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Article

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

1
Department of Electrical Engineering, Amirkabir University of Technology, Tehran 159163-4311, Iran
2
Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran 196976-4499, Iran
3
Cross Labs, Cross-Compass Ltd., Tokyo 104-0045, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Ron Zevenhoven
Energies 2021, 14(23), 8035; https://doi.org/10.3390/en14238035
Received: 22 October 2021 / Revised: 18 November 2021 / Accepted: 19 November 2021 / Published: 1 December 2021
(This article belongs to the Special Issue Power System Planning and Quality Control)
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. View Full-Text
Keywords: bidirectional LSTM; deep learning; generation expansion planning (GEP); lifetime; planning; power system bidirectional LSTM; deep learning; generation expansion planning (GEP); lifetime; planning; power system
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MDPI and ACS Style

Dehghani, M.; Taghipour, M.; Sadeghi Gougheri, S.; Nikoofard, A.; Gharehpetian, G.B.; Khosravy, M. A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime. Energies 2021, 14, 8035. https://doi.org/10.3390/en14238035

AMA Style

Dehghani M, Taghipour M, Sadeghi Gougheri S, Nikoofard A, Gharehpetian GB, Khosravy M. A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime. Energies. 2021; 14(23):8035. https://doi.org/10.3390/en14238035

Chicago/Turabian Style

Dehghani, Majid, Mohammad Taghipour, Saleh Sadeghi Gougheri, Amirhossein Nikoofard, Gevork B. Gharehpetian, and Mahdi Khosravy. 2021. "A Deep Learning-Based Approach for Generation Expansion Planning Considering Power Plants Lifetime" Energies 14, no. 23: 8035. https://doi.org/10.3390/en14238035

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