IDEAS home Printed from
   My bibliography  Save this article

Performance evaluation of power demand scheduling scenarios in a smart grid environment


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


Smart grid technology is considered as the ultimate solution to challenges that emerge from the increasing power demands, the subsequent increase in pollution, and the outmoded power grid infrastructure. The successful implementation of the smart grid is mainly driven by the utilization of modern communication technologies, which aim at the provision of advanced demand side management mechanisms, such as demand response. In this paper, we present and analyze four power-demand scheduling scenarios that aim to reduce the peak demand in a smart grid infrastructure. The proposed scenarios consider that each consumer is equipped with a certain number of appliances of different power demands and different operational times, while the percentage of consumers that agree to participate in the demand scheduling program is also incorporated in our models. We provide the analysis for the determination of the peak demand in a residential area, based on recursive formulas. The proposed analysis is validated through simulations; the accuracy of the analytical models is found to be quite satisfactory. Moreover, we unveil the consistency and necessity of the proposed scenarios and corresponding analytical models.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:142:y:2015:i:c:p:164-178
    DOI: 10.1016/j.apenergy.2014.12.060

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    1. Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2011. "Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market," Applied Energy, Elsevier, vol. 88(7), pages 2548-2558, July.
    2. Venkatesan, Naveen & Solanki, Jignesh & Solanki, Sarika Khushalani, 2012. "Residential Demand Response model and impact on voltage profile and losses of an electric distribution network," Applied Energy, Elsevier, vol. 96(C), pages 84-91.
    3. Valenzuela, Jorge & Thimmapuram, Prakash R. & Kim, Jinho, 2012. "Modeling and simulation of consumer response to dynamic pricing with enabled technologies," Applied Energy, Elsevier, vol. 96(C), pages 122-132.
    4. Gyamfi, Samuel & Krumdieck, Susan & Urmee, Tania, 2013. "Residential peak electricity demand response—Highlights of some behavioural issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 71-77.
    5. Kwag, Hyung-Geun & Kim, Jin-O, 2014. "Reliability modeling of demand response considering uncertainty of customer behavior," Applied Energy, Elsevier, vol. 122(C), pages 24-33.
    6. Rastegar, Mohammad & Fotuhi-Firuzabad, Mahmud & Aminifar, Farrokh, 2012. "Load commitment in a smart home," Applied Energy, Elsevier, vol. 96(C), pages 45-54.
    7. Kwag, Hyung-Geun & Kim, Jin-O, 2012. "Optimal combined scheduling of generation and demand response with demand resource constraints," Applied Energy, Elsevier, vol. 96(C), pages 161-170.
    8. Wissner, Matthias, 2011. "The Smart Grid - A saucerful of secrets?," Applied Energy, Elsevier, vol. 88(7), pages 2509-2518, July.
    9. 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.
    10. Ferreira, R.S. & Barroso, L.A. & Carvalho, M.M., 2012. "Demand response models with correlated price data: A robust optimization approach," Applied Energy, Elsevier, vol. 96(C), pages 133-149.
    11. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    12. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. 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.
    2. Awais Manzoor & Nadeem Javaid & Ibrar Ullah & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes," Energies, MDPI, Open Access Journal, vol. 10(9), pages 1-28, August.
    3. Gruber, J.K. & Huerta, F. & Matatagui, P. & Prodanović, M., 2015. "Advanced building energy management based on a two-stage receding horizon optimization," Applied Energy, Elsevier, vol. 160(C), pages 194-205.
    4. Reihani, Ehsan & Motalleb, Mahdi & Thornton, Matsu & Ghorbani, Reza, 2016. "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, Elsevier, vol. 183(C), pages 445-455.
    5. Gonçalves, Ivo & Gomes, Álvaro & Henggeler Antunes, Carlos, 2019. "Optimizing the management of smart home energy resources under different power cost scenarios," Applied Energy, Elsevier, vol. 242(C), pages 351-363.
    6. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    7. Roth, Lucas & Lowitzsch, Jens & Yildiz, Özgür & Hashani, Alban, 2016. "The impact of (co-) ownership of renewable energy production facilities on demand flexibility," MPRA Paper 73562, University Library of Munich, Germany.
    8. Jelena Lukić & Miloš Radenković & Marijana Despotović-Zrakić & Aleksandra Labus & Zorica Bogdanović, 2017. "Supply chain intelligence for electricity markets: A smart grid perspective," Information Systems Frontiers, Springer, vol. 19(1), pages 91-107, February.
    9. Rabiee, Abdorreza & Sadeghi, Mohammad & Aghaeic, Jamshid & Heidari, Alireza, 2016. "Optimal operation of microgrids through simultaneous scheduling of electrical vehicles and responsive loads considering wind and PV units uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 721-739.
    10. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
    11. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.


    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:appene:v:142:y:2015:i:c:p:164-178. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.