Presentations

Abstract

The integration of battery storage into wind power plants is increasingly recognized as a key enabler for enhancing flexibility, reducing market risks, and unlocking new revenue streams. However, the true value of this integration depends on the ability to manage assets intelligently across technical, market, and regulatory dimensions. This study examines how AI-driven optimization frameworks can improve the efficiency and profitability of hybrid wind-storage plants. Using the Spanish electricity market as a case study, we compare conventional operational methods, where storage is applied primarily for smoothing output and meeting basic dispatch compliance, and an advanced optimization framework that integrates weather forecasts, production forecasts, and price indicators with a scheduling layer accounting for technical limits such as state of charge, depth of discharge, and long-term battery degradation constraints. By embedding forecasts and optimization into the control process, hybrid plants are better positioned to align production with market conditions, capture higher revenues during peak-value periods, reduce imbalance costs, and minimize curtailment. At the same time, the approach ensures compliance with both technical and regulatory requirements, demonstrating that enhanced profitability does not need to come at the expense of reliability or asset lifetime. The findings highlight that hybrid assets managed under flexible optimization frameworks can turn variability into value, strengthening the competitiveness of wind power while supporting the integration of renewables in liberalized electricity markets.