Abstract
The construction of large renewable energy projects is characterized by the great uncertainties associated with their administrative complexity and their constructive characteristics. For proper management, it is necessary to undertake a thorough project risk assessment prior to construction. The work presented in this paper is based on a hierarchical risk structure identified by a group of experts, from which a Probabilistic Fuzzy Sets with Analysis Hierarchy Process (PFSAHP) was applied. This probabilistic analysis approach used expert opinion based on the Monte Carlo Method that allows for extracting more information from the original data. In addition, the coherence of the experts’ opinions is assessed using a novel parameter known as Confidence Level, which allows for adjusting the opinions of experts and weighting their judgments regarding impact and probability according to their coherence. This model has the advantage of offering a risk analysis in the early stages of the management of renewable energy projects in which there is no detailed information. This model is also more accurate than the classic fuzzy methodology when working with complete distribution functions, whilst it avoids the loss of information that results from the traditional mathematical operations with Fuzzy numbers. To test the model, it was applied to a 250 MW photovoltaic solar plant construction project located in southeast of Spain (Region of Murcia). As a result of the application of the proposed method, risk rankings are obtained with respect to the cost, the time, the scope and from a general point of view of the project.
Highlights
In some countries, the reduction or disappearance of economic incentives to produce from renewable energy sources has caused a drastic decline in the investment in new projects
The chief contributions of this work are: 1) it introduces a new parameter known as Confidence level, which allows for variations in the opinions of experts according to their coherence and weighting their judgements regarding impact and probability, 2) it employs a probabilistic analysis of the opinion functions instead of applying the mathematical simplifications used in other methods of fuzzy analysis, which allows for extracting more information from the same data
This paper presents a new methodology for risk analysis that is both qualitative and quantitative, based on probabilistic analysis, detailed risk definitions, and expert opinion
Summary
The reduction or disappearance of economic incentives to produce from renewable energy sources has caused a drastic decline in the investment in new projects. The chief contributions of this work are: 1) it introduces a new parameter known as Confidence level, which allows for variations in the opinions of experts according to their coherence and weighting their judgements regarding impact and probability, 2) it employs a probabilistic analysis of the opinion functions instead of applying the mathematical simplifications used in other methods of fuzzy analysis, which allows for extracting more information from the same data This approach provides a smooth application with a rapid and thorough methodology that could be readily used for both qualitative and quantitative risk analyses based on linguistic assessments in the early stages of large-scale renewable construction projects. The column matrix Pgm is generated from the questionnaires, from RP opinions with respect to the general project framework (RPim), as shown in Eq (2),
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