Title :
Stochastic model predictive control method for microgrid management
Author :
Hooshmand, Ali ; Poursaeidi, Mohammad H. ; Mohammadpour, Javad ; Malki, Heidar A. ; Grigoriads, Karolos
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Houston, Houston, TX, USA
Abstract :
This paper presents a stochastic model predictive control method for managing a microgrid. In order to reliably provide the required power for costumers, the proposed method enables the microgrid to use the renewable energy sources as much as possible while keeping the storage device to its maximum state of charge and minimizing the power generated by the micro gas turbine. The performance and effectiveness of the proposed method will be finally illustrated by simulating a microgrid model consisting of three nodes including a renewable generation source and a battery, customers, and a micro gas turbine.
Keywords :
distributed power generation; energy storage; gas turbines; power generation control; power generation economics; power system management; predictive control; renewable energy sources; stochastic systems; customer services; microgas turbine; microgrid management; microgrid model; renewable energy sources; renewable generation source; state of charge; stochastic model predictive control method; storage device; Batteries; Chebyshev approximation; Cost function; Dynamic programming; Predictive control; Stochastic processes; Wind turbines; Microgrid; dynamic programming; empirical mean; renewable energy sources; stochastic model predictive control; storage device;
Conference_Titel :
Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4577-2158-8
DOI :
10.1109/ISGT.2012.6175660
