The rapid integration of renewable energy sources, such as wind and solar power, into modern electricity systems has introduced challenges in balancing supply and demand, managing grid congestion, and ensuring efficient energy market participation. This thesis develops a framework for optimizing the portfolio management of hybrid power plants (HPPs) under uncertainty. HPPs combine renewable generation, energy storage (batteries), and flexible demand (electrolyzers) to improve grid efficiency and market participation.

This thesis develops a framework for optimizing HPP portfolio management under uncertainty, specifically targeting participation in the day-ahead market, the Dutch aFRR market, and the strategic use of passive imbalance. Two complementary optimization methods were developed and compared. Stochastic programming (SP) manages uncertainties in wind generation, activations, and day-ahead market prices using scenario-based approaches. Adaptive robust optimization (ARO) employs worst-case scenarios defined by budgeted polyhedral uncertainty sets for wind generation and activations, while using scenarios for day-ahead market price uncertainty. The ARO framework leverages duality theory and a column-and-constraint generation algorithm (CCGA) to iteratively refine robust solutions. Both methods were implemented within a shrinking horizon framework, which allowed for iterative decision-making at each time-step and tested the robustness of the first-stage decisions.

A case study using real-world Dutch market data demonstrated that both methods achieved zero violations by leveraging passive imbalance. ARO consistently delivered more robust first-stage decisions and higher revenue by effectively utilizing activation capacity. Additionally, a parameter study revealed the trade-offs between robustness and revenue, offering valuable insights into the flexibility and effectiveness of each method under varying conditions. This work highlights the potential of hybrid power plants to improve operational reliability and profitability in modern electricity systems