Cascade reservoir flood control scheduling optimization method and system coupled with four-vector optimization algorithm

Abstract

The invention discloses a cascade reservoir flood control scheduling optimization method and system coupled with a four-vector optimization algorithm. The method comprises the following steps: obtaining an initial scheduling track and a neighborhood space of each reservoir level meeting constraint conditions; decomposing the cascade reservoir flood control scheduling optimization problem into a plurality of two-stage sub-optimization problems; a four-vector optimization algorithm is embedded in each two-stage sub-optimization problem, a better scheduling track is sought, and the operation is repeated until all the two-stage sub-optimization problems are optimized; the neighborhood space range is shrunk, and loop iteration calculation optimization is carried out to approach a globally optimal solution. The four-vector optimization algorithm is applied to the reservoir group scheduling problem for the first time, the solution space information is fully extracted and captured skillfully by coupling the four-vector optimization algorithm, comprehensive enumeration among all discrete states of two-stage sub-optimization problems is replaced, the number of parameters is small, the principle is simple, implementation is easy, and the method is suitable for large-scale popularization and application. The method has excellent characteristics of high search capability and high calculation precision.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir group optimization dispatching, and in particular to a cascade reservoir flood control dispatching optimization method and system coupled with a four-vector optimization algorithm. Background Art

[0002] In order to promote benefits and eliminate harm, store flood water to compensate for drought, regulate runoff, protect the lives and property of the people, and promote the stable and healthy development of the economy and society, a large number of reservoir projects have been built and put into operation. With the rapid increase in the number of reservoir groups in major river basins, the conventional flood control dispatching of a single reservoir in the design stage can no longer meet the objective requirements, making the joint flood control optimization dispatching of cascade reservoir groups an important means to deal with floods.

[0003] From a mathematical point of view, the optimal dispatching of cascade reservoirs for flood control is a complex control system problem with multiple stages, multiple variables, multiple constraints and high dimensions, and its optimal solution is not easy to obtain. The stepwise optimization method (POA) is an improved dynamic programming method for solving multi-stage optimization problems. First, the multi-stage decision optimization problem is decomposed into a series of two-stage optimization problems. In each calculation stage, the variables of other stages are fixed, and only the objective function values ​​of the current stage and the adjacent two stages are optimized. In this way, the calculation is repeated in a cycle from stage to stage until the stop condition is reached. This method reduces the comprehensive combination of discrete states of feasible solutions and improves the solution efficiency compared with the dynamic programming method. However, when the number of reservoir groups is large, there are also problems such as "dimensionality curse", premature convergence and low solution efficiency, which makes it difficult to meet the actual dispatching needs. For this reason, it is urgent to effectively improve POA to improve the computational efficiency and solution accuracy of the optimal dispatching problem of cascade reservoirs for flood control. With the rapid development of computer science, various intelligent evolutionary algorithms have emerged, providing a possibility for optimizing and solving the dispatching problem of reservoir groups.

[0004] The FourVector Intelligent Metaheuristic (FVIM) is a novel intelligent evolutionary algorithm proposed in April 2024. It has strong global development capabilities and local exploration capabilities. Its uniqueness is that after initializing a certain number of population individuals, it uses the four best individuals in the population to calculate the average value, and generates new individuals to guide the movement of the group to continuously track the optimal result.

[0005] Therefore, in order to efficiently solve the flood control optimization scheduling problem of cascade reservoirs, the applicant considered cleverly coupling the four-vector optimization algorithm with simple principles, few parameters and strong search capabilities in the calculation process of each stage of POA, so as to replace the comprehensive enumeration between all discrete states of the two-stage sub-optimization problem, so as to improve the problems of dimensionality curse and premature convergence of POA. Summary of the invention

[0006] In order to overcome the shortcomings of the above-mentioned technology, the present invention provides a cascade reservoir flood control scheduling optimization method and system coupled with a four-vector optimization algorithm, which solves the problems of dimensionality curse and premature convergence when using POA in the optimization of cascade reservoir flood control scheduling, and couples the four-vector optimization algorithm to the POA two-stage optimization problem. It has excellent search ability and calculation accuracy, can shorten the determination time of the cascade reservoir flood control scheduling optimization plan, and can obtain the optimal plan for cascade reservoir flood control scheduling more quickly and accurately.

