Coordinated Distributed MPC for Load Frequency Control of Power System With Wind Farms

Load frequency control (LFC) is crucial for the operation and design of modern electric power systems. This becomes quite challenging, as more wind power is included into the power system. This paper proposes a coordinated distributed model predictive control (DMPC) for the LFC of a power system tha...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2017-06, Vol.64 (6), p.5140-5150
Main Authors: Xiangjie Liu, Yi Zhang, Lee, Kwang Y.
Format: Article
Language:English
Subjects:
Computer simulation
Control systems
Controllers
Distributed model predictive control
Electric power systems
Frequency control
Load distribution (forces)
load frequency control (LFC)
Optimization
Pitch (inclination)
Power system stability
Predictive control
Stress concentration
Wind farms
Wind power
Wind power generation
Wind speed
wind turbine
Wind turbines
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Summary:Load frequency control (LFC) is crucial for the operation and design of modern electric power systems. This becomes quite challenging, as more wind power is included into the power system. This paper proposes a coordinated distributed model predictive control (DMPC) for the LFC of a power system that includes inherently variable wind-power generations. This DMPC communicates power system measurement and prediction data, and considers the information of other controllers for their local objective to realize effective coordination. The controllers solve the optimization problem while considering given constraints, e.g., generation rate constraints, wind speed, pitch angle, and load input constraints for each area. Since the wind-power output depends largely on the wind speed, different optimization modes for the DMPC were used. Both simulation and experimental tests of a four-area interconnected power system LFC, which consists of thermal plants, hydro units, and a wind farm, demonstrate the improved efficiency of the coordinated DMPC.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2016.2642882