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Modeling Epistemic Uncertainty in Offshore Wind Farm Production Capacity to Reduce Risk
Author(s) -
Zitrou Athena,
Bedford Tim,
Walls Lesley
Publication year - 2022
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/risa.13846
Subject(s) - production (economics) , offshore wind power , environmental science , submarine pipeline , uncertainty quantification , natural resource economics , risk analysis (engineering) , environmental resource management , business , engineering , computer science , economics , wind power , microeconomics , geotechnical engineering , electrical engineering , machine learning
Financial stakeholders in offshore wind farm projects require predictions of energy production capacity to better manage the risk associated with investment decisions prior to construction. Predictions for early operating life are particularly important due to the dual effects of cash flow discounting and the anticipated performance growth due to experiential learning. We develop a general marked point process model for the times to failure and restoration events of farm subassemblies to capture key uncertainties affecting performance. Sources of epistemic uncertainty are identified in design and manufacturing effectiveness. The model then captures the temporal effects of epistemic and aleatory uncertainties across subassemblies to predict the farm availability‐informed relative capacity (maximum generating capacity given the technical state of the equipment). This performance measure enables technical performance uncertainties to be linked to the cost of energy generation. The general modeling approach is contextualized and illustrated for a prospective offshore wind farm. The production capacity uncertainties can be decomposed to assess the contribution of epistemic uncertainty allowing the value of gathering information to reduce risk to be examined.