TY  - JOUR
AU  - Wenzel, Moritz
AU  - De Din, Edoardo
AU  - Zimmer, Marcel
AU  - Benigni, Andrea
TI  - Gaussian Process Supported Stochastic MPC for Distribution Grids
JO  - IEEE open journal of control systems
VL  - 4
SN  - 2694-085X
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2025-03546
SP  - 332-348
PY  - 2025
AB  - The efficacy of control systems for distribution grids can be influenced by different sources of uncertainty. Stochastic Model Predictive Control (SMPC) can be employed to compensate for such uncertainties by integrating their probability distribution into the control problem. An efficient SMPC algorithm for online control applications is the stochastic tube SMPC, which is able to treat the evaluation of the chance constraints analytically. However, this approach is efficient only when the calculation of the constraint back-off is applied to a linear model. To address this issue, this work employs Gaussian Processes to approximate the nonlinear part of the power flow equations based on offline training, which is integrated into the SMPC formulation. The resulting SMPC is first validated and then tested on a benchmark system, comparing the results with Deterministic MPC and SMPC that excludes Gaussian Processes. The proposed SMPC proves to be more efficient in terms of cost minimization, reference tracking and voltage violationreduction.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001569592700004
DO  - DOI:10.1109/OJCSYS.2025.3601836
UR  - https://juser.fz-juelich.de/record/1045550
ER  -