Evolutionary Oppositional Moth Flame Optimization for Renewable and Sustainable Wind Energy Based Economic Dispatch: Evolutionary OMFO for R and SW Energy-Based Economic Dispatch

Evolutionary Oppositional Moth Flame Optimization for Renewable and Sustainable Wind Energy Based Economic Dispatch: Evolutionary OMFO for R and SW Energy-Based Economic Dispatch

Sunanda Hazra (Department of Electrical Engineering, Central Institute of Plastics Engineering and Technology, Haldia, West Bengal, India) and Provas Kumar Roy (Department of Electrical Engineering, Kalyani Government Engineering College, Kalyani, West Bengal, India)
Copyright: © 2019 |Pages: 20
DOI: 10.4018/IJAEC.2019100104

Abstract

Fossil fuel power has limited its penetration into the power system network for the intermittency and unpredictability coordination. That's why, renewable wind energy incorporating load dispatch becomes a promising system. In this regard, this article proposes an economic load dispatch (ELD) in the existence of renewable wind technology for consuming less fossil fuel energy. For the stochastic scenery of wind speed, the Weibull probability density function (PDF) is used. To boost up the convergence swiftness and advance the simulation results, opposition-based learning (OBL) is integrated with the basic moth flame optimization (MFO) technique, which depends on the social dealings of the moth in nature. The performance of OMFO is evaluated through four cases and each case consists of three different load demands. The simulation results by these methods along with various other existing algorithms in the literature are presented to demonstrate the validity and usefulness of the proposed OMFO.
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1. Introduction

Cabbalistic research has been expanded all around the globe for the progress of renewable, sustainable (Gaing, 2003) and competent energy systems to diminish the extensive use of fossil fuels. Renewable energy-oriented (Surender & Bijwe, 2015) economic load dispatch (ELD) has become a necessary tool for power system operation and control in recent electricity market situation that requires optimal profit with less pollution. The principal objective of renewable wind economic dispatch is to manage the committed generators, wind turbines (Jeddi & Vahidinasab, 2014) output in such a way that the total fuel cost is minimized by optimal arranging the generators constraints (Hazra et al., 2015) and alleviate the specific load demand in a precise period (Roy, 2013).

Some numbers of solution method such as dynamic programming (DP) (Chang et al., 1990), interior point method (IPM) (Farag et al.,1995), and mathematical decomposition (Habibollahzadeh & Bubenko, 1986) are used for the minimization problem. These traditional techniques do not tackle adequately in solving economic optimization problems due to the complexities in dealing with a range of non-linear constraints and also for large time –consuming. Now a days, some literature is considered a single objective cost minimization problem by applying evolutionary algorithms to avoidance the poor local optimal and slow convergence rate. Several populations based iteratively enhanced meta-heuristic algorithms are developed, such as particle swarm optimization (PSO) (Lu et al., 2010), krill herd algorithm (Mandal et al., 2014), chemical reaction optimization (CRO) (Roy & Hazra, 2015), predator- prey optimization (PPO) (Hazra & Roy, 2015) are represented as alternatives to a traditional methods as well as to achieving the global best result. Bai et al. (2016) proposed an artificial bee colony (ABC) to deal with the insecurity of wind power in their present investigation for load dispatch problem. Hetzer et al. (2008) momentarily discussed the wind power underestimation cost and overestimation cost of accessible renewable power generation. Wind generation output is difficult to predict (Panigrahi et al., 2010) due to the stochastic nature of wind resources, while now a day’s most of these works are used to represent the variability of wind, known as Weibull distribution (Shi et al., 2012). Chen et al. (2015) proposed distribution management oriented renewable energy generation using novel interval optimization. Abbaspour et al. (2016) established the best possible process scheduling of renewable wind power included with condensed storage of air energy. Ghulam Mohy-ud-din (2017) considered the variable scenery of generated renewable power using a backtracking search algorithm. Moghimi et al. (2012) implemented a mixed- integer for wind hydropower scheduling stochastic framework. Zhang et al. (2013) presented PSO with a little world arrangement to explain a replica for wind-park scheduling power generation. Moreover, Jin et al. (2016) proposed the best possible day-ahead preparation including urban energy systems considering the reconfigurable capability. Aghaei et al. (2013) suggested integrating wind power programming framework in a scenario-based stochastic dynamic load transmit problem over the 24-h time span.

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