Deterministic and stochastic MPC algorithms for minimizing mechanical stresses in wind farms

We consider the problem of dispatching Wind-Farm (WF) power demand to individual Wind Turbines (WTs) with the goal of minimizing mechanical stresses. We assume wind is strong enough to let each WT produce the required power and propose different closed-loop Model Predictive Control (MPC) dispatching algorithms. Similarly to existing approaches based on MPC, our methods do not require changes in WT hardware but only software changes in the SCADA system of the WF. However, differently from other MPC schemes, we augment the model of a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty in wind predictions over the MPC control horizon. This allows us to develop both stochastic and deterministic MPC algorithms. In order to compare different MPC schemes and demonstrate improvements over classic open-loop schedulers, we use simulations based on the SimWindFarm toolbox for MatLab.