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

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

Abstract

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
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    References listed on IDEAS

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    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.

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