CN114757579A - Reservoir group flood control optimal scheduling method under complex engineering system - Google Patents
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Abstract
The invention discloses a reservoir flood control optimal scheduling method under a complex engineering system, which belongs to the technical field of reservoir scheduling and comprises the following steps: step one, a hydrological model serves as an external model to provide inflow at the upstream and the interval of a reservoir, and a downstream one-dimensional and two-dimensional hydraulic model serves as an external model to feed back hydrological characteristic value indexes; determining a target function and constraint conditions, and establishing a reservoir group flood control optimal scheduling model; thirdly, solving a reservoir group flood control scheduling model by applying a particle group algorithm to generate a flood control optimal scheduling scheme; step four, manually intervening reservoir outflow and target control water level to generate a new flood control scheduling scheme to form a flood control scheduling scheme set; and fifthly, optimizing a final flood control scheduling scheme by using the variable fuzzy model. The invention can integrally consider forecasting and scheduling, and realizes the intellectualization of flood control decision and the maximization of flood control benefit on the premise of ensuring the flood control safety of the reservoir and the safety of downstream flood control objects.
Description
Technical Field
The invention relates to the technical field of reservoirs, in particular to a reservoir group flood control optimal scheduling method under a complex engineering system.
Background
In research on flood control dispatching of reservoir groups, a conventional method is an early analysis method, which takes a dispatching rule as an operation basis and is a semi-empirical semi-theoretical reservoir flood control dispatching method, selects a typical flood series from historical hydrological data, depends on dispatching experience of a dispatching decision maker, and commands flood control dispatching operation by means of a flood control dispatching graph and the like, is relatively simple and easy to operate, but depends on the experience of the dispatching worker and some experience graphs, belongs to a semi-empirical method, the conventional dispatching of the prior reservoir basically adopts an independent dispatching mode, does not consider the hydrological compensation and reservoir capacity compensation effects seen by a plurality of upstream and downstream reservoir groups, is reasonable in dispatching of a single reservoir, but easily causes flood control loss of the downstream of the reservoir for the probability of flood encounter in the whole region, therefore, the combined flood control optimized dispatching of the whole reservoir group is considered, the scheduling of the whole flood control project system is coordinated, the flood control benefit maximization is realized, and the method is an important way for flood control and disaster reduction.
Reservoir group flood control scheduling under complicated engineering system relates to hydraulic engineering such as a plurality of reservoirs, stagnant flood district and floodgate pump, not only relates to the dispatch of reservoir, and carries out the joint scheduling with river course flood evolution, hold stagnant flood district, mainly shows: firstly, the dispatching model has complex coupling relation and hydrologic model relation, the dispatching involves a plurality of forecasting sections, the forecasting data of the sections is used as the input data of the dispatching model, and the hydrologic models adopted by each section are different; in relation to a river flood evolution model, the upstream ex-warehouse flow evolution and interval inflow superposition of the series reservoir are used as the warehousing flow of the downstream reservoir, and the seamless connection with the river evolution model (a Maskikyo model and a one-dimensional hydrodynamic model) is required; when the stagnant flood area is started or a break embankment scheme is set, the model is seamlessly connected with a two-dimensional evolution hydrodynamic model of the stagnant flood area; the scheduling process is complex, the start and stop or water level control rules of different stagnant flood areas are involved, and the difficulty of increasing the restriction and optimization is increased; the dispatching feedback mechanism is complex, and a feasible optimized dispatching scheme is realized based on the feedback readjustment of the reservoir dispatching flood regulation indexes and the flood storage and stagnation area flood regulation indexes; the dispatching process is manually intervened, such as setting the reservoir outlet flow, controlling the water level and setting the control conditions of the involved dispatching gate pump, so that the dispatching initial conditions and the constraint processing mechanism are complicated.
