Simulation and optimization for unit commitment using a region-based sampling (RBS) algorithm

Joshua Darville, Nurcin Celik

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

1 Scopus citations

Abstract

Energy demand is a global crisis as climate changes and the population continues to rise. Hence, is it imperative to produce and distribute energy efficiently; this is commonly referred to as the unit commitment problem. Simulation and optimization are separate approaches to this problem that can synchronize with each other to compensate their unique deficiencies such as the uncertainties associated with simulating renewable power generation. In this paper, we propose a new region-based sampling (RBS) algorithm to determine which demand points to consider based on the region’s priority within the community along with a microgrid (MG) optimization model for each scenario. A case study was conducted on a synthetic microgrid to assess the performance of this approach. The results show that energy supplied but not used (overgeneration) was reduced by eighty percent between the first and second replication according to the sampling region selected by the RBS algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IISE Annual Conference
EditorsL. Cromarty, R. Shirwaiker, P. Wang
PublisherInstitute of Industrial and Systems Engineers, IISE
Pages1424-1430
Number of pages7
ISBN (Electronic)9781713827818
StatePublished - 2020
Event2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020 - Virtual, Online, United States
Duration: Nov 1 2020Nov 3 2020

Publication series

NameProceedings of the 2020 IISE Annual Conference

Conference

Conference2020 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2020
Country/TerritoryUnited States
CityVirtual, Online
Period11/1/2011/3/20

Keywords

  • Agent-based simulation
  • Machine learning
  • Optimization
  • Power systems
  • Smart grid

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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