Reinforcement Learning Based Cooperative P2P Energy Trading between DC Nanogrid Clusters with Wind and PV Energy Resources. (arXiv:2209.07744v1 [cs.LG])

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