Uncertainty management in multiobjective electric vehicle integrated optimal power flow based hydrothermal scheduling of renewable power system for environmental sustainability.

The combined heat and power economic dispatch (CHPED) and optimal power flow (OPF) are two power system optimization issues that are simultaneously studied in this work on IEEE-57 bus and IEEE 118-bus power network. The main contribution of the proposed work is to determine the OPF of CHPED problem on the IEEE 57 bus and IEEE 118 bus systems. Secondly, renewable energy sources such as wind-solar-EV are integrated with the aforesaid systems for lowering fuel cost, emission, active power loss (APL), aggregated voltage deviation (AVD), voltage stability index (VSI) and also cost, emision, APL, AVD, VSI are reduced simultaneously considering different cases for multi-objective functions.Proposed sine-cosine algorithm (SCA) embedded with quasi-oppositional based learning (QOBL), known as QOSCA is used to balance the exploration and exploitation ability in order to overcome shortcomings and provide global optimal solutions. Utilizing statistical analysis, the suggested technique's robustness has been assessed. Moreover, an analysis of variance (ANOVA) test and box plot are used to thoroughly investigate this data to provide a more precise assessment of QOSCA's robustness. After integrating wind-solar and EV, the numerical analysis for IEEE 57 bus and IEEE 118-bus utilizing QOSCA for single objective over generation cost is reduced by 21%, emission is reduced by 17.5%, APL is reduced by 0.17% and 2.59%. Additionally, the suggested method (QOSCA) is applied to a multiobjective function while taking AVD and VSI into account. This resulted in a reduction in AVD by 0.37% and VSI by 0.24%, demonstrating the superiority of the suggested method. Furthermore, it has been demonstrated that the computational efficiency in complex systems is 24% faster than that of conventional optimization methods.