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With the expansion and infilling of urban areas, the demand for electric power is driving the design and capacity of distribution substations to their thermal limits. Distribution transformer substations are increasingly required to be compact, reliable, safe, and intelligent. To efficiently utilize city space and to support the intermittent load flows imposed by smart-grid features, such as distributed generation, the transformers are expected to operate close to or occasionally over their ratings, with stalled or little air circulation inside the safety enclosure. Dynamic thermal models with physically validated convection and radiation heat-transfer components are essential for the real-time thermal rating of substations. Natural convection via the air inside the cabin to the outside ambient air plays the major role in cooling down a transformer. In this study a scale model of a prefabricated substation is examined to draft a numerical solution which is based on stack ventilation principles. A clear and expandable first principle approach is used to quantify heat transfer through ventilation openings. Measurements from actual cabins and 3-D finite element method simulations are used to validate the numerical model. [ABSTRACT FROM PUBLISHER]

In order to enable the key applications provided by smart grid, a dynamic distribution system state estimator has to function properly giving the real time knowledge of a distribution network load profiles. However, due to unavailability of enough measurements and lagging communication of the measured data, the current system cannot readily access measurements in real time. Therefore a practical load modeling technique has to be applied to provide real time distribution system load profile estimates with the respective confidence intervals. Nevertheless, modeling individual customer loads is far statistically complicated and computationally prohibitive for real time application. The customer loads are not always normally distributed but rather a combination of normal distribution, lognormal distribution, generalized pareto and many others. In this study, models of primary heating type load classes are used to represent individual customers. A statistical formulation based on clustered customer groups' hourly consumption model is used to calculate aggregated substation loads and day ahead load forecasts with the respective confidence intervals. The stochasticity of customer loads when aggregated at distribution substation is investigated for four customer type load classes. [ABSTRACT FROM AUTHOR]