Quantification of carbon emissions and savings in smart grids
In this research, carbon emissions and carbon savings in the smart grid are modelled and quantified. Carbon emissions are defined as the product of produced energy and the corresponding carbon factor. The carbon savings are estimated as the difference between the conventional and improved energy usage multiplied by the corresponding carbon factor. An adaptive seasonal model based on the hyperbolic tangent function (HTF) is developed to define seasonal and daily trends of electricity demand and the resultant carbon emissions. A stochastic model describing profiles of energy usage and carbon emissions for groups of consumers is developed. The flexibility of the HTF for modelling cycles of energy consumption is demonstrated and discussed with several case studies. The analytical description to determine electricity grid carbon intensity in the UK is derived, using the available fuel mix data from the Elexon portal.
The uncertain realisation of energy data is forecasted and assimilated using the ensemble Kalman filter (EnKF). The numerical optimisation of carbon emissions and savings in the smart grid is further performed using the ensemble-based closed-loop production optimisation scheme (EnOpt). The EnOpt involves the optimisation of fuel costs and carbon emissions (maximisation of carbon savings) in the smart grid subject to the operational control constraints. The software codes for the based on the application of EnKF and EnOpt are developed, and the optimization of energy, cost and emissions is performed. The numerical simulation shows the ability of EnKF in forecasting and assimilating the energy data, and the robustness of the EnOpt in optimising costs and carbon savings. 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.
The final stage of work includes the quantification of carbon emissions and savings in the UK electricity ancillary services. The ancillary services such as Short Term Operating Reserve, Triad, Fast Reserve, Frequency Control by Demand Management and smart meter roll-out are included, with various types of smart interventions. The ancillary services are modelled with appropriate configurations and assumptions in power plants used in the energy industry. This enables the comparison of emissions between the business-as-usual (BAU) and the smart solutions applied, thus deriving the carbon savings. Several case studies involving the modelling and analysing ancillary services are successfully performed. Thus, the project represents novel analytical and numerical techniques applied in the fast-growing UK market of smart energy solutions.
Overall, the derived electricity grid carbon factor with uncertainties provides a useful tool for the assessment of the carbon intensity across the network grid. High carbon intensity may indicate high energy demand (demand stress), and more BAU non-renewable energy generation than renewables. In EnKF, the ensemble representation of uncertain energy consumption and generation provides a useful numerical tool in prediction and assimilation of energy data. With the EnKF algorithm, the uncertain trends of energy generation and consumption can be predicted using the input data. In EnOpt, running and controlling complex smart grids allows to account emissions and renewable energy in a closed-loop optimal control. It can be used by aggregators, distribution network operators, and National Grid, under the regulations of the UK Office of Gas and Electricity Markets (Ofgem) to quantify and minimise the trade-offs between costs and carbon emissions of the economic dispatch problem. The ancillary services framework as developed allows the network operators develop an optimal operating strategy for greener control of generating fleets through ancillary services in order to ensure sustainability and minimal environmental impact of the power grid.
Finally, a model has been developed for external companies based on the model of ancillary services. The model allows one to forecast of the potential carbon savings based on self-generation by in comparison with BAU generation. The proposed model provides all economical and environmental solution for business decisions based on both costs and environmental benefits of optimised energy generation.
- Lau, E.T., Yang, Q., Stokes, L., Taylor, G.A., Forbes, A.B., Clarkson, P. Wright, P. and Livina, V.N. “Carbon savings in UK demand side response programmes”, Applied Energy, 159: 478-489, August 2015.
- Lau, E.T., Yang, Q., Forbes, A.B., Wright, P. and Livina, V.N. “Modelling Carbon Emissions in Electrical Systems”, Energy Conversion and Management, 80(59): 573-581, April 2014.
- Lau, E.T., Yang, Q., Taylor, G.A., Forbes, A.B. and Livina, V.N. “Optimisation of costs and carbon savings in relation to the economic dispatch problem as associated with power system operation, Electric Power Systems Research, 140: 173-183, July 2016.
- Lau, E.T., Yang, Q., Forbes, A.B. and Livina, V.N. “Application of Ensemble Kalman Filter in forecasting the electricity grid carbon factor”, International Journal of Electrical Energy, 3(4), December 2015.
- Lau, E.T. and Livina, V.N. “Assessment of carbon savings of the British Telecommunications (BT) participation in the Triad programme of the National Grid (NG)”, National Physical Laboratory, London, Tech. Report, 2015.
- Lau, E.T. and Livina, V.N. “Carbon savings of demand side response of a UK energy aggregator”, National Physical Laboratory, London, Tech. Report, ISSN: 1754-2960, 2015.
Conference paper (In Print/Press)
- Lau, E.T., Yang, Q., Taylor, G.A., Forbes, A.B., Wright, P. and Livina, V.N. “Optimization of Carbon Emissions in Smart Grids”, 49th International Universities’ Power Engineering Conference, Cluj-Napoca, Romania, 1-4th September 2014, DOI: 10.1109/UPEC.2014.6934796.
- Lau, E.T., Yang, Q., Taylor, G.A., Forbes, A.B., Wright, P. and Livina, V.N. “The UK electricity demand side response: carbon savings analysis”, 12th International Conference on the European Energy Market, Lisbon, 20-22th May 2015, DOI: 10.1109/EEM.2015.7216719.