Intelligent ANFIS-controlled solar PV energy management system with GTO-based multi-agent optimization for smart grid applications
DOI:
https://doi.org/10.65746/jbrha113Keywords:
solar PV, adaptive neuro-fuzzy inference system (ANFIS) controller, group teaching optimization, multi-agent system (MAS), energy, voltageAbstract
A solar power plant is a large-scale facility that converts sunlight into electricity using photovoltaic (PV) panels or to supply renewable energy to the grid or commercial users. The proposed solution offers a solar power plant’s intelligent energy management framework along with cutting-edge optimization methods and hybrid energy sources. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller, the architecture integrates a solar PV module, utility grid supply, and battery storage. By managing solar irradiance uncertainty and dynamic demand situations, the ANFIS controller effectively controls power flow between generating, storage, and load components. To guarantee steady functioning and longer battery life, a charger subsystem controls battery charging and discharging. Additionally, a Multi-Agent System (MAS) based on Group Teaching Optimization (GTO) is used to optimize energy distribution among various loads, including EVs, drones, and portable gadgets, enhancing overall system dependability and efficiency. In contemporary smart grid contexts, the combination of intelligent control and metaheuristic optimization improves energy efficiency, reduces reliance on grid power, and promotes sustainable and autonomous energy management.
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Copyright (c) 2026 Saravanakumar Samy, Ashok Kumar Loganathan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
