Operational Scenario Generation and Forecasting for Integrated Energy Systems
Yubin Wang, Yixian Liu, Qiang Yang
Abstract
The integrated energy system (IES) is considered to be an efficient paradigm for low-carbon energy provision. The accurate modeling of inherent operational uncertainties in IES is of paramount importance for its optimal planning and energy management. This article develops a generation and forecasting approach of IES operational scenarios based on Wasserstein generative adversarial network (WGAN) for the characterization of IES operational uncertainties. The proposed solution can efficiently generate high-quality IES operational scenarios consisting of multivariate uncertain variables without explicit statistical assumptions. In addition, the well-trained WGAN is integrated into a constrained optimization problem to realize scenario forecasting in compliance with certain information (i.e., the past observation and point forecasting) for a specific coming period, and it is not restricted by the forecast look-ahead horizons. The proposed solution is assessed through a range of qualitative and quantitative evaluations, and the numerical results validate its effectiveness.