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Optimal Bidding Scheduling of Virtual Power Plants Using a Dual-MILP (Mixed-Integer Linear Programming) Approach under a Real-Time Energy Market

Author

Listed:
  • Seung-Jin Yoon

    (Power Electric System Research Laboratory, Korea Institute of Energy Research, Jeju 63357, Republic of Korea)

  • Kyung-Sang Ryu

    (Power Electric System Research Laboratory, Korea Institute of Energy Research, Jeju 63357, Republic of Korea)

  • Chansoo Kim

    (Power Electric System Research Laboratory, Korea Institute of Energy Research, Jeju 63357, Republic of Korea)

  • Yang-Hyun Nam

    (Power Electric System Research Laboratory, Korea Institute of Energy Research, Jeju 63357, Republic of Korea)

  • Dae-Jin Kim

    (Power Electric System Research Laboratory, Korea Institute of Energy Research, Jeju 63357, Republic of Korea)

  • Byungki Kim

    (Power Electric System Research Laboratory, Korea Institute of Energy Research, Jeju 63357, Republic of Korea)

Abstract

In recent years, the energy industry has increased the proportion of renewable energy sources, which are sustainable and carbon-free. However, the increase in renewable energy sources has led to grid instability due to factors such as the intermittent power generation of renewable sources, forecasting inaccuracies, and the lack of metering for small-scale power sources. Various studies have been carried out to address these issues. Among these, research on Virtual Power Plants (VPP) has focused on integrating unmanaged renewable energy sources into a unified system to improve their visibility. This research is now being applied in the energy trading market. However, the purpose of VPP aggregators has been to maximize profits. As a result, they have not considered the impact on distribution networks and have bid all available distributed resources into the energy market. While this approach has increased the visibility of renewables, an additional method is needed to deal with the grid instability caused by the increase in renewables. Consequently, grid operators have tried to address these issues by diversifying the energy market. As regulatory method, they have introduced real-time energy markets, imbalance penalty fees, and limitations on the output of distributed energy resources (DERs), in addition to the existing day-ahead market. In response, this paper proposes an optimal scheduling method for VPP aggregators that adapts to the diversifying energy market and enhances the operational benefits of VPPs by using two Mixed-Integer Linear Programming (MILP) models. The validity of the proposed model and algorithm is verified through a case study analysis.

Suggested Citation

  • Seung-Jin Yoon & Kyung-Sang Ryu & Chansoo Kim & Yang-Hyun Nam & Dae-Jin Kim & Byungki Kim, 2024. "Optimal Bidding Scheduling of Virtual Power Plants Using a Dual-MILP (Mixed-Integer Linear Programming) Approach under a Real-Time Energy Market," Energies, MDPI, vol. 17(15), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3773-:d:1446926
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    References listed on IDEAS

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    1. Meng, Yuan & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "A Holistic P2P market for active and reactive energy trading in VPPs considering both financial benefits and network constraints," Applied Energy, Elsevier, vol. 356(C).
    2. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    3. Zhang, Rufeng & Chen, Yan & Li, Zhengmao & Jiang, Tao & Li, Xue, 2024. "Two-stage robust operation of electricity-gas-heat integrated multi-energy microgrids considering heterogeneous uncertainties," Applied Energy, Elsevier, vol. 371(C).
    4. Li, Zhengmao & Wu, Lei & Xu, Yan & Wang, Luhao & Yang, Nan, 2023. "Distributed tri-layer risk-averse stochastic game approach for energy trading among multi-energy microgrids," Applied Energy, Elsevier, vol. 331(C).
    5. Nasiri, Nima & Zeynali, Saeed & Ravadanegh, Sajad Najafi & Marzband, Mousa, 2021. "A hybrid robust-stochastic approach for strategic scheduling of a multi-energy system as a price-maker player in day-ahead wholesale market," Energy, Elsevier, vol. 235(C).
    6. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.
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