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Data Driven Optimization of Energy Management in Residential Buildings with Energy Harvesting and Storage

Author

Listed:
  • Nadia Ahmed

    (Donald Bren School of Information and Computer Science, University of California, Irvine, CA 92697, USA)

  • Marco Levorato

    (Donald Bren School of Information and Computer Science, University of California, Irvine, CA 92697, USA)

  • Roberto Valentini

    (Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, 67100 L’Aquila, Italy)

  • Guann-Pyng Li

    (Donald Bren School of Information and Computer Science, University of California, Irvine, CA 92697, USA)

Abstract

This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the utility, and consumer preferences into a unified Markov decision process. The embedded optimization problem is formulated to determine the proportion of energy drawn from the battery and the grid to minimize a cost function capturing a user-defined tradeoff between battery degradation and financial expense by user preferences. Numerical results are based on real-world weather data for Golden, Colorado, and load traces. The results illustrate the ability of the system to limit battery degradation assessed using the Rain flow counting method for lithium ion batteries.

Suggested Citation

  • Nadia Ahmed & Marco Levorato & Roberto Valentini & Guann-Pyng Li, 2020. "Data Driven Optimization of Energy Management in Residential Buildings with Energy Harvesting and Storage," Energies, MDPI, vol. 13(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2201-:d:353294
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    References listed on IDEAS

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    1. Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
    2. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    3. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Hoon Lee & Jin-Wook Kang & Bong-Yeon Choi & Kyung-Min Kang & Mi-Na Kim & Chang-Gyun An & Junsin Yi & Chung-Yuen Won, 2021. "Energy Management System of DC Microgrid in Grid-Connected and Stand-Alone Modes: Control, Operation and Experimental Validation," Energies, MDPI, vol. 14(3), pages 1-26, January.
    2. Goldsworthy, M. & Moore, T. & Peristy, M. & Grimeland, M., 2022. "Cloud-based model-predictive-control of a battery storage system at a commercial site," Applied Energy, Elsevier, vol. 327(C).
    3. Kang, Hyuna & Jung, Seunghoon & Lee, Minhyun & Hong, Taehoon, 2022. "How to better share energy towards a carbon-neutral city? A review on application strategies of battery energy storage system in city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).

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