CN115719041B - Multi-target flood control optimal dispatching method and system for reservoir gate group - Google Patents
Multi-target flood control optimal dispatching method and system for reservoir gate group Download PDFInfo
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Abstract
The invention belongs to the technical field of reservoir gate dispatching, and provides a multi-target flood control optimal dispatching method for reservoir gate groups, which comprises the following steps: establishing a plurality of objective functions according to reservoir information, and constructing a multi-objective optimization model according to the objective functions; the plurality of objective functions specifically comprise a minimum objective function of the initial flood, a minimum objective function of the maximum drainage flow, a minimum objective function of the highest running water level, a minimum objective function of the warehouse-in flow and a minimum objective function of the warehouse-out flow; establishing constraint conditions of a multi-objective optimization model; and calculating an optimal solution of the multi-objective optimization model according to the constraint conditions and the improved gray wolf algorithm. According to the invention, the conditions for optimization are added, so that the optimization scheduling of the reservoir gate group is satisfied, and the convergence speed and the accuracy of an optimization model are improved.
Description
Technical Field
The invention belongs to the technical field of reservoir gate dispatching, and particularly relates to a multi-target flood control optimal dispatching method and system for reservoir gate groups.
Background
The water level of the river reach rises sharply in the rainy season or the ice and snow melting period, and huge harm is brought to life production of people after flood exceeds a certain limit, so that the reservoir gate plays a vital role in flood control period in the flood season, and the improvement of the flood control capability of reservoir groups becomes the key point of numerous engineering researches. The traditional reservoir flood control optimal dispatching model only takes the highest peak value of the discharge flow as an optimal target, considers single optimizing condition, only can ensure that one reservoir is in an optimal dispatching state, and cannot meet the optimal dispatching of a reservoir gate group.
The intelligent algorithm of the existing reservoir flood control optimal dispatching model mainly comprises a genetic algorithm and a particle swarm algorithm, wherein the genetic algorithm mainly converges on an initial optimal dispatching population through intersection and variation iteration, the optimal solution is not easy to obtain by the iteration method, and the iteration convergence time is overlong; the particle swarm algorithm converges too fast, and local convergence is easy to cause, and searching for the optimal solution is inaccurate.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art, and provides a multi-target flood control optimization scheduling method and system for a reservoir gate group, which increase the conditions for optimization, so that the optimal scheduling of the reservoir gate group is met, and the convergence speed and the accuracy of an optimization model are improved.
In order to achieve the above object of the present invention, according to a first aspect of the present invention, there is provided a multi-objective flood control optimizing and dispatching method for a reservoir gate group, comprising the steps of: establishing a plurality of objective functions according to reservoir information, and constructing a multi-objective optimization model according to the plurality of objective functions, wherein the plurality of objective functions specifically comprise an excess flood minimum objective function, a maximum drainage flow minimum objective function, a highest running water level minimum objective function, a warehouse-in flow minimum objective function and a warehouse-out flow minimum objective function; establishing constraint conditions of a multi-objective optimization model; and calculating an optimal solution of the multi-objective optimization model according to the constraint conditions and the improved gray wolf algorithm.
Further, the multi-objective optimization model is specifically: minf=f 1 +F 2 +F 3 +F 4 +F 5 The method comprises the steps of carrying out a first treatment on the surface of the minF represents the solution of the multi-objective optimization model, F 1 Representing excess flood of reservoir, F 2 Representing the maximum downward leakage flow peak value of the gate, F 3 For the highest running water level of reservoir group, F 4 Indicating the flow rate of warehouse entry, F 5 Indicating the reservoir traffic.
