Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid

Abstract

Renewable sources such as solar PV and wind are stochastic in nature, hence their integration with emerging isolated microgrid (MG) is challenging especially with regards to stability issues. An accurate prediction model of wind and solar sources is necessary to analyze the uncertainty in MG system and to encourage the reliable participation of wind and solar power in the energy market. The advancement in deep learning methods has made it possible to develop a multi-step forecasting model unlike shallow neural networks (SNNs). The time series forecasting using SNN and Recurrent Neural Network (RNN) suffers from the problem of vanishing/exploding gradient while training. To eliminate this problem the long short-term memory (LSTM) RNN has been used in this study for wind speed and solar irradiance prediction. The forecasted solar and wind power is applied to analyze the load frequency behavior and the response of nonrenewable sources for sudden rise and fall in load power demand and PI controller is used to mitigate frequency deviation to ensure the stability of the MG power system.