Elitist Rao Algorithms and R-Method for Optimization of Energy Systems

Abstract

Identifying, examining, and optimizing the impact of different parameters in a renewable energy system can significantly help determine its efficiency. Furthermore, since these systems have several objectives such as power output, system efficiency, investment cost, economic, and ecological factors, it is often not preferable to present the optimum system parameters considering just one objective. This article proposes three improved versions of recently developed Rao algorithms named elitist Rao algorithms to find optimum system parameters of renewable energy systems. In addition, a new multi-attribute decision-making method named R-method is proposed for selecting the best solution from the Pareto-fronts obtained using the elitist Rao algorithms. The proposed algorithms are tested using 30 single-objective unconstrained benchmark functions, and the significance of improvement over basic Rao algorithms is validated using the Friedman statistical test. Later, the proposed algorithms’ performances are tested in multi- and many-objective optimization scenarios of a solar-assisted Stirling heat engine system and a turbocharged direct injection diesel engine system. Furthermore, the proposed algorithms’ effectiveness is presented in terms of hypervolume, coverage, and spacing metrics. Also, the performances of the proposed algorithms in single-, multi-, and many-objective optimization are compared with the other algorithms from the literature and found to be superior or competitive.