Time Series Model for Wind Turbine Losses: Assessing Regional and Temporal Variability, Loss Interactions, and Market Values

National assessments of wind power losses typically rely on static loss factors and focus on annual energy production (AEP), implicitly assuming a one-to-one correspondence between energetic losses and their economic significance. This paper challenges this assumption by applying a time-resolved, national-scale loss modeling framework to the German onshore wind fleet for the year 2024, simulating all turbines at 10-minute resolution to quantify losses due to wake, noise-related curtailment, and bat protection measures, including their interactions. We find that wake losses dominate energetically, while those from noise and bat curtailment are smaller in magnitude. Accounting for interactions demonstrates that the combined loss differs from the sum of individual contributions. By weighting losses with hourly day-ahead electricity prices, we introduce the loss market value (LMV) as a metric to assess their economic relevance. The results show that energetic magnitude and economic relevance are largely decoupled: wake losses exhibit an LMV close to unity (≈ 1.1), whereas bat curtailment shows a substantially higher LMV (≈ 1.5). Noise-related losses are concentrated in low-price night hours, resulting in an LMV below unity (≈ 0.8).