Further, the excess flood minimum objective function is specifically: wherein F is 1 The method is characterized in that the method comprises the following steps of representing excess flood of a reservoir, T represents the total scheduling time, T represents the T-th period, M represents the total gate number of the reservoir, M represents the M-th gate and Q m,t,E Representing excess flood of the mth gate in the t-th period, and Deltat representing the scheduled unit period; the minimum objective function of the maximum downflow is specifically: /> Wherein F is 2 Represents the maximum downward leakage flow peak value of the gate, Q m,t A maximum value of the downward leakage flow of the mth gate in the t-th period; the highest running water level and lowest objective function are specifically: />Wherein F is 3 For the highest operating level of the reservoir group, n represents the nth reservoir, +.>The highest water level of the nth reservoir during flood control; the minimum objective function of the warehouse-in flow is specifically as follows: />Wherein F is 4 Indicating the flow of warehouse entry, NIs the total number of reservoirs, n is the nth reservoir, Q n,t,in Representing the warehousing flow rate of the nth reservoir in the nth period; the minimum objective function of the ex-warehouse flow is specifically as follows: /> Wherein F is 5 Represent the warehouse flow, Q n,t,out Indicating the discharge flow rate of the nth reservoir in the nth period.
Further, the step of calculating an optimal solution of the multi-objective optimization model according to the constraint condition and the improved wolf algorithm specifically includes: s1: determining the upper limit and the lower limit of the multi-objective optimization model according to the constraint conditions; s2: setting algorithm parameters of an improved wolf algorithm, initializing dynamic weights in a wolf population and position formula of the improved wolf algorithm, and initializing control coefficients in the improved wolf algorithm, wherein the control coefficients comprise random vectors, search range coefficients and convergence factors of the search range coefficients; s3: calculating the fitness value of each wolf in the wolf population, and giving the fitness value sequence to the head wolves and the hunting wolves; updating the position of the hunting wolf according to the position formula, and updating the control coefficient; s4: judging whether the multi-objective optimization model reaches a convergence condition, if so, outputting the position of the head wolf, and ending the operation; if the convergence condition is not reached, returning to the step S3.
Further, the first wolf comprises a first wolf, a second wolf and a third wolf;
the step S4 specifically comprises the following steps: judging whether the multi-objective optimization model reaches a convergence condition, if so, outputting the position of the first head wolf, and ending the operation; if the convergence condition is not reached, returning to the step S3.
Further, the excess flood of the reservoir is set as a first wolf, the maximum lower discharge peak value of the gate is set as a second wolf, the highest running water level of the reservoir group is set as a third wolf, and the rest targets are set as hunting wolves.
Further, the dynamic weights include a first weight, a second weight, and a third weight; first weightThe calculation formula of (2) is as follows:the calculation formula of the second weight is as follows: />The calculation formula of the third weight is as follows: />Wherein omega 1 Represents a first weight, ω 2 Representing the second weight, ω 3 Represents a third weight, X 1 Representing the moving distance of the hunting wolf to the first head wolf, X 2 Representing the moving distance of the hunting wolf to the second head wolf, X 3 Representing the distance the hunting wolf moves to the third wolf.
Further, the location formula is specifically:wherein X represents the current iteration position of the hunting wolf, X (l+1) represents the next iteration position of the hunting wolf, ω 1 Represents a first weight, ω 2 Representing the second weight, ω 3 Represents a third weight, X 1 Representing the moving distance of the hunting wolf to the first head wolf, X 2 Representing the moving distance of the hunting wolf to the second head wolf, X 3 Representing the distance the hunting wolf moves to the third wolf.
Further, the constraint conditions include a water balance constraint, a reservoir level upper and lower limit constraint, a reservoir level change constraint, a reservoir lower discharge flow limit constraint and a reservoir gate maximum lower discharge flow constraint.
In order to achieve the above object of the present invention, according to a second aspect of the present invention, there is provided a reservoir gate group multi-objective flood control optimization scheduling system, in which any one of the above reservoir gate group multi-objective flood control optimization scheduling methods is used in an operation process, the system comprising a creation module, a constraint module and a calculation module; the creation module is used for creating a plurality of objective functions according to reservoir information and also used for creating a multi-objective optimization model according to the plurality of objective functions; the constraint module is used for establishing constraint conditions of the multi-objective optimization model; the calculation module is used for calculating the optimal solution of the multi-objective optimization model according to the constraint conditions and the improved wolf algorithm.