[0007] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0008] A cascade reservoir flood control dispatch optimization method coupled with a four-vector optimization algorithm comprises the following steps:

[0009] 1) Determine the initial calculation conditions, including the objective function, constraints and decision variables of the optimal operation of cascade reservoir flood control;

[0010] 2) Setting calculation parameters; the calculation parameters include the number of reservoirs N, the number of scheduling stages T, the maximum number of iterations M, the population number Y of the four-vector optimization algorithm and the maximum number of optimization searches G;

[0011] 3) Using artificial experience decision-making or conventional methods to generate the initial dispatching trajectory of each reservoir at each stage that meets the constraints and neighborhood space Ω=[p i,j ,q i,j ],1≤i≤N,1≤j≤T; where represents the initial trajectory of the water level in front of the dam of reservoir i at stage j; p i,j and q i,j They represent the upper and lower limits of the water level of reservoir i at stage j respectively;

[0012] 4) Set the number of iterations m = 1,

[0013] 5) Decompose the optimal operation problem of cascade reservoirs with the current iteration number m into T-1 two-stage step-by-step optimization sub-problems;

[0014] 6) Set the number of stages j = 1;

[0015] 7) For the jth two-stage stepwise optimization subproblem, the four-vector optimization algorithm is coupled to perform optimal search to obtain the best scheduling trajectory, that is, the optimal individual X generated iteratively α ;

[0016] 8) Compare the current scheduling trajectory The optimal individual X generated by the four-vector optimization algorithm α , if the optimal individual is better than the current scheduling trajectory, the current scheduling trajectory is replaced by the optimal individual; otherwise, no processing is performed;

[0017] 9) Let j = j + 1. If j ≤ T-1, go to step 7); otherwise, shrink the neighborhood space and let p i,j =p i,j ×λ,q i,j =q i,j ×λ, λ is the neighborhood shrinkage factor, go to step 10);

[0018] 10) Let m=m+1, if m>M, go to step 11), otherwise go to step 5);

[0019] 11) Terminate the calculation and output the optimal dispatching trajectory after the iteration is completed, that is, obtain the optimized flood control dispatching plan for the cascade reservoirs.

[0020] Preferably, the step 7) comprises:

[0021] 7.1) Execute population initialization operation of four-vector optimization algorithm;

[0022] 7.2) Execute the population iterative update operation of the four-vector optimization algorithm;

[0023] 7.3) Perform the four-vector optimization algorithm to find the elite solution operation.

[0024] Preferably, the step 7.1) comprises the following steps:

[0025] In the N-dimensional neighborhood space, let Y individuals form a population X = {X1, X2, ..., X Y}, the maximum number of optimization searches is G, and the position of the yth individual in the N-dimensional neighborhood space during the g-th optimization search is expressed as 1≤i≤N,1≤y≤Y,1≤g≤G; at the same time, define the best individual, second best individual, third best individual, and fourth best individual in the population as X α , X β , X δ and X μ ;

[0026] Set g = 1, and perform a Initialize, where The calculation formula is as follows: Among them, r is a random number between 0 and 1;

[0027] Calculate all individuals The fitness of the individual X is compared and selected. α , suboptimal individual X β , the third best individual X δ , the fourth best individual X μ :

[0028]

[0029] and

[0030] and and

[0031] and and and

[0032] Where f() is the objective function of the optimal scheduling of cascade reservoir flood control, which is used to calculate the fitness of each individual.

[0033] Preferably, the step 7.2) comprises the following steps:

[0034] For Y individuals Perform iterative updates to generate corresponding new individuals The calculation formula is as follows:

[0035]

[0036] in, and The calculation formulas are as follows:

[0037]

[0038]

[0039] In the formula, r1, r2, r3, r4, r5, r6, r7, r8, r9, r 10 、r 11 、r 12 are all random numbers between 0 and 1; θ is the step size control parameter, which is set to decrease continuously as the optimization progresses, so as to gradually narrow the search range and gradually approach the optimal result;

[0040] Compare and if Indicates new individual Better than individual Then replace Otherwise, no treatment is performed; when all Y individuals After performing the iterative update operation, proceed to step 7.3).

