Multi-criteria Preference Aggregation Framework for Sustainable Energy Planning

In the energy field, the decisions need to take into consideration several factors such as the needs of the population, the environment, suitability, capital cost, sustainability, political goals and the actors involved, with their interests and preferences. The lack of homogeneity in all the factors that must be consider makes it necessary to design a process that guides the analysis process of any type of decision-maker. Decision analysis methods have been developed to aid decision-makers identify a problem, determine the criteria to be consider and their importance, recognize the stakeholders that need to be involved and pose the different alternatives to resolve or to best address the problem. These techniques range from simple to more mathematically oriented ones, from single criterion evaluation to multiple criteria, and from purely qualitative or quantitative to mixed techniques. Within the field of decision analysis, multi-criteria techniques are better suited to aid in decision situations in the energy field as these decisions require several considerations beside economic ones. This thesis uses theories and notions of decision analysis to construct a framework to be used in any energy related decision situations by non-experts. The framework tackles common challenges faced by multi-criteria decision analysis methods, including the identification of stakeholders and decision-makers, the aggregation of various decision-makers, preferences and heterogeneous inputs, and the selection of suitable criteria, alternatives and methods.

Name dc:type.qualificationname

Doctor of Philosophy

Level dc:type.qualificationlevel

Doctoral Thesis

Grantor dc:publisher.institution

University of Dundee

Year dc:date.issued

2018

Author dc:creator

• Santos-Ramos, Raquel

Advisors dc:contributor.advisor

• Bergmann, Eric
• Liao, Xuanli

• Multi-criteria
• Decision making
• Decision analysis
• Aggregate preference

Language dc:language

eng

Identifier

oai:discovery.dundee.ac.uk:studenttheses/a5e857bf-cb9d-462d-bb52-3f3f625e758b

OAI identifier oai:identifier

oai:discovery.dundee.ac.uk:studenttheses/a5e857bf-cb9d-462d-bb52-3f3f625e758b