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Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Zafar Iqbal & Nadeem Javaid & Syed Muhammad Mohsin & Syed Muhammad Abrar Akber & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling," Energies, MDPI, vol. 11(10), pages 1-31, October.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2861-:d:177498
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    References listed on IDEAS

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    1. Muhammad Babar Rasheed & Nadeem Javaid & Ashfaq Ahmad & Mohsin Jamil & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh, 2016. "Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing," Energies, MDPI, vol. 9(8), pages 1-25, July.
    2. Zafar Iqbal & Nadeem Javaid & Saleem Iqbal & Sheraz Aslam & Zahoor Ali Khan & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2018. "A Domestic Microgrid with Optimized Home Energy Management System," Energies, MDPI, vol. 11(4), pages 1-39, April.
    3. Nadeem Javaid & Sardar Mehboob Hussain & Ibrar Ullah & Muhammad Asim Noor & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations," Energies, MDPI, vol. 10(8), pages 1-29, August.
    4. Muhammad Babar Rasheed & Nadeem Javaid & Muhammad Awais & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh & Zafar Iqbal & Qaisar Javaid, 2016. "Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes," Energies, MDPI, vol. 9(7), pages 1-30, July.
    5. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    6. Danish Mahmood & Nadeem Javaid & Nabil Alrajeh & Zahoor Ali Khan & Umar Qasim & Imran Ahmed & Manzoor Ilahi, 2016. "Realistic Scheduling Mechanism for Smart Homes," Energies, MDPI, vol. 9(3), pages 1-28, March.
    7. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
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    Cited by:

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    2. Zubair Khalid & Ghulam Abbas & Muhammad Awais & Thamer Alquthami & Muhammad Babar Rasheed, 2020. "A Novel Load Scheduling Mechanism Using Artificial Neural Network Based Customer Profiles in Smart Grid," Energies, MDPI, vol. 13(5), pages 1-23, February.
    3. Syed Muhammad Mohsin & Tahir Maqsood & Sajjad Ahmed Madani, 2022. "Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
    4. Taha Selim Ustun, 2022. "Power Systems Imitate Nature for Improved Performance Use of Nature-Inspired Optimization Techniques," Energies, MDPI, vol. 15(17), pages 1-2, August.
    5. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    6. Makhadmeh, Sharif Naser & Khader, Ahamad Tajudin & Al-Betar, Mohammed Azmi & Naim, Syibrah & Abasi, Ammar Kamal & Alyasseri, Zaid Abdi Alkareem, 2019. "Optimization methods for power scheduling problems in smart home: Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    7. Lorenzo Bartolucci & Stefano Cordiner & Vincenzo Mulone & Marina Santarelli, 2019. "Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems," Energies, MDPI, vol. 12(12), pages 1-18, June.

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