Wind and solar power are known to be highly influenced by weather events and may ramp up or down
abruptly. Such events in the power production influence not only the availability of energy, but also
the stability of the entire power grid. By analysing significant amounts of data from several regions
around the world with resolutions of seconds to minutes, we provide strong evidence that renewable
wind and solar sources exhibit multiple types of variability and nonlinearity in the time scale of seconds
and characterise their stochastic properties. In contrast to previous findings, we show that only the
jumpy characteristic of renewable sources decreases when increasing the spatial size over which the
renewable energies are harvested. Otherwise, the strong non-Gaussian, intermittent behaviour in the
cumulative power of the total field survives even for a country-wide distribution of the systems. The
strong fluctuating behaviour of renewable wind and solar sources can be well characterised by
Kolmogorov-like power spectra and q-exponential probability density functions. Using the estimated
potential shape of power time series, we quantify the jumpy or diffusive dynamic of the power. Finally
we propose a time delayed feedback technique as a control algorithm to suppress the observed short
term non-Gaussian statistics in spatially strong correlated and intermittent renewable sources
