Driven by the rapid integration of Renewable Energy Sources (RESs) and the growing elec- trification of transport, heating, and industry, the Dutch power grid is being fundamentally reshaped. While essential for meeting climate goals, these developments introduce significant operational challenges, including higher uncertainty in power production and congestion risks. Existing approaches for Congestion Management (CM) often neglect the stochastic nature of RESs generation, rely on simplified network representations, or overlook real-world market constraints.
This thesis addresses these gaps by formulating the Dutch market-based CM problem as a Chance-Constrained Model Predictive Control (CC-MPC) framework. A linearized model of the Dutch high-voltage network is employed within a CC-MPC scheme that incorporates flexibility offers through integer decision variables. Uncertainty in RESs generation is cap- tured using an Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model for each production region in the network, enabling a probabilistic treatment of forecast errors. To mitigate conservatism in the chance constraints, a Reinforcement Learning (RL) approach is introduced to adaptively tune the uncertainty model. The resulting stochastic disturbance trajectories are used in a sampling-based approximation of the CC-MPC, optimising conges- tion mitigation decisions under uncertainty.
The proposed methodology is validated using real-world data from the Dutch energy data ex- change platform Energie Data Services Nederland (EDSN), including operational data from Grid Operators Platform for AnCillary Services (GOPACS), the national CM platform. Re- sults demonstrate that the RL-enhanced CC-MPC achieves improved constraint satisfaction compared to other methods. Overall, this work contributes to the current literature by devel- oping a rigorous framework for market-based CM under uncertainty, aimed at ensuring the reliable and cost-effective operation of future renewable-dominated power systems.

In a world with limited resources and a growing awareness of the destructive effects greenhouse gasses, the concept of sustainability is becoming more and more prominent in research. Some of the biggest polluters are the aerial shipping and travel industries, for this reason it is vital that they become more sustainable. Therefore a transition from fossil fuels to electric is needed. To completely transition to electric flight seems impossible but nevertheless big companies such as Airbus and start-ups such as Volocopter have started research and development of eVTOL's which are meant as urban air mobility (UAM) vehicles.

This thesis will explore the design of a control system for such an eVTOL. This thesis will be combined with two other theses to create a full theoretical design of an eVTOL. The other two theses will describe the physical design of the eVTOL, the energy storage system and the power train.

The end result of this thesis will be a description of the complete control system for this eVTOL, from what kind of sensors it needs, the mathematical model describing the eVTOL, the design of the control algorithm used, and simulations of the eVTOL in vertical flight.