Modelling carbon emissions in electric systems

•We model carbon emissions in electric systems.•We estimate emissions in generated and consumed energy with UK carbon factors.•We model demand profiles with novel function based on hyperbolic tangents.•We study datasets of UK Elexon database, Brunel PV system and Irish SmartGrid.•We apply Ensemble Kalman Filter to forecast energy data in these case studies. We model energy consumption of network electricity and compute Carbon emissions (CE) based on obtained energy data. We review various models of electricity consumption and propose an adaptive seasonal model based on the Hyperbolic tangent function (HTF). We incorporate HTF to define seasonal and daily trends of electricity demand. We then build a stochastic model that combines the trends and white noise component and the resulting simulations are estimated using Ensemble Kalman Filter (EnKF), which provides ensemble simulations of groups of electricity consumers; similarly, we estimate carbon emissions from electricity generators. Three case studies of electricity generation and consumption are modelled: Brunel University photovoltaic generation data, Elexon national electricity generation data (various fuel types) and Irish smart grid data, with ensemble estimations by EnKF and computation of carbon emissions. We show the flexibility of HTF-based functions for modelling realistic cycles of energy consumption, the efficiency of EnKF in ensemble estimation of energy consumption and generation, and report the obtained estimates of the carbon emissions in the considered case studies.