of cascade reservoirs, including characteristic parameters of each reservoir, water level and storage capacity curves, downstream water level and flow relationship curves, hydropower station unit output characteristic curves, reservoir water inflow process and other data and scheduling documents such as the joint operation plan of cascade reservoirs.

[0065] 2) According to the joint scheduling plan of cascade reservoirs, the releasable flood control storage capacity of the cascade reservoirs at each stage that changes with flood characteristics is obtained; based on the basic information and the releasable flood control storage capacity, the objective function and constraints for the optimal scheduling of the gradual release of the flood control storage capacity of the cascade reservoirs are constructed, and the calculation parameters are set, including the number of reservoirs N, the number of scheduling stages T, the maximum number of iterations M, and the maximum number of class gradient searches K.

[0066] According to the cascade reservoir scheduling plan, under the premise that the flood control storage capacity of the cascade reservoirs can be released at different periods of the flood season, in order to coordinate the release rhythm of the flood control storage capacity of each reservoir, improve the benefits of the cascade reservoirs, and improve the efficiency of flood resource utilization during the flood season, the main considerations of the constructed cascade reservoir flood control storage capacity gradual release optimization scheduling model are: the inflow runoff process of each reservoir at different scheduling stages of the flood season is known, and under the premise of meeting the flood control safety of the basin and the requirements of the flood control storage capacity that can be released by the cascade reservoirs at each stage, the flood control storage capacity of the cascade reservoirs is gradually released, thereby achieving maximum power generation.

[0067] Therefore, the objective function of the optimization scheduling model for the gradual release of flood control storage capacity of cascade reservoirs is as follows:

[0068]

[0069] Where: F is the total power generation during the gradual release of the flood control storage capacity of the cascade reservoirs; N is the number of reservoirs; i is the reservoir number, and i = 1, 2, ..., N; T is the total number of stages in which the flood control storage capacity of the cascade reservoirs is gradually released; j is the stage number, and j = 1, 2, ..., T; A i is the output coefficient of reservoir i; Q i,j is the power generation flow of reservoir i in stage j (m 3 / s); H i,j is the average power generation head of reservoir i in stage j after deducting the head loss (m); Δt is the stage duration (h).

[0070] The various constraints that need to be met for the gradual release of flood control storage capacity of cascade reservoirs for optimal scheduling mainly include:

[0071] (1) Water balance constraints of each reservoir: V i,j =V i,j-1 +(I i,j-1 -O i,j-1 )×Δt,

[0072] (2) Hydraulic connection constraints of each reservoir: Ii,j =O i-1,j +R i,j ,

[0073] (3) Water level constraints of each reservoir:

[0074] (4) Constraints on outflow from each reservoir: O i,j =Q i,j +S i,j ,

[0075] (5) Constraints on discharge capacity of each reservoir: i,j ≤O i,max (Z i,j ),

[0076] (6) Output constraints of each reservoir: P min i,j ≤A i Q i,j ·H i,j ≤P max i,j ,

[0077] (7) Cascade reservoirs can release flood control storage capacity constraints:

[0078] (8) Constraints on total output of cascade reservoirs:

[0079] (9) Non-negative constraint: All variables are non-negative.

[0080] Where V i,j is the storage capacity of reservoir i at the end of stage j (m 3 );I i,j is the inflow of reservoir i in stage j (m 3 / s); O i,j is the outflow of reservoir i in stage j (m 3 / s); and are the minimum outflow of reservoir i in stage j (m 3 / s) and maximum outbound flow (m 3 / s); S i,j is the water discharge of reservoir i at stage j (m 3 / s); R i,j is the interval flow of reservoir i in stage j (m 3 / s); Z i,j is the water level before the dam of reservoir i at stage j (m); and are the lowest and highest water levels in front of the dam of reservoir i at stage j (m); O i,max (Z i,j ) is the water level Z corresponding to reservoir i at stage j i,j Maximum discharge capacity (m 3 / s); P min i,j and P max i,j are the minimum output (kW) and maximum output (kW) of reservoir i in stage j; V i 0 is the storage capacity corresponding to the flood control limit water level of reservoir i (m 3 );SV j is the total releasable flood control storage capacity of the cascade reservoirs at stage j (m 3 );NP j is the minimum total output of the cascade reservoir in stage j (kW).