On the basis of the existing reservoir flood control dispatching rules, the invention changes the independent dispatching modes in the past, couples the external hydrological model and the hydraulic model, adopts the means of reservoir group combined dispatching to reduce the loss of reservoir downstream flood control, establishes the reservoir group combined optimized dispatching model with the criteria of minimum reservoir flood control capacity occupation, minimum reservoir flood control capacity occupation at the end of the dispatching period and minimum flood control protection object water level over-alarm, solves the model by using the particle group algorithm, and combines the generation of a new manual experience intervention scheme, and optimizes a satisfactory dispatching scheme by using a variable fuzzy model for the multi-dispatching scheme, thereby obtaining more satisfactory effect.
Disclosure of Invention
The invention aims to provide a reservoir group flood control optimal scheduling method under a complex engineering system. Providing inflow boundaries of an upstream reservoir region and a reservoir region by taking the hydrological model as an external model, and taking a downstream one-dimensional and two-dimensional hydraulic model as the external model; constructing a reservoir group flood control optimal scheduling model by combining a reservoir group flood control scheduling target; solving the scheduling model by adopting a particle swarm coupling constraint processing method to obtain an initial optimized scheduling scheme, setting different targets and engineering conditions by adopting a manual intervention mode to regenerate a new scheme, and finally obtaining a flood control optimized scheduling scheme set; and finally, carrying out scheme optimization by adopting a variable fuzzy optimization method to form a final reservoir group flood control optimization scheduling scheme.
In order to achieve the above effects, the present invention provides the following technical solutions: a reservoir group flood control optimal scheduling method under a complex engineering system comprises the following steps:
step one, a hydrological model is used as an external model to provide inflow boundaries of an upstream reservoir region and a reservoir region, and a one-dimensional and two-dimensional downstream hydraulics model is used as an external model;
determining a target function, constraint conditions and scheduling parameters, and establishing a reservoir group flood control optimal scheduling model;
thirdly, solving a reservoir group flood control scheduling model by applying a particle group algorithm to generate a flood control optimal scheduling scheme;
step four, manually intervening reservoir outflow and target control water level to generate a new flood control scheduling scheme to form a flood control scheduling scheme set;
and fifthly, evaluating the flood control scheduling scheme by using the variable fuzzy model, and preferably selecting the final flood control scheduling scheme.
Further, the method comprises the following steps: according to the operation steps in the first step, the establishment of the downstream hydraulic model requires the water level and flow characteristic values of a plurality of feedback key nodes.
Further, the method comprises the following steps: according to the operation steps in the second step, the objective function comprises minimum occupation of flood control capacity of the reservoir
Minimum flood control storage capacity occupation of dispatching end reservoir
Minimum height of flood protection object water level over-alarm
Further, in the above-mentioned case,the method comprises the following steps: according to the operation steps in the second step, the constraint conditions comprise water balance constraint
Reservoir water level restriction
Reservoir water level time interval amplitude-variation constraint
Further, the method comprises the following steps: according to the operation steps in the second step, the constraint conditions further comprise reservoir flow capacity constraint
Reservoir level constraints at the beginning of the dispatch period
Flood protection protected object safety constraints
T1, 2.. times.t, all major variables in step two are non-negative constraints, all of which are non-negative values.
Further, the method comprises the following steps: according to the operation steps in the third step, the particle and velocity update calculation formula:
further, the method comprises the following steps: according to the operation steps in step three, i is 1, 2. K1, 2,. K; m is the number of particles; k is the total number of iterations, c1And c2Is a learning factor.
Further, the method comprises the following steps: according to the operation steps in the third step, the third step further comprises
Step 301, randomly initializing the random position and the flight speed of a particle swarm, initializing a swarm size M, an inertia coefficient w, a learning factor c, a maximum algebra K and the like;
step 302, evaluating individual fitness of the particles, wherein if the particles are evolved for the first time, pBest is assigned to each particle and a global extremum is assigned to a population optimal particle;
step 303, comparing the fitness of each particle with the individual extremum pBest, and if the fitness of each particle is better, updating the current individual extremum;
step 304, comparing the fitness of each particle with the global extreme value gBest, and if the fitness is better, updating the current global extreme value;
605, after the current pBest and gBest are determined, updating the speed and the position of the particles according to the particle and a speed updating calculation formula to generate a new population;
step 306, whether the termination condition is met is generally set to reach a preset maximum iteration number or a good enough adaptive value is found, and if the termination condition is met, the calculation is finished; otherwise, go to step 302 to continue the evolution.
