Economic Control of Hybrid Energy Systems Composed of Wind Turbine and Battery

An Economic Nonlinear Model Predictive Controller (ENMPC) is designed for a wind turbine and battery based hybrid energy system. An explicit consideration of cyclic damages within the controller is implemented via externalization of Rainflow based cycle counting (RFC) algorithm from the Model Predictive Controller (MPC). This is achieved using Parametric Online Rainflow counting (PORFC) approach. Additionally, impact of stress history is considered directly inside the optimization problem by employing a stress residue which also helps overcome the limitation of using shorter horizon for cyclic damage estimation. The designed MPC controller is implemented using the state-of-the-art ACADOS framework. The performance of the controller is assessed in closed loop with a hybrid plant model consisting of a NREL 5MW onshore wind turbine and a 1MWh/1MW Li-ion battery. Simulation output indicates that the formulated controller results in profit gain with respect to a realistic base-case controller. Moreover, the formulated controller is found to conveniently handle model complexities, non-linearities, and system constraints resulting in suitable dynamic performance. An economically optimal closed-loop operation of the grid-connected hybrid plant is achieved, where the controller, using PORFC algorithm, optimizes a realistic monetary objective while explicitly considering the requirements from the electricity grid.