SCIENTIA SINICA Informationis, Volume 48 , Issue 10 : 1316-1332(2018) https://doi.org/10.1360/N112018-00076

Hierarchical distributed model predictive control of hybrid wind and solar generation system

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  • ReceivedApr 1, 2018
  • AcceptedJun 28, 2018
  • PublishedOct 26, 2018


Distributed networked generation (DNG), which incorporates renewable energy and clean energy into information networks, has become increasingly important for modern power systems. In DNG, wind power and solar power generation are considered as intermittent systems with multiple constraints. The coordinated optimization of the wind power and solar power generation can effectively meet the load demand while guaranteeing the safety of power grids by reducing the wear and tear of generating units and prolonging the lifetime of grids. This study aims to construct a hierarchical distributed model predictive control (HDMPC) for large-scale, geographically dispersed wind-solar power generation systems. In HDMPC, the upper layer utilizes an iterative distributed control strategy to coordinate the power dispatch. Thus, it attains economic objectives, including the reduction in the torsional shaft torque transmitted to the gearbox in the wind turbine system and the generation cost. The lower layer utilizes the supervisory predictive control to realize economic and tracking properties. HDMPC enables the plug-and-play of distributed energy through the coordinated optimization of subsystems. Simulation experiments validated the advantages of the proposed method, which can meet the demands of safe, reliable, highly efficient, flexible, and interactive microgrid controlling.

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