Further, the method comprises the following steps: according to the operation steps in the fourth step, manual intervention is carried out in two modes: if the reservoir has outflow intervention, the reservoir with the intervention carries out flood regulation calculation according to given outflow, and an external hydraulic model carries out simulation calculation again; if no intervention occurs, the original scheme is kept unchanged; and if the target water level value of the controlled object is manually intervened, performing reservoir group optimal scheduling calculation again to generate a new scheduling scheme.
Further, according to the operation step of the step five, the n scheduling schemes and the m scheduling indexes are written as follows:
the method for normalizing the target with the larger and more excellent type is
The method for normalizing the more optimal index for the smaller index is
Wherein V and A are respectively large and small operators, and after normalization, a relative membership matrix is formed
Order to
Then r isg＝(rg1,...,rgm)TRepresents an ideal optimal solution, rb＝(rb1,...,rbm)TExpressing an ideal bad scheme, wherein the membership degree of each scheme to the ideal good scheme is as follows: u ═ U1,...,un)，
And selecting a corresponding optimal scheduling scheme according to the maximum membership degree principle.
The invention provides a reservoir group flood control optimal scheduling method under a complex engineering system, which has the following beneficial effects:
according to the invention, flood forecasting and scheduling can be considered integrally under a complex engineering system, the scheduling of the whole flood control engineering system can be coordinated, and the maximization of flood control benefit is realized; the manual experience is combined to intervene in the scheduling scheme, so that the practicability of flood prevention decision is improved; the invention aims at the scheme optimization model of a multi-scheduling scheme set, realizes the intelligent optimal flood control scheduling scheme, and finds the scheme with the maximum flood control benefit value of the series-parallel reservoir group under the condition of emphasizing the coordination of reservoir safety and flood control object safety, thereby having higher practicability in practice.
Drawings
Fig. 1 is a flow chart of a method for optimizing and scheduling flood control of a reservoir group under a complex engineering system according to the invention;
FIG. 2 is a schematic diagram of particle movement of a particle swarm algorithm of the reservoir swarm flood control optimized dispatching method under a complex engineering system according to the present invention;
fig. 3 is a topological structure diagram of a reservoir group according to the reservoir group flood control optimal scheduling method in a complex engineering system, wherein a reservoir A and a reservoir D are upstream and downstream cascade reservoirs respectively, A, D reservoirs, the reservoir A and the reservoir B form a parallel reservoir group, F is a downstream flood control point, and A and B are flood stagnant areas. And inflow processes are provided among key nodes at the upstream, the interval and the downstream of the reservoir by external hydrological models. The whole series-parallel reservoir forms a complex series-parallel reservoir group system.
Detailed Description
The invention provides a technical scheme that: referring to fig. 1, a reservoir flood control optimal scheduling method under a complex engineering system includes the following steps:
step one, a hydrological model is used as an external model to provide inflow boundaries of an upstream reservoir region and a reservoir region, and a one-dimensional and two-dimensional downstream hydraulics model is used as an external model;
determining a target function, constraint conditions and scheduling parameters, and establishing a reservoir group flood control optimal scheduling model;
thirdly, solving a reservoir group flood control scheduling model by applying a particle group algorithm to generate a flood control optimal scheduling scheme;
step four, manually intervening reservoir outflow and target control water level to generate a new flood control scheduling scheme to form a flood control scheduling scheme set;
and fifthly, evaluating the flood control scheduling scheme by using the variable fuzzy model, and preferably selecting the final flood control scheduling scheme.
