Abstract
Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.
Highlights
There are various concepts and ideas relating to how to design the future energy supply to achieve the climate goals set out in the Paris Agreement of 2015
The analysis of the energy system’s transformation was subject to an interval of five years, starting from today’s energy system and progressing up to the year 2050. This can be run in linear programming (LP) or quadratic programming (QP) mode based on the implementation of investment costs
The consideration of uncertainties in energy system models plays a key role in improving the results of these models and the quality of energy scenarios
Summary
There are various concepts and ideas relating to how to design the future energy supply to achieve the climate goals set out in the Paris Agreement of 2015. The underlying decision-making process is supported by different types of energy system models Most of these focus on the identification of cost-efficient options to supply future energy demand [1]. In particular, are affected by this so-called ‘penny switching effect’, which leads to a complete switch to other technologies through marginal variations in the corresponding investment costs [13,14,15]. This results in the underestimation of certain technologies or even of entire supply chains. The model’s results are expected to comprise a wider range of technologies, be more robust, and, more realistic
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