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Effective scheduling of residential energy storage systems under dynamic pricing

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  • Yoon, Yourim
  • Kim, Yong-Hyuk

Abstract

We address the control of a residential energy storage system under dynamic pricing, for scenarios with and without local electricity generation, by combining a dynamic programming approach with real-time correction of predictions of load and generated power. We performed simulations using energy generation and consumption data for 64 residences in the Pecan Street Project, and a range of seasonal dynamic price tables. Our algorithm was more effective than other approaches in reducing electricity costs under most tariffs, especially when the amount of electricity generated locally is small.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:87:y:2016:i:p2:p:936-945
    DOI: 10.1016/j.renene.2015.09.072
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    References listed on IDEAS

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    1. Jaeyeong Yoo & Byungsung Park & Kyungsung An & Essam A. Al-Ammar & Yasin Khan & Kyeon Hur & Jong Hyun Kim, 2012. "Look-Ahead Energy Management of a Grid-Connected Residential PV System with Energy Storage under Time-Based Rate Programs," Energies, MDPI, vol. 5(4), pages 1-19, April.
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    3. Severin Borenstein, 2005. "The Long-Run Efficiency of Real-Time Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-116.
    4. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    Cited by:

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    2. Jiyoung Eum & Yongki Kim, 2020. "Analysis on Operation Modes of Residential BESS with Balcony-PV for Apartment Houses in Korea," Sustainability, MDPI, vol. 13(1), pages 1-9, December.
    3. Seung-Ju Lee & Yourim Yoon, 2020. "Electricity Cost Optimization in Energy Storage Systems by Combining a Genetic Algorithm with Dynamic Programming," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
    4. Tariq, Zaid Bin & Khalid, Qasim & Ikram, Jahangir & Arshad, Naveed, 2017. "An approach to operate high-powered legacy electrical appliances on small scale solar PV systems," Renewable Energy, Elsevier, vol. 104(C), pages 238-247.
    5. 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.
    6. Mehdi Dhifli & Abderezak Lashab & Josep M. Guerrero & Abdullah Abusorrah & Yusuf A. Al-Turki & Adnane Cherif, 2020. "Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid," Energies, MDPI, vol. 13(12), pages 1-18, June.
    7. Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
    8. Philippe de Bekker & Sho Cremers & Sonam Norbu & David Flynn & Valentin Robu, 2023. "Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm," Energies, MDPI, vol. 16(5), pages 1-26, March.
    9. Kyritsis, A. & Voglitsis, D. & Papanikolaou, N. & Tselepis, S. & Christodoulou, C. & Gonos, I. & Kalogirou, S.A., 2017. "Evolution of PV systems in Greece and review of applicable solutions for higher penetration levels," Renewable Energy, Elsevier, vol. 109(C), pages 487-499.

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