Title :
Optimized Midterm Preventive Maintenance Outage Scheduling of Thermal Generating Units
Author :
Abiri-Jahromi, Amir ; Fotuhi-Firuzabad, Mahmud ; Parvania, Masood
Author_Institution :
Electr. Eng. Dept., Sharif Univ. of Technol., Tehran, Iran
Abstract :
This paper addresses the midterm preventive maintenance outage scheduling problem of thermal generating units which is becoming increasingly important due to the aging of power generation fleet. In this context, a novel midterm preventive maintenance outage scheduler is proposed based on decision tree and mixed integer linear formation which explicitly considers the thermal units aging momentum in terms of failure rate. This allows the system operators to determine the thermal units´ maintenance outage window based on the cost/benefit analysis of preventive maintenance tasks while optimizing the time interval between consecutive maintenance tasks. Additionally, the division of the year-long midterm horizon into several time blocks in the proposed model provides a unique opportunity for parallel processing and computational saving. The proposed approach is tested on the IEEE Reliability Test System (IEEE-RTS). The results presented reveal the accuracy and efficiency of the proposed approach.
Keywords :
decision trees; integer programming; linear programming; power generation reliability; power generation scheduling; preventive maintenance; thermal power stations; IEEE Reliability Test System; IEEE-RTS; benefit analysis; computational saving; consecutive maintenance tasks; cost analysis; decision tree; failure rate; maintenance outage window; mixed integer linear formation; optimized midterm preventive maintenance outage scheduling; parallel processing; power generation fleet aging; thermal generating units; thermal units aging momentum; time blocks; time interval optimization; Aging; Markov processes; Materials; Power system reliability; Preventive maintenance; Reliability; Aging process; midterm preventive maintenance scheduling; mixed integer linear programming; resource allocation and optimization;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2011.2182362