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Tuning the Complexity of Photovoltaic Array Models to Meet Real-time Constraints of Embedded Energy Emulators

Author

Listed:
  • Emanuele Lattanzi

    () (Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy)

  • Matteo Dromedari

    () (Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy)

  • Valerio Freschi

    () (Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy)

  • Alessandro Bogliolo

    () (Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy)

Abstract

Reproducibility of experimental conditions is a fundamental requirement for designing energy efficient, self-sustainable wireless sensor networks (WSNs). At the same time, it represents a significant challenge because of the variability and the unpredictability of many energy harvesting sources, and because of the dynamic operating conditions of the devices to which energy is supplied. Energy source emulation is considered a suitable solution to enable the exploration of the design space of networked embedded systems. However, in order to guarantee the compatibility with real-time performance of resource-constrained embedded platforms, particular attention has to be paid to the complexity of the models. In this paper, we propose an approach aimed at tuning the complexity of models of photovoltaic (PV) arrays implemented on a target embedded emulator, featuring low cost and small form factor. Experimental results performed on different models of PV array, show that the proposed solution is flexible and accurate enough to meet the real-time constraints of typical sensor networks applications without impairing the precision in the emulation of the energy sources.

Suggested Citation

  • Emanuele Lattanzi & Matteo Dromedari & Valerio Freschi & Alessandro Bogliolo, 2017. "Tuning the Complexity of Photovoltaic Array Models to Meet Real-time Constraints of Embedded Energy Emulators," Energies, MDPI, Open Access Journal, vol. 10(3), pages 1-17, February.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:278-:d:91632
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    References listed on IDEAS

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    1. repec:gam:jeners:v:9:y:2016:i:5:p:326:d:69101 is not listed on IDEAS
    2. Silvano Vergura, 2016. "A Complete and Simplified Datasheet-Based Model of PV Cells in Variable Environmental Conditions for Circuit Simulation," Energies, MDPI, Open Access Journal, vol. 9(5), pages 1-12, April.
    3. Mohammadmehdi Seyedmahmoudian & Saad Mekhilef & Rasoul Rahmani & Rubiyah Yusof & Ehsan Taslimi Renani, 2013. "Analytical Modeling of Partially Shaded Photovoltaic Systems," Energies, MDPI, Open Access Journal, vol. 6(1), pages 1-17, January.
    4. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
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    1. repec:eee:rensus:v:80:y:2017:i:c:p:430-452 is not listed on IDEAS

    More about this item

    Keywords

    photovoltaic (PV) array models; energy emulation; embedded systems;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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