Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-10-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/10/2861 |
| _version_ | 1798004015241363456 |
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| author | Zafar Iqbal Nadeem Javaid Syed Muhammad Mohsin Syed Muhammad Abrar Akber Muhammad Khalil Afzal Farruh Ishmanov |
| author_facet | Zafar Iqbal Nadeem Javaid Syed Muhammad Mohsin Syed Muhammad Abrar Akber Muhammad Khalil Afzal Farruh Ishmanov |
| author_sort | Zafar Iqbal |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-11T12:16:52Z |
| format | Article |
| id | doaj.art-27df0b9aeba244a0afaf2d029fb680c8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:16:52Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-27df0b9aeba244a0afaf2d029fb680c82022-12-22T04:24:15ZengMDPI AGEnergies1996-10732018-10-011110286110.3390/en11102861en11102861Performance Analysis of Hybridization of Heuristic Techniques for Residential Load SchedulingZafar Iqbal0Nadeem Javaid1Syed Muhammad Mohsin2Syed Muhammad Abrar Akber3Muhammad Khalil Afzal4Farruh Ishmanov5Department of Computer Science, PMAS Arid Agriculture University, Rawalpindi 46000, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad 44000, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad 44000, PakistanSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, PakistanDepartment of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, KoreaWith 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%.http://www.mdpi.com/1996-1073/11/10/2861demand side managementload schedulinghome energy management systemoptimization techniques |
| spellingShingle | Zafar Iqbal Nadeem Javaid Syed Muhammad Mohsin Syed Muhammad Abrar Akber Muhammad Khalil Afzal Farruh Ishmanov Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling Energies demand side management load scheduling home energy management system optimization techniques |
| title | Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling |
| title_full | Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling |
| title_fullStr | Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling |
| title_full_unstemmed | Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling |
| title_short | Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling |
| title_sort | performance analysis of hybridization of heuristic techniques for residential load scheduling |
| topic | demand side management load scheduling home energy management system optimization techniques |
| url | http://www.mdpi.com/1996-1073/11/10/2861 |
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