IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i5p683-d98533.html
   My bibliography  Save this article

Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market

Author

Listed:
  • Zora Luburić

    (Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb 10000, Croatia)

  • Hrvoje Pandžić

    (Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb 10000, Croatia)

  • Tomislav Plavšić

    (Croatian Transmission System Operator Ltd., Kupska 4, Zagreb 10000, Croatia
    This paper is an extended version of our paper published in Luburić, Z.; Pandžić, H.; Plavšić, T. Comparison of Energy Storage Operation in Vertically Integrated and Market-Based Power System. In Proceedings of the IEEE International Conference on Environment and Electrical Engineering 2016, Florence, Italy, 7–10 June 2016.)

Abstract

The aim of this paper is to compare the operational pattern of an energy storage system (ESS) in a vertically-integrated utility and in a deregulated market environment for different levels of wind integration. As the main feature of a vertically-integrated utility is a centralized decision-making process, all of the investment and operating decisions are made with a single goal of minimizing the overall system operating costs. As a result, an ESS in such an environment is operated in a way that is optimal for the overall system economics. On the other hand, the system operator in a deregulated market has less power over the system resources, and commitment and dispatch decisions are a result of the market clearing procedure. In this setting, the ESS owner aims at maximizing its profit, which might not be in line with minimizing overall system operating costs or maximizing social welfare. To compare the ESS operation in these two environments, we analyze the storage operation in two different settings. The first one is a standard unit commitment model with the addition of centrally-controlled storage. The second one is a bilevel model, where the upper level is a coordinated ESS profit maximization problem, while the lower level a simulated market clearing. The case study is performed on a standardized IEEE RTS-96 system. The results show a reduction in the generation dispatch cost, online generation capacity and wind curtailment for both models. Moreover, ESS significantly increases social welfare in the market-based environment.

Suggested Citation

  • Zora Luburić & Hrvoje Pandžić & Tomislav Plavšić, 2017. "Assessment of Energy Storage Operation in Vertically Integrated Utility and Electricity Market," Energies, MDPI, vol. 10(5), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:5:p:683-:d:98533
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/5/683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/5/683/
    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. 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).
    3. Pilar Meneses de Quevedo & Javier Contreras, 2016. "Optimal Placement of Energy Storage and Wind Power under Uncertainty," Energies, MDPI, vol. 9(7), pages 1-18, July.
    4. Ramteen Sioshansi & Paul Denholm & Thomas Jenkin, 2012. "Market and Policy Barriers to Deployment of Energy Storage," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 2).
    5. Kyriakopoulos, Grigorios L. & Arabatzis, Garyfallos, 2016. "Electrical energy storage systems in electricity generation: Energy policies, innovative technologies, and regulatory regimes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1044-1067.
    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. Haiteng Han & Hantao Cui & Shan Gao & Qingxin Shi & Anjie Fan & Chen Wu, 2018. "A Remedial Strategic Scheduling Model for Load Serving Entities Considering the Interaction between Grid-Level Energy Storage and Virtual Power Plants," Energies, MDPI, vol. 11(9), pages 1-19, September.

    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. Faezeh Akhavizadegan & Lizhi Wang & James McCalley, 2020. "Scenario Selection for Iterative Stochastic Transmission Expansion Planning," Energies, MDPI, vol. 13(5), pages 1-18, March.
    2. 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.
    3. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    4. Noori, Ehsan & Khazaei, Ehsan & Tavaro, Mehdi & Bardideh, Farhad, 2019. "Economically Operation of Power Utilities Base on MILP Approach," MPRA Paper 95910, University Library of Munich, Germany.
    5. Howard, B. & Waite, M. & Modi, V., 2017. "Current and near-term GHG emissions factors from electricity production for New York State and New York City," Applied Energy, Elsevier, vol. 187(C), pages 255-271.
    6. 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.
    7. Hain, Martin & Kargus, Tobias & Schermeyer, Hans & Uhrig-Homburg, Marliese & Fichtner, Wolf, 2022. "An electricity price modeling framework for renewable-dominant markets," Working Paper Series in Production and Energy 66, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    8. Munoz, Francisco D. & Pumarino, Bruno J. & Salas, Ignacio A., 2017. "Aiming low and achieving it: A long-term analysis of a renewable policy in Chile," Energy Economics, Elsevier, vol. 65(C), pages 304-314.
    9. Le Cadre, Hélène & Mezghani, Ilyès & Papavasiliou, Anthony, 2019. "A game-theoretic analysis of transmission-distribution system operator coordination," European Journal of Operational Research, Elsevier, vol. 274(1), pages 317-339.
    10. 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.
    11. Trine K. Boomsma, 2019. "Comments on: A comparative study of time aggregation techniques in relation to power capacity-expansion modeling," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 406-409, October.
    12. Majid Al-Gwaiz & Xiuli Chao & Owen Q. Wu, 2017. "Understanding How Generation Flexibility and Renewable Energy Affect Power Market Competition," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 114-131, February.
    13. Varawala, Lamia & Dán, György & Hesamzadeh, Mohammad Reza & Baldick, Ross, 2023. "A generalised approach for efficient computation of look ahead security constrained optimal power flow," European Journal of Operational Research, Elsevier, vol. 310(2), pages 477-494.
    14. 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.
    15. Sun, Mingyang & Cremer, Jochen & Strbac, Goran, 2018. "A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 546-555.
    16. 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.
    17. ARAVENA, Ignacio & PAPAVASILIOU, Anthony, 2016. "An Asynchronous Distributed Algorithm for solving Stochastic Unit Commitment," LIDAM Discussion Papers CORE 2016038, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    18. 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.
    19. 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.
    20. Doubleday, Kate & Lara, José Daniel & Hodge, Bri-Mathias, 2022. "Investigation of stochastic unit commitment to enable advanced flexibility measures for high shares of solar PV," Applied Energy, Elsevier, vol. 321(C).

    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:10:y:2017:i:5:p:683-:d:98533. 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.