Abstract

     The use of renewable energy technologies is a key factor for sustainable development but their selection from several alternatives is a difficult task that relies on the careful assessment of relevant criteria. While Multiple Criteria Decision Making (MCDM) methods have been used successfully in various renewable energy technology selection problems, the decision process becomes more challenging when preferential judgements are made on the basis of non-homogenous and imprecise input data, and when there is uncertainty due to disparities among decision makers. This paper presents a hybrid MCDM method capable of overcoming these problems by taking into account quantitative and qualitative data under a probabilistic environment in the context of group decision making. In this method, qualitative data is fuzzified and used along with quantitative data to develop a hybrid model. A coefficient factor allows decision makers to vary the weight of each quantitative model so that the resultant criteria weights and overall alternatives’ scores consider both subjective considerations and objective information. An example is presented to showcase the usability of the method developed for ranking and evaluating renewable energy technologies in the mining industry. In addition, the impact of different coefficient factors on the final results was assessed by means of sensitivity analysis. The results indicate that the method developed is able to minimise the loss of valuable objective information, caused by the subjective bias of qualitative weights during the evaluations, by adjusting the coefficient factors of the hybrid model during the calculations.

Introduction

     Energy-generating technologies that depend on non-renewable fossil fuels result in significant environmental challenges, such as increasing greenhouse gas (GHG) emissions, which lead to climate change (Disli et al., 2016, Li et al., 2020). In response to these challenges, it is important to better exploit renewable energy technologies (e.g. wind and solar), which are low-cost, clean and sustainable (Cunden et al., 2020, Dincer, 2000).

     The selection of renewable energy technologies is a complex and multidisciplinary problem that mainly refers to the performance of the technologies concerning multiple criteria such as environmental, social, technical and economic (Wu et al., 2018). In order to evaluate holistically and select the technologies that have a higher performance appropriately, decision makers need to have methodological tools that incorporate both quantitative and qualitative analyses of the multiple criteria. Decision makers should, therefore, make use of the best tools available to evaluate the performance criteria of renewable energy technologies. Choosing the best renewable energy technology to use among various alternatives considering conflicting criteria is thus considered a Multiple Criteria Decision Making (MCDM) problem (Büyüközkan & Güleryüz, 2017).

Conclusions

     A hybrid MCDM method was proposed and was applied to the selection of renewable energy technologies in the mining industry, which faces an increase in energy demand as high grade ores are depleted and the demand for metals and minerals, including those required for renewable energy technologies, increases. The large scale of mining operations makes it very important to consider renewable energy options in order to contribute to the sustainability of the operations.

     Three renewable technology alternatives, namely onshore wind, concentrated solar power, and solar photovoltaic, were assessed taking into account both subjective considerations and objective information with respect to five sustainability criteria. The selected criteria were potential total power generation, GHG emissions, area requirement, levelised energy cost, and prospective jobs creation. An objective weight was obtained using data compiled from the literature, whereas a subjective weight was obtained from the judgements and preferences of four experts. The proposed method was then employed to compute the criteria weights and the alternatives’ scores.