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Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data

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  • Giannelos, Spyros
  • Pudjianto, Danny
  • Strbac, Goran

Abstract

This paper investigates optimal day-ahead operation of a building-scale energy hub equipped with photovoltaics and a battery. Electricity demand and PV availability are uncertain and are represented in two ways: (i) thin-tailed normal distributions and (ii) kernel density estimation (KDE) fitted to empirical CityLearn data. For each representation we evaluate (a) deterministic Monte Carlo analysis, where the hub is optimised separately for 1 000 daily scenarios, and (b) a two-stage stochastic optimisation that fixes one set of decisions for hours 0–11 and adapts for hours 12–23 after conditions are observed. Gaussian inputs yield clustered costs (mean= $51.6, σ= $0.2) and a 99 % CVaR below $52, suggesting negligible risk. KDE inputs raise the Monte Carlo mean to $80.6 and lift the 99 % CVaR to $114, exposing heavy-tailed risk. Within the stochastic model the identical first-stage policy costs $79.0 with Gaussian data but only $71.3 with KDE, as recourse exploits sunny scenarios and trims the 95 % CVaR from $106.4 to $93.5. Consequently, Gaussian assumptions obscure true operating costs and financial exposure, whereas incorporating empirically derived KDE uncertainty within stochastic optimisation both lowers the average cost and provides stronger protection against extreme cost outcomes.

Suggested Citation

  • Giannelos, Spyros & Pudjianto, Danny & Strbac, Goran, 2025. "Smart home economic operation under uncertainty: comparing monte carlo and stochastic optimization using gaussian and KDE-based data," Operations Research Perspectives, Elsevier, vol. 15(C).
    • Handle: RePEc:eee:oprepe:v:15:y:2025:i:c:s2214716025000247
    DOI: 10.1016/j.orp.2025.100348
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    References listed on IDEAS

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    1. Spyros Giannelos & Xi Zhang & Tai Zhang & Goran Strbac, 2024. "Multi-Objective Optimization for Pareto Frontier Sensitivity Analysis in Power Systems," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    2. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    3. Spyros Giannelos, 2025. "Reinforcement Learning in Energy Finance: A Comprehensive Review," Energies, MDPI, vol. 18(11), pages 1-41, May.
    4. G., Varathan & J., Belwin Edward, 2024. "A review of uncertainty management approaches for active distribution system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(C).
    5. Ana Cabrera-Tobar & Alessandro Massi Pavan & Giovanni Petrone & Giovanni Spagnuolo, 2022. "A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids," Energies, MDPI, vol. 15(23), pages 1-38, December.
    6. 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.
    7. 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.
    8. 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.
    9. Spyros Giannelos & Tai Zhang & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2024. "Investments in Electricity Distribution Grids: Strategic versus Incremental Planning," Energies, MDPI, vol. 17(11), pages 1-13, June.
    10. 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).
    11. Sakki, G.K. & Tsoukalas, I. & Kossieris, P. & Makropoulos, C. & Efstratiadis, A., 2022. "Stochastic simulation-optimization framework for the design and assessment of renewable energy systems under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    12. Spyros Giannelos & Stefan Borozan & Goran Strbac, 2022. "A Backwards Induction Framework for Quantifying the Option Value of Smart Charging of Electric Vehicles and the Risk of Stranded Assets under Uncertainty," Energies, MDPI, vol. 15(9), pages 1-22, May.
    13. 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.
    14. Spyros Giannelos & Anjali Jain & Stefan Borozan & Paola Falugi & Alexandre Moreira & Rohit Bhakar & Jyotirmay Mathur & Goran Strbac, 2021. "Long-Term Expansion Planning of the Transmission Network in India under Multi-Dimensional Uncertainty," Energies, MDPI, vol. 14(22), pages 1-27, November.
    15. 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).
    16. Spyros Giannelos & Stefan Borozan & Marko Aunedi & Xi Zhang & Hossein Ameli & Danny Pudjianto & Ioannis Konstantelos & Goran Strbac, 2023. "Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids," Energies, MDPI, vol. 16(13), pages 1-15, June.
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    1. Tai Zhang & Goran Strbac, 2025. "Artificial Intelligence Applications for Energy Storage: A Comprehensive Review," Energies, MDPI, vol. 18(17), pages 1-44, September.

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