| Title |
Probabilistic Load Flow for Assessment of Wind Power Integration Using a C-Vine Copula Function |
| Authors |
이륜경(Ryungyeong Lee) ; 신훈영(Hunyoung Shin) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.1.035 |
| Keywords |
Probabilistic Load flow; vine copula; wind power generation location optimization; wind power |
| Abstract |
Owing to the increase in renewable energy, the power system is exposed to more variability and uncertainty, which increases the difficulty in analyzing the power systems. Thus, an accurate power system analysis considering the stochastic characteristics of renewable energy is required. In this paper, we propose a Monte-Carlo based probabilistic load flow method that takes into account the statistical dependencies between the power generation from the multiple wind sites. To this end, the vine copula method was employed to capture their dependencies and the marginal distribution of the power generation from the multiple wind sites is modeled using a 3-parameter Weibull distribution. In addition, we suggested the chance-constrained optimization problem for properly allocating a target wind power capacity to the buses. From the numerical experiment conducted on the IEEE 39-bus system, it has been demonstrated that the proposed method can more accurately identify the potential risk with the modeling of the dependencies between the power generation from the multiple wind sites as compared to the deterministic approach or probabilistic load flow based on the random sampling method. |