Nußbaum, FinnFinnNußbaumTeimourzadeh Baboli, PayamPayamTeimourzadeh BaboliBecker, ChristianChristianBecker2026-10-292026-10-292026-098th International Conference on Smart Energy Systems and Technologies, SEST 2026https://hdl.handle.net/11420/65618The increasing electrification of heating and mobility, as well as distributed generation introduce significant uncertainty into distribution grids, complicating demand predictions and grid operation. This paper presents a Monte Carlo-based framework to quantify the impact of correlated weather and behavioural uncertainties on optimized household scheduling and grid loading. Forecast errors of temperature, solar irradiation, inflexible load, and mobility demand are modelled and propagated through a home energy management system and probabilistic power flow analysis. Results show that uncertainty in household power and grid loading is mainly driven by photovoltaic generation and electric vehicle charging, while temperature primarily affects daily energy. Dynamic tariffs reduce overall uncertainty, whereas flat tariffs lead to pronounced peaks. Despite high household-level variability, aggregation limits uncertainty at the grid level, and severe congestion events remain predictable.enDistribution GridForecast UncertaintyHousehold SchedulingLow-Carbon TechnologiesMonte Carlo SimulationNatural Sciences and Mathematics::537: Electricity and ElectronicsConference Paper10.1109/SEST67798.2026.11712353