Using quasi-oppositional butterfly optimization algorithm, a probabilistic optimal power flow for a combined tidal and electric vehicle renewable energy system

Aiming to reduce operating expenses and emissions while satisfying system restrictions, optimal power flow, or OPF, is essential for effective and sustainable power system management. Quasi-Oppositional Based Learning (QOBL) is added to the butterfly optimization algorithm in this research to improve convergence and solution accuracy when solving the OPF issue. When tested on the IEEE 57-bus system, the suggested QOBOA outperforms current optimization methods in terms of transmission loss and voltage profile improvement. Combining renewable energy sources is crucial for efficient electricity generation because fossil fuel sources are becoming more and more improved every day. The suggested solutions integrate renewable energy sources, such as tidal and electric vehicles, to reduce the demand for fossil fuels in the generation of electricity. Furthermore, the suggested approach has a great deal of promise for improving the adaptability and resilience of contemporary power grids, particularly in light of the growing integration of decentralized energy resources (DERs) and renewable energy sources. This strategy can help future smart grid systems operate more effectively, sustainably, and dependably by facilitating quicker decision-making and enhancing the coordination of dispersed assets.