An Adaptive Model with Joint Chance Constraints for a Hybrid Wind-Conventional Generator System

We study the problem of scheduling a hybrid wind-conventional generator system to make it dispatchable, with the aim of profit maximization. Our models ensure that with high probability we satisfy the day-ahead energy promised by the model, using combined output of the conventional and wind generators. We consider two scenarios, which differ in whether the conventional generator must commit to its schedule prior to observing the wind-power realizations or has the flexibility to adapt in near real-time to these realizations. We investigate the relative synergy between the conventional generator and the wind farm in these two scenarios. From a computational perspective, the non-adaptive model is relatively tractable, benefiting from a strong extended-variable formulation as an integer program. The adaptive model is a two-stage stochastic integer program with joint chance constraints. Such Graduate Program in Operations Research & Industrial Engineering, The University of Texas at Austin, Austin, Texas, USA Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois, USA Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA