Reducing Losses in a Deregulated Power System with Teaching Learning Based Optimization Algorithm

The robust Newton–Raphson method is suggested to solve the power flow equations. Newton power flow algorithms do not automatically minimize objective function such as real power losses. Hence, this paper presents teaching learning based optimization (TLBO) approach to minimize power lossesby the optimal allocation of reactive power sources considering placement and value in restructured power systems while at the same time satisfying various equality and inequality constraints. Reconstruction of power industries has brought fundamental changes to both power system operation and planning. Moreover,proper location of capacitors and finding the best combination among a large number of potential combinations are important for maintaining a stable and secure operation of a deregulated system, which benefits the system with reducing the total reactive power generation cost, minimizing the total power losses, and increasing available real power transfer capabilities. Therefore, the independent system operator (ISO) does not worry about compensating power losses. This study reviews the method of minimization of power losses, then we investigate the deregulated environment without losses. Next, a new evolutionary method is applied on IEEE 30 bus test system. And its superior performance is compared to particle swarm optimization(PSO) and Genetic Algorithm (GA). Simulationresults have been presented in order to show the effectiveness of the proposed approach for reactive power planning. They must produce reactive power for keeping magnitude bus voltages in their proper magnitudes.