Joint day-ahead power procurement and load scheduling using stochastic alternating direction method of multipliers

Xiangfeng Wang, Mingyi Hong, Tsung Hui Chang, Meisam Razaviyayn, Zhi-Quan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this work, we consider the joint day-ahead power bidding and load scheduling problem for the smart grid system, in the presence of uncertain energy demand and renewable energy generation. We formulate the problem as a convex stochastic program in which the renewable energy generation and energy demand are modeled as random variables. The objective is to minimize the cost in the day-ahead market as well as the cost due to real-time power imbalance, by simultaneously selecting: 1) the amount of power to buy in the day-ahead market and 2) the schedule for the controllable load. We propose a stochastic alternating direction method of multipliers (S AD-MM) to solve the resulting convex stochastic optimization problem and analyze its convergence. The effectiveness of the proposed approach is demonstrated via numerical experiments using real solar power data.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7754-7758
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

Keywords

  • Smart grid
  • alternating direction method of multipliers
  • day-ahead power procurement
  • demand side management
  • stochastic programming

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