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

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  • 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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
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    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.

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