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Cooperative distributed model predictive control for wind farms
Author(s) -
Spudić V.,
Conte C.,
Baotić M.,
Morari M.
Publication year - 2014
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2136
Subject(s) - model predictive control , constraint (computer aided design) , controller (irrigation) , computer science , turbine , control theory (sociology) , distributed power , distributed element model , scheme (mathematics) , mathematical optimization , grid , power (physics) , wind power , transmission (telecommunications) , grid code , control (management) , distributed control system , operator (biology) , electric power system , engineering , mathematics , repressor , artificial intelligence , mathematical analysis , chemistry , biology , telecommunications , biochemistry , geometry , quantum mechanics , transcription factor , agronomy , mechanical engineering , physics , electrical engineering , gene
SUMMARY This paper focuses on cooperative distributed model predictive control (MPC) of wind farms, where the farms respond to active power control commands issued by the transmission system operator. A distributed MPC scheme is proposed, which aims at satisfying the requirements imposed by the grid code while minimizing the farm‐wide mechanical structure fatigue. The distributed MPC control law is defined by a global finite‐horizon optimal control problem, which is solved at every time step by distributed optimization. The computational approach is completely distributed, that is, every turbine evaluates its own globally optimal input by considering local measurements and communicating to neighboring turbines only. Two MPC versions are compared, in the first of which the farm‐wide power output constraint is implemented as a hard constraint, whereas in the second, it is implemented as a soft constraint. As for distributed optimization methods, the alternating direction method of multipliers as well as a dual decomposition scheme based on fast gradient updates are compared. The performance of the proposed distributed MPC controller, as well as the performance of the distributed optimization methods used for its operation, are compared in the simulation on four exemplary scenarios. The results of the simulations imply that the use of cooperative distributed MPC in wind farms is viable both from a performance and from a computational viewpoint. Copyright © 2014 John Wiley & Sons, Ltd.