Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects

Abstract

Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where—although lacking in historical data—large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor’s impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).

Domain-specific risk assessment using integrated simulation: a case study of an onshore wind project

“…As a new type of construction, onshore wind farms are associated with a relatively large amount of risk and uncertainty (Gatzert and Kosub 2016;Somi et al 2020;Mohamed et al, 2020b). Accurately estimating the impact of risk to ensure adequate cost and time contingencies is particularly important in wind farm construction due to electricity production requirements mandated in power purchase agreements, with contracts imposing liquidated damages of up to 50 000 CAD per turbine/day for any delays in the operation date.…”

“…Types of distributions used in previous studies are summarized in Table S2. Due to a lack of historical data, a fuzzy-based multivariate method for determining risk impact distributions recently proposed by Mohamed et al (2020b) was…”

“…The probability distributions for cost and schedule risk impact were determined using the method introduced by Mohamed et al (2020b) for input modeling of MCS in wind farm construction. First, the root causes/scenarios of the risk factors were determined and evaluated.…”

“…Risk factors with correlated schedule and cost impacts were represented by a bivariate distribution using a normal copula (Mohamed et al, 2020b). A copula package in R (Yan 2007) was used to implement the multivariate modeling of the cost-and schedule-risk impact…”

“…Although considered a superior approach, previous MCS-CPM-based models consider cost and schedule impacts of a risk factor as independent variables (Table S2). While a method for considering the dependency between cost and schedule impacts through bivariate distributions has been recently 10 proposed (Mohamed et al 2020b), the method has not been applied to a real case study. As such, its functionality and practical utility for the evaluation of real case data remains unknown.…”