An Asynchronous Online Negotiation Mechanism for Real-Time Peer-to-Peer Electricity Markets

Zhenwei Guo, Pierre Pinson, Qianhong Wu, Shibo Chen, Qinmin Yang, Zaiyue Yang

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Participants in electricity markets are becoming more proactive because of the fast development of DERs and DSM, which also boosts the emergence of P2P market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. Therefore, a practicable real-time P2P market mechanism is in urgent need. However, it is technically challenging to deploy P2P mechanisms in real-time, since they most often involve a heavy computation burden, while the time available for negotiation in real-time is very short. Our core contribution is to design a novel asynchronous online optimization framework to enable the real-time P2P market negotiation mechanism, which can greatly reduce the computation and communication burden from two aspects. First, a novel online consensus ADMM algorithm is proposed, which can greatly reduce the computation complexity since only one iteration is performed for each agent in every time period. Second, the market operates in an asynchronous mode so that all agents can freely trade without waiting for idle neighboring agents. The sublinear regret upper bound is proved for our online algorithm, which indicates that social welfare can be maximized in the long run on time average.
Original languageEnglish
JournalIEEE Transactions on Power Systems
Number of pages13
ISSN0885-8950
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Asynchronous online consensus ADMM
  • Convex functions
  • Electricity supply industry
  • forgetting factor
  • non-stationary regret
  • Optimization
  • Peer-to-peer computing
  • Real-time P2P markets
  • Real-time systems
  • Renewable energy sources
  • Upper bound

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