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A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime

Author

Listed:
  • Majid Dehghani

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran)

  • Mohammad Taghipour

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran)

  • Saleh Sadeghi Gougheri

    (Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran)

  • Amirhossein Nikoofard

    (Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran)

  • Gevork B. Gharehpetian

    (Department of Electrical Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran)

  • Mahdi Khosravy

    (Cross Labs, Cross-Compass Ltd., Tokyo 104-0045, Japan)

Abstract

In Generation Expansion Planning (GEP), the power plants lifetime is one of the most important factors which to the best knowledge of the authors, has not been investigated in the literature. In this article, the power plants lifetime effect on GEP is investigated. In addition, the deep learning-based approaches are widely used for time series forecasting. Therefore, a new version of Long short-term memory (LSTM) networks known as Bi-directional LSTM (BLSTM) networks are used in this paper to forecast annual peak load of the power system. For carbon emissions, the cost of carbon is considered as the penalty of pollution in the objective function. The proposed approach is evaluated by a test network and then applied to Iran power system as a large-scale grid. The simulations by GAMS (General Algebraic Modeling System, Washington, DC, USA) software show that due to consideration of lifetime as a constraint, the total cost of the GEP problem decreases by 5.28% and 7.9% for the test system and Iran power system, respectively.

Suggested Citation

  • Majid Dehghani & Mohammad Taghipour & Saleh Sadeghi Gougheri & Amirhossein Nikoofard & Gevork B. Gharehpetian & Mahdi Khosravy, 2021. "A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime," Energies, MDPI, vol. 14(23), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8035-:d:692849
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    References listed on IDEAS

    as
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    Cited by:

    1. Tomonobu Senjyu & Mahdi Khosravy, 2022. "Power System Planning and Quality Control," Energies, MDPI, vol. 15(14), pages 1-2, July.
    2. Seyed Hamed Jalalzad Mahvizani & Hossein Yektamoghadam & Rouzbeh Haghighi & Majid Dehghani & Amirhossein Nikoofard & Mahdi Khosravy & Tomonobu Senjyu, 2022. "A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy," Energies, MDPI, vol. 15(3), pages 1-16, February.
    3. Bibi Ibrahim & Luis Rabelo & Alfonso T. Sarmiento & Edgar Gutierrez-Franco, 2023. "A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics," Energies, MDPI, vol. 16(13), pages 1-29, July.
    4. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    5. Ali Ahmadian & Kumaraswamy Ponnambalam & Ali Almansoori & Ali Elkamel, 2023. "Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning," Energies, MDPI, vol. 16(2), pages 1-17, January.

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