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A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources

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  • Mansouri, S.A.
  • Ahmarinejad, A.
  • Nematbakhsh, E.
  • Javadi, M.S.
  • Esmaeel Nezhad, A.
  • Catalão, J.P.S.

Abstract

This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration.

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  • Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001311
    DOI: 10.1016/j.energy.2022.123228
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    6. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).
    7. Kermani, Mostafa & Chen, Peiyuan & Göransson, Lisa & Bongiorno, Massimo, 2022. "A comprehensive optimal energy control in interconnected microgrids through multiport converter under N−1 criterion and demand response program," Renewable Energy, Elsevier, vol. 199(C), pages 957-976.
    8. Lu, Xi & Xia, Shiwei & Gu, Wei & Chan, Ka Wing, 2022. "A model for balance responsible distribution systems with energy storage to achieve coordinated load shifting and uncertainty mitigation," Energy, Elsevier, vol. 249(C).
    9. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2022. "A Stochastic-IGDT model for energy management in isolated microgrids considering failures and demand response," Applied Energy, Elsevier, vol. 317(C).
    10. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).
    11. Cristina Sousa & Evaldo Costa, 2022. "Types of Policies for the Joint Diffusion of Electric Vehicles with Renewable Energies and Their Use Worldwide," Energies, MDPI, vol. 15(20), pages 1-19, October.
    12. Mohammed M. Alhaider & Ziad M. Ali & Mostafa H. Mostafa & Shady H. E. Abdel Aleem, 2023. "Economic Viability of NaS Batteries for Optimal Microgrid Operation and Hosting Capacity Enhancement under Uncertain Conditions," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
    13. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    14. Tong, Ziqiang & Mansouri, Seyed Amir & Huang, Shoujun & Rezaee Jordehi, Ahmad & Tostado-Véliz, Marcos, 2023. "The role of smart communities integrated with renewable energy resources, smart homes and electric vehicles in providing ancillary services: A tri-stage optimization mechanism," Applied Energy, Elsevier, vol. 351(C).
    15. Silva, Jéssica Alice A. & López, Juan Camilo & Guzman, Cindy Paola & Arias, Nataly Bañol & Rider, Marcos J. & da Silva, Luiz C.P., 2023. "An IoT-based energy management system for AC microgrids with grid and security constraints," Applied Energy, Elsevier, vol. 337(C).

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