Learning a Local Trading Strategy: Deep Reinforcement Learning for Grid-scale Renewable Energy Integration

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dc.contributor.authorJu, Caleb
dc.contributor.authorCrozier, Constance
dc.date.accessioned2024-12-26T21:06:58Z
dc.date.available2024-12-26T21:06:58Z
dc.date.issued2025-01-07
dc.description.abstractVariable 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.379
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.otherc2111c30-e1fd-42a8-ac6e-c56655d426d8
dc.identifier.urihttps://hdl.handle.net/10125/109221
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectResilient Networks
dc.subjectbattery storage, deep reinforcement learning, pv generation, renewable energy
dc.titleLearning a Local Trading Strategy: Deep Reinforcement Learning for Grid-scale Renewable Energy Integration
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage3147

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