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

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

Applied Energy, 2022, vol. 306, issue PB, No S0306261921013350

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.

Keywords: Peer-to-peer; Buildings; Multi-energy copuling; Mixed-integer linear programming; Parametric optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2021.118040

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