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Design of an optimal P2P energy trading market model using bilevel stochastic optimization

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  • Singh, Kamini
  • Gadh, Rajit
  • Singh, Anoop
  • Lal Dewangan, Chaman

Abstract

In a smart grid, with the emergence of behind-the-meter distributed energy resources (DER) integration, multiple consumers are emerging as prosumers. A prosumer’s chance of maximizing the payoff through peer-to-peer (P2P) trading is bringing a prolific change in the existing energy market. However, for any successful P2P trading, it requires a mutual agreement on participation (peer-matching) between a prosumer (seller) and a consumer (buyer). The presented work projects a two-level stochastic P2P energy trading model between the neighborhood peers in the distributed electricity market (DEM). A systematic and structured peer-matching mechanism, namely ‘enTrade’, is designed and introduced to accelerate the real-time P2P trading between the players (buyer & seller). The algorithm is developed into two parts. In the first part, we consider the players’ stochastic behavior in decision-making, and a joint probability density function is used to design the players ‘stochastic optimal bidding strategy. In the second part, the ‘enTrade’ uses a game theory-based leader–follower model to maximize the likelihood of players ‘bid matching for a successful P2P energy trading in DEM and optimizes the player’s payoff. The developed algorithm uses the transmission constraints to show that the voltage loss between the trading nodes is within the limit. Further, a low-voltage distribution transformer (LVDT) node-27 flow bus is used to showcase the efficacy of the presented work for a successful P2P transaction between the ‘enTrade’ matched-up peers.

Suggested Citation

  • Singh, Kamini & Gadh, Rajit & Singh, Anoop & Lal Dewangan, Chaman, 2022. "Design of an optimal P2P energy trading market model using bilevel stochastic optimization," Applied Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014507
    DOI: 10.1016/j.apenergy.2022.120193
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    References listed on IDEAS

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