Dynamic Control, Modeling and Sizing of Hybrid Power Plants : Investigating the optimum usage of energy storage for Fortum’s hydropower

Abstract: The rapidly evolving Nordic Power System demands enhanced flexibility and robustness in electricity production. The traditional role of hydropower plants in regulating the grid frequency has been challenged by new criteria for dynamic stability, which some units struggle to meet due to their relatively poor dynamic performance. This study addresses this challenge by investigating the potential of integrating optimal energy storage systems with hydropower plants. This study aimed to develop a tool that could streamline the process of converting a traditional hydropower plant into a hybrid unit using an optimal energy storage system. The problem is complex and requires an innovative approach that combines electrical engineering expertise with cutting-edge machine-learning algorithms. A comprehensive hydropower plant model, including governor control and mechanical and hydraulic subsystems, was developed and integrated with an energy storage system model to form a hybrid unit. This model was validated using real power plant data. Three distinct XGBoost Regressor models were trained using data samples generated from the optimized hybrid unit. These models aim to predict power and energy requirements for an optimal energy storage solution, including an estimation of wear and tear reduction. The XGBoost Power Regressor achieved a prediction accuracy of 92 % and the XGBoost Energy Regressor demonstrated a 95 % accuracy. The XGBoost Movement Regressor, indicating wear and tear, boasted an accuracy greater than 99 %. The integration of energy storage systems can significantly mitigate wear and tear on a hydropower plant, with reductions of up to 85 % or more. The results indicate that integrating energy storage systems with hydropower units can substantially enhance the dynamic performance, reduce wear and tear and enable the plants to meet the demanding requirements of providing frequency regulation services in the Nordic Power System. The findings of this study culminate in a robust and user-friendly tool capable of accurately estimating optimal energy storage requirements for any hydropower plant tasked with meeting frequency regulation service demands.