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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Process and Energy Systems Engineering
Volume 12 - 2024 |
doi: 10.3389/fenrg.2024.1492243
This article is part of the Research Topic Urban Multi-energy System Networks with High Proportion of Renewable Energy View all 7 articles
Microgrid System for Electric Vehicle Charging Station Integrated with Renewable Energy Sources using Hybrid DOA-SBNN Approach
Provisionally accepted- 1 Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India
- 2 College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
- 3 Department of Electrical Engineering, University of business and technology, Jeddah, Saudi Arabia
Microgrid-equipped electric vehicle charging stations offer economical and sustainable power sources. In addition to supporting eco-friendly mobility, the technology lowers grid dependency and improves energy reliability. The manuscript introduces hybrid technique for efficient Electric Vehicle (EV) Charging integrating Dollmaker Optimization Algorithm (DOA) and Spatial Bayesian Neural Networks (SBNN). This method optimizes the joint operation of Photovoltaic (PV), Wind turbine (WT), Super-capacitors' (SCs') and Battery Energy Storage Systems (BESS) in microgrids to enhance EV charging station efficiency, reliability, and power quality while reducing grid outages. SBNN predicts EV load demand for improved efficiency and reliability, while DOA manages Microgrid (MG) fluctuations to ensure seamless EV charging. The MG system features a Four Phase-Inductor Coupled Interleaved Boost Converter (FP-ICIBC) and Fractional Order Proportional Integral Derivative
Keywords: AC, alternating current, BESS, Battery Energy Storage Systems, CS, Charging Stations, DC, Direct current, DOA, Dollmaker Optimization Algorithm, DNN, Deep Neural Network, EV, Electric Vehicle, FP-ICIBC Four-Phase Inductor Coupled Interleaved Boost Converter, FOPID, Fractional Order Proportional Integral Controller
Received: 06 Sep 2024; Accepted: 02 Dec 2024.
Copyright: © 2024 Sai Eswar, Arun Noyal Doss, Shorfuzzaman and Elrashidi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Kommoju Naga Durga Veera Sai Eswar, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, 600 033, Tamil Nadu, India
Mohan Arun Noyal Doss, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, 600 033, Tamil Nadu, India
Ali Elrashidi, Department of Electrical Engineering, University of business and technology, Jeddah, Saudi Arabia
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