Trajectory Based Robust Optimization Applied to the Case of Electricity Facilities Investment with Significant Penetration of Renewables

Pierre Cayet (1, 2) , Arash Farnoosh (1, 2)

1 IFPEN - IFP Energies nouvelles
2 IFP School

As large scale penetration of renewables into electric systems requires increasing flexibility from dispatchable production units, the electricity mix must be designed in order to address brutal variations of residual demand. Inspired from the philosophy of Distributionally Robust Optimization (DRO), we propose a trajectory ambiguity set including residual demand trajectories verifying both support and variability criterion using ambiguous quantile information. We derive level-maximizing, level-minimizing and variability-maximizing residual demand trajectories using two algorithms based on forward-backward path computation. This set of limiting trajectories allows us to make investment decisions robust to extreme levels and brutal variations of residual demand. We provide a numerical experiment using a MILP (Mixed-Integer Linear Programming) investment and unit commitment model in the case of France and discuss the results.

Mots clés: OR in energy; Uncertainty modelling; Decision analysis; Renewables; Robust optimization

Domaines: Economies et finances

Pierre Cayet, Arash Farnoosh. Trajectory Based Robust Optimization Applied to the Case of Electricity Facilities Investment with Significant Penetration of Renewables: Cahiers de l'Economie, Série Recherche, n° 140. 2021. ⟨hal-03206638⟩