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Article Dans Une Revue IEEE Transactions on Control Systems Technology Année : 2023

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

Résumé

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-energybased power generation sources into the power grid translates into emerging uncertainties which compromise the system's safety, stability and performance. This paper proposes a model-based predictive controller 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 optimisation 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.
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Dates et versions

hal-04174489 , version 1 (01-08-2023)

Identifiants

Citer

Nouha Dkhili, Sorin Olaru, Alessio Iovine, Guillaume Giraud, Jean Maeght, et al.. Data-Based Predictive Control for Power Congestion Management in Subtransmission Grids Under Uncertainty. IEEE Transactions on Control Systems Technology, 2023, 31 (5), pp.2146-2158. ⟨10.1109/TCST.2023.3291556⟩. ⟨hal-04174489⟩
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