Premium
A comparison of distributed MPC schemes on a hydro‐power plant benchmark
Author(s) -
Maestre J. M.,
Ridao M. A.,
Kozma A.,
Savorgnan C.,
Diehl M.,
Doan M. D.,
Sadowska A.,
Keviczky T.,
De Schutter B.,
Scheu H.,
Marquardt W.,
Valencia F.,
Espinosa J.
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.2154
Subject(s) - benchmark (surveying) , power station , hydroelectricity , renewable energy , computer science , power (physics) , model predictive control , mathematical optimization , control (management) , mathematics , engineering , artificial intelligence , physics , geodesy , quantum mechanics , geography , electrical engineering
SUMMARY In this paper, we analyze and compare five distributed model predictive control (DMPC) schemes using a hydro‐power plant benchmark. Besides being one of the most important sources of renewable power, hydro‐power plants present very interesting control challenges. The operation of a hydro‐power valley involves the coordination of several subsystems over a large geographical area in order to produce the demanded energy while satisfying constraints on water levels and flows. In particular, we test the different DMPC algorithms using a 24‐h power tracking scenario in which the hydro‐power plant is simulated with an accurate nonlinear model. In this way, it is possible to provide qualitative and quantitative comparisons between different DMPC schemes implemented on a common benchmark, which is a type of assessment rare in the literature. Copyright © 2014 John Wiley & Sons, Ltd.