Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid

Loau Tawfak Al-Bahrani and Jagdish Chandra Patra

Energy, 2018, vol. 147, issue C, 1070-1091

Abstract: Optimization of fuel cost function of large-scale thermal generating units under several constraints in smart power grid is a challenging problem. Because of these constraints, the fuel cost function becomes multimodal, discontinuous and non-convex. Although the global particle swarm optimization with inertia weight (GPSO-w) algorithm is a popular optimization technique, it is not capable of solving such complex problems satisfactory. In this paper, a novel multi-gradient PSO (MG-PSO) algorithm is proposed to solve such a challenging problem. In MG-PSO algorithm, two phases, called Exploration phase and Exploitation phase, are used. In the Exploration phase, the m particles are called Explorers and undergo multiple episodes. In each episode, the Explorers use a different negative gradient to explore new neighbourhood whereas in the Exploitation phase, the m particles are called Exploiters and they use one negative gradient that is less than that of the Exploration phase, to exploit a best neighborhood. This diversity in negative gradients provides a balance between global search and local search. The effectiveness of the MG-PSO algorithm is demonstrated using four (medium and large) power generation systems. Superior performance of the MG-PSO algorithm over several PSO variants in terms of several performance measures has been shown.

Keywords: Multi-gradient particle swarm optimization; Exploration and exploitation phases; Economic dispatch; Power constraints; Prohibited operating zones; Valve-point loading effects (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:147:y:2018:i:c:p:1070-1091

DOI: 10.1016/j.energy.2017.12.052

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