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Demand Side Management and its Possibilities in Jordan

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The increase in energy consumption and energy bills in Jordan have been escalating rapidly, which requires a special concern as a large percent of the energy is imported. The need for the reducing peak demand of the distribution network is essential to decrease the overall electricity generation cost. This study was aimed at presenting a model for a home that manages its energy consumption, and the maximum savings possible if the load shifting to off-peak times was applied. It also introduced a tariff that is more concerned in time of use rather than consumption only. The power consumption profile is collected for a sample house. The profile for a week was registered and graphed. The pricing suggested was calculated per day. Moreover, some samples applied worldwide were discussed to find a suitable model. It was found that a saving rate of 16% is achievable if the time of use charge is applied. Additionally, a peak load reduction of 3.5 kWH average per day (in the peak hours) is possible.
Rocznik
Strony
29--33
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
  • Electrical Engineering Department, The University of Jordan, Amman, Jordan
  • Mechanical Engineering Department, The University of Jordan, Amman, Jordan
  • Mechanical Engineering Department, The University of Jordan, Amman, Jordan
Bibliografia
  • 1. Bahrami Sh., Parniani M. and Vafaeimehr V. 2012. A modified approach for residential load scheduling using smart meters. Innovative Smart Grid Technologies (ISGT Europe). Berlin, Germany, 14–17 Oct. 2012.
  • 2. Demand side flexibility in the Nordic electricity market: From a distribution system operator perspective. Copenhagen, 2017.
  • 3. Diekerhof M., Schwarz S., Monti A. 2016. Distributed optimization for electro-thermal heating units. IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Ljubljana, Slovenia, 1–6.
  • 4. Ferdous J. et al. 2017. Optimal dynamic pricing for trading-off user utility and operator profit in smart grid. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–13. DOI: 10.1109/TSMC.2017.2764442.
  • 5. Gao G., Lo K. and Lu J., 2017. Risk assessment due to electricity price forecast uncertainty in UK electricity market. In: 52nd International Universities Power Engineering Conference (UPEC), Heraklion, Crete, Greece, 2017, pp. 1–6.
  • 6. http://emrc.gov.jo/images/electric/electmax2017.pdf. 27/12/2017
  • 7. https://particulier.edf.fr/en/home/energy-at-home/electricity/blue-tariff.html. 27/12/2017
  • 8. Mortaji H., Hock Ow S., M. Moghavvemi, Almurib H.A.F. 2017. Load shedding and smart-direct load control using internet of things in smart grid demand response management. IEEE Transactions on Industry Applications, 53(6), 5155–5163.
  • 9. Naus J., Spaargaren G., Van Vliet B.J.M., Van der Horst H.M. 2014. Smart grids, information flows and emerging domestic energy practices. Energy Policy, 68, 436–446.
  • 10. Parvathy S., Patne N.R., Jadhav A.M., 2016. A smart demand side management mechanism for domestic energy consumers with major HVAC load. International Conference on Electrical Power and Energy Systems, Bhopal, India, 504–511. DOI: 10.1109/ICEPES.2016.7915982.
  • 11. Roscoe J. and Ault G. 2010. Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response. IET Renewable Power Generation, 4, 369–382.
  • 12. Roy T., Das A. and Ni. Z. 2017. Optimization in load scheduling of a residential community using dynamic pricing. In: IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC: IEEE. DOI: 10.1109/ISGT.2017.8086087.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bab10c21-2b91-4efd-8d46-947a5c431b9f
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