Maximizing power transfer in transmission networks with TCPS: A non-linear programming-genetic algorithm approach

This study presents an advanced methodology for maximizing the performance of Thyristor-Controlled Phase Shifters (TCPS) in order to enhance power transfer capacity within transmission networks. As a flexible AC transmission system (FACTS) device, TCPS plays a crucial role in providing dynamic control over bus voltage and angle, thereby improving the power system overall operational efficiency. The proposed approach utilizes a Non-Linear Programming (NLP) framework integrated with a Genetic Algorithm (GA) to achieve maximum active forward power flow in transmission lines. A voltage source converter (VSC)-based TCPS is strategically placed between the swing and load buses in a three-bus power system. The operating range under the forward power flow condition is determined by analyzing voltage and line current data obtained through varied phase-shifting configurations of the TCPS. The optimal phase shift value is determined through GA-based convergence tracking and NLP optimization, leading to the maximization of forward power flow between the two buses. The simulation results demonstrate its effectiveness in successfully tracking and converging to the maximum power flow (MPF) point within 40 iterations. Moreover, the convergence efficiency remains consistently at 100% over 10 consecutive runtimes, while the computational time for the GA component is less than 20 seconds. The outcomes of this study highlight the novelty and superiority of the proposed methodology compared to existing approaches in the literature. The findings have significant implications for enhancing the reliability and stability of power systems in the face of increasing demand and complex grid conditions.