Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach

Abstract

In this paper, an integrative approach is proposed to link integrated assessment modelling results with a novel portfolio analysis framework for robust modelling. The approach is applied for identifying optimal technological portfolios for power generation in the EU towards climate change mitigation, in a timescale until 2050. The technologies considered include photovoltaics, concentrated solar power, wind, nuclear, biomass and carbon capture and storage. The proposed approach links data from the Global Change Assessment Model (GCAM), namely subsidy curves for emissions reduction and energy security for the six power generation technologies until 2050, with other decision support methods, in the aim of managing the inherent uncertainty and assessing the robustness of the optimal portfolios. The modelling results are then integrated in a bi-objective evaluation model for portfolio analysis. The model treats uncertainty stochastically, using a Monte Carlo simulation algorithm and the Iterative Trichotomic Approach, and defines specific portfolios of electricity generation technologies as the most robust. The results are presented and discussed, mainly in terms of highlighting the robustness of the Pareto optimal solutions, which is essential for policymakers to be more confident when selecting technology portfolios that feature a high degree of uncertainty, regarding their vulnerability to different future developments. By aggregating the results to one robust technological portfolio, the proposed approach features the potential to subsequently be linked to a deterministic model.

Integrated policy assessment and optimisation over multiple sustainable development goals in Eastern Africa

“…In this study, we tried to account for this uncertainty by connecting integrated assessment and portfolio analysis with robustness analysis. Although the combination of these methods, allowing for policy optimisation over different objectives, has been pretested in a different setting (Forouli et al 2019b), the use of different SSPs as a form of deterministic uncertainty has been a novel approach that suits the purpose of this study, but yet has to be proven in future work. Alternatively, optimal subsidy portfolios could be identified separately for each SSP, robust to stochastic uncertainty.…”

“…(O'Neill et al 2014) in the GCAM model. Key parts on the proposed methodology are explained byForouli et al (2019a) as well as in section A3 of the SM Forouli et al (2019b). …”

AUGMECON-Py: A Python Framework for Multi-Objective Linear Optimisation Under Uncertainty

“…The AUGMECON-R algorithm, as well as its predecessors (AUGME-CON2 [2] and AUGMECON [3]) are widely used in literature and practice for the timely optimisation of complex systems that feature multiple evaluation criteria, constraints of different nature, and numerous decision variables. The fields of application of the AUGMECON algorithms include inter alia supply chain management , energy and climate action [25][26][27], energy planning [28][29][30][31][32][33][34][35][36], waste management [37][38][39], investment portfolio analysis [40,41], transportation [39,40], project selection [41,42], and network optimisation and planning [43][44][45][46][47]. AUGMECON-R, which is the most recent member of the AUGME-CON family of methods, allows for easy and timely solution of very demanding (in terms of time and processing requirements) problems of numerous objective functions.…”

The desirability of transitions in demand: Incorporating behavioural and societal transformations into energy modelling

“…Apart from co-developing inputs, effort must be put into enhancing the robustness of modelling outcomes and providing policymakers with information on the level of certainty over selecting feasible technologies or policies [165] , [166] , [167] . Significant work in the modelling community must also be done in improving transparency, by opening the scientific processes to stakeholders.…”