Research Article: Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects

Date Published: June 13, 2019

Publisher: Public Library of Science

Author(s): Luis Serrano-Gomez, Jose Ignacio Munoz-Hernandez, Rajagopalan Srinivasan.

http://doi.org/10.1371/journal.pone.0215943

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.

Partial Text

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. However, the maturity of renewable technologies has led to a fall in the costs of the main technological components. This has, in turn, led to a resurgence of the photovoltaic sector, in which activity is focused on the large plants, which, even at current market prices, provide sufficient profitability due to economies of scale. It is important to bear in mind that undertaking a large project entails managing budgets to the value of tens of millions of euros, in addition to considerable administrative complexity. Such large projects are often processed at the level of Central Governments and National Transport Network Operators, which entails slower and more laborious procedures compared with those of local and regional organizations. If a construction project of a large photovoltaic plant is developed without conducting an analysis of the possible risks of the project from an early stage, this could incur considerable losses for the promoter of the company. Various definitions of risk have been proposed in the literature, although perhaps more appropriate for renewable projects is that adopted by Perry and Hayes [1] and Chapman and Ward [2]. These authors regard risk as ‘exposure to the possibility of economic or financial loss or gain, physical damage or injury, or delay, as a consequence of the uncertainty associated with the pursuing a particular course of action’.

Classic Fuzzy methods for risk analysis share the same general structure [24]:

For those experts where CL is greater than the minimum threshold set, the opinions coefficient is estimated by the weighting of RI and RP experts using Eqs (10) and (11),
WRIim=CL(RI)im∑n=1mCL(RI)in(10)WRPim=CL(RP)im∑n=1mCL(RP)in(11)
where WRIim and WRPim are the opinions weighting coefficients of RI or RP, CL (RI) and CL (RP) are the Confidence Level calculated with Eqs (5) and (6), respectively, for each risk i and expert m.

To study and apply the proposed risk assessment methodology, we have chosen as a case study a 250 MW photovoltaic plant project located in the village of Jumilla, in the Region of Murcia (Southeast of Spain). This project is in the early phase of study and initial administrative treatment. Given the plant size and the project budget, initially estimated at 250 million euros, the application of a risk assessment methodology is essential from the very beginning of the project, since it allows for identifying the key risks and setting the risk response strategy. The project area is close to 600 Ha, with 1 million polycrystalline photovoltaic panels over fixed structure, whose energy will be channeled into central power inverters 1MVA.

A comprehensive risk analysis method such as the one presented in this paper is critical for adequate risk response planning. To obtain a risk ranking, working directly with distribution functions, the Mode value is chosen as the most frequent value, which is equivalent to the centroid method employed in the classic fuzzy methodology.

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. Unlike the classic fuzzy sets methodology that works with triangular numbers that are eventually simplified, this work uses whole probabilistic functions and Monte Carlo simulation to obtain more complete results for the same database, thus allowing for a better analysis. The main points to note are as follows:

 

Source:

http://doi.org/10.1371/journal.pone.0215943

 

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