Power Flow Analysis of 330 kV Nigerian Transmission Network Using Smell Agent Optimization Technique

The Nigerian 330 kV power transmission network is a crucial component of the nation's power sector, facilitating the efficient transfer of electrical energy across diverse regions. Operating at a voltage level of 330 kilovolts, it addresses the growing energy demands for industrial, commercial, and residential needs. This research explores the design, challenges, and opportunities of the network, emphasizing the need for addressing voltage profile issues, optimizing reactive power support, and enhancing overall resilience. Power flow studies are conducted to understand complex bus voltages, real and reactive power injection, and system behavior during operation. The work formulates the power flow problem as an optimization task, aiming to minimize mismatches between scheduled and calculated values. The traditional power flow equations are presented in polar form, providing a foundation for the subsequent application of the Smell Agent Optimization (SAO) technique. SAO, inspired by olfactory systems, operates in sniffing, trailing, and random modes. It simulates the dispersion of "smell molecules" representing optimal solutions. The algorithm dynamically adjusts agent positions based on the perceived smell strength, contributing to efficient optimization in power flow studies. The power flow analysis of the Nigerian 330 kV network using SAO reveals critical information such as bus voltages, phase angles, and power flows. The results, presented in tables 1 and 2 and figures 4 to 8 demonstrate that SAO effectively maintains voltage magnitudes of all buses within acceptable limits of ±5? (0.95 and 1.05 p. u) with the exception of only three buses and optimizes real and reactive power flows, and minimizes losses. In conclusion, the application of SAO in power flow studies proves effective in ensuring the secure and reliable operation of the Nigerian 330 kV power transmission network. By fine-tuning power flow parameters, SAO contributes to stability, efficiency, and optimal performance. The study emphasizes the importance of advanced optimization techniques in managing complex power systems and achieving a resilient, efficient, and sustainable energy infrastructure.