Applying Reinforcement Learning Method to Optimize an Energy Hub Operation in the Smart Grid

被引：0

作者：

Rayati, M. ^{[1
]}

Sheikhi, A. ^{[1
]}

Ranjbar, A. M. ^{[1
]}

机构：

[1] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) | 2015年

关键词：

Smart Energy Hub (S. E. Hub); Smart Grids; Reinforcement Learning (RL); Energy Management System; Optimization; FUTURE; SYSTEM;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

New days, the concepts of "Smart Grid" and "Energy Hub" have been introduced to improve the operation of the energy systems. This paper introduces a new conception entitling Smart Energy Hub (S. E. Hub), as a multi-carrier energy system in a smart grid environment. To show the application of this novel idea, we present a residential S. E. Hub which employs Reinforcement Learning (RL) method for finding a near optimal solution. The simulation results show that by applying the S. E. Hub model and then using the proposed method for a residential customer, running cost is reduced substantially. While, comparing with the classical ones, the RL method does not require any data about the environment and either equipment's parameters.

引用

页数：5

共 23 条

[1] Optimal Operation of Residential Energy Hubs in Smart Grids [J].