On quantifying uncertainty for project selection: the case of a renewable energy sources investment

Konstantinos Kirytopoulos, Athanasios Rentizelas, Georgios Tziralis

Research output: Contribution to conferencePaper

Abstract

The selection of a project among different alternatives, considering the limited resources of a company (organisation), is an added value process that determines the prosperity of an undertaken project (investment). This applies also to the “booming” Renewable Energy Sector, especially under the circumstances established by the recent activation of the Kyoto protocol and by the plethora of available choices for renewable energy sources (RES) projects. The need for a reliable project selection method among the various alternatives is, therefore, highlighted and, in this context, the paper proposes the NPV function as one of possible criteria for the selection of a RES project. Furthermore, it differentiates from the typical NPV calculation process by adding the concept of a probabilistic NPV approach through Monte Carlo simulation. Reality is non-deterministic, so any attempt of modelling it by using a deterministic approach is by definition erroneous. The paper ultimately proposes a process of substituting the point with a range estimation, capable of quantifying the various uncertainty factors and in this way elucidate the accomplishment possibilities of eligible scenarios. The paper is enhanced by a case study showing how the proposed method can be practically applied to support the investment decision, thus enabling the decision makers to judge its effectiveness and usefulness.
Original languageEnglish
Number of pages11
Publication statusPublished - 24 Jan 2006
Event World Renewable Energy & Environment Conference (WREEC) 2006 - Tripoli, Libya
Duration: 1 Jan 2006 → …

Conference

Conference World Renewable Energy & Environment Conference (WREEC) 2006
CountryLibya
Period1/01/06 → …

Keywords

  • quantifying uncertainty
  • project selection
  • renewable energy sources investment
  • Monte Carlo
  • uncertainty factors

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