  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 {\it 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.
