CN111915164B - Cascade reservoir group full ecological factor fine scheduling control method and system - Google Patents

Cascade reservoir group full ecological factor fine scheduling control method and system Download PDF

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CN111915164B
CN111915164B CN202010682898.0A CN202010682898A CN111915164B CN 111915164 B CN111915164 B CN 111915164B CN 202010682898 A CN202010682898 A CN 202010682898A CN 111915164 B CN111915164 B CN 111915164B
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牛文静
冯仲恺
蒋志强
刘帅
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Abstract

The invention provides a method and a system for fine scheduling control of all ecological elements of a cascade reservoir group, wherein the population is initialized randomly under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals; calculating the individual fitness value in the current population, and updating the global optimal position of the population and the historical optimal position of the individual; introducing individual historical optimal positions and random individual positions in an elite individual set to increase population diversity and effectively avoid premature convergence of individuals; a refined search strategy is introduced, so that the population convergence precision is effectively improved; updating the positions of all individuals in the population through iterative calculation until the maximum iterative times of the population are reached; and outputting the global optimal position of the current population as a final scheduling process of the cascade hydropower station. Compared with the classic intelligent optimization method, the method has high robustness, and can effectively reduce the ecological water shortage of the whole cascade reservoir, thereby achieving the purpose of protecting the watershed ecology; meanwhile, the method has the advantages of simple principle and high solving precision.

Description

Cascade reservoir group full ecological factor fine scheduling control method and system
Technical Field
The invention belongs to the field of cascade reservoir group ecological scheduling, and particularly relates to a cascade reservoir group full ecological element fine scheduling control method and system.
Background
The results of many operating large cascade hydroelectric power station groups show that: the construction and operation of the hydropower station group bring huge social and economic benefits, and simultaneously change the hydrological situation of natural runoff and bring serious threat to the health of the ecological system of the drainage basin.
The main purpose of reservoir construction is to adjust water flow to play the role of blocking flood and storing water, but can bring adverse effects such as reservoir sediment deposition, water temperature rise, water quality deterioration and serious scouring of downstream riverbeds. In order to improve the negative influence of reservoir construction and operation on the surrounding ecological environment, reservoir managers begin to consider the influence on the environment while bringing economic benefit to the reservoir, but the existing literature data are difficult to provide a uniform ecological flow process. Therefore, domestic related experts carry out intensive research on ecological scheduling of the reservoir, and the purpose of environmental protection is achieved mainly by improving the traditional operation mode of the reservoir or gradually restoring the architecture and the function of the ecological system around the watershed. However, the traditional optimization method (such as linear programming, dynamic programming and the like) has the defects of 'dimension disaster' and the like when the ecological scheduling of the cascade reservoir is solved.
Disclosure of Invention
Aiming at the defects or requirements of the prior art, the invention aims to provide a cascade reservoir group full ecological element fine scheduling control method and system, so that the defects of 'dimension disaster' and the like existing in the traditional optimization method (such as linear programming, dynamic programming and the like) in the step reservoir ecological scheduling solving process are overcome.
In order to achieve the above object, as an aspect of the present invention, a method for fine scheduling control of all ecological elements of a cascade reservoir group is provided, comprising the steps of:
(1) setting the maximum iteration times as the individual water level values of all hydropower stations in the reservoir group at different moments
Figure GDA0003580805410000029
When the iteration times k is equal to 1, randomly initializing the population under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals, and taking the initial population as a current population;
(2) calculating the fitness of each individual in the current population, taking the position of each individual in the current population as a historical optimal position, and updating the historical optimal positions of all individuals and the global optimal position in the current population;
(3) on the basis of the temporary population obtained after updating the positions of all individuals in the population in the step (2), selecting the first G individuals with better fitness to establish an elite individual set; introducing the individual historical optimal position and the random individual position in the elite individual set to all temporary population individuals to increase the population diversity to obtain a diversity population; updating the positions of the individual diversity populations through a refined search strategy to form next generation populations;
(4) Let k be k +1, if
Figure GDA0003580805410000021
Taking the next generation population as the current population, and repeatedly executing the step (2) and the step (3); otherwise, stopping calculation, and outputting the global optimal individual of the current population as the optimal scheduling process.
