info:eu-repo/semantics/article
Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method
Fecha
2020-05Registro en:
Pringles, Rolando Marcelo; Olsina, Fernando Gabriel; Penizzotto Bacha, Franco Victor; Valuation of defer and relocation options in photovoltaic generation investments by a stochastic simulation-based method; Pergamon-Elsevier Science Ltd; Renewable Energy; 151; 5-2020; 846-864
0960-1481
CONICET Digital
CONICET
Autor
Pringles, Rolando Marcelo
Olsina, Fernando Gabriel
Penizzotto Bacha, Franco Victor
Resumen
Risk management is crucial when committing investments in electricity markets. Investment projects for the generation of electricity are capital-intensive, in large part irreversible and future performance is subject to high uncertainty. Fortunately, most power generation projects have strategic flexibility for handling uncertainty and for mitigating risks under unfavorable scenarios. Modern corporate finance recognizes Real Option analysis (ROA) as the correct way to value investment projects with these characteristics. Due to both, environmental concerns and escalation of fuel prices, electricity generation from renewable sources has grown dramatically worldwide over the last decade. Renewable investment projects share many of the features mentioned. As such, option valuation methods should be applied to estimate the monetary value of flexibility in renewable energy investments. This work presents an appropriate methodology for assessing the economic value of a photovoltaic power plant under uncertainties. ROA is applied to determine the value of delaying the investment decision while waiting for better market information that would reduce acquisition costs due to progress in solar technology. The flexibility of relocating the solar facility in the future upon the appearance of a more attractive site in terms of cost, network accessibility or regulatory policies is also valued. The problem of option valuation is solved through stochastic simulation combined with recursive approximate dynamic programming techniques. The methodology developed might be used by investors for more efficient decision-making and by regulatory agencies for designing adequate support policies that encourage investment in renewable energy generation.