# Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning

arXiv: https://arxiv.org/abs/2109.05024 · HTML: https://mlsanity.com/paper/2109.05024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning](https://mlsanity.com/paper/1711.03127/) (2017)
- [Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China](https://mlsanity.com/paper/1909.11711/) (2019)
- [Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning](https://mlsanity.com/paper/1912.03408/) (2019)
- [Exploring market power using deep reinforcement learning for intelligent bidding strategies](https://mlsanity.com/paper/2011.04079/) (2020)

## Abstract (arXiv)

A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy gradient algorithm to optimise the charging and discharging behaviour of a battery within such a system. Our approach outputs a continuous action space when it charges and discharges the battery, and can function well in a stochastic environment. We show good performance of this algorithm by lowering the expenditure of a single household on electricity to almost 1AUD for large batteries across selected weeks within a year.