Decision-Support System for Construction Risk Management in Onshore Wind Projects

  • Author / Creator
    Mohamed, Emad
  • Wind energy is emerging as a primary source of renewable energy in Canada, attracting over $23 billion in investment. Steadily increasing, a total capacity of 31,640 MW of wind energy must be installed by 2040 to meet the requirements of the Paris Agreement on Climate, requiring the construction of new Canadian wind farms and supporting infrastructure. As with other types of construction, the execution phase of wind farm projects is associated with unanticipated risks (e.g., weather-related challenges and unknown stakeholder interactions), which create uncertainty during project execution. Uninformed decisions made in response to such risks can lead projects to deviate from original objectives, resulting in time and cost overruns, safety issues, and quality deficiencies.
    Risk management has become a popular approach in the construction industry to reduce project uncertainties and risks for improved decision-making. However, previous research studies do not address the distinctive characteristics, unique risks, and data limitations associated with wind farm construction, restricting the ability of practitioners to adequately assess the risks affecting the construction phase of onshore wind projects—particularly in the Canadian wind energy sector. In particular, the identification of project-specific (i.e., contextual) risk factors still relies heavily on traditional risk identification techniques that are demanding in terms of time and effort. This, together with a lack of historical data and methods to deal with data insufficiency, hinder the use of advanced quantitative techniques, such as simulation, to assess risks. Finally, distinctive characteristics, including location-bias to high wind speeds, impose unique challenges during the execution of these projects that are not addressed by existing methods.
    This thesis describes the development of a novel decision-support system designed to address current limitations by facilitating and enhancing the identification, analysis, and assessment of risk factors affecting the construction phase of onshore wind farm projects. The decision-support system was developed by adopting existing analytical methods and simulation. First, critical generic risk factors affecting onshore wind projects in Canada were identified. Then, a context-driven approach for identifying project-specific risk factors was developed. Once risk factors were identified, a method to enhance the input modelling of these risk factors for quantitative risk assessment was proposed. Next, a domain-specific risk assessment method was proposed for onshore wind projects. Finally, since adverse weather was identified as the most critical risk factor affecting the construction phase of onshore wind projects in Canada, a simulation-based approach was proposed to more effectively model weather risk.
    This research contributes to the state-of-the-art by (1) providing a systematic and thorough analysis—focused exclusively on the construction phase—of the risk factors affecting onshore wind projects, (2) identifying the most critical risk factors in onshore wind projects in Canada using a hybrid multi-criteria approach; (3) developing a context-driven approach that considers the specific characteristics of a project to facilitate the identification of project risks; (4) developing an integrated simulation approach for assessing risks in onshore wind projects that considers both the cost and time impact of risks; (5) proposing a method for deriving probability distributions of a risk factor’s impact using fuzzy logic and multivariate analysis to enhance input modelling for improved Monte Carlo simulation; and (6) developing a simulation-based approach that allows decision-makers to dynamically and rapidly assess the impact of upcoming weather conditions on project performance during lookahead scheduling.

  • Subjects / Keywords
  • Graduation date
    Fall 2021
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.