Operations and maintenance (O&M) expenses can vary greatly from one energy solution to another. While a solar farm or geothermal system may need minimal ongoing maintenance, wind turbines require a skilled crew to keep them operating efficiently.
In this research, the authors use a scaled-down wind farm case study to demonstrate the potential of Reinforcement Learning (RL) in identifying an optimal O&M policy and to show the ease of use of AnyLogic simulation software and Pathmind reinforcement learning tool.