Optimal Control Policies for Power Demand Scheduling in the Smart Grid

Koutsopoulos, Iordanis; Tassiulas, Leandros

doi:10.1109/jsac.2012.120704

Cited by 84 publications

(91 citation statements)

References 14 publications

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“…PDM has been studied in the context of preemptible jobs (i.e., jobs that can be interrupted partway through execution) [1,2]. In this paper, we consider the scenario where jobs are non-preemptible, and thus must run to completion once started.…”

Section: Related Workmentioning

confidence: 99%

“…Peaks in power demand are proportionally more expensive to generate and provision for (since more infrastructure is required), so it is advantageous to schedule power-consuming jobs in such a way as to minimize peak demand. This problem has been previously formalized as the Peak demand minimization (PDM) problem, and has been studied extensively [1][2][3][4][5][6][7][8]. The basic formulation of the problem is as follows: Each job j is non-preemptible, meaning once it begins execution, it must run to completion without any interruptions.…”

Section: Introductionmentioning

confidence: 99%

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Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Yaw

Mumey

2017

Algorithms

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Yaw

Mumey

2017

Algorithms

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“…forall jobs j Pick a random i ∈ I j with probability x i,j Set s j to be the start time of interval i endforall 1 An interval [s j , s j + l j ) is valid for job j iff a j ≤ s j and s j + l j ≤ d j .…”

Section: Algorithm 1 Roundlpmentioning

confidence: 99%

“…Peaks in power demand are proportionally more expensive to generate and provision for (since more infrastructure is required), so it is advantageous to schedule power consuming jobs in such as way as to minimize peak demand. This problem has been previously formalized as the Peak Demand Minimization (PDM) problem and has been studied extensively [1][2][3][4][5][6][7][8]. The basic formulation of the problem is as follows: Each job j is non-preemptible, meaning once it begins execution, it must run to completion without any interruptions.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Non-preemptible Jobs to Minimize Peak Demand

Yaw¹,

Mumey²

2017

Preprint

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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.

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“…Certainly, the description of the generator expenses associated with producing electric energy as functions of the production volumes in the form of a piecewise linear function is a simplification of the grid regularities, since, generally, these expenses are suggested to be described by non-decreasing convex functions for each base load power plant [41].…”

Section: Remarkmentioning

confidence: 99%

A game-theoretic approach to optimizing the scale of incorporating renewable sources of energy and electricity storing systems in a regional electrical grid

Belenky

2015

Energy Syst

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The problem of developing a decision support system for estimating a) the scale of incorporating available renewable sources of energy (such as solar and wind energy) in a part of a country's electrical grid (called a regional electrical grid further in this paper), and b) the scale of storing electricity in this (regional) electrical grid to make these renewable sources of electric power competitive with traditional power generators (such as fossil-fuel and nuclear ones) and to reduce the cost of acquiring electricity from all the electric power generating facilities in the grid is considered. In the framework of this system, renewable sources of energy are viewed as electricity generating facilities under both existing and expected electricity prices, and the uncertainty of energy supply from them and the uncertainty of the grid customer demand for electricity during every 24 h are taken into account. A mathematical model underlying the system allows one to study the interaction of all the grid elements as a game with a finite (more than three) number of players on a polyhedron of connected player strategies (i.e., strategies that cannot be chosen by the players independently of each other) in a finite-dimensional space. It is shown that solving both parts of the problem under consideration is reducible to finding Nash equilibrium points in this game.

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Optimal Control Policies for Power Demand Scheduling in the Smart Grid

Cited by 84 publications

References 14 publications

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

Scheduling Non-preemptible Jobs to Minimize Peak Demand

A game-theoretic approach to optimizing the scale of incorporating renewable sources of energy and electricity storing systems in a regional electrical grid

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