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Title: Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Our paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We then focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. These results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.
Authors:
ORCiD logo [1] ; ORCiD logo [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Montana State Univ., Bozeman, MT (United States). Gianforte School of Computing
Publication Date:
Report Number(s):
LA-UR-17-26381
Journal ID: ISSN 1999-4893; ALGOCH
Grant/Contract Number:
AC52-06NA25396; CNS-1156475
Type:
Accepted Manuscript
Journal Name:
Algorithms
Additional Journal Information:
Journal Volume: 10; Journal Issue: 4; Journal ID: ISSN 1999-4893
Publisher:
MDPI
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE; National Science Foundation (NSF)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Computer Science; Mathematics
OSTI Identifier:
1408842

Yaw, Sean, and Mumey, Brendan. Scheduling Non-Preemptible Jobs to Minimize Peak Demand. United States: N. p., Web. doi:10.3390/a10040122.
Yaw, Sean, & Mumey, Brendan. Scheduling Non-Preemptible Jobs to Minimize Peak Demand. United States. doi:10.3390/a10040122.
Yaw, Sean, and Mumey, Brendan. 2017. "Scheduling Non-Preemptible Jobs to Minimize Peak Demand". United States. doi:10.3390/a10040122. https://www.osti.gov/servlets/purl/1408842.
@article{osti_1408842,
title = {Scheduling Non-Preemptible Jobs to Minimize Peak Demand},
author = {Yaw, Sean and Mumey, Brendan},
abstractNote = {Our paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We then focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. These results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.},
doi = {10.3390/a10040122},
journal = {Algorithms},
number = 4,
volume = 10,
place = {United States},
year = {2017},
month = {10}
}