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

Abstract

Solar photovoltaic (PV) technology 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 regard to these potential returns. In order to manage these uncertainties, an innovative probabilistic approach using Bayesian networks has been applied to the techno-economic analysis of domestic solar PV. Empirical datasets from over 600 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-consumption with regard to 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 illustrate the sensitivity of PV investment returns to parameters such as PV self-consumption, PV degradation rates and geographical location and quantify inherent uncertainties when evaluating the impact of sector-specific PV adoption upon economic indicators. The outcomes are discussed in terms of the value and impact of this new Bayesian approach in terms of supporting robust and rigorous policy and investment decision-making, especially in post-subsidy contexts globally.

Strategic business risk evaluation for sustainable energy investment and stakeholder engagement: A proposal for energy policy development in the Middle East through Khalifa funding and land subsidies

“…DCF and NPV are methods to find the present value of future returns on investments. An abundance of literature can be found where people have evaluated energy projects through DCF and NPV (Aba, Ladeinde, & Afimia, 2019; Leicester, Goodier, & Rowley, 2016). MA analyse the several important criteria to decide on the establishment of energy projects.…”

Sources of Uncertainty for Monte Carlo Modelling with Bayesian Analysis for Sub-Saharan Off-Grid Solar PV Systems

“…This study builds upon previous research by the authors [7], which presents sustainability data from 65 installed off-grid PV systems in Malawi and combines the data in a novel way, using Monte Carlo with Bayesian analysis for the first time to compare the sustainability of different projects. In [7] projects are scored within 4 impact categories: technical, economic, social, and organisational. An aggregated (total) sustainability 'score' is then proposed as a good early measure of project sustainability.…”