Data-Based Predictive Control for Power Congestion Management in Subtransmission Grids under Uncertainty.

The energy transition of power grids has spawned a large spectrum of new technical challenges at the design, deployment, and operation levels. From a control standpoint, the integration of renewable-energy-based power generation sources into the power grid translates into emerging uncertainties which compromise the system's safety, stability, and performance. This article proposes a model-based predictive controller (MPC) that incorporates the stochastic nature of these sources into its feedback decision-making policy. The overarching objective is to balance upholding operational constraints of power lines with smart power generation curtailment and energy storage strategies. The proposed method introduces a novel characterization of disturbance trajectory scenarios, and their incorporation into the optimization problem is detailed leading to a robust congestion management strategy. Simulation results are discussed with respect to a baseline of a trend-based disturbance estimation.