International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 1  (January 2017), Pages:  1-9


Title: Time based device clustering for domestic power scheduling

Author(s):  Muhammad Adnan Aziz 1,*, Ijaz Mansoor Qureshi 2, Tanweer Ahmad Cheema 1, Aqdas Naveed Malik 3

Affiliation(s):

1Department of Electronic Engineering, ISRA University, Islamabad Campus, Islamabad, Pakistan
2Department of Electrical Engineering, AIR University, Islamabad, Pakistan
3Department of Electronic Engineering, International Islamic University, Islamabad, Pakistan

https://doi.org/10.21833/ijaas.2017.01.001

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Abstract:

Domestic consumers can reduce their electricity expenditures by shifting their loads to slots of low power usage during demand response (DR) in a smart grid (SG) power system. Efficient shifting of loads can be used to reduce the peak-to-average (PAR) of power network, which is highly desirable for the reliability of SG. Methodologies available in literature only address the problem of power scheduling for a small set of consumers and underperforms for large population. This paper presents clustered community based home energy management system (CCHEMS), which performs better for a huge consumer set. CCHEMS is based on clustering consumer devices according to operating time overlap. Activation time slots (ATS) of clustered devices under user defined constraints are subjected to particle swarm optimization (PSO) to attain optimum power demand. Real time electricity price (RTEP) and modified inclined block rate (IBR) is employed to contain the power demand under appropriate limits. Results confirm that CCHEMS is better than non-clustered optimization, 9% in cost reduction and 24% in PAR trimming for a population of 1000 consumers. 

© 20167 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Home energy management system, Clustered Community based HEMS, Smart grid, Incline block rate, Particle swarm optimization

Article History: Received 5 September 2016, Received in revised form 20 December 2016, Accepted 21 December 2016

Digital Object Identifier: https://doi.org/10.21833/ijaas.2017.01.001

Citation:

Aziz MA, Qureshi IM, Cheema TA, and  Malik AN (2017). Time based device clustering for domestic power scheduling. International Journal of Advanced and Applied Sciences, 4(1): 1-9

http://www.science-gate.com/IJAAS/V4I1/Aziz.html


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