The invention has the technical principle and beneficial effects that: according to the invention, a plurality of objective functions are introduced, a multi-objective optimization model with minimum excess flood, minimum maximum flood discharge flow of a reservoir gate group, minimum running water level of a reservoir group, minimum reservoir storage flow and minimum reservoir delivery flow is constructed, and constraint conditions are introduced and an improved gray wolf algorithm is introduced to calculate the optimal solution of the multi-objective optimization model. The improved wolf algorithm is divided into leading wolves and hunting wolves according to the class system of the wolves, the leading wolves send instructions for searching the hunting position to the rest hunting wolves, and the hunting wolves continuously search hunting objects and repeatedly update the hunting position to finally surround and obtain hunting objects, namely the optimal solution is found. The improved gray wolf algorithm introduces dynamic weight, so that the leading wolf can distinguish the leading ability of the hunting wolf, the higher the leading weight of the hunting wolf, the higher the hunting ability of hunting waves, the higher the optimizing ability, the more accurate the optimal solution, the local optimization and convergence can be effectively avoided, the iteration times can be reduced, and the convergence speed of the multi-objective optimizing function can be accelerated.
Drawings
FIG. 1 is a schematic diagram of steps of a reservoir gate group multi-objective flood control optimization scheduling method according to the invention;
FIG. 2 is a schematic diagram of the steps for computing the optimal solution of the multi-objective optimization model according to the constraint and the modified gray wolf algorithm according to the present invention;
fig. 3 is a schematic structural diagram of a multi-objective flood control optimal dispatching system for reservoir gate groups according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and defined, it should be noted that the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanical or electrical, or may be in communication with each other between two elements, directly or indirectly through intermediaries, as would be understood by those skilled in the art, in view of the specific meaning of the terms described above.
As shown in fig. 1, the embodiment provides a multi-objective flood control optimization scheduling method for a reservoir gate group, which comprises the following steps: establishing a plurality of objective functions according to reservoir information, and constructing a multi-objective optimization model according to the plurality of objective functions, wherein the plurality of objective functions specifically comprise an excess flood minimum objective function, a maximum drainage flow minimum objective function, a highest running water level minimum objective function, a warehouse-in flow minimum objective function and a warehouse-out flow minimum objective function; establishing constraint conditions of a multi-objective optimization model; and calculating an optimal solution of the multi-objective optimization model according to the constraint conditions and the improved gray wolf algorithm.
In this embodiment, the reservoir information includes a dam monitoring water level, a front gate monitoring water level, all gate water levels, gate widths, and gate opening heights of the reservoir; the method further comprises preprocessing and calculating reservoir information before establishing the plurality of objective functions.
Further, the multi-objective optimization model is specifically: minf=f 1 +F 2 +F 3 +F 4 +F 5 The method comprises the steps of carrying out a first treatment on the surface of the MinF represents multiple ordersSolution of target optimization model, F 1 Representing excess flood of reservoir, F 2 Representing the maximum downward leakage flow peak value of the gate, F 3 For the highest running water level of reservoir group, F 4 Indicating the flow rate of warehouse entry, F 5 Indicating the reservoir traffic. Compared with the prior art, the multi-objective optimization model constructed by the implementation is comprehensive in consideration, and not only is the maximum drainage flow considered, but also the reservoir water level, excess flood, warehouse-in flow and warehouse-out flow are included, so that the actual situation of flood control of a reservoir gate group is met, and the damage caused by flood is reduced.
Preferably, the flood period when flood comes may bring about destructive influence, not only considering the flood control safety of the downstream but also considering the flood amount of the reservoir, so that the excess flood amount is included in a plurality of objective functions, and the excess flood amount is controlled to reduce the loss of the flood basin to the surroundings and the hazard time caused by flood in the flood period; the minimum objective function of excess flood is specifically:
wherein F is 1 The method is characterized in that the method comprises the following steps of representing excess flood of a reservoir, T represents the total scheduling time, T represents the T period, M represents the total gate number of the reservoir, M represents the M gate and Qm ,t,E Representing the excess flood of the mth gate in the t-th period, and Δt representing the scheduled unit period.
