IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6221-d898615.html
   My bibliography  Save this article

A Unit Commitment Model Considering Feasibility of Operating Reserves under Stochastic Optimization Framework

Author

Listed:
  • Heejung Park

    (School of Energy Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

Grid integration of renewable resources such as solar and wind energy can significantly raise the level of uncertainty in power systems, making the scheduled operation of generating units difficult. Therefore, the importance of operating reserves is more emphasized to prevent disruption by sudden changes in outputs of generators. In this paper, a stochastic unit commitment (UC) model to reflect uncertainty due to a large amount of renewable resources is proposed, in which upward and downward operating reserves are deployed simultaneously, and feasibility of the reserves is examined to make the deployed reserves supplied reliably. Uncertain parameters considered in the model are wind power availability, solar direct normal irradiance, and electric load. Two-stage stochastic programming is applied to the mathematical formulation, where UC decisions including dispatch are modeled as non-anticipative variables at the first stage, and redispatch decisions to serve realized electric demand are made at the second stage as recourse. By solving the UC problem, feasible and reliable stochastic UC and dispatch solutions can be provided to power system operators.

Suggested Citation

  • Heejung Park, 2022. "A Unit Commitment Model Considering Feasibility of Operating Reserves under Stochastic Optimization Framework," Energies, MDPI, vol. 15(17), pages 1-22, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6221-:d:898615
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6221/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/17/6221/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anthony Papavasiliou & Shmuel S. Oren, 2013. "Multiarea Stochastic Unit Commitment for High Wind Penetration in a Transmission Constrained Network," Operations Research, INFORMS, vol. 61(3), pages 578-592, June.
    2. Matthias Nowak & Werner Römisch, 2000. "Stochastic Lagrangian Relaxation Applied to Power Scheduling in a Hydro-Thermal System under Uncertainty," Annals of Operations Research, Springer, vol. 100(1), pages 251-272, December.
    3. Paatero, Jukka V. & Lund, Peter D., 2007. "Effects of large-scale photovoltaic power integration on electricity distribution networks," Renewable Energy, Elsevier, vol. 32(2), pages 216-234.
    4. Tuohy, Aidan & Meibom, Peter & Denny, Eleanor & O'Malley, Mark, 2009. "Unit commitment for systems with significant wind penetration," MPRA Paper 34849, University Library of Munich, Germany.
    5. John A. Muckstadt & Sherri A. Koenig, 1977. "An Application of Lagrangian Relaxation to Scheduling in Power-Generation Systems," Operations Research, INFORMS, vol. 25(3), pages 387-403, June.
    6. PAPAVASILIOU, Anthony & OREN, Schmuel S., 2013. "Multiarea stochastic unit commitment for high wind penetration in a transmission constrained network," LIDAM Reprints CORE 2500, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    7. Marne C. Cario & Barry L. Nelson, 1998. "Numerical Methods for Fitting and Simulating Autoregressive-to-Anything Processes," INFORMS Journal on Computing, INFORMS, vol. 10(1), pages 72-81, February.
    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. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, 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. Yonghan Feng & Sarah Ryan, 2016. "Solution sensitivity-based scenario reduction for stochastic unit commitment," Computational Management Science, Springer, vol. 13(1), pages 29-62, January.
    2. Abdul Rauf & Mahmoud Kassas & Muhammad Khalid, 2022. "Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    3. De Vos, K. & Stevens, N. & Devolder, O. & Papavasiliou, A. & Hebb, B. & Matthys-Donnadieu, J., 2019. "Dynamic dimensioning approach for operating reserves: Proof of concept in Belgium," Energy Policy, Elsevier, vol. 124(C), pages 272-285.
    4. Johnson, Samuel C. & Papageorgiou, Dimitri J. & Mallapragada, Dharik S. & Deetjen, Thomas A. & Rhodes, Joshua D. & Webber, Michael E., 2019. "Evaluating rotational inertia as a component of grid reliability with high penetrations of variable renewable energy," Energy, Elsevier, vol. 180(C), pages 258-271.
    5. Aghaei, Jamshid & Nikoobakht, Ahmad & Siano, Pierluigi & Nayeripour, Majid & Heidari, Alireza & Mardaneh, Mohammad, 2016. "Exploring the reliability effects on the short term AC security-constrained unit commitment: A stochastic evaluation," Energy, Elsevier, vol. 114(C), pages 1016-1032.
    6. Wang, Bo & Wang, Shuming & Zhou, Xianzhong & Watada, Junzo, 2016. "Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties," Energy, Elsevier, vol. 111(C), pages 18-31.
    7. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    8. Álvaro Lorca & X. Andy Sun & Eugene Litvinov & Tongxin Zheng, 2016. "Multistage Adaptive Robust Optimization for the Unit Commitment Problem," Operations Research, INFORMS, vol. 64(1), pages 32-51, February.
    9. Jeanne Aslak Petersen & Ditte Heide-Jørgensen & Nina Detlefsen & Trine Boomsma, 2016. "Short-term balancing of supply and demand in an electricity system: forecasting and scheduling," Annals of Operations Research, Springer, vol. 238(1), pages 449-473, March.
    10. Jeanne Aslak Petersen & Ditte Mølgård Heide-Jørgensen & Nina Kildegaard Detlefsen & Trine Krogh Boomsma, 2016. "Short-term balancing of supply and demand in an electricity system: forecasting and scheduling," Annals of Operations Research, Springer, vol. 238(1), pages 449-473, March.
    11. Meng, Fanyi & Bai, Yang & Jin, Jingliang, 2021. "An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm," Renewable Energy, Elsevier, vol. 178(C), pages 13-24.
    12. Jan Abrell & Friedrich Kunz, 2015. "Integrating Intermittent Renewable Wind Generation - A Stochastic Multi-Market Electricity Model for the European Electricity Market," Networks and Spatial Economics, Springer, vol. 15(1), pages 117-147, March.
    13. Melamed, Michal & Ben-Tal, Aharon & Golany, Boaz, 2018. "A multi-period unit commitment problem under a new hybrid uncertainty set for a renewable energy source," Renewable Energy, Elsevier, vol. 118(C), pages 909-917.
    14. Briest, Gordon & Lauven, Lars-Peter & Kupfer, Stefan & Lukas, Elmar, 2022. "Leaving well-worn paths: Reversal of the investment-uncertainty relationship and flexible biogas plant operation," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1162-1176.
    15. Fattahi, Salar & Ashraphijuo, Morteza & Lavaei, Javad & Atamtürk, Alper, 2017. "Conic relaxations of the unit commitment problem," Energy, Elsevier, vol. 134(C), pages 1079-1095.
    16. Jianqiu Huang & Kai Pan & Yongpei Guan, 2021. "Multistage Stochastic Power Generation Scheduling Co-Optimizing Energy and Ancillary Services," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 352-369, January.
    17. David Schönheit & Dominik Möst, 2019. "The Effect of Offshore Wind Capacity Expansion on Uncertainties in Germany’s Day-Ahead Wind Energy Forecasts," Energies, MDPI, vol. 12(13), pages 1-23, July.
    18. Schulze, Tim & Grothey, Andreas & McKinnon, Ken, 2017. "A stabilised scenario decomposition algorithm applied to stochastic unit commitment problems," European Journal of Operational Research, Elsevier, vol. 261(1), pages 247-259.
    19. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    20. Victor M. Zavala & Kibaek Kim & Mihai Anitescu & John Birge, 2017. "A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties," Operations Research, INFORMS, vol. 65(3), pages 557-576, June.

    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:gam:jeners:v:15:y:2022:i:17:p:6221-:d:898615. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.