Aggregation of Group Prioritisations for Energy Rationing with an Additive Group Decision Model: A Case Study of the Swedish Emergency Preparedness Planning in case of Power Shortage
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
The backbone of our industrialised society and economy is electricity. To avoid a catastrophic situation, a plan for how to act during a power shortage is crucial. Previous research shows that decision models provide support to decision makers providing efficient energy rationing during power shortages in the Netherlands, United States and Canada. The existing research needs to be expanded with a group decision model to enable group decisions. This study is conducted with a case study approach where the Swedish emergency preparedness plan in case of power shortage, named Styrel, is explored and used to evaluate properties of a proposed group decision model. The study consist of a qualitative phase and a quantitative phase including a Monte Carlo simulation of group decisions in Styrel evaluated with correlation analysis. The qualitative results show that participants in Styrel experience the group decisions as time-consuming and unstructured. The current decision support is not used in neither of the two counties included in the study, with the motivation that the preferences provided by the decision support are misleading. The proposed group decision model include a measurable value function assigning values to priority classes for electricity users, an additive model to represent preferences of individual decision makers and an additive group decision model to aggregate preferences of several individual decision makers into a group decision. The conducted simulation indicate that the proposed group decision model evaluated in Styrel is sensitive to significant changes and more robust to moderate changes in preference differences between priority classes.
Place, publisher, year, edition, pages
2016. , 99 p.
Energy rationing, decision analysis, preference aggregation rules, Monte-Carlo simulation, correlation analysis.
Other Engineering and Technologies not elsewhere specified
IdentifiersURN: urn:nbn:se:miun:diva-27987Local ID: IG-V16-A2-006OAI: oai:DiVA.org:miun-27987DiVA: diva2:938708
Subject / course
Industrial Organization and Economy IE1
Master of Science in Industrial Engineering and Management TINDA 300 higher education credits
Larsson, Aron, Docent
Olsson, Leif, Docent