Predicting renewable energy generation using LSTM for risk assessment of local level power networksopen accessLocal level 전력네트워크의 리스크 평가를 위한 LSTM을 활용한 재생에너지 발전량 예측 모델 개발
- Authors
- Ryu H.-S.; Lee Y.-R.; Kim M.-K.
- Issue Date
- Jun-2020
- Publisher
- Korean Institute of Electrical Engineers
- Keywords
- Local Level Network; Long Short-Term Memory; Renewable Power Forecasting; Severity Risk Index; Uncertainty Modeling
- Citation
- Transactions of the Korean Institute of Electrical Engineers, v.69, no.6, pp 783 - 791
- Pages
- 9
- Journal Title
- Transactions of the Korean Institute of Electrical Engineers
- Volume
- 69
- Number
- 6
- Start Page
- 783
- End Page
- 791
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/41770
- DOI
- 10.5370/KIEE.2020.69.6.783
- ISSN
- 1975-8359
2287-4364
- 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. © 2020 Korean Institute of Electrical Engineers. All rights reserved.
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Collections - College of Engineering > School of Energy System Engineering > 1. Journal Articles
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