Integrated energy system scheduling considering the correlation of uncertainties

Abstract

Under the circumstances of “double carbon”, it is imminent to promote the transition of energy. Integrated energy systems (IES) are one of the critical technologies for the energy transition. To achieve optimal operation of IES with uncertainty to reduce system operational cost and carbon emission. In this paper, we propose an optimal scheduling method considering the correlation between uncertainties with source-source, load-load, and source-load. Scenario generation with Wasserstein deep convolutional generative adversarial network-gradient penalty (WDCGAN-GP) captures the correlation between uncertainties. The laddered carbon transaction mechanism is introduced to control carbon emissions. The simulation analysis of the method is performed with the case of the park integrated energy system (PIES) to explore the impact of correlation on IES scheduling. The simulation results show that the carbon emission of laddered carbon transactions is reduced by 8.75% compared with the conventional, while the total cost is increased by 11.09%; on the basis, after considering the correlation, the carbon emission is reduced by 6.22%, and the total cost is decreased by 5.62%. Clearly, the correlation provides financial and environmental benefits to the system. This study provides theory instruction for IES scheduling under uncertainty, which with enormous potential for engineering applications.