Optimal residential community demand response scheduling in smart grid

Sibo Nan, Ming Zhou and Gengyin Li

Applied Energy, 2018, vol. 210, issue C, 1280-1289

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 demand response scheduling model for the novel residential community incorporating the current circumstances and the future trends of demand response programs. In this paper, smart residential loads are firstly classified into different categories according to various demand response programs. Secondly, a complete scheduling scheme is modeled based on the dispatch of residential loads and distributed generation. The presented model reduces the cost of user’s electricity consumption and decreases the peak load and peak-valley difference 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: Smart residential community; Controllable load; Distributed generation; Demand response (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (63)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626191730819X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:210:y:2018:i:c:p:1280-1289

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2017.06.066

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().