[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/2208828.2208835acmotherconferencesArticle/Chapter ViewAbstractPublication Pagese-energyConference Proceedingsconference-collections
research-article

A delay based optimization scheme for peak load reduction in the smart grid

Published: 09 May 2012 Publication History

Abstract

A measurement campaign based on commodity wireless sensors shows that the majority of thermostatic loads in a user premise are described by periodic pulse waves. The superposition of these loads results to high peak power demand and costs in the network. We propose a novel first stage of optimization in the smart grid which reduces external on/off command flow for demand response between the controller and the smart appliances. A phase management scheme is developed that defines optimal time shifts (delays) on the periodic loads in order to provide peak power reduction over a limited time horizon. A gradient descent optimization technique, based on Taylor series, is applied to determine the phases of the pulses in discrete time steps. A centralized control scheme is explored, applied from the controller of the smart grid to smart devices that fall within its administrative domain. It is found that respectable peak power reduction can be achieved by the centralized scheme with a drawback the redundant data transfer in the network. The main advantage by implementing the proposed algorithm is that direct on/off control of the smart grid upon the smart devices of the users can be avoided. As a consequence, user discomfort is reduced and higher penetration of smart grid services is expected.

References

[1]
IEEE Standards, IEEE P.1701--1705, 2011.
[2]
M. J. Neely, A. S. Tehrani, A. G. Dimakis, 'Efficient algorithms for renewable energy allocation to delay tolerant consumers', in Proc. IEEE Conf. Smart Grid Communic., Oct. 2010.
[3]
K. Hamilton and N. Gulhar, "Taking Demand Response to the Next Level", IEEE Power and Energy Mag., vol.8, no.3, pp.60--65, May/June 2010.
[4]
L. Chen, N. Li, L. Jiang, S. H. Low, 'Optimal demand response: problem formulation and deterministic case', in Control and Optimization Theory for Electric Smart Grids, Springer, 2011.
[5]
Jean-Yves Le Boudec, D. C. Tomozei, 'Satisfiability of elastic demand in the smart grid', in Proc. Int. Conf. on Smart Grids, Green Communications and IT Energy-aware Technologies, ENERGY 2011, May 2011.
[6]
I. Koutsopoulos and L. Tassiulas, "Control and Optimization meet the Smart Power Grid: Scheduling of power demands for optimal energy management", Proc. Int. Conf on Energy-Efficient Computing and Networking (E-Energy), 2011.
[7]
G. Koutitas, 'Control of flexible smart devices in the smart grid', IEEE Trans. Smart Grids, (under review).
[8]
L. Chen et al., "Two Market Models for Demand Response in Power Networks," Proc. IEEE Int'l. Conf. Smart Grid Commun., 2010.
[9]
M. Baghaie, S. Moeller, B. Krishnamachari, 'Energy routing of the future grid: A stochastic network optimization approach', in Proc. Power System Technology, 2010.
[10]
http://www.beywatch.eu

Cited By

View all
  • (2018)Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable EnergyEnergies10.3390/en1108210411:8(2104)Online publication date: 13-Aug-2018
  • (2018)Online Energy Management for Smart Communities with Heterogeneous Demands2018 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2018.8647752(1-6)Online publication date: Dec-2018
  • (2018)Averting the privacy risks of smart metering by local data preprocessingPervasive and Mobile Computing10.1016/j.pmcj.2014.10.00216:PA(171-183)Online publication date: 24-Dec-2018
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
e-Energy '12: Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
May 2012
250 pages
ISBN:9781450310550
DOI:10.1145/2208828
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • IEEE-CS\DATC: IEEE Computer Society

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 May 2012

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. demand load control
  2. smart grid
  3. smart sensors/actuators network

Qualifiers

  • Research-article

Funding Sources

Conference

e-Energy'12
Sponsor:
  • IEEE-CS\DATC

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 11 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2018)Online Energy Management and Heterogeneous Task Scheduling for Smart Communities with Residential Cogeneration and Renewable EnergyEnergies10.3390/en1108210411:8(2104)Online publication date: 13-Aug-2018
  • (2018)Online Energy Management for Smart Communities with Heterogeneous Demands2018 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2018.8647752(1-6)Online publication date: Dec-2018
  • (2018)Averting the privacy risks of smart metering by local data preprocessingPervasive and Mobile Computing10.1016/j.pmcj.2014.10.00216:PA(171-183)Online publication date: 24-Dec-2018
  • (2018)Storage-Saving Bi-dimensional Privacy-Preserving Data Aggregation in Smart GridsSecurity with Intelligent Computing and Big-data Services10.1007/978-3-319-76451-1_30(322-329)Online publication date: 29-Mar-2018
  • (2018)Verifiable Privacy-Preserving Payment Mechanism for Smart GridsInternet and Distributed Computing Systems10.1007/978-3-030-02738-4_5(52-63)Online publication date: 17-Oct-2018
  • (2016)An efficient consumption optimisation for dense neighbourhood area demand management2016 IEEE International Energy Conference (ENERGYCON)10.1109/ENERGYCON.2016.7514063(1-5)Online publication date: Apr-2016
  • (2015)Cloud Computing Applications for Smart Grid: A SurveyIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2014.232137826:5(1477-1494)Online publication date: 1-May-2015
  • (2015)Applying limited-preemptive scheduling to peak load reduction in smart buildings2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA)10.1109/ETFA.2015.7301454(1-8)Online publication date: Sep-2015
  • (2014)Impact of demand-response on the efficiency and prices in real-time electricity marketsProceedings of the 5th international conference on Future energy systems10.1145/2602044.2602052(171-182)Online publication date: 11-Jun-2014
  • (2013)Periodic Flexible Demand: Optimization and Phase Management in the Smart GridIEEE Transactions on Smart Grid10.1109/TSG.2013.22468744:3(1305-1313)Online publication date: Sep-2013
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media