[0041] Preferably, the calculation formula of the step size control parameter θ is as follows:

[0042] θ = 1.5×(1-m / M).

[0043] Preferably, the step 7.3) comprises the following steps:

[0044] Combined with the population iterative update operation to obtain Y individuals Recompare and select the new optimal individual X α , suboptimal individual X β , the third best individual X δ , the fourth best individual X μ , to be used in the next optimization search;

[0045] When g=g+1, if g≤G, go to step 7.2); otherwise, it indicates that the iterative optimization of the coupled four-vector optimization algorithm has been completed for the current stage, and go to step 8).

[0046] Preferably, in step 9), the neighborhood shrinkage factor λ is 0.5 to 0.95.

[0047] The present invention also provides a cascade reservoir flood control dispatch optimization system coupled with a four-vector optimization algorithm, which is used to implement the above-mentioned cascade reservoir flood control dispatch optimization method; the cascade reservoir flood control dispatch optimization system includes:

[0048] The initialization module is used to determine the objective function, constraints and decision variables of the optimal operation of cascade reservoir flood control, and generate the initial operation trajectory and neighborhood space that meet the constraints based on artificial experience or conventional methods under various constraints;

[0049] The stepwise optimization decomposition module is used to decompose the flood control optimization scheduling problem of cascade reservoirs at the current iteration number into multiple two-stage sub-optimization problems;

[0050] The four-vector optimization algorithm optimization module performs optimization search to obtain the best scheduling trajectory;

[0051] The scheduling trajectory comparison and replacement module is used to compare the current trajectory with the optimal individual in the four-vector optimization algorithm to continuously optimize and replace the current scheduling trajectory;

[0052] The output module is used to repeatedly execute the step-by-step optimization decomposition module, the four-vector optimization algorithm optimization module and the scheduling trajectory comparison and replacement module until the preset iteration stop condition is met, the optimal scheduling trajectory is obtained, and it is output as the final cascade reservoir flood control optimization scheduling result.

[0053] Preferably, the four-vector optimization algorithm optimization module includes three submodules, namely:

[0054] The population initialization operation submodule is used to initialize the population of the optimization algorithm according to the calculation principle of the four-vector optimization algorithm, and to find out the best individual, the second best individual, the third best individual, and the fourth best individual;

[0055] The population iterative update operation submodule is used to iteratively update the population individuals based on the best individual, the second best individual, the third best individual, and the fourth best individual to continuously optimize the population individuals;

[0056] The elite solution search operation submodule is used to find the best individual, the second best individual, the third best individual, and the fourth best individual in preparation for the next population iterative update operation.

[0057] The present invention also provides a computer device, including 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 scheduling optimization method coupled with the four-vector optimization algorithm.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] The present invention iteratively executes scheduling trajectory optimization by coupling the calculation operations of the four-vector optimization algorithm, avoiding the comprehensive combination of all discrete states of the reservoir group in each stage, and iteratively updating by generating a population in the neighborhood space, fully exploring and utilizing the search information to increase the possibility of reaching the global optimum, thereby significantly improving the calculation efficiency, thereby shortening the calculation process of the cascade reservoir flood control scheduling optimization plan, and the cascade reservoir flood control scheduling optimization plan can be obtained more quickly and accurately.

