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Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility

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  • Lee, Zachary E.
  • Zhang, K. Max

Abstract

Transactive energy systems use dynamic electricity markets to present an improved way to control large numbers of distributed energy resources while maintaining user privacy, autonomy and scalability. However, this access to dynamic electricity prices can introduce negative emergent behavior like high peak loads if not adequately regulated by grid operators. To solve this, this paper proposes a regulated peer-to-peer market structure for residential prosumers. Prosumers bid energy demand or supply curves into a peer-to-peer market, which can then be directly used by the utility to quantify and dispatch demand flexibility. A utility surcharge mechanism can then be employed to dispatch this flexibility, and achieve significant improvements to reliability. This framework is tested in a simulation environment of electrified residential buildings with significantly enhanced fidelity representing the real-world housing stock. New York State and the month of January was chosen for the demonstration purpose, considering that January is one of the most challenging months for fully electrified systems in cold climates as solar production occurs at opposite times of the highest heating load. Results show that the surcharge can reducing peak load and ramp rates by nearly 50%, while also encouraging consumption of locally generated electricity.

Suggested Citation

  • Lee, Zachary E. & Zhang, K. Max, 2023. "Regulated peer-to-peer energy markets for harnessing decentralized demand flexibility," Applied Energy, Elsevier, vol. 336(C).
  • Handle: RePEc:eee:appene:v:336:y:2023:i:c:s0306261923000363
    DOI: 10.1016/j.apenergy.2023.120672
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    References listed on IDEAS

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    Cited by:

    1. Hussain, Shahid & Irshad, Reyazur Rashid & Pallonetto, Fabiano & Hussain, Ihtisham & Hussain, Zakir & Tahir, Muhammad & Abimannan, Satheesh & Shukla, Saurabh & Yousif, Adil & Kim, Yun-Su & El-Sayed, H, 2023. "Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles," Applied Energy, Elsevier, vol. 352(C).
    2. Wu, Chun & Chen, Xingying & Hua, Haochen & Yu, Kun & Gan, Lei & Shen, Jun & Ding, Yi, 2024. "Peer-to-peer energy trading optimization for community prosumers considering carbon cap-and-trade," Applied Energy, Elsevier, vol. 358(C).
    3. You, Zhengjie & Lumpp, Sebastian Dirk & Doepfert, Markus & Tzscheutschler, Peter & Goebel, Christoph, 2024. "Leveraging flexibility of residential heat pumps through local energy markets," Applied Energy, Elsevier, vol. 355(C).
    4. Escobar, Eros D. & Betancur, Daniel & Manrique, Tatiana & Isaac, Idi A., 2023. "Model predictive real-time architecture for secondary voltage control of microgrids," Applied Energy, Elsevier, vol. 345(C).

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    Keywords

    P2P; Renewable energy; Building systems; Microgrid;
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