Further, the kth generation ith individual position is expressed as:
Figure GDA0003580805410000022
wherein N represents the number of power stations, T represents the number of time periods, i is more than or equal to 1 and less than or equal to m, and m represents the population scale;
Figure GDA0003580805410000023
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure GDA0003580805410000024
for the upper water level limit of the nth hydropower station during the t-th period,
Figure GDA0003580805410000025
the lower limit of the water level of the nth hydropower station in the t-th period.
Further, in the step (2), fitness of each individual in the current population is calculated by adopting a penalty function method, and the individual is
Figure GDA0003580805410000026
Is adapted to
Figure GDA0003580805410000027
Comprises the following steps:
Figure GDA0003580805410000028
wherein, yn,tIs the n-thWater shortage, Delta, at the t-th time of the planttHours for the t-th period; a isbIs the penalty coefficient of the b-th inequality constraint; thetabIs the violation value of the b-th inequality constraint, and χ is the total number of inequality constraints; beta is alIs the penalty coefficient for the ith equality constraint; phi is alIs the violation of the ith equality constraint, and η is the total number of equality constraints.
Further, the updating of the historical optimal positions of all the individuals and the global optimal position in the current population in the step (2) includes:
By
Figure GDA0003580805410000031
Updating the historical optimal locations of all individuals by
Figure GDA0003580805410000032
Updating the global optimal position in the current population;
wherein:
Figure GDA0003580805410000033
representing the historical optimal position of the ith individual in the k-1 generation,
Figure GDA0003580805410000034
to represent
Figure GDA0003580805410000035
The degree of fitness of (a) to (b),
Figure GDA0003580805410000036
denotes the fitness of the ith individual of the kth generation, gBestkRepresenting the global optimal position of the population of the kth generation.
Further, in the step (3),
by
Figure GDA0003580805410000037
Obtaining a diversity population;
wherein the content of the first and second substances,
Figure GDA0003580805410000038
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure GDA0003580805410000039
is the kth generation r in the population1Historical optimal position of j dimension of individual, r1Randomly selected individual subscripts in the population;
Figure GDA00035808054100000310
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure GDA00035808054100000311
is the kth generation r in the elite set2Position of individual j dimension, r2Randomly selected individual subscripts in the elite set.
Further, in the step (3),
by
Figure GDA00035808054100000312
Updating the individual positions of the diversity population to form a next generation population;
wherein the content of the first and second substances,
Figure GDA00035808054100000313
delta is an intermediate variable, Gauss (0,1) is a normally distributed random number,
Figure GDA00035808054100000314
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure GDA00035808054100000315
searching the j dimension position of an individual for the ith refinement of the kth generation in the population;
Figure GDA00035808054100000316
the maximum iteration number of the population is obtained;
Figure GDA00035808054100000317
is the kth generation r in the elite set3The individual isPosition in j dimension, r 3Is a randomly selected individual subscript in the elite set.
As another aspect of the present invention, there is provided a step reservoir group full ecological element fine scheduling control system, comprising:
the initialization module is used for setting the maximum iteration times as the individual water level values of all hydropower stations in the reservoir group at different moments
Figure GDA0003580805410000041
When the iteration number k is 1, randomly initializing a population under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals, and taking the initial population as a current population;
the fitness calculation module is used for calculating the fitness of each individual in the current population, taking the position of each individual in the current population as a historical optimal position, and updating the historical optimal positions of all the individuals and the global optimal position in the current population;
the position updating module is used for updating the positions of all individuals of the population based on the fitness calculating module to obtain a temporary population, and selecting the first G individuals with better fitness to establish an elite individual set; introducing the individual historical optimal position and the random individual position in the elite individual set to all temporary population individuals to increase the population diversity to obtain a diversity population; updating the positions of the various population individuals through a refined search strategy to form a next generation population;
And the output module is used for repeatedly executing the operation from the fitness calculating module to the position updating module by taking the next generation population as the current population until a preset iteration stop condition is met, and outputting the globally optimal individual of the current population as the optimal scheduling process.
In general, compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:
the population is initialized randomly under the constraint of the water level value of the reservoir to obtain an initial population containing a plurality of individuals; calculating the individual fitness value in the current population, and updating the global optimal position of the population and the historical optimal position of the individual; introducing individual historical optimal positions and random individual positions in an elite individual set to increase population diversity and effectively avoid premature convergence of individuals; and a refined search strategy is introduced, so that the population convergence precision is effectively improved, and the transition from exploration to development can be well completed. In conclusion, compared with the classical intelligent optimization method, the method has high robustness, and can effectively reduce the ecological water shortage of the whole cascade reservoir, thereby achieving the purpose of protecting the watershed ecology. Meanwhile, the method solves the problem of cascade reservoir ecological scheduling, and is simple in principle and high in solving precision.
