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Dynamic pricing for decentralized energy trading in micro-grids

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  • Liu, Youbo
  • Zuo, Kunyu
  • Liu, Xueqin (Amy)
  • Liu, Junyong
  • Kennedy, Jason M.

Abstract

The fast deployment of distributed energy resources in the electric power system has highlighted the need for an efficient energy trading transactive model, without the need for centralized dispatch. In this field, a particular challenge is the determination of an effective pricing scheme that is able to produce benefits for all participants. In this paper, a novel dynamic pricing methodology is presented, offering a market-oriented means to drive decentralized energy trading and to optimize financial benefits for owners of distributed energy resources. Firstly, a price-responsive model for each type of distributed energy resource is investigated. Particularly, the decoupled State of Charge function is proposed to calculate the value of a single charging/discharging action for energy storage systems. In addition, an adaptable three-tiered framework is designed, including micro-grid balancing, aggregator scheduling, and trading optimization. By launching Tier I, II, and III, the spot prices for participants are iteratively updated and optimized in inner-micro-grid, inner-aggregator, and inter-aggregators level. The framework is able to maximize the financial savings from renewable energy, and meanwhile, provide a dynamic price signal to assist stakeholders in determining response actions and trading strategies. A realistic case is simulated using Java Agent Development framework based multi-agent modeling. The results indicate that the presented methodology enables decentralized energy trading and permits easier marketization of micro-grids with a high share of distributed energy resources.

Suggested Citation

  • Liu, Youbo & Zuo, Kunyu & Liu, Xueqin (Amy) & Liu, Junyong & Kennedy, Jason M., 2018. "Dynamic pricing for decentralized energy trading in micro-grids," Applied Energy, Elsevier, vol. 228(C), pages 689-699.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:689-699
    DOI: 10.1016/j.apenergy.2018.06.124
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    16. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    17. Ju, Liwei & Tan, Qinliang & Lin, Hongyu & Mei, Shufang & Li, Nan & Lu, Yan & Wang, Yao, 2020. "A two-stage optimal coordinated scheduling strategy for micro energy grid integrating intermittent renewable energy sources considering multi-energy flexible conversion," Energy, Elsevier, vol. 196(C).
    18. Adia Khalid & Sheraz Aslam & Khursheed Aurangzeb & Syed Irtaza Haider & Mahmood Ashraf & Nadeem Javaid, 2018. "An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid," Energies, MDPI, vol. 11(12), pages 1-17, December.
    19. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K., 2020. "Investigating the impact of P2P trading on power losses in grid-connected networks with prosumers," Applied Energy, Elsevier, vol. 263(C).
    20. Mangirdas Morkunas & Gintaras Cernius & Gintare Giriuniene, 2019. "Assessing Business Risks of Natural Gas Trading Companies: Evidence from GET Baltic," Energies, MDPI, vol. 12(14), pages 1-14, July.
    21. Janko, Samantha & Johnson, Nathan G., 2020. "Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks," Applied Energy, Elsevier, vol. 277(C).

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