This thesis has the aim to evaluate the profitability of an investment in a photovoltaic system when considering uncertain energy prices and costs. We determine the optimal investment timing using a standard Grid Parity model and then compare it with the optimal timing determined using a Grid Parity model taking into account the presence of an option value to invest. This model required the calibration of two Geometric Brownian Motions (GBM) that have been used to express the future paths of prices and costs. The GBM’s drift and volatility of the prices have been calibrated through an analysis of the trend and the volatility of the time series Prezzo Unico Nazionale, that is the price formed in the Italian Power Exchange (IPEX). On the other hand, the estimation of the parameters related to the costs of the PV plant has been performed applying the learning curve approach to the evolution of the PV sector and considering the stocks’ volatility of the main companies producing PV modules. The two GBM processes calibrated in this way then have been included in the stochastic Grid Parity model with the aim to forecast the timing at which the investment in a PV plant becomes profitable, involving in the evaluation also the uncertainty surrounding the future paths of prices and costs. In the end of the thesis, we will show that the stochastic Grid Parity leads to considerable differences with respect to the standard version about the optimal time that a rational agent should choose to invest in the PV plant.
Investing in Solar Energy under Price and Cost Uncertainty
Righetto, Davide
2021/2022
Abstract
This thesis has the aim to evaluate the profitability of an investment in a photovoltaic system when considering uncertain energy prices and costs. We determine the optimal investment timing using a standard Grid Parity model and then compare it with the optimal timing determined using a Grid Parity model taking into account the presence of an option value to invest. This model required the calibration of two Geometric Brownian Motions (GBM) that have been used to express the future paths of prices and costs. The GBM’s drift and volatility of the prices have been calibrated through an analysis of the trend and the volatility of the time series Prezzo Unico Nazionale, that is the price formed in the Italian Power Exchange (IPEX). On the other hand, the estimation of the parameters related to the costs of the PV plant has been performed applying the learning curve approach to the evolution of the PV sector and considering the stocks’ volatility of the main companies producing PV modules. The two GBM processes calibrated in this way then have been included in the stochastic Grid Parity model with the aim to forecast the timing at which the investment in a PV plant becomes profitable, involving in the evaluation also the uncertainty surrounding the future paths of prices and costs. In the end of the thesis, we will show that the stochastic Grid Parity leads to considerable differences with respect to the standard version about the optimal time that a rational agent should choose to invest in the PV plant.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14247/12016