A Novel Grid Load Management Technique Using Electric Water Heaters and Q-Learning

被引：0

作者：

Al-Jabery, Khalid ^{[1
]}

Wunsch, Don C. ^{[1
]}

Xiong, Jinjun ^{[2
]}

Shi, Yiyu ^{[1
]}

机构：

[1] Missouri Univ Sci & Technol, ECE Dept, Rolla, MO 65401 USA

[2] IBM TJ Watson Res Ctr, Yorktown Hts, NY 10562 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM) | 2014年

关键词：

Electric water heaters; Reinforcement learning; Markov decision process; Q-learning; grid demand;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper describes a novel technique for controlling demand-side management (DSM) by optimizing the power consumed by Domestic Electric Water Heaters (DEWH) while maintaining customer satisfaction. The system has 18 states based on three factors: instantaneous grid load, water consumption, and the temperature of the water supplied. The current state of the system is defined based on its fuzzy membership for each factor. The resulting model represents a Semi-Markov decision process (SMDP) with two possible actions, "On" and "Off." Rewards are assigned for each action-state pairs proportionally to the fuzzy membership of the system in the new state. A simulation study was conducted to compare the proposed method with three previous approaches. The proposed method demonstrated better performance in reducing the overall grid power demand and flattening its peaks. Furthermore, it provides better rate of customers' satisfaction than the uncontrolled operation.

引用

页码：776 / 781

页数：6

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