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

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  • 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.

Suggested Citation

  • Zheng, J.H. & Xiao, Wenting & Wu, C.Q. & Li, Zhigang & Wang, L.X. & Wu, Q.H., 2023. "A gradient descent direction based-cumulants method for probabilistic energy flow analysis of individual-based integrated energy systems," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222031760
    DOI: 10.1016/j.energy.2022.126290
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    References listed on IDEAS

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    Cited by:

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    6. Wang, Y.X. & Chen, J.J. & Zhao, Y.L. & Xu, B.Y., 2024. "Incorporate robust optimization and demand defense for optimal planning of shared rental energy storage in multi-user industrial park," Energy, Elsevier, vol. 301(C).

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