Abstract
In this paper, a modified Rao-2 (MRao-2) algorithm is proposed to solve the problem of optimal power flow (OPF) in a power system incorporating renewable energy sources (RES). Quasi-oppositional and Levy flight methods are used to improve the performance of the Rao algorithm. To demonstrate effectiveness of the MRao-2 technique, it is tested on two standard test systems: an IEEE 30-bus system and an IEEE 118-bus system. The objective function of the OPF is the minimization of fuel cost in five scenarios. The IEEE 30-bus system reflects fuel cost minimization in three scenarios (without RES, with RES, and with RES under contingency state), while the IEEE 118-bus system reflects fuel cost minimization in two scenarios (without RES and with RES). The achieved results of various scenarios using the suggested MRao-2 technique are compared with those obtained using five recent techniques: Atom Search Optimization (ASO), Turbulent Flow of Water-based Optimization (TFWO), Marine Predators Algorithm (MPA), Rao-1, Rao-3 algorithms, as well as the conventional Rao-2 algorithm. Those comparisons confirm the superiority of the MRao-2 technique over those other algorithms in solving the OPF problem.
Highlights
In recent decades, the optimal power flow (OPF) problem has had an important role in the operation and planning of electrical systems [1]
The IEEE 30-bus and IEEE 118-bus systems are used to prove the efficient performance of the proposed modified Rao-2 (MRao-2)
A new technique has been proposed for finding the optimum solution to the OPF problem incorporating renewable energy sources considering the fuel cost, transmission loss, emission, and improvement of the voltage profile
Summary
The optimal power flow (OPF) problem has had an important role in the operation and planning of electrical systems [1]. Different heuristic techniques are utilized to solve the OPF problem such as a multi-objective hybrid firefly and PSO (MOHFPSO) [10], modified grasshopper optimization algorithm (MGOA) [11], forced initialized differential evolution algorithm [12], an adaptive multiple teams perturbation-guiding Jaya (AMTPG-Jaya) technique [13], modified Sine-Cosine algorithm (MSCA) [14], Developed Grey Wolf Optimizer (DGWO) [15], improved salp swarm algorithm (ISSA) [16], Barnacles Mating Optimizer (BMO) [17], and Lévy Coyote optimization algorithm (LCOA) [18] These three versions of the Rao algorithm have been recently published, many optimization problems have been solved using them and using their modifications such as the photovoltaic cell parameter estimation [19,20,21,22], design optimization of mechanical system components [23], selected thermodynamic [24], Optimal weight design of a spur gear train [25], 2D truss structures [26], multi-objective design optimization of selected heat sinks [27], optimal reactive power dispatch with renewable energy and time-varying demand uncertainty [28], and Classification of Parkinson disease [29]. I=1 where ai, b, and ci are the cost coefficients of ith generator
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