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Optimal Scheduling Approach on Smart Residential Community Considering Residential Load Uncertainties

  • Sibo Nan
  • Ming ZhouEmail author
  • Gengyin Li
  • Yong Xia
Original Article
  • 3 Downloads

Abstract

With the reformation of electric power market and the development of smart grid technology, smart residential community, a new residential demand side entity, tends to play an important role in demand response program. This paper presents a day-ahead demand response scheduling model for the novel residential community considering the impact of various residential load uncertainties. In this paper, each residential load with uncertainties is firstly modeled using Copula, and the random scenarios are generated by Monte Carlo simulation. Secondly, the residential loads are classified into different categories according to various demand response strategies, and an optimal scheduling scheme for residential loads and distributed generation is modeled. Finally, the generated scenarios are integrated into the scheduling model to form the complete optimal residential demand stochastic scheduling scheme. The presented model can reduce the cost of user’s electricity consumption and decrease the peak load, load peak-valley difference, and the energy consumption of residential load profile without bringing discomfort to the users, through which residential community can participate in demand response efficiently. Besides, this model can also provide support for the decision of electricity pricing strategies under power market development.

Keywords

Copula Demand response scheduling Residential load uncertainties Smart residential community Stochastic modeling 

Notes

Acknowledgements

This work is supported by National Key Research and Development Program of China (2016YFB0901100) and National Nature Science Foundation of China (51577061).

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Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.State Key Laboratory for Alternate Electrical Power System with Renewable Energy SourcesNorth China Electric Power UniversityBeijingChina
  2. 2.State Grid Jiangsu Electric Power Co., LtdNanjingChina

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