Power Flow Optimization by Integrating Novel Metaheuristic Algorithms and Adopting Renewables to Improve Power System Operation

Mohana Alanazi; Abdulaziz Alanazi; Almoataz Y. Abdelaziz; Pierluigi Siano

Power Flow Optimization by Integrating Novel Metaheuristic Algorithms and Adopting Renewables to Improve Power System Operation

2022

Mohana Alanazi | Abdulaziz Alanazi | Almoataz Y. Abdelaziz | Pierluigi Siano

The present study merges the teaching and learning algorithm (TLBO) and turbulent flow of water optimization (TFWO) to propose the hybrid TLTFWO. The main purpose is to provide optimal power flow (OPF) of the power network. To this end, the paper also incorporated photovoltaics (PV) and wind turbine (WT) generating units. The estimated output power of PVs/WTs and voltage magnitudes of PV/WT buses are included, respectively, as dependent and control (decision) variables in the mathematical expression of OPF. Real-time wind speed and irradiance measurements help estimate and predict the power generation by WT/PV units. An IEEE 30-bus system is also used to verify the accuracy and validity of the suggested OPF and the hybrid TLTFWO method. Moreover, a comparison is made between the suggested approach and the competing algorithms in solving the OPF problem to demonstrate the capability of the TLTFWO from robustness and efficiency perspectives.

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