LSTM auto-encoder based representative scenario generation method for hybrid hydro-PV power system

The increasing penetration of renewable energy sources causes complex uncertainties of the power system. To capture such uncertainties in power system planning, an important step is to generate representative scenarios. In this work, a long short term memory (LSTM) auto-encoder based approach is proposed to generate representative scenarios in an integrated hydro-photovoltaic (PV) power generation system, which consists of feature extraction by LSTM Encoder, scenario clustering in feature domain by combining gap statistics method and K-means++, and representative scenario reconstruction by using LSTM Decoder. Compared with traditional scenario selection and generation methods, the proposed method can better capture the patterns of multivariate time-series data in both temporal and spatial dimensions. A case study in southwest China is used to demonstrate the effectiveness of the proposed method, which outperforms other existing methods by achieving the lowest SSE and DBI indices of 0.89 and 0.12, respectively, and obtaining the best SIL and CHI scores of 0.93 and 2.30, respectively, In addition, the case study shows the proposed model setup works more stable for scenario generation.