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Energy Hub Operation Under Uncertainty: Monte Carlo Risk Assessment Using Gaussian and KDE-Based Data

Author

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  • Spyros Giannelos

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Danny Pudjianto

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Tai Zhang

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

  • Goran Strbac

    (Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK)

Abstract

Energy hubs integrating onsite renewable generation and battery storage provide cost-efficient solutions for meeting building electricity requirements. This study presents methods for modeling uncertainties in load demand and solar generation, ranging from normal distribution assumptions to distributions sourced from CityLearn 2.3.0. We also implement kernel density estimation (KDE) to represent the non-parametric distribution characteristics of actual data. Through Monte Carlo simulation, we emphasize the value of robust, data-driven methodologies in optimizing energy hub operations under realistic uncertainty conditions and effectively conducting risk assessment. The CityLearn real-world data confirms that the non-Gaussian nature of building-level energy demand and solar PV electricity output is most accurately represented through KDE, leading to more precise cost projections for the considered energy hub.

Suggested Citation

  • Spyros Giannelos & Danny Pudjianto & Tai Zhang & Goran Strbac, 2025. "Energy Hub Operation Under Uncertainty: Monte Carlo Risk Assessment Using Gaussian and KDE-Based Data," Energies, MDPI, vol. 18(7), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1712-:d:1623451
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    References listed on IDEAS

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    1. K. Ramakrishna Kini & Fouzi Harrou & Muddu Madakyaru & Ying Sun, 2023. "Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis," Energies, MDPI, vol. 16(15), pages 1-25, August.
    2. J. N. Hooker, 2007. "Planning and Scheduling by Logic-Based Benders Decomposition," Operations Research, INFORMS, vol. 55(3), pages 588-602, June.
    3. Spyros Giannelos & Alexandre Moreira & Dimitrios Papadaskalopoulos & Stefan Borozan & Danny Pudjianto & Ioannis Konstantelos & Mingyang Sun & Goran Strbac, 2023. "A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector," Energies, MDPI, vol. 16(6), pages 1-37, March.
    4. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, December.
    5. Hariri, A. M. A. & Potts, C. N., 1989. "A branch and bound algorithm to minimize the number of late jobs in a permutation flow-shop," European Journal of Operational Research, Elsevier, vol. 38(2), pages 227-226, January.
    6. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    7. Bunn, Derek W. & Oliveira, Fernando S., 2007. "Agent-based analysis of technological diversification and specialization in electricity markets," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1265-1278, September.
    8. Dong, Zihang & Zhang, Xi & Zhang, Linan & Giannelos, Spyros & Strbac, Goran, 2024. "Flexibility enhancement of urban energy systems through coordinated space heating aggregation of numerous buildings," Applied Energy, Elsevier, vol. 374(C).
    9. Spyros Giannelos & Predrag Djapic & Danny Pudjianto & Goran Strbac, 2020. "Quantification of the Energy Storage Contribution to Security of Supply through the F-Factor Methodology," Energies, MDPI, vol. 13(4), pages 1-15, February.
    10. Leprince, Julien & Schledorn, Amos & Guericke, Daniela & Dominkovic, Dominik Franjo & Madsen, Henrik & Zeiler, Wim, 2023. "Can occupant behaviors affect urban energy planning? Distributed stochastic optimization for energy communities," Applied Energy, Elsevier, vol. 348(C).
    11. Warut Pannakkong & Vu Thanh Vinh & Nguyen Ngoc Minh Tuyen & Jirachai Buddhakulsomsiri, 2023. "A Reinforcement Learning Approach for Ensemble Machine Learning Models in Peak Electricity Forecasting," Energies, MDPI, vol. 16(13), pages 1-20, July.
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    Cited by:

    1. Spyros Giannelos, 2025. "Reinforcement Learning in Energy Finance: A Comprehensive Review," Energies, MDPI, vol. 18(11), pages 1-41, May.

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