Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

Kumar, S. Phani; Naresh, Ram

doi:10.1016/j.ijepes.2007.06.001

Cited by 107 publications

(43 citation statements)

References 16 publications

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“…Avg. time (s) TPNN [9] 154808.5 -ALM [9] 154739 -PSO [18] 154705 - Table 2 reports the comparison of result for system 1 obtained by the proposed MCSA and other methods including Genetic algorithm in [2], Binary coded GA (BCGA) and real coded GA (RCGA) in [3], classical evolutionary programming (CEP), Fast EP (FEP) and improved Fast EP (IFEP) in [5], global constriction PSO (GCPSO), global weight factor PSO (GWPSO), Local constriction PSO (LCPSO) and local weight factor PSO (LWPSO) in [6], enhanced PSO (EPSO) in [7], and differential evolutionary (DE) in [9]. As observed from the table, the proposed method obtains better minimum cost, average cost and maximum cost than all methods available in the table.…”

Section: Case 1: Two Systems With Quadratic Fuel Cost Function Of Thementioning

confidence: 99%

“…In recent decades, several artificial intelligence algorithms, such as genetic algorithm (GA) [2][3], two-phase neural network [4], evolutionary programming technique (EP) [5], particle swarm optimization (PSO) [6][7], differential evolution [8][9], and clonal selection algorithm (CSA) [10] have been widely and successfully applied for solving the ST-CHTS problems where quadratic and/or nonconvex fuel cost function of thermal units are considered. Among the methods, GA is the worst one since it obtains very high fuel cost, high constraint violation and long execution time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

Nguyen¹,

Vo²

2016

IJGDC

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Section: Case 1: Two Systems With Quadratic Fuel Cost Function Of Thementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solving Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

Nguyen¹,

Vo²

2016

IJGDC

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“…Implementation of elite-preserving operator can be done by directly copying the best 10% chromosomes from the current population to the next generation [67]. …”

Section: Elite-preserving Operatormentioning

confidence: 99%

Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem

Farag¹,

El-Shorbagy²,

Eldesoky³

et al. 2015

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This paper presents a new algorithm for solving unit commitment (UC) problems using a binaryreal coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit; while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results. KeywordsUnit Commitment (UC), Genetic Algorithm (GA), k-Means Clustering Technique M. A. Farag et al.

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“…The mutation rate changes self-adaptively with the increase of the generation number and which is decided by the individual fitness and the overall performance of population (Kumar and Naresh, 2007)…”

Section: Mutationmentioning

confidence: 99%

Multi-objective ecological reservoir operation based on water quality response models and improved genetic algorithm: A case study in Three Gorges Reservoir, China

Huang

Sun

et al. 2014

Engineering Applications of Artificial Intelligence

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a b s t r a c tThis study proposes a self-adaptive GA-aided multi-objective ecological reservoir operation model (SMEROM) and applies it to water quality management in the Xiangxi River near to the Three Gorges Reservoir, China. The SMEROM integrates statistical water quality models, multi-objective reservoir operations, and a self-adaptive GA within a general framework. Among them, the statistical water quality models of the Xiangxi River are formulated to deal with the relationships between reservoir operation and water quality, which are embedded in constraints of the SMEROM. The multiple objective functions, including maximizing hydropower generation, minimizing loss of flood control, minimizing rate of flood risk, maximizing the average remaining capacity of flood control and maximizing the benefit of shipping, are considered simultaneously to obtain comprehensive benefit among the environment, society and economy. The weighting method is employed to convert the multiple objectives to a single objective. To solve the complex SMEROM, an improved self-adaptive GA is employed through incorporating simulated binary crossover and self-adaptive mutation. To demonstrate the advantage of the developed SMEROM model, the solutions through ecological reservoir operation are compared with those through the traditional reservoir operation and the practical operation in 2011, in terms of water quality, reservoir operation and objective function values. The results show that most of benefit in the ecological operation is better than that in the traditional or practical operations except for the hydropower benefit and loss benefit of flood control. This is because flood control and environmental protection are reasonably considered in the ecological operation.

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Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

Cited by 107 publications

References 16 publications

Solving Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

Solving Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem

Multi-objective ecological reservoir operation based on water quality response models and improved genetic algorithm: A case study in Three Gorges Reservoir, China

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Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem

Cited by 107 publications

References 16 publications

Solving Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

Solving Short-Term Cascaded Hydrothermal Scheduling Problem Using Modified Cuckoo Search Algorithm

Binary-Real Coded Genetic Algorithm Based &lt;i&gt;k&lt;/i&gt;-Means Clustering for Unit Commitment Problem

Multi-objective ecological reservoir operation based on water quality response models and improved genetic algorithm: A case study in Three Gorges Reservoir, China

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Binary-Real Coded Genetic Algorithm Based <i>k</i>-Means Clustering for Unit Commitment Problem