One of the main objectives of a peer-to-peer energy market is to efficiently manage distributed energy resources while creating additional financial benefits for the participants. Cooperative game theory offers such a framework, and the Shapley value, a cooperative game payoff allocation based on the participants’ marginal contributions made to the local energy coalition, is shown to be fair and efficient. However, its high computational complexity limits the size of the game. In order to improve this peer-to-peer cooperative scheme’s scalability, this paper investigates and adapts a stratified sampling method for the Shapley value estimation. It then proposes a multi-step sampling strategy to further reduce the computation time by dividing the samples into incremental parts and terminating the sampling process once a certain level of estimation performance is achieved. Finally, selected case studies demonstrate the effectiveness of the proposed method, which is able to scale up the game from 20 players to 100 players.
|Journal||International Journal of Control, Automation and Systems|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
Recommended by Associate Editor Soohee Han under the direction of Editor-in-Chief Keum-Shik Hong. This work was supported in part by the Engineering and Physical Sciences Research Council under Grants EP/N03466X/1, EP/S000887/1, and EP/S031901/1, and in part by the Oxford Martin Programme on Integrating Renewable Energy.
© 2021, ICROS, KIEE and Springer.
- Cooperative game theory
- Energy management
- Energy storage
- P2P energy sharing
- Shapley value