Solution of optimal power flow using evolutionary-based algorithms
This paper applies two reliable and efficient evolutionary-based methods named Shuffled Frog Leaping Algorithm (SFLA) and Grey Wolf Optimizer (GWO) to solve Optimal Power Flow (OPF) problem. OPF is one of the essential functions of electrical power generation control and operation. It aims to estimate the optimal settings of real generator output power, bus voltage, reactive power compensation devices, and transformer tap setting. The objective function of OPF is to minimize total production cost while maintaining the power system operation within its security limit constrains. SFLA and GWO are meta-heuristic evolutionary-based methods. SFLA mimics the frogs’ behavior while searching for food. GWO mimics the grey wolfs behavior while hunting for prey. These methods are simulated and tested on the standard IEEE 30-bus and 57-bus test systems. Moreover, the performances of the applied algorithms are analyzed and compared with each other as well as with other existing optimization techniques. The obtained results show the strengths of the two applied methods and the superiority of them in comparison to the other methods.
Keywords: Optimal power flow, Shuffled Frog Leaping, Memeplex, Grey Wolf Optimizer