Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response
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 e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/9/1646 |
| _version_ | 1798034987350491136 |
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| author | Hyung-Chul Jo Rakkyung Ko Sung-Kwan Joo |
| author_facet | Hyung-Chul Jo Rakkyung Ko Sung-Kwan Joo |
| author_sort | Hyung-Chul Jo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T20:50:56Z |
| format | Article |
| id | doaj.art-6593f06202604f94af23804b42f12e13 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T20:50:56Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6593f06202604f94af23804b42f12e132022-12-22T04:03:50ZengMDPI AGEnergies1996-10732019-04-01129164610.3390/en12091646en12091646Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand ResponseHyung-Chul Jo0Rakkyung Ko1Sung-Kwan Joo2Distributed Power System Research Center, Korea Electrotechnology Research Institute, Gyeongsangnam-do 51543, KoreaThe School of Electrical Engineering, Korea University, Seoul 02841, KoreaThe School of Electrical Engineering, Korea University, Seoul 02841, KoreaPeriodic 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.https://www.mdpi.com/1996-1073/12/9/1646demand responsegenerator maintenance schedulingelectricity supply and demandtransformation of mixed integer polynomial programming |
| spellingShingle | Hyung-Chul Jo Rakkyung Ko Sung-Kwan Joo Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response Energies demand response generator maintenance scheduling electricity supply and demand transformation of mixed integer polynomial programming |
| title | Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response |
| title_full | Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response |
| title_fullStr | Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response |
| title_full_unstemmed | Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response |
| title_short | Generator Maintenance Scheduling Method Using Transformation of Mixed Integer Polynomial Programming in a Power System Incorporating Demand Response |
| title_sort | generator maintenance scheduling method using transformation of mixed integer polynomial programming in a power system incorporating demand response |
| topic | demand response generator maintenance scheduling electricity supply and demand transformation of mixed integer polynomial programming |
| url | https://www.mdpi.com/1996-1073/12/9/1646 |
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