Two-stage stochastic power generation scheduling in microgrids

A. A. Eajal, E. F. El-Saadany, Yousef Elrayani, K. Ponnambalam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In this study, the power scheduling problem in μ-grids is investigated taking the uncertainties in power demand and wind power into account. The problem is formulated as a stochastic mixed-integer linear optimization problem with the objective being minimizing the total μ-grid cost. The objective is subject to a set of operational constraints imposed on the generating units and the system itself. A two-stage stochastic programming method has been applied to find the optimal power generation schedule for a μ-grid. The developed approach was implemented in a General Algebraic Modeling System platform (GAMS). The developed method was tested on a μ-grid consisting of eight dispatchable units and a wind turbine. To demonstrate the necessity of uncertainty modeling, the value of the stochastic solution (VSS) and the expected value of perfect information (EVPI) were used to compare the stochastic power schedule obtained with the deterministic one.

Original languageBritish English
Title of host publicationCanadian Conference on Electrical and Computer Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479930999
DOIs
StatePublished - 17 Sep 2014
Event2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering, CCECE 2014 - Toronto, Canada
Duration: 4 May 20147 May 2014

Publication series

NameCanadian Conference on Electrical and Computer Engineering
ISSN (Print)0840-7789

Conference

Conference2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering, CCECE 2014
Country/TerritoryCanada
CityToronto
Period4/05/147/05/14

Keywords

  • Generation scheduling
  • renewable energy
  • stochastic optimization
  • uncertainty
  • μ-grid

Fingerprint

Dive into the research topics of 'Two-stage stochastic power generation scheduling in microgrids'. Together they form a unique fingerprint.

Cite this