A data envelopment analysis based evaluation of sustainable energy generation portfolio scenarios.

Generating secure, affordable, and clean energy requires careful evaluation of the costs and associated risks of different energy generation sources. Portfolio optimisation models are commonly used in this regard to help diversify risks associated with generation sources. In recent times, energy policies often require the consideration of the environmental and social effects of such activity. Consequently, sustainability has become a key factor in making energy mix planning decisions. To incorporate sustainability considerations in energy mix planning, the conventional approach has been to add indicators for environmental and social costs to the total generation cost for each available technology in a portfolio optimisation model. However, this approach to developing a sustainable generation mix may not effectively address all dimensions of sustainability. In most cases, the economic dimension is prioritised over social and environmental factors. We examine how various aggregation methods impact the preference among the sources and the optimal portfolio mix and propose aggregation methods that effectively incorporate all sustainability dimensions. We observed that technology ranking based on multiplicative, pairwise interaction, and multilinear aggregation options aligns better with our sustainability goals than additive aggregation. By adopting these methods of aggregation, we were able to include more renewable and clean energy sources in our optimal portfolios. • Sustainable energy mix literature relies on the additive aggregation of costs. • Additive aggregation allows compensation across sustainability dimensions. • We examine various cost aggregation methods. • Various optimization models are developed to test aggregation methods • More sustainable portfolios are generated through multiplicative aggregation. [ABSTRACT FROM AUTHOR]