Model Validation by Statistical Methods on a Monte-Carlo Simulation of Residential Low Voltage Grid


This paper presents validation of a Monte-Carlo simulation built for supporting electricity grid capacity planning. The base simulation model is developed to represent the power usages of electricity at residential low voltage grid. The simulation allows generating detailed load profiles of individual households, and also produces aggregated load profiles at transformer levels. The research is aimed at finding the probabilities of overloads that may cause faults. In this paper, statistical methods are selected and applied to validate the simulation results by comparing them with: 1. current commonly available data provided by electricity distribution operators (DNOs) of aggregated load data at transformers; 2. sampled household daily load profiles. These statistical methods for simulation validation can be referenced for other simulation-based studies especially on grid strategic planning and design.


model validation Monte-Carlo simulation statistical method electricity low voltage grid load profiles 


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  1. 1.
    Sargent, R.G.: Verification and Validation of Simulation Models. In: Chick, S., Sanchez, P., Ferrin, D., Morrice, D. (eds.) Proc. Winter Simulation Conference, pp. 37–48 (2003)Google Scholar
  2. 2.
    Schlesinger, et al.: Terminology for model credibility. Simulation 32(3), 103–104 (1979)CrossRefGoogle Scholar
  3. 3.
    Du, W., Bots, P.W.G., Slootweg, J.G.: Monte Carlo Simulation of Generating Stochastic Load Profiles in Electricity Distribution Grid Asset Planning. In: CIRED Conference, Germany (2011)Google Scholar
  4. 4.
    Du, W.: Modeling Electric Vehicles at Residential Low Voltage Grid. In: CIRED Conference, Germany (2011)Google Scholar
  5. 5.
    Soliman, S., Al-Kandari, A.M.: ‘Ekectrical load forecasting: modeling and model construction. Butterworth-Heinemann, Amsterdam (2010)Google Scholar
  6. 6.
    van Reeuwijk, L.P., Houba, V.J.G.: Basic statistical Tools. In: Guidelines for Quality Management in Soil and Plant Laboratories. International Soil Reference and Information Centre (ISRIC), Wageningen (1998)Google Scholar

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© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Delft University of TechnologyDelftThe Netherlands

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