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.
The ongoing world-wide increase of installed photovoltaic (PV) power attracts notice to weather-induced PV power output variability. Understanding the underlying spatiotemporal volatility of solar radiation is essential to the successful outlining and stable operation of future power grids. This paper concisely reviews recent advances in the characterization of irradiance variability, with an emphasis on small spatial and temporal scales (respectively less than about 10 km and 1 min), for which comprehensive data sets have recently become available. Special attention is given to studies dealing with the quantification of variability using such unique data, the analysis and modeling of spatial smoothing, and the evaluation of temporal averaging.
Abstract. Characterizing spatiotemporal irradiance variability is important for the successful grid integration of increasing numbers of photovoltaic (PV) power systems. Using 1 Hz data recorded by as many as 99 pyranometers during the HD(CP) 2 Observational Prototype Experiment (HOPE), we analyze field variability of clear-sky index k * (i.e., irradiance normalized to clear-sky conditions) and sub-minute k * increments (i.e., changes over specified intervals of time) for distances between tens of meters and about 10 km. By means of a simple classification scheme based on k * statistics, we identify overcast, clear, and mixed sky conditions, and demonstrate that the last of these is the most potentially problematic in terms of short-term PV power fluctuations. Under mixed conditions, the probability of relatively strong k * increments of ±0.5 is approximately twice as high compared to increment statistics computed without conditioning by sky type. Additionally, spatial autocorrelation structures of k * increment fields differ considerably between sky types. While the profiles for overcast and clear skies mostly resemble the predictions of a simple model published by Hoff and Perez (2012), this is not the case for mixed conditions. As a proxy for the smoothing effects of distributed PV, we finally show that spatial averaging mitigates variability in k * less effectively than variability in k * increments, for a spatial sensor density of 2 km −2 .
Abstract. Observations of turbulence are analysed for the afternoon and evening transition (AET) during the BoundaryLayer Late Afternoon and Sunset Turbulence (BLLAST) experimental field campaign that took place in Lannemezan (foothills of the Pyrenees) in summer 2011. The case of 2 July is further studied because the turbulence properties of the lower atmosphere (up to 300 m above ground level) were sampled with the Meteorological Mini Aerial Vehicle (M 2 AV) from turbulently mixed to stably stratified atmospheric conditions. Additionally, data from radiosoundings, 60 m tower and UHF wind profiler were taken together with the model results from a high-resolution mesoscale simulation of this case. Weak large-scale winds and clear-sky conditions were present on the studied AET case favouring the development of slope winds and mountain-plain circulations. It is found that during the AET the anisotropy of the turbulent eddies increases as the vertical motions are damped due to the stably stratified conditions. This effect is enhanced by the formation of a low-level jet after sunset. Finally, the comparison of the anisotropy ratio computed from the different sources of observations allow us to determine the most relevant scales of the motion during the AET in such a complex terrain region.
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