Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models
James Price and
Applied Energy, 2017, vol. 195, issue C, 356-369
In this study we describe a novel formulation of the so-called modelling to generate alternatives (MGA) methodology and use it to explore the near cost optimal solution space of the global energy-environment-economy model TIAM-UCL. Our implementation specifically aims to find maximally different global energy system transition pathways and assess the extent of their diversity in the near optimal region. From this we can determine the stability of the results implied by the least cost pathway which in turn allows us to both identify whether there are any consistent insights that emerge across MGA iterations while at the same time highlighting that energy systems that are very similar in cost can look very different. It is critical that the results of such an uncertainty analysis are communicated to policy makers to aid in robust decision making. To demonstrate the technique we apply it to two scenarios, a business as usual (BAU) case and a climate policy run. For the former we find significant variability in primary energy carrier consumption across the MGA iterations which then projects further into the energy system leading to, for example, large differences in the portfolio of fuels used in and emissions from the electricity sector. When imposing a global emissions constraint we find, in general, less variability than the BAU case. Consistent insights do emerge with oil use in transport being a robust finding across all MGA iterations for both scenarios and, in the mitigation case, the electricity sector is seen to reliably decarbonise before transport and industry as total system cost is permitted to increase. Finally, we compare our implementation of MGA to the so-called Hop-Skip-Jump formulation, which also seeks to obtain maximally different solutions, and find that, when applied in the same way, the former identifies more diverse transition pathways than the latter.
Keywords: Modeling; Energy systems optimisation; Uncertainty; Mathematical methods; Robust decision making (search for similar items in EconPapers)
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