Ju, CalebCrozier, Constance2024-12-262024-12-262025-01-07978-0-9981331-8-8c2111c30-e1fd-42a8-ac6e-c56655d426d8https://hdl.handle.net/10125/109221Variable renewable generation increases the challenge of balancing power supply and demand. Grid-scale batteries co-located with generation can help mitigate this misalignment. This paper explores the use of reinforcement learning (RL) for operating grid-scale batteries co-located with solar power. Our results show RL achieves an average of 61% (and up to 96%) of the approximate theoretical optimal (non-causal) operation, outperforming advanced control methods on average. Our findings suggest RL may be preferred when future signals are hard to predict. Moreover, RL has two significant advantages compared to simpler rules-based control: (1) that solar energy is more effectively shifted towards high demand periods, and (2) increased diversity of battery dispatch across different locations, reducing potential ramping issues caused by super-position of many similar actions.10Attribution-NonCommercial-NoDerivatives 4.0 InternationalResilient Networksbattery storage, deep reinforcement learning, pv generation, renewable energyConference Paper10.24251/HICSS.2025.379