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arXiv Machine Learning By Meiyi Li, Lucas Ruda, Javad Mohammadi

LOOP-MAC: Learning-Based Multi-Agent Coordination with Guaranteed Local Feasibility in a Virtual Power Plant

Read the original on arXiv Machine Learning →

LOOP-MAC is a decentralized learning-based framework for coordinating distributed energy resources in a virtual power plant. It guarantees local feasibility by construction and reduces the aggregate commitment gap through a two-stage decision process: learned coordination with neural optimizers and a local adjustment step that rebalances schedules without extra communication. In a 100-agent test, five communication rounds plus adjustment lowered the commitment gap from 5.1% to 0.75% and the optimality gap from 3.93% to 3.07%, with per-agent computation around 1.1 ms.

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