Quasi-oppositional chemical reaction optimization for combined economic emission dispatch in power system considering wind power uncertainties. (December 2019) - Hazra, Sunanda; Roy, Provas Kumar
Graphical abstract: Graphical abstract of the proposed research work and the proposed QOCRO methodology Highlights: Quasi-oppositional based learning (QOCRO) is integrated with Chemical Reaction Optimization (CRO). Quasi-oppositional CRO (QOCRO) is proposed to solve wind based combined EED (WCEED) problem. Performance of QOCRO is compared with CRO and other optimization techniques available in literature. Performance of QOCRO is found to be more encouraging to solve WCEED problems. Abstract: In this article, chemical reaction optimization (CRO) is proposed to solve wind-based combined economic emission dispatch problem (WCEED) in order to minimize thermal-wind electrical energy cost and emissions formed by fossil-fuelled power plants, concurrently. Moreover, to improve the solution superiority and convergence speed quasi-opposition based learning (QOBL) is included with basic CRO algorithm. The proposed CRO and QOCRO approaches are implemented to discover the optimal generation of wind and thermal generators in order to minimize the individual objective of fuel cost, emission and compromising solutions is also evaluated. Wind power generation is modelled by the piecewise linear approximation method. Due to uncertainty in nature, cost of wind is sweeping by counting underestimation and overestimation cost of existing wind power. The performance of CRO and QOCRO is evaluated through three test systems and the simulation results, as well as statistical results obtained by these methods along with different other algorithms available in the literature, are presented to demonstrate the validity and effectiveness of the proposed CRO and QOCRO schemes for practical applications. Moreover, a non-dominated sorting CRO and QOCRO are employed to approximate the set of Pareto solution through the evolutionary optimization process.