| Title |
Predicting Renewable Energy Generation Using LSTM for Risk Assessment of Local Level Power Networks |
| Authors |
유호성(Ho-Sung Ryu) ; 이용래(Yong-Rae Lee) ; 김문겸(Mun-Kyeom Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2020.69.6.783 |
| Keywords |
Long Short-Term Memory; Uncertainty Modeling; Renewable Power Forecasting; Local Level Network; Severity Risk Index |
| Abstract |
Low uncertainty is essential when operating the power system in a stable state. Recently, the uncertainty in the power systems has increased due to the growth of renewable energy. This paper proposes a method to reduce the uncertainty of the power systems including renewable energy by using Long Short-term Memory (LSTM) algorithm. Through repeated simulation, the optimal LSTM model of each renewable unit is created. probabilistic scenario is created by monte-carlo simulation and k-means clustering algorithm, and then we assess risk for each scenario through a test system created with reference to the actual system. To validate the superiority of the proposed method, the risk assessment are conducted through local level test system. The results demonstrate that the optimal LSTM model reduces the risk index compared to other predicted models. |