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Probabilistic energy flow for multi-carrier energy systems

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  • Khorsand, Hosein
  • Seifi, Ali Reza

Abstract

This paper investigates energy flow of Multi-Carrier Energy Systems using Point Estimate Method and Monte Carlo simulation considering uncertainties, which may happen in the Electrical, Natural Gas, and District Heating Networks all together. These uncertainties could occur from the probabilistic behavior of loads or unforeseen faults. An innovative probabilistic energy flow of Multi-Carrier Energy Systems based on Point Estimate Method has been presented in this paper. Results for two different case studies are investigated and compared against those achieved from the Monte Carlo simulation. The results prove that the presented point estimate schemes have precise results, smaller computational burden, and time, comparing than Monte Carlo simulation method.

Suggested Citation

  • Khorsand, Hosein & Seifi, Ali Reza, 2018. "Probabilistic energy flow for multi-carrier energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 989-997.
  • Handle: RePEc:eee:rensus:v:94:y:2018:i:c:p:989-997
    DOI: 10.1016/j.rser.2018.07.008
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    References listed on IDEAS

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    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
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    3. Shabanpour-Haghighi, Amin & Seifi, Ali Reza, 2015. "Multi-objective operation management of a multi-carrier energy system," Energy, Elsevier, vol. 88(C), pages 430-442.
    4. Abdullah, M.A. & Agalgaonkar, A.P. & Muttaqi, K.M., 2013. "Probabilistic load flow incorporating correlation between time-varying electricity demand and renewable power generation," Renewable Energy, Elsevier, vol. 55(C), pages 532-543.
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    Cited by:

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    3. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    4. Yu Huang & Shuqin Li & Peng Ding & Yan Zhang & Kai Yang & Weiting Zhang, 2019. "Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program," Energies, MDPI, vol. 12(20), pages 1-21, October.
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    6. 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).

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