[0081] 3) Using artificial experience decision-making or conventional methods to generate the initial state of the water level process of the cascade reservoir flood control storage capacity gradually released to meet various constraints And calculate the discrete step length of each reservoir at each stage Δ=(Δ i,j ) N×T , the calculation formula is as follows:

[0082]

[0083] Where, and Δ i,j They represent the initial state of reservoir i at stage j; and Z i,j They represent the upper and lower limits of the water level of reservoir i at stage j, respectively.

[0084] 4) Initialize the number of iterations m = 1,

[0085] 5) Execute the gradient-like stepwise optimization method, the calculation steps of which are as follows:

[0086] 5.1) Decompose the optimal scheduling problem of gradually releasing flood control reservoir capacity into T-1 two-stage problems;

[0087] 5.2) For each two-stage problem, perform gradient-like depth search in turn.

[0088] The basic principle of gradient-like deep search is to gradually approach the optimal solution along the effective direction based on the gradient method. It uses individuals to search along several equidistant spaces and randomly generates equidistant spaces based on angle patterns to obtain the optimal evolutionary direction until the iterative search is completed.

[0089] Now take the state of the sth two-stage in the mth iteration Take the gradient-like deep search optimization problem as an example to explain it in detail.

[0090] 5.2.1) Initialize the number of class gradient searches k = 1;

[0091] 5.2.2) Status Perform gradient-like depth search to generate gradient-like states The calculation formula is as follows:

[0092]

[0093] Where, α1, α2, ..., α N-1 are N-1 random angles with values ​​in [0,2π]. According to the characteristics of trigonometric functions, the gradient-like equidistant space parameters D1, D2, ..., D N The value of is between -1 and 1, ensuring At the same time, g is the step size control parameter, which is set to gradually decrease as the iteration progresses to gradually shrink the search range of the class gradient. Its calculation formula is as follows:

[0094] g = 1-m / M;

[0095] 5.2.3) Comparing Class Gradient States and current status if Better than The current state is replaced by the gradient-like state; otherwise, no processing is performed.

[0096] 5.2.4) Let k = k + 1. If k ≤ K, it indicates that the gradient-like depth search has been completed in the current stage, and go to step 5.2.5); otherwise, go to step 5.2.2) and continue to perform the gradient-like depth search.

[0097] 5.2.5) Determine whether all two-stage sub-problems have been subjected to gradient-like depth search. If so, go to step 6); otherwise, go to step 5.2.1) and perform gradient-like depth search on the remaining two-stage sub-problems in sequence.

[0098] 6) Let m=m+1. If m>M, go to step 7); otherwise, go to step 5) and continue to perform gradient-like depth search.

[0099] 7) Stop the calculation, output the final optimal trajectory state, and obtain the optimized scheduling process of gradually releasing the flood control storage capacity of the cascade reservoirs that takes into account the optimal benefits, that is, obtain the optimized scheduling process of gradually releasing the flood control storage capacity of the cascade reservoirs with the largest total power generation.

[0100] A cascade reservoir flood control capacity gradual release optimization scheduling system for implementing the above method comprises:

[0101] The initialization module is used to construct the optimization objective function and constraints for the gradual release of the flood control storage capacity of the cascade reservoirs, and generate the water level trajectory of the gradual release of the flood control storage capacity of the cascade reservoirs that meets the constraints based on manual experience decision-making or conventional dynamic programming methods;

[0102] The step-by-step optimization decomposition module is used to decompose the optimization scheduling problem of the gradual release of flood control storage capacity of cascade reservoirs at the current iteration number into multiple two-stage step-by-step optimization sub-problems;

[0103] The gradient-like deep search module performs gradient-like deep search based on the step-by-step optimization decomposition module, including gradient-like parameter generation, gradient-like state generation, state comparison and replacement, stop condition judgment and other functions to obtain a better optimization state;

[0104] The output module is used to repeatedly execute the step-by-step optimization decomposition module and the gradient-like deep search module until the preset iteration stop condition is met, thereby obtaining the optimal scheduling result, which serves as the water level optimization process for gradually releasing the flood control storage capacity of the final cascade reservoir.

