A Golden Jackal based optimal operation and optimization of a virtual power plant involving renewable resources

Abstract

Abstract This paper reports the optimum operation of a virtual power plant (VPP) to reduce the reliance on coal and gas-based energy generation. This practice not only promotes the incorporation of renewable-based energy sources but also reduces the reliance on fossil-based resources which are limited in the reserves. The selected VPP system comprises small hydropower, wind turbine, solar photovoltaics and fuel cells accompanied by a co-generation unit. The proposed VPP is also equipped with a storage facility applicable as a flexible storage option i.e., an electric vehicle followed by an energy storage system. In addition, the primary objective of this work is to lessen the overall cost associated with the system along with a reduction in the generated emissions. Moreover, a Golden Jackal-based Optimization inspired by nature is utilized in a single and multi-objective framework to attain the desired target objectives. Furthermore, the scheduling of the VPP system is carried out on a day-ahead basis and the outcome in terms of cost and emission is compared with the available work in the literature and the result reveals the effectiveness of the system in terms of quality solutions sets for the objectives, minimum computational time and better convergence behavior. In particular, there is a reduction in cost by 2.1% and 0.62% when compared with the salp swarm algorithm and beluga whale algorithm followed by 2.12% and 1.18% in emission. Furthermore, the cost/emission of the VPP system is also abridged by employing a Pareto-based approach which shows the suitability and effectiveness of the developed system.