Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response

Authors
Jo, Hyung-ChulKo, RakkyungJoo, Sung-Kwan
Issue Date
1-May-2019
Publisher
MDPI
Keywords
demand response; generator maintenance scheduling; electricity supply and demand; transformation of mixed integer polynomial programming
Citation
ENERGIES, v.12, no.9
Indexed
SCIE
SCOPUS
Journal Title
ENERGIES
Volume
12
Number
9
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/65487
DOI
10.3390/en12091646
ISSN
1996-1073
Abstract
Periodic preventive maintenance of generators is required to maintain the reliable operation of a power system. However, generators under maintenance cannot supply electrical energy to the power system; therefore, it is important to determine an optimal generator maintenance schedule to facilitate efficient supply. The schedule should consider various constraints of the reliability-based demand response program, power system security, and restoration. Determining the optimal generator maintenance schedule is generally formulated as a non-linear optimization problem, which leads to difficulties in obtaining the optimal solution when the various power system constraints are considered. This study proposes a generator maintenance scheduling (GMS) method using transformation of mixed integer polynomial programming in a power system incorporating demand response. The GMS method is designed to deal with various system requirements and characteristics of demand response within a power system. A case study is conducted using data from the Korean power system to demonstrate the effectiveness of the proposed method for determining the optimal maintenance schedule. The results show that the proposed GMS method can be used to facilitate the efficient and reliable operation of a power system, by considering the applicable system constraints.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > School of Electrical Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Joo, Sung Kwan photo

Joo, Sung Kwan
College of Engineering (School of Electrical Engineering)
Read more

Altmetrics

Total Views & Downloads

BROWSE