  Various peer-to-peer energy markets have emerged in recent years in an
attempt to manage distributed energy resources in a more efficient way. One of
the main challenges these models face is how to create and allocate incentives
to participants. Cooperative game theory offers a methodology to financially
reward prosumers based on their contributions made to the local energy
coalition using the Shapley value, but its high computational complexity limits
the size of the game. This paper explores a stratified sampling method proposed
in existing literature for Shapley value estimation, and modifies the method
for a peer-to-peer cooperative game to improve its scalability. Finally,
selected case studies verify the effectiveness of the proposed coalitional
stratified random sampling method and demonstrate results from large games.
