期刊
IEEE TRANSACTIONS ON SMART GRID
卷 11, 期 5, 页码 4513-4521出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2020.2986333
关键词
Batteries; Degradation; Mathematical model; Machine learning; Aging; Stress; Energy storage; energy arbitrage; battery degradation; deep reinforcement learning; noisy networks
资金
- Smart Energy Network Demonstrator Project [32R16P00706]
- European Regional Development Fund
- Department for Business, Energy & Industrial Strategy, U.K.
- Royal Society Research [RGS/R1/191395, EP/S031863/1]
- EPSRC [EP/R030243/1, EP/P004636/1, EP/S000887/1, EP/L001063/1] Funding Source: UKRI
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.
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