Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration

• URL: http://arxiv.org/abs/2411.15422v1
• Date: Sat, 23 Nov 2024 02:55:38 GMT
• Title: Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
• Authors: Caleb Ju, Constance Crozier,
• Abstract summary: This paper explores the use of reinforcement learning 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.
• Score: 0.0
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• Abstract: Variable 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.

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