A probabilistic power flow prediction and situation awareness method based on long and short term memory network

With the massive integration of renewable energy into the power system, the randomness and volatility of power generation in the power system are increasing day by day. These characteristics have a great impact on the direction and size of power flow in the power grid. This paper presents a probabilistic power flow prediction and situation awareness method based on long and short term memory network. This paper first introduces the probability model based on wind power generation, photovoltaic power generation, demand side load, electric vehicle charging, generator set; Secondly, based on the NATAF transformation method, several probability models are transformed by de-correlation standard normal distribution, and a probability scheduling model with minimum cost of multi-party coordination and complementarity is established. Then, a probabilistic power flow solution based on long short-term memory network is proposed for the probabilistic scheduling model. Finally, an actual power grid is taken as an example to verify and compare the proposed algorithms, and the results prove the effectiveness of the proposed methods.