By Topic

Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
S. C. Chan ; Department of Electrical & Electronic Eng, University of Hong Kong, Hong Kong, SAR, Hong Kong ; K. M. Tsui ; H. C. Wu ; Yunhe Hou
more authors

With the promises of smart grids, power can be more efficiently and reliably generated, transmitted, and consumed over conventional electricity systems. Through the two-way flow of information between suppliers and consumers, the grids can also adapt more readily to the increased penetration of renewable energy sources and encourage users' participation in energy savings and cooperation through the demand-response (DR) mechanism. An important issue in smart grids is therefore how to manage DR to reduce peak electricity load and hence future investment in thermal generations and transmission networks, and better utilize renewable energies to reduce our dependence on hydrocarbon. Effective DR depends critically on demand management and price/load/renewable energy forecasting, which call for sophisticated signal processing and optimization techniques. The objectives of this article are to: 1) introduce to the signal processing community the concept of smart grids, especially on the problems of price/load forecasting and DR management (DRM) and optimization, 2) highlight related signal processing applications and state-of-the-art methodologies, and 3) share the authors' research experience through concrete examples on price predictions and DRM and optimization, with emphasis on recursive online solutions and future challenges.

Published in:

IEEE Signal Processing Magazine  (Volume:29 ,  Issue: 5 )