[0060] The method of the present invention has a simple principle, few control parameters, is easy to implement, and has a small amount of calculation. It exhibits efficient optimization capability and optimization accuracy, and can be applied to solving multi-stage, multi-variable, multi-constraint, and high-dimensional cascade reservoir flood control optimization scheduling problems, providing a new method for faster and more accurate acquisition of cascade reservoir flood control scheduling plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a cascade reservoir flood control dispatch optimization method coupled with a four-vector optimization algorithm according to the present invention;

[0062] Figure 2 This is a diagram showing the calculation results of flood control and lake cruise dispatching of Reservoir A in the cascade reservoirs under the 2% water inflow condition in 1998 in an embodiment of the present invention;

[0063] Figure 3 This is a diagram showing the calculation results of flood control optimization dispatching of Reservoir B in the cascade reservoirs under the 2% water inflow condition in 1998 in an embodiment of the present invention;

[0064] Figure 4This is a diagram showing the calculation results of flood control optimization dispatching of Reservoir C in the cascade reservoirs under the 2% water inflow condition in 1998 in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to better explain the present invention, the main contents of the present invention are further explained below in conjunction with specific embodiments, but the contents of the present invention are not limited to the following embodiments.

[0066] In order to reduce the flood control burden and flood losses downstream of the reservoir group, and to provide technical support for real-time flood control scheduling decisions, the maximum peak shaving criterion is currently widely used to construct the optimal scheduling objective function. To this end, based on the maximum peak shaving criterion, the cascade reservoir flood control optimization scheduling model is generally described as follows: given the initial water level, final water level, inflow flood process, and interval flood process of each reservoir during the scheduling period, while satisfying the complex constraints of the water level and flow of each reservoir and the upstream and downstream protection objects, the optimization goal is to minimize the sum of the squares of the flow process of each flood control object. The specific mathematical expression is:

[0067]

[0068] Where: f is the square sum of the flow process of the flood control object; N is the number of reservoirs; i is the reservoir number, i = 1, 2, ..., N; T is the total number of stages in the dispatch period; j is the stage number, j = 1, 2, ..., T; O i,j is the outflow of reservoir i at stage j (m 3 / s); R i,j is the interval flow from reservoir i to the downstream reservoir or downstream flood control section at stage j (m 3 / s).

[0069] The constraints that need to be considered in the optimization calculation of flood control operation of cascade reservoirs mainly include:

[0070] (1) Water balance constraints of each reservoir:

[0071] (2) Hydraulic connection constraints of each reservoir:

[0072] (3) Water level constraints of each reservoir:

[0073] (4) Constraints on outflow from each reservoir:

[0074] (5) Constraints on discharge capacity of each reservoir:

[0075] (6) Initial and final water level constraints of each reservoir: Z i,0 =Z i,start ,Z i,T =Z i,end ;

[0076] (7) Non-negative constraint: All variables are non-negative values.

[0077] 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 at stage j (m 3 / s); i,j is the outflow of reservoir i at stage j (m 3 / s); Δt is the stage duration (h); and are the minimum outflow of reservoir i in stage j (m 3 / s) and maximum outbound flow (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); Z i,start and Z i,end are the initial reservoir water level (m) and the controlled water level (m) at the end of the dispatching period of reservoir i, respectively.

[0078] By analyzing the above objective functions and constraints, it can be seen that the optimal scheduling of cascade reservoirs for flood control covers many complex constraints such as water level, storage capacity, flow rate, etc. of each reservoir at each stage. It has complex characteristics such as large scale, high dimension, multi-stage, strong constraint, and nonlinearity. When POA is used to solve this optimal scheduling problem, as the number of reservoirs, calculation stages, and discrete numbers continue to increase, the amount of calculation increases exponentially, and there are problems such as the curse of dimensionality and premature convergence.

[0079] In order to efficiently solve the flood control optimization scheduling problem of cascade reservoirs, the method of the present invention considers cleverly coupling a four-vector optimization algorithm with simple principle, few parameters and strong search ability in the calculation process of each stage of POA, so as to replace the comprehensive enumeration between all discrete states of the two-stage sub-optimization problem, so as to improve the problems of dimensionality curse and premature convergence of POA.

