IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v8y2015i10p11667-11684d57240.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/8/10/11667/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/8/10/11667/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    2. Benghanem, M., 2011. "Optimization of tilt angle for solar panel: Case study for Madinah, Saudi Arabia," Applied Energy, Elsevier, vol. 88(4), pages 1427-1433, April.
    3. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    4. Angelis-Dimakis, Athanasios & Biberacher, Markus & Dominguez, Javier & Fiorese, Giulia & Gadocha, Sabine & Gnansounou, Edgard & Guariso, Giorgio & Kartalidis, Avraam & Panichelli, Luis & Pinedo, Irene, 2011. "Methods and tools to evaluate the availability of renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(2), pages 1182-1200, February.
    5. Manuela Sechilariu & Fabrice Locment & Baochao Wang, 2015. "Photovoltaic Electricity for Sustainable Building. Efficiency and Energy Cost Reduction for Isolated DC Microgrid," Energies, MDPI, vol. 8(8), pages 1-23, July.
    6. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    7. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    8. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    3. Nadeem Javaid & Sakeena Javaid & Wadood Abdul & Imran Ahmed & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid," Energies, MDPI, vol. 10(3), pages 1-27, March.
    4. Labeodan, Timilehin & Aduda, Kennedy & Boxem, Gert & Zeiler, Wim, 2015. "On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction – A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1405-1414.
    5. Zhang, Jingrui & Wu, Yihong & Guo, Yiran & Wang, Bo & Wang, Hengyue & Liu, Houde, 2016. "A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints," Applied Energy, Elsevier, vol. 183(C), pages 791-804.
    6. Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.
    7. Mohammad Ali Taghikhani & Behnam Zangeneh, 2022. "Optimal energy scheduling of micro-grids considering the uncertainty of solar and wind renewable resources," Journal of Scheduling, Springer, vol. 25(5), pages 567-576, October.
    8. Arslan, Okan & Karasan, Oya Ekin, 2013. "Cost and emission impacts of virtual power plant formation in plug-in hybrid electric vehicle penetrated networks," Energy, Elsevier, vol. 60(C), pages 116-124.
    9. Jin, Ming & Feng, Wei & Liu, Ping & Marnay, Chris & Spanos, Costas, 2017. "MOD-DR: Microgrid optimal dispatch with demand response," Applied Energy, Elsevier, vol. 187(C), pages 758-776.
    10. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    11. Ettore Bompard & Daniele Grosso & Tao Huang & Francesco Profumo & Xianzhang Lei & Duo Li, 2018. "World Decarbonization through Global Electricity Interconnections," Energies, MDPI, vol. 11(7), pages 1-29, July.
    12. Mahtta, Richa & Joshi, P.K. & Jindal, Alok Kumar, 2014. "Solar power potential mapping in India using remote sensing inputs and environmental parameters," Renewable Energy, Elsevier, vol. 71(C), pages 255-262.
    13. Faris Alqurashi & Rached Nciri & Abdulrahman Alghamdi & Chaouki Ali & Faouzi Nasri, 2022. "Control of the Solar Radiation Reception Rate (SRRR) Using a Novel Poly-Tilted Segmented Panel (PTSP) in the Region of Makkah, Saudi Arabia," Energies, MDPI, vol. 15(7), pages 1-15, March.
    14. Moreno, Manuel & Novales, Alfonso & Platania, Federico, 2019. "Long-term swings and seasonality in energy markets," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1011-1023.
    15. Simona-Vasilica Oprea & Adela Bâra & Adriana Reveiu, 2018. "Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders," Energies, MDPI, vol. 11(1), pages 1-31, January.
    16. Abbaspour, M. & Satkin, M. & Mohammadi-Ivatloo, B. & Hoseinzadeh Lotfi, F. & Noorollahi, Y., 2013. "Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)," Renewable Energy, Elsevier, vol. 51(C), pages 53-59.
    17. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    18. Zhong, Qing & Tong, Daoqin, 2020. "Spatial layout optimization for solar photovoltaic (PV) panel installation," Renewable Energy, Elsevier, vol. 150(C), pages 1-11.
    19. Hammar, Linus & Ehnberg, Jimmy & Mavume, Alberto & Cuamba, Boaventura C. & Molander, Sverker, 2012. "Renewable ocean energy in the Western Indian Ocean," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4938-4950.
    20. Asad, R. & Kazemi, A., 2014. "A novel distributed optimal power sharing method for radial dc microgrids with different distributed energy sources," Energy, Elsevier, vol. 72(C), pages 291-299.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:11667-11684:d:57240. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.