Pick to Learn for Stochastic Economic Model Predictive Control with an Expected Shortfall Constraint

This paper proposes a method to find probabilistic
guarantees for a scenario-based stochastic economic model
predictive control (SEMPC) scheme with an empirical
expected shortfall (EES) constraint. The objective function
includes the minimisation of an average economic cost,
desirable from an operational perspective, while keeping
the risk of high costs under control through satisfaction
of the EES constraint. The pick-to-learn (P2L) method is
employed to find probabilistic guarantees for the solution
of the proposed SEMPC problem. The suggested framework is
applied to a water distribution network, and the results
show a balance between economic performance and
probabilistic guarantees at each time step.