Vote of Thanks Note for AI-Solution

Executive Summary

An Artificial Intelligence (AI)-based forecasting solution has been developed to address generation scheduling deviations caused by tracker wind stow operation in utility-scale solar plants.

The solution integrates wind forecast data with tracker operating logic to estimate generation loss during high-wind conditions and prepare a more realistic generation schedule. This initiative is expected to improve forecasting accuracy, reduce DSM penalties, and demonstrate the practical application of AI in renewable energy operations.

Business Challenge

Accurate generation forecasting is essential for minimizing DSM penalties and maximizing revenue realization.

In tracker-based solar plants, forecasting is generally carried out using weather parameters such as solar irradiance and cloud cover. During high-wind conditions, trackers automatically move to protective stow positions, which reduces energy generation.

Conventional forecasting tools generally do not account for tracker wind stow operation. As a result, actual generation may deviate from scheduled generation, leading to avoidable DSM penalties and reduced forecasting accuracy.

Key Impacts

Generation loss during high-wind events.
Schedule deviation even under otherwise clear-weather conditions.
Increase in DSM penalties.
Reduced predictability of plant performance.

AI-Based Solution

To address this challenge, an AI-driven Wind Stow-Aware Scheduling Model has been developed.

The model considers:

Wind speed forecast data.
Tracker wind stow operating philosophy.
Estimated generation loss corresponding to different stow angles.

Based on these inputs, the model predicts the likely impact of tracker wind stow operation on plant generation and provides a revised generation schedule aligned with expected plant performance.

Outcome Achieved

The Below comparison between conventional and AI Based forecast indicates improved scheduling alignment and lower DSM exposure when the AI-based method is used for high-wind operating conditions.

Generation Gap Reduction 338.86 MWh about 61.8% lower	DSM Penalty Reduction Rs. 1,63,233 about 31.6% lower	Business Impact Improved schedule accuracy reduced deviation risk

Date	Forecasting Method	Schedule (MWh)	Schedule after Curtailment (MWh)	Generation (MWh)	Generation Gap (MWh)	DSM Penalty (Rs.)
28.05.2026	Conventional	5,032.25	4,984.88	4,436.29	548.59	5,16,686
29.05.2026	AI-Based	4,349.72	3,681.01	3,471.27	209.73	3,53,453

Key Outcome Interpretation

The generation gap reduced from 548.59 MWh under the conventional method to 209.73 MWh under the AI-based method.
The DSM penalty reduced from Rs. 5,16,686 to Rs. 3,53,453, resulting in an indicative reduction of Rs. 1,63,233.
The outcome demonstrates that incorporating wind stow logic in forecasting can improve schedule reliability during high-wind periods.

Expected Benefits

Financial Benefits

Reduction in DSM penalties arising from wind stow-related generation deviations.
Observed outcome shows DSM penalty reduction of Rs. 1,63,233 in the sample comparison.
Potential savings in DSM penalties associated with high-wind events through better wind stow-aware scheduling.
Improved revenue realization through enhanced scheduling accuracy.

Operational Benefits

Improved accuracy of generation forecasting.
Better scheduling decisions during adverse weather conditions.
Enhanced visibility of wind stow impact on plant performance.
Improved coordination with Load Dispatch Centres.

Strategic Benefits

Demonstrates the successful adoption of AI in power plant operations.
Establishes a framework for AI-driven decision-making across renewable assets.
Creates further opportunities for AI applications in forecasting, asset management, and performance optimization.

Innovation Highlights

This initiative represents a practical and cost-effective implementation of Artificial Intelligence to solve a recurring operational challenge in solar power generation.

The solution converts operational knowledge of tracker behavior into a predictive decision-support tool that can improve commercial outcomes without requiring additional hardware investment.

Conclusion

The AI-Based Wind Stow-Aware Generation Forecasting Solution is an innovative initiative that combines operational expertise with Artificial Intelligence to improve scheduling accuracy during high-wind conditions.

The solution has the potential to significantly reduce DSM penalties, improve forecasting reliability, and strengthen GIPCL's position as a technology-driven renewable energy utility. Successful implementation of this model will also open the path for broader AI adoption across operational and commercial functions.

Acknowledgement and Vote of Thanks

I would like to place on record my sincere appreciation and vote of thanks for the valuable guidance, mentorship, and encouragement received throughout this initiative.

The guidance in AI utilization, prompt engineering, and structured problem-solving provided a strong foundation for developing this solution. It helped translate an operational challenge into a practical AI-enabled business solution.

I gratefully acknowledge and deeply appreciate the support, vision, and continuous motivation behind this effort.

SM (RE O&M)

CGM (RE)	GM (IT)

MD (For Information)