Composite indicators for security of energy supply using ordered weighted averaging

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Abstract

In this paper we propose to use an aggregation rule derived from the Group Decision Theory, and based on the ranks of a set of individual indicators, for building a family of composite indicators for the security of energy supply. This family of composite indicators depends on a parameter associated with the risk-averse level of the decision maker, which can hence vary continuously from risk-prone to risk-averse. It represents a valuable and objective instrument to evaluate the degree of security of energy supply of different countries without entering into controversial matters related to the choice of the weights. This methodology can be used to aggregate either ranks or normalized values of the individual indicators. We apply it to a set of individual indicators, accounting for different dimensions of the security of supply and derived from the outputs of an energy system model. We study the evolution in time of the countries’ performances, as well as the propagation of the uncertainties associated with the individual indicators to the composite. We also studied the robustness of such composite indicators with respect to the risk-averse level.

Introduction

Together with economic competitiveness and sustainability, the security of energy supply is one of the main objectives of the European energy policy [1]. Energy security became such an important issue on the political agenda because the EU is highly dependent on imports of energy sources, the energy costs are rising and there are concerns with regards to the climate change and environmental issues such as damage to the ecosystems caused by the energy chain. Moreover, unanticipated cuts in the supply of energy sources, such as the Russian gas crisis from 2006 and 2009, had negative effects for the concerned EU Member States (MS).

In particular, the 2009 gas crisis have been followed by a EC proposal of a new (draft) regulation on gas supply security that will oblige member states to follow supply and infrastructure standards to ensure that they can maintain normal supplies during the coldest of winters. The profiles of the member states are very different with regards to the types of infrastructures they are using (some have LNG terminals, some have gas storage facilities and some have national production), and to their main suppliers. Taking into account this diversity, the draft regulation leaves to the member states the possibility to decide which are the best measures to improve the previously mentioned standards.

The security of supply has been defined as the availability of a regular supply of energy at an affordable price [2]. It has mainly four dimensions (see Ref. [3], for instance), which are the availability of the resources, their accessibility (which include geopolitical, financial and human constraints and existence of the necessary infrastructure), the acceptability from an environmental point of view and the affordability. Depending on the context, researchers or decision makers might use one or several of these dimensions to measure the extent of the energy security for different countries, but there is no such thing as an ideal measure. The use of several indicators helps assessing a multi-dimensional reality, but a composite indicator (CI) is an added value, summarizing the information, benchmarking the countries and monitoring the efforts made to improve the security of supply and the time trends.

A certain number of composite or simple indicators for security of supply have been developed in the recent years. Among them the composite vulnerability index developed in Ref. [4] is used in the World Energy Council’s study on the Europe Vulnerability to Energy Crises [5] and the Supply/Demand index [6] aims to provide a model, based on standards, for the MS to assess energy supply security. The indicators for security of supply are overviewed in Ref. [7]. Finally, Energy Policy has consecrated in 2010 a special volume “Energy security: concepts and indicators with regular papers” to this topic [8].

Guidelines for the development of composite indicators have recently been issued by the OECD and the JRC [9], aiming to improve the techniques currently used to build them. One the main objections against the use of composite indicators is that the aggregation is an arbitrary process of allocating weights to combine the individual indicators. The choice of the aggregation rule for the component indicators and the choice of the weight are hence important points in developing composite indicators. In our opinion, one of the weak points of the previously mentioned CI for security of supply is exactly the way they are aggregated.

Our paper is not just a paper about a new composite indicator on security of supply, but a paper about a methodology of building a composite indicator for energy security, which takes into account the risk-averse level of the decision maker in a range varying continuously from risk-prone to risk-averse. This methodology provides the possibility to define a threshold for a critical situation of a country or a group of countries and could be a starting point for policy decisions and/or actions. It also allows the MS to be aware of the level of risk for which they achieve an acceptable degree of energy security. Finally, as it covers the whole range of risk-averse levels, it should not lead to political disputes if used in the policy decision context, as it is sometimes the case when weights that might be considered arbitrary are assigned to the individual indicators.

