Economic analysis of grid tied hybrid solar wind system with electric vehicle CS using COA-QNN approach

This paper proposes a novel hybrid strategy for energy management in a grid-connected solar-wind hybrid system with electric vehicles (EVs) charging station (CS). The proposed hybrid method is the combined execution of the Coati Optimization Algorithm (COA) and Quantum Neural Network (QNN). Hence named as, COA-QNN Approach. The major purpose of this strategy is to minimize the power exchanged with the grid and reduce the annual cost of the system. This is achieved by optimizing the interaction between the utility grid and system components, including wind turbines, solar panels and EVs. The COA-QNN Approach ensures efficient power exchange and considers the cost implications of energy bought from and supplied to the grid. This technique introduces a more effective way to manage energy flow and cost in hybrid renewable energy systems, leveraging the strengths of the COA and QNN to enhance overall system performance and economic viability. By then, using the MATLAB platform, the proposed technique's efficacy is compared to other techniques that are now in use. The proposed strategy outperforms all existing approaches, including Heap Based Optimization (HBO), Wild Horse Optimizer (WHO), and Particle Swarm Optimization (PSO).The proposed technique costs $0.1968 per kW annually.