Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning
This work addresses cost reduction for households with solar battery systems, but it is incremental as it applies an existing method to a specific domain.
The paper tackled optimizing battery charging and discharging in a domestic solar photovoltaic system using deep reinforcement learning, resulting in reduced electricity expenditure to nearly $1AUD per week for large batteries.
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.