Research on Robust Optimal Power Flow of VSC-MTDC AC / DC Uncertain System
- Zhongjian Kang, Yao Chen, Yan Li
- Corresponding Author
- Zhongjian Kang
Available Online December 2017.
- https://doi.org/10.2991/anit-17.2018.18How to use a DOI?
- DE-PCPDIPM, long timescale, robust optimization, uncertainty, VSC-MTDC, wind power accommodation
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/anit-17.2018.18 DO - https://doi.org/10.2991/anit-17.2018.18 ID - Kang2017/12 ER -