Economic nonlinear model predictive control of fatigue for a hybrid wind-battery generation system. Issue 3 (1st May 2022)

• Record Type: Journal Article
• Authors: Anand, Abhinav; Loew, Stefan; Bottasso, Carlo L.
• Is Part Of: Journal of physics. Volume 2265:Issue 3(2022)
• Publication Date: 2022-05-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2265/3/032106
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: An economic nonlinear model predictive controller (ENMPC) is formulated for a wind turbine-battery hybrid generation system. The controller aims to maximize the operational profit of the generator by balancing between generated wind power and turbine tower fatigue as well as battery cyclic fatigue. Other than the tower fore-aft fatigue, tower side-side fatigue is also considered to assess impact on overall economic performance. A moving horizon estimator (MHE) is formulated to provide meaningful initialization to the ENMPC in presence of plant model mismatch.The formulated controller utilizes the parametric online rainflow counting (PORFC) approach for direct cyclic fatigue cost minimization within ENMPC. The closed-loop simulation shows significantly higher profit compared to a realistic base-case scenario and relatively higher profit compared to another economic controller.