强化学习
马尔可夫决策过程
计算机科学
电池(电)
电力市场
套利
储能
数学优化
电
马尔可夫过程
人工智能
工程类
功率(物理)
电气工程
统计
金融经济学
量子力学
物理
经济
数学
作者
Jun Cao,Dan Harrold,Zhong Fan,Thomas Morstyn,David Healey,Kang Li
出处
期刊:IEEE Transactions on Smart Grid
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:11 (5): 4513-4521
被引量:149
标识
DOI:10.1109/tsg.2020.2986333
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI