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Application of Markov decision process in generating units maintenance scheduling

Rajabi Ghahnavie, A ; Sharif University of Technology | 2006

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  1. Type of Document: Article
  2. DOI: 10.1109/PMAPS.2006.360308
  3. Publisher: 2006
  4. Abstract:
  5. An important issue in power system planning is maintenance scheduling of generating units. In a traditional power system, the problem of maintenance scheduling is of high importance and has various technical and economical constraints. Different methods have been used to solve the problem. On the other hand, changes in legal environment of electricity industry poses new bound and requirements on the maintenance scheduling problem. This paper presents a new approach on maintenance scheduling of generating units in a generating company (GENCO). The proposed approach uses Markov Decision Process (MDP) to minimize total costs of unserved energy and reserve. The impacts on maintenance scheduling of different factors such as unit failure and variation in maintenance duration are determined. The proposed approach has less constraints compared to traditional maintenance scheduling methods. The method is then applied to a sample GENCO to investigate capabilities and limitations of proposed method. © Copyright KTH 2006
  6. Keywords:
  7. Electric industry ; Electric power systems ; Electric power transmission networks ; Industry ; Markov processes ; Mobile telecommunication systems ; Power transmission ; Probability ; Scheduling ; Wireless telecommunication systems ; Applied (CO) ; Electricity industry ; Generating company ; Generating units ; International conferences ; Maintenance scheduling ; Maintenance scheduling problems ; Markov Decision Process (MDP) ; New approaches ; Power system planning ; power systems ; Probabilistic methods ; Total costs ; Traditional power system ; Maintenance
  8. Source: 2006 9th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS, Stockholm, 11 June 2006 through 15 June 2006 ; 2006 ; 9171783520 (ISBN); 9789171783523 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4202320