Power Management of Hybrid System Using Coronavirus Herd Immunity Optimizer Algorithm

Abstract

Hybrid renewable energy systems (HRESs) that merge wind and solar power with energy storage offer a trustworthy and affordable alternative for remote consumers. Energy storage integrates variable wind and solar energy, while energy management enhances system reliability, reduces costs, and minimizes environmental impact. This paper proposes a novel methodology called the coronavirus herd immunity optimizer (CHIO) for modeling and sizing HRESs. The CHIO algorithm uniquely balances exploration and exploitation phases inspired by herd immunity principles, setting it apart from traditional optimization methods. It addresses the optimization problem of minimizing the system's overall net present cost, aiming to reduce the cost of energy (COE) while improving system reliability. We investigate the efficacy of the CHIO method in solving hybrid system design issues and compare its performance to other popular optimization strategies, such as cuckoo search (CS) and particle swarm optimization (PSO). The results demonstrate that CHIO achieves superior solutions to the optimization problem, producing energy with a lower COE and higher reliability compared to PSO and CS.