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metadata.dc.type: doctoralThesis
Título : Stochastic Model Predictive Control for Robust Operation of Distribution Systems
Autor : Velarde Rueda, Pablo
metadata.dc.contributor.advisor: Maestre Torreblanca, José
Fecha de publicación : 2017
Editorial : España: Universidad de Sevilla
Citación : Velarde Rueda Pablo. (2017). Stochastic Model Predictive Control for Robust Operation of Distribution Systems. (Trabajo de titulación del Doctorado en Ingeniería Automática electrónica y de Telecomunicaciones). Universidad de Sevilla. España. 144 p.
Descripción : There are many systems in which uncertainties are present in their model, either in the same system escription or as disturbances. Many random variables can be mentioned: the electrical demand of a generation network, the amount of rainfall in an irrigation system, the number of people occupying a room in a system of heating, among others. They are examples of stochastic systems, in which the idea of scenarios can be considered for their solution. In particular, the stochastic model predictive control seeks to generate a solution for several scenarios that can be established under a probabilistic condition. In this work, an analysis and comparison regarding performance among the three well-known stochastic model predictive control (MPC) approaches, namely, multi-scenario, tree-based, and chance-constrained model predictive control are carried out. The possibility of application in several distribution sectors is also analyzed. Moreover, some improvements are proposed in terms of robustness. To this end, the stochastic MPC controllers are designed and implemented in a real renewablehydrogen-based microgrid as well as to the drinking water network of Barcelona via simulation. Finally, an application of chance constrained MPC to inventory management in a hospital pharmacy, is also presented.
URI : http://repositorio.educacionsuperior.gob.ec/handle/28000/5124
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