Summary This paper proposes an improved moth flame optimization (IMFO) algorithm to effectively solve the optimal power flow (OPF) problems. The concept of moth flame optimization (MFO) is inspired from the movement of moth towards the moon direction. IMFO is mainly based on the concept of MFO with modifying the path of moths in new spirals around the flame. Standard IEEE 30‐bus, IEEE 57‐bus and IEEE 118‐bus test systems are used to validate and prove the efficiency and robustness of IMFO algorithm. The validation of the proposed algorithm is based on 15 case studies in terms of different single and multi‐objective functions: fuel cost minimization, gas emission reduction, active power loss minimization, voltage profile improvement, and voltage stability enhancement. The simulation results of the proposed algorithm are compared with those obtained by other well‐known optimization techniques. The obtained results demonstrate the capability and robustness of IMFO algorithm to solve OPF problems. The results reveal that IMFO algorithm is capable of finding precise and better OPF solutions compared with the other techniques. A comparison among the convergence characteristics of IMFO technique and the other techniques proves the prevalence of IMFO to attain the optimal power flow solution with fast convergence.

No abstract

Summary Unified power flow controller (UPFC) is utilized to regulate the bus voltage as well as the power flow through a power system. A solution of optimal power flow (OPF) problems with UPFC is a crucial and complex task due to the required modifications for considering the parameters of UPFC. This work proposes a simplified UPFC modeling into an OPF code in order to avoid the programming complexity. Moreover, the solution of an OPF problem with a UPFC model is obtained by a recent physical‐based optimization technique called lightning attachment procedure optimization (LAPO). Different objective functions including minimizing fuel cost and fuel cost with valve point effect (VPE), reduction of emission, improvement of voltage profile, and improving the voltage stability index are considered. The proposed algorithm is tested using the standard IEEE 30‐bus system. The results obtained by the proposed algorithm are compared with those obtained by other optimization techniques. However, the obtained results verify the applicability of the simplified UPFC model in OPF.

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.

No abstract