Ergodic Energy Management Leveraging Resource Variability in Distribution Grids

Gang Wang, Vasileios Kekatos, Antonio J. Conejo, Georgios B Giannakis

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.

Original languageEnglish (US)
Article number7419267
Pages (from-to)4765-4775
Number of pages11
JournalIEEE Transactions on Power Systems
Volume31
Issue number6
DOIs
StatePublished - Nov 1 2016

Fingerprint

Energy management
Reactive power
Electricity
Electric potential
Compensation and Redress

Keywords

  • Energy management
  • active power curtailment
  • dual decomposition
  • reactive power compensation
  • stochastic optimization

Cite this

Ergodic Energy Management Leveraging Resource Variability in Distribution Grids. / Wang, Gang; Kekatos, Vasileios; Conejo, Antonio J.; Giannakis, Georgios B.

In: IEEE Transactions on Power Systems, Vol. 31, No. 6, 7419267, 01.11.2016, p. 4765-4775.

Research output: Contribution to journalArticle

@article{3865561f843144708bea4b484f4799f5,
title = "Ergodic Energy Management Leveraging Resource Variability in Distribution Grids",
abstract = "Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.",
keywords = "Energy management, active power curtailment, dual decomposition, reactive power compensation, stochastic optimization",
author = "Gang Wang and Vasileios Kekatos and Conejo, {Antonio J.} and Giannakis, {Georgios B}",
year = "2016",
month = "11",
day = "1",
doi = "10.1109/TPWRS.2016.2524679",
language = "English (US)",
volume = "31",
pages = "4765--4775",
journal = "IEEE Transactions on Power Systems",
issn = "0885-8950",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

TY - JOUR

T1 - Ergodic Energy Management Leveraging Resource Variability in Distribution Grids

AU - Wang, Gang

AU - Kekatos, Vasileios

AU - Conejo, Antonio J.

AU - Giannakis, Georgios B

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.

AB - Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.

KW - Energy management

KW - active power curtailment

KW - dual decomposition

KW - reactive power compensation

KW - stochastic optimization

UR - http://www.scopus.com/inward/record.url?scp=84959419905&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84959419905&partnerID=8YFLogxK

U2 - 10.1109/TPWRS.2016.2524679

DO - 10.1109/TPWRS.2016.2524679

M3 - Article

VL - 31

SP - 4765

EP - 4775

JO - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

IS - 6

M1 - 7419267

ER -