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Microgrid Assisted Design for Remote Areas

Author

Listed:
  • Guodong Liu

    (Grid Components & Control Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Zhi Li

    (Grid Components & Control Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Yaosuo Xue

    (Grid Components & Control Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Kevin Tomsovic

    (Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA)

Abstract

In this work, we present a three-stage multiobjective mixed-integer linear programming (MILP) for the optimal expansion planning and operation of isolated multienergy microgrids in remote areas. By selecting the optimal distributed generators (DGs) and energy storage systems (ESSs) mix selection, siting, sizing, and scheduling in the remote microgrid, the proposed model is targeted to minimize the annualized total cost of microgrids while enhancing the performance of the system, i.e., minimizing the voltage deviations and line power loss. To represent the electricity and heat flow between generation resources and various electrical, heating, and cooling loads in the isolated microgrid, linearized power flow, and heat flow constraints are employed in the proposed optimization model. The available capacity of DGs and ESSs are modeled as discrete constants instead of continuous variables for practical purpose. Numerical simulation results on a remote microgrid consisting of DGs, ESSs, and various loads validate the proposed method.

Suggested Citation

  • Guodong Liu & Zhi Li & Yaosuo Xue & Kevin Tomsovic, 2022. "Microgrid Assisted Design for Remote Areas," Energies, MDPI, vol. 15(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3725-:d:819068
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    References listed on IDEAS

    as
    1. Kaab, Ali & Sharifi, Mohammad & Mobli, Hossein & Nabavi-Pelesaraei, Ashkan & Chau, Kwok-wing, 2019. "Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production," Energy, Elsevier, vol. 181(C), pages 1298-1320.
    2. Elianne Mora & Jenny Cifuentes & Geovanny Marulanda, 2021. "Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks," Energies, MDPI, vol. 14(23), pages 1-26, November.
    3. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    4. Askarzadeh, Alireza, 2017. "Distribution generation by photovoltaic and diesel generator systems: Energy management and size optimization by a new approach for a stand-alone application," Energy, Elsevier, vol. 122(C), pages 542-551.
    5. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    6. Hakimi, Seyed Mehdi & Hasankhani, Arezoo & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market," Applied Energy, Elsevier, vol. 298(C).
    7. Ignacio Herrero & Pablo Rodilla & Carlos Batlle, 2020. "Evolving Bidding Formats and Pricing Schemes in USA and Europe Day-Ahead Electricity Markets," Energies, MDPI, vol. 13(19), pages 1-21, September.
    8. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2019. "Optimal operational planning of scalable DC microgrid with demand response, islanding, and battery degradation cost considerations," Applied Energy, Elsevier, vol. 237(C), pages 695-707.
    9. Ning Xin & Laijun Chen & Linrui Ma & Yang Si, 2022. "A Rolling Horizon Optimization Framework for Resilient Restoration of Active Distribution Systems," Energies, MDPI, vol. 15(9), pages 1-14, April.
    10. Michael Stadler & Zack Pecenak & Patrick Mathiesen & Kelsey Fahy & Jan Kleissl, 2020. "Performance Comparison between Two Established Microgrid Planning MILP Methodologies Tested On 13 Microgrid Projects," Energies, MDPI, vol. 13(17), pages 1-24, August.
    11. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    12. Liu, Guodong & Jiang, Tao & Ollis, Thomas B. & Zhang, Xiaohu & Tomsovic, Kevin, 2019. "Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics," Applied Energy, Elsevier, vol. 239(C), pages 83-95.
    13. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    14. Hasankhani, Arezoo & Hakimi, Seyed Mehdi, 2021. "Stochastic energy management of smart microgrid with intermittent renewable energy resources in electricity market," Energy, Elsevier, vol. 219(C).
    15. Muhammad Zahid Khan & Chaoxu Mu & Salman Habib & Waleed Alhosaini & Emad M. Ahmed, 2021. "An Enhanced Distributed Voltage Regulation Scheme for Radial Feeder in Islanded Microgrid," Energies, MDPI, vol. 14(19), pages 1-19, September.
    16. Wu, Di & Ma, Xu & Huang, Sen & Fu, Tao & Balducci, Patrick, 2020. "Stochastic optimal sizing of distributed energy resources for a cost-effective and resilient Microgrid," Energy, Elsevier, vol. 198(C).
    17. Ruiming Liu & Shengtie Wang & Guangchen Liu & Sufang Wen & Jianwei Zhang & Yuechao Ma, 2022. "An Improved Virtual Inertia Control Strategy for Low Voltage AC Microgrids with Hybrid Energy Storage Systems," Energies, MDPI, vol. 15(2), pages 1-21, January.
    18. Rahmat Khezri & Amin Mahmoudi & Hirohisa Aki & S. M. Muyeen, 2021. "Optimal Planning of Remote Area Electricity Supply Systems: Comprehensive Review, Recent Developments and Future Scopes," Energies, MDPI, vol. 14(18), pages 1-29, September.
    19. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2021. "A novel genetic LSTM model for wind power forecast," Energy, Elsevier, vol. 223(C).
    20. Wang, Yi & Rousis, Anastasios Oulis & Strbac, Goran, 2020. "On microgrids and resilience: A comprehensive review on modeling and operational strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    21. Yu, Jiah & Ryu, Jun-Hyung & Lee, In-beum, 2019. "A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system," Applied Energy, Elsevier, vol. 247(C), pages 212-220.
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    3. Marvin Lema & Wilson Pavon & Leony Ortiz & Ama Baduba Asiedu-Asante & Silvio Simani, 2022. "Controller Coordination Strategy for DC Microgrid Using Distributed Predictive Control Improving Voltage Stability," Energies, MDPI, vol. 15(15), pages 1-15, July.

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