Elsevier

Energy

Volume 95, 15 January 2016, Pages 602-611
Energy

Which energy mix for the UK (United Kingdom)? An evolutive descriptive mapping with the integrated GAIA (graphical analysis for interactive aid)–AHP (analytic hierarchy process) visualization tool

https://doi.org/10.1016/j.energy.2015.12.009Get rights and content

Highlights

  • We introduce a descriptive visual analysis tool for the analytic hierarchy process.

  • The method has been implemented as an open-source preference elicitation tool.

  • We analyse user preferences in the energy sector using this method.

  • The tool also provides a way to visualize temporal preferences changes.

  • The main negative temporal shift in the ranking was found for the nuclear energy.

Abstract

Although Multi-Criteria Decision Making methods have been extensively used in energy planning, their descriptive use has been rarely considered. In this paper, we add an evolutionary description phase as an extension to the AHP (analytic hierarchy process) method that helps policy makers to gain insights into their decision problems. The proposed extension has been implemented in an open-source software that allows the users to visualize the difference of opinions within a decision process, and also the evolution of preferences over time. The method was tested in a two-phase experiment to understand the evolution of opinions on energy sources. Participants were asked to provide their preferences for different energy sources for the next twenty years for the United Kingdom. They were first asked to compare the options intuitively without using any structured approach, and then were given three months to compare the same set of options after collecting detailed information on the technical, economic, environmental and social impacts created by each of the selected energy sources. The proposed visualization method allow us to quickly discover the preference directions, and also the changes in their preferences from first to second phase. The proposed tool can help policy makers in better understanding of the energy planning problems that will lead us towards better planning and decisions in the energy sector.

Introduction

Energy planning is an important process that has long-range implications but unfortunately, the process is not trivial as it involves many stakeholders with different backgrounds, and has to be analysed in many contexts including the social, economic, environmental and technical contexts. To facilitate this process, MCDM (multi-criteria decision making) methods have been extensively used to prioritize available options after assessing and synthesizing all the individual preferences [1]. However, the aggregative approaches like AHP (analytic hierarchy process) have low explanatory power and results are often not enough to reach to a consensual decision by stakeholders, especially when they have divergent views [2]. It is therefore necessary to identify the points of agreements and disagreements before initiating a negotiation process. In response to this need, GAIA (graphical analysis for interactive aid) [3] was developed to capture different views of DMs (decision-makers) with respect to many criteria and to display them graphically. The GAIA method was initially proposed to complement PROMETHEE which is widely used in strategic decision making [3]. GAIA has recently been combined with Geographical Information Systems [4] and FlowSort [5].

In this paper, we propose to combine the GAIA method with AHP and show its usefulness in the area of energy policy making. We investigate the visualization of preferences and their evolution in situations where additional information is acquired during the decision making process. The proposed combination (AHP-GAIA) displays a graphical representation that can easily highlight the presence of any like-minded decision makers/agents or opposite minds, and can also reflect changes in their preferences over time. This hybrid method has been applied in a two-phase experiment. In the first phase, participants were asked to rank seven energy sources in the United Kingdom for the next 20 years. Each participant compared the options in pairs without any specific tool. For the second phase, the participants were informed about the widely-used criteria to evaluate the energy sources, and were asked to produce a documented report on how well the energy sources were fulfilling these criteria. Three months later, the participants submitted their reports that showed their analyses of the seven energy sources with the help of AHP. The adapted GAIA approach was then used to visualize the change in participants' preferences. In both phases, the solar energy has been found to be the most preferred choice, while coal remained the least preferred. Interestingly, the dispersion of the opinions decreased in the second phase i.e. more participants were found in agreement with each other after performing the detailed analysis of the selected energy sources.

The proposed technique of AHP-GAIA has been implemented in an open-source software tool – called PriEsT – that helps visualize all the preferences of multiple stakeholders in a single plot. We believe that the proposed technique can help policy makers towards understanding the preferences of each stakeholder and therefore can help towards better justification, better communication and even towards better ways for negotiation.

The remaining paper is structured as follow: Section 2 reviews the literature on energy planning with multi-criteria methods. Section 3 introduces the AHP and GAIA methods; Section 4 then proposes the use of GAIA in AHP group decision making. Section 5 presents the experimental analysis and their results; and Section 6 concludes the paper with possible future work.

Section snippets

Literature review

Energy planning is becoming more and more complex as demands are increasing for more energy while at the same time, challenges like environmental impact, safety, security, and economic viability are all pressing for a need to have better technology and tools in the field of energy production and planning. Although several methods have been proposed to assist the energy planning process, the two most widely-used techniques found in literature are the use of simulation and multi-criteria decision

Basics on AHP

AHP is a widely-used MCDM technique with the following two important features as compared to other MCDM methods [64]:

  • The decision problem in AHP can be decomposed into a multi-level hierarchical structure of criteria. The criteria at the lowest level of the hierarchy are considered ‘atomic’ in a sense that they could not be decomposed further. The alternatives are then placed below these ‘atomic’ criteria.

  • All the evaluations are provided through pairwise comparisons and priorities are derived

Constructing the GAIA plan

The idea of GAIA is to represent multidimensional information in a low dimensional space with as much information as possible. For example, a decision problem that involves six criteria will have six-dimensional scores assigned to each alternative, which is impossible to visualize in a conventional Euclidean space. This is sometimes referred to as “curse of dimensionality”. To solve this problem, GAIA borrows the idea of dimensionality reduction from principle component analysis – a widely used

Case study

The GAIA-AHP hybrid group decision making method and visualization has been used in a two-phase experiment to investigate the difference of intuitive versus informed and structured decision in the energy sector. Postgraduate students in the Portsmouth Business School were asked to estimate the importance of seven sources of energy production (coal, gas, nuclear, oil, solar, tidal and wind) for the next twenty years for the United Kingdom. Students are an important voting class and it is

Conclusion

Energy planning is a complex problem that has been often solved with multi-criteria decision methods. These methods have the strength to incorporate technical and subjective conflicting appreciations. In this paper, we complement AHP with a visualisation tool in an open-source software tool that helps visualize the preferences of multiple stakeholders in a single plot. This descriptive feature allows policy makers to better understand the preferences of stakeholders, and has the ability to

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