Drawings
Fig. 1 is a schematic flow chart of a method for fine scheduling control of all ecological elements of a cascade reservoir group according to an embodiment of the present invention;
FIG. 2(a) is a schematic diagram of a box-type water supply system using the method of the present invention at a minimum ecological demand of 75% of the incoming water frequency;
FIG. 2(b) is a schematic diagram of a box-shaped case of the method of the present invention at a water frequency of 80% of the minimum ecological requirement provided by the embodiment of the present invention;
FIG. 2(c) is a box-type schematic illustration of the method of the present invention at a minimum ecological demand of 85% incoming water frequency, according to an embodiment of the present invention;
FIG. 2(d) is a box-type schematic representation of the method of the present invention at a minimum ecological demand of 90% incoming water frequency, as provided by an embodiment of the present invention.
Detailed Description
In order to make the objects and methods of the invention more clear and intuitive, the invention is described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The invention provides a method and a system for finely scheduling and controlling all ecological elements of a cascade reservoir group, which are used for increasing the diversity of the group by applying an extreme value search strategy of a group learning algorithm and improving the convergence precision of the group by applying a fine search strategy, thereby effectively reducing the ecological water shortage of a watershed cascade system and providing a scientific basis for ecological scheduling of the cascade reservoir.
The invention takes the minimum total water shortage of the whole cascade reservoir system of the drainage basin as an objective function, and the mathematical form is as follows:
Figure GDA0003580805410000061
Figure GDA0003580805410000062
in the formula: n is the number of step reservoirs; t is the total time interval number of scheduling; f is the total water shortage of the whole cascade reservoir;
Figure GDA0003580805410000063
the maximum ecological flow demand of the nth power station in the tth time period;
Figure GDA0003580805410000064
the minimum ecological flow requirement is set for the nth power station in the tth time period; o isn,tThe total ex-warehouse flow is the t time period of the nth power station; y isn,tFor the water shortage, Delta, at the t-th time interval of the nth planttThe t-th interval hours.
The constraints that need to be satisfied are as follows:
(1) and (3) water balance constraint:
Figure GDA0003580805410000065
wherein, Vn,tThe storage capacity of the nth hydropower station in the t period; q. q.sn,tThe interval flow of the nth hydropower station in the t period is obtained; i isn,tThe warehousing flow of the nth hydropower station in the t-th time period is obtained; o isn,tThe flow of the nth hydropower station in the t period is taken out of the reservoir; q n,tGenerating flow for the nth hydropower station in the t period; sn,tThe water reject flow for the nth hydropower station in the t period; un is an upstream power station directly connected to the nth hydroelectric power stationNumber of the cells.
(2) Reservoir water level restraint from beginning to end:
Figure GDA0003580805410000066
wherein the content of the first and second substances,
Figure GDA0003580805410000067
at an initial water level for the nth hydropower station;
Figure GDA0003580805410000068
the end-of-term level for the nth hydroelectric power station.
(3) And (3) power generation flow restriction:
Figure GDA0003580805410000069
wherein the content of the first and second substances,
Figure GDA00035808054100000610
the lower limit of the generating flow of the nth hydropower station in the t period;
Figure GDA00035808054100000611
generating flow upper limit for the nth hydropower station in the t period;
(4) water head balance constraint:
Figure GDA00035808054100000612
wherein Hn,tHead for nth hydropower station at tth time period; zn,tThe water level of the nth hydropower station in front of the dam in the tth time period; dn,tThe nth hydropower station has a water level downstream of the t-th period.
(5) And (3) power station output restraint:
Figure GDA00035808054100000613
wherein the content of the first and second substances,
Figure GDA00035808054100000614
the output upper limit of the nth hydropower station in the t period is set;
Figure GDA0003580805410000071
the lower limit of the output of the nth hydropower station in the t period is shown.
