The optimization of the Operation and Maintenance (O&M) of energy systems equipped with Prognostics and Health Management (PHM) capabilities can be framed as a sequential decision process, which can be addressed by Reinforcement Learning (RL). However, using RL algorithms requires specific skills, whereas the understanding of the possibly counter-intuitive solutions proposed by RL is not straifhtforward. To sidestep both issues, we use Pathmind, a software tool which enables effectively exploiting the RL capabilities without deep knowledge of machine learning. Pathmind is encoded in the Anylogic environment, which is an Agent-Based simulation software that simplifies the system modeling and allows easily visualizing the effects of the optimized policy. A scaled-down wind farm case study is used to demonstrate the potential of RL in identifying an optimal O&M policy and to show the ease of use of Pathmind and AnyLogic.

Agent-based modeling and reinforcement learning for optimizing energy systems operation and maintenance: the pathmind solution

Pinciroli L.;Baraldi P.;Zio E.
2020-01-01

Abstract

The optimization of the Operation and Maintenance (O&M) of energy systems equipped with Prognostics and Health Management (PHM) capabilities can be framed as a sequential decision process, which can be addressed by Reinforcement Learning (RL). However, using RL algorithms requires specific skills, whereas the understanding of the possibly counter-intuitive solutions proposed by RL is not straifhtforward. To sidestep both issues, we use Pathmind, a software tool which enables effectively exploiting the RL capabilities without deep knowledge of machine learning. Pathmind is encoded in the Anylogic environment, which is an Agent-Based simulation software that simplifies the system modeling and allows easily visualizing the effects of the optimized policy. A scaled-down wind farm case study is used to demonstrate the potential of RL in identifying an optimal O&M policy and to show the ease of use of Pathmind and AnyLogic.
2020
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference
978-981-14-8593-0
AnyLogic
Operation and Maintenance
Optimization
Pathmind
Reinforcement Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1181255
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