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

J Cao, D Harrold, Z Fan, T Morstyn, D Healey, K Li

Research output: Contribution to journalArticlepeer-review


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 historical U.K. 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.
Original languageUndefined/Unknown
Pages (from-to)4513 - 4521
JournalIEEE Transactions on Smart Grid
Issue number5
Publication statusPublished - 8 Apr 2020


  • Energy storage
  • energy arbitrage
  • battery degradation
  • Deep Reinforcement Learning
  • noisy networks

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