A Novel Quasi-Oppositional Learning-Based Chaos-Assisted Sine Cosine Algorithm for Hybrid Energy Integrated Dynamic Economic Emission Dispatch

Abstract

In the current era of industrialization, renewable energy (such as wind and solar energy) plays an significant role in power generation sector, and it helps to decrease the generation cost and the environmental pollution. In this work, hybrid source of energy (like solar and wind energy) has been integrated with the traditional dynamic economic emission dispatch (EED) (DEED) problem to formulate a new model, i.e., complicated constrained hybrid energy integrated DEED problem, which may be able to generate less polluted power than the traditional DEED problem. Here, the quasi-opposition learning (QOL) approach and chaotic dynamics have been introduced in a novel sine cosine algorithm (SCA) to enhance its convergence and diversity. Effectiveness of the presented solution of the dynamic thermal-wind-solar EED problem has been properly validated by solving three aspects, i.e., the cost, the emission and the combined cost-emission using QOL-based chaotic SCA. The use of solar and wind energy in the DEED problem reduces the generating costs by 8% and 20.84%, respectively, compared to traditional and wind-integrated DEED. The robustness of the proposed modified SCA has been presented by comparing the results with the results offered by SCA and other recently published algorithms.