CN111915164A - Fine scheduling control method and system for full ecological elements of cascade reservoir group - Google Patents

Fine scheduling control method and system for full ecological elements of cascade reservoir group Download PDF

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CN111915164A
CN111915164A CN202010682898.0A CN202010682898A CN111915164A CN 111915164 A CN111915164 A CN 111915164A CN 202010682898 A CN202010682898 A CN 202010682898A CN 111915164 A CN111915164 A CN 111915164A
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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 randomly initialized under the constraint of the reservoir water level value to obtain an initial population containing a plurality of individuals; calculating individual fitness values 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

Fine scheduling control method and system for full ecological elements of cascade reservoir group
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.
In 1973 + 2019, eight national environmental protection conferences were held in turn, and a series of important decisions are made by the nation from the strategic policy of overall environmental protection to the solution of the problem of environmental damage. In recent years, around the three major security war of blue sky, private water and clean soil, a series of policies such as 'opinions about promoting and implementing ultra-low emission of steel industry', 'action plan for protecting and repairing Yangtze river and attacking solidness war', 'construction and pilot point working scheme for non-waste cities' and the like are launched, and a new situation of national ecological environment protection is created. 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, there is provided a step reservoir group full ecological element fine scheduling control method, including the steps of:
(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 BDA0002586491610000027
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;
(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; introducing individual historical optimal positions and random individual positions in an elite individual set to all temporary population individuals to increase 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 equal k +1, if
Figure BDA0002586491610000021
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 ith individual position of the kth generation is represented as:
Figure BDA0002586491610000022
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 BDA0002586491610000023
Figure BDA0002586491610000026
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure BDA0002586491610000024
for the upper water level limit of the nth hydropower station during the t-th period,
Figure BDA0002586491610000025
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 BDA0002586491610000031
Is adapted to
Figure BDA0002586491610000032
Comprises the following steps:
Figure BDA0002586491610000033
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 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 BDA0002586491610000034
Updating the historical optimal locations of all individuals by
Figure BDA0002586491610000035
Updating the global optimal position in the current population;
wherein:
Figure BDA0002586491610000036
representing the historical optimal position of the ith individual in the k-1 generation,
Figure BDA0002586491610000037
to represent
Figure BDA0002586491610000038
The degree of fitness of (a) to (b),
Figure BDA0002586491610000039
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 BDA00025864916100000310
Obtaining a diversity population;
wherein,
Figure BDA00025864916100000311
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure BDA00025864916100000312
is the kth generation r in the population1Historical optimal position of j dimension of individual, r1Randomly selected individual subscripts in the population;
Figure BDA00025864916100000313
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure BDA00025864916100000314
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 BDA00025864916100000315
Updating the individual positions of the diversity population to form a next generation population;
wherein,
Figure BDA0002586491610000041
Figure BDA0002586491610000042
as an intermediate variable, Gauss (0,1) is a normally distributed random number,
Figure BDA0002586491610000043
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure BDA0002586491610000044
searching the j dimension position of an individual for the ith refinement of the kth generation in the population;
Figure BDA0002586491610000045
the maximum iteration number of the population is obtained;
Figure BDA0002586491610000046
is the kth generation r in the elite set3Position of individual j dimension, r3Randomly selected individual subscripts in the elite set.
As another aspect of the present invention, there is provided a cascade reservoir group full ecological element fine scheduling control system, including:
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 BDA0002586491610000047
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 individual historical optimal positions and random individual positions in an elite individual set to all temporary population individuals to increase 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;
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.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
the method comprises the steps of initializing populations randomly under the constraint of reservoir water level values to obtain an initial population containing a plurality of individuals; calculating individual fitness values 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 step-step reservoir group full-ecological element fine scheduling control method according to an embodiment of the present invention;
FIG. 2(a) is a schematic box-shaped representation of the method of the present invention at a minimum ecological demand of 75% incoming water frequency provided by the embodiments of the present invention;
FIG. 2(b) is a schematic box-shaped illustration 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 BDA0002586491610000061
Figure BDA0002586491610000062
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 BDA0002586491610000063
the maximum ecological flow demand of the nth power station in the tth time period;
Figure BDA0002586491610000064
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 BDA0002586491610000065
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; qn,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; u shapenThe number of upstream power plants connected directly to the nth hydroelectric power plant.
(2) Reservoir water level restraint from beginning to end:
Figure BDA0002586491610000066
wherein,
Figure BDA0002586491610000067
at an initial water level for the nth hydropower station;
Figure BDA0002586491610000068
the end-of-term water level for the nth hydroelectric power station.
(3) And (3) power generation flow restriction:
Figure BDA0002586491610000069
wherein,
Figure BDA00025864916100000610
the lower limit of the generating flow of the nth hydropower station in the t period;
Figure BDA00025864916100000611
generating flow upper limit for the nth hydropower station in the t period;
(4) water head balance constraint:
Figure BDA0002586491610000071
wherein Hn,tHead for nth hydropower station at t 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 BDA0002586491610000072
wherein,
Figure BDA0002586491610000073
the output upper limit of the nth hydropower station in the t period is set;
Figure BDA0002586491610000074
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 BDA0002586491610000075
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 BDA0002586491610000076
Is generated in a manner of
Figure BDA0002586491610000077
Is [0,1 ]]Random numbers are evenly distributed in intervals.
Figure BDA0002586491610000078
The lower limit of the water level of the nth hydropower station in the t period;
Figure BDA0002586491610000079
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 BDA00025864916100000710
Is adapted to
Figure BDA00025864916100000711
The calculation formula is as follows:
Figure BDA00025864916100000712
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 BDA00025864916100000713
Figure BDA0002586491610000081
in the formula:
Figure BDA0002586491610000082
representing the historical optimal position of the ith individual in the k-1 generation;
Figure BDA0002586491610000083
to represent
Figure BDA0002586491610000084
The fitness of (2);
Figure BDA0002586491610000085
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 BDA0002586491610000086
in the formula:
Figure BDA0002586491610000087
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure BDA0002586491610000088
is the kth generation r in the population1Position of individual j dimension, r1Randomly selected individual subscripts in the population;
Figure BDA0002586491610000089
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure BDA00025864916100000810
is the kth generation r in the elite set2Position of individual j dimension, r2Randomly selected individual subscripts in the elite set.
(5) And (3) improving the population convergence precision by using a refined search strategy of a group learning algorithm:
Figure BDA00025864916100000811
Figure BDA00025864916100000812
Figure BDA00025864916100000813
in the formula: is an intermediate variable; gauss (0,1) is a normally distributed random number;
Figure BDA00025864916100000814
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure BDA00025864916100000815
searching the j dimension position of an individual for the ith refinement of the kth generation in the population;
Figure BDA00025864916100000816
the maximum iteration number of the population is obtained;
Figure BDA00025864916100000817
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 BDA00025864916100000818
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 BDA0002586491610000091
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 BDA0002586491610000092
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 a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (7)

