A dynamic behavioral model of the long-term development of solar photovoltaic generation driven by feed-in tariffs

This work aims to assess the impact of renewable energy incentives, particularly that of the feed-in tariff (FiT), on the long-term development of solar photovoltaics (PVs). With this aim, the paper introduces a dynamic model based on nonlinear delay differential algebraic equations to simulate the evolution of the PV capacity and its commitment in the power grid. The model assumes the FiT budget, the PV cost and willingness of the public to install PVs as the main drivers for solar PV installations. In particular, the learning-by-doing concept to model the PV cost and consequently the PV deployment is proposed for the first time in this paper. The accuracy of the model is validated against historical data of two of the biggest PV markets in the world driven by FiT, namely, Italy during 2008–2014, and Germany during 2000–2014. A sensitivity analysis based on the Italian PV market is carried out to identify the impact of the parameters of the proposed model. Results indicate that the proposed model is a valuable tool that can help policymakers in the decision-making process, such as the definition of the FiT price and the duration of the incentives.