Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

Zafar Iqbal, Nadeem Javaid, Syed Muhammad Mohsin, Syed Muhammad Abrar Akber, Muhammad Khalil Afzal and Farruh Ishmanov
Additional contact information
Zafar Iqbal: Department of Computer Science, PMAS Arid Agriculture University, Rawalpindi 46000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Syed Muhammad Mohsin: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Syed Muhammad Abrar Akber: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Muhammad Khalil Afzal: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
Farruh Ishmanov: Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea

Energies, 2018, vol. 11, issue 10, 1-31

Abstract: With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.

Keywords: demand side management; load scheduling; home energy management system; optimization techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/10/2861/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/10/2861/ (text/html)

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:gam:jeners:v:11:y:2018:i:10:p:2861-:d:177498

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().