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Top1. Introduction
According to renewable portfolio benchmark, the reliability of the power generation sector depends primarily on modular and environment friendly wind energy. Moreover, electricity generated by the renewable sources is the most needful to develop and make pollution free country (Cory & Swezey,2007; Hazra et al., 2015). To overcome the increasing load demand, optimal generation scheduling of hybrid wind thermal[WT] should be adjusted in a specific way that the total operational charges and pollution is diminished by allowing a mixture of constraints (Khare et al.,2013). Due to the stochastic scenery of renewable wind resources, wind generation output is difficult to predict (Panigrahi et al., 2012), so the efficiency of these wind sources is less. A few years back, this difficulty has been considered for a progress of economic load dispatch (ELD) (Roy, 2012); while now a day’s research focus on wind energy generator (WEG)units together (Sahin et al., 2013; Hazra &Roy, 2015), with exact cost functions. Accessibility of wind power is supposed to prepare load dispatch problem with constraint (Ren, 2010; Roy & Hazra, 2015). Weibull distribution is described by valid statistics to signify the variability of wind, known as Weibull distribution (Shi et al., 2012; Hazra et al., 2019).
Wind powered source incorporating thermal power systems are solved by traditional and non-traditional optimization is reported in different literatures (Hetzer & Yu, 2008; Jin et al., 2014; Thapar et al.,2011; Olaofe & Folly,2013; Fersi & Chtourou, 2019). ET solutions are described using traditional methods like Lagrange relaxation (LR) (Salam & Hamdan, 1998), linear programming (LP) method (Farag et al., 1995), and neural network(Lee et al., 1998). But the aforesaid algorithms may not execute adequately due to the nonlinearity of the problem. Therefore, by using randomly generated solutions, many populations based meta-heuristic techniques i.e., teaching learning based optimization algorithm (TLBO) (Roy, 2013), krill herd algorithm (Mandal et al., 2014), modified sub-gradient search (MSS) (Fadil et al., 2012), particle swarm optimization (PSO) (Lu et al., 2010; Dubey et al., 2014), are developed as alternatives to traditional methods. From the literature review, it is noticed that few researchers solved the optimal scheduling as well as generation cost and emission minimization problem in a mixture renewable power system using recently developed meta-heuristic optimization.