Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways. (15th June 2020)

• Record Type: Journal Article
• Authors: Pilpola, Sannamari; Lund, Peter D.
• Is Part Of: Energy. Volume 201(2020)
• Publication Date: 2020-06-15
• Subjects: Energy system modelling -- Uncertainty -- Monte Carlo uncertainty analysis -- Scenarios -- Finland; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.117652
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Decarbonization is an important goal of the future energy transition, but its modelling is also subject to several uncertainties. Here we investigate the impacts of such uncertainties through analyzing the overall performance and operation of a modelled national energy system undergoing deep decarbonization. Finland was chosen as a case, as it intends to become carbon-neutral already by 2035. Uncertainties in costs, energy consumption, and renewable resource potential and how they affect the operation of a modelled energy system is analyzed using a Monte Carlo method linked to a national energy system model with hourly resolution. The importance of the different uncertainties for the overall system indicators such as annual cost, CO2 emissions, and reliability are assessed. The impacts on different modelled low-carbon pathways are compared. For the Finnish case study, the projected energy consumption seems to be the most important uncertainty factor for the future energy system scenarios (e.g. for the CO2 emissions), followed by the production of wind power and the potential of biomass. The results indicate that addressing input uncertainties will be highly relevant for energy system modelling when pursuing decarbonization. None of the modelled cost-optimal decarbonization pathways stands out as fully resilient in this respect. Highlights: The impacts of uncertainties on low-carbon energy system modelling are analyzed. Monte Carlo analysis is used to examine uncertainties' effects on modelled systems. Finland is used as a case with the year 2050 data. Energy consumption seems to be the most significant uncertainty factor.