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Schedule-Based Operation Method Using Market Data for an Energy Storage System of a Customer in the Ontario Electricity Market

Author

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  • Pyeong-Ik Hwang

    (Department of Electrical Engineering, Chosun University, 309 Pilmun-Daero, Dong-Gu, Gwangju 61452, Korea)

  • Seong-Chul Kwon

    (Korea Electric Power Research Institute (KEPRI), Korea Electric Power Corporation (KEPCO), 105 Munji-Ro, Yuseong-Gu, Deajeon 34056, Korea)

  • Sang-Yun Yun

    (Department of Electrical Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea)

Abstract

A new operation method for an energy storage system (ESS) was proposed to reduce the electricity charges of a customer paying the wholesale price and participating in the industrial conservation initiative (ICI) in the Ontario electricity market of Canada. Electricity charges were overviewed and classified into four components: fixed cost, electricity usage cost, peak demand cost, and Ontario peak contribution cost (OPCC). Additionally, the online market data provided by the independent electricity system operator (IESO), which operates the Ontario electricity market, were reviewed. From the reviews, it was identified that (1) the portion of the OPCC in the electricity charges increased continuously, and (2) large errors can sometimes exist in the forecasted data given by the IESO. In order to reflect these, a new schedule-based operation method for the ESS was proposed in this paper. In the proposed method, the operation schedule for the ESS is determined by solving an optimization problem to minimize the electricity charges, where the OPCC is considered and the online market data provided by the IESO is used. The active power reference for the ESS is then calculated from the scheduled output for the current time interval. To reflect the most recent market data, the operation schedule and the active power reference for the ESS are iteratively determined for every five minutes. In addition, in order to cope with the prediction errors, methods to correct the forecasted data for the current time interval and secure the energy reserve are presented. The results obtained from the case study and actual operation at the Penetanguishene microgrid test bed in Ontario are presented to validate the proposed method.

Suggested Citation

  • Pyeong-Ik Hwang & Seong-Chul Kwon & Sang-Yun Yun, 2018. "Schedule-Based Operation Method Using Market Data for an Energy Storage System of a Customer in the Ontario Electricity Market," Energies, MDPI, vol. 11(10), pages 1-26, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2683-:d:174386
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    References listed on IDEAS

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    1. Yoon, Yourim & Kim, Yong-Hyuk, 2016. "Effective scheduling of residential energy storage systems under dynamic pricing," Renewable Energy, Elsevier, vol. 87(P2), pages 936-945.
    2. Minh Y Nguyen & Dinh Hung Nguyen & Yong Tae Yoon, 2012. "A New Battery Energy Storage Charging/Discharging Scheme for Wind Power Producers in Real-Time Markets," Energies, MDPI, vol. 5(12), pages 1-14, December.
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    1. Dimitrios Drosos & Grigorios L. Kyriakopoulos & Garyfallos Arabatzis & Nikolaos Tsotsolas, 2020. "Evaluating Customer Satisfaction in Energy Markets Using a Multicriteria Method: The Case of Electricity Market in Greece," Sustainability, MDPI, vol. 12(9), pages 1-19, May.

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