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

ABSTRACT: 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

SUBMITTER: Bahabri MO 

PROVIDER: S-EPMC12749602 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature