IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v52y2022i4p344-362.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2022.1120
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2022.1120?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gérard P. Cachon & Kaitlin M. Daniels & Ruben Lobel, 2017. "The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 368-384, July.
    2. Harish Guda & Upender Subramanian, 2019. "Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives," Management Science, INFORMS, vol. 67(5), pages 1995-2014, May.
    3. Meng Liu & Erik Brynjolfsson & Jason Dowlatabadi, 2021. "Do Digital Platforms Reduce Moral Hazard? The Case of Uber and Taxis," Management Science, INFORMS, vol. 67(8), pages 4665-4685, August.
    4. Gordon Burtch & Yili Hong & Ravi Bapna & Vladas Griskevicius, 2018. "Stimulating Online Reviews by Combining Financial Incentives and Social Norms," Management Science, INFORMS, vol. 64(5), pages 2065-2082, May.
    5. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    6. Clay Campaigne & Shmuel S. Oren, 2016. "Firming renewable power with demand response: an end-to-end aggregator business model," Journal of Regulatory Economics, Springer, vol. 50(1), pages 1-37, August.
    7. Wozabal, David & Rameseder, Gunther, 2020. "Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity," European Journal of Operational Research, Elsevier, vol. 280(2), pages 639-655.
    8. Jeffrey D. Camm & Amitabh S. Raturi & Shigeru Tsubakitani, 1990. "Cutting Big M Down to Size," Interfaces, INFORMS, vol. 20(5), pages 61-66, October.
    9. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    10. Kjetil Høyland & Stein W. Wallace, 2001. "Generating Scenario Trees for Multistage Decision Problems," Management Science, INFORMS, vol. 47(2), pages 295-307, February.
    11. Fazlalipour, Pary & Ehsan, Mehdi & Mohammadi-Ivatloo, Behnam, 2019. "Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets," Energy, Elsevier, vol. 171(C), pages 689-700.
    12. Yu, Songyuan & Fang, Fang & Liu, Yajuan & Liu, Jizhen, 2019. "Uncertainties of virtual power plant: Problems and countermeasures," Applied Energy, Elsevier, vol. 239(C), pages 454-470.
    13. Kai Huang & Shabbir Ahmed, 2009. "The Value of Multistage Stochastic Programming in Capacity Planning Under Uncertainty," Operations Research, INFORMS, vol. 57(4), pages 893-904, August.
    14. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2018. "Multi market bidding strategies for demand side flexibility aggregators in electricity markets," Energy, Elsevier, vol. 149(C), pages 120-134.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.
    2. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    3. Kristine Klock Fleten & Ellen Krohn Aasgård & Liyuan Xing & Hanne Høie Grøttum & Stein-Erik Fleten & Odd Erik Gundersen, 2024. "Applying and benchmarking a stochastic programming-based bidding strategy for day-ahead hydropower scheduling," Computational Management Science, Springer, vol. 21(2), pages 1-24, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. 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.
    3. Reza Nadimi & Masahito Takahashi & Koji Tokimatsu & Mika Goto, 2024. "The Reliability and Profitability of Virtual Power Plant with Short-Term Power Market Trading and Non-Spinning Reserve Diesel Generator," Energies, MDPI, vol. 17(9), pages 1-19, April.
    4. Jiang, Yuzheng & Dong, Jun & Huang, Hexiang, 2024. "Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm," Energy, Elsevier, vol. 306(C).
    5. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    6. Amit Kumer Podder & Sayemul Islam & Nallapaneni Manoj Kumar & Aneesh A. Chand & Pulivarthi Nageswara Rao & Kushal A. Prasad & T. Logeswaran & Kabir A. Mamun, 2020. "Systematic Categorization of Optimization Strategies for Virtual Power Plants," Energies, MDPI, vol. 13(23), pages 1-46, November.
    7. Wen Diao & Baojun Jiang & Lin Tian, 2023. "Competition between P2P ride‐sharing platforms and traditional taxis," Production and Operations Management, Production and Operations Management Society, vol. 32(12), pages 3801-3815, December.
    8. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    9. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    10. Hu, Xinru & Zhou, Shuiyin & Luo, Xiaomeng & Li, Jianbin & Zhang, Chi, 2024. "Optimal pricing strategy of an on-demand platform with cross-regional passengers," Omega, Elsevier, vol. 122(C).
    11. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    12. Yongwook Paik & Christos A. Makridis, 2023. "The social value of a ridesharing platform: a hedonic pricing approach," Empirical Economics, Springer, vol. 64(5), pages 2125-2150, May.
    13. Omar Besbes & Francisco Castro & Ilan Lobel, 2021. "Surge Pricing and Its Spatial Supply Response," Management Science, INFORMS, vol. 67(3), pages 1350-1367, March.
    14. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    15. Saif Benjaafar & Ming Hu, 2020. "Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 93-101, January.
    16. Amirmahdi Tafreshian & Neda Masoud & Yafeng Yin, 2020. "Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions," Service Science, INFORMS, vol. 12(2-3), pages 44-60, June.
    17. Tong Xing & Hongyu Lin & Zhongfu Tan & Liwei Ju, 2019. "Coordinated Energy Management for Micro Energy Systems Considering Carbon Emissions Using Multi-Objective Optimization," Energies, MDPI, vol. 12(23), pages 1-27, November.
    18. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    19. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    20. Nikhil Garg & Hamid Nazerzadeh, 2022. "Driver Surge Pricing," Management Science, INFORMS, vol. 68(5), pages 3219-3235, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:52:y:2022:i:4:p:344-362. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.