Scheduling Non-preemptible Jobs to Minimize Peak Demand

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 these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.

A simulated annealing algorithm for the allocation of production resources in the food catering industry

“…Dealing with the allocation of processing resources several contributions are intended to the minimisation of the workload peaks. The minimisation of peaks has a long tradition in job scheduling and resource allocation problems, both in production systems and in power grids management (Ranjan et al, 2014;Yaw and Mumey, 2017). As for power grids which need for the balance of power load among the users, the workload in production systems needs to be balanced between several processing resources (e.g.…”