Deep Reinforcement Learning Based Energy Storage Arbitrage With Accurate Lithium-ion Battery Degradation Model

Journal article

Accurate estimation of battery degradation cost is one of the main barriers for battery participating on the energy arbitrage market. This paper addresses this problem by using a model-free deep reinforcement learning (DRL) method to optimize the battery energy arbitrage considering an accurate battery degradation model. Firstly, the control problem is formulated as a Markov Decision Process (MDP). Then a noisy network based deep reinforcement learning approach is proposed to learn an optimized control policy for storage charging/discharging strategy. To address the uncertainty of electricity price, a hybrid Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) model is adopted to predict the price for the next day. Finally, the proposed approach is tested on the the historical UK wholesale electricity market prices. The results compared with model based Mixed Integer Linear Programming (MILP) have demonstrated the effectiveness and performance of the proposed framework.

Authors: Cao, J; Harrold, D; Fan, Z; Morstyn, T; Healey, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Journal: IEEE Transactions on Smart Grid
• Volume: 14
• Issue: 8
• Pages: 4513-4521
• Publication date: 2020-04-08
• Acceptance date: 2020-04-03
• DOI: 10.1109/tsg.2020.2986333
• EISSN: 1949-3061
• ISSN: 1949-3053
• Language: English
• Keywords: Energy arbitrage; Energy storage; FFR; Deep reinforcement learning; Battery degradation; Noisy Networks