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Operations Research Helps the Optimal Bidding of Virtual Power Plants

Author

Listed:
  • Daeho Kim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Gyeongbuk 37673, Republic of Korea)

  • Hyungkyu Cheon

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Gyeongbuk 37673, Republic of Korea)

  • Dong Gu Choi

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Gyeongbuk 37673, Republic of Korea; Open Innovation Big Data Center, Pohang University of Science and Technology, Gyeongbuk 37673, Republic of Korea)

  • Seongbin Im

    (H Energy Co. Ltd., Gyeongbuk 37666, Republic of Korea)

Abstract

As distributed energy resources (DERs) continue to emerge, a new cloud-based information technology platform business model, the virtual power plant (VPP), is being introduced into the electricity market. The competitiveness of VPPs mainly depends on data analytics and operational technologies. Among the several operational problems, we focus on the optimal bidding decision problem in the day-ahead market. The bidding decision is a VPP’s commitment to supply the market with electricity from uncertain DERs, thereby affecting the VPP’s profits. Based on a collaboration with a VPP company in South Korea, H Energy Co. Ltd., we formulate a Markov decision process model for the problem and use a stochastic dynamic programming-based solution approach. This is the first study under the incentive-based market structure. To describe the uncertainty in the power supply from DERs, we build frameworks to generate scenario trees or lattices. Additionally, we apply heuristic techniques to reduce the computational burden. Through a pilot test based on real data, we verify the performance and practicality of our proposed model and solution approach. The case company has begun implementing the model and solution approach on its platform and has found that performance has improved after using advanced forecasting models for DERs.

Suggested Citation

  • Daeho Kim & Hyungkyu Cheon & Dong Gu Choi & Seongbin Im, 2022. "Operations Research Helps the Optimal Bidding of Virtual Power Plants," Interfaces, INFORMS, vol. 52(4), pages 344-362, July.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:4:p:344-362
    DOI: 10.1287/inte.2022.1120
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    References listed on IDEAS

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    Cited by:

    1. Kim, Seokwoo & Choi, Dong Gu, 2024. "A sample robust optimal bidding model for a virtual power plant," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1101-1113.

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