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Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources

Author

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  • Xiangyu Kong

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Jingtao Yao

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

  • Zhijun E

    (State Grid Tianjin Electric Power Company, Tianjin 300010, China)

  • Xin Wang

    (State Grid Tianjin Electric Power Company, Tianjin 300010, China)

Abstract

In generation expansion planning, sustainable generation expansion planning is gaining more and more attention. Based on the comprehensive consideration of generation expansion planning economics, technology, environment, and other fields, this paper analyzes the sustainable development of power supply planning evaluation indicators and builds a multi-objective generation expansion planning decision model considering sustainable development. According to the target variables in the model, the variables such as attribute variables are divided into different subsets, and the logical relationship analysis method between different nodes is obtained based on Dynamic Bayesian network theory, which reduces the complexity of the planning model problem. The application examples show the feasibility and effectiveness of the proposed model and the solution method.

Suggested Citation

  • Xiangyu Kong & Jingtao Yao & Zhijun E & Xin Wang, 2019. "Generation Expansion Planning Based on Dynamic Bayesian Network Considering the Uncertainty of Renewable Energy Resources," Energies, MDPI, vol. 12(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2492-:d:243734
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    References listed on IDEAS

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    Cited by:

    1. Seyed Hamed Jalalzad & 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.
    2. 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.

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