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An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes

Author

Listed:
  • Omaji Samuel

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Sakeena Javaid

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Nadeem Javaid

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Syed Hassan Ahmed

    (Department of Computer Science, Georgia Southern University, Statesboro, GA 30460, USA)

  • 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

Presently, the advancements in the electric system, smart meters, and implementation of renewable energy sources (RES) have yielded extensive changes to the current power grid. This technological innovation in the power grid enhances the generation of electricity to meet the demands of industrial, commercial and residential sectors. However, the industrial sectors are the focus of power grid and its demand-side management (DSM) activities. Neglecting other sectors in the DSM activities can deteriorate the total performance of the power grid. Hence, the notion of DSM and demand response by way of the residential sector makes the smart grid preferable to the current power grid. In this circumstance, this paper proposes a home energy management system (HEMS) that considered the residential sector in DSM activities and the integration of RES and energy storage system (ESS). The proposed HEMS reduces the electricity cost through scheduling of household appliances and ESS in response to the time-of-use (ToU) and critical peak price (CPP) of the electricity market. The proposed HEMS is implemented using the Earliglow based algorithm. For comparative analysis, the simulation results of the proposed method are compared with other methods: Jaya algorithm, enhanced differential evolution and strawberry algorithm. The simulation results of Earliglow based optimization method show that the integration of RES and ESS can provide electricity cost savings up to 62.80% and 20.89% for CPP and ToU. In addition, electricity cost reduction up to 43.25% and 13.83% under the CPP and ToU market prices, respectively.

Suggested Citation

  • Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3155-:d:182840
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    References listed on IDEAS

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    Cited by:

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    2. Christodoulos Spagkakas & Dimitrios Stimoniaris & Dimitrios Tsiamitros, 2023. "Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm," Energies, MDPI, vol. 16(19), pages 1-17, September.
    3. Sung-Ho Park & Akhtar Hussain & Hak-Man Kim, 2019. "Impact Analysis of Survivability-Oriented Demand Response on Islanded Operation of Networked Microgrids with High Penetration of Renewables," Energies, MDPI, vol. 12(3), pages 1-22, January.
    4. Waqas Ahmad & Nasir Ayub & Tariq Ali & Muhammad Irfan & Muhammad Awais & Muhammad Shiraz & Adam Glowacz, 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine," Energies, MDPI, vol. 13(11), pages 1-17, June.
    5. Herie Park, 2020. "Human Comfort-Based-Home Energy Management for Demand Response Participation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    6. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    7. Christian Pfeiffer & Markus Puchegger & Claudia Maier & Ina V. Tomaschitz & Thomas P. Kremsner & Lukas Gnam, 2020. "A Case Study of Socially-Accepted Potentials for the Use of End User Flexibility by Home Energy Management Systems," Sustainability, MDPI, vol. 13(1), pages 1-19, December.
    8. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    9. Doğukan Aycı & Ferhat Öğüt & Ulaş Özen & Bora Batuhan İşgör & Sinan Küfeoğlu, 2021. "Energy Optimisation Models for Self-Sufficiency of a Typical Turkish Residential Electricity Customer of the Future," Energies, MDPI, vol. 14(19), pages 1-24, September.
    10. Cheng-Ta Tsai & Yu-Shan Cheng & Kuen-Huei Lin & Chun-Lung Chen, 2021. "Effects of a Battery Energy Storage System on the Operating Schedule of a Renewable Energy-Based Time-of-Use Rate Industrial User under the Demand Bidding Mechanism of Taipower," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    11. Adamu Sani Yahaya & Nadeem Javaid & Fahad A. Alzahrani & Amjad Rehman & Ibrar Ullah & Affaf Shahid & Muhammad Shafiq, 2020. "Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism," Sustainability, MDPI, vol. 12(8), pages 1-28, April.

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