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Algorithms 2017, 10(4), 122; https://doi.org/10.3390/a10040122

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

1
Los Alamos National Laboratoy, Los Alamos, NM 87545, USA
2
Gianforte School of Computing, Montana State University, Bozeman, MT 59717,USA
*
Author to whom correspondence should be addressed.
Received: 22 September 2017 / Revised: 20 October 2017 / Accepted: 25 October 2017 / Published: 28 October 2017
(This article belongs to the Special Issue Algorithms for Hard Problems: Approximation and Parameterization)
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Abstract

This 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 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. Our 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. View Full-Text
Keywords: peak demand minimization; job scheduling; approximation algorithms; smart grid peak demand minimization; job scheduling; approximation algorithms; smart grid
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Yaw, S.; Mumey, B. Scheduling Non-Preemptible Jobs to Minimize Peak Demand. Algorithms 2017, 10, 122.

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