Market-Based Congestion Man- agement in the Dutch Transmis- sion Grid Using Reinforcement Learning Enhanced Chance- Constrained MPC
J. van der Weerd (TU Delft - Mechanical Engineering)
B. De Schutter – Mentor (TU Delft - Delft Center for Systems and Control)
F. Cordiano – Mentor (TU Delft - Team Bart De Schutter)
A. Riccardi – Mentor (TU Delft - Team Bart De Schutter)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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.