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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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    8. 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.
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    Cited by:

    1. Tai Zhang & Goran Strbac, 2025. "Novel Artificial Intelligence Applications in Energy: A Systematic Review," Energies, MDPI, vol. 18(14), pages 1-51, July.
    2. Qirui Ding & Weicheng Cui, 2025. "Stochastic Biomechanical Modeling of Human-Powered Electricity Generation: A Comprehensive Framework with Advanced Monte Carlo Uncertainty Quantification," Energies, MDPI, vol. 18(18), pages 1-32, September.
    3. Spyros Giannelos, 2025. "Reinforcement Learning in Energy Finance: A Comprehensive Review," Energies, MDPI, vol. 18(11), pages 1-41, May.
    4. Mehrdad Ghahramani & Daryoush Habibi & Seyyedmorteza Ghamari & Hamid Soleimani & Asma Aziz, 2025. "Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling," Clean Technol., MDPI, vol. 7(3), pages 1-37, September.
    5. Tai Zhang & Goran Strbac, 2025. "Artificial Intelligence Applications for Energy Storage: A Comprehensive Review," Energies, MDPI, vol. 18(17), pages 1-44, September.
    6. Naixuan Zhu & Guilian Wu & Hao Chen & Nuoling Sun, 2025. "Resilience Enhancement for Distribution Networks Under Typhoon-Induced Multi-Source Uncertainties," Energies, MDPI, vol. 18(13), pages 1-21, June.

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