Fig. 1 is a schematic flow chart of a method for fine scheduling and controlling all ecological elements of a cascade reservoir group according to an embodiment of the present invention, and the method includes the following specific steps:
(1) and selecting power stations participating in calculation, and performing serial coding by taking the water level value of each power station at different moments as independent variables, wherein any one individual in the population represents the water level value of the cascade reservoir in the whole scheduling period. Let the iteration number k be 1 and randomly generate an initial population in the search space, then the ith individual position of the kth generation is represented as:
Figure GDA0003580805410000072
Wherein N represents the number of power stations; t represents the number of periods; and i is more than or equal to 1 and less than or equal to m, wherein m represents the population scale. In the initial population, the water level value of the nth power station of the kth generation in the tth time period
Figure GDA0003580805410000073
Is generated in a manner of
Figure GDA0003580805410000074
Is [0,1 ]]Random numbers are evenly distributed in intervals.
Figure GDA0003580805410000075
The lower limit of the water level of the nth hydropower station in the t period;
Figure GDA0003580805410000076
the upper water level limit of the nth hydropower station in the t-th period.
(2) Calculating the fitness of all individuals in the population by using a penalty function method, and then generating the ith individual
Figure GDA0003580805410000077
Is adapted to
Figure GDA0003580805410000078
The calculation formula is as follows:
Figure GDA0003580805410000079
in the formula,. DELTA.tThe number of hours of the t-th period. a isbIs the penalty coefficient of the b-th inequality constraint; thetabIs the violation value of the b-th inequality constraint, and χ is the total number of inequality constraints; beta is alIs the penalty coefficient for the ith equality constraint; phi is alIs the violation of the ith equality constraint and η is the total number of equality constraints.
(3) Updating the historical optimal positions of all the individuals and the global optimal position of the population,
Figure GDA00035808054100000710
Figure GDA00035808054100000711
in the formula:
Figure GDA00035808054100000712
representing the historical optimal position of the ith individual in the k-1 generation;
Figure GDA00035808054100000713
to represent
Figure GDA00035808054100000714
The fitness of (2);
Figure GDA00035808054100000715
denotes the ith individual fitness, gBest, of the k generationkRepresenting the global optimal position of the kth generation of population;
(4) updating an elite individual set in the population, wherein the elite individual set is the first G individuals with better fitness in the population, and then increasing the diversity of the population by using an extremum search strategy of a group learning algorithm, thereby effectively avoiding premature convergence of the individuals:
Figure GDA0003580805410000081
In the formula:
Figure GDA0003580805410000082
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure GDA0003580805410000083
is the kth generation r in the population1Position of individual j dimension, r1Randomly selected individual subscripts in the population;
Figure GDA0003580805410000084
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure GDA0003580805410000085
is the kth generation r in the elite set2Position of individual in j dimension, r2Is a randomly selected individual subscript in the elite set.
(5) And (3) improving the population convergence precision by using a refined search strategy of a group learning algorithm:
Figure GDA0003580805410000086
Figure GDA0003580805410000087
Figure GDA0003580805410000088
in the formula: delta is an intermediate variable; gauss (0,1) is a normally distributed random number;
Figure GDA0003580805410000089
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure GDA00035808054100000810
to be planted toThe position of the jth dimension of the ith generation of the ith refinement search individuals in the group;
Figure GDA00035808054100000811
the maximum iteration number of the population is obtained;
Figure GDA00035808054100000812
is the kth generation r in the elite set3Position of individual j dimension, r3Randomly selected individual subscripts in the elite set.
(6) Let k be k + 1. If it is
Figure GDA00035808054100000813
Returning to the step (2); otherwise, stopping calculation, and enabling the global optimal individual gBest of the current populationkAs the output of the optimal scheduling process.
The invention is further described below with reference to the figures and examples.
The invention takes five power stations of Hongjiadu, Dongfeng, gufengying, Wujiang du and broussonetia on the Wujiang river dry stream as implementation objects, and the corresponding parameters are set as m is 30,
Figure GDA00035808054100000814
Each constraint damage penalty coefficient is set to 10000.
In order to verify the high efficiency of the invention, a Genetic Algorithm (GA), a Differential Evolution (DE), and a Cuckoo Search Algorithm (CS) were used as comparison methods, and all the methods were independently run for 10 times. The minimum ecological flow demand and S four incoming water frequencies (75%, 80%, 85% and 90%) are selected as implementation working conditions, and the statistical results of the four methods under the minimum ecological flow demand under the four incoming water frequencies are listed in Table 1; the statistical results in table 1 include the optimum, median, mean, worst, and standard deviation. As can be seen from table 1, the algorithm of the method of the present invention is superior to other methods in all cases with respect to all statistical indicators. For example, when the frequency of incoming water from each reservoir is set to 90%, the method of the present invention can increase the optimum values by about 89%, 58% and 84% with respect to GA, DE and CS, respectively. The method provided by the invention can effectively reduce the ecological water shortage of the cascade ecosystem. Therefore, the method is a novel solving method for the cascade reservoir ecological dispatching and can provide scientific basis for the dispatching operation of the cascade reservoir.
