An enhanced multiobjective particle swarm optimisation algorithm for optimum utilisation of hybrid renewable energy systems

Abstract

Wind-photovoltaic (PV) with backup energy storage system like battery bank and diesel generator-based hybrid energy system (HES) becomes popular day by day because it gives the reliable energy resources with a decrease in the price of turbines, PV array, battery banks and diesel generator with reduced emissions of fuel. The optimisation techniques are mainly used to minimise the total cost of the system, which incorporates the initial investment cost, running cost, maintenance cost and get-back prices of each component, to supply reliable electricity to meet out the electrical energy demand of the remote area, where the transmission cost is very high for such areas consisting of huge dense forests, remote areas, etc. The comparison of the performance characteristics of each hybrid system has been carried out using particle swarm optimisation (PSO) and genetic algorithm. The hybrid system configuration is derived mainly based on the meteorological data of wind speed, solar radiation and the ambient atmospheric temperature. This paper has been proposed to minimise the cost of energy and the loss of power supply probability using enhanced multiobjective PSO algorithm for the optimum utilisation of HES to make the system economical and more reliable for the household applications.

KEYWORDS:

• Cost of energy
• loss of power supply probability
• hybrid energy systems
• renewable energy fraction
• particle swarm optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).