Preferably, the maximum downward discharge flow of the reservoir gate group is controlled to be minimum, so that the pressure of the downstream of the reservoir gate group can be reduced, the downstream reservoir receives smaller impact, and the safety of the downstream reservoir is protected; therefore, the plurality of objective functions include a maximum downward leakage flow minimum objective function, and the maximum downward leakage flow minimum objective function is specifically:
wherein F is 2 Represents the maximum downward leakage flow peak value of the gate and Qm ,t The maximum discharge flow of the mth gate of the t-th periodA value;
preferably, the highest operating water level of the control reservoir gate group is within the safe limit, so that the safety of the reservoir can be protected as much as possible, and the reservoir can better cope with the emergency, therefore, the highest operating water level lowest target function is included in the multiple target functions, and the highest operating water level lowest target function is specifically:
wherein F is 3 Is the highest running water level of the reservoir group, n represents the nth reservoir,the highest water level of the nth reservoir during flood control;
preferably, the flood flow rate change span in the flood season is very large, so that the running water level of the reservoir is considered, the storage flow rate is monitored at any time, and the storage flow rate of the reservoir gate group is rapidly changed, so that the downstream is influenced. Therefore, the plurality of objective functions comprise a minimum objective function of reservoir flow, flood control risks can be reduced by controlling the warehousing flow, and the minimum objective function of the warehousing flow is specifically as follows:
wherein F is 4 Represents the flow rate of the warehouse, N is the total number of reservoirs, N is the nth reservoir, Q n,t,in Representing the warehousing flow rate of the nth reservoir in the nth period;
preferably, the excessive delivery flow affects the power generation, the peer, the comprehensive water supply and the downstream ecology of the downstream power station, so that the multiple objective functions comprise a minimum delivery flow objective function, and the minimum delivery flow objective function is specifically:
wherein F is 5 Represent the warehouse flow, Q n,t,out Indicating the discharge flow rate of the nth reservoir in the nth period.
In the embodiment, the excess flood, the maximum value of the drainage flow, the highest running water level, the warehousing flow and the ex-warehouse flow of the reservoir are obtained by designing and monitoring a reservoir gate.
As shown in fig. 2, the steps for calculating the optimal solution of the multi-objective optimization model according to the constraint condition and the improved wolf algorithm specifically include:
s1: determining the upper limit and the lower limit of the multi-objective optimization model according to the constraint conditions; the upper limit and the lower limit of the multi-objective optimization model comprise the total water quantity in storage, the water quantity out of storage, the upper limit and the lower limit of the water level of the reservoir, the upper limit and the lower limit of the water level change of the reservoir, the lower discharge flow limit of the reservoir and the maximum lower discharge flow value;
s2: setting algorithm parameters of an improved wolf algorithm, wherein the algorithm parameters comprise maximum iteration number iter max Population size N and dimension d; initializing a wolf population N for improving a wolf algorithm i (i=1, 2,3,4, … …, n) and dynamic weights in the positional formula, initializing control coefficients in the modified gray wolf algorithm, the control coefficients including a random vector C, a search range coefficient a and a convergence factor a of the search range coefficient;
s3: calculating the fitness value of each wolf in the wolf population, and giving the fitness value sequence to the head wolf and the hunting wolf omega wolf; updating the position of the hunting wolf omega wolf according to a position formula;
s4: judging whether the multi-objective optimization model reaches a convergence condition, if so, outputting the position of the head wolf, and ending the operation; if the convergence condition is not reached, the control coefficient is updated, and the step S3 is returned.
Preferably, the first wolf includes a first wolf α wolf, a second wolf β wolf and a third wolf δ wolf, and step S4 is specifically: judging whether the multi-objective optimization model reaches a convergence condition, if so, outputting the position of the first head wolf, and ending the operation; if the convergence condition is not reached, updating the control coefficient and the dynamic weight in the position formula, and returning to the step S3.
In the traditional wolf algorithm, the leading ability of the first wolf to the omega wolf to be captured is behind, the positions and weights of the alpha wolf, the beta wolf and the delta wolf are not distinguished, so that the model is converged prematurely and the iteration times are too many, and an optimal solution cannot be obtained. Compared with other intelligent algorithms, the improved gray wolf algorithm has the advantages that the optimization iteration speed is high, the global optimization and the local optimization searching can be balanced, and the optimization result precision is higher when the parameter variable of the gray wolf algorithm is improved.
