Energy Hub Optimal Sizing in the Smart Grid; Machine Learning Approach

被引：0
作者：
Sheikhi, A. [1 ]
Rayati, M. [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 Grids; Smart Energy Hub (S. E. Hub); Energy Management System; Reinforcement Learning (RL); Optimal size; financial analysis; COMBINED HEAT; REINFORCEMENT; POWER; EXPLORATION;
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：
The interests in "Energy Hub" (EH) and "Smart Grid" (SG) concepts have been increasing, in recent years. The synergy effect of the coupling between electricity and natural gas grids and utilizing intelligent technologies for communicating, may change energy management in the future. A new solution entitling "Smart Energy Hub" (S. E. Hub) that models a multi-carrier energy system in a SG environment studied in this paper. Moreover, the optimal size of CHP, auxiliary boiler, absorption chiller, and also transformer unit as main elements of a S. E. Hub is determined. Authors proposed a comprehensive cost and benefit analysis to optimize these elements and apply Reinforcement Learning (RL) algorithm for solving the optimization problem. To confirm the proposed method, a residential customer has been investigated as an S. E. Hub in a dynamic electricity pricing market.
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页数：5
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