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A Robust Participation in the Load Following Ancillary Service and Energy Markets for a Virtual Power Plant in Western Australia

Author

Listed:
  • Behnaz Behi

    (School of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

  • Philip Jennings

    (School of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

  • Ali Arefi

    (School of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

  • Ali Azizivahed

    (School of Electrical and Data Engineering, University of Technology Sydney, Broadway, NSW 2007, Australia)

  • Almantas Pivrikas

    (School of Engineering and Energy, Murdoch University, Murdoch, WA 6150, Australia)

  • S. M. Muyeen

    (Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

  • Arian Gorjy

    (Innogreen Technologies Pty Ltd., Perth, WA 6000, Australia)

Abstract

Virtual power plants (VPPs) are an effective platform for attracting private investment and customer engagement to speed up the integration of green renewable resources. In this paper, a robust bidding strategy to participate in both energy and ancillary service markets in the wholesale electricity market is proposed for a realistic VPP in Western Australia. The strategy is accurate and fast, so the VPP can bid in a very short time period. To engage customers in the demand management schemes of the VPP, the gamified approach is utilized to make the exercise enjoyable while not compromising their comfort levels. The modelling of revenue, expenses, and profit for the load-following ancillary service (LFAS) is provided, and the effective bidding strategy is developed. The simulation results show a significant improvement in the financial indicators of the VPP when participating in both the LFAS and energy markets. The payback period can be improved by 3 years to the payback period of 6 years and the internal rate of return (IRR) by 7.5% to the IRR of 18% by participating in both markets. The accuracy and speed of the proposed bidding strategy method is evident when compared with a mathematical method.

Suggested Citation

  • Behnaz Behi & Philip Jennings & Ali Arefi & Ali Azizivahed & Almantas Pivrikas & S. M. Muyeen & Arian Gorjy, 2023. "A Robust Participation in the Load Following Ancillary Service and Energy Markets for a Virtual Power Plant in Western Australia," Energies, MDPI, vol. 16(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3054-:d:1108870
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    References listed on IDEAS

    as
    1. Behnaz Behi & Ali Baniasadi & Ali Arefi & Arian Gorjy & Philip Jennings & Almantas Pivrikas, 2020. "Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study," Energies, MDPI, vol. 13(10), pages 1-24, May.
    2. Moghaddam, Saeed Zolfaghari & Akbari, Tohid, 2018. "Network-constrained optimal bidding strategy of a plug-in electric vehicle aggregator: A stochastic/robust game theoretic approach," Energy, Elsevier, vol. 151(C), pages 478-489.
    3. Wang, Han & Riaz, Shariq & Mancarella, Pierluigi, 2020. "Integrated techno-economic modeling, flexibility analysis, and business case assessment of an urban virtual power plant with multi-market co-optimization," Applied Energy, Elsevier, vol. 259(C).
    4. Behnaz Behi & Ali Arefi & Philip Jennings & Arian Gorjy & Almantas Pivrikas, 2021. "Advanced Monitoring and Control System for Virtual Power Plants for Enabling Customer Engagement and Market Participation," Energies, MDPI, vol. 14(4), pages 1-19, February.
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