| Summary: | Power sectors, as the world’s demand for electricity is increasing, are recognized to be as significant contributors of CO2 emissions in a fossil-fuel based economy. Low carbon energy systems are thus being developed, promoted and deployed as part of the solution portfolios to address climate change. However, certain issues are associated with each technology such that each one needs to be deployed in appropriate scenarios. Optimal selection of such systems should consider the technical, economic, environmental and social aspects of the decision problem. In addition, some emerging technologies may have imprecise information which make it difficult to understand the behavior of the alternatives with respect to some criteria with certainty. The decision maker also needs to conduct trade-off analysis when prioritizing the alternatives in a complex problem involving multiple conflicting criteria. In this work, a Stochastic Fuzzy Analytic Network Process (SFANP) model was developed and applied in the prioritization of low carbon energy systems considering such uncertainty. This technique decomposes the complex problem into a hierarchic network structure and derives priority weights to rank the alternatives. The decision model incorporated the ambiguity-type uncertainty wherein a calibrated fuzzy scale was used to represent the judgment in pairwise comparisons of alternatives and criteria. Monte Carlo simulations were also done for the uncertainty analysis of the priorities derived from the model. An illustrative case study in the Philippines was presented. The case study involves biomass, geothermal, solar, hydro, and wind power which were evaluated with respect to tangible criteria such as levelized cost of electricity, carbon footprint, land footprint and water footprints, as well as, intangible criteria such as maturity of technology, social acceptance, and social benefits.
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