Reliability Assessment of Energy Hubs Using Advanced Monte Carlo Simulation Approaches: A Comprehensive Framework
Keywords:
reliability assessment, energy hub, kernel density estimation, monte carlo simulationAbstract
Energy hubs represent a key concept in the integration of multiple energy carriers, allowing for efficient coordination of various energy sources, conversion technologies, and storage systems. However, the reliability of these complex systems remains a significant challenge due to uncertainties in renewable energy generation, load patterns, and potential component failures. This paper introduces a comprehensive framework for reliability assessment of energy hubs using advanced Monte Carlo simulation techniques. We propose a novel approach that combines traditional sequential Monte Carlo simulation with kernel density estimation (KDE) for more accurate representation of uncertainties in renewable energy resources and load demands. The framework additionally incorporates cyber-physical interdependencies by modeling both physical components and their supporting cyber infrastructure. Two detailed case studies demonstrate the effectiveness of the proposed methodology: (1) a small-scale residential energy hub with solar photovoltaics, battery storage, and grid connection; and (2) a large-scale multi-carrier energy hub serving a commercial district with diverse energy conversion technologies. Results indicate that conventional Gaussian-based uncertainty modeling significantly underestimates operational risks, with our KDE-based approach revealing up to 56% higher expected operational costs and substantially greater variability in reliability metrics. The paper provides practical insights for energy hub designers and operators while offering a robust computational tool for reliability-oriented planning and operation of future energy systems.
Downloads
References
[1] M. Geidl and G. Andersson, "Optimal power flow of multiple energy carriers," IEEE Trans. Power Syst.,
vol. 22, no. 1, pp. 145-155, 2007.
[2] M. Mohammadi, Y. Noorollahi, B. Mohammadi-Ivatloo, H. Yousefi, and S. Jalilinasrabady, "Optimal
scheduling of energy hubs in the presence of uncertainty-A review," J. Energy Manage. Technol., vol. 1, no.
1, pp. 1-17, 2017.
[3] G. Varathan and J. Belwin Edward, "A review of uncertainty management approaches for active distribution
system planning," Renew. Sustain. Energy Rev., vol. 205, p. 114808, 2024.
[4] A. Dolatabadi, M. Jadidbonab, and B. Mohammadi-Ivatloo, "Short-term scheduling strategy for wind-based
energy hub: A hybrid stochastic/IGDT approach," IEEE Trans. Sustain. Energy, vol. 10, no. 1, pp. 438-448,
2019.
[5] M. Moeini-Aghtaie, A. Safdarian, Z. Parvini, and F. Aramoun, "Optimal stochastic short-term scheduling
of renewable energy hubs taking into account the uncertainties of the renewable sources," in Operation,
Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs, Springer, Cham, pp. 35-59, 2018.
[6] R. Billinton and R. N. Allan, "Reliability evaluation of power systems," Springer Science & Business
Media, 1996.
[7] J. He, Z. Yuan, X. Yang, W. Huang, Y. Tu, and Y. Li, "Reliability modeling and evaluation of urban multienergy systems: A review of the state of the art and future challenges," IEEE Access, vol. 8, pp. 67395-67413,
2020.
[8] X. Yan, J. Li, P. Zhao, N. Liu, L. Wang, B. Yue, and D. Liu, "Review on reliability assessment of energy
storage systems," IET Smart Grid, vol. 7, no. 1, pp. 10-31, 2024.
[9] J. Faraji, M. Aslani, H. Hashemi-Dezaki, A. Ketabi, and S. M. Muyeen, "Reliability analysis of cyberphysical energy hubs: A Monte Carlo approach," IEEE Trans. Smart Grid, vol. 14, no. 4, pp. 2896-2909, 2023.
[10] M. Aslani, J. Faraji, H. Hashemi-Dezaki, and A. Ketabi, "A novel clustering-based method for reliability
assessment of cyber-physical microgrids considering cyber interdependencies and information transmission
errors," Applied Energy, vol. 310, p. 118551, 2022.
[11] R. Billinton and W. Li, "Reliability assessment of electric power systems using Monte Carlo methods,"
Springer Science & Business Media, 1994.
[12] S. Giannelos, D. Pudjianto, T. Zhang, and G. Strbac, "Energy hub operation under uncertainty: Monte
Carlo risk assessment using Gaussian and KDE-based data," Energies, vol. 18, no. 7, p. 1712, 2025.
[13] M. Memari, A. Karimi, and H. Hashemi-Dezaki, "Clustering-based reliability assessment of smart grids
by fuzzy c-means algorithm considering direct cyber-physical interdependencies and system uncertainties,"
Applied Energy, vol. 309, p. 118437, 2022.
[14] S. F. Myhre, "Reliability assessment tool for modern electrical distribution systems–A Monte Carlo
simulation approach," Norwegian University of Science and Technology, 2023.
[15] A. Stankovski, "Methods for assessing the impact of the energy transition on the security and reliability
of the Swiss and interconnected European power systems," ETH Zurich, 2024.
[16] M. Shabanian-Poodeh, R. A. Hooshmand, and B. Mozafari, "Resilience enhancement strategies for energy
systems in the face of natural calamities and cyber threats: A comprehensive review," IEEE Access, vol. 13,
pp. 12345-12367, 2025.
[17] C. Yan, S. Liu, L. Xie, and Z. Bie, "A reliability model for integrated energy system considering multienergy correlation," IEEE Trans. Power Syst., vol. 36, no. 4, pp. 3648-3661, 2021.
[18] M. Aslani, H. Hashemi-Dezaki, and A. Ketabi, "Analytical reliability evaluation method of smart micro‐
grids considering the cyber failures and information transmission system faults," IET Renewable Power
Generation, vol. 16, no. 8, pp. 1632-1644, 2022.
[19] J. Wang, C. Fu, K. Chen, and J. Zhai, "Reliability and availability analysis of redundant BCHP (building
cooling, heating and power) system," Energy, vol. 61, pp. 531-540, 2013.
[20] G. Koeppel and G. Andersson, "Reliability modeling of multi-carrier energy systems," Energy, vol. 34,
no. 3, pp. 235-244, 2009.
