Improved evolutionary algorithms for economic load dispatch optimization problems

Orike, Sunny; Corne, David

doi:10.1109/ukci.2012.6335753

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…A simple MA introduces a local search phase into a GA after crossover and mutation have been performed during the evolutionary process. Since their emergence, MAs and subsequent variants of MAs have been applied to a wide variety of problems, including educational timetabling [2,10,56,60,59], multi-objective optimisation problems [31,24], permanent-magnet synchronous motor (PMSM) design [11], Optimal Placement of PMUs [37], permutation flow shop scheduling [28], the economic load dispatch problem [51,52], protein folding [33], HIV therapy design [44], the quadratic assignment problem [39] and the travelling salesman problem [42,5,26]. As well as the application of MAs to practical optimisation problems, a number of studies have sought to understand the concepts underpinning MAs and the behaviour of memes [34].…”

Section: Memetic Algorithmsmentioning

confidence: 99%

A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

zcan

Drake²,

Altnta

et al. 2016

Applied Soft Computing

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Section: Memetic Algorithmsmentioning

confidence: 99%

A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

zcan

Drake²,

Altnta

et al. 2016

Applied Soft Computing

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“…Furthermore, treatments of operational constraints are very difficult using the classical approach. The primary objective of OPF is to obtain the optimal state of the control variables by minimizing an objective function for a particular power system while keeping all constraints (equality and inequality constraints) within limits [3]. These classical techniques such as LaGrange based methods, linear programming (LP), non-linear programming (NLP) and quadratic programming (QP) methods present some limitations in their implementation.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence technique for solving optimal power flow problem in the Nigeria power system

Obinna Emmanuel Okwuosa,

Aninye Emmanuel Anazia,

Emeka Emmanuel Ezendiokwelu

2023

World J. Adv. Eng. Technol. Sci.

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This paper deals on the implementation of the particle swarm optimization method for solving power flow problems in the Nigeria power system. The difficulty of solving optimal power flow (OPF) problems increases significantly with increasing network size and complexity. Some of the weakness of the conventional methods include: limited ability in solving real-world large scale optimization problems, weakness in handling constraints, poor convergence and slow computational time. The results gotten by implementing the Particle Swarm Optimization (PSO) algorithm in Nigeria 330kV 52-Bus network show that the total active power and reactive power losses were substantially reduced to 0.03MW and 0.05MVAR. The system time of convergence was faster at 0.5seconds. Also, the maximum line active and reactive power losses on line 1 to 2 were reduced greatly to 0.00MW and 0.01MVAR respectively. These show that the system was better optimized with the PSO algorithm than the conventional method.

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“…In Addition, various constraints may play a causative role in increasing the dimension of the DED problem. Therefore, requiring large computational resources, excessive numerical iterations, and enourmous calculation efforts [4]. To make more convenient formulation to the numerical methods for solving the DED problem optimally, Dantzig-Wolfe DW is used to solve DED problems for units with quadratic functions for the fuel cost and considering various equality and inequality constraints.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization Performance: Comparison of Dynamic Economic Dispatch with Dantzig-Wolfe Decomposition

Ghani

Hussein²,

Jano

et al. 2016

TELKOMNIKA

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Economic Dispatch (ED) Keywords: particle swarm optimization (PSO), Dantzig-Wolfe decomposition, problem formulation, dynamic economic dispatch (DED)Copyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionDynamic economic dispatch (DED) problem is one of the essential operational matters in power system. The main objective of employing optimization technique is to minimize the total operation costs of the generation units while fullfilling all required constraints. Several traditional methods are used to solve this problem like Lagrangian and gradient methods [1][2]. Solving DED problem using these classical methods essentially assumes a monotonic increase in the curves representing the incremental cost of generators with a function characterized by a piecewise linearization. However, in practical system, the nonlinear characteristics of these generation units may not comply with such linearization assumption and resulting in an infeasibilty status [3]. Large-scale power system involving large number of generating units with contradicting constraints and possible non-smooth cost function requires longer solution time. In Addition, various constraints may play a causative role in increasing the dimension of the DED problem. Therefore, requiring large computational resources, excessive numerical iterations, and enourmous calculation efforts [4]. To make more convenient formulation to the numerical methods for solving the DED problem optimally, Dantzig-Wolfe DW is used to solve DED problems for units with quadratic functions for the fuel cost and considering various equality and inequality constraints. Although the DW method have been utilized to find solution for complex DED optimization problems, some problems have been identified in DW due to complicated problem formulation [1][2].Dynamic economic dispatch (DED) is a technique to dispatch the generating units to the anticipated demands for electrical power over a specified time interval at minimum operational

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Improved evolutionary algorithms for economic load dispatch optimization problems

Cited by 7 publications

References 17 publications

A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

A self-adaptive Multimeme Memetic Algorithm co-evolving utility scores to control genetic operators and their parameter settings

Swarm intelligence technique for solving optimal power flow problem in the Nigeria power system

Particle Swarm Optimization Performance: Comparison of Dynamic Economic Dispatch with Dantzig-Wolfe Decomposition

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