International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 5, October 2023, pp. 4835~4844 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i5.pp4835-4844  4835 Journal homepage: http://ijece.iaescore.com Optimal power flow with distributed energy sources using whale optimization algorithm TentuPapi Naidu 1,2 , Ganapathy Balasubramanian 3 , Bathina Venkateshwara Rao 4 1 Department of Electrical and Electronics Engineering, Annamalai University, Chidambaram, India 2 Department of Electrical and Electronics Engineering, Lendi Institute of Engineering and Technology, Andhra Pradesh, India 3 Department of Electrical Engineering, Government College of Engineering, Tirunelveli, India 4 Department of Electrical and Electronics Engineering, V. R. Siddhartha Engineering College, Andhra Pradesh, India Article Info ABSTRACT Article history: Received Nov 26, 2022 Revised Jan 17, 2023 Accepted Feb 4, 2023 Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSO- GWO). Keywords: Fuel cost Optimal power flow Optimization Solar power Whale optimization algorithm This is an open access article under the CC BY-SA license. Corresponding Author: Bathina Venkateshwara Rao Department of Electrical and Electronics Engineering, V. R. Siddhartha Engineering College Andhra Pradesh-520007, India Email: drbvrao@vrsiddhartha.ac.in 1. INTRODUCTION Proper planning is necessary for improved usage of resources already existing in the power system. optimal power flow (OPF) has recently emerged as a popular issue for realizing the optimal planning of a real-timesystemfunction is very much necessary for achieving operation and control of modern power systems. Various objectives including power losses, emissions, and voltage stability are taken into account for optimizing the variable regulation using OPF. Different traditional optimization strategies for tackling OPF problems have been presented in the literature [1] and these procedures have occasionally failed to provide the desired effects. This can be overcome employing by a heuristic approach and metaheuristic methodology that take into account the randomization of controlled parameters. Various authors used a variety of strategies to address the OPF problem, some of which are described below. Particle swarm optimization (PSO) was used by [2] authors to solve the OPF. The PSO examined for IEEE thirty Bus test case for reduced goal operation like voltage stability improvement, power loss and total fuel cost. In [3] grey