A gradient descent direction based-cumulants method for probabilistic energy flow analysis of individual-based integrated energy systems. (15th February 2023) - Zheng, J.H.; Xiao, Wenting; Wu, C.Q.; Li, Zhigang; Wang, L.X.; Wu, Q.H.
Abstract: This study presents an individual-based model of heterogeneous integrated energy systems, which is composed of sub-energy system individuals and coupling individuals. Different evolution rules are depicted within one individual to form an accurate model of a complex system. Based on the model, a gradient descent direction iterative method (GDDM) is proposed for energy flow calculation of integrated energy systems to improve its convergence performance. Furthermore, a GDDM-based cumulants method (GDDM-CM) is presented to analyse the probabilistic energy flow distribution throughout the system. To verify the effectiveness of the proposed method, simulation studies have been undertaken in a modified complex integrated energy system. The results show that GDDM performs better than non-gradient descent method (NGDM). It converges faster and is insensitive to the initial point. Besides, GDDM-CM can obtain similar results as monte-carlo sampling method does and its calculation accuracy is better than that of the point estimate method. Moreover, the proposed GDDM-CM can greatly reduce the calculation time compared with the other two methods. Utilizing the GDDM-CM, the coupling effects of different individuals are investigated and the weak points in each individual can be identified. Highlights: Individual-based models are established for heterogeneous integrated energy systems. A gradient descent direction iterative method (GDDM) is proposed for energy flow calculation. A GDDM-based cumulants method is utilized to analyse probabilistic energy flow.