Grey Wolf Optimization-Based Global MPPT for Solar-Assisted Hybrid Energy Systems in Oil and Gas Production Facilities

Teuku Zulfadli; Muliadi; Yassir; Zamzami; Salahuddin

doi:10.29017/scog.v49i1.2056

Authors

Teuku Zulfadli Politeknik Negeri Lhokseumawe https://orcid.org/0000-0002-9579-2228
Muliadi Universitas Iskandarmuda https://orcid.org/0000-0002-8808-0927
Yassir Politeknik Negeri Lhokseumawe
Zamzami Politeknik Negeri Lhokseumawe
Salahuddin Politeknik Negeri Lhokseumawe

DOI:

https://doi.org/10.29017/scog.v49i1.2056

Keywords:

solar module, grey wolf optimization, partial shading, hybrid energy system

Abstract

Partial shading in photovoltaic (PV) modules produces multiple power peaks that reduce system efficiency if the global maximum power point is not properly tracked. This condition commonly occurs in oil and gas production facilities due to shadows from industrial structures, requiring MPPT methods with reliable global tracking capability. This study evaluates the Grey Wolf Optimization (GWO) algorithm for global MPPT under eight partial shading scenarios (12.5%–100%) using MATLAB/Simulink simulation. The results show that GWO successfully tracks the global maximum power point under single-, double-, and triple-peak conditions. Under 12.5% shading, the system produces 277.0 W with an efficiency of 99.78%; under 25% shading, it produces 266.7 W with an efficiency of 99.85%; and under 37.5% shading, it produces 204.2 W with an efficiency of 99.90%. Across all scenarios, the algorithm achieves efficiencies above 99% with an average efficiency of 99.61%, which is higher than the 97.20% reported in previous studies. This efficiency improvement of approximately 2–4% increases the contribution of solar energy in PV–diesel hybrid systems and potentially reduces fuel consumption while improving power supply reliability for critical loads in oil and gas production facilities. Unlike conventional metaheuristic approaches such as PSO-MPPT, Flower Pollination Algorithm (FPA), and Differential Evolution (DE), which are sensitive to parameter tuning or prone to premature convergence, the proposed GWO implementation employs a hierarchical three-agent update mechanism (α, β, δ) that enhances global exploration capability across complex multi-peak P–V characteristics. This distinguishes the present study from prior GWO-based MPPT work that relied solely on the alpha agent for position update.

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