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

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.