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An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch

Author

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  • Shehab Al-Sakkaf

    (Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Mahmoud Kassas

    (Electrical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Muhammad Khalid

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

  • Mohammad A. Abido

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

Abstract

This work presents the operation of an autonomous direct current (DC) DC microgrid for residential house controlled by an energy management system based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supply house load demand. The microgrid consists of photovoltaic (PV), wind turbine, fuel cell, battery energy storage and diesel generator. The size of the battery energy storage is determined based on the battery sizing algorithm depending on the generation of renewables during all seasons of the year in the eastern region of Saudi Arabia. Two scenarios are considered in this work. In the first scenario: the microgrid consists of solar PV, wind turbine, battery energy storage and fuel cell. The fuzzy logic controller is optimized using an artificial bee colony technique in order to increase the system energy saving efficiency and to reduce the cost. In the second scenario: wind turbine is replaced by a diesel generator, also the rated power of the fuel cell is reduced. In this scenario, a new method is proposed to reduce the generation cost of the dispatchable sources in the microgrid by considering economic dispatch within the optimized fuzzy logic energy management system. To obtain the most suitable technique for solving the economic dispatch problem, three optimization techniques were used which are particle swarm optimization, genetic algorithm and artificial bee colony based on real environmental data and real house load demand. A comparison in terms of energy saving between the two scenarios and a comparison in terms of cost reduction between conventional economic dispatch method and the proposed method are presented.

Suggested Citation

  • Shehab Al-Sakkaf & Mahmoud Kassas & Muhammad Khalid & Mohammad A. Abido, 2019. "An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch," Energies, MDPI, vol. 12(8), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1457-:d:223604
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    References listed on IDEAS

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    Cited by:

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    9. Md Jahidur Rahman & Tahar Tafticht & Mamadou Lamine Doumbia & Ntumba Marc-Alain Mutombo, 2021. "Dynamic Stability of Wind Power Flow and Network Frequency for a High Penetration Wind-Based Energy Storage System Using Fuzzy Logic Controller," Energies, MDPI, vol. 14(14), pages 1-18, July.
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    13. Taha Selim Ustun & S. M. Suhail Hussain, 2019. "Secure Communication Modeling for Microgrid Energy Management System: Development and Application," Energies, MDPI, vol. 13(1), pages 1-14, December.
    14. Simona-Vasilica Oprea & Adela Bâra & Ștefan Preda & Osman Bulent Tor, 2020. "A Smart Adaptive Switching Module Architecture Using Fuzzy Logic for an Efficient Integration of Renewable Energy Sources. A Case Study of a RES System Located in Hulubești, Romania," Sustainability, MDPI, vol. 12(15), pages 1-27, July.
    15. Ali Ahmad & Syed Abdul Rahman Kashif & Arslan Ashraf & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "Coordinated Economic Operation of Hydrothermal Units with HVDC Link Based on Lagrange Multipliers," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    16. Seydali Ferahtia & Ali Djeroui & Tedjani Mesbahi & Azeddine Houari & Samir Zeghlache & Hegazy Rezk & Théophile Paul, 2021. "Optimal Adaptive Gain LQR-Based Energy Management Strategy for Battery–Supercapacitor Hybrid Power System," Energies, MDPI, vol. 14(6), pages 1-16, March.
    17. Ahmed Rashwan & Alexey Mikhaylov & Tomonobu Senjyu & Mahdiyeh Eslami & Ashraf M. Hemeida & Dina S. M. Osheba, 2023. "Modified Droop Control for Microgrid Power-Sharing Stability Improvement," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    18. Hegazy Rezk & Rania M. Ghoniem & Seydali Ferahtia & Ahmed Fathy & Mohamed M. Ghoniem & Reem Alkanhel, 2022. "A Comparison of Different Renewable-Based DC Microgrid Energy Management Strategies for Commercial Buildings Applications," Sustainability, MDPI, vol. 14(24), pages 1-22, December.
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    20. Rodriguez, Mauricio & Arcos–Aviles, Diego & Martinez, Wilmar, 2023. "Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms," Applied Energy, Elsevier, vol. 335(C).

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