IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm
View Sample PDF
Author(s): Sunanda Hazra (Department of Electrical Engineering, Central Institute of Plastics Engineering and Technology, Haldia, West Bengal, India), Tapas Pal (Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India)and Provas Kumar Roy (Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India)
Copyright: 2019
Volume: 10
Issue: 1
Pages: 20
Source title: International Journal of Swarm Intelligence Research (IJSIR)
Editor(s)-in-Chief: Yuhui Shi (Southern University of Science and Technology (SUSTech), China)
DOI: 10.4018/IJSIR.2019010103

Purchase

View Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm on the publisher's website for pricing and purchasing information.

Abstract

This article presents an integrated approach towards the economical operation of a hybrid system which consists of conventional thermal generators and renewable energy sources like windmills using a grasshopper optimization algorithm (GOA). This is based on the social interaction nature of the grasshopper, considering a carbon tax on the emissions from the thermal unit and uncertainty in wind power availability. The Weibull distribution is used for nonlinearity of wind power availability. A standard system, containing six thermal units and two wind farms, is used for testing the dispatch model of three different loads. The GOA results are compared with those obtained using a recently developed quantum-inspired particle swarm optimization (QPSO) optimization technique available in the literature. The simulation results demonstrate the efficacy and ability of GOA over the QPSO algorithm in terms of convergence rate and minimum fitness value. Performance analysis under wind power integration and emission minimization further confirms the supremacy of the GOA algorithm.

Related Content

Fan Liu. © 2024. 21 pages.
Kai Zhang, Zi Tang. © 2024. 21 pages.
. © 2024.
Jing Liu, Shoubao Su, Haifeng Guo, Yuhua Lu, Yuexia Chen. © 2024. 11 pages.
Fazli Wahid, Rozaida Ghazali, Lokman Hakim Ismail, Ali M. Algarwi Aseere. © 2023. 13 pages.
Yifu Chen, Jun Li, Lin Zhang. © 2023. 31 pages.
Jatin Soni, Kuntal Bhattacharjee. © 2023. 15 pages.
Body Bottom