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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

With the increasing integration of HVDC tie-lines, the regional power systems in both the energy-exporting area and the energy-importing area have been gradually evolving into “strong DC, weak AC” systems. In this paper, a multi-period transmission expansion planning optimization model is proposed for an energy-exporting power grid with hybrid AC/DC interface. While the existing literature has not considered the dynamic security problem in TEP, this paper adopts the conventional total transfer capacity (TTC) index to evaluate the security limit of hybrid AC/DC interface under different transmission expansion schemes. Multiple objectives are considered to reduce the investment cost while promoting the consumption of renewables by enhancing the total transfer capacity of hybrid AC/DC interface. The non-dominated sorting genetic algorithm-II (NSGA-II) is used to compute the optimal solution for the proposed multi-period multi-objective transmission expansion planning problem. A case study on the modified IEEE 39-bus system is presented to demonstrate the effectiveness of the proposed method.

Details

Title
Multi-Period Transmission Expansion Planning for Renewables-Rich Power Grid Enabling Transfer Capacity Enhancement of Hybrid AC/DC Interface
Author
Shen, Li 1 ; Jiang, Li 1 ; Wang, Qing 1 ; Wen, Yiyu 1 ; Liu, Tingjian 2 

 State Grid Southwest Branch Corporation, State Grid Corporation of China, Chengdu 610095, China 
 College of Electrical Engineering, Sichuan University, Chengdu 610065, China 
First page
2170
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
19961073
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2785193931
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.