An Improved Genetic Algorithm Approach to the Unit Commitment/Economic Dispatch Problem

Ponciroli, Roberto; Stauff, Nicolas; Ramsey, J.; Ganda, Francesco; Vilim, Richard B.

doi:10.1109/tpwrs.2020.2986710

Cited by 65 publications

(22 citation statements)

References 11 publications

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“…A typical GA consists of a random population of individuals, which are the possible solutions to that problem. Subsequently, these individuals undergo selection, crossover, and mutation processes in a large number of iterations so that the optimal solution is found 25,26 . In this algorithm, for each individual, a fitness value is assigned, and finally, all individuals are sorted based on their fitness values.…”

Section: Mechanism Of Genetic Algorithmmentioning

confidence: 99%

“…Subsequently, these individuals undergo selection, crossover, and mutation processes in a large number of iterations so that the optimal solution is found. 25,26 In this algorithm, for each individual, a fitness value is assigned, and finally, all individuals are sorted based on their fitness values. In the process of optimization, a chromosome is assigned for each individual in the random population.…”

Section: Introductionmentioning

confidence: 99%

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A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

Mirsaeidi

Devkota

et al. 2022

Int J Numerical Modelling

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Due to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocation of DGs. To validate the accuracy of the obtained results from GA, another nature-inspired optimization algorithm, cuckoo search, is also deployed. The simulation results on IEEE 30 and 118 bus systems indicate that the proposed strategy not only can effectively reduce the total network active and reactive power losses but also lead to the improvement of network voltage profile.

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Section: Mechanism Of Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

Mirsaeidi

Devkota

et al. 2022

Int J Numerical Modelling

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show abstract

“…Graphical representation of the adopted procedure to perform the cross-over of selected chromosomes. [3] The mixed integer problem in this work is approached as a parallelizable problem that makes use of the genetic algorithm solution method. Genetic algorithms are general purpose optimization techniques based on principles inspired from biological evolution as the "survival of the fittest" [4].…”

Section: Genetic Algorithm Solvermentioning

confidence: 99%

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Vilim

Nguyen

Wang

et al. 2020

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“…Besides, they can manage non-convex fuel cost functions with non-linear constraints. Some of the most commonly used algorithms are artificial neural network [29], simulated annealing [30], grey wolf optimizer [31], genetic algorithm, and particle swarm optimization technique [32,33]. Also, hybrid meta-heuristic optimization techniques have been applied to the UC problem considering the advantages and features of each optimization technique [34,35].…”

Section: Introductionmentioning

confidence: 99%

Hybridization of Manta-Ray Foraging Optimization Algorithm with Pseudo Parameter-Based Genetic Algorithm for Dealing Optimization Problems and Unit Commitment Problem

2022

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The manta ray foraging optimization algorithm (MRFO) is one of the promised meta-heuristic optimization algorithms. However, it can stick to a local minimum, consuming iterations without reaching the optimum solution. So, this paper proposes a hybridization between MRFO, and the genetic algorithm (GA) based on a pseudo parameter; where the GA can help MRFO to escape from falling into the local minimum. It is called a pseudo genetic algorithm with manta-ray foraging optimization (PGA-MRFO). The proposed algorithm is not a classical hybridization between MRFO and GA, wherein the classical hybridization consumes time in the search process as each algorithm is applied to all system variables. In addition, the classical hybridization results in an extended search algorithm, especially in systems with many variables. The PGA-MRFO hybridizes the pseudo-parameter-based GA and the MRFO algorithm to produce a more efficient algorithm that combines the advantages of both algorithms without getting stuck in a local minimum or taking a long time in the calculations. The pseudo parameter enables the GA to be applied to a specific number of variables and not to all system variables leading to reduce the computation time and burden. Also, the proposed algorithm used an approximation for the gradient of the objective function, which leads to dispensing derivatives calculations. Besides, PGA-MRFO depends on the pseudo inverse of non-square matrices, which saves calculations time; where the dependence on the pseudo inverse gives the algorithm more flexibility to deal with square and non-square systems. The proposed algorithm will be tested on the test functions that the main MRFO failed to find their optimum solution to prove its capability and efficiency. In addition, it will be applied to solve the unit commitment (UC) problem as one of the vital power system problems to show the validity of the proposed algorithm in practical applications. Finally, several analyses will be applied to the proposed algorithm to illustrate its effectiveness and reliability.

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An Improved Genetic Algorithm Approach to the Unit Commitment/Economic Dispatch Problem

Cited by 65 publications

References 11 publications

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

A power loss minimization strategy based on optimal placement and sizing of distributed energy resources

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Hybridization of Manta-Ray Foraging Optimization Algorithm with Pseudo Parameter-Based Genetic Algorithm for Dealing Optimization Problems and Unit Commitment Problem

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