Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 12, Issue 4, Pages 3390-3403Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3063960
Keywords
Pricing; Distribution networks; Peer-to-peer computing; Indexes; Transactive energy; Reactive power; Smart grids; Distributed optimization; energy pricing; peer-to-peer market; transactive energy; and smart grid
Categories
Funding
- National Science Foundation [ECCS-1554626, TSG-01001-2020]
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This paper proposes a distributed pricing strategy for P2P transactive energy systems, considering voltage and line congestion management, and introduces a new mutual reputation index for product differentiation between prosumers. The effectiveness of the approach is validated through software simulations, demonstrating scalability, faster convergence, easy implementation, and maximum social welfare/profit.
In recent years, the rapid growth of active consumers in the distribution networks transforms the modern power markets' structure more independent, flexible, and distributed. Specifically, in the recent trend of peer-to-peer (P2P) transactive energy systems, the traditional consumers became prosumers (producer+consumer) who can maximize their energy utilization by sharing it with neighbors without any conventional arbitrator in the transactions. Although a distributed energy pricing scheme is inevitable in such systems to make optimal decisions, it is challenging to establish under the influence of non-linear physical network constraints with limited information. Therefore, this paper presents a distributed pricing strategy for P2P transactive energy systems considering voltage and line congestion management, which can be utilized in various power network topologies. This paper also introduces a new mutual reputation index as a product differentiation between the prosumers to consider their bilateral trading willingness. In this paper, a Fast Alternating Direction Method of Multipliers (F-ADMM) algorithm is realized instead of the standard ADMM algorithm to improve the convergence rate. The effectiveness of the proposed approach is validated through software simulations. The result shows that the algorithm is scalable, converges faster, facilitates easy implementation, and ensures maximum social welfare/profit.
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