Scaling up repowering decisions: a farm-level financial decision support system for onshore wind energy assets

The aging of wind energy assets and the expiration of government subsidies make decisions on continued operation, repowering, and decommissioning increasingly critical for wind farm operators. Existing studies provide valuable insights into wind energy investment under uncertainty, but often provide limited consideration of turbine-level heterogeneity when analyzing decisions from a wind farm perspective. Following Design Science Research, this study proposes a Decision Support System (DSS) for wind farm operations that integrates several methodological components with distinct roles. Spatial and regulatory feasibility assessment identifies whether turbines and sites are eligible for repowering. Stochastic process simulation generates uncertain paths of electricity prices, turbine lifetimes, energy yields, and costs. Least Squares Monte Carlo-based real options valuation evaluates repowering timing for eligible turbines, and Monte Carlo-based net present value analysis assesses decommissioning timing for turbines without repowering eligibility. Building on these turbine-level valuations, the DSS applies a rule-based clustering approach to translate individual decision timings into batch-level coordinated strategies at the farm scale. The artifact is evaluated using a real wind farm in central Germany. The results indicate that, under the given assumptions, repowering-eligible turbines exhibit a pronounced early exercise window, whereas non-eligible turbines tend to be decommissioned at the end of their lifetimes. At the farm level, the system identifies three decision scenarios and compares their expected total returns. The batch-execution strategy, in which actions are carried out at each cluster’s timing, achieves the highest expected return in the case study. This study provides a reusable information-systems-based methodological approach and case-based evidence for coordinating repowering and decommissioning decisions in wind farms. The findings suggest that the proposed DSS can translate complex uncertainties into executable farm-level strategies and improve transparency in strategic decision-making.