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Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy

Author

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  • Haesum Ali

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea)

  • Akhtar Hussain

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

  • Van-Hai Bui

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea)

  • Jinhong Jeon

    (Division of Smart Distribution Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea)

  • Hak-Man Kim

    (Department of Electrical Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406840, Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Korea)

Abstract

Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks.

Suggested Citation

  • Haesum Ali & Akhtar Hussain & Van-Hai Bui & Jinhong Jeon & Hak-Man Kim, 2019. "Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy," Energies, MDPI, vol. 12(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3701-:d:271570
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    References listed on IDEAS

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    1. 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.
    2. Van-Hai Bui & Akhtar Hussain & Hak-Man Kim, 2017. "Diffusion Strategy-Based Distributed Operation of Microgrids Using Multiagent System," Energies, MDPI, vol. 10(7), pages 1-21, July.
    3. Hee-Jun Cha & Dong-Jun Won & Sang-Hyuk Kim & Il-Yop Chung & Byung-Moon Han, 2015. "Multi-Agent System-Based Microgrid Operation Strategy for Demand Response," Energies, MDPI, vol. 8(12), pages 1-15, December.
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    Cited by:

    1. Asfand Yar Ali & Akhtar Hussain & Ju-Won Baek & Hak-Man Kim, 2020. "Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles," Energies, MDPI, vol. 14(1), pages 1-20, December.
    2. Tostado-Véliz, Marcos & Kamel, Salah & Hasanien, Hany M. & Turky, Rania A. & Jurado, Francisco, 2022. "Uncertainty-aware day-ahead scheduling of microgrids considering response fatigue: An IGDT approach," Applied Energy, Elsevier, vol. 310(C).
    3. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    4. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    5. Costa, Vinicius B.F. & Bonatto, Benedito D. & Silva, Patrícia F., 2022. "Optimizing Brazil's regulated electricity market in the context of time-of-use rates and prosumers with energy storage systems," Utilities Policy, Elsevier, vol. 79(C).

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