Abstract
The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques.
Highlights
optimal power flow (OPF) problems have become a strenuous task for optimal operation of the power systems
Many conventional methods have been developed in order to solve the OPF problem such as NLP [3], LP [4], QP [5], Newton’s Method [6], IP [7]
Developed Grey Wolf Optimizer (DGWO) has been proposed to efficiently solve the OPF problem and avoid the stagnation problems of the traditional GWO. This technique is based on modifying the grey wolf optimizer by employing a random mutation for enhancing its exploration process
Summary
OPF problems have become a strenuous task for optimal operation of the power systems. Energies 2018, 11, 1692 these methods face some problems in solving nonlinear or non-convex objective functions These methods may fall into local minima, new optimization algorithms have been proposed to avoid the shortcomings of these methods. Several optimization techniques have been employed for solving the OPF considering the valve-point loading effect such as ABC [17], GSA [18], SFLA [20], SOS [24], BSA [25] and Hybrid Particle Swarm Optimization and Differential Evolution [28]. The conventional and some meta-heuristics methods could not efficiently solve the OPF problem, several new or modified versions of optimization techniques have been proposed. A new developed version of GWO is proposed to effectively solve the OPF problem.
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