Co-planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

被引:0
|
作者
Zare Ghaleh Seyyedi, Abbas [1 ]
Mahmoudi Rashid, Sara [2 ]
Akbari, Ehsan [3 ]
Nejati, Seyed Ashkan [4 ]
Khalafian, Farshad [5 ]
Siano, Pierluigi [6 ,7 ]
机构
[1] Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
[2] Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
[3] Department of Electrical Engineering, Mazandaran University of Science and Technology, Babol, Iran
[4] School of Engineering, Newcastle University, Newcastle, United Kingdom
[5] Department of Electrical Engineering, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran
[6] Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, Italy
[7] Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, South Africa
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Economics - Electric power transmission - Expansion - Investments - Meteorology - Stochastic models - Stochastic programming;
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学科分类号
摘要
Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient-constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching-learning-based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6-Bus and IEEE 89-Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network. © 2022 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
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页码:4830 / 4845
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