Enhancing Renewable Energy Integration: A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks
Ali S. Alghamdi1,*, Mohamed A. Zohdy2, Saad Aldoihi3,4
1 Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah, 11952, Saudi Arabia
2 Electrical and Computer Engineering Department, Oakland University, Rochester, MI, 48309, USA
3 Institute of Earth and Space Science, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia
4 Computer Science and Systems Engineering, Institute Polytechnique de Paris, Palaiseau Cedex, France
* Corresponding Author: Ali S. Alghamdi. Email: 
Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.048839
Received 20 December 2023; Accepted 07 March 2024; Published online 10 April 2024
Abstract
In the contemporary era, the global expansion of electrical grids is propelled by various renewable energy sources (RESs). Efficient integration of stochastic RESs and optimal power flow (OPF) management are critical for network optimization. This study introduces an innovative solution, the Gaussian Bare-Bones Levy Cheetah Optimizer (GBBLCO), addressing OPF challenges in power generation systems with stochastic RESs. The primary objective is to minimize the total operating costs of RESs, considering four functions: overall operating costs, voltage deviation management, emissions reduction, voltage stability index (VSI) and power loss mitigation. Additionally, a carbon tax is included in the objective function to reduce carbon emissions. Thorough scrutiny, using modified IEEE 30-bus and IEEE 118-bus systems, validates GBBLCO’s superior performance in achieving optimal solutions. Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios: total cost with valve point effects, total cost with emission and carbon tax, total cost with prohibited operating zones, active power loss optimization, voltage deviation optimization and enhancing voltage stability index (VSI). GBBLCO outperforms conventional techniques in each scenario, showcasing rapid convergence and superior solution quality. Notably, GBBLCO navigates complexities introduced by valve point effects, adapts to environmental constraints, optimizes costs while considering prohibited operating zones, minimizes active power losses, and optimizes voltage deviation by enhancing the voltage stability index (VSI) effectively. This research significantly contributes to advancing OPF, emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima. GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems, offering a promising solution for the evolving needs of renewable energy-integrated power grids.
Keywords
Renewable energy integration; optimal power flow; stochastic renewable energy sources; gaussian-bare-bones levy cheetah optimizer; electrical network optimization; carbon tax optimization
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