Robust optimisation of electricity-heating integrated energy system based on data-driven PV interval estimation

Short-term interval estimation can effectively and precisely quantify the uncertainties of renewable energy, accurately represent the range of fluctuations of uncertain variables in robust optimisation of electricity-heating integrated energy system (EHIES) and it is getting crucial for reliable and flexible operation of renewable dominated new energy systems. The authors present a multivariate data-driven short-term PV power interval prediction model that consists of multiple layers, including one-dimensional convolutional layer, ultra-lightweight subspace attention mechanism (ULSAM), bidirectional long and short-term memory (BiLSTM), quantile regression (QR) and kernel density estimation (KDE). The one-dimensional convolutional layer and ULSAM can extract sequential features and highlight key information from the data; the BiLSTM processes time series data in both directions and conveys historical information; the QR and KDE models generate interval prediction with a given confidence level. Based on the proposed interval estimation, a refined PV uncertainty set can be established and adopted by robust optimal scheduling of EHIES utilising min-max-min algorithm. The simulation results have demonstrated the estimation accuracy and adaptability to various weather scenarios.