Optimal reservoir operation using stochastic model predictive control
Name
958653329-MIT.pdf
Description
Full printable version
Size
7.61 MB
Format
Adobe PDF
Checksum (MD5)
27d416770643bcc413a7aa31e8b8ad0a
Author(s)
Sahu, Reetik Kumar
Advisor(s)
Dennis McLaughlin.
Date Issued
2016
Publisher
Massachusetts Institute of Technology
Abstract
Dynamical systems are subjected to various random external forcings that complicate theie control. In order to achieve optimal performance, these systems need to continually adapt to external disturbances in real time. This capability is provided by feedback based control strategies that derive an optimal control from the current state of the system. Model Predictive Control(MPC) is one such feedback-based technique. This thesis explores the application of a stochastic version of MPC to a reservoir system. The reservoir system is designed to maximize the revenue generated from the hydroelectricity while simultaneously obeying several exogenous constraints. An ensemble based version of the stochastic MPC technique is studied and applied to the reservoir to determine the optimal water release strategies. Further analysis is performed to understand the sensitivity of different parameters in the MPC technique.
Description
Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-65).
Subjects
Computation for Design and Optimization Program.
MIT Department
Massachusetts Institute of Technology. Computation for Design and Optimization Program
Terms of Use
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