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Optimal power management of dependent microgrid considering distribution market and unused power capacity

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  • MansourLakouraj, Mohammad
  • Shahabi, Majid
  • Shafie-khah, Miadreza
  • Ghoreishi, Niloofar
  • Catalão, João P.S.

Abstract

This study presents an optimal power management for a microgrid (MG) in distribution market environment. The MG operator is able to have interactions with the distribution market operator (DMO) and adjacent MG (AMG) operator to supply its local loads. The DMO regulates the electricity market, and assigns the electricity price and power profile for the MG. The motivation behind the use of The DMO is that it works as an entity between MG and independent system operator (ISO) in order to guarantee a flexible operation by reducing power fluctuation and reduce the unintentional peak loads in low market price hours. The unused power capacity in AMG is used during the islanded hours through an additional interconnection point connected to the MG. Using this method reduces the need for installing new generation resources, which will be a practical and economical solution for MG developer. This MG which is able to provide power from distribution market and the AMG with two interconnection points is named dependent MG (DMG). A market-based stochastic model containing distribution market constraints and AC power flow formulations employs conditional value at risk (CVaR) methodology to capture the loads and wind uncertainties.

Suggested Citation

  • MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Ghoreishi, Niloofar & Catalão, João P.S., 2020. "Optimal power management of dependent microgrid considering distribution market and unused power capacity," Energy, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:energy:v:200:y:2020:i:c:s0360544220306587
    DOI: 10.1016/j.energy.2020.117551
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    References listed on IDEAS

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    1. Shams, Mohammad H. & Shahabi, Majid & Khodayar, Mohammad E., 2018. "Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response," Energy, Elsevier, vol. 155(C), pages 326-338.
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    5. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
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    Cited by:

    1. Saeian, Hosein & Niknam, Taher & Zare, Mohsen & Aghaei, Jamshid, 2022. "Coordinated optimal bidding strategies methods of aggregated microgrids: A game theory-based demand side management under an electricity market environment," Energy, Elsevier, vol. 245(C).
    2. Zandrazavi, Seyed Farhad & Guzman, Cindy Paola & Pozos, Alejandra Tabares & Quiros-Tortos, Jairo & Franco, John Fredy, 2022. "Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles," Energy, Elsevier, vol. 241(C).
    3. Shiduo Jia & Xiaoning Kang, 2022. "Multi-Objective Optimal Scheduling of CHP Microgrid Considering Conditional Value-at-Risk," Energies, MDPI, vol. 15(9), pages 1-21, May.
    4. MansourLakouraj, Mohammad & Shahabi, Majid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal market-based operation of microgrid with the integration of wind turbines, energy storage system and demand response resources," Energy, Elsevier, vol. 239(PB).

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