NES Comparison (ASENES)

Introduction

The International Project on Innovative Nuclear Reactors and Fuel Cycles (INPRO) was established in 2000 with the goal of ensuring a sustainable nuclear energy supply to help meet 21st century global energy needs. INPRO’s activities are centred on the key concepts of global nuclear energy sustainability and the development of long-range nuclear energy strategies, so that nuclear energy is and remains available to meet national energy needs in interested IAEA Member States.
Innovative technologies to support future sustainable nuclear energy systems (NESs) are being analyzed/developed in many countries worldwide. With multiplicity of such developments the need periodically arises in Member States to evaluate the status, prospects, benefits and risks associated with development of particular technologies as compared to others, in order to prioritize/adjust the allotment of financing and other resources within national programmes on innovative nuclear technology development.
The existing NES evaluation tools require reasonably detailed design information for the components of a NES. For evolutionary systems such information is typically available, while future innovative NESs that are still under development normally lack sufficient design information for a full-scope assessment. Moreover, development of innovative systems could benefit from comparative analysis and evaluation, which is not among the objectives of most of the existing assessment tools. The INPRO collaborative project on Key indicators for innovative nuclear energy systems (KIND) had the objective to develop guidance and tools for comparative evaluation of the status, prospects, benefits and risks associated with development of innovative nuclear technologies for a more distant future [1,2]. A comparative evaluation of NESs requires judgment aggregation due to a multi-criteria character of the problem. Performance indicators characterizing different aspects associated with resource consumption, economy, proliferation risks, country specifics and waste management are conflicting by nature: increasing in a certain indicator may be associated with decreasing in other ones. Lack of common methodologies for decision-making when it is necessary to take into account a set of contradictory indicators in the NES performance comparative evaluation and, in particular, in the area of NESs sustainability assessments, complicates the procedure of formulating a coordinated vision of preferable technological and institutional solutions balanced on different costs, benefits, and risks. Wide application of multi-criteria decision analysis for judgments aggregation allows searching for compromises among the conflicting factors that determine the NES performance and calculating corresponding trade-off rates; carrying out a comparative evaluation of alternatives as well as choosing, ranking, and sorting corresponding options. Uncertainty analysis based on state-of-the art methods also should be included in the evaluations, thereby providing better grounds for judgments and enabling the decision-maker to reach a conclusion about the stability of the ranking results. The paper presents results illustrated the expedience of elaboration and application of a possible approach to NES/scenario options comparative evaluation based on multi-attribute value theory (MAVT) performed within the project. The article also provides recommendations regarding MAVT applicability for the KIND project objectives showing added value that may be obtained by implementation of the method for NESs comparative evaluation.

Comparative evaluation of NES/scenario options based on the MAVT method

Multi-Criteria Decision Analysis (MCDA) is a tool aimed at supporting decision makers who are faced with making numerous and conflicting assessments and intend to highlight conflicts and find compromises in the decision making process [3,4]. The MCDA problems consist of a finite number of alternatives, explicitly known in the beginning of the decision support process. Each alternative is represented by its performance in multiple criteria. The problem may be defined as finding the best alternative for a decision maker, or finding a set of acceptable trade-off alternatives. Studies properly organized on the MCDA base represent a process not only formally operating with a set of mathematical methods and various analytical tools, but also leading to a comprehensive understanding of the problem and its elaboration. MCDA does not provide a ‘right solution’, in this regard it would be correct to talk about a compromise or a trade-off solution, paying special attention to an analysis of the solution stability to various methods used and their model parameters.

MCDA application to NESs comparative evaluation

The decision support process starts with identification of the decision-maker, the group of experts and the stakeholders (persons interested in a certain decision), and further goes through the following steps: problem formulation, formulation of alternatives, criteria identification, performance indicators assessment, selection of MCDA method, uncertainty and sensitivity analysis, final conclusions and recommendations. A large number of MCDA techniques (value-based, outranking, reference-based, other/hybrid methods) have been developed to deal with different kinds of problems: simple scoring model, Analytic Hierarchy Process (AHP), Multi-Attribute Value Theory (MAVT), Multi-Attribute Utility Theory (MAUT), Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), etc [5]. The MCDA application provides more detailed differentiation of the alternatives, for specific costs, benefits and associated risks and allows different trade-off options. In numerous studies it was shown that application of the various MCDA methods (based on the different theoretical frameworks and calculation algorithms) for multi-criteria comparative evaluation of the NESs performance and sustainability, despite some differences in the ranking alternatives, leads to well-coordinated and consistent results. The final choice of the most appropriate method for a particular problem should be made on the basis of the problem context analysis and the initial information quality provided by subject matter experts. Despite the fact that both simple scoring models and more sophisticated MCDA methods may be used for NES comparative evaluations at both technology- and scenario-levels, it is recommended to apply simple methods for judgment aggregation in the framework of the KIND project given the purpose of application to less mature technologies and the project audience. The MAVT is one of such methods, which has found wide application for different applied problems in general and, particularly, in the nuclear engineering field [5,6]. A wide experience of applying this method summarized in different publications and the extensive set of recommendations and software tools for the method are the main obstacles which may be considered as reasons to select this method as a basic one for the NESs comparative evaluation, but it does not limit experts to implementation of other MCDA methods. MAVT is a quantitative comparison method used to combine different measures of costs, risks and benefits along with expert and decision-maker preferences into a high-level aggregated performance index – multi-attribute value function. The foundation of the MAVT method is the use of single-attribute value functions. Every indicator has a single-attribute value function created for it. These functions transform diverse indicators evaluated in ‘natural’ scale to one common, dimensionless scale (from 0 to 1), in accordance with experts’ and decision-maker’s judgments. These scores are used in further calculations. The single-attribute value functions are weighted according to the corresponding indicators importance. To identify the preferred alternative, experts should multiply each normalized alternative’s scores on corresponding weighting factors for all of an alternative’s indicators, which reflect the experts’ and decision-maker’s preferences. The total scores (multi-attribute value function) indicate the ranks of the alternatives. The preferred alternative will have the highest total score. The MAVT method is quite flexible; it allows implementation of different approaches to comparing and differentiating alternatives as well as interpreting the ranking results. Within the NESs comparative evaluation, this method may also be used in different ways. If the MAVT method is correctly used, it will certainly provide identification of the merits and demerits of NESs being compared, and their ranking according to their performance based on experts and decision makers’ judgments and preferences. In this regards it is necessary to provide recommendations for a full cycle of MAVT application for NESs comparative evaluations: scoring scale selection for indicators’ assessment; risk attitude parameter identification; single-attribute value function shape evaluation; weighting factor identification; uncertainty and sensitivity analysis; results representation. The main goals of these recommendations are to provide an acceptable MAVT method resolution and results interpretation techniques. Next section provides a number of such general and specific recommendations aimed at meeting this goal as well as providing some additional information which is necessary to judge regarding the merits and demerits of NESs being considered. Comparative evaluation procedure Due to the MAVT method is rather adjustable allowing implementation of different approaches to ranking options being considered there is a need to elaborate recommendations aimed at increasing the MAVT method resolution by reducing the risks of alternatives indistinguishability and ranking results sensitivity to model parameters. The Monte-Carlo statistical analysis was carried out to work out and detail the NESs comparative evaluation procedure and to elaborate recommendations aimed at reducing these risks, which indicating directions of modifying model parameters to provide better alternatives differentiation. Below they are mentioned. Multi-attribute value function The general form of the multi-attribute value function is

In the expression ui(xi) is the single-attribute value function and ki is the weight for i indicator. k is a scaling constant that characterizes the interaction effect between different indicators.