Assessing Credit Risk in Wind Energy Project Finance: A Monte Carlo Simulation Approach

Abstract

Non-recourse project finance has emerged as a dominant financing structure for wind energy infrastructure, coinciding with the sector’s evolution from a niche application to a globally established asset class. Notwithstanding this growth, credit risk remains inconsistently modeled: no dedicated, widely accepted quantitative framework exists to capture the distinct risk characteristics of wind energy project finance. This thesis addresses this gap by developing a Monte Carlo simulation-based model specifically calibrated to the unique cash flow dynamics of wind energy assets. The model estimates probability of default, expected loss, loss given default, value-at-risk, and expected shortfall, while also enabling scenario-based sensitivity testing under varying contractual and financial conditions. The model is then applied to a real-world case study to assess both the technical feasibility and practical relevance of the framework. The findings from its development and application confirm that the model effectively captures the dynamic evolution of credit risk across the debt tenor and provides quantitative insights into how revenue certainty and liquidity reserve structures influence lender exposure. Accordingly, the research proposes a structured, adaptable framework that complements existing credit assessment methods and supports risk analysis, scenario evaluation, and transaction structuring in wind energy project finance.