Study of Meta-heuristic Optimization Methodologies for Design of Hybrid Renewable Energy Systems

Abstract

Exponentially rising hydrocarbon fuel consumption creates several environmental issues that have necessitated the integration of renewable energy systems (RES) into the grid. Solar photovoltaic and wind energy constitutes the most matured hybrid renewable energy system (HRES) alternative technology against conventional fossil fuels as it is pollution-free, easily available, low-price, and available in abundance. The intermittent nature of solar irradiation and fluctuation in wind speed makes the system design inappropriate, making it either oversized or undersized. Due to this, deploying solar PV-Wind-based HRES is becoming either expensive or inefficient. Thus, there is an immediate requirement for optimization problem-solving methodologies to minimize the HRES costs. This paper mainly focuses on a critical comparative analysis of the many applied and promising optimization methodologies. The methodologies used to design the optimal capacity of HRES, are categorized as traditional, modern, and hybrid on the basis of identified objective functions, decision variables, and evaluation indicators. While most of the present studies have been based on the technical reliability and economical perspective of HRES, in this paper the environmental indicators have got major emphasis. The facts emerging out of the study indicate that application of hybrid meta-heuristic optimization techniques for engineering applications is growing significantly due to their flexibility and efficiency. The application of economical indicators was observed to be more prevalent compared to other reliability and environmental indicators. However, the percentage of multi-objective functions of economical and reliability is a growing trend for the optimal design process of HRES.