[0080] like Figure 1 As shown, the present invention provides a cascade reservoir flood control scheduling optimization method coupled with a four-vector optimization algorithm, comprising the following steps:

[0081] 1) Determine the initial calculation conditions, including the objective function, constraints and decision variables of the optimal operation of cascade reservoir flood control;

[0082] 2) Setting calculation parameters; the calculation parameters include the number of reservoirs N, the number of scheduling stages T, the maximum number of iterations M, the population number Y of the four-vector optimization algorithm and the maximum number of optimization searches G;

[0083] 3) Using artificial experience decision-making or conventional methods to generate the initial trajectory of each reservoir at each stage that meets the constraints and neighborhood space Ω=[p i,j ,q i,j ],1≤i≤N,1≤j≤T; where represents the initial trajectory of the water level in front of the dam of reservoir i at stage j; p i,j and q i,j They represent the upper and lower limits of the water level of reservoir i at stage j respectively;

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

[0085] 5) Decompose the optimal operation problem of cascade reservoirs with the current iteration number m into T-1 two-stage step-by-step optimization sub-problems;

[0086] 6) Set the number of stages j = 1;

[0087] 7) For the j-th two-stage stepwise optimization subproblem, the four-vector optimization algorithm is coupled to perform optimal search to obtain the best trajectory. The specific steps are as follows:

[0088] 7.1) Perform population initialization operation of the four-vector optimization algorithm.

[0089] In the above N-dimensional neighborhood space, let Y individuals form a population X = {X1, X2, ..., X Y}, the maximum number of optimization searches is G, and the position of the yth individual in the N-dimensional space during the gth optimization search is expressed as There are 1≤i≤N,1≤y≤Y,1≤g≤G; one individual represents the water level of each reservoir in all periods. At the same time, the best individual, second best individual, third best individual, and fourth best individual in the population are defined as X α , X β , X δ and X μ .

[0090] Set g = 1, and perform the following operations on Y individuals in the population: Initialize, The specific calculation formula is: Here, r is a random number between 0 and 1.

[0091] At the same time, calculate Y individuals The fitness of the individual X is compared and selected.α , suboptimal individual X β , the third best individual X δ , the fourth best individual X μ ,have:

[0092]

[0093] and

[0094] and and

[0095] and and and

[0096] Among them, f() is the objective function of the optimal scheduling of cascade reservoir flood control, which is used to calculate the fitness of each individual.

[0097] 7.2) Perform population iterative update operations of the four-vector optimization algorithm.

[0098] For Y individuals Perform iterative updates to generate corresponding new individuals The specific calculation formula is:

[0099]

[0100] in, and The calculation formulas are as follows:

[0101]

[0102] In the formula, r1, r2, r3, r4, r5, r6, r7, r8, r9, r 10 、r 11 、r 12 are all random numbers between 0 and 1. At the same time, θ is the step size control parameter, which is set to decrease continuously as the optimization progresses, so as to gradually narrow the search range and gradually approach the optimal result. Its calculation formula is as follows:

[0103] θ = 1.5 × (1-m / M);

[0104] Compare and if Indicates new individual Better than individual Then replace Otherwise, no processing is done. After performing the iterative update operation, proceed to step 7.3).

[0105] 7.3) Perform the four-vector optimization algorithm to find the elite solution operation.

[0106] Combined with the population iterative update operation to obtain Y individuals Recompare and select the new optimal individual X α , suboptimal individual X β , the third best individual X δ , the fourth best individual X μ , to be used in the next optimization search;

[0107] When g=g+1, if g≤G, go to step 7.2); otherwise, it indicates that the iterative optimization of the coupled four-vector optimization algorithm has been completed for the current stage, and go to step 8);

[0108] 8) Compare current trajectory The optimal individual X generated by the four-vector optimization algorithm α , if the optimal individual is better than the current trajectory, the current trajectory is replaced by the optimal individual; otherwise, no processing is performed.

[0109] 9) Let j = j + 1. If j ≤ T-1, go to step 7); otherwise, shrink the neighborhood space and let p i,j =p i,j ×λ,q i,j =q i,j ×λ, λ is the neighborhood shrinkage factor, which is generally between 0.5 and 0.95, and then proceed to step 10);

[0110] 10) Let m=m+1, if m>M, go to step 11), otherwise go to step 5);

[0111] 11) Terminate the calculation and output the optimal water level process of each reservoir.

