LAPSE:2019.1405
Published Article

LAPSE:2019.1405
Tuning the Complexity of Photovoltaic Array Models to Meet Real-time Constraints of Embedded Energy Emulators
December 10, 2019
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.
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.
Record ID
Keywords
embedded systems, energy emulation, photovoltaic (PV) array models
Subject
Suggested Citation
Lattanzi E, Dromedari M, Freschi V, Bogliolo A. Tuning the Complexity of Photovoltaic Array Models to Meet Real-time Constraints of Embedded Energy Emulators. (2019). LAPSE:2019.1405
Author Affiliations
Lattanzi E: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy [ORCID]
Dromedari M: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy
Freschi V: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy
Bogliolo A: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy [ORCID]
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Dromedari M: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy
Freschi V: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy
Bogliolo A: Department of Pure and Applied Sciences (DiSPeA), University of Urbino, Piazza della Repubblica 13, 61029 Urbino, Italy [ORCID]
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Journal Name
Energies
Volume
10
Issue
3
Article Number
E278
Year
2017
Publication Date
2017-02-27
ISSN
1996-1073
Version Comments
Original Submission
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PII: en10030278, Publication Type: Journal Article
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LAPSE:2019.1405
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https://doi.org/10.3390/en10030278
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[v1] (Original Submission)
Dec 10, 2019
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Dec 10, 2019
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https://psecommunity.org/LAPSE:2019.1405
Record Owner
Calvin Tsay
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