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Residential Demand Response model and impact on voltage profile and losses of an electric distribution network

Author

Listed:
  • Venkatesan, Naveen
  • Solanki, Jignesh
  • Solanki, Sarika Khushalani

Abstract

This paper develops a model for Demand Response (DR) by utilizing consumer behavior modeling considering different scenarios and levels of consumer rationality. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices for different types of consumers. These price elasticity matrices (PEMs) are utilized to calculate the level of Demand Response for a given consumer considering a day-ahead real time pricing scenario. DR models are applied to the IEEE 8500-node test feeder which is a real world large radial distribution network. A comprehensive analysis has been performed on the effects of demand reduction and redistribution on system voltages and losses. Results show that considerable DR can boost in system voltage due for further demand curtailment through demand side management techniques like Volt/Var Control (VVC).

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:84-91
    DOI: 10.1016/j.apenergy.2011.12.076
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    Citations

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    Cited by:

    1. 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.
    2. Dupont, B. & Dietrich, K. & De Jonghe, C. & Ramos, A. & Belmans, R., 2014. "Impact of residential demand response on power system operation: A Belgian case study," Applied Energy, Elsevier, vol. 122(C), pages 1-10.
    3. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
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    10. 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.
    11. Xu, Fang Yuan & Zhang, Tao & Lai, Loi Lei & Zhou, Hao, 2015. "Shifting Boundary for price-based residential demand response and applications," Applied Energy, Elsevier, vol. 146(C), pages 353-370.
    12. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    13. Anees, Amir & Chen, Yi-Ping Phoebe, 2016. "True real time pricing and combined power scheduling of electric appliances in residential energy management system," Applied Energy, Elsevier, vol. 165(C), pages 592-600.
    14. Petinrin, J.O. & Shaabanb, Mohamed, 2016. "Impact of renewable generation on voltage control in distribution systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 770-783.
    15. repec:gam:jsusta:v:10:y:2018:i:2:p:466-:d:131191 is not listed on IDEAS
    16. Mahboubi-Moghaddam, Esmaeil & Nayeripour, Majid & Aghaei, Jamshid, 2016. "Reliability constrained decision model for energy service provider incorporating demand response programs," Applied Energy, Elsevier, vol. 183(C), pages 552-565.
    17. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    18. Manbachi, Moein & Farhangi, Hassan & Palizban, Ali & Arzanpour, Siamak, 2016. "Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification," Applied Energy, Elsevier, vol. 174(C), pages 69-79.
    19. repec:eee:rensus:v:96:y:2018:i:c:p:411-419 is not listed on IDEAS
    20. 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.
    21. 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.
    22. repec:eee:appene:v:212:y:2018:i:c:p:997-1012 is not listed on IDEAS
    23. De Coninck, Roel & Helsen, Lieve, 2016. "Quantification of flexibility in buildings by cost curves – Methodology and application," Applied Energy, Elsevier, vol. 162(C), pages 653-665.
    24. repec:eee:appene:v:205:y:2017:i:c:p:1538-1547 is not listed on IDEAS
    25. Zakariazadeh, Alireza & Homaee, Omid & Jadid, Shahram & Siano, Pierluigi, 2014. "A new approach for real time voltage control using demand response in an automated distribution system," Applied Energy, Elsevier, vol. 117(C), pages 157-166.

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