TABLE 1 (units: billions of cubic meters)
Figure GDA0003580805410000091
Fig. 2(a) -2 (d) show the box type diagrams at different incoming water frequencies with minimum ecological requirements. As can be seen from fig. 2(a) -2 (d), as the water supply decreases, the water shortage of the cascade hydroelectric system increases, and the objective function value obtained by the method of the present invention is more concentrated and smaller than that obtained by other methods, which illustrates that the method of the present invention is an effective solving tool for solving the cascade reservoir ecological scheduling.
It will be understood by those skilled in the art that the foregoing is only an exemplary embodiment of the present invention, and is not intended to limit the invention to the particular forms disclosed, since various modifications, substitutions and improvements within the spirit and scope of the invention are possible and within the scope of the appended claims.

Claims (5)

1. A cascade reservoir group full ecological element fine scheduling control method is characterized by comprising the following steps:
(1) setting the maximum iteration times as follows by taking the water level values of all hydropower stations in the reservoir group at different moments as individuals
Figure FDA00035808054000000110
When the iteration number k is 1, randomly initializing a population under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals, and taking the initial population as a current population; the minimum total water shortage of the whole cascade reservoir of the drainage basin is taken as an objective function, and the objective function is as follows:
Figure FDA0003580805400000011
Figure FDA0003580805400000012
In the formula: n is the number of step reservoirs; t is the total time interval number of scheduling; f is the total water shortage of the whole cascade reservoir;
Figure FDA0003580805400000013
the maximum ecological flow requirement of the nth power station in the t time period is met;
Figure FDA0003580805400000014
the minimum ecological flow requirement is set for the nth power station in the tth time period; o isn,tThe total ex-warehouse flow is the t time period of the nth power station; y isn,tFor the water shortage, Delta, at the t-th time interval of the nth planttThe number of hours of the t period;
(2) calculating the fitness of each individual in the current population, taking the position of each individual in the current population as a historical optimal position, and updating the historical optimal positions of all the individuals and the global optimal position in the current population;
(3) based on the temporary population obtained after updating the positions of all individuals in the population in the step (2), selecting the first G individuals with better fitness to establish an elite individual set; for all temporary population individuals, the method comprises
Figure FDA0003580805400000015
Obtaining a diversity population; wherein the content of the first and second substances,
Figure FDA0003580805400000016
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure FDA0003580805400000017
is the kth generation r in the population1Historical optimal position of j dimension of individual, r1Randomly selected individual subscripts in the population;
Figure FDA0003580805400000018
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure FDA0003580805400000019
is the kth generation r in the set of elite individuals2Position of individual j dimension, r2Randomly selecting individual subscripts from the set of elite individuals;
Then by
Figure FDA0003580805400000021
Updating the individual positions of the diversity population to form a next generation population; wherein the content of the first and second substances,
Figure FDA0003580805400000022
delta is an intermediate variable, Gauss (0,1) is a random number with normal distribution,
Figure FDA0003580805400000023
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA0003580805400000024
searching the j dimension position of an individual for the ith refinement of the kth generation in the population;
Figure FDA0003580805400000025
the maximum iteration number of the population is obtained;
Figure FDA0003580805400000026
is the kth generation r in the set of elite individuals3Position of individual j dimension, r3Randomly selecting an individual subscript from the set of elite individuals;
(4) let k equal k +1, if
Figure FDA0003580805400000027
The next generation population is taken as the current population, and the step (2) and the step (3) are repeatedly executed) (ii) a Otherwise, stopping calculation, and outputting the global optimal individual of the current population as the optimal scheduling process.
2. The method of claim 1, wherein the ith generation of individual positions is represented as:
Figure FDA0003580805400000028
wherein N represents the number of power stations, T represents the number of time periods, i is more than or equal to 1 and less than or equal to m, and m represents the population scale;
Figure FDA0003580805400000029
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA00035808054000000210
for the upper water level limit of the nth hydropower station during the t-th period,
Figure FDA00035808054000000211
the lower limit of the water level of the nth hydropower station in the t-th period.
