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Infrastructure Resilience Conference 2018

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Resilience Assessment of Countries’ Electricity Supply According to Different Aggregation Functions

The development of future resilient energy systems requires evaluation methodologies capable of accounting for multiple assessment criteria, variable data typology and stakeholders’ perspectives. Handling these diverse and potentially conflicting requirements can be conducted through effective and efficient decision support tools. In this research, a structured Multiple Criteria Decision Aiding (MCDA) process is proposed to conduct resilience assessment of nation-wide energy systems, i.e. countries. This presentation will focus on two main contributions of our research project, (i) the selection of the resilience indicators and (ii) the comparison of the performance of a set of countries according to different aggregation functions. The selection of the resilience indicators started from a broad list of indicators that was assessed according to four key features, (i) relevance; (ii) credibility of the data; (iii) accessibility of the data; and (iv) applicability. Only those indicators performing positively on these features were selected for the next step, namely the statistical analysis with Cronbach alpha, a measure of scale reliability and indicators relevance, which was eventually confirmed. The last part of the presentation will focus on the application of different aggregation algorithms (all providing a score of countries) for the resilience indicators. They account for different levels of compensation (i.e. acceptance of trade-offs) between the indicators and this allows the visualization of the countries performance according to different preference structures the decision-makers (DMs) could have. For example, when a fully compensatory method is applied a very poor performance on some indicators can be compensated by a very good performance on others. On the contrary, a non-compensatory function does not allow for this acceptance and the overall valuation is downsized according to the valuation of the worst indicators.   The use of these various aggregation methods can support development agencies interested in considering DMs with different levels of trade-offs acceptance. This can help for instance in shaping energy policies of countries where minimum performance goals can be guaranteed and highly compensatory plans are excluded at an early stage.

Marco Cinelli
Singapore-ETH Centre
Singapore

Patrick Gasser
Singapore-ETH Centre
Singapore

Miłosz Kadziński
Poznan University of Technology
Poland

Matteo Spada
Paul Scherrer Institute
Switzerland

Peter Burgherr
Paul Scherrer Institute
Switzerland

 

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