Estimation of the Shapley Value of a Peer-to-Peer Energy Sharing Game using Coalitional Stratified Random Sampling

TL;DR

This paper proposes a modified stratified sampling method to efficiently estimate the Shapley value in large peer-to-peer energy sharing games, addressing computational challenges in cooperative game theory.

Contribution

It introduces an improved stratified sampling approach tailored for peer-to-peer energy games, enhancing scalability and estimation accuracy.

Findings

The method accurately estimates Shapley values in large-scale energy sharing games.

Case studies demonstrate the approach's effectiveness and scalability.

Results show significant computational efficiency gains.

Abstract

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.