PLANTING: Computing High Spatio-temporal Resolutions of Photovoltaic Potential of 3D City Models

  • Syed Monjur Murshed
  • Amy Lindsay
  • Solène Picard
  • Alexander Simons
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Photovoltaic (PV) production from the sun significantly contributes to the sustainable generation of energy from renewable resources. With the availability of detailed 3D city models across many cities in the world, accurate calculation of PV energy production can be performed. The goal of this paper is to introduce and describe PLANTING, a numerical model to estimate the solar irradiance and PV potential at the resolution of individual building surfaces and hourly time steps, using 3D city models. It considers the shading of neighboring buildings and terrains to perform techno-economic PV potential assessment with indicators such as installed power, produced electrical energy, levelized cost of electricity on the horizontal, vertical and tilted surfaces of buildings in a city or district. It is developed within an open-source architecture using mostly non-proprietary data formats, software and tools. The model has been tested on many cities in Europe and as a case study, the results obtained on the city of Lyon in France are explained in this paper. PLANTING is flexible enough to allow the users to choose PV installation settings, based on which solar irradiance and energy production calculations are performed. The results can also be aggregated at coarser spatial (building, district) and temporal (daily, monthly, annual) resolutions or visualized in 3D maps. Therefore, it can be used as a planning tool for decision makers or utility companies to optimally design the energy supply infrastructure in a district or city.

Keywords

Solar irradiance Photovoltaic potential Building surfaces 3D city model CityGML Python 

Notes

Acknowledgements

We are grateful to EDF R&D and Métropole de Lyon for funding this research within the projects “Smart and Low Carbon Cities” and “Modélisation Urbaine Gerland (MUG)”, respectively. Our heartiest gratitude to the city of Lyon and Laboratoire LIRIS for providing the 3D city model of Lyon. We would like to thank City of Karlsruhe for the permission of using the CityGML data for evaluating the model. We are very grateful to Gilbert El Hajje and Emmanuel Boyere of EDF R&D for comparison and cross validation of the model. We also acknowledge Fabrice Casciani, Monika Heyder, Pierre Imbert, Omar Benhamid, Alexandru Nichersu, Céline De Pin, Alice Duval, Manfred Wieland and other colleagues for their input. Finally, our sincere gratitude extends to the editors and two anonymous referees for their insightful comments, which helped us to improve the manuscript.

Supplementary material

461293_1_En_2_MOESM1_ESM.zip (32 kb)
Supplementary material 1 (ZIP 32 kb)

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Syed Monjur Murshed
    • 1
  • Amy Lindsay
    • 2
  • Solène Picard
    • 3
  • Alexander Simons
    • 1
  1. 1.European Institute for Energy ResearchKarlsruheGermany
  2. 2.EDF Inc. Innovation LabLos AltosUSA
  3. 3.École Supérieure D’ÉlectricitéGif-sur-YvetteFrance

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