Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for +publisher:"University of Houston" +contributor:("Mohammadpour, Javad"). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters


University of Houston

1. 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 methods. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

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. http://hdl.handle.net/10657/859.

MLA Handbook (7th Edition):

Hooshmand, Ali 1982-. “Predictive Energy Management Methods for Smart Grids.” 2012. Web. 30 Nov 2020.

Vancouver:

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

.