University of Houston
Hooshmand, Ali 1982-.
Predictive Energy Management Methods for Smart Grids.
Degree: PhD, Electrical Engineering, 2012, University of Houston
In this dissertation, we propose energy management methods for power systems in
the context of smart grids. In this regard, we consider new management problems for
various conﬁgurations of smart grids, microgrids, as well as the power system generation. For diﬀerent scenarios, we consider grid connection and distributed generations
such as photovoltaic cells, wind turbine, and microgas turbines as energy sources. In
addition, the eﬀects and advantages of storage devices in smart grids operation are
investigated by including them as one of the system components.
For microgrids operation, we consider a microgrid both in islanded mode and
grid-tied mode of operation. In these modes, we develop and solve new optimization
problems which aim to minimize the cost of energy within a microgrid to supply the
load and maximize the lifetime of battery units simultaneously. Next, we extend the
concept and consider a network of microgrids which are able to collaborate with each
other. By proposing a cooperative optimization problem for microgrids network, we
will show that the total cost of energy would be minimized.
On the generation side, we investigate the economic dispatch problem for power
systems which include renewable sources among energy providers. In this case, we will
illustrate that conventional approaches for considering renewable energy sources in
the dispatching problem will not be functional anymore. In addition, we will develop
a new method which can be an appropriate alternative for conventional approach.
Finally, we will investigate the advantages of storage devices in the aforementioned
economic dispatch problem.
Model predictive control (MPC) policies, in both deterministic and stochastic forms, are employed to solve the underlying optimization problems. Several solution
methods such as stochastic dynamic programming, linear programming, etc., will be
employed to solve the MPC optimization problems. Numerous testbeds and experimental data including IEEE 14-bus system and California ISO data will be utilized
to demonstrate the eﬃciency and optimality of the proposed energy management
Advisors/Committee Members: Malki, Heidar A. (advisor), Mohammadpour, Javad (advisor), Shieh, Leang-San (committee member), Han, Zhu (committee member), Chen, Yuhua (committee member).
Subjects/Keywords: Smart grids; Model predictive control; Energy Systems; Power systems; Electrical engineering
to Zotero / EndNote / Reference
APA (6th Edition):
Hooshmand, A. 1. (2012). Predictive Energy Management Methods for Smart Grids. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/859
Chicago Manual of Style (16th Edition):
Hooshmand, Ali 1982-. “Predictive Energy Management Methods for Smart Grids.” 2012. Doctoral Dissertation, University of Houston. Accessed November 30, 2020.
MLA Handbook (7th Edition):
Hooshmand, Ali 1982-. “Predictive Energy Management Methods for Smart Grids.” 2012. Web. 30 Nov 2020.
Hooshmand A1. Predictive Energy Management Methods for Smart Grids. [Internet] [Doctoral dissertation]. University of Houston; 2012. [cited 2020 Nov 30].
Available from: http://hdl.handle.net/10657/859.
Council of Science Editors:
Hooshmand A1. Predictive Energy Management Methods for Smart Grids. [Doctoral Dissertation]. University of Houston; 2012. Available from: http://hdl.handle.net/10657/859