In this embodiment, excess flood of reservoir, F 2 Representing the maximum downward leakage flow peak value of the gate, F 3 For the highest running water level of reservoir group, F 4 Indicating the flow rate of warehouse entry, F 5 The method comprises the steps of showing that the reservoir flow is a wolf individual in the wolf population, setting excess flood of a reservoir as a first wolf, setting a maximum lower discharge flow peak value of a gate as a second wolf, setting the highest running water level of the reservoir population as a third wolf, and setting the rest wolf individuals as hunting wolves.
Specifically, in step S3, the adaptation values are sequentially assigned to α wolf, β wolf, δ wolf and ω wolf according to the order of the adaptation values.
Because the omega wolves are led by the alpha wolves, the beta wolves and the delta wolves, the moving distance of the omega wolves to the alpha wolves, the beta wolves and the delta wolves is obtained according to the positions of the alpha wolves, the beta wolves and the delta wolves and the control coefficient of the current iteration times, and the dynamic weight and the moving distance are brought into a position formula to obtain the position of the updated omega wolves; the dynamic weights include a first weight ω 1 Second weight omega 2 And a third weight omega 3 ；
Specifically, the positional formula is as follows:
wherein, X is as followsShowing the position of the current iteration of the hunting wolf, X (l+1) represents the position of the next iteration of the hunting wolf, ω 1 Represents a first weight, ω 2 Representing the second weight, ω 3 Represents a third weight, X 1 Representing the moving distance of the hunting wolf to the first head wolf, X 2 Representing the moving distance of the hunting wolf to the second head wolf, X 3 Representing the moving distance of the hunting wolf to the third wolf; in particular, the method comprises the steps of,
X 1 ＝X α -A 1 ·D α
X 2 ＝X β -A 2 ·D β
X 3 ＝X δ -A 3 · D δ
wherein D is α ，D β ，D δ Respectively represent the relative positions of alpha wolf, beta wolf, delta wolf and the rest wolf, X α ，X β ，X δ Respectively representing the specific positions of alpha wolf, beta wolf and delta wolf in the hunting process of the current iteration times; a is that 1 ，A 2 ，A 3 Respectively representing a first search range coefficient, a second search range coefficient and a third search range coefficient; specifically:
D α ＝|C 1 X α -X|
D β ＝|C 2 X β -X|
D δ ＝|C 3 X δ -X|
wherein C is 1 、C 2 、C 3 Representing the first random variable, the second random variable, and the third random variable, respectively. As the number of iterations increases, the position of ωwolf is also changing.
The calculation formula of the first weight is as follows:
the calculation formula of the second weight is as follows:
the calculation formula of the third weight is as follows:
wherein omega 1 Represents a first weight, ω 2 Representing the second weight, ω 3 Represents a third weight, X 1 Representing the moving distance of the hunting wolf to the first head wolf, X 2 Representing the moving distance of the hunting wolf to the second head wolf, X 3 Representing the distance the hunting wolf moves to the third wolf.
Specifically, the control parameter is obtained from the following formula:
A＝2a·rd 1 -α
C＝2rd 2
wherein C is a random vector, A is a search range coefficient, a is a convergence factor of the search range coefficient A, l is the current iteration number, along with the continuous iteration, the value of a is continuously changed, rd 2 And rd 1 Random constant vector, iter, of (0, 1) max For a set maximum number of iterations.
Specifically, random vectors C and rd 2 In the related, the range of the random vector C is (0, 2), the random vector C gives a random weight proportion to the position of the wolf individual relative to the prey, the weight proportion is randomly reduced from 2 to 0, and the random vector C gives a random weight proportion to the position of the wolf individual, so that the wolf group is hunting at the optimal position, an optimal solution is obtained, and local optimization is avoided;
the search range coefficient A is a coefficient for controlling the wolf group to approach the hunting object to be reduced, the search range coefficient A is controlled by the convergence factor a, the convergence factor a is continuously reduced because the wolf group is required to be continuously close to the hunting object during hunting, meanwhile, the random range of the controlled search range coefficient A is continuously reduced, when A >1, the wolf group is continuously searched at the hunting object and the current position, the search range is large and the optimal solution is searched, and when A <1, the wolf group attacks the hunting object to enter local search.
