Probabilistic analysis of solar photovoltaic self-consumption using Bayesian Network Models
journal contributionposted on 21.12.2015 by Philip Leicester, Paul Rowley, Chris Goodier
Any type of content formally published in an academic journal, usually following a peer-review process.
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.
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).
- Architecture, Building and Civil Engineering