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agrivoltaics-supply-side-management

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Optimizes Supply Side Management with Agrivoltaics by Artificial Intelligence

agrivoltaicsanalysisbayesiandatadevelopmentmodelingseriestime
Versions collected1
Direct dependencies18From release 0.1.0
Last observed21 Oct, 09:27 UTC

README

Version 0.1.0 documentation

Agrivoltaics Supply Side Management

Optimizes Supply Side Management with Agrivoltaics (Solar sharing between Photovoltaics and Agriculture) by Artificial Intelligence.

Installation

Use the package manager pip to install agrivoltaics-supply-side-management.

Case 1

When using by cloning this GIT repository:

pip install -e [ABSOLUTE PATH TO THIS PROJECT]

Case 2

When using a package available in PyPI:

pip install agrivoltaics-supply-side-management

Solvers

We use Python library for mathematical optimization, called Pyomo. As in any mathematical optimization tool, it requires a solver.

For Linear Programming, glpk is used as a solver by default following Pyomo tutorial.

In Macintosh, install it through Homebrew:

brew install glpk

For other platforms, see https://www.gnu.org/software/glpk/

Usage

[To be added]

References

[1] P. E. Campana, B. Stridh, S. Amaducci, and M. Colauzzi, “Optimisation of vertically mounted agrivoltaic systems,” Journal of Cleaner Production, vol. 325, p. 129091, Nov. 2021, doi: 10.1016/j.jclepro.2021.129091. [2] C. B. Honsberg, R. Sampson, R. Kostuk, G. Barron-Gafford, S. Bowden, and S. Goodnick, “Agrivoltaic Modules Co-Designed for Electrical and Crop Productivity,” in 2021 IEEE 48th Photovoltaic Specialists Conference (PVSC), Jun. 2021, pp. 2163–2166. doi: 10.1109/PVSC43889.2021.9519011. [3] B. Willockx, B. Uytterhaegen, B. Ronsijn, B. Herteleer, and J. Cappelle, “A standardized classification and performance indicators of agrivoltaic systems.,” Oct. 2020. doi: 10.4229/EUPVSEC20202020-6CV.2.47. [4] H-W, Heldt, ”Plant Biochemistry,” Elsevier Academic Press, Burlington, MA, USA, 2005. [5] R. W. Langhans and T. W. Tibbits, “Plant Growth Chamber Handbook - Chapter 1 Radiation,” in Plant Growth Chamber Handbook, Iowa State University, 1997. [6] O. A. Martin, R. Kumar, and J. Lao, "Bayesian Modeling and Computation in Python," Boca Ratón, 2021. [Online]. Available: https://bayesiancomputationbook.com [7] R. Retkute, S. E. Smith-Unna, R. W. Smith, A. J. Burgess, O. E. Jensen, G. N. Johnson, S. P. Preston, E. H. Murchie, ”Exploiting heterogeneous environments: does photosynthetic acclimation optimize carbon gain in fluctuating light?,” Journal of Experimental Botany, May 2015, vol. 66, no. 9, pp. 2437–2447, doi: 10.1093/jxb/erv055.

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Versions 1

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VersionPublishedDirect dependenciesOpen version
0.1.018 Nov 202218

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