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Das Ende der Grundlast
(2015)
Carpe diem: A novel approach to select representative days for long-term power system modeling
(2016)
In order to explore scenarios on the future of power systems, a variety of numerical models have been developed. As the share of variable renewable energy sources, particularly wind and solar, is projected to significantly increase, accounting for their temporal and spatial variability becomes ever more important in developing sound long-term scenarios. Computational restrictions prevent many long-term power system models being developed with an hourly resolution; instead they use time slices that aggregate periods with similar load and renewable electricity generation levels. There is to date no reproducible and validated method to derive and select time slices for power system models with multiple fluctuating time series. In this paper, we present a novel and effective method that is easily applied to input data for all kinds of power system models. We utilize this procedure in the long-term power system model LIMES-EU and show that a small number of representative days developed in this way are sufficient to reflect the characteristic fluctuations of the input data. Alongside a validation of the method, we discuss the conditions under which seasonal differentiation, and the use of representative weeks instead of days, is necessary.
Several studies have shown that the revenue of wind power generators on spot markets (“market value”) diminishes with increasing deployment. This “value drop” is mostly observed in power markets that are dominated by thermal power plants, such as in Germany. This paper assesses the wind market value in power systems where hydroelectric stations with large reservoirs prevail, such as in Sweden. Due to their dispatch flexibility, such hydropower compensates for wind power output variability and thereby mitigates the wind power value drop. The market value of electricity from wind declines with penetration in both types of power systems, but it tends to decline at a slower rate if hydropower is present. This paper presents empirical evidence on the relevance of this effect derived from market data and numerical model results. Our results indicate that when moving from 0% to 30% wind penetration, hydropower mitigates the value drop by a third. As a result, 1 MWh of wind energy is worth 18% more in Sweden than in Germany. Sensitivity analyses indicate high robustness despite large parameter uncertainty: in 80% of all sensitivities, wind energy is valuable 12–29% more in Sweden than in Germany. The benefits of hydropower seem to level off at around 20% wind penetration. This suggests that the hydro flexibility is “exhausted” at this level. Low wind speed wind turbines, carbon pricing, and upgrades of hydropower generation capacity can lever the added value of hydro flexibility further. Not only is wind energy more valuable in the presence of hydropower, hydroelectricity also becomes more valuable if paired with wind power.
The global energy system is undergoing a major transition, and in energy planning and decision-making across governments, industry and academia, models play a crucial role. Because of their policy relevance and contested nature, the transparency and open availability of energy models and data are of particular importance. Here we provide a practical how-to guide based on the collective experience of members of the Open Energy Modelling Initiative (Openmod). We discuss key steps to consider when opening code and data, including determining intellectual property ownership, choosing a licence and appropriate modelling languages, distributing code and data, and providing support and building communities. After illustrating these decisions with examples and lessons learned from the community, we conclude that even though individual researchers' choices are important, institutional changes are still also necessary for more openness and transparency in energy research.