IET-RPG.2015.0360.pdf (906.53 kB)
Probabilistic analysis of solar photovoltaic self-consumption using Bayesian Network Models
journal contribution
posted on 2015-12-21, 14:43 authored by Philip LeicesterPhilip Leicester, Paul Rowley, Chris GoodierChris GoodierIn 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 GenerationVolume
10Issue
4Pages
448-455Citation
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/Acceptance date
2015-11-28Publication date
2016-04-01Copyright date
2016Notes
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
1752-1424Publisher version
Language
- en