ANFIS-Based Energy Management for a Grid-Connected PV-Battery Hybrid System at Renewable Energy Integration Demonstrator of Indonesia
Article Sidebar
Main Article Content
Abstract
This paper presents the design and performance evaluation of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Energy Management System (EMS) for a grid-connected PV–battery hybrid system. The proposed EMS aims to coordinate the power exchange among the photovoltaic (PV) array, battery storage, and the utility grid to ensure reliable operation and optimal renewable utilization. The ANFIS controller receives three inputs PV power, load power, and battery state of charge (SOC) and generates the battery reference power () to regulate the charging and discharging processes. The system was trained using approximately 1600 data sets, with 30% used for testing and checking, achieving a final root-mean-square (RMS) error of 0.0589. The proposed control strategy was validated through three test scenarios: (1) constant load with variable irradiance, (2) variable load with constant irradiance, and (3) simultaneous variations of load and irradiance. Simulation results show that the ANFIS-based EMS effectively maintains power balance, prevents battery overcharge or deep discharge, and stabilizes SOC within the 30–80% range. In addition, the controller efficiently manages power sharing between the PV system, battery, and grid, ensuring optimal energy utilization and reduced grid dependency. Overall, the results confirm that the proposed EMS demonstrates adaptive, stable, and intelligent performance suitable for smart microgrid applications.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright
Submission of a manuscript implies that the submitted work has not been published before (except as part of a thesis or report, or abstract); that it is not under consideration for publication elsewhere; that its publication has been approved by all co-authors. If and when the manuscript is accepted for publication, the author(s) still hold the copyright and retain publishing rights without restrictions. Authors or others are allowed to multiply article as long as not for commercial purposes. For the new invention, authors are suggested to manage its patent before published. The license type is CC-BY-NC 4.0.
Disclaimer
No responsibility is assumed by publisher and co-publishers, nor by the editors for any injury and/or damage to persons or property as a result of any actual or alleged libelous statements, infringement of intellectual property or privacy rights, or products liability, whether resulting from negligence or otherwise, or from any use or operation of any ideas, instructions, procedures, products or methods contained in the material therein.