Specifically, the method comprises the following steps: according to the operation steps in the first step, the establishment of the downstream hydraulic model requires the water level and flow characteristic values of a plurality of feedback key nodes.
Specifically, the method comprises the following steps: according to the operation steps in the second step, the objective function comprises minimum occupied capacity of the flood control storage of the reservoir
Minimum flood control storage capacity occupation of dispatching end reservoir
Minimum height of flood protection object water level over-alarm
Specifically, the method comprises the following steps: according to the operation steps in the second step, the constraint conditions comprise water balance constraint
Reservoir water level restriction
Reservoir water level time interval amplitude-variation constraint
Specifically, the method comprises the following steps: according to the operation steps in the second step, the constraint conditions further comprise reservoir flow capacity constraint
Reservoir water level constraints at the beginning of dispatch period
Flood protection protected object safety constraints
T1, 2., T, all major variables in step two are non-negative constraints, all of which are non-negative values.
Specifically, the method comprises the following steps: according to the operation steps in the third step, the particle and speed update calculation formula:
specifically, the method comprises the following steps: according to the operation steps in step three, i is 1, 2., M; k1, 2,. K; m is the number of particles; k is the total number of iterations, c1And c2Is a learning factor.
Specifically, the method comprises the following steps: according to the operation steps in the third step, the third step also comprises
Step 301, randomly initializing the random position and the flight speed of a particle swarm, initializing a swarm size M, an inertia coefficient w, a learning factor c, a maximum algebra K and the like;
step 302, evaluating the individual fitness of the particles, wherein if the particles are evolved for the first time, the individual extreme value pBest is assigned to each particle and the global extreme value is assigned to the optimal particle of the population;
step 303, comparing the fitness of each particle with the individual extreme value pBest, and if the fitness of each particle is better than the individual extreme value pBest, updating the current individual extreme value;
step 304, comparing the fitness of each particle with the global extreme value gBest, and if the fitness is better, updating the current global extreme value;
step 305, after the current pBest and gBest are determined, the speed and the position of the particles are updated according to the particle and speed updating calculation formula, and a new population is generated;
step 306, whether the termination condition is met is generally set to reach a preset maximum iteration number or a good enough adaptive value is found, and if the termination condition is met, the calculation is finished; otherwise, go to step 302 to continue the evolution.
Specifically, the method comprises the following steps: according to the operation steps in the fourth step, manual intervention is carried out in two forms: if the reservoir has outflow intervention, the reservoir with the intervention carries out flood regulation calculation according to given outflow, and an external hydraulic model carries out simulation calculation again; if no intervention occurs, the original scheme is kept unchanged; and if the target water level value of the controlled object is manually intervened, performing reservoir group optimal scheduling calculation again to generate a new scheduling scheme.
Specifically, according to the operation steps in the step five, the n scheduling schemes and the m scheduling targets are written as follows:
then r isg＝(rg1,...,rgm)TRepresents an ideal optimal solution, rb＝(rb1,...,rbm)TExpressing an ideal bad scheme, wherein the membership degree of each scheme to the ideal good scheme is as follows: u ═ U1,...,un)，
And selecting a corresponding optimal scheduling scheme according to the maximum membership degree principle.
The method of the examples was performed for detection analysis and compared to the prior art to yield the following data:
| accuracy of prediction | Efficiency of scheduling | Convenience for workers | |
| Examples | Is higher than | Is higher than | Is higher than |
| Prior Art | Is lower than | Is lower than | Lower is |
According to the table data, in the embodiment, the hydrological model is used as an external model to provide inflow boundaries between the upstream and reservoir regions of the reservoir, and the one-dimensional and two-dimensional downstream hydraulic models are used as external models; constructing a reservoir group flood control optimal scheduling model by combining a reservoir group flood control scheduling target; solving the scheduling model by adopting a particle swarm coupling constraint processing method to obtain a flood control optimal scheduling scheme, setting different targets and engineering conditions by adopting a manual intervention mode to regenerate a new scheme, and finally obtaining a flood control optimal scheduling scheme set; and finally, carrying out scheme optimization by adopting a variable fuzzy optimization method to form a final reservoir group flood control optimal scheduling scheme.
