Research
Stochastic optimization and risk-aware decision support for power system operation and planning.
My research focuses on how uncertainty shapes power system operation and how optimization can support better decisions under these conditions. Although this work is technical in nature, it has also been shaped by people, places, and conversations across different settings. The map below offers a more personal way to explore that side of the journey.
Research Impact
Global Footprint of My Research
Education, conference papers, talks, field visits, training, and awards connected to this research.
As renewable generation increases, uncertainty propagates into both congestion management and real-time balancing, coupling decisions across time horizons that are often treated independently in practice.
I develop stochastic optimization frameworks that capture uncertainty continuously and quantify its impact on operational decisions. This enables a transparent understanding of how probabilistic forecasts influence congestion patterns, redispatch actions, and balancing requirements, supporting more efficient and risk-aware system operation.
01
Congestion management and balancing under uncertainty
This research focuses on the coordinated treatment of congestion management and balancing, which are traditionally addressed independently. By modeling their interaction explicitly, the proposed frameworks enable more efficient operational planning and avoid cost increases and risk exposure caused by decoupled decision-making.
02
Quantifying the impact of uncertainty on operational decisions
A central objective is to understand how uncertainty affects decisions rather than treating it as an abstract input. This includes quantifying how probabilistic forecasts of renewable generation and demand influence congestion patterns, redispatch actions, and balancing requirements, thereby providing a transparent link between uncertainty and system operation.
03
Optimization under non-Gaussian uncertainty
The work develops stochastic optimization approaches that operate in continuous uncertainty spaces without relying on Gaussian assumptions. By capturing skewness and bounded behavior in renewable generation, these methods provide a more realistic representation of uncertainty and improve the reliability of operational decisions.
04
Polynomial Chaos Expansion for scalable uncertainty propagation
Intrusive Polynomial Chaos Expansion is leveraged to propagate uncertainty through nonlinear power system models in a computationally tractable manner. This enables the evaluation of statistical properties of system states and costs without relying on large sets of discrete scenarios, improving scalability with respect to uncertainty dimensions.
05
Hybrid AC/DC grids and HVDC controllability
The research investigates how HVDC technologies influence congestion management strategies and operational flexibility. By incorporating controllable DC links into optimization frameworks, it becomes possible to analyze their role in reducing congestion costs and balancing risks in future meshed AC/DC grids.
Methods and frameworks
Uncertainty and modeling
Applications and systems