[0105] The present invention also provides a computer device comprising a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the above-mentioned method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs.

[0106] The method of the present invention is further described below through specific examples.

[0107] Example

[0108] Take the cascade reservoir group consisting of four large reservoirs A, B, C and D in the upper reaches of a river as an example.

[0109] The engineering development mission of this cascade of reservoirs is primarily focused on power generation, while also taking into account flood control, navigation, and the promotion of local economic and social development. The cascade reservoirs have a large total regulating storage capacity, reserved flood control storage capacity, and installed power generation capacity, providing significant comprehensive benefits in flood control, power generation, navigation, ecological conservation, and water replenishment during the dry season.

[0110] According to the joint scheduling scheme for cascade reservoirs, during real-time scheduling, when a cascade reservoir does not need to perform flood control operations in its region or cooperate with upstream reservoirs in flood control operations, the cascade reservoir flood control storage capacity reservation method can be optimized in a timely manner while ensuring flood control safety, and the cascade reservoir flood control storage capacity can be gradually released. To this end, the proposed method of the present invention is applied to the problem of optimizing the scheduling of the gradual release of cascade reservoir flood control storage capacity.

[0111] The optimized scheduling period of this embodiment is the flood season in July. During this period, under the premise of meeting flood control safety, the cascade reservoirs can gradually release flood control storage capacity in early, mid and late July, respectively, in combination with the water supply situation and flood control situation needs. Generally, the capacity can be 993 million m3 respectively. 3 2.493 billion m 3 and 2.993 to 6.493 billion m 3 The calculation for the end of July should be made in light of the specific flood control situation. Five typical water inflow frequencies in July, 15%, 30%, 50%, 70%, and 90%, were selected as examples of high-water years, moderate-water years, normal-water years, moderate-water years, and low-water years, respectively. The proposed method and POA were used to optimize the gradual release of flood control storage capacity in cascade reservoirs. Table 1 compares the results.

[0112] Table 1: Comparison of the results of the method of the present invention and POA calculation

[0113]

[0114] From Table 1 and Figure 2 It can be seen that for different typical water inflow conditions, such as abundant years, moderately abundant years, normal years, moderately dry years, and dry years, the power generation calculated by the method of the present invention is similar to that of POA, but the calculation advantages are reflected in:

[0115] (1) From the perspective of power generation, both the method of the present invention and POA gradually approach the global optimal solution, but the power generation of the method of the present invention is greater than that of POA, and the calculation accuracy is improved, indicating that after adopting the gradient-like method, the local depth search capability of the method of the present invention is better.

[0116] (2) The calculation time of the method of the present invention is significantly shorter than that of POA, which is only about 10% of the calculation time of POA. As the calculation scale of the hydropower station increases, the calculation performance advantage becomes more prominent.

[0117] (3) By Figure 2 It can be seen that compared with the iterative calculation process of POA, the method of the present invention can quickly approach the global optimal solution in the early stage of iteration, and the search capability is very powerful.

[0118] From the above analysis, it can be seen that compared with POA, the method of the present invention can, on the one hand, increase power generation and have higher calculation accuracy, and on the other hand, save calculation time and have higher calculation efficiency. It is feasible and effective to apply it to the optimization calculation of the gradual release of flood control storage capacity of cascade reservoirs.

[0119] Figures 3 to 6The changes in the water levels of the four large reservoirs A, B, C, and D in this embodiment under the conditions of normal water years are listed, and the changes in the flood control storage capacity released by each reservoir are listed. The scheduling results all meet the constraints of various water level, storage capacity, flow, etc., and each reservoir gradually releases the flood control storage capacity. The power generation head and flow of each reservoir are optimized. The leading reservoir intercepts and stores water in advance to raise the reservoir level and exert the head and water volume effect. The water levels of other reservoirs are steadily raised, which comprehensively improves the total power generation of the cascade reservoirs and can effectively support the scheduling, operation, management and production practice of the cascade reservoirs.

