Advanced Probabilistic Power Flow Method Using Vine Copulas for Wind Power Capacity Expansion
- 주제(키워드) Wind power generation , Wind speed , Power system stability , Power systems , Renewable energy sources , Probability distribution , Load flow , Probabilistic power flow , wind power , vine copula , Wasserstein distance , bulk power systems
- 주제(기타) Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications
- 설명문(일반) [Lee, Ryungyeong; Shin, Hunyoung] Hongik Univ, Dept Elect & Elect Engn, Seoul 04066, South Korea; [Kim, Gyeongmin; Hur, Jin] Ewha Womans Univ, Coll Engn, Dept Climate & Energy Syst Engn, Seoul 03760, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- OA유형 gold
- 발행기관 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행년도 2022
- URI http://www.dcollection.net/handler/ewha/000000203107
- 본문언어 영어
- Published As https://doi.org/10.1109/ACCESS.2022.3218644
초록/요약
As the use of renewable energy is continuously increasing, power systems are currently exposed to greater uncertainty and variability, which can lead to severe power system stability issues. Therefore, a power system analysis tool should be devised to assess the impact of renewable energy integration along with an accurate modeling of their stochastic characteristics. In this study, an advanced probabilistic power flow (PPF) method is developed using vine copulas that captures the complex dependency of the stochastic wind power generated from multiple wind sites. The proposed method also involves the use of a function for selecting the probability models of wind speeds by regions in a sophisticated manner. The effectiveness of the proposed method is tested on an IEEE bus system as well as, on a South Korean power system with thousands of buses and transmission lines using PSS/E with Python API. The simulations demonstrate that the proposed method can more accurately evaluate the power system risks with the sophisticated modeling of wind power in multiple sites as compared to the deterministic approach or the PPF with independent sampling.
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