Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning

Luca Pinciroli, Piero Baraldi, Guido Ballabio, Michele Compare and Enrico Zio

Renewable Energy, 2022, vol. 183, issue C, 752-763

Abstract: Equipment of renewable energy systems are being supported by Prognostics & Health Management (PHM) capabilities to estimate their current health state and predict their Remaining Useful Life (RUL). The PHM health state estimates and RUL predictions can be used for the optimization of the systems Operation and Maintenance (O&M). This is an ambitious and challenging task, which requires to consider many factors, including the availability of maintenance crews, the variability of energy demand and production, the influence of the operating conditions on equipment performance and degradation and the long time horizons of renewable energy systems usage. We develop a novel formulation of the O&M optimization as a sequential decision problem and we resort to Deep Reinforcement Learning (DRL) to solve it. The proposed solution approach combines proximal policy optimization, imitation learning, for pre-training the learning agent, and a model of the environment which describes the renewable energy system behavior. The solution approach is tested by its application to a wind farm O&M problem. The optimal solution found is shown to outperform those provided by other DRL algorithms. Also, the approach does not require to select a-priori a maintenance strategy, but, rather, it discovers the best performing policy by itself.

Keywords: Renewable energy systems; Wind farm; Operation and maintenance; Prognostics and health management; Optimization; Deep reinforcement learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:183:y:2022:i:c:p:752-763

DOI: 10.1016/j.renene.2021.11.052

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