[0120] In summary, the present invention first collects basic data and scheduling plans for flood season scheduling of cascade reservoirs, constructs the objective function and constraints for the optimized scheduling of the gradual release of flood control storage capacity of cascade reservoirs, and gives the initial state of the water level process of the gradual release of flood control storage capacity of cascade reservoirs; then, based on the stepwise optimization method (POA) as the basic framework, the multi-stage cascade reservoir flood control storage capacity gradual release optimized scheduling problem is decomposed into multiple two-stage sub-problems; then, a gradient-like method is embedded in the calculation process of each sub-problem, and a gradient-like deep search and update is performed on the initial state; finally, the global optimal solution is successively approximated through iterative optimization, and the optimized scheduling process of the gradual release of flood control storage capacity of cascade reservoirs that takes into account the optimal benefits is output.

[0121] The present invention performs stage-by-stage dimensionality reduction on the optimization scheduling problem of gradually releasing the flood control storage capacity of cascade reservoirs. It combines equidistant spatial parameters based on angle patterns and step control parameters based on iterative processes to generate diverse search information to seek the optimization direction of the scheduling trajectory. It avoids the comprehensive combination of all discrete states of each reservoir at each stage, reduces computational complexity, improves local deep search capabilities, avoids the problem of the curse of dimensionality, and is suitable for the optimization scheduling of large-scale cascade reservoir groups.

[0122] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0123] Other parts not described belong to the prior art.

Claims

1. A method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs, characterized by: The steps include: 1) Collect basic data on flood season operation of cascade reservoirs and joint operation plan of cascade reservoirs; 2) according to the joint scheduling plan for cascade reservoirs, obtaining the releasable flood control storage capacity of the cascade reservoirs at each stage as it changes with flood characteristics; based on the basic information and the releasable flood control storage capacity, constructing the objective function and constraints for the optimal scheduling of the gradual release of the flood control storage capacity of the cascade reservoirs, and setting calculation parameters; the calculation parameters include the number of reservoirs N, the number of scheduling stages T, the maximum number of iterations M, and the maximum number of class gradient searches K; 3) Using artificial experience decision-making or conventional methods to obtain the initial state of the water level process of the cascade reservoir flood control storage capacity gradually released to meet the constraints And calculate the discrete step length of each reservoir at each stage Δ=(Δ i,j ) N×T , the calculation formula is as follows: Where, and Δ i,j They represent the initial state of reservoir i at stage j; and Z i,j Respectively represent the upper and lower limits of the water level of reservoir i at stage j; i = 1, 2, ..., N; j = 1, 2, ..., T; 4) Initialize the number of iterations m = 1; 5) performing a gradient-like stepwise optimization method; 5.1) Decompose the optimal scheduling problem of gradually releasing flood control reservoir capacity into T-1 two-stage problems; 5.2) For each two-stage problem, perform gradient-like depth search in turn; 5.2.1) Initialize the number of class gradient searches k = 1; 5.2.2) Status Perform gradient-like depth search to generate gradient-like states in, is the state of the sth two-stage in the mth iteration; the gradient-like state The calculation formula is as follows: Where, α1, α2, ..., α N-1 are N-1 random angles with values ​​in [0,2π]. According to the characteristics of trigonometric functions, the gradient-like equidistant space parameters D1, D2, ..., D N The value of is between -1 and 1, ensuring At the same time, g is the step size control parameter, which is set to gradually decrease as the iteration progresses to gradually shrink the search range of the gradient class; 5.2.3) Comparing Class Gradient States and current status if Better than The current state is replaced by the gradient-like state, otherwise no processing is done; 5.2.4) Let k = k + 1. If k ≤ K, the gradient-like depth search has been completed in the current stage, and go to step 5.2.5). Otherwise, go to step 5.2.2) and continue the gradient-like depth search. 5.2.5) Determine whether all two-stage subproblems have been subjected to gradient-like depth search. If so, go to step 6). Otherwise, go to step 5.2.1) and perform gradient-like depth search on the remaining two-stage subproblems in sequence. 6) Let m = m + 1. If m > M, go to step 7); otherwise, go to step 5) and continue to perform gradient-like depth search. 7) Stop the calculation and output the final optimal trajectory state, that is, obtain the optimized scheduling process of gradually releasing the flood control storage capacity of the cascade reservoirs with the optimal benefits.

