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A framework for participation of prosumers in peer-to-peer energy trading and flexibility markets

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Listed:
  • Khorasany, Mohsen
  • Shokri Gazafroudi, Amin
  • Razzaghi, Reza
  • Morstyn, Thomas
  • Shafie-khah, Miadreza

Abstract

As the owners of distributed energy resources (DER), prosumers can actively manage their power supply and consumption and partake in new energy services. In order to enable prosumers to benefit from their participation in energy services, innovative market models need to be designed. This paper proposes a framework for local energy and flexibility trading within distribution networks, in which prosumers participate in a peer-to-peer (P2P) market to trade energy with each other based on their preferences. The P2P market is cleared in a decentralized manner with direct interaction of seller and buyer prosumers. Then, the distribution system operator (DSO) checks the network constraints based on the energy scheduling of prosumers. If the network constraints are not satisfied, the DSO calculates the flexibility that is required in each feeder to avoid network issues. Triggered by the requested flexibility by the DSO, prosumers in each feeder form a community and participate in a flexibility market, in which they can offer their flexibility in response to the DSO’s request. An iterative auction is employed to clear the flexibility market, which enables the prosumers to independently decide on their offered flexibility, while the DSO adjusts the flexibility price to minimize its costs. The proposed framework is tested on a real-world distribution network. Simulations based on a number of case studies indicate that through the proposed framework, the DSO can avoid network constraints violation by employing prosumers’ flexibility. Besides, participation in the P2P and flexibility trading reduces the net energy costs of the prosumers in different community by an average of 17.09%.

Suggested Citation

  • Khorasany, Mohsen & Shokri Gazafroudi, Amin & Razzaghi, Reza & Morstyn, Thomas & Shafie-khah, Miadreza, 2022. "A framework for participation of prosumers in peer-to-peer energy trading and flexibility markets," Applied Energy, Elsevier, vol. 314(C).
  • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922003312
    DOI: 10.1016/j.apenergy.2022.118907
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    References listed on IDEAS

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    1. Gazafroudi, Amin Shokri & Khorasany, Mohsen & Razzaghi, Reza & Laaksonen, Hannu & Shafie-khah, Miadreza, 2021. "Hierarchical approach for coordinating energy and flexibility trading in local energy markets," Applied Energy, Elsevier, vol. 302(C).
    2. Khorasany, Mohsen & Razzaghi, Reza & Shokri Gazafroudi, Amin, 2021. "Two-stage mechanism design for energy trading of strategic agents in energy communities," Applied Energy, Elsevier, vol. 295(C).
    3. Park, Sung-Won & Zhang, Zhong & Li, Furong & Son, Sung-Yong, 2021. "Peer-to-peer trading-based efficient flexibility securing mechanism to support distribution system stability," Applied Energy, Elsevier, vol. 285(C).
    4. Ahmad, Ali & Kashif, Syed Abdul Rahman & Saqib, Muhammad Asghar & Ashraf, Arslan & Shami, Umar Tabrez, 2019. "Tariff for reactive energy consumption in household appliances," Energy, Elsevier, vol. 186(C).
    5. Thomas Morstyn & Niall Farrell & Sarah J. Darby & Malcolm D. McCulloch, 2018. "Using peer-to-peer energy-trading platforms to incentivize prosumers to form federated power plants," Nature Energy, Nature, vol. 3(2), pages 94-101, February.
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    Citations

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    Cited by:

    1. 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).
    2. Yang, Peiwen & Fang, Debin & Wang, Shuyi, 2022. "Optimal trading mechanism for prosumer-centric local energy markets considering deviation assessment," Applied Energy, Elsevier, vol. 325(C).
    3. Bożena Gajdzik & Magdalena Jaciow & Radosław Wolniak & Robert Wolny & Wieslaw Wes Grebski, 2023. "Energy Behaviors of Prosumers in Example of Polish Households," Energies, MDPI, vol. 16(7), pages 1-26, March.
    4. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    5. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Peer-to-Peer trading with Demand Response using proposed smart bidding strategy," Applied Energy, Elsevier, vol. 327(C).

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