Towards DQN Reinforcement Learning for energy management for bidirectional charging of EV’s
Year: 2026
Type: article
Abstract: This study applies Deep Q-Network (DQN) reinforcement learning to optimize bidirectional EV charging in a microgrid with dynamic pricing and renewable energy. The environment includes an EV, wind turbine, stationary battery, flexible household loads, and grid connection. DQN agents learn to minimize energy costs by charging during low-price periods and discharging during high-price windows. Simulations across four scenarios show improved cumulative rewards and grid efficiency. Future work will address stochastic elements, realistic EV availability, and continuous action spaces to enhance adaptability and performance in real-world applications.
Source: OJS
Institution: Offenburg University of Applied Sciences
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7141700368
FWCI:
Cites:
Cited by:
Related to: