Time based device clustering for domestic power scheduling

Aziz, Muhammad Adnan; Qureshi, Ijaz Mansoor; Cheema, Tanweer Ahmad; Malik, Aqdas Naveed

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

Full Text - PDF XML

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.

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

References:

Abushnaf J, Rassau A and Włodzimierz G (2015). Impact of dynamic energy pricing schemes on a novel multi-user home energy management system. Electric Power Systems Research, 125: 124-132. https://doi.org/10.1016/j.epsr.2015.04.003

Adika CO and Wang L (2014). Autonomous appliance scheduling for household energy management. IEEE Transactions on Smart Grid, 5(2):673-682. https://doi.org/10.1109/TSG.2013.2271427

Aggarwal A, Kunta S and Verma PK (2010). A proposed communications infrastructure for the smart grid. In the IEEE Innovative Smart Grid Technologies (ISGT '10): 1-5. https://doi.org/10.1109/ ISGT.2010.5434764 https://doi.org/10.1109/ISGT.2010.5434764

Ameren Illinois Power Co (2015). Real-time pricing for residential customers. Available online at: https://www2.ameren.com/retailenergy/realtimeprices.aspx [Accessed on February 12, 2016].

Borenstein S (2008). Equity effects of increasing-block electricity pricing. Working paper CSEM WP 180, Center for the Study of Energy Markets. Berkeley, University of California, CA. Available online at: http://escholarship.org/uc/item/ 3sr1h8nc

Chavali P, Yang P and Nehorai A (2014). a distributed algorithm of appliance scheduling for home energy management system. IEEE Transactions on Smart Grid, 5(1):282-290. https://doi.org/10.1109/TSG.2013.2291003

Chen CY, Tsoul YP, Liao SC and Lin CT (2009). Implementing the design of smart home and achieving energy conservation. In the 7th IEEE International Conference on Industrial Informatics (INDIN '09): 273-276. https://doi.org/10.1109/INDIN.2009.5195 816.

Deilami S, Masoum AS, Moses PS and Masoum MAS (2011). Real-Time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Transactions on Smart Grid, 2(3):456-467. https://doi.org/10.1109/TSG.2011.2159816

Ipakchi A and Albuyeh F (2009). Grid of the future. IEEE Power and Energy Magazine, 7(2):52-62. https://doi.org/10.1109/MPE.2008.931384

Kennedy J and Eberhart R (1995). Particle swarm optimization. In the Proceedings of IEEE International Conference on Neural Networks, 4:1942-1948. https://doi.org/10.1109/ICNN. 1995.488968 https://doi.org/10.1109/ICNN.1995.488968

Kim J, Byun J, Jeong D, Choi M, Kang B and Park S (2015). An IoT-based home energy management system over dynamic home area networks. International Journal of Distributed Sensor Networks, Special issue on Enhancing Internet and Distributed Computing Systems with Wireless Sensor Networks, 2015: Article ID 828023, 15 pages. https://doi.org/10.1155/2015/828023

Kim TT and Poor HV (2007). Scheduling power consumption with price uncertainty. IEEE Transactions on Smart Grid, 2(3):519-527. https://doi.org/10.1109/TSG.2011.2159279

Lien CH, Bai YW and Lin MB (2007). Remote-controllable power outlet system for home power management. IEEE Transactions on Consumer Electronics, 53(4):1634-1641. https://doi.org/10.1109/TCE.2007.4429263

Mohsenian-Rad AH and Garcia AL (2010). Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 1(2):120-133. https://doi.org/10.1109/TSG.2010.2055903

Ozturk Y, Senthilkumar D, Kumar S and Lee G (2013). An intelligent home energy management system to improve demand response. IEEE Transactions on Smart Grid, 4(2):694-701. https://doi.org/10.1109/TSG.2012.2235088

Rastegar M, Fotuhi-Firuzabad MF and Aminifar F (2012). Load commitment in a smart home. Applied Energy, 96: 45–54. https://doi.org/10.1016/j.apenergy.2012.01.056

Sierra E, Hossian A, Britos P, Rodriguez D and Garcia-Martinez R (2007). Fuzzy control for improving energy management within indoor building environments. In the IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA '07):412-416. https://doi.org/10.1109/CERMA.2007.4367722

Xiong G, Chen C, Kishore S and Yener A (2011). Smart (in-home) power scheduling for demand response on the smart grid. In the IEEE PES Innovative Smart Grid Technologies (ISGT '11): 1-7. https://doi.org/10.1109/ISGT.2011.5759154

Zhao Z, Lee WC, Shin Y and Song KB (2013). An optimal power scheduling method for demand response in home energy management system. IEEE Transactions on Smart Grid, 4(3):1391-1400. https://doi.org/10.1109/TSG.2013.2251018