[0112] The present invention also provides a cascade reservoir flood control dispatch optimization system coupled with a four-vector optimization algorithm, which is used to implement the cascade reservoir flood control dispatch optimization method coupled with the four-vector optimization algorithm, including:

[0113] The initialization module is used to determine the objective function, constraints and decision variables of the optimal operation of cascade reservoir flood control, and generate the initial operation trajectory and neighborhood space that meet the constraints based on artificial experience or conventional methods under various constraints;

[0114] The stepwise optimization decomposition module is used to decompose the flood control optimization scheduling problem of cascade reservoirs at the current iteration number into multiple two-stage sub-optimization problems;

[0115] The optimization module of the four-vector optimization algorithm performs optimization search to obtain the best scheduling trajectory. It includes three sub-modules: a population initialization operation sub-module, which is used to initialize the population of the optimization algorithm according to the calculation principle of the four-vector optimization algorithm, and find the optimal individual, the second-best individual, the third-best individual, and the fourth-best individual; a population iterative update operation sub-module, which is used to iteratively update the population individuals based on the optimal individual, the second-best individual, the third-best individual, and the fourth-best individual to continuously optimize the population individuals; and a search for elite solution operation sub-module, which is used to find the optimal individual, the second-best individual, the third-best individual, and the fourth-best individual in preparation for the next population iterative update operation.

[0116] The scheduling trajectory comparison and replacement module is used to compare the current trajectory with the optimal individual in the four-vector optimization algorithm to continuously optimize and replace the current scheduling trajectory;

[0117] The output module is used to repeatedly execute the step-by-step optimization decomposition module, the four-vector optimization algorithm optimization module and the scheduling trajectory comparison and replacement module until the preset iteration stop condition is met, the optimal scheduling trajectory is obtained, and it is output as the final cascade reservoir flood control optimization scheduling result.

[0118] The present invention also provides a computer device, including 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 scheduling optimization method coupled with the four-vector optimization algorithm.

[0119] The technical solution of the present invention will be further explained below by taking a large controlled cascade reservoir group on a certain river as an example.

[0120] The river is about 1,571 km long and has a drainage area of ​​about 136,000 km. 2 The basin has a complex terrain, with mountains and valleys interlaced, a well-developed water system, and abundant hydropower resources. From upstream to downstream, there are three large reservoirs, A, B and C, with a total flood control capacity of 4.5 billion m 3 , plays an important role in flood control and effectively ensures the overall flood control safety of the basin.

[0121] Taking the four typical design floods of 5%, 3.33%, 2% and 1% in 1998 as examples, the method of the present invention and POA were used to carry out the optimization calculation of flood control for cascade reservoirs. The specific calculation results are shown in Table 1 and Figures 2 to 4 shown.

[0122] Table 1: Comparison of the calculation results of the method of the present invention and POA

[0123]

[0124] As shown in Table 1, for the four frequency design floods, the method of the present invention has outstanding performance in terms of optimization results and calculation time compared with the conventional POA method, mainly because: (1) in the calculation results, the target value of the method of the present invention is smaller, indicating that the cascade reservoirs have played a good interception effect for the four floods; (2) in terms of calculation time, the method of the present invention takes less time, saves calculation time, and improves the calculation rate. It can be seen that the method of the present invention has high calculation accuracy and high efficiency. It is reasonable and feasible to couple the four-vector optimization algorithm in POA, which provides a new means for the optimization calculation of actual cascade reservoir flood control scheduling.

[0125] like Figures 2 to 4 The figure shows the calculation results of the flood control optimization dispatching of the cascade reservoirs of the above-mentioned rivers under the condition of 2% water inflow in 1998 using the method of the present invention. The dispatching results all meet the constraints of water level, storage capacity, flow rate and the like. The outflow of the last level C reservoir is very stable, which effectively solves the problem of too frequent gate operation caused by too large changes in outflow flow during actual flood control dispatching, indicating that the method of the present invention exhibits excellent flood control, peak cutting and peak shifting effects.

