Optimized Reactive Power Distribution for Enhancing Power System Efficiency Using a Generalized Teaching-Learning-Based Optimization Algorithm

Electrical energy generation in power systems aims to minimize the total production cost of active units within the power network, making it one of the most critical aspects of modern power systems. Optimal reactive power distribution is a key approach to ensuring the reliable and economical operation of power systems. The primary objective of reactive power distribution in power networks is to determine the control variables that minimize the objective function while adhering to system constraints. In this paper, a hybrid optimization algorithm is introduced, combining the Multi-Objective Teaching-Learning-Based Optimization (MTLBO) method with a fuzzy decision-making algorithm to solve the Reactive Power Dispatch (RPD) problem. The proposed method addresses multiple objectives, including reducing active power losses, improving voltage profiles, and enhancing network security. To assess the effectiveness of the proposed approach, simulations were performed on IEEE ۵۷-bus and ۱۱۸-bus test systems. The simulation results confirm the efficiency and superiority of the proposed method compared to conventional optimization techniques.

Keywords:

Optimal Reactive Power Distribution , MTLBO Algorithm , Fuzzy Decision-Making Algorithm , System Loss Reduction