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Multi-Agent Deep reinforcement learning for EV aggregator bidding in Renewable-Dominated electricity markets
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DOI：10.1016/j.ijepes.2025.111444.png)
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Abstract
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• A unified multi-agent market model simulates strategic interactions under high renewable penetration. • The framework combines fleet scheduling and bidding via MADDPG with Prioritized Experience Replay. • Adaptive bidding strategies are learned in continuous action spaces. • The simulation on a 30-bus system proves improved efficiency and reliability versus baselines.
Keywords:
Electric vehicle aggregator
Renewable energy integration
Multi-agent reinforcement learning
Electricity market bidding
Deep deterministic policy gradient
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