Further, the constraint conditions include a water balance constraint, a reservoir level upper and lower limit constraint, a reservoir level change constraint, a reservoir lower discharge flow limit constraint and a reservoir gate maximum lower discharge flow constraint.
Specifically, the formula of the water balance constraint is as follows:
V n,t ＝V n,t-1 +(Q n,t,in -Q n,t,out )Δt
wherein V is n,t The reservoir capacity of the nth reservoir in the nth period is V n,t-1 For the reservoir capacity of the nth reservoir in the t-1 period, Q n,t,in Represents the flow rate of the storage of the nth reservoir in the t-th period, Q n,t,out Represents the discharge flow rate of the nth reservoir in the t-th period, and Δt represents the scheduled unit period.
The formula of the upper and lower limit constraint of the reservoir water level is as follows:
wherein Z is n,t Is the water level of the nth reservoir at the end of the nth period,respectively representing the lowest reservoir level and the highest reservoir level of the nth reservoir in the nth period.
The formula of the reservoir water level change constraint is as follows:
|Z n,t -Z n,t-1 |＜＜ΔZ n,t
wherein Z is n,t For the water level at the end of the nth period of the nth reservoir, deltaZ n,t Is the maximum water level allowed by the nth period of the nth reservoir.
The formula of the limit constraint of the reservoir drainage flow is as follows:
wherein Q is n,t,out For the delivery flow of the nth reservoir in the nth period,the lowest discharge flow and the highest discharge flow of the nth reservoir at the nth period are respectively shown.
The formula of the maximum downward discharge flow constraint of the reservoir gate is as follows:
wherein Q is m,t Is the maximum value of the downdraft flow of the mth gate of the t-th period,is the constraint of the maximum value of the downdraft flow of the mth gate in the t period.
As shown in fig. 3, the embodiment provides a multi-target flood control optimizing and dispatching system for a reservoir gate group, wherein any one of the multi-target flood control optimizing and dispatching methods for the reservoir gate group is used in the operation process, and the system comprises a creation module, a constraint module and a calculation module; the creation module is used for creating a plurality of objective functions according to reservoir information and also used for creating a multi-objective optimization model according to the plurality of objective functions; the constraint module is used for establishing constraint conditions of the multi-objective optimization model; the calculation module is used for calculating the optimal solution of the multi-objective optimization model according to the constraint conditions and the improved wolf algorithm.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (6)
1. The multi-target flood control optimal dispatching method for the reservoir gate group is characterized by comprising the following steps of:
establishing a plurality of objective functions according to reservoir information, and constructing a multi-objective optimization model according to the objective functions; the plurality of objective functions specifically comprise an excess flood minimum objective function, a maximum drainage flow minimum objective function, a highest running water level minimum objective function, a warehouse-in flow minimum objective function and a warehouse-out flow minimum objective function;
establishing constraint conditions of a multi-objective optimization model; calculating an optimal solution of the multi-objective optimization model according to constraint conditions and an improved gray wolf algorithm;
in the improved wolf algorithm, the first wolf comprises a first wolf, a second wolf and a third wolf; setting excess flood of a reservoir as a first wolf, setting a maximum lower discharge peak value of a gate as a second wolf, setting the highest running water level of a reservoir group as a third wolf, and setting other targets as hunting wolves;
the step of calculating the optimal solution of the multi-objective optimization model according to the constraint condition and the improved wolf algorithm specifically comprises the following steps:
s1: determining the upper limit and the lower limit of the multi-objective optimization model according to the constraint conditions;
s2: setting algorithm parameters of an improved wolf algorithm, initializing dynamic weights in a wolf population and position formula of the improved wolf algorithm, and initializing control coefficients in the improved wolf algorithm, wherein the control coefficients comprise random vectors, search range coefficients and convergence factors of the search range coefficients;
s3: calculating the fitness value of each wolf in the wolf population, and giving the fitness value sequence to the head wolves and the hunting wolves; updating the position of the hunting wolf according to the position formula, and updating the control coefficient;
s4: judging whether the multi-objective optimization model reaches a convergence condition, if so, outputting the position of the first head wolf, and ending the operation; if the convergence condition is not reached, returning to the step S3;
the multi-objective optimization model specifically comprises the following steps:
minF＝F 1 +F 2 +F 3 +F 4 +F 5
minF represents the solution of the multi-objective optimization model, F 1 Representing excess flood of reservoir, F 2 Representing the maximum downward leakage flow peak value of the gate, F 3 For the highest running water level of reservoir group, F 4 Indicating the flow rate of warehouse entry, F 5 Indicating the reservoir traffic.
2. The optimal dispatching method for multi-objective flood control of reservoir gate groups according to claim 1, wherein the minimum objective function of excess flood is as follows:
wherein F is 1 The method is characterized in that the method comprises the following steps of representing excess flood of a reservoir, T represents the total scheduling time, T represents the T-th period, M represents the total gate number of the reservoir, M represents the M-th gate and Q m,t,E Representing excess flood of the mth gate in the t-th period, and Deltat representing the scheduled unit period;
the minimum objective function of the maximum downflow is specifically:
wherein F is 2 Represents the maximum downward leakage flow peak value of the gate, Q m,t A maximum value of the downward leakage flow of the mth gate in the t-th period;
the highest running water level and lowest objective function are specifically:
wherein F is 3 Is the highest running water level of the reservoir group, n represents the nth reservoir,the highest water level of the nth reservoir during flood control;
the minimum objective function of the warehouse-in flow is specifically as follows:
wherein F is 4 Represents the flow rate of the warehouse, N is the total number of reservoirs, N is the nth reservoir, Q n,t,in Representing the warehousing flow rate of the nth reservoir in the nth period;
the minimum objective function of the ex-warehouse flow is specifically as follows:
wherein F is 5 Represent the warehouse flow, Q n,t,out Indicating the discharge flow rate of the nth reservoir in the nth period.
3. The multi-objective flood control optimization scheduling method for the reservoir gate group according to claim 1, wherein the dynamic weights comprise a first weight, a second weight and a third weight;
the calculation formula of the first weight is as follows:
the calculation formula of the second weight is as follows:
the calculation formula of the third weight is as follows:
wherein omega 1 Represents a first weight, ω 2 Representing the second weight, ω 3 Represents a third weight, X 1 Representing the moving distance of the hunting wolf to the first head wolf, X 2 Representing the moving distance of the hunting wolf to the second head wolf, X 3 Representing the distance the hunting wolf moves to the third wolf.
4. The optimal dispatching method for multi-target flood control of the reservoir gate group as claimed in claim 3, wherein the position formula is specifically as follows:
wherein X represents the current iteration position of the hunting wolf, X (l+1) represents the next iteration position of the hunting wolf, ω 1 Represents a first weight, ω 2 Representing the second weight, ω 3 Represents a third weight, X 1 Representing the moving distance of the hunting wolf to the first head wolf, X 2 Representing the moving distance of the hunting wolf to the second head wolf, X 3 Representing the distance the hunting wolf moves to the third wolf.
5. A multi-objective flood control optimization scheduling method for a reservoir gate group as claimed in claim 1,2,3 or 4, wherein the constraint conditions include water balance constraint, reservoir level upper and lower limit constraint, reservoir level change constraint, reservoir lower discharge limit constraint and reservoir gate maximum lower discharge limit constraint.
6. The multi-target flood control optimal dispatching system for the reservoir gate group is characterized in that the multi-target flood control optimal dispatching method for the reservoir gate group according to any one of claims 1-5 is used in the operation process, and the system comprises a creation module, a constraint module and a calculation module;
the creation module is used for creating a plurality of objective functions according to reservoir information and also used for creating a multi-objective optimization model according to the plurality of objective functions;
the constraint module is used for establishing constraint conditions of the multi-objective optimization model;
the calculation module is used for calculating the optimal solution of the multi-objective optimization model according to the constraint conditions and the improved wolf algorithm.
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