[0126] In summary, considering that POA is calculated stage by stage, there is only one current trajectory state and no concept of population. In the process of calculating each trajectory stage by stage, based on the operation of the four-vector optimization algorithm, a number of individuals are first randomly generated in the neighborhood to form a group, and then the population iterative update and elite solution search are repeatedly performed to gradually optimize the trajectory process of each stage, so as to achieve the reasonable and organic coupling of POA and the four-vector optimization algorithm. The method of the present invention provides a cascade reservoir flood control scheduling optimization method and system coupled with a four-vector optimization algorithm, takes the lead in applying the novel four-vector optimization algorithm to the reservoir group scheduling problem, cleverly couples the four-vector optimization algorithm to fully extract and capture the solution space information, and implements population initialization operations, population iterative update operations, and elite solution search operations to seek scheduling trajectories with better results, replacing the comprehensive enumeration between all discrete states of the two-stage sub-optimization problem. The principle is simple, the control parameters are few, and it is easy to implement. It has the excellent characteristics of strong search ability and high calculation accuracy. It is a new strategy for efficiently solving the optimization scheduling problem of large-scale cascade reservoirs, and provides a new method for obtaining cascade reservoir flood control scheduling schemes.

[0127] It will be easily understood by those skilled in the art that the above description is only 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 protection scope of the present invention.

Claims

1. A method for optimizing flood control dispatching of cascade reservoirs coupled with a four-vector optimization algorithm, characterized in that: The following steps are involved: 1) Determine the initial calculation conditions, including the objective function, constraints and decision variables of the optimal operation of cascade reservoir flood control; 2) Setting calculation parameters; the calculation parameters include the number of reservoirs N, the number of scheduling stages T, the maximum number of iterations M, the population number Y of the four-vector optimization algorithm and the maximum number of optimization searches G; 3) Using artificial experience decision-making or conventional methods to generate the initial dispatching trajectory of each reservoir at each stage that meets the constraints and neighborhood space Ω=[p i,j ,q i,j ],1≤i≤N,1≤j≤T; where represents the initial trajectory of the water level in front of the dam of reservoir i at stage j; p i,j and q i,j They represent the upper and lower limits of the water level of reservoir i at stage j respectively; 4) Set the number of iterations m = 1, 5) Decompose the optimal operation problem of cascade reservoirs with the current iteration number m into T-1 two-stage step-by-step optimization sub-problems; 6) Set the number of stages j = 1; 7) For the jth two-stage stepwise optimization subproblem, the four-vector optimization algorithm is coupled to perform optimal search to obtain the best scheduling trajectory, that is, the optimal individual X generated iteratively α ; 8) Compare the current scheduling trajectory The optimal individual X generated by the four-vector optimization algorithm α , if the optimal individual is better than the current scheduling trajectory, the current scheduling trajectory is replaced by the optimal individual; otherwise, no processing is performed; 9) Let j = j + 1. If j ≤ T-1, go to step 7); otherwise, shrink the neighborhood space and let p i,j =p i,j ×λ,q i,j =q i,j ×λ, λ is the neighborhood shrinkage factor, go to step 10); 10) Let m=m+1, if m>M, go to step 11), otherwise go to step 5); 11) Terminate the calculation and output the optimal dispatching trajectory after the iteration is completed, that is, obtain the optimized flood control dispatching plan for the cascade reservoirs.

2. The method for optimizing flood control dispatching of cascade reservoirs according to claim 1, characterized in that: The step 7) comprises: 7.1) Execute population initialization operation of four-vector optimization algorithm; 7.2) Execute the population iterative update operation of the four-vector optimization algorithm; 7.3) Perform the four-vector optimization algorithm to find the elite solution operation.

3. The method for optimizing flood control dispatching of cascade reservoirs according to claim 2 is characterized in that: The step 7.1) comprises the following steps: In the N-dimensional neighborhood space, let Y individuals form a population X = {X1, X2, ..., X Y }, the maximum number of optimization searches is G, and the position of the yth individual in the N-dimensional neighborhood space during the g-th optimization search is expressed as At the same time, the best individual, the second best individual, the third best individual, and the fourth best individual in the population are defined as X α , X β , X δ and X μ ; Set g = 1, and perform a Initialize, where The calculation formula is as follows: Among them, r is a random number between 0 and 1; Calculate all individuals The fitness of the individual X is compared and selected. α , suboptimal individual X β , the third best individual X δ , the fourth best individual X μ : and and and and and and Where f() is the objective function of the optimal scheduling of cascade reservoir flood control, which is used to calculate the fitness of each individual.

