DocumentCode :
135068
Title :
Electric service restoration using microgrids
Author :
Ansari, Bananeh ; Mohagheghi, Salman
Author_Institution :
Electr. Eng. & Comput. Sci. Dept., Colorado Sch. of Mines, Golden, CO, USA
fYear :
2014
fDate :
27-31 July 2014
Firstpage :
1
Lastpage :
5
Abstract :
A Microgrid-assisted methodology is proposed in this paper for electric service restoration in distribution networks. Success of restoration algorithms is closely tied with the availability of sufficient capacity on the restoration path(s). When the restoration path is at or near full capacity, some capacity relief may be achieved through the usage of Microgrids. By increasing the generation level of its micro-generators, reducing the demand of its demand responsive loads, or, as the last resort, by islanding from the grid, a Microgrid can help alleviate the congestion on the restoration path, and therefore assist the service restoration algorithm. The methodology put forth in this paper considers a potential restoration circuit equipped with Microgrids, and provides the most cost-effective operational solution while achieving the targeted restoration capacity. Network operational constraints, demand variations, and fluctuations in energy pricing are all taken into account. The problem is formulated as a mixed-integer nonlinear programming problem, and is implemented on a modified version of the IEEE 123-node test distribution system.
Keywords :
demand side management; distributed power generation; distribution networks; integer programming; nonlinear programming; power system restoration; IEEE 123-node test distribution system; capacity relief; demand responsive loads; demand variations; distribution networks; electric service restoration; energy pricing fluctuations; islanding; microgenerators; microgrid-assisted methodology; mixed-integer nonlinear programming problem; network operational constraints; restoration capacity; restoration circuit; restoration paths; service restoration algorithm; Artificial neural networks; Density estimation robust algorithm; Distributed power generation; Genetic algorithms; Load management; Microgrids; Optimization; Distribution network; Microgrid; demand response; distributed energy resources; electric service restoration; mixed-integer nonlinear programming;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
PES General Meeting | Conference & Exposition, 2014 IEEE
Conference_Location :
National Harbor, MD
Type :
conf
DOI :
10.1109/PESGM.2014.6939103
Filename :
6939103
Link To Document :
بازگشت