The invention provides a reservoir group flood control optimal scheduling method under a complex engineering system, which comprises the following steps: step one, a hydrological model serves as an external model to provide inflow boundaries of reservoir upstream and reservoir intervals, a one-dimensional and two-dimensional downstream hydraulic model serves as an external model, the downstream hydraulic model needs to feed back characteristic values of water levels and flow of key nodes, step two, objective functions, constraint conditions and scheduling parameters are determined, a reservoir group flood control optimal scheduling model is established, and the objective functions comprise minimum reservoir flood control capacity occupation of reservoir
In the formula, VRiShowing the relationship of the water level of the ith reservoir to the reservoir capacity,
the minimum occupied storage capacity of the flood control storage of the reservoir at the end of the dispatching period is represented by the water level of the reservoir at the beginning of the t period of the ith reservoir
The height of the flood limit water level and the flood control protection object water level over-alarm actually executed by the ith reservoir is the minimum
In the formula (I), the compound is shown in the specification,
the water level of the kth flood control protection object in the t period is obtained by calculation of a basin flood evolution model,
the guaranteed water level of the kth flood control protection object is represented, alpha is a control coefficient and is used for increasing the danger weight (alpha is more than or equal to 1) when the water is high, and the constraint condition comprises water balance constraint
The average warehousing flow rate of the ith reservoir in the period t,
the lower discharge quantity of the ith reservoir in the time period t and the reservoir water level constraint
The highest allowable water level of the ith reservoir in the flood season is generally flood control high water level or design flood level, and the variation amplitude restriction of the reservoir water level time interval
ΔZRiThe maximum allowable variation constraint condition for the water level of the adjacent time interval of the ith reservoir also comprises reservoir flow capacity constraint
Reservoir water level constraints at the beginning of dispatch period
Safety restriction for initial reservoir water level and flood protection object in dispatching stage of ith reservoir
T is 1,2, the constraint is optional constraint which can ensure the safety of the kth flood control protected object, when large floods occur in a drainage basin, the constraint is carefully selected, and a feasible scheduling scheme completely does not exist due to the constraint, a reservoir group flood control optimized scheduling model takes a reservoir as a main scheduling object, a hydrological model serves as an external model to provide inflow boundaries between the upstream and reservoir regions of the reservoir, a one-dimensional and two-dimensional downstream hydraulic model serves as an external model to simulate the flood process of a downstream riverway, the water level and the flow characteristic value of a key node are fed back, a particle group coupling constraint processing method is adopted to solve the reservoir group flood control optimized scheduling model, the reservoir group optimized scheduling model carries out outflow decision value adjustment according to the self flood control characteristic value of the reservoir and the hydrological characteristic value of the key node at the downstream fed back by the external model, and finally an optimized scheduling scheme is generated through interactive iteration, then setting different targets by adopting a manual intervention mode to regenerate a new scheme, forming a flood control scheduling scheme set by the scheme, and finally performing scheduling scheme optimization by adopting a variable fuzzy optimization method to form a final reservoir groupA flood control dispatching scheme, wherein all main variables in the step two are non-negative constraints, all the variables are non-negative values, the flood control of a reservoir group is optimized and dispatched, the occupation of the flood control reservoir capacity of the reservoir and the disaster severity of a flood control protection object are considered, the occupation of the upstream flood control reservoir capacity is smaller from the viewpoint of dam safety and subsequent flood control potential, and vice versa from the viewpoint of the safety and the economical efficiency of the flood control protection object, therefore, when a large flood occurs in a drainage basin, two types of dispatching targets have natural contradictory conflict characteristics, the total K drainage basin flood control protection objects to be considered are arranged, the reservoirs participating in the optimized dispatching share M seats, the drainage basin reservoir group dispatching period is determined based on a flood forecasting period, is uniformly divided into T dispatching periods, the period is long at, an optimized dispatching model is coupled with a drainage basin flood forecasting model and a flood evolution model, and is continuously calculated in the whole dispatching period, step of time Δ t2Scheduling period by Δ t2Is divided into T2In the searching process, each particle firstly generates the speed and the position at random, then the speed and the position are dynamically updated through an individual extreme value pBest of the particle in the past generation searching process and a global extreme value gBest of the whole group until the optimal solution is found, and the particle i adopts a real number coding mode of a one-dimensional array and is expressed as x, so that the optimal solution is foundi＝(xi,1,xi,2,...,xi,d,...,xi,P·L) The flying speed is vi＝(vi,1,vi,2,...,vi,d,...,vi,P·L) Wherein, the gene position xi,dRepresenting one position of the dispatch line, d ═ 1 · L + L, P ═ 1, 2., P, L ═ 1, 2., L, P is the total number of dispatch lines, the particle and velocity update calculation formula:
i 1, 2., M, K1, 2., K, M being the number of particles, K being the total number of iterations, c1And c2Is a learning factor, c1Reflecting the influence of individual pole value on the optimizing speed, c2The parameter reflects the influence of the global extreme on its optimization speed, if c1When c is 0, the particles have no cognitive ability and the convergence rate is increased, but when a complicated problem is handled, the problem tends to be locally optimal, and c is a case2If equal to 0, the particles are mutually independent, no group shares information, the probability of obtaining the global optimal solution is very small by completely depending on self experience, and the value is generally c1＝c22, w is an inertia weight, so that the particle keeps the motion inertia and the trend of expanding a search space, and the global and local search capability is balanced, and experiments prove that w is gradually reduced (such as linear reduction) along with evolution, namely, w is larger at the early stage of the evolution, so that the global search of emphatic particles is facilitated, the local search of emphatic particles with smaller w at the later stage is beneficial to obviously improving the convergence performance of the algorithm, and the general values w are as the values w ∈ [0.4,0.9 ∈ [ ]]，
Maximum speed
Greater accuracy of the search for the solution vector
The method is beneficial to global search, and conversely, is beneficial to local search, is generally set to be 10% -40% of the maximum value range of each dimension, and the speed updating formula consists of three parts: the previous velocity, which represents the degree to which the particle maintains the previous velocity, maintains the ability of the algorithm to expand the search space, the "cognitive" part, which represents the affirmation of the particle's own successful experience and avoids trapping into the local area by random perturbationsThe 'society' part reflects the information sharing and cooperation among the particles, so that the particles have strong enough global searching capability, the particles continuously adjust the positions thereof according to individual historical experience and global sharing information under the combined action of the three parts, and finally find the optimal solution of the problem, wherein the particle speed formula is
And
the vector of (1) is shown in the attached figures of the specification, and the third step also comprises the steps of step 301, randomly initializing the random position and the flight speed of the particle swarm, initializing the size M of the population, the inertia coefficient w, the learning factor c, the maximum algebra K and the like, step 302, evaluating the individual fitness of the particles, if the first evolution is carried out, assigning the individual extreme value pBest to each particle and the global extreme value to the optimal particle of the population, step 303, comparing the fitness of each particle with the individual extreme value pBest, if the fitness is better, updating the current individual extreme value, step 304, comparing the fitness of each particle with the global extreme value gBest, if the fitness is better, updating the current global extreme value, step 305, after determining the current pBest and gBest, updating the speed and the position of the particle according to the particle and a speed updating calculation formula, generating a new population, and step 306, whether the termination condition is met, generally setting to reach a preset maximum iteration number or finding a good enough adaptive value, if the adaptive value is met, finishing the calculation, otherwise, turning to the step 302 to continue evolution, manually intervening to generate