Stochastic Multi Objective Short Term Hydrothermal Scheduling Using Particle Swarm Optimization

Umayal, S. P.; Kamaraj, N.

doi:10.1109/indcon.2005.1590220

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…In [30], an improved PSO algorithm with the constriction factor has been put forward to deal with the bid based dynamic ED problem. The PSO algorithm has been hybridized with the sequential quadratic programming (SQP) method to deal with the ED problem with the valve-point effects [128]. In [129], a CEED problem (which considers the fuel cost, the emission and the variance of generation mismatch) has been figured out by using the PSO algorithm.…”

Section: Economic Dispatchmentioning

confidence: 99%

A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

Fang

Liu

Chen

et al. 2023

IJNDI

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Survey/review study A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization Jingzhong Fang 1, Weibo Liu 1,*, Linwei Chen 2, Stanislao Lauria 1, Alina Miron 1, and Xiaohui Liu 1 1 Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom 2 The School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom * Correspondence: Weibo.Liu2@brunel.ac.uk Received: 18 October 2022 Accepted: 28 November 2022 Published: Abstract: Particle swarm optimization (PSO) is a popular heuristic method, which is capable of effectively dealing with various optimization problems. A detailed overview of the original PSO and some PSO variant algorithms is presented in this paper. An up-to-date review is provided on the development of PSO variants, which include four types i.e., the adjustment of control parameters, the newly-designed updating strategies, the topological structures, and the hybridization with other optimization algorithms. A general overview of some selected applications (e.g., robotics, energy systems, power systems, and data analytics) of the PSO algorithms is also given. In this paper, some possible future research topics of the PSO algorithms are also introduced.

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Section: Economic Dispatchmentioning

confidence: 99%

A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

Fang

Liu

Chen

et al. 2023

IJNDI

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“…The paper formulated the multi-objective problem considering various competitive objectives fuel cost, NOx, SOx, CO2, variation of generation mismatch. All objectives are weighted as per the importance and added together to form the final objective function [58]. Ravinder Maan, et al their paper described, a new particle swarm optimization framework used to deal with the equality and inequality constraints in ELD problem.…”

Section: Ismail Ziane Et Al Provide Multi-objective Simulatedmentioning

confidence: 99%

Optimization techniques for economic load dispatch problem in power plant

2021

SUDORJ

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This paper presents survey of optimization techniques used to solve the problem of economic load dispatch in power stations. Since there is no single method available for solving all economic dispatch problems efficiently, thus a number of different optimization methods have been developed to solve this problem. These techniques were divided into three types depending on the efficiency of the solution: stochastic process techniques, statistical methods, and mathematical programming techniques which divided to local optimization, and global optimization. It is found that is better to use hybrid techniques to overcome load dispatch problems so as to achieve significant improvements in computation time, convergence properties, solution quality, or parameter robustness.

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“…The main objective of optimal SMHTS problem is to allocate total expected power generation among hydro and thermal plants so as to minimise the expected production cost, NO x emission, SO 2 emission and CO 2 emission of thermal plants while satisfying various constraints such as expected system load demand, water availability constraints in hydel plants, the expected hydro and thermal power generation output limits over a scheduled time horizon. Several authors have applied various approaches such as non‐linear programming method [11], progressive optimality algorithm [12], fuzzy decision making (FDM) approach [13, 14], real coded genetic algorithm [15], particle swarm optimisation [16], etc. to solve SMHTS problem.…”

Section: Introductionmentioning

confidence: 99%