Single and Multi-Objective Optimal Power Flow Based on JAYA Algorithm with Teaching-Learning Based Optimization

This paper deals with the Optimal Power Flow (OPF) in an IEEE standard bus (30-bus) power system and presents a multi-objective optimization approach to minimize generation costs, active power losses and voltage deviations. The OPF problem is of critical importance for the reliable, efficient and economical operation of power systems. However, the solution to this problem is complex due to its nonlinear nature and large number of constraints. Conventional methods are often insufficient to overcome the nonlinear challenges inherent in OPF. In addressing these challenges, this study employs metaheuristic algorithms, namely Teaching-Learning Based Optimisation (TLBO), JAYA and hybrid TLBO-JAYA, to enhance the efficiency and convergence speed of the solution process. To manage the multi-objective nature of the problem, Pareto optimisation is utilised to identify a solution set that balances conflicting objectives. The outcomes demonstrate that the hybrid TLBO-JAYA algorithm offers a balanced enhancement in terms of generation cost, active power loss and voltage stability, thereby providing a versatile and efficient solution framework for contemporary power systems. These findings underscore the potential of hybrid metaheuristic algorithms in addressing complex multi-objective optimisation problems in power systems.