Economic Feasibility Analysis of Photovoltaic Systems Using Bayesian Networks

The economic viability of photovoltaic (PV) systems is subject to spatial and temporal uncertainties that can pose significant financial risks to the investors of such systems. This paper presents a decision-making model using Bayesian networks to assess the economic viability of PV systems, by considering the inherent spatial and temporal uncertainties and their impacts on the annual electricity generation of a PV system. The application of the proposed model is shown in a 6.5 kWh PV system installed on a house located in Corvallis, OR. Results show that by using the Bayesian networks, the probability of different states of electricity generation can be estimated and used for quantitative decision making. Furthermore, sensitivity analysis is performed to demonstrate how the key factors in the PV system can affect the annual electricity generation. The proposed model contributes to the body of knowledge in the valuation of renewable energy investments through the application of Bayesian networks. It is expected that the proposed model can help investors in PV systems make scientific and informed decisions.