Chaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

Arul Rajagopalan, Padmanathan Kasinathan, Karthik Nagarajan, Vigna Kumaran Ramachandaramurthy, Velusami Sengoden, Srinivasan Alavandar

Research output: Contribution to journalArticle

Abstract

The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self-adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11-generating units (without security), IEEE-30 bus system, and IEEE-118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.

Original languageEnglish
Article numbere12026
JournalInternational Transactions on Electrical Energy Systems
DOIs
Publication statusPublished - 01 Jan 2019

Fingerprint

Search Algorithm
Interior
Economics
Generator
Harmony Search
Unit
Speed of Convergence
Costs
Performance Metrics
Violate
Differential Evolution
Particle Swarm Optimization Algorithm
Genetic Algorithm
Particle swarm optimization (PSO)
Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

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abstract = "The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self-adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11-generating units (without security), IEEE-30 bus system, and IEEE-118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.",
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Chaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints. / Rajagopalan, Arul; Kasinathan, Padmanathan; Nagarajan, Karthik; Ramachandaramurthy, Vigna Kumaran; Sengoden, Velusami; Alavandar, Srinivasan.

In: International Transactions on Electrical Energy Systems, 01.01.2019.

Research output: Contribution to journalArticle

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