Semimonthly

ISSN 1000-1026

CN 32-1180/TP

+Advanced Search 中文版
Intelligent Energy Management of Industrial Loads Considering Participation in Demand Response Program
Author:
Affiliation:

1. School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; 2. Department of Electrical & Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei; 3. State Grid Henan Electric Power Corporation, Zhengzhou 450052, China

Abstract:

With the development of smart grids and promotion of demand responses, intelligent energy management at the demand side has attracted extensive attention. The advanced automation level and vast energy consumption of an industrial load makes it a good choice for implementing intelligent energy management. Given this background, a framework of an intelligent energy management system for industrial loads is presented first, with different types of power demands and management of photovoltaic power generation taken into consideration. Then, based on the state task network, power demand models are developed for production devices and temperature-controlled devices with thermal comfort of the production environment considered. On this basis, an optimal operation model of the intelligent energy management system is presented with the energy-purchasing cost and energy-selling income of the industrial load concerned taken into account. Finally, a typical industrial load is employed to demonstrate the essential characteristics of the developed method, and its demand response performance and benefits of the presented intelligent energy management system are addressed as well.

Keywords:

Foundation:

Get Citation
[1]SHI Junyi, WEN Fushuan, CUI Pengcheng, et al. Intelligent Energy Management of Industrial Loads Considering Participation in Demand Response Program[J]. Automation of Electric Power Systems,2017,41(14):45-53. DOI:10.7500/AEPS20170203002
Copy
Share
History
  • Received:February 03,2017
  • Revised:June 08,2017
  • Adopted:March 06,2017
  • Online: May 12,2017
  • Published: