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Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids

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  • Raji Atia

    (Graduate School of Energy and Environment Science, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka 940-2188, Japan)

  • Noboru Yamada

    (Graduate School of Energy and Environment Science, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka 940-2188, Japan)

Abstract

This paper considers the contribution of independent owners (IOs) operating within microgrids (MGs) toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG) and a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for demand response (DR), DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used for testing the algorithm and analyzing the potential for IO/MG investments and their strategies.

Suggested Citation

  • Raji Atia & Noboru Yamada, 2016. "Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids," Energies, MDPI, vol. 9(3), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:176-:d:65378
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    References listed on IDEAS

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    3. Ahmed Alzahrani & Hussain Alharthi & Muhammad Khalid, 2019. "Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics," Energies, MDPI, vol. 13(1), pages 1-16, December.
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    5. Rosa Morales González & Shahab Shariat Torbaghan & Madeleine Gibescu & Sjef Cobben, 2016. "Harnessing the Flexibility of Thermostatic Loads in Microgrids with Solar Power Generation," Energies, MDPI, vol. 9(7), pages 1-24, July.
    6. Liu, Zifa & Chen, Yixiao & Zhuo, Ranqun & Jia, Hongjie, 2018. "Energy storage capacity optimization for autonomy microgrid considering CHP and EV scheduling," Applied Energy, Elsevier, vol. 210(C), pages 1113-1125.
    7. Khalilpour, Kaveh R. & Lusis, Peter, 2020. "Network capacity charge for sustainability and energy equity: A model-based analysis," Applied Energy, Elsevier, vol. 266(C).
    8. Hun-Chul Seo, 2017. "New Configuration and Novel Reclosing Procedure of Distribution System for Utilization of BESS as UPS in Smart Grid," Sustainability, MDPI, vol. 9(4), pages 1-16, March.
    9. Jichun Liu & Zhengbo Chen & Yue Xiang, 2019. "Exploring Economic Criteria for Energy Storage System Sizing," Energies, MDPI, vol. 12(12), pages 1-17, June.
    10. Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    11. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
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