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Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Journal Article · · Algorithms
DOI:https://doi.org/10.3390/a10040122· OSTI ID:1408842
 [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Montana State Univ., Bozeman, MT (United States). Gianforte School of Computing
+ Show Author Affiliations
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.
Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
National Science Foundation (NSF); USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1408842
Report Number(s):
LA-UR-17-26381
Journal Information:
Algorithms, Journal Name: Algorithms Journal Issue: 4 Vol. 10; ISSN ALGOCH; ISSN 1999-4893
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

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Adaptive Energy Consumption Scheduling for Connected Microgrids Under Demand Uncertainty journal July 2013
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Social Networking Reduces Peak Power Consumption in Smart Grid journal May 2015
Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid journal January 2016
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Wind Integration National Dataset (WIND) Toolkit
  • Maclaurin, Galen; Draxl, Caroline; Hodge, Bri-Mathias
  • DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory https://doi.org/10.25984/1822195
dataset January 2014

Cited By (2)

Approximation Algorithms for Demand Strip Packing text January 2021
Approximation Algorithms for Demand Strip Packing preprint January 2021

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97 MATHEMATICS AND COMPUTING
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Mathematics