Abstract
This research explores the optimization of Electric Vehicle (EV) charging strategies within DERMS-enabled smart grids using a multi-agent-based approach. The research deals with the issue of operating EV charging without affecting the grid stability, peak loads, and renewable energy sources. The approach is formulated around simulation of multi-agent systems (MAS) communication with Distributed Energy Resource Management Systems (DERMS) in a real-time manner to dynamically respond to charging schedule based on grid demand and availability of energy. Some of the major findings indicate that energy costs can be minimized by using the MAS, grid stability is boosted, and the whole energy charging processes become more efficient. The system's ability to prioritize EV charging based on factors like renewable energy availability and grid load leads to significant reductions in peak demand and better integration of renewable resources. This study concludes that optimization using multi-agents can become a key to the development of smart grid technologies and e-mobility development in general.
Keywords
EV Charging, Smart Grids, Energy Optimization, Grid Stability, Multi-Agent Systems, Renewable Energy
Citations
IRE Journals:
Arjun Pedapati
"Multi-Agent-Based Optimization of EV Charging Strategies within DERMS-Enabled Smart Grids" Iconic Research And Engineering Journals Volume 8 Issue 8 2025 Page 975-986
IEEE:
Arjun Pedapati
"Multi-Agent-Based Optimization of EV Charging Strategies within DERMS-Enabled Smart Grids" Iconic Research And Engineering Journals, 8(8)
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