a new scheduling scheme, if the reservoirs A, B and C have outflow intervention, performing flood regulation calculation on the reservoir in which the intervention occurs according to the given outflow, and externally connecting a hydraulics model for re-simulation calculation; if no intervention occurs, the original scheme data result is kept unchanged; if the reservoir A has outflow interference, recalculating the evolution model between the reservoir A and the reservoir D to obtain the inflow process of the reservoir D, wherein the reservoir D is determined according to the inflow processGiven inflow, carrying out flood regulation calculation, and externally connecting a hydraulics model for re-simulation calculation; if the reservoir A is not subjected to manual intervention, the manual intervention flow of the reservoir D is the same as that of the reservoir C or the reservoir B; if the target water level value of the flood control object is changed through manual intervention, performing reservoir group optimal scheduling calculation again to generate a new scheduling scheme; finally, a flood control scheduling scheme set is formed, step five, flood control scheduling scheme evaluation optimization is carried out by using a variable fuzzy model, and the n scheduling schemes and the m scheduling indexes are written into the following forms:
the method for normalizing the target with the larger and more excellent type is
The method for normalizing the more optimal index for the smaller index is
Wherein V and A are operators respectively for getting larger and smaller, and after normalization, a relative membership matrix is formed
Order to
Then
Represents an ideal optimal solution, rb＝(rb1,...,rbm)TExpressing an ideal bad scheme, wherein the membership degree of each scheme to the ideal good scheme can be obtained by the following formula: u ═ U1，…un)，
And selecting a corresponding optimal scheduling scheme according to the maximum membership degree principle.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A reservoir group flood control optimal scheduling method under a complex engineering system is characterized by comprising the following steps:
s1, the hydrological model serves as an external model to provide inflow boundaries between the upstream and reservoir regions of the reservoir, and the one-dimensional and two-dimensional downstream hydraulic models serve as external models;
s2, determining a target function, constraint conditions and scheduling parameters, and establishing a reservoir group flood control optimal scheduling model;
s3, solving a reservoir group flood control scheduling model by applying a particle group algorithm to generate a flood control optimal scheduling scheme;
s4, manually intervening reservoir outflow and target control water level to generate a new flood control scheduling scheme to form a flood control scheduling scheme set;
and S5, performing flood control scheduling scheme evaluation by using the variable fuzzy model, and preferably selecting a final flood control scheduling scheme.
2. The optimal flood control dispatching method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation step in the S1, the hydrological model provides inflow boundaries of the upstream and the reservoir regions of the reservoir, and the downstream hydraulics model needs to feed back the water level and the flow characteristic value of the key node.
3. The optimal flood control dispatching method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation steps in S2, the objective function includes minimum occupied capacity of reservoir flood control
Scheduling end of termMinimum occupied capacity of reservoir for flood control
Minimum height of flood protection object water level over-alarm
4. The flood control optimal scheduling method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation step in S2, the constraint condition includes a water balance constraint
Reservoir water level restriction
Reservoir water level time interval amplitude-changing constraint
5. The flood control optimal scheduling method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operating step in S2, the constraints further include a reservoir flow capacity constraint
Reservoir water level constraints at the beginning of dispatch period
Flood protection protected object safety constraints
And all main variables in the step two are non-negative constraints, and all the variables are non-negative values.
6. The optimal flood control dispatching method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation step in S3, the particle and velocity update calculation formula:
7. the optimal scheduling method for flood control of reservoir groups under the complex engineering system according to claim 6, characterized by comprising the following steps: according to the operation in S3, the i is 1, 2. K1, 2,. K; m is the number of particles; k is the total number of iterations, c1And c2Is a learning factor.
