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<p>As Saudi Arabia accelerates its transition to intelligent, sustainable energy systems under Vision 2030, advanced control of renewable microgrids becomes critical. This study proposes an artificial neural network-based hierarchical intelligent control framework for a fully renewable hybrid microgrid powering a residential villa in Jeddah, Saudi Arabia. The system integrates a 36&#xa0;kW solar PV array, 10&#xa0;kW wind turbine system, and 100 kWh lithium-ion battery to meet a daily load of 177.5 kWh. Neural networks are embedded across all control layers: MLP-based MPPT for solar and wind sources, NARMA-L2 for battery management, and an intelligent EMS for system-wide coordination. Simulations conducted in MATLAB/Simulink, under realistic variable irradiance (0.1–0.8&#xa0;kW/m²) and wind speeds (3.5–5&#xa0;m/s) characteristic of Jeddah, quantitatively demonstrate the superior performance of the proposed framework. Specifically, the MLP-based MPPT controllers achieves a power tracking enhancement of up to 12% compared to conventional MPPT methods, significantly improving energy harvesting efficiency. The NARMA-L2 battery management controller exhibits a reduction in DC bus voltage error by approximately 10% and an improvement in recovery time by over 5% when benchmarked against traditional PI control, underscoring its enhanced stability and responsiveness. Furthermore, the intelligent EMS successfully maintained real-time load balancing and optimized battery operation across five distinct dynamic load scenarios, confirming its robustness. The simulation results validate the effectiveness, resilience, and scalability of the proposed neural network-based hierarchical control system, positioning it as a promising solution for smart residential microgrids in challenging coastal environments.</p>

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Artificial neural network based hierarchical intelligent control framework for a residential microgrid

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月23日

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  • Mohammed O. Bahabri,
  • Sreerama Kumar Ramdas,
  • Hussam A. Banawi

摘要

As Saudi Arabia accelerates its transition to intelligent, sustainable energy systems under Vision 2030, advanced control of renewable microgrids becomes critical. This study proposes an artificial neural network-based hierarchical intelligent control framework for a fully renewable hybrid microgrid powering a residential villa in Jeddah, Saudi Arabia. The system integrates a 36 kW solar PV array, 10 kW wind turbine system, and 100 kWh lithium-ion battery to meet a daily load of 177.5 kWh. Neural networks are embedded across all control layers: MLP-based MPPT for solar and wind sources, NARMA-L2 for battery management, and an intelligent EMS for system-wide coordination. Simulations conducted in MATLAB/Simulink, under realistic variable irradiance (0.1–0.8 kW/m²) and wind speeds (3.5–5 m/s) characteristic of Jeddah, quantitatively demonstrate the superior performance of the proposed framework. Specifically, the MLP-based MPPT controllers achieves a power tracking enhancement of up to 12% compared to conventional MPPT methods, significantly improving energy harvesting efficiency. The NARMA-L2 battery management controller exhibits a reduction in DC bus voltage error by approximately 10% and an improvement in recovery time by over 5% when benchmarked against traditional PI control, underscoring its enhanced stability and responsiveness. Furthermore, the intelligent EMS successfully maintained real-time load balancing and optimized battery operation across five distinct dynamic load scenarios, confirming its robustness. The simulation results validate the effectiveness, resilience, and scalability of the proposed neural network-based hierarchical control system, positioning it as a promising solution for smart residential microgrids in challenging coastal environments.