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An Energy Management Service for the Smart Office

Author

Listed:
  • Cristina Rottondi

    (Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, P.zza L. da Vinci, 32, Milan 20133, Italy)

  • Markus Duchon

    (Fortiss GmbH, Guerickestrasse 25, 80805 Munich, Germany)

  • Dagmar Koss

    (Fortiss GmbH, Guerickestrasse 25, 80805 Munich, Germany)

  • Andrei Palamarciuc

    (Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, P.zza L. da Vinci, 32, Milan 20133, Italy)

  • Alessandro Pití

    (Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, P.zza L. da Vinci, 32, Milan 20133, Italy)

  • Giacomo Verticale

    (Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, P.zza L. da Vinci, 32, Milan 20133, Italy)

  • Bernhard Schätz

    (Fortiss GmbH, Guerickestrasse 25, 80805 Munich, Germany)

Abstract

The evolution of the electricity grid towards the smart grid paradigm is fostering the integration of distributed renewable energy sources in smart buildings: a combination of local power generation, battery storage and controllable loads can greatly increase the energetic self-sufficiency of a smart building, enabling it to maximize the self-consumption of photovoltaic electricity and to participate in the energy market, thus taking advantage of time-variable tariffs to achieve economic savings. This paper proposes an energy management infrastructure specifically tailored for a smart office building, which relies on measured data and on forecasting algorithms to predict the future patterns of both local energy generation and power loads. The performance is compared to the optimal energy usage scheduling, which would be obtained assuming the exact knowledge of the future energy production and consumption trends, showing gaps below 10% with respect to the optimum.

Suggested Citation

  • Cristina Rottondi & Markus Duchon & Dagmar Koss & Andrei Palamarciuc & Alessandro Pití & Giacomo Verticale & Bernhard Schätz, 2015. "An Energy Management Service for the Smart Office," Energies, MDPI, vol. 8(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:11667-11684:d:57240
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    References listed on IDEAS

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    Cited by:

    1. Stefan Arens & Karen Derendorf & Frank Schuldt & Karsten Von Maydell & Carsten Agert, 2018. "Effect of EV Movement Schedule and Machine Learning-Based Load Forecasting on Electricity Cost of a Single Household," Energies, MDPI, vol. 11(11), pages 1-19, October.
    2. Alessandro Pitì & Giacomo Verticale & Cristina Rottondi & Antonio Capone & Luca Lo Schiavo, 2017. "The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case," Energies, MDPI, vol. 10(2), pages 1-25, February.
    3. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
    4. Antimo Barbato & Cristiana Bolchini & Angela Geronazzo & Elisa Quintarelli & Andrei Palamarciuc & Alessandro Pitì & Cristina Rottondi & Giacomo Verticale, 2016. "Energy Optimization and Management of Demand Response Interactions in a Smart Campus," Energies, MDPI, vol. 9(6), pages 1-20, May.
    5. Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    6. Kai Ma & Yege Bai & Jie Yang & Yangqing Yu & Qiuxia Yang, 2017. "Demand-Side Energy Management Based on Nonconvex Optimization in Smart Grid," Energies, MDPI, vol. 10(10), pages 1-17, October.
    7. Danish Mahmood & Nadeem Javaid & Nabil Alrajeh & Zahoor Ali Khan & Umar Qasim & Imran Ahmed & Manzoor Ilahi, 2016. "Realistic Scheduling Mechanism for Smart Homes," Energies, MDPI, vol. 9(3), pages 1-28, March.

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