We are well on our way, with wind turbines delivering power equivalent to 43.6% of Denmark’s electricity consumption in 2017. Since the installation of the first wind turbines in Denmark in the 70’s, the total capacity of installed wind turbines has been increasing to almost 6000 MW:
Especially the Western regions of Denmark (DK1) can produce a lot of wind power, due to strong winds reaching Denmark from the Atlantic ocean. If all those wind turbines were operating at their maximum capacity at the same time, it would be enough to support roughly 8 million homes, if we adopt a relatively high annual energy consumption of 6000 kWh per year per household. However, all those wind turbines are never producing at their maximum capcity - luckily, because the Danish electricity system probably would not be able to support it!
Wind is definitely the source of power to go for in Denmark, where solar power is hard to get in winter. That said, reaching the goal of 100% renewable energy by 2050 is not just a matter of putting up more wind turbines. Wind speeds fluctuate and a whole line of research (and business) is currently dedicated to predict those fluctuations. Evern with prefect predictions of how the wind (and hence the wind power) is going to fluctuate, we cannot fully take advantage of the wind power produced if the power system is not designed in a flexible, that for example knows when to store or re-directing excess power.
I work in a group at DTU that searched for ways to reshape and store wind power as gas and heating in Denmark. For that work, I am involved in the EPIMES project: Enhancing wind Power Integration through optimal use of cross-sectoral flexibility in an integrated Multi-Energy System, a project which is a collaboration between DTU CEE and Chinese partners, including Tsinghua University in Beijing. On the Danish side, much work has gone into studying the flexibility options in a normal residential house, where energy can be stored as heat and smart planning using Model Predictive Control, can ensure that heating takes place mostly at times with low electricity prices (see a recent paper on this here).
My work so far has been to analyze wind power production time series, with the purpose of identifying fluctuations and converting them to flexibility demands on responsive loads, such as residential houses with smart controls. A great tool for the analysis of a fluctuating signal, is Fourier analysis which I have exploited in order to separate low frequency fluctuations from high frequency ones. Below is an example for a week in 2018, where I have identified dips in national wind power production on different timescales: