Metaheuristic Moth-Flame Optimization Applied on Renewable Wind Energy Incorporating Load Transmit Penetration

Sunanda Hazra and Provas Kumar Roy
Additional contact information
Sunanda Hazra: Central Institute of Plastics Engineering and Technology, Haldia, India
Provas Kumar Roy: Kalyani Government Engineering College, Kalyani, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2021, vol. 12, issue 1, 185-210

Abstract: Ubiquitous and ecologically friendly renewable wind energy are promising options to execute the energy requirement as well as to reducing emission. Conventional thermal power economic transmit (ET) problem including wind generator model deals with minimizing the generation cost and pollutant emission by fulfilling variety of constraints. The stochastic scenery of wind speed and the discrepancy charges of overestimation and underestimation wind cost, which is essentially a random variable, are taken into account by introducing Weibull probability density function (W-pdf). In order to generate optimal generation scheduling under renewable energy environment, moth flame optimization (MFO) algorithm is proposed, and it is tested on three different benchmark load systems. It is observed that the newly developed enhanced MFO method is proficient, and it can provide lower generation cost and smaller pollutant emission for real-world problems.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2021010110 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:12:y:2021:i:1:p:185-210

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().