Optimal power flow solution with uncertain RES using augmented grey wolf optimization

Khan, Inam Ullah and Javaid, Nadeem and Akurugoda Gamage, Kelum and Taylor, C. James and Ma, Xiandong (2020) Optimal power flow solution with uncertain RES using augmented grey wolf optimization. In: 2020 IEEE International Conference on Power Systems Technology (POWERCON) :. IEEE International Conference on Power Systems Technology (POWERCON) . IEEE. ISBN 9781728163512

Text (20powercon_gwo)
20powercon_gwo.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.
Download (372kB)

Abstract

This work focuses on implementing the optimal power flow (OPF) problem, considering wind, solar and hydropower generation in the system. The stochastic nature of renewable energy sources (RES) is modelled using Weibull, Lognormal and Gumbel probability density functions. The system-wide economic aspect is examined with additional cost functions such as penalty and reserve costs for under and overestimating the imbalance of RES power outputs. Also, a carbon tax is imposed on carbon emissions as a separate objective function to enhance the contribution of green energy. For solving the optimization problem, a simple and efficient augmentation to the basic grey wolf optimization (GWO) algorithm is proposed, in order to enhance the algorithm's exploration capabilities. The performance of the new augmented GWO (AGWO) approach, in terms of robustness and scalability, is confirmed on IEEE-30, 57 and 118 bus systems. The obtained results of the AGWO algorithm are compared with modern heuristic techniques for a case of OPF incorporating RES. Numerical simulations indicate that the proposed method has better exploration and exploitation capabilities to reduce operational costs and carbon emissions.