Probabilistic evaluation of solar photovoltaic systems using Bayesian Networks: a discounted cash flow assessment

Solar PV technology (PV) is now a key contributor worldwide in the transition towards low carbon electricity systems. To date, PV commonly receives subsidies in order to accelerate adoption rates by increasing investor returns. However, many aleatory and epistemic uncertainties exist with regards these potential returns. In order to manage these uncertainties, a probabilistic approach using Bayesian networks has been applied to the techno-economic analysis of domestic solar PV. Using the UK as a representative case study, empirical datasets from over 400 domestic PV systems, together with national domestic electricity usage datasets, have been used to generate and calibrate prior probability distributions for PV yield and domestic electricity consumption respectively for typical urban housing stock. Subsequently, conditional dependencies of PV self-use with regards PV generation and household electricity consumption have been simulated via stochastic modelling using high temporal resolution demand and PV generation data. A Bayesian network model is subsequently applied to deliver posterior probability distributions of key parameters as part of a discounted cash flow analysis. The results indicate the sensitivity of investment returns to specific parameters (including PV self-consumption, PV degradation rates and geographical location), and quantify inherent uncertainties when using economic indicators for the promotion of PV adoption. The results’ implications for potential rates of sector-specific adoption are discussed, and implications for policy makers globally are presented with regards energy policy imperatives, as well as fiscal imperatives of meeting investors’ requirements in terms of returns on investment in a post-subsidy context.