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Partially observable Markov decision processes for optimal operations of gas transmission networks

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  • Compare, Michele
  • Baraldi, Piero
  • Marelli, Paolo
  • Zio, Enrico

Abstract

We develop a decision-support framework based on Partially Observable Markov Decision Processes (POMDPs) for the management of Gas Transmission Networks (GTNs) operations, encoding realistic degradation state estimations provided by Prognostics and Health Management (PHM) systems, while considering demand variations and the effects of the management decisions on the GTN degradation evolution. This Operation and Maintenance (O&M) framework allows optimally operating a GTN. Furthermore, the economic impact of using PHM systems with different accuracy levels can be estimated. The approach is shown with reference to a GTN of the literature.

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  • Compare, Michele & Baraldi, Piero & Marelli, Paolo & Zio, Enrico, 2020. "Partially observable Markov decision processes for optimal operations of gas transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:reensy:v:199:y:2020:i:c:s0951832019302753
    DOI: 10.1016/j.ress.2020.106893
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    Cited by:

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    5. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Arismendi, Renny & Barros, Anne & Grall, Antoine, 2021. "Piecewise deterministic Markov process for condition-based maintenance models — Application to critical infrastructures with discrete-state deterioration," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Guo, Chunhui & Liang, Zhenglin, 2022. "A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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