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).
Highlights
Wind and solar energy are expected to lead the future transformation of the global electricity sector, with onshore and offshore wind predicted to produce about 35% of total electricity demands by [1]
The proposed approach addresses this limitation by allowing a risk analyst to (1) reliably assess the risk impact based on subjective knowledge and expertise, (2) consider the root causes of a risk factor when calculating its impact, (3) model the dependence between the cost and schedule impacts of a risk factor that has both cost and schedule impact, (4) reduce biases in expert evaluation through the decomposition of a risk factor, and (5) overcome the limitation for using Monte Carlo simulation (MCS) in practice [68]
Input modeling is the first step in MCS-based risk assessment of construction projects
Summary
Wind and solar energy are expected to lead the future transformation of the global electricity sector, with onshore and offshore wind predicted to produce about 35% of total electricity demands by [1]. To reach the targeted installation capacity, considerable investments in the construction of renewable energy infrastructure are being made [1]. As a relatively novel type of infrastructure, wind farm construction is characterized by a lack of relevant literature and a scarcity of historical data. The development of risk management plans for these types of projects, are highly dependent on the collection of expert knowledge [4]. While the boom in the wind energy industry has encouraged new contractors to engage in the construction of these projects, a lack of data represents a challenge for new contractors when conducting risk management. Inadequate risk identification and assessment can have a detrimental impact on these large-scale projects, resulting in negative effects on cost, time, quality, and safety, while simultaneously discouraging contractors from engaging in wind farm construction
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