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

• URL: http://arxiv.org/abs/2109.05024v1
• Date: Fri, 10 Sep 2021 10:59:14 GMT
• Title: Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning
• Authors: Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
• Abstract summary: 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 algorithm to optimise the charging and discharging behaviour of a battery within such a system.
• Score: 69.68068088508505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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