Decision-Making for Electricity Retailers: A Brief Survey

被引：113

作者：

Yang, Jiajia ^{[1
]}

Zhao, Junhua ^{[2
]}

Luo, Fengji ^{[3
]}

Wen, Fushuan ^{[4
,5
,6
]}

Dong, Zhao Yang ^{[1
]}

机构：

[1] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

[2] Chinese Univ Hong Kong Shenzhen, Shenzhen 518100, Peoples R China

[3] Univ Sydney, Sch Civil Engn, Sydney, NSW 2006, Australia

[4] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China

[5] Univ Teknol Brunei, Bandar Seri Begawan BE1410, Brunei

[6] Elect Power Res Inst, CSG, Guangzhou 510080, Guangdong, Peoples R China

来源：

IEEE TRANSACTIONS ON SMART GRID | 2018年 / 9卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Terms Electricity retail; retail pricing; retail energy forecasting; demand side management; decision-making; EXTREME LEARNING-MACHINE; LOAD DISAGGREGATION; OPTIMAL STRATEGY; SELLING PRICE; POWER GRIDS; MARKET; MODEL; DEMAND; RISK; ENERGY;

D O I：

10.1109/TSG.2017.2651499

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：

With the continuous development of smart grid and further restructuring of power industry, modern power systems have been transformed to complex cyber-physical systems characterized with high renewable energy penetrations, distributed facilities, advanced metering, and communication technologies, as well as ever-increasing customer awareness. These new development trends pose significant challenges for electricity retailers and call for innovative decision-making methods. To help researchers and engineers have a better overview of the state-of-the-art on electricity retail decision-making schemes, this paper aims to survey the latest progress on this subject. Some critical and open issues in this field are also discussed.

引用

页码：4140 / 4153

页数：14

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