The set of individual indicators (ID) used to built the CI is used more for illustrative purposes, and even if they cover dimensions of energy security, such as demand, supply and environmental aspects, they might not be considered as the optimal set of ID. They have however the advantage of being computed from the outputs of an energy system model (PRIMES) under the baseline scenario [10], and, as projections until 2030 are available, the time trends can be computed too.

The aggregation rule we will use here is derived from the Group Decision Theory and aggregates ranks. Some authors propose to generate an aggregated ranking by applying preference aggregation methods (such as value function based methods) [11]. In these cases additional inter-criteria information is required, such as the underlying functional form and the associated parameters and particular characteristics (tradeoff values, for example). This means that intense interactions with the decision makers (DM) are required and, in many real cases, strong assumptions on desirable properties are made (preference independence, for instance [11]). In some cases, a simple unordered linear combination may be used but different DM preferences cannot be considered.

Softer techniques such as outranking aggregation methods (such as ELECTRE and derived methods [12]) demand lesser effort, but also require some preference information not always available, such as the coherence of the ID family involved and the indifference, preference and veto thresholds of each ID.

A simple and efficient method to aggregate ranks, which requires minimal information, is the Borda: Fuse model [13]. It is based on a political election strategy named Borda-count. The Borda–Fuse works by assigning a score to each country for each individual indicator to be merged. The score per country depends on the rank position of the country, i.e. for a list of m ranked countries, the top country receives a score of m, the next country receives m−1 and so on until a score of 1 is assigned to the last country. The scores assigned for a given country by different ID are added up to form the total score and the countries are ranked from highest to lowest according to this total score. The method fixes the scores, and obviously the preference of the DM cannot be incorporated.

We propose here the use of the Ordered Weighted Averaging (OWA) [14] aggregation rule, which allows us to test both compensatory and non-compensatory aggregations, and to embed expert preferences in the setup of importance weights.

In a compensatory aggregation rule, weights express trade-offs between indicators. A deficit in one dimension can thus be compensated by a surplus in another. An example of compensatory aggregation rule is the linear aggregation. However, a non-compensatory logic might be necessary if different goals are equally legitimate and important, as in the case of indices that include physical, social and economic data. If an increase in economic performance cannot compensate for a loss in social cohesion or a worsening in environmental sustainability, then the compensatory aggregation rule is no longer suitable. A non-compensatory multi-criteria approach could assure non-compensability by finding a compromise between two or more legitimate goals. This approach retains only ordinal information, i.e. those countries having a greater advantage (disadvantage) in individual indicators [9].

The paper is organized as follows. Section 2 contains an overview of the aggregation approach. Section 3 presents a composite indicator for measuring security of the energy supply for European MS. Uncertainty analysis is employed to assess its robustness. Finally, in Section 4 we present the conclusions.

Section snippets

Aggregation approach

“Given multiple criteria and multiple alternatives, the goal is to aggregate the criteria information and to obtain an overall ranking of alternatives” [15]. The previous quote is a typical example of situations studied in Group Decision Theory and even in the case of a single DM [16].

In the last years, data fusion techniques have become one of the most promising approaches for merging results.

Among fusion techniques, the OWA operator provides a family of aggregation operators parameterized by

Framework

Energy security is an issue of common EU concern, and it might even become Europe’s foreign policy unifying purpose, the next “big thing” for the EU, following Ref. [23]. The security of energy supply, defined as the availability of reliable and affordable supplies of energy, has two time aspects: a short term and a long term. In the short term, the concern is about how to minimize the disruptive impacts of an unanticipated cut in supply or rise in price. In the long term, the concern is more

Conclusions

We have provided a family of composite indicators for measuring the security of energy supply. They have been built using the same aggregation rule derived from the Group Decision Theory, but using different weights associated with different risk-averse levels, covering the whole range between risk-prone and risk-averse. They can be used as decision-making instruments, to trade-off between the different interests of the different MS. They should be less controversial than other composite

References (27)

  • World Energy Council. Europe’s vulnerability to energy crises,...
  • Scheepers MJJ, Seebregts, AJ, Jong, JJ de Maters, JM. EU standards for energy security of supply. ECN report number...
  • A Löschel et al.

    Energy security: concepts and indicators with regular papers

    Energy Policy

    (2010)
  • Cited by (0)

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