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Enhancing scalability of peer-to-peer energy markets using adaptive segmentation method

Mohsen Khorasany, Yateendra Mishra, Behrouz Babaki and Gerard Ledwich

Article (2019)

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Cite this document: Khorasany, M., Mishra, Y., Babaki, B. & Ledwich, G. (2019). Enhancing scalability of peer-to-peer energy markets using adaptive segmentation method. Journal of Modern Power Systems and Clean Energy, 7(4), p. 791-801. doi:10.1007/s40565-019-0510-0
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This paper proposes an adaptive segmentation method as a market clearing mechanism for peer-to-peer (P2P) energy trading scheme with large number of market players. In the proposed method, market players participate in the market by announcing their bids. In the first step, players are assigned to different segments based on their features, where the balanced k-means clustering method is implemented to form segments. These segments are formed based on the similarity between players, where the amount of energy for trade and its corresponding price are considered as features of players. In the next step, a distributed method is employed to clear the market in each segment without any need to private information of players. The novelty of this paper relies on developing an adaptive algorithm for dividing large number of market players into multiple segments to enhance scalability of the P2P trading by reducing data exchange and communication overheads. The proposed approach can be used along with any distributed method for market clearing. In this paper, two different structures including community-based market and decentralized bilateral trading market are used to demonstrate the efficacy of the proposed method. Simulation results show the beneficial properties of the proposed segmentation method.

Uncontrolled Keywords

Energy trading; Market segmentation; Distributed optimization; Peer-to-peer market; Alternating direction method of multipliers

Open Access document in PolyPublie
Subjects: 2500 Génie électrique et électronique > 2501 Réseaux électriques
2950 Mathématiques appliquées > 2956 Optimisation et théories de commande optimale
Department: Département de mathématiques et de génie industriel
Research Center: Non applicable
Date Deposited: 04 Jul 2022 16:13
Last Modified: 05 Jul 2022 01:20
PolyPublie URL: https://publications.polymtl.ca/4972/
Document issued by the official publisher
Journal Title: Journal of Modern Power Systems and Clean Energy (vol. 7, no. 4)
Publisher: Springer Nature
Official URL: https://doi.org/10.1007/s40565-019-0510-0


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