More efficient approaches to the generation of near-optimal alternatives


The starting point of the SEEDS project workflow is the use of an energy system optimisation model to generate multiple, equally-feasible and economically-comparable alternatives for the decarbonisation of the energy system, which will serve as a basis for stakeholder engagement and discussion. Approaches to generate such decision options are well-known known in the literature under the name of “modelling to generate alternatives” (MGA). The one we use in the SEEDS project is a recent advancement of conventional MGA, called SPORES, which explicitly looks for alternatives that differ not only in terms of technology mix, but also in the way they locate capacity of each technology at the sub-national scale, for instance different ways of locating wind farms.

Looking for alternatives across both the technological and the spatial dimensions can be quite burdensome, which is why one of the first tasks in the SEEDS project is to increase the computational efficiency of SPORES, and of modelling to generate alternatives in general. The first insights from this research task are available as: