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Título

Robust optimization based energy dispatch in smart grids considering simultaneously multiple uncertainties: Load demands and energy prices

AutorNassourou, M.; Puig, Vicenç; Blesa, Joaquim
Palabras claveModel predictive control
Demand uncertainty
Robust optimization
Energy prices uncertainty
Smart grid
Fecha de publicación2017
EditorElsevier
CitaciónIFAC papers online 50(1): 6755-6760 (2017)
ResumenSolving the problem of energy dispatch in a heterogeneous complex system is not a trivial task. The problem becomes even more complex considering uncertainties in demands and energy prices. This paper discusses the development of several Economic Model Predictive Control (EMPC) based strategies for solving an energy dispatch problem in a smart micro-grid. The smart grid components are described using control-oriented model approach. Considering uncertainty of load demands and energy prices simultaneously, and using an economic objective function, leads to a non-linear non-convex problem. The technique of using an affine dependent controller is used to convexify the problem. The goal of this research is the development of a controller based on EMPC strategies that tackles both endogenous and exogenous uncertainties, in order to minimize economic costs and guarantee service reliability of the system. The developed strategies have been applied to a hybrid system comprising some photovoltaic (PV) panels, a wind generator, a hydroelectric generator, a diesel generator, and some storage devices interconnected via a DC Bus. Additionally, a comparison between the standard EMPC, and its combination with MPC tracking in single-layer and two-layer approaches was also carried out based on the daily cost of energy production.
DescripciónTrabajo presentado al 20th IFAC (International Federation of Automatic Control) World Congress, celebrado en Toulouse (Francia) del 9 al 14 de julio de 2017.
Versión del editorhttps://doi.org/10.1016/j.ifacol.2017.08.1175
URIhttp://hdl.handle.net/10261/166499
Identificadoresdoi: 10.1016/j.ifacol.2017.08.1175
issn: 1474-6670
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