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Estimation of Large-Scale Solar Rooftop PV Potential for Smart Grid Integration: A Methodological Review

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Sustainable Interdependent Networks

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 145))

Abstract

Roof-mounted photovoltaic (PV) panels are currently one of the most promising sources of renewable energy in urban areas. Yet, the optimized use of these rooftop PV systems requires an estimate of the potential supply. This chapter presents a review of the existing methodologies to explore the suitability of different methods to estimate the solar PV potential at regional and national scale. A thorough potential study for solar PV over rooftops requires the estimation of multiple variables, including (1) the horizontal components of solar radiation (global, diffuse, direct, extraterrestrial radiations) at the location of interest, (2) the shadowing effects over rooftops, (3) the rooftop slope and aspect distributions, and the rooftop shape, (4) the solar radiation over the tilted rooftops, and (5) the available rooftop area for PV installation. The goal of the present chapter is to review different methods for solar rooftop PV potential, independently from each other. A comparison is given based on the regional characteristics, the scale of study, the availability of data, and the level of accuracy. The methods include physical and empirical models , geostatistical methods, constant-value methods, sampling methods, geographic information systems (GIS) and light detection and ranging (LiDAR) -based methods, and finally machine learning methods. We present for each of them the main principle and theoretical background, as well as a literature review of the most significant studies that applied the method in various contexts. We also discuss the main advantages and disadvantages of the methods as well as present which methods are more suitable to estimate the above-mentioned variables.

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Acknowledgements

This research has been financed partly by the Commission for Technology and Innovation (CTI) within the SCCER Future Energy Efficient Buildings and Districts, FEEB&D (CTI.2014.0119) and partly by Swiss National Science Foundation under Mobility Fellowship P300P2 174514.

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Authors and Affiliations

  1. LESO-PB, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland

    Dan Assouline & Jean-Louis Scartezzini

  2. Sustainable Urban Development Programme, Department for Continuing Education, University of Oxford, Rewley House, 1 Wellington Square, Oxford, OX12JA, UK

    Nahid Mohajeri

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  1. Dan Assouline
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Correspondence to Dan Assouline .

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  1. Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    M. Hadi Amini

  2. Florida International University, Miami, Florida, USA

    Kianoosh G. Boroojeni

  3. Florida International University, Miami, Florida, USA

    S.S. Iyengar

  4. University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  5. Aalborg University, Aalborg East, Denmark

    Frede Blaabjerg

  6. Department of Electrical and Computer Engineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, California, USA

    Asad M. Madni

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Assouline, D., Mohajeri, N., Scartezzini, JL. (2018). Estimation of Large-Scale Solar Rooftop PV Potential for Smart Grid Integration: A Methodological Review. In: Amini, M., Boroojeni, K., Iyengar, S., Pardalos, P., Blaabjerg, F., Madni, A. (eds) Sustainable Interdependent Networks. Studies in Systems, Decision and Control, vol 145. Springer, Cham. https://doi.org/10.1007/978-3-319-74412-4_11

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