Optimal power flow incorporating wind uncertainty using bald eagle search and transit search optimization algorithms
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Abstract
The optimal power flow (OPF) problem is a key issue in determining the most efficient and secure operating point for a power system. This problem has become particularly complex with the high penetration of renewable energy resources, due to the intermittent and stochastic nature of these resources. In this paper, the OPF is solved using the recently developed algorithms called the Bald Eagle Search (BES) and Transit Search Optimizer (TSO), taking into account the uncertainties associated with the power outputs of the wind generation systems. The paper examines several objective functions, such as minimizing thermal and wind generation costs, transmission losses, voltage deviations, and greenhouse gas emissions. Comparative analyses were carried out on standard and modified IEEE 30-bus and 57-bus test systems, in comparison with recent developed optimization algorithms, including the Equilibrium Optimizer (EO), the Marine Predators Algorithm (MPA), the Artificial Ecosystem-based Optimizer (AEO) and the Slime Mould Algorithm (SMA). The obtained results demonstrate the effectiveness and robustness of the BES and TSO algorithms in solving the OPF problem incorporating renewable energies while optimizing overall system performance and minimizing its environmental impact.
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