8. The flood control optimal scheduling method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation step in S3, the step three further includes
S301, randomly initializing the random position and the flight speed of a particle swarm, initializing a swarm size M, an inertia coefficient w, a learning factor c, a maximum algebra K and the like;
s302, evaluating the individual fitness of the particles, and if the particles are evolved for the first time, assigning an individual extreme value pBest to each particle and assigning a global extreme value to a population optimal particle;
s303, comparing the fitness of each particle with the individual extremum pBest, and if the fitness of each particle is better, updating the current individual extremum;
s304, comparing the fitness of each particle with a global extreme value gBest, and if the fitness is better, updating the current global extreme value;
s305, after the current pBest and gBest are determined, the speed and the position of the particles are updated according to the particle and speed updating calculation formula, and a new population is generated;
s306, whether a termination condition is met is generally set to reach a preset maximum iteration number or a good enough adaptive value is found, and if the termination condition is met, the calculation is finished; otherwise, go to S302 to continue the evolution.
9. The flood control optimal scheduling method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation step in S4, manual intervention takes two forms: if the reservoir has outflow intervention, the reservoir with the intervention carries out flood regulation calculation according to given outflow, and an external hydraulic model carries out simulation calculation again; if no intervention occurs, the original scheme is kept unchanged; and if the target water level value of the controlled object is manually intervened, performing reservoir group optimal scheduling calculation again to generate a new scheduling scheme.
10. The optimal flood control dispatching method for reservoir groups under the complex engineering system according to claim 1, characterized by comprising the following steps: according to the operation step in S5, the n scheduling schemes, m scheduling indexes are written as follows:
the method for normalizing the target with the larger and more excellent type is
The method for normalizing the more optimal index for the smaller index is
Wherein V and A are operators respectively for getting larger and smaller, and after normalization, a relative membership matrix is formed
Order to
Then r isg＝(rg1,...,rgm)TRepresents an ideal optimal solution, rb＝(rb1,...,rbm)TRepresenting ideal bad schemes, wherein the membership degree of each scheme to the ideal good schemes is as follows: u ═ U1,...,un)，
And selecting an optimal scheduling scheme according to the maximum membership degree principle.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116227800A (en) * | 2022-11-09 | 2023-06-06 | 中国水利水电科学研究院 | Parallel reservoir group flood control optimal scheduling scheme generation method based on flood control pressure value |
| CN116307533A (en) * | 2023-02-21 | 2023-06-23 | 珠江水利委员会珠江水利科学研究院 | Reservoir group flood control dispatching intelligent method, system and medium |
| CN116777135B (en) * | 2023-05-12 | 2023-11-14 | 大连海事大学 | Reservoir-impounded flood area refined flood control combined dispatching method based on efficient optimization algorithm |
| CN117150975A (en) * | 2023-10-31 | 2023-12-01 | 长江三峡集团实业发展(北京)有限公司 | Hydrodynamic model parameter optimization and hydrodynamic process simulation method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116227800A (en) * | 2022-11-09 | 2023-06-06 | 中国水利水电科学研究院 | Parallel reservoir group flood control optimal scheduling scheme generation method based on flood control pressure value |
| CN116227800B (en) * | 2022-11-09 | 2023-12-22 | 中国水利水电科学研究院 | Parallel reservoir group flood control optimal scheduling scheme generation method based on flood control pressure value |
| CN116307533A (en) * | 2023-02-21 | 2023-06-23 | 珠江水利委员会珠江水利科学研究院 | Reservoir group flood control dispatching intelligent method, system and medium |
| CN116307533B (en) * | 2023-02-21 | 2023-10-20 | 珠江水利委员会珠江水利科学研究院 | Reservoir group flood control dispatching intelligent method, system and medium |
| CN116777135B (en) * | 2023-05-12 | 2023-11-14 | 大连海事大学 | Reservoir-impounded flood area refined flood control combined dispatching method based on efficient optimization algorithm |
| CN117150975A (en) * | 2023-10-31 | 2023-12-01 | 长江三峡集团实业发展(北京)有限公司 | Hydrodynamic model parameter optimization and hydrodynamic process simulation method and device |
| CN117150975B (en) * | 2023-10-31 | 2024-01-26 | 长江三峡集团实业发展(北京)有限公司 | Hydrodynamic model parameter optimization and hydrodynamic process simulation method and device |
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