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.
@article{arxiv.2109.05024,
title = {Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning},
author = {Alexander J. M. Kell and A. Stephen McGough and Matthew Forshaw},
journal= {arXiv preprint arXiv:2109.05024},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:2011.04079