Research on Robust Optimal Power Flow of VSC-MTDC AC / DC Uncertain System

DOI

10.2991/anit-17.2018.18How to use a DOI?

Keywords

DE-PCPDIPM, long timescale, robust optimization, uncertainty, VSC-MTDC, wind power accommodation

Abstract

For the purpose of large-scale wind power accommodation in a voltage source converter based multi-terminal high voltage direct current (VSC-MTDC) AC/DC system, this study proposed a robust optimal power flow strategy for VSC-MTDC AC/DC uncertain systems. The strategy is suitable for long timescale application while considering the uncertainty in the power production of wind farms. The optimization mathematical model was established using cassette uncertainty set, which takes economy and security metrics into account. The economically optimal decision on limit maximum conservative solutions for all uncertain sets is obtained using the Differential Evolution and Predictor-Corrector Primal-Dual Interior Point Method (DE-PCPDIPM) hybrid optimization algorithm. Wind power transmission efficiency and effectiveness in MTDC is improved by the op-timization of VSC control command value and cooperative control among converter stations. The study also proposed to alleviate wind power accommodation problem effectively through coordi-nated optimization with AC power grid dispatching. The effectiveness of the robust optimization strategy was verified on the IEEE 14-bus system, and the effect of parameter uncertainty range on the robust optimization decision is analyzed. The results showed the improvement in economy op-eration of the VSC-MTDC AC/DC uncertain system.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhongjian Kang
AU  - Yao Chen
AU  - Yan Li
PY  - 2017/12
DA  - 2017/12
TI  - Research on Robust Optimal Power Flow of VSC-MTDC AC / DC Uncertain System
BT  - Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
PB  - Atlantis Press
SP  - 101
EP  - 112
SN  - 1951-6851
UR  - https://doi.org/10.2991/anit-17.2018.18
DO  - 10.2991/anit-17.2018.18
ID  - Kang2017/12
ER  -