2. The method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs according to claim 1 is characterized by: In the step 1), the basic data include characteristic parameters of each reservoir, water level and storage capacity curve, downstream water level and flow relationship curve, power station unit output characteristic curve, and reservoir water inflow process.

3. The method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs according to claim 1 is characterized by: In step 2), the objective function is: Where: F is the total power generation during the gradual release of the flood control storage capacity of the cascade reservoirs; N is the number of reservoirs; i is the reservoir number, and i = 1, 2, ..., N; T is the number of stages in the scheduling period during which the flood control storage capacity of the cascade reservoirs is gradually released; j is the stage number, and j = 1, 2, ..., T; A i is the output coefficient of reservoir i; Q i,j is the power generation flow of reservoir i in stage j, m 3 / s;H i,j is the average power generation head of reservoir i in stage j after deducting the head loss, m; Δt is the stage duration, h.

4. The method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs according to claim 1 is characterized by: In step 2), the constraints include: (1) Water balance constraints of each reservoir: (2) Hydraulic connection constraints of each reservoir: (3) Water level constraints of each reservoir: (4) Constraints on outflow from each reservoir: (5) Constraints on discharge capacity of each reservoir: (6) Output constraints of each reservoir: (7) Cascade reservoirs can release flood control storage capacity constraints: (8) Constraints on total output of cascade reservoirs: (9) Non-negative constraint: all variables are non-negative; Where V i,j is the storage capacity of reservoir i at the end of stage j, m 3 ;I i,j is the inflow of reservoir i in stage j, m 3 / s;O i,j is the outflow of reservoir i in stage j, m 3 / s; and are the minimum outflow and maximum outflow of reservoir i in stage j, m 3 / s;S i,j is the water discharge of reservoir i at stage j, m 3 / s;R i,j is the interval flow of reservoir i in stage j, m 3 / s;Z i,j is the water level before the dam of reservoir i at stage j, m; and are the lowest and highest water levels in front of the dam of reservoir i at stage j, m; O i,max (Z i,j ) is the water level Z corresponding to reservoir i at stage j i,j Maximum discharge capacity, m 3 / s;P min i,j and P max i,j are the minimum and maximum output of reservoir i in stage j, kW; V i 0 is the storage capacity corresponding to the flood control limit water level of reservoir i, m 3 SV j is the total releasable flood control storage capacity of the cascade reservoirs at stage j, m 3 NP j is the minimum total output of the cascade reservoir in stage j, kW.

5. The method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs according to claim 1 is characterized by: The calculation formula of the step length control parameter g is: g=1-m / M.

6. A system for optimizing the gradual release of storage capacity for flood control in cascade reservoirs, for implementing the method for optimizing the gradual release of storage capacity for flood control in cascade reservoirs as claimed in claim 1, characterized in that: include: The initialization module is used to construct the optimization objective function and constraints for the gradual release of the flood control storage capacity of the cascade reservoirs, and generate the water level trajectory of the gradual release of the flood control storage capacity of the cascade reservoirs that meets the constraints based on manual experience decision-making or conventional dynamic programming methods; The step-by-step optimization decomposition module is used to decompose the optimization scheduling problem of the gradual release of flood control storage capacity of cascade reservoirs at the current iteration number into multiple two-stage step-by-step optimization sub-problems; The gradient-like deep search module performs gradient-like deep search based on the step-by-step optimization decomposition module, including gradient-like parameter generation, gradient-like state generation, state comparison and replacement, and stop condition judgment functions to obtain a better optimization state; The output module is used to repeatedly execute the step-by-step optimization decomposition module and the gradient-like deep search module until the preset iteration stop condition is met, thereby obtaining the optimal scheduling result, which serves as the water level optimization process for gradually releasing the flood control storage capacity of the final cascade reservoir.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load the at least one program to execute the method for optimizing the scheduling of the gradual release of flood control storage capacity of cascade reservoirs as described in any one of claims 1 to 5.

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