Open AccessTwo‐stage optimal MPC for hybrid energy storage operation to enable smooth wind power integration
Author(s) -
Guo Tingting,
Zhu Yuwei,
Liu Youbo,
Gu Chenghong,
Liu Junyong
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.1178
Subject(s) - wind power , energy storage , intermittency , computer science , state of charge , grid , model predictive control , renewable energy , schedule , smoothing , control theory (sociology) , battery (electricity) , engineering , electrical engineering , power (physics) , mathematics , turbulence , physics , geometry , control (management) , quantum mechanics , computer vision , thermodynamics , operating system , artificial intelligence
The large‐scale penetration of wind generation imposes challenges on the security of power system operation due to the intermittency and stochastic volatility. Hybrid energy storage system (HESS), which combines battery banks and super‐capacitors, is applied in this study to smooth wind fluctuations to facilitate the grid‐friendly integration. To optimally schedule HESS charge/discharge in an online receding horizon, a novel two‐stage model predictive control (MPC) scheme is proposed. The first stage determines the initial charge/discharge profiles for battery banks at a large time scale, while the second stage quantifies the optimal amendment to update them and determines the operation strategies for super‐capacitors at a smaller time scale. According to the rolling update of wind forecasting, the two‐stage model is successively solved in a receding horizon to generate the most appropriate operation strategies. The proposed method also optimises the state of charge of HESS, yielding sufficient margin to cope with wind uncertainties, making HESS operation more reliable and robust. The case study demonstrates that the proposed model can enable an effective smoothing effect on wind generation volatility by fully utilising energy storage systems of various distinct characteristics, providing a powerful tool to facilitate the smooth integration in a large scale in practice.