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Probabilistic transmission expansion planning to maximize the integration of wind power

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  • Jadidoleslam, Morteza
  • Ebrahimi, Akbar
  • Latify, Mohammad Amin

Abstract

In many electricity markets, wind power producers are paid by the Locational Marginal Price (LMP) of the bus where they are located. Therefore, transmission network and its future expansion plans can play a determinative role in revenue and profitability of a wind power project. This paper aims to exploit this potential of the transmission expansion planning (TEP) studies which can lead to private investment absorption for development of the wind power. To this end, a framework for transmission and wind power expansion planning is developed and modeled as a stochastic bi-level optimization problem. The upper-level problem represents wind power and transmission investment decisions. The lower-level comprises two optimization problems based on the optimal power flow including market clearing and reliability assessment of the bulk system. An approach based on the multi-objective shuffled frog leaping algorithm is proposed to cope with the multi-objective, bi-level, and non-linear nature of the model. The feasibility and effectiveness of the proposed methodology is demonstrated in the IEEE-RTS test system. The obtained results show that adoption a proper strategy for TEP can lead to more private investment absorption in wind power without a significant additional, even with a lower, transmission investment cost.

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  • Jadidoleslam, Morteza & Ebrahimi, Akbar & Latify, Mohammad Amin, 2017. "Probabilistic transmission expansion planning to maximize the integration of wind power," Renewable Energy, Elsevier, vol. 114(PB), pages 866-878.
    • Handle: RePEc:eee:renene:v:114:y:2017:i:pb:p:866-878
    DOI: 10.1016/j.renene.2017.07.063
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    5. Xie, Shiwei & Hu, Zhijian & Zhou, Daming & Li, Yan & Kong, Shunfei & Lin, Weiwei & Zheng, Yunfei, 2018. "Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network," Applied Energy, Elsevier, vol. 219(C), pages 207-225.
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