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Probabilistic analysis of solar photovoltaic self-consumption using Bayesian Network Models

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journal contribution
posted on 21.12.2015, 14:43 by Philip Leicester, Paul Rowley, Chris Goodier
In order to assess the systemic value and impacts of multiple PV systems in urban areas, detailed analysis of on-site electricity consumption and of solar PV yield at relatively high temporal resolution is required, together with an understanding of the impacts of stochastic variations in consumption and PV generation. In this study, measured and simulated time series data for consumption and PV generation at 5 and 1 minute resolution for a large number of domestic PV systems are analysed, and a statistical evaluation of self-consumption carried out. The results show a significant variability of annual PV self-consumption across the sample population, with typical median annual self-consumption of 31% and inter-quartile range of 22-44%. 10% of the dwellings exceed a self-consumption of 60% with 10% achieving 14% or less. The results have been used to construct a Bayesian Network model capable of probabilistically analysing self-consumption given consumption and PV generation. This model provides a basis for rapid detailed analysis of the techno-economic characteristics and socio-economic impacts of PV in a range of built environment contexts, from single building to district scales.

Funding

The authors wish to acknowledge the financial support of the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom through EPSRC grants EP/K022229/1 (WISE PV - Whole System Impacts and Socio-economics of wide scale PV integration) and EP/K02227X/1 (PV2025 - Potential Costs and Benefits of Photovoltaics for UK-Infrastructure and Society).

History

School

  • Architecture, Building and Civil Engineering

Published in

IET Renewable Power Generation

Citation

LEICESTER, P.A., ROWLEY, P. and GOODIER, C.I., 2015. Probabilistic analysis of solar photovoltaic self-consumption using Bayesian Network Models. IET Renewable Power Generation, 10 (4), pp. 448-455.

Publisher

Institution of Engineering and Technology (IET)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Publication date

2015

Notes

This item is an Open Access article published by IET and made available under the terms of the Creative Commons Attribution Licence, (http://creativecommons.org/licenses/by/3.0/)

ISSN

1752-1424

Language

en

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