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Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation

Integrated Assessment Models (IAMs) are omnipresent in energy policy analysis. Even though IAMs can successfully handle uncertainty pertinent to energy planning problems, they render multiple variables as outputs of the modelling. Therefore, policy makers are faced with multiple energy development scenarios and goals. Specifically, technical, environmental, and economic aspects are represented by multiple criteria, which, in turn, are related to conflicting objectives. Preferences of decision makers need to be taken into account in order to facilitate effective energy planning. Multi-criteria decision making (MCDM) tools are relevant in aggregating diverse information and thus comparing alternative energy planning options. The paper aims at ranking European Union (EU) energy development scenarios based on several IAMs with respect to multiple criteria. By doing so, we account for uncertainty surrounding policy priorities outside the IAM. In order to follow a sustainable approach, the ranking of policy options is based on EU energy policy priorities: energy efficiency improvements, increased use of renewables, reduction in and low mitigations costs of GHG emission. The ranking of scenarios is based on the estimates rendered by the two advanced IAMs relying on different approaches, namely TIAM and WITCH. The data are fed into the three MCDM techniques: the method of weighted aggregated sum/product assessment (WASPAS), the Additive Ratio Assessment (ARAS) method, and technique for order preference by similarity to ideal solution (TOPSIS). As MCDM techniques allow assigning different importance to objectives, a sensitivity analysis is carried out to check the impact of perturbations in weights upon the final ranking. The rankings provided for the scenarios by different MCDM techniques diverge, first of all, due to the underlying assumptions of IAMs. Results of the analysis provide valuable insights in integrated application of both IAMs and MCDM models for developing energy policy scenarios and decision making in energy sector.


Keywords: Optimization; Integrated assessment models; Energy sector development scenarios; Multi-criteria decision making; EU energy policy priorities; Sustainability.


Baležentis, T.; Štreimikienė, D. 2017. Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation, Applied Energy 185(1):862–871. http://dx.doi.org/10.1016/j.apenergy.2016.10.085. ISSN 0306-2619 [ASFA2 – Ocean Technology, Policy and Non–Living Resources; Applied Mechanics Reviews; BMT Abstracts; Biotechnology Research Abstracts; Chemical Abstracts; Compendex; Current Contents; International Petroleum Abstracts/Offshore Abstracts; Engineering Index Monthly ;Energy Information Abstracts; Engineering Abstracts; Environmental Periodicals Bibliography; Environmental Sciences & Pollution Management; GEOBASE; GeoRef; OCLC Contents Alert; Pollution Abstracts; Public Affairs Information Service Bulletin; Science Citation Index; Web of Science; Engineering Information Database EnCompass LIT (Elsevier); Scopus; Science Citation Index Expanded; CSA Technology Research Database; Biotechnology and Bioengineering Abstracts; Energy & Power Source; Environment Complete; Environment Index; Academic Search (EBSCO); Current Abstracts (EBSCO); TOC Premier; CSA Engineering Research Database (Cambridge Scientific Abstracts); CSA Sustainability Science Abstracts (Cambridge Scientific Abstracts)].


Impact Factor: 5.746

5-Year Impact Factor: 6.222

© 2016 Elsevier Ltd. All rights reserved.



Multi-criteria ranking of energy generation scenarios with Monte Carlo simulation


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