4. The method for optimizing flood control dispatching of cascade reservoirs according to claim 3 is characterized in that: The step 7.2) comprises the following steps: For Y individuals Perform iterative updates to generate corresponding new individuals The calculation formula is as follows: in, and The calculation formulas are as follows: In the formula, r1, r2, r3, r4, r5, r6, r7, r8, r9, r 10 、r 11 、r 12 are all random numbers between 0 and 1; θ is the step size control parameter, which is set to decrease continuously as the optimization progresses, so as to gradually narrow the search range and gradually approach the optimal result; Compare and if Indicates new individual Better than individual Then replace Otherwise, no treatment is performed; when all Y individuals After performing the iterative update operation, proceed to step 7.3).

5. The method for optimizing flood control dispatching of cascade reservoirs according to claim 4 is characterized in that: The calculation formula of the step size control parameter θ is as follows: θ = 1.5×(1-m / M).

6. The method for optimizing flood control dispatching of cascade reservoirs according to claim 4 is characterized in that: The step 7.3) comprises the following steps: Combined with the population iterative update operation to obtain Y individuals Recompare and select the new optimal individual X α , suboptimal individual X β , the third best individual X δ , the fourth best individual X μ , to be used in the next optimization search; When g=g+1, if g≤G, go to step 7.2); otherwise, it indicates that the iterative optimization of the coupled four-vector optimization algorithm has been completed for the current stage, and go to step 8).

7. The method for optimizing flood control dispatching of cascade reservoirs according to claim 1, characterized in that: In the step 9), the neighborhood shrinkage factor λ is 0.5 to 0.

95.

8. A cascade reservoir flood control dispatch optimization system coupled with a four-vector optimization algorithm, characterized in that: Used to implement the cascade reservoir flood control dispatch optimization method described in any one of claims 1 to 7; The cascade reservoir flood control dispatch optimization system comprises: The initialization module is used to determine the objective function, constraints and decision variables of the optimal operation of cascade reservoir flood control, and generate the initial operation trajectory and neighborhood space that meet the constraints based on artificial experience or conventional methods under various constraints; The stepwise optimization decomposition module is used to decompose the flood control optimization scheduling problem of cascade reservoirs at the current iteration number into multiple two-stage sub-optimization problems; The four-vector optimization algorithm optimization module performs optimization search to obtain the best scheduling trajectory; The scheduling trajectory comparison and replacement module is used to compare the current trajectory with the optimal individual in the four-vector optimization algorithm to continuously optimize and replace the current scheduling trajectory; The output module is used to repeatedly execute the step-by-step optimization decomposition module, the four-vector optimization algorithm optimization module and the scheduling trajectory comparison and replacement module until the preset iteration stop condition is met, the optimal scheduling trajectory is obtained, and it is output as the final cascade reservoir flood control optimization scheduling result.

9. The cascade reservoir flood control dispatch optimization system according to claim 8 is characterized in that: The four-vector optimization algorithm optimization module includes three sub-modules, namely: The population initialization operation submodule is used to initialize the population of the optimization algorithm according to the calculation principle of the four-vector optimization algorithm, and to find out the best individual, the second best individual, the third best individual, and the fourth best individual; The population iterative update operation submodule is used to iteratively update the population individuals based on the best individual, the second best individual, the third best individual, and the fourth best individual to continuously optimize the population individuals; The elite solution search operation submodule is used to find the best individual, the second best individual, the third best individual, and the fourth best individual in preparation for the next population iterative update operation.

10. A computer device, characterized in that: It 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 cascade reservoir flood control scheduling optimization method coupled with a four-vector optimization algorithm as described in any one of claims 1 to 7.

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