IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v69y2014icp455-469.html
   My bibliography  Save this article

Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments

Author

Listed:
  • Vardakas, John S.
  • Zorba, Nizar
  • Verikoukis, Christos V.

Abstract

In this paper we present and analyze online and offline scheduling models for the determination of the maximum power consumption in a smart grid environment. The proposed load models consider that each consumer's residence is equipped with a certain number of appliances of different power demands and different operational times, while the appliances' feature of alternating between ON and OFF states is also incorporated. Each load model is correlated with a scheduling policy that aims to the reduction of the power consumption through the compression of power demands or the postponement of power requests. Furthermore, we associate each load model with a proper dynamic pricing process in order to provide consumers with incentives to contribute to the overall power consumption reduction. The evaluation of the load models through simulation reveals the consistency and the accuracy of the proposed analysis.

Suggested Citation

  • Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2014. "Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments," Energy, Elsevier, vol. 69(C), pages 455-469.
    • Handle: RePEc:eee:energy:v:69:y:2014:i:c:p:455-469
    DOI: 10.1016/j.energy.2014.03.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544214003016
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2014.03.037?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blumsack, Seth & Fernandez, Alisha, 2012. "Ready or not, here comes the smart grid!," Energy, Elsevier, vol. 37(1), pages 61-68.
    2. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    3. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    4. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    5. Centolella, Paul, 2010. "The integration of Price Responsive Demand into Regional Transmission Organization (RTO) wholesale power markets and system operations," Energy, Elsevier, vol. 35(4), pages 1568-1574.
    6. Faruqui, Ahmad & Hledik, Ryan & Tsoukalis, John, 2009. "The Power of Dynamic Pricing," The Electricity Journal, Elsevier, vol. 22(3), pages 42-56, April.
    7. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    8. Kathleen Spees & Lester Lave, 2008. "Impacts of Responsive Load in PJM: Load Shifting and Real Time Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 101-122.
    9. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
    10. Faruqui, A. & Hajos, A. & Hledik, R.M. & Newell, S.A., 2010. "Fostering economic demand response in the Midwest ISO," Energy, Elsevier, vol. 35(4), pages 1544-1552.
    11. Alagoz, B.B. & Kaygusuz, A. & Karabiber, A., 2012. "A user-mode distributed energy management architecture for smart grid applications," Energy, Elsevier, vol. 44(1), pages 167-177.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Premarathne, Uthpala Subodhani, 2015. "Reliable context-aware multi-attribute continuous authentication framework for secure energy utilization management in smart homes," Energy, Elsevier, vol. 93(P1), pages 1210-1221.
    2. Tushar, Wayes & Lan, Lan & Withanage, Chathura & Sng, Hui En Karen & Yuen, Chau & Wood, Kristin L. & Saha, Tapan Kumar, 2020. "Exploiting design thinking to improve energy efficiency of buildings," Energy, Elsevier, vol. 197(C).
    3. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2015. "Performance evaluation of power demand scheduling scenarios in a smart grid environment," Applied Energy, Elsevier, vol. 142(C), pages 164-178.
    4. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
    5. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    6. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2017. "A flexible control strategy of plug-in electric vehicles operating in seven modes for smoothing load power curves in smart grid," Energy, Elsevier, vol. 118(C), pages 197-208.
    7. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2016. "Power demand control scenarios for smart grid applications with finite number of appliances," Applied Energy, Elsevier, vol. 162(C), pages 83-98.
    8. Zhou, Kaile & Yang, Shanlin, 2015. "Demand side management in China: The context of China’s power industry reform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 954-965.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boukettaya, Ghada & Krichen, Lotfi, 2014. "A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications," Energy, Elsevier, vol. 71(C), pages 148-159.
    2. Wang, Yong & Li, Lin, 2015. "Time-of-use electricity pricing for industrial customers: A survey of U.S. utilities," Applied Energy, Elsevier, vol. 149(C), pages 89-103.
    3. Galo, Joaquim J.M. & Macedo, Maria N.Q. & Almeida, Luiz A.L. & Lima, Antonio C.C., 2014. "Criteria for smart grid deployment in Brazil by applying the Delphi method," Energy, Elsevier, vol. 70(C), pages 605-611.
    4. Woo, C.K. & Li, R. & Shiu, A. & Horowitz, I., 2013. "Residential winter kWh responsiveness under optional time-varying pricing in British Columbia," Applied Energy, Elsevier, vol. 108(C), pages 288-297.
    5. Soares, Ana & Antunes, Carlos Henggeler & Oliveira, Carlos & Gomes, Álvaro, 2014. "A multi-objective genetic approach to domestic load scheduling in an energy management system," Energy, Elsevier, vol. 77(C), pages 144-152.
    6. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    7. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    8. Olmos, Luis & Ruester, Sophia & Liong, Siok-Jen & Glachant, Jean-Michel, 2011. "Energy efficiency actions related to the rollout of smart meters for small consumers, application to the Austrian system," Energy, Elsevier, vol. 36(7), pages 4396-4409.
    9. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    10. Fletcher, James & Malalasekera, Weeratunge, 2016. "Development of a user-friendly, low-cost home energy monitoring and recording system," Energy, Elsevier, vol. 111(C), pages 32-46.
    11. Guo, Peiyang & Li, Victor O.K. & Lam, Jacqueline C.K., 2017. "Smart demand response in China: Challenges and drivers," Energy Policy, Elsevier, vol. 107(C), pages 1-10.
    12. Greening, Lorna A., 2010. "Demand response resources: Who is responsible for implementation in a deregulated market?," Energy, Elsevier, vol. 35(4), pages 1518-1525.
    13. Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.
    14. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    15. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    16. Alagoz, B. Baykant & Kaygusuz, Asim & Akcin, Murat & Alagoz, Serkan, 2013. "A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market," Energy, Elsevier, vol. 59(C), pages 95-104.
    17. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    18. Daniel Adelman & Canan Uçkun, 2019. "Dynamic Electricity Pricing to Smart Homes," Operations Research, INFORMS, vol. 67(6), pages 1520-1542, November.
    19. Zhang, Yunchao & Islam, Md Monirul & Sun, Zeyi & Yang, Sijia & Dagli, Cihan & Xiong, Haoyi, 2018. "Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program," International Journal of Production Economics, Elsevier, vol. 206(C), pages 261-267.
    20. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:69:y:2014:i:c:p:455-469. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.