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arXiv Machine Learning By Pablo Benalcazar, Maciej Kalka, Wilian Guam\'an, Jacek Kami\'nski

Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities

Read the original on arXiv Machine Learning →

The paper compares rule‑based and reinforcement‑learning (RL) pricing mechanisms for peer‑to‑peer electricity trading in residential photovoltaic communities. Rule‑based benchmarks—bill‑sharing, mid‑market rate, and supply‑demand‑ratio pricing—outperform the best RL policy in a PV‑only setup, while RL policies achieve higher community savings when battery storage is added. Across both configurations, SDR‑shaped pricing outperforms multiplier‑based parameterization, but benefit distribution remains heterogeneous among households.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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