Welcome to the Electrical Power Engineering Unit (EPEU) | Service de Génie électrique of the Faculty of Engineering of the University of Mons. The unit of about 20 people has teaching and research activities in power systems, electricity markets, power electronics, electrical machines and computational electromagnetics.
EPEU is an effective member of the Research Institute for Energy of UMONS. Its research activities involve two different streams of focus:
Power Systems and Markets Research
Power & Energy Conversion
EPEU is fully equipped with modern state-of-art testing instruments and data acquisition platforms (Labview, dSPACE) to undertake standard measurements on most electrical equipments, as well as with specific simulation and calculation softwares (PSIM, Comsol Multiphysics, Neplan, etc.). It hosts also a High-Voltage laboratory dedicated to teaching and industrial testing and measurement (breakdown voltage, leakage current, etc.). Non-destructive tests are possible mainly for dielectric loss and capacitance measurement (Schering bridge) and for resistivity measurement (measuring cell).
The unit gets funding from regional (e.g. Walloon Region DG06), national (e.g. FNRS) and European (e.g. ESA's Networking/Partnering Initiative) agencies as well as from industrial partners (e.g. in the context of the ALSTOM chair).
See below for News and Latest Publications
Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: An exact and physically-bounded formulation
in European Journal of Operational Research (Elsevier)
Authors: A. Arrigo, C. Ordoudis, J. Kazempour, Z. De Grève, J.-F. Toubeau and F. Vallée
In the context of transition towards sustainable, cost-efficient and reliable energy systems, the improve- ment of current energy and reserve dispatch models is crucial to properly cope with the uncertainty of weather-dependent renewable power generation. In contrast to traditional approaches, distributionally robust optimization offers a risk-aware framework that provides performance guarantees when the distri- bution of uncertain parameters is not perfectly known. In this paper, we develop a distributionally robust chance-constrained optimization with a Wasserstein ambiguity set for energy and reserve dispatch, and provide an exact reformulation. While preserving the exactness, we then improve the model by enforcing physical bounds on the uncertainty space, resulting in a bilinear program. We solve the resulting bilinear model with an iterative algorithm which is computationally efficient and has convergence guarantee. A thorough out-of-sample analysis is performed to compare the proposed model against a scenario-based stochastic program. We also compare the performance of the proposed exact reformulation against an ex- isting approximate technique in the literature, built upon a conditional-value-at-risk measure. We even- tually show that the proposed physically-bounded exact reformulation outperforms the other methods by achieving a cost-optimal yet reliable trade-offbetween reserve procurement and load curtailment.