A Novel Elite Opposition-Based Learning Combined Aquila Optimizer for Optimization of Hybrid Power System Considering UPFC

This paper applies an intelligent elite opposition-based learning (EOBL) technique to enhance the aquila optimizer's (AO) performance.The proposed EOBL combined AO (EOBL-AO) is utilized to optimize the power generation cost in the presence of the valve-point loading condition for a hybrid power system considering conventional thermal generator (CTG) and intermittent wind energy (WE) and plug-in electric vehicle (PEV). Uncertain features of WE and PEV are estimated using a probabilistic approach. Furthermore, the location and parameters of a single unified power flow controller (UPFC) are optimized to ensure the reliable and secure operation of the hybrid power system. The simulation is conducted on the IEEE 30-bus test grid system for two simulation cases. The proposed technique's performance is validated by comparing the results to AO and a quasi-oppositional AO (QOAO). The results show that the proposed technique outperforms the competitors by improving generation costs by 0.1% and 0.14% over QOAO and AO in case 1 and 0.12% and 0.14% over QOAO and AO in case 2. It is proved from the result analysis that the proposed technique outperforms AO and QOAO regarding accuracy and convergence ability.