Optimal power flow solution in power systems using a novel Sine-Cosine algorithm

• Published in: International journal of electrical power & energy systems Vol. 99; pp. 331 - 343
• Main Authors: Attia, Abdel-Fattah, El Sehiemy, Ragab A., Hasanien, Hany M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2018
• Subjects: Fuel cost minimization; Optimal solution of power flow problem; Reactive power allocation; Sine- cosine search algorithm; Fuel cost minimization; Optimal solution of power flow problem; Reactive power allocation; Sine- cosine search algorithm
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2018.01.024

•A modified Sine-Cosine algorithm is enhancing the OPF solution feasibility.•Levy flights enhance the proposed algorithm ability to avoid local optima.•Adapting agents number based on the objective function will speed up the algorithm.•It is validated on standard systems for operational and economic indices.•The proposed algorithm effectiveness is proven compared with previous methods. This paper presents the application of a novel algorithm, which is based on a modified Sine-Cosine technique for solving the optimal power flow (OPF) problem. It is a highly coupled non-linear constrained optimization problem. The modified Sine-Cosine algorithm (MSCA) aims at reducing the computational time with a sufficient improvement in finding the optimal solution and feasibility. Levy flights are added to the original Sine-Cosine algorithm to enhance the ability to focus on optimal and avoids local optima. Decreasing search agents number based on the objective function speeds up the MSCA. The MSCA presents a simple and robust solution for the OPF problem under different objective functions. The MSCA is validated with solving the OPF problem for a number of benchmark test systems, namely the IEEE-30 bus and IEEE 118-bus systems under selected objective functions, which are based on operational and economic performance indices of the power system. The proposed MSCA is compared with other optimization methods to illustrate the effectiveness and potential of the SCA and MSCA algorithms.