1. A step 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 FDA0002586491600000017
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;
(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; introducing individual historical optimal positions and random individual positions in an elite individual set to all temporary population individuals to increase 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 equal k +1, if
Figure FDA0002586491600000011
Taking the next generation population as the current population, and repeatedly executing the step (2) and the step (3); otherwise, stop the calculation, anAnd outputting the global optimal individual of the current population as an optimal scheduling process.
2. The method of claim 1, wherein the ith generation of individual positions is represented as:
Figure FDA0002586491600000012
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 FDA0002586491600000013
Figure FDA0002586491600000014
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA0002586491600000015
for the upper water level limit of the nth hydropower station during the t-th period,
Figure FDA0002586491600000016
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 FDA0002586491600000021
Is adapted to
Figure FDA0002586491600000022
Comprises the following steps:
Figure FDA0002586491600000023
wherein, yn,tFor the water shortage, Delta, at the t-th time interval of the nth planttIs the t thHours of the session; 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.
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 FDA0002586491600000024
Updating the historical optimal locations of all individuals by
Figure FDA0002586491600000025
Updating the global optimal position in the current population;
wherein:
Figure FDA0002586491600000026
representing the historical optimal position of the ith individual in the k-1 generation,
Figure FDA0002586491600000027
to represent
Figure FDA0002586491600000028
The degree of fitness of (a) to (b),
Figure FDA0002586491600000029
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 method according to claim 4, wherein, in step (3),
by
Figure FDA00025864916000000210
Obtaining a diversity population;
wherein,
Figure FDA00025864916000000211
the position of the j dimension of the ith variant individual in the kth generation of the population;
Figure FDA00025864916000000212
is the kth generation r in the population1Historical optimal position of j dimension of individual, r1Randomly selected individual subscripts in the population;
Figure FDA00025864916000000213
is [0,1 ]]Random numbers uniformly distributed in intervals;
Figure FDA00025864916000000214
is the kth generation r in the elite set2Position of individual j dimension, r2Randomly selected individual subscripts in the elite set.
6. The method according to claim 5, wherein, in step (3),
by
Figure FDA00025864916000000215
Updating the individual positions of the diversity population to form a next generation population;
wherein,
Figure FDA0002586491600000031
as an intermediate variable, Gauss (0,1) is a normally distributed random number,
Figure FDA0002586491600000032
is [0,1 ]]Random numbers are evenly distributed in the interval, and the random numbers are distributed in the interval,
Figure FDA0002586491600000033
for the ith refinement of the kth generation in the populationSearching the position of the j dimension of the individual;
Figure FDA0002586491600000034
the maximum iteration number of the population is obtained;
Figure FDA0002586491600000035
is the kth generation r in the elite set3Position of individual j dimension, r3Randomly selected individual subscripts in the elite set.
7. 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 FDA0002586491600000036
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 individual historical optimal positions and random individual positions in an elite individual set to all temporary population individuals to increase 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;
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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Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105956714A (en) * 2016-05-21 2016-09-21 华能澜沧江水电股份有限公司 Novel group searching method for optimal scheduling of cascade reservoir groups
CN106203689A (en) * 2016-07-04 2016-12-07 大连理工大学 A kind of Hydropower Stations cooperation Multiobjective Optimal Operation method
CN108710970A (en) * 2018-05-07 2018-10-26 华中科技大学 A kind of parallel dimension reduction method of Multiobjective Scheduling of huge Hydro Power Systems with Cascaded Reservoirs
CN109523059A (en) * 2018-10-19 2019-03-26 华中科技大学 A kind of step hydroelectric station reservoir ecological dispatching intelligent optimization method and system
CN110222938A (en) * 2019-05-10 2019-09-10 华中科技大学 A kind of Hydropower Stations head relation cooperative optimization method and system
CN110363343A (en) * 2019-07-11 2019-10-22 水利部交通运输部国家能源局南京水利科学研究院 A kind of the GROUP OF HYDROPOWER STATIONS intelligent optimization dispatching method and system of mixed self-adapting
CN110598919A (en) * 2019-08-28 2019-12-20 华中科技大学 Method and system for dynamically regulating and controlling cascade hydropower stations
CN110751365A (en) * 2019-09-11 2020-02-04 华中科技大学 Multi-target balanced scheduling method and system for cascade reservoir group
CN110766210A (en) * 2019-10-12 2020-02-07 华中科技大学 Short-term optimized scheduling method and system for cascade reservoir group

