Technical Due Diligence Assessment and Bayesian Belief Networks Methodology for Wind Power Projects
Independent thesis Advanced level (degree of Master (One Year)), 40 credits / 60 HE creditsStudent thesis
A Technical Due Diligence (TDD) investigation is an important step in the process of obtaining financing, or in mergers and acquisitions, for a wind power project. The investigation, the scope of which varies depending on the stage and nature of the project, involves reviewing important documentation relating to different aspects of the project, assessing potential risks in terms of the quality of the information available and suggesting mitigation or other risk management measures where required.
A TDD assessment can greatly benefit from increased objectivity in terms of the reviewed aspects as it enables a sharper focus on the important risk elements and also provides a better appreciation of the investigated parameters. This master’s thesis has been an attempt to introduce more objectivity in the technical due diligence process followed at the host company. Thereafter, a points-based scoring system was devised to quantify the answered questions. The different aspects under investigation have a complex interrelationship and the resulting risks can be viewed as an outcome of a causal framework.
To identify this causal framework the concept of Bayesian Belief Networks has been assessed. The resulting Bayesian Networks can be considered to provide a holistic framework for risk analysis within the TDD assessment process. The importance of accurate analysis of likelihood information for accurate analysis of Bayesian analysis has been identified. The statistical data set for the right framework needs to be generated to have the right correct setting for Bayesian analysis in the future studies.
The objectiveness of the TDD process can be further enhanced by taking into consideration the capability of the investing body to handle the identified risks and also benchmarking risky aspects with industry standards or historical precedence.
Place, publisher, year, edition, pages
2013. , 156 p.
Wind Power, Risk Management, Technical Due Diligence, Bayesian Belief Networks
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-224063OAI: oai:DiVA.org:uu-224063DiVA: diva2:715159
Mecal Indepent eXperts B.V., Enschede, The Netherlands
Master Programme in Wind Power Project Management
2013-06-05, 14:00 (English)
Uzunoglu, Bahri, Associate ProfessorRoeloffzen, Frank
Sørensen, Jens Nørkær, Professor