VELARDE RUEDA, PABLO ANIBAL, Maestre, J. M. , Ocampo-Martinez, C. , Bordons, C. , IEEE

Si

Proceedings Paper

Científica

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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015000256&doi=10.1109%2fECC.2016.7810454&partnerID=40&md5=53f112a31436a78574ec19d8d049bba2

01/01/2016

000392695300201

2-s2.0-85015000256

10.1109/ECC.2016.7810454

In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i.e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems.

Hydrogen; Model predictive control; Predictive control systems; Renewable energy resources; Robust control; Chance-constrained model; Control techniques; Microgrid; Multi scenarios; Renewable energy generation; Renewable hydrogens; Robust model predictive control; Scenario tree; Tree-based; Uncertainty; Stochastic systems