A multi-period mixed-integer linear optimisation of future electricity supply considering life cycle costs and environmental impacts

A multi-period mixed-integer linear programming model has been developed to help explore future pathways for electricity supply where costs and carbon reduction are a priority. The model follows a life cycle approach and can optimise on costs and on a number of environmental objectives. To illustrate the application, the model has been optimised on two objectives: whole system costs and global warming potential (GWP) using the UK as an example. Four different scenarios have been considered up to 2060, each assuming different electricity demand and carbon reduction targets. When optimising on system costs, they range from £156.6 bn for the least carbon-constrained scenario with moderate increase in electricity demand to £269.9 bn for the scenario with high demand and requiring 100% decarbonisation of electricity supply by 2035. In optimisation on GWP, negative carbon emissions are achieved in all scenarios, ranging from −0.5 to −1.28 Gt CO2 eq. over the period, owing to biomass carbon capture and storage. Optimising on the GWP also reduces significantly other environmental impacts at costs comparable to optimised costs. This research shows that meeting carbon targets will require careful planning and consideration of objectives other than costs alone to ensure that optimal rather than suboptimal solutions are found for a more sustainable electricity supply.