Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
arXiv:2606.31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources. To enable efficient integration of EVs while minimizing costs for users and avoiding network overloads, implicit coordination between EVs is required. This work compares two independent multi-agent reinforcement learning approaches for optimizing such decentralized EV charging: contextual combinatorial band
The rapid increase in Electric Vehicle adoption necessitates immediate solutions for managing grid demand effectively, especially as renewable energy integration scales.
Efficient EV charging is critical to prevent grid overloads and high energy costs, directly impacting energy infrastructure investment and consumer adoption of EVs.
Approaches to managing large-scale EV charging are shifting towards more decentralized, AI-driven coordination, reducing reliance on centralized grid control.
- · EV owners
- · Smart grid technology providers
- · Renewable energy producers
- · Energy management software companies
- · Traditional utility companies (if slow to adapt)
- · Fossil fuel power generators
- · Local grid infrastructure lacking smart capabilities
Widespread adoption of smart charging optimizes grid stability and reduces electricity costs for EV users.
Decentralized AI-driven energy management becomes a standard for integrating other distributed energy resources, accelerating grid modernization.
This could lead to 'energy sovereignty' for communities, managing their own microgrids with local generation and storage optimized by AI.
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Read at arXiv cs.AI