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105956714A (en) * 2016-05-21 2016-09-21 华能澜沧江水电股份有限公司 Novel group searching method for optimal scheduling of cascade reservoir groups
CN106203689A (en) * 2016-07-04 2016-12-07 大连理工大学 A kind of Hydropower Stations cooperation Multiobjective Optimal Operation method
CN108710970A (en) * 2018-05-07 2018-10-26 华中科技大学 A kind of parallel dimension reduction method of Multiobjective Scheduling of huge Hydro Power Systems with Cascaded Reservoirs
CN109523059A (en) * 2018-10-19 2019-03-26 华中科技大学 A kind of step hydroelectric station reservoir ecological dispatching intelligent optimization method and system
CN110222938A (en) * 2019-05-10 2019-09-10 华中科技大学 A kind of Hydropower Stations head relation cooperative optimization method and system
CN110363343A (en) * 2019-07-11 2019-10-22 水利部交通运输部国家能源局南京水利科学研究院 A kind of the GROUP OF HYDROPOWER STATIONS intelligent optimization dispatching method and system of mixed self-adapting
CN110598919A (en) * 2019-08-28 2019-12-20 华中科技大学 Method and system for dynamically regulating and controlling cascade hydropower stations
CN110751365A (en) * 2019-09-11 2020-02-04 华中科技大学 Multi-target balanced scheduling method and system for cascade reservoir group
CN110766210A (en) * 2019-10-12 2020-02-07 华中科技大学 Short-term optimized scheduling method and system for cascade reservoir group

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHONG-KAI FENG: ""Ecological operation of cascade hydropower reservoirs by elite-guide gravitational search algorithm with Lévy flight local search and mutation"", 《JOURNAL OF HYDROLOGY》 *

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