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Parametric optimization-based peer-to-peer energy trading among commercial buildings considering multiple energy conversion

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  • Zhang, Heng
  • Zhang, Shenxi
  • Hu, Xiao
  • Cheng, Haozhong
  • Gu, Qingfa
  • Du, Mengke

Abstract

A peer-to-peer energy trading framework of buildings considering multi-energy coupling is presented in this study to improve energy efficiency and achieve cost savings. Microsources, such as photovoltaic and micro-combined heat and power, are used to meet electricity and heating demands, while the problem of unbalanced power is solved via the main grid and other peers through peer-to-peer energy trading. All peers aim to maximize their revenue and create trading strategies (i.e., trading period and volume) based on the time-of-use tariff released by the main grid. The trading model established by each peer is a parametric optimization-based problem because each peer must consider the trading willingness of other peers when formulating strategies. A new mixed-integer linear programming problem to maximize total profit can be finally formulated to obtain an equilibrium solution that satisfies all peers. Results show that the proposed peer-to-peer transaction approach can reduce energy bills of buildings. Daily purchased electricity from the main grid decreases by 8.9% through peer-to-peer energy trading between different buildings. In addition, compared with the building peer-to-peer transaction that only considers electricity energy management, the amount of purchased electricity from the main network can be reduced by 4.2% a day when multi-energy coupling is considered in peer-to-peer.

Suggested Citation

  • Zhang, Heng & Zhang, Shenxi & Hu, Xiao & Cheng, Haozhong & Gu, Qingfa & Du, Mengke, 2022. "Parametric optimization-based peer-to-peer energy trading among commercial buildings considering multiple energy conversion," Applied Energy, Elsevier, vol. 306(PB).
  • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013350
    DOI: 10.1016/j.apenergy.2021.118040
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    Cited by:

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    4. Qiu, Dawei & Xue, Juxing & Zhang, Tingqi & Wang, Jianhong & Sun, Mingyang, 2023. "Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading," Applied Energy, Elsevier, vol. 333(C).

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