IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p158-d126102.html
   My bibliography  Save this article

Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties

Author

Listed:
  • Ryusuke Konishi

    (Graduate School of Science and Technology, Keio University, Yokohama 223-8522, Japan
    Japan Science and Technology Agency (JST), CREST, Kawaguchi 332-0012, Japan)

  • Akiko Takeda

    (Japan Science and Technology Agency (JST), CREST, Kawaguchi 332-0012, Japan
    Institute of Statistical Mathematics, Tachikawa 190-8562, Japan)

  • Masaki Takahashi

    (Japan Science and Technology Agency (JST), CREST, Kawaguchi 332-0012, Japan
    Faculty of Science and Technology, Keio University, Yokohama 223-8522, Japan)

Abstract

In deregulated electricity markets, minimizing the procurement costs of electricity is a critical problem for procurement agencies (PAs). However, uncertainty is inevitable for PAs and includes multiple factors such as market prices, photovoltaic system (PV) output and demand. This study focuses on settlements in multi-period markets (a day-ahead market and a real-time market) and the installation of energy storage systems (ESSs). ESSs can be utilized for time arbitrage in the day-ahead market and to reduce the purchasing/selling of electricity in the real-time market. However, the high costs of an ESS mean the size of the system needs to be minimized. In addition, when determining the size of an ESS, it is important to identify the size appropriate for each role. Therefore, we employ the concept of a “slow” and a “fast” ESS to quantify the size of a system’s role, based on the values associated with the various uncertainties. Because the problem includes nonlinearity and non-convexity, we solve it within a realistic computational burden by reformulating the problem using reasonable assumptions. Therefore, this study identifies the optimal sizes of ESSs and procurement, taking into account the uncertainties of prices in multi-period markets, PV output and demand.

Suggested Citation

  • Ryusuke Konishi & Akiko Takeda & Masaki Takahashi, 2018. "Optimal Sizing of Energy Storage Systems for the Energy Procurement Problem in Multi-Period Markets under Uncertainties," Energies, MDPI, vol. 11(1), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:158-:d:126102
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/158/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/158/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ryusuke Konishi & Masaki Takahashi, 2017. "Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis," Energies, MDPI, vol. 10(10), pages 1-20, September.
    2. Mauricio B. C. Salles & Junling Huang & Michael J. Aziz & William W. Hogan, 2017. "Potential Arbitrage Revenue of Energy Storage Systems in PJM," Energies, MDPI, vol. 10(8), pages 1-19, July.
    3. Chakraborty, Shantanu & Okabe, Toshiya, 2016. "Robust energy storage scheduling for imbalance reduction of strategically formed energy balancing groups," Energy, Elsevier, vol. 114(C), pages 405-417.
    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. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid & Josep M. Guerrero, 2018. "A Stochastic Model Predictive Control Approach for Joint Operational Scheduling and Hourly Reconfiguration of Distribution Systems," Energies, MDPI, vol. 11(7), pages 1-19, July.
    2. Thibaut Théate & Sébastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Energies, MDPI, vol. 13(23), 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. Gonocruz, Ruth Anne Tanlioco & Yoshida, Yoshikuni & Ozawa, Akito & Aguirre, Rodolfo A. & Maguindayao, Edward Joseph H., 2023. "Impacts of agrivoltaics in rural electrification and decarbonization in the Philippines," Applied Energy, Elsevier, vol. 350(C).
    2. Denholm, Paul & Nunemaker, Jacob & Gagnon, Pieter & Cole, Wesley, 2020. "The potential for battery energy storage to provide peaking capacity in the United States," Renewable Energy, Elsevier, vol. 151(C), pages 1269-1277.
    3. Ahmed Alzahrani & Hussain Alharthi & Muhammad Khalid, 2019. "Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics," Energies, MDPI, vol. 13(1), pages 1-16, December.
    4. Zezhong Li & Xiangang Peng & Yilin Xu & Fucheng Zhong & Sheng Ouyang & Kaiguo Xuan, 2023. "A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response," Mathematics, MDPI, vol. 12(1), pages 1-21, December.
    5. Ikeda, Shunnosuke & Nishimura, Naoki & Sukegawa, Noriyoshi & Takano, Yuichi, 2023. "Prescriptive price optimization using optimal regression trees," Operations Research Perspectives, Elsevier, vol. 11(C).
    6. Simpson, J.G. & Hanrahan, G. & Loth, E. & Koenig, G.M. & Sadoway, D.R., 2021. "Liquid metal battery storage in an offshore wind turbine: Concept and economic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    7. Zhou, Jian & Tsianikas, Stamatis & Birnie, Dunbar P. & Coit, David W., 2019. "Economic and resilience benefit analysis of incorporating battery storage to photovoltaic array generation," Renewable Energy, Elsevier, vol. 135(C), pages 652-662.
    8. Bennett, Jeffrey A. & Simpson, Juliet G. & Qin, Chao & Fittro, Roger & Koenig, Gary M. & Clarens, Andres F. & Loth, Eric, 2021. "Techno-economic analysis of offshore isothermal compressed air energy storage in saline aquifers co-located with wind power," Applied Energy, Elsevier, vol. 303(C).
    9. Ikechi Emmanuel, Michael & Denholm, Paul, 2022. "A market feedback framework for improved estimates of the arbitrage value of energy storage using price-taker models," Applied Energy, Elsevier, vol. 310(C).
    10. Hojnik, Jana & Ruzzier, Mitja & Fabri, Stephanie & Klopčič, Alenka Lena, 2021. "What you give is what you get: Willingness to pay for green energy," Renewable Energy, Elsevier, vol. 174(C), pages 733-746.
    11. Pimnapat Bhumkittipich & Hideaki Ohgaki, 2018. "Development Strategy for Sustainable Solar Home System in the Akha Upland Community of Thailand," Energies, MDPI, vol. 11(6), pages 1-14, June.
    12. Dawid Chudy & Adam Leśniak, 2021. "Advantages of Applying Large-Scale Energy Storage for Load-Generation Balancing," Energies, MDPI, vol. 14(11), pages 1-17, May.
    13. Brown, Patrick R. & O'Sullivan, Francis M., 2020. "Spatial and temporal variation in the value of solar power across United States electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    14. Ondeck, Abigail D. & Edgar, Thomas F. & Baldea, Michael, 2018. "Impact of rooftop photovoltaics and centralized energy storage on the design and operation of a residential CHP system," Applied Energy, Elsevier, vol. 222(C), pages 280-299.
    15. Andreolli, Francesca & D’Alpaos, Chiara & Moretto, Michele, 2022. "Valuing investments in domestic PV-Battery Systems under uncertainty," Energy Economics, Elsevier, vol. 106(C).
    16. Tsianikas, Stamatis & Zhou, Jian & Birnie, Dunbar P. & Coit, David W., 2019. "Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems," Applied Energy, Elsevier, vol. 256(C).
    17. Chakraborty, Shantanu & Baarslag, Tim & Kaisers, Michael, 2020. "Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives," Applied Energy, Elsevier, vol. 259(C).
    18. Marte Fodstad & Mats Mathisen Aarlott & Kjetil Trovik Midthun, 2017. "Value-Creation Potential from Multi-Market Trading for a Hydropower Producer," Energies, MDPI, vol. 11(1), pages 1-15, December.

    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:11:y:2018:i:1:p:158-:d:126102. 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.