Stochastic Model Predictive Control for Robust Operation of Distribution Systems

 

Authors
Velarde Rueda, Pablo
Format
DoctoralThesis
Status
publishedVersion
Description

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.

Publication Year
2017
Language
spa
Topic
CONTROL PREDICTIVO
SISTEMAS ENERG?TICOS
SISTEMAS DE DISTRIBUCI?N
CONTROL ESTOC?STICO
Repository
Repositorio SENESCYT
Get full text
http://repositorio.educacionsuperior.gob.ec/handle/28000/5124
Rights
openAccess
License