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Energy trading in the distribution system using a non-model based game theoretic approach

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  • Bhatti, Bilal Ahmad
  • Broadwater, Robert

Abstract

A comprehensive energy trading market is proposed at the distribution level using a model-free, game theoretic approach. The proposed market is modeled as a non-cooperative, multiplayer game, and a Nash equilibrium solution is obtained using an extremum seeking algorithm. For the game model, a non-cooperative market structure is proposed with an embedded notion of each player’s reputation. Besides that, an index is proposed that tracks the commitments of each player, rewards them according to their past behavior, and improves market reliability. Moreover, the proposed index, referred to as a Market Reputation Index, promotes fairness by rewarding good players and also encourages installation of an energy management system with accurate forecasting. A new, low-complexity, model free approach (i.e., Extremum seeking) is demonstrated to model Nash seeking behavior of players and a unique Nash equilibrium solution is sought for the proposed game. Detailed case studies demonstrate how the game is setup and the convergence to Nash equilibrium is achieved. Results are analyzed to show the usefulness of the market reputation index in improving reliability and fairness. It is also shown that the proposed market results in an increased local generation, higher payoffs (profits) for the participating players, and lower market clearing prices.

Suggested Citation

  • Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:48
    DOI: 10.1016/j.apenergy.2019.113532
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    6. Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
    7. Jing, Rui & Xie, Mei Na & Wang, Feng Xiang & Chen, Long Xiang, 2020. "Fair P2P energy trading between residential and commercial multi-energy systems enabling integrated demand-side management," Applied Energy, Elsevier, vol. 262(C).
    8. Wicak Ananduta & Sergio Grammatico, 2022. "Equilibrium Seeking and Optimal Selection Algorithms in Peer-to-Peer Energy Markets," Games, MDPI, vol. 13(5), pages 1-13, October.
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