An Enhanced Hybrid Rhino Herd–PSO Optimizer for Optimal Technical and Economic Operation of Power Systems Considering Environmental Concerns

Abstract

The distributed generation based on renewable energy is gaining importance due to increased concerns about energy costs, energy security, and environmental issues. To effectively demonstrate the combination of different generators under various limitations, such as those imposed by energy systems using traditional fossil fuel generators and the incorporation of renewable energy sources, this article highlights the great potential for economic and efficient operation with environmental benefits. The reasons why the use of optimal power flow is regarded as the most significant operational challenge in power systems and the use of single- and multi-objective frameworks to analyze various objective functions are investigated. The objective functions are considered to reflect the power system’s technical, economic, and environmental requirements, including generation cost minimization, environmental pollution emission reduction, and voltage profile. Additionally, the article addresses the complexity of irregular mix structures such as solar and wind power and justifies the usage of swarm intelligence and an evolutionary approach to get the best solution. Furthermore, the article utilizes and validates the Rhino Herd Optimizer (RH), which is a modern type of swarm-based meta-heuristic search system, on various electrical power systems to evaluate the outcomes of an RH optimizer and show the capabilities.