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Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

Author

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  • Akhtar Hussain

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

  • Van-Hai Bui

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

  • Hak-Man Kim

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

Abstract

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

Suggested Citation

  • Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids," Energies, MDPI, vol. 10(7), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:882-:d:103187
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    References listed on IDEAS

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    8. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2017. "Fuzzy Logic-Based Operation of Battery Energy Storage Systems (BESSs) for Enhancing the Resiliency of Hybrid Microgrids," Energies, MDPI, vol. 10(3), pages 1-19, February.
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    Cited by:

    1. Andrea Mazza & Hamidreza Mirtaheri & Gianfranco Chicco & Angela Russo & Maurizio Fantino, 2019. "Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks," Energies, MDPI, vol. 13(1), pages 1-20, December.
    2. 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.
    3. Anh-Duc Nguyen & Van-Hai Bui & Akhtar Hussain & Duc-Huy Nguyen & Hak-Man Kim, 2018. "Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    5. Daniel Reich & Giovanna Oriti, 2021. "Rightsizing the Design of a Hybrid Microgrid," Energies, MDPI, vol. 14(14), pages 1-22, July.
    6. Akhtar Hussain & Van-Hai Bui & Ju-Won Baek & Hak-Man Kim, 2020. "Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation," Energies, MDPI, vol. 13(1), pages 1-18, January.
    7. Vadim Avkhimenia & Matheus Gemignani & Tim Weis & Petr Musilek, 2022. "Deep Reinforcement Learning-Based Operation of Transmission Battery Storage with Dynamic Thermal Line Rating," Energies, MDPI, vol. 15(23), pages 1-15, November.
    8. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    9. Mahdi Karami Darabi & Hamed Ganjeh Ganjehlou & Amirreza Jafari & Morteza Nazari-Heris & Gevork B. Gharehpetian & Mehrdad Abedi, 2021. "Evaluating the Effect of Demand Response Programs (DRPs) on Robust Optimal Sizing of Islanded Microgrids," Energies, MDPI, vol. 14(18), pages 1-20, September.

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