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Optimal Transmission Expansion Planning with Long-Term Solar Photovoltaic Generation Forecast

Author

Listed:
  • Siripat Somchit

    (Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Palamy Thongbouasy

    (Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Chitchai Srithapon

    (Department of Electrical Engineering, KTH Royal Institute of Technology, 11428 Stockholm, Sweden)

  • Rongrit Chatthaworn

    (Department of Electrical Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

Solar PhotoVoltaics (PV) integration into the electricity grids significantly increases the complexity of Transmission Expansion Planning (TEP) because solar PV power generation is uncertain and difficult to predict. Therefore, this paper proposes the optimal planning method for transmission expansion combined with uncertain solar PV generation. The problem of uncertain solar PV generation is solved by using Long Short-Term Memory (LSTM) for forecasting solar radiation with high accuracy. The objective function is to minimize total system cost, including the investment cost of new transmission lines and the operating cost of power generation. The optimal TEP problem is solved by the Binary Differential Evolution (BDE) algorithm. To investigate and demonstrate the performance of the proposed method, the IEEE 24-bus system and solar radiation data in Thailand are selected as a study case for TEP. The MATPOWER program written in MATLAB software is used for solving optimal power flow problems. Simulation results show that the proposed optimal TEP method combined with forecasting solar PV power generation using the LSTM can reduce the total system cost of the transmission expansion by 9.12% compared with the cost obtained by the TEP using solar radiation from statistical data.

Suggested Citation

  • Siripat Somchit & Palamy Thongbouasy & Chitchai Srithapon & Rongrit Chatthaworn, 2023. "Optimal Transmission Expansion Planning with Long-Term Solar Photovoltaic Generation Forecast," Energies, MDPI, vol. 16(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1719-:d:1062753
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    References listed on IDEAS

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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    3. Hamdi Abdi & Mansour Moradi & Sara Lumbreras, 2021. "Metaheuristics and Transmission Expansion Planning: A Comparative Case Study," Energies, MDPI, vol. 14(12), pages 1-23, June.
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