Web: http://arxiv.org/abs/2209.07744

Sept. 19, 2022, 1:11 a.m. | Sangkeum Lee, Hojun Jin, Sarvar Hussain Nengroo, Taewook Heo, Yoonmee Doh, Chungho Lee, Dongsoo Har

cs.LG updates on arXiv.org arxiv.org

In order to replace fossil fuels with the use of renewable energy resources,
unbalanced resource production of intermittent wind and photovoltaic (PV) power
is a critical issue for peer-to-peer (P2P) power trading. To resolve this
problem, a reinforcement learning (RL) technique is introduced in this paper.
For RL, graph convolutional network (GCN) and bi-directional long short-term
memory (Bi-LSTM) network are jointly applied to P2P power trading between
nanogrid clusters based on cooperative game theory. The flexible and reliable
DC nanogrid …

arxiv energy p2p reinforcement reinforcement learning resources trading

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