Abstract
The optimal power flow (OPF) is a vital tool for optimizing the control parameters of a power system by considering the desired objective functions subject to system constraints. Metaheuristic algorithms have been proven to be well-suited for solving complex optimization problems. The whale optimization algorithm (WOA) is one of the well-regarded metaheuristics that is widely used to solve different optimization problems. Despite the use of WOA in different fields of application as OPF, its effectiveness is decreased as the dimension size of the test system is increased. Therefore, in this paper, an effective whale optimization algorithm for solving optimal power flow problems (EWOA-OPF) is proposed. The main goal of this enhancement is to improve the exploration ability and maintain a proper balance between the exploration and exploitation of the canonical WOA. In the proposed algorithm, the movement strategy of whales is enhanced by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking. To validate the proposed EWOA-OPF algorithm, a comparison among six well-known optimization algorithms is established to solve the OPF problem. All algorithms are used to optimize single- and multi-objective functions of the OPF under the system constraints. Standard IEEE 6-bus, IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems are used to evaluate the proposed EWOA-OPF and comparative algorithms for solving the OPF problem in diverse power system scale sizes. The comparison of results proves that the EWOA-OPF is able to solve single- and multi-objective OPF problems with better solutions than other comparative algorithms.
Highlights
Over the past decades, metaheuristic algorithms (MAs) have become more prevalent in solving optimization problems in various fields of industry and science [1]
This paper proposes an effective whale optimization algorithm for solving the optimal power flow problem (EWOA-OPF)
The comparison of results proves that the EWOA-OPF can solve single- and multi-objective OPF problems with better solutions than other comparative algorithms
Summary
Metaheuristic algorithms (MAs) have become more prevalent in solving optimization problems in various fields of industry and science [1]. Non-linear programming [22], Newton algorithm [23], and quadratic programming [24] are some of the classical optimization algorithms that have been employed to tackle the OPF problem These algorithms can sometimes find the global optimum solution, they have some drawbacks such as getting trapped in local optima, a high sensitivity to initial potions, and the inability to deal with non-differentiable objective functions [25,26,27]. The EWOA-OPF improves the movement strategy of whales by introducing two new movement strategies: (1) encircling the prey using Levy motion and (2) searching for prey using Brownian motion that cooperate with canonical bubble-net attacking The reason for these changes is to maintain an appropriate balance between exploration and exploitation and enhance the exploration ability of the WOA, resulting in more precise solutions when solving problems.
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