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

arXiv: https://arxiv.org/abs/2411.15422 · HTML: https://mlsanity.com/paper/2411.15422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 4 of 4)

- [Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning](https://mlsanity.com/paper/1711.03127/) (2017)
- [Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning](https://mlsanity.com/paper/2106.06061/) (2021)
- [Arbitraging Variable Efficiency Energy Storage using Analytical Stochastic Dynamic Programming](https://mlsanity.com/paper/2108.06000/) (2021)
- [ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning](https://mlsanity.com/paper/1911.01203/) (2019)

## Abstract (arXiv)

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.