Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering
This addresses scalability issues for prosumers in energy markets, but it is incremental as it adapts an existing clustering method to a known bottleneck.
The paper tackled the exponential computational complexity of a cooperative game model for peer-to-peer energy sharing by applying K-means clustering to energy profiles, resulting in significantly improved scalability while maintaining high financial incentives for prosumers.
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.