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

Author

Listed:
  • 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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