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Design, architecture and implementation of a residential energy box management tool in a SmartGrid

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  • Ioakimidis, Christos S.
  • Oliveira, Luís J.
  • Genikomsakis, Konstantinos N.
  • Dallas, Panagiotis I.

Abstract

This paper presents the EB (energy box) concept in the context of the V2G (vehicle-to-grid) technology to address the energy management needs of a modern residence, considering that the available infrastructure includes micro-renewable energy sources in the form of solar and wind power, the electricity loads consist of “smart” and conventional household appliances, while the battery of an EV (electric vehicle) plays the role of local storage. The problem is formulated as a multi-objective DSP (dynamic stochastic programming) model in order to maximize comfort and lifestyle preferences and minimize cost. Combining the DSP model that controls the EB operation with a neural network based approach for simulating the thermal model of a building, a set of scenarios are examined to exemplify the applicability of the proposed energy management tool. The EB is capable of working under real-time tariff and placing bids in electricity markets both as a stand-alone option and integrated in a SmartGrid paradigm, where a number of EBs are managed by an aggregator. The results obtained for the Portuguese tertiary electricity market indicate that this approach has the potential to compete as an ancillary service and sustain business with benefits for both the microgrid and residence occupants.

Suggested Citation

  • Ioakimidis, Christos S. & Oliveira, Luís J. & Genikomsakis, Konstantinos N. & Dallas, Panagiotis I., 2014. "Design, architecture and implementation of a residential energy box management tool in a SmartGrid," Energy, Elsevier, vol. 75(C), pages 167-181.
    • Handle: RePEc:eee:energy:v:75:y:2014:i:c:p:167-181
    DOI: 10.1016/j.energy.2014.07.068
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    Cited by:

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    2. Thomas, Dimitrios & D’Hoop, Gaspard & Deblecker, Olivier & Genikomsakis, Konstantinos N. & Ioakimidis, Christos S., 2020. "An integrated tool for optimal energy scheduling and power quality improvement of a microgrid under multiple demand response schemes," Applied Energy, Elsevier, vol. 260(C).
    3. Filipe Barata & José Igreja, 2018. "Energy Management in Buildings with Intermittent and Limited Renewable Resources," Energies, MDPI, vol. 11(10), pages 1-21, October.
    4. 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.
    5. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    6. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    7. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    8. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    9. Al Essa, Mohammed Jasim M., 2019. "Home energy management of thermostatically controlled loads and photovoltaic-battery systems," Energy, Elsevier, vol. 176(C), pages 742-752.
    10. Jalali, Mohammad Majid & Kazemi, Ahad, 2015. "Demand side management in a smart grid with multiple electricity suppliers," Energy, Elsevier, vol. 81(C), pages 766-776.

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