Energy transition in transportation under cost uncertainty- an assessment based on robust optimization

Claire Nicolas, Stephane Tchung-Ming and Emmanuel Hache

No 2016-29, EconomiX Working Papers from University of Paris Nanterre, EconomiX

Abstract: To improve energy security and ensure the compliance with stringent climate goals, the European Union is willing to step up its efforts to accelerate the development and deployment of electrification, and in general, of alternative fuels and propulsion methods. Yet, the costs and benefits of imposing norms on vehicle or biofuel mandates should be assessed in light of the uncertainties surrounding these pathways, in terms of e.g. cost of these new technologies. By using robust optimization, we are able to introduce uncertainty simultaneously on a high number of cost parameters without notably impacting the computing time of our model (a French TIMES paradigm model). To account for the different nature of the uncertain parameters we model two kinds of uncertainty propagation with time. We then apply this formal setting to French energy system under carbon constraint. As uncertainty increases, as does technology diversification to hedge against it. In the transportation sector, low-carbon alternatives (CNG, electricity) appear consistently as hedges against cost variations, along with biofuels. Policy implications of diversification strategies are of importance; in that sense, the work undertaken here is a step towards the design of robust technology-oriented energy policies.

Keywords: Robust optimization; Climate change; Energy transition; Transportation policy. (search for similar items in EconPapers)
JEL-codes: C61 O33 Q47 R40 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2016
New Economics Papers: this item is included in nep-ene, nep-env, nep-reg and nep-sog
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Working Paper: Energy transition in transportation under cost uncertainty, an assessment based on robust optimization (2016)
Working Paper: Energy transition in transportation under cost uncertainty- an assessment based on robust optimization (2016)
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