3. The method according to claim 2, wherein the fitness of each individual in the current population is calculated in step (2) by a penalty function method, and the individual is selected from the group consisting of
Figure FDA00035808054000000212
Is adapted to
Figure FDA00035808054000000213
Comprises the following steps:
Figure FDA00035808054000000214
wherein, yn,tFor the water shortage, Delta, at the t-th time interval of the nth planttHours for the t-th period; a isbIs the penalty coefficient of the b-th inequality constraint; thetabIs the b th oneViolation of equality constraints, χ being the total number of inequality constraints; beta is alIs the penalty coefficient for the ith equality constraint; phi is alIs the violation of the ith equality constraint and η is the total number of equality constraints.
4. The method of claim 3, wherein updating the historical optimal locations and the global optimal locations in the current population for all individuals in step (2) comprises:
by
Figure FDA0003580805400000031
Updating the historical optimal locations of all individuals by
Figure FDA0003580805400000032
Updating the global optimal position in the current population;
wherein:
Figure FDA0003580805400000033
representing the historical optimal position of the ith individual in the k-1 generation,
Figure FDA0003580805400000034
to represent
Figure FDA0003580805400000035
The degree of fitness of (a) to (b),
Figure FDA0003580805400000036
denotes the fitness of the ith individual of the kth generation, gBestkRepresenting the global optimal position of the population of the kth generation.
5. The utility model provides a meticulous dispatch control system of full ecological factor of step reservoir crowd which characterized in that includes:
the initialization module is used for setting the maximum iteration times as the individual water level values of all hydropower stations in the reservoir group at different moments
Figure FDA0003580805400000037
When the iteration number k is 1, randomly initializing a population under the constraint of a reservoir water level value to obtain an initial population containing a plurality of individuals, and taking the initial population as a current population; the minimum total water shortage of the whole cascade reservoir of the drainage basin is taken as an objective function, and the objective function is as follows:
Figure FDA0003580805400000038
Figure FDA0003580805400000039
In the formula: n is the number of step reservoirs; t is the total scheduling time interval number; f is the total water shortage of the whole cascade reservoir;
Figure FDA00035808054000000310
the maximum ecological flow demand of the nth power station in the tth time period;
Figure FDA00035808054000000311
the minimum ecological flow requirement is set for the nth power station in the tth time period; o isn,tThe total ex-warehouse flow is the t time period of the nth power station; y isn,tFor the water shortage, Delta, at the t-th time interval of the nth planttThe number of hours of the t period;
the fitness calculation module is used for calculating the fitness of each individual in the current population, taking the position of each individual in the current population as a historical optimal position, and updating the historical optimal positions of all the individuals and the global optimal position in the current population;
the position updating module is used for updating the positions of all individuals of the population based on the fitness calculating module to obtain a temporary population, and selecting the first G individuals with better fitness to establish an elite individual set; for all temporary population individuals, the method comprises
Figure FDA00035808054000000312
Obtaining a diversity population; wherein the content of the first and second substances,
Figure FDA0003580805400000041
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure FDA00035808054000000410
is the kth generation r in the population1Historical optimal position of j dimension of individual, r1Randomly selected individual subscripts in the population;
Figure FDA0003580805400000042
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure FDA0003580805400000043
for the kth generation of the set of elite individuals 2Position of individual j dimension, r2Randomly selecting individual subscripts from the set of elite individuals;
then by
Figure FDA0003580805400000044
Updating the individual positions of the diversity population to form a next generation population; wherein the content of the first and second substances,
Figure FDA0003580805400000045
delta is an intermediate variable, Gauss (0,1) is a normally distributed random number,
Figure FDA0003580805400000046
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA0003580805400000047
searching the j dimension position of an individual for the ith refinement of the kth generation in the population;
Figure FDA0003580805400000048
for maximum number of iterations of the population；
Figure FDA0003580805400000049
Is the kth generation r in the set of elite individuals3Position of individual j dimension, r3Randomly selecting individual subscripts from the set of elite individuals;
and the output module is used for repeatedly executing the operation from the fitness calculation module to the position updating module by taking the next generation population as the current population until a preset iteration stop condition is met, and outputting the globally optimal individual of the current population as the optimal scheduling process.
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