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Optimizing the Size of Autonomous Hybrid Microgrids with Regard to Load Shifting

Author

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  • Alexander Lavrik

    (Department of Electrical Engineering, Saint Petersburg Mining University, 2 21st Line, 199106 Saint Petersburg, Russia)

  • Yuri Zhukovskiy

    (Department of Electrical Engineering, Saint Petersburg Mining University, 2 21st Line, 199106 Saint Petersburg, Russia)

  • Pavel Tcvetkov

    (Department of Economics, Organization and Management, Saint Petersburg Mining University, 2 21st Line, 199106 Saint Petersburg, Russia)

Abstract

The article proposes a method of multipurpose optimization of the size of an autonomous hybrid energy system consisting of photovoltaic, wind, diesel, and battery energy storage systems, and including a load-shifting system. The classical iterative Gauss–Seidel method was applied to optimize the size of a hybrid energy system in a remote settlement on Sakhalin Island. As a result of the optimization according to the minimum net present value criterion, several optimal configurations corresponding to different component combinations were obtained. Several optimal configurations were also found, subject to a payback period constraint of 5, 6, and 7 years. Optimizing the size of the hybrid power system with electric load shifting showed that the share of the load not covered by renewable energy sources decreases by 1.25% and 2.1%, depending on the parameters of the load shifting model. Net present cost and payback period also decreased, other technical and economic indicators improved; however, CO 2 emissions increased due to the reduction in the energy storage system.

Suggested Citation

  • Alexander Lavrik & Yuri Zhukovskiy & Pavel Tcvetkov, 2021. "Optimizing the Size of Autonomous Hybrid Microgrids with Regard to Load Shifting," Energies, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5059-:d:616289
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    References listed on IDEAS

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    Cited by:

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    3. Elena Sosnina & Andrey Dar’enkov & Andrey Kurkin & Ivan Lipuzhin & Andrey Mamonov, 2022. "Review of Efficiency Improvement Technologies of Wind Diesel Hybrid Systems for Decreasing Fuel Consumption," Energies, MDPI, vol. 16(1), pages 1-38, December.
    4. Ali M. Eltamaly & Zeyad A. Almutairi & Mohamed A. Abdelhamid, 2023. "Modern Optimization Algorithm for Improved Performance of Maximum Power Point Tracker of Partially Shaded PV Systems," Energies, MDPI, vol. 16(13), pages 1-22, July.
    5. Pavel Ilyushin & Aleksandr Kulikov & Konstantin Suslov & Sergey Filippov, 2022. "An Approach to Assessing Spatial Coherence of Current and Voltage Signals in Electrical Networks," Mathematics, MDPI, vol. 10(10), pages 1-15, May.
    6. Pannee Suanpang & Pattanaphong Pothipassa & Kittisak Jermsittiparsert & Titiya Netwong, 2022. "Integration of Kouprey-Inspired Optimization Algorithms with Smart Energy Nodes for Sustainable Energy Management of Agricultural Orchards," Energies, MDPI, vol. 15(8), pages 1-18, April.
    7. Aleksandr Skamyin & Yaroslav Shklyarskiy & Vasiliy Dobush & Iuliia Dobush, 2021. "Experimental Determination of Parameters of Nonlinear Electrical Load," Energies, MDPI, vol. 14(22), pages 1-14, November.

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