ANNOUNCEMENTS
As India continues to scale up its renewable energy ambitions, integrating variable wind power into the grid brings new operational and regulatory challenges, especially under the Deviation Settlement Mechanism (DSM). This study examines real-time operational data from a 300 MW wind pooling station in Karnataka (2024) to assess how data availability and seasonal wind variability influence DSM penalties. While initial correlation analysis showed a weak statistical relationship between data availability and forecasting error (r ≈ –0.0288), further in-depth analysis revealed that even short-term data gaps can trigger significant penalties during volatile wind periods. However, once data quality crosses a certain threshold (~93% availability), weather variability more than the not data gaps, becomes the dominant source of error, especially during the monsoon season.
The study supports concerns raised by Prayas (Energy Group), which criticized the 2022 DSM regulation amendments for lacking impact assessments. Findings confirm that uniform ±10% error bands are not well suited to the unpredictable nature of wind energy, and strongly supports PEG’s call for seasonally adaptive DSM limits. Furthermore, the study offers evidence-based suggestions for how Qualified Coordinating Agencies (QCAs) must evolve under upcoming ±5% DSM bands. These include adopting AI-based early warning systems, improving turbine-level visibility, and shifting from reactive to proactive scheduling.
By showing how penalties are often caused by a combination of forecast limitations, weather volatility, and system constraints, this report also highlights the limits of QCA and generator control under current rules. It recommends that future research explore climate-informed forecasting strategies, substation-level risk metrics, and smarter reserve pooling models. As the DSM framework becomes stricter, maintaining grid reliability and investor confidence will require policies that are not only data-driven but also sensitive to seasonal patterns, location-specific risks, and the realities of operational flexibility.
KEYWORDS: Wind Energy Forecasting, Deviation Settlement Mechanism (DSM), Qualified Coordinating Agencies (QCA), SCADA Data Quality, Forecast Accuracy, Renewable Energy Integration, Seasonal Wind Variability, Grid Reliability, DSM Penalties, Adaptive Regulation, Climate-Informed Scheduling, Karnataka, India, ±5% Error Band, Energy Policy.
