  Among the various market structures under peer-to-peer energy sharing, one
model based on cooperative game theory provides clear incentives for prosumers
to collaboratively schedule their energy resources. The computational
complexity of this model, however, increases exponentially with the number of
participants. To address this issue, this paper proposes the application of
K-means clustering to the energy profiles following the grand coalition
optimization. The cooperative model is run with the "clustered players" to
compute their payoff allocations, which are then further distributed among the
prosumers within each cluster. Case studies show that the proposed method can
significantly improve the scalability of the cooperative scheme while
maintaining a high level of financial incentives for the prosumers.

Computational Engineering, Finance, and Science;Computer Science and Game Theory;Machine Learning;General Economics;Optimization and Control;Economics