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The Life Cycle Cost (LCC) of energy systems including Renewable Energy Sources (RES) strongly depends on the Operation and Maintenance (O&M) costs. Nowadays, many components of these energy systems are equipped with Prognostics & Health Management (PHM) capabilities, for estimating their current and future health states. This information is intended to be used for the optimization of O&M. It is an ambitious and challenging objective as the uncertain information brought by PHM must be combined with other factors influencing O&M, such as the limited availability of maintenance crews, the variability of energy demand and production, the long-time horizons of energy systems. In this work, we formalize the O&M optimization of RES-based energy systems equipped with PHM as a sequential decision problem over a long-time horizon and we solve it by Deep Reinforcement Learning (DRL). The proposed methodology is applied to a small wind farm. Strengths and weaknesses are analyzed by means of a comparison with state-of-the-art O&M policies.

The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.

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