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Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm

Author

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  • Md Mamun Ur Rashid

    (Department of Electrical & Electronic Engineering, Bangladesh University of Engineering & Technology (BUET), Dhaka 1000, Bangladesh
    Department of Electrical & Electronic Engineering, National Institute of Textile Engineering and Research (NITER), Dhaka 1350, Bangladesh)

  • Majed A. Alotaibi

    (Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abdul Hasib Chowdhury

    (Department of Electrical & Electronic Engineering, Bangladesh University of Engineering & Technology (BUET), Dhaka 1000, Bangladesh)

  • Muaz Rahman

    (Department of Electrical & Electronic Engineering, National Institute of Textile Engineering and Research (NITER), Dhaka 1350, Bangladesh)

  • Md. Shafiul Alam

    (K. A. CARE Energy Research & Innovation Center (ERIC), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Md. Alamgir Hossain

    (School of Engineering & Information Technology, The University of New South Wales, Canberra 2612, Australia
    Department of Electrical & Electronic Engineering, Dhaka University of Engineering and Technology (DUET), Gazipur 1700, Bangladesh)

  • Mohammad A. Abido

    (K. A. CARE Energy Research & Innovation Center (ERIC), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
    Electrical Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

Abstract

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.

Suggested Citation

  • Md Mamun Ur Rashid & Majed A. Alotaibi & Abdul Hasib Chowdhury & Muaz Rahman & Md. Shafiul Alam & Md. Alamgir Hossain & Mohammad A. Abido, 2021. "Home Energy Management for Community Microgrids Using Optimal Power Sharing Algorithm," Energies, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1060-:d:501134
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    References listed on IDEAS

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    Cited by:

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    2. Enas Taha Sayed & Abdul Ghani Olabi & Abdul Hai Alami & Ali Radwan & Ayman Mdallal & Ahmed Rezk & Mohammad Ali Abdelkareem, 2023. "Renewable Energy and Energy Storage Systems," Energies, MDPI, vol. 16(3), pages 1-26, February.
    3. Fahad Saleh Al-Ismail & Md Shafiul Alam & Md Shafiullah & Md Ismail Hossain & Syed Masiur Rahman, 2023. "Impacts of Renewable Energy Generation on Greenhouse Gas Emissions in Saudi Arabia: A Comprehensive Review," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    4. Minseok Jang & Hyun-Cheol Jeong & Taegon Kim & Sung-Kwan Joo, 2021. "Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs," Energies, MDPI, vol. 14(19), pages 1-12, September.
    5. Sylwia Sysko-Romańczuk & Grzegorz Kluj & Liliana Hawrysz & Łukasz Rokicki & Sylwester Robak, 2021. "Scalable Microgrid Process Model: The Results of an Off-Grid Household Experiment," Energies, MDPI, vol. 14(21), pages 1-26, November.
    6. Nur Najihah Abu Bakar & Josep M. Guerrero & Juan C. Vasquez & Najmeh Bazmohammadi & Yun Yu & Abdullah Abusorrah & Yusuf A. Al-Turki, 2021. "A Review of the Conceptualization and Operational Management of Seaport Microgrids on the Shore and Seaside," Energies, MDPI, vol. 14(23), pages 1-31, November.

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