Posters

Abstract

Wind energy portfolios are increasingly diverse, spanning geographies, project sizes, and operational conditions. Yet, traditional approaches to uncertainty aggregation often overestimate risk by assuming perfect correlation between project-level production variabilities. In reality, there is some decorrelation between project uncertainties, which has the effect of reducing total portfolio production uncertainty, also known as the portfolio benefit. Unravelling these correlations and accurately simulating portfolio production is the challenge. In this presentation, we perform a portfolio benefit analysis on a company’s portfolio consisting of 31 operational wind farms. We further compare the relative benefits of including a range of proposed wind farms, highlighting the added diversity each brings to the portfolio. METHODOLOGY The analysis focused on 31 wind farms distributed across the world, encompassing diverse climates, terrains, and operational conditions. Four key uncertainty categories were identified to establish correlations between projects: wind resource, historical representativeness, operational period and correlation, and plant downtime adjustments. A high-level annual approach based on P50s and annual uncertainties was employed. This included the following steps: 1. Correlation Matrices: Correlation matrices were calculated for each uncertainty category to quantify interdependencies between projects. 2. Covariance Matrices: Multiplication of correlation matrices with uncertainty values produced covariance matrices, encapsulating the relationship between uncertainties across the portfolio. 3. Monte Carlo Sampling: Covariance matrices were sampled using a Monte Carlo approach to generate annual average energy production (AEP) estimates for each project. Over one million iterations were conducted to preserve correlations and ensure statistical robustness. 4. Portfolio Synergy Analysis: Results highlighted how diverse assets complement one another, mitigating variability and reducing portfolio-level uncertainty. RESULTS The portfolio benefit analysis revealed significant insights into portfolio-level performance. While individual project uncertainties ranged widely, the aggregated portfolio uncertainty was markedly lower, decreasing from 8.2% without the portfolio benefit to 5.7% with the benefit. Further, when evaluating new projects to add to the portfolio, some projects were able to reduce the portfolio uncertainty by up to an additional 0.3%, while others provided negligible benefit. DISCUSSION This case study underscores the transformative potential of portfolio benefit analysis in renewable energy portfolio management. By quantifying and leveraging inter-project correlations, such an analysis provides a more nuanced understanding of production variability, turning a perceived challenge into a strategic advantage. The incorporation of Monte Carlo simulations and machine learning ensures that the analysis is both comprehensive and scalable, adaptable to portfolios of varying sizes and compositions. FUTURE WORK While this analysis focuses on annual uncertainty metrics, future work will expand to address daily and hourly variability, which are increasingly critical in real-time energy markets with day-ahead forecasting requirements. Additionally, future efforts will link portfolio production uncertainty to revenue uncertainty, considering the impact of various Power Purchase Agreement (PPA) structures. This evolution will enhance the practical application of portfolio benefit analysis, supporting more accurate market participation and financial planning.