A gradient descent direction based-cumulants method for probabilistic energy flow analysis of individual-based integrated energy systems

J.H. Zheng, Wenting Xiao, C.Q. Wu, Zhigang Li, L.X. Wang and Q.H. Wu

Energy, 2023, vol. 265, issue C

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.

Keywords: Integrated energy system; Individual-based model; Probabilistic energy flow; Gradient descent direction iterative method; GDDM-based cumulants method (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222031760
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:265:y:2023:i:c:s0360544222031760

DOI: 10.1016/j.energy.2022.126290

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().