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Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants

Author

Listed:
  • Rakkyung Ko

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Sung-Kwan Joo

    (The School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

Virtual power plants (VPPs) have been widely researched to handle the unpredictability and variable nature of renewable energy sources. The distributed energy resources are aggregated to form into a virtual power plant and operate as a single generator from the perspective of a system operator. Power system operators often utilize the incentives to operate virtual power plants in desired ways. To maximize the revenue of virtual power plant operators, including its incentives, an optimal portfolio needs to be identified, because each renewable energy source has a different generation pattern. This study proposes a stochastic mixed-integer programming based distributed energy resource allocation method. The proposed method attempts to maximize the revenue of VPP operators considering market incentives. Furthermore, the uncertainty in the generation pattern of renewable energy sources is considered by the stochastic approach. Numerical results show the effectiveness of the proposed method.

Suggested Citation

  • Rakkyung Ko & Sung-Kwan Joo, 2019. "Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants," Energies, MDPI, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:67-:d:300742
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    References listed on IDEAS

    as
    1. Rakkyung Ko & Daeyoung Kang & Sung-Kwan Joo, 2019. "Mixed Integer Quadratic Programming Based Scheduling Methods for Day-Ahead Bidding and Intra-Day Operation of Virtual Power Plant," Energies, MDPI, vol. 12(8), pages 1-16, April.
    2. Mashayekh, Salman & Stadler, Michael & Cardoso, Gonçalo & Heleno, Miguel, 2017. "A mixed integer linear programming approach for optimal DER portfolio, sizing, and placement in multi-energy microgrids," Applied Energy, Elsevier, vol. 187(C), pages 154-168.
    3. Ehsan, Ali & Yang, Qiang, 2019. "Scenario-based investment planning of isolated multi-energy microgrids considering electricity, heating and cooling demand," Applied Energy, Elsevier, vol. 235(C), pages 1277-1288.
    4. Armendáriz, M. & Heleno, M. & Cardoso, G. & Mashayekh, S. & Stadler, M. & Nordström, L., 2017. "Coordinated microgrid investment and planning process considering the system operator," Applied Energy, Elsevier, vol. 200(C), pages 132-140.
    5. Jaeyong Chae & Sung-Kwan Joo, 2017. "Demand Response Resource Allocation Method Using Mean-Variance Portfolio Theory for Load Aggregators in the Korean Demand Response Market," Energies, MDPI, vol. 10(7), pages 1-14, June.
    6. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
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    Cited by:

    1. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    2. Siqin, Zhuoya & Niu, DongXiao & Wang, Xuejie & Zhen, Hao & Li, MingYu & Wang, Jingbo, 2022. "A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission," Energy, Elsevier, vol. 260(C).

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