ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

Quantification of Carbon Emissions and Savings in Smart Grids

12 September 2016, 10:20 – 10:40


Submitted by
Eng Lau
Eng Lau (Queen Mary University of London), Qingping Yang (Brunel University London), Gareth Taylor (Brunel University London), Alistair Forbes (National Physical Laboratory), Valerie Livina (National Physical Laboratory)
The UK national energy system experiences large and increasing loads on the infrastructure, as well as uncertainty in energy consumption due to: variable demand in colder seasons and peak hours of the day; contribution of intermittent green generators; insufficient storage facilities; increasing complexity of the grid components. Such factors have led to economical and environmental stresses on the national energy system, among which is the need to reduce carbon emissions produced during energy generation. We attempted to model and quantify carbon emissions and carbon savings in the smart grids to address this problem.

We define carbon emissions as the product of the activity (energy) and the corresponding carbon factor. Carbon savings are estimated as the difference between the conventional and improved energy
usage multiplied by the corresponding carbon factor. Given high-resolution energy generation data (Elexon portal), we estimate dynamical grid carbon factor based on the available national fuel
mix, with quantification of uncertainties, given the known ranges of carbon factors for each fuel.

An adaptive seasonal model based on the hyperbolic tangent function (HTF) is developed to reproduce seasonal trends of electricity consumption and the resultant carbon emissions for groups of consumers. Energy consumption and generation data are forecast and assimilated using the ensemble Kalman filter (EnKF). Numerical optimisation of carbon savings is further performed following the ensemble-based Closed-loop Production Optimisation Scheme (EnOpt), where the EnKF is combined with the EnOpt procedure. The EnOpt involves the optimisation of fuel costs
and carbon emissions in the smart grid subject to the operational control constraints. The proposed approach addresses the complexity and diversity of the power grid and may be implemented at the
level of the transmission operator in collaboration with the operational wholesale electricity market and distribution network operators.

As an application, we quantify carbon emissions and savings in demand response (DR) programmes, such as Short Term Operating Reserve (STOR), Triad, Fast Reserve, Frequency Control
by Demand Management (FCDM) and smart meter roll-out (Irish pilot project). DR programmes are modelled with appropriate configurations and assumptions on power plants technological cycles
used in the energy industry. This enables the comparison of emissions between the conventional routines and smart solutions applied, thus deriving carbon savings. Several industrial case studies
of DR participants are successfully performed.

Uncertainty estimation is performed for those carbon factors of individual fuels used in electricity generations in specific power plants, using the data. Monte Carlo simulations of random samplings are performed in order to quantify the corresponding uncertainties for the resultant carbon emissions and savings. This enables the comparison of carbon emissions between the conventional and the improved solutions, with quantification of uncertainties.
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