Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162463
Title: Optimal network charge for peer-to-peer energy trading: a grid perspective
Authors: Yang, Yu
Chen, Yue
Hu, Guoqiang
Spanos, Costas J.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Yang, Y., Chen, Y., Hu, G. & Spanos, C. J. (2022). Optimal network charge for peer-to-peer energy trading: a grid perspective. IEEE Transactions On Power Systems. https://dx.doi.org/10.1109/TPWRS.2022.3185585
Journal: IEEE Transactions on Power Systems 
Abstract: Peer-to-peer (P2P) energy trading is a promising market scheme to accommodate the increasing distributed energy resources (DERs). However, how P2P to be integrated into the existing power systems remains to be investigated. In this paper, we apply network charge as a means for the grid operator to attribute transmission loss and ensure network constraints for empowering P2P transaction. The interaction between the grid operator and the prosumers is modeled as a Stackelberg game, which yields a bi-level optimization problem. We prove that the Stackelberg game admits an equilibrium network charge price. Besides, we propose a method to obtain the network charge price by converting the bi-level optimization into a single-level mixed-integer quadratic programming (MIQP), which can handle a reasonable scale of prosumers efficiently. Simulations on the IEEE bus systems show that the proposed optimal network charge is favorable as it can benefit both the grid operator and the prosumers for empowering the P2P market, and achieves near-optimal social welfare. Moreover, the results show that the presence of energy storage will make the prosumers more sensitive to the network charge price changes.
URI: https://hdl.handle.net/10356/162463
ISSN: 0885-8950
DOI: 10.1109/TPWRS.2022.3185585
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPWRS.2022.3185585.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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