Otto matic pawear inargi sistam series#

It covers important aspects such as CO2 emissions and use of freshwater for cooling of power plants, and estimates their sensitivity to the changing climatic conditions.

Changing weather conditions affect the operation of the European power systems. The output of renewable energy sources fluctuates depending on the availability of wind, cloud cover, or water levels in reservoirs, while the output of dispatchable generators, such as gas turbines, must be adapted accordingly to ensure that supply and demand are balanced at all times. The link between meteorology and power systems also manifests itself through other aspects such as the demand for electricity, affecting the operation of power markets, and thus power prices, emissions, and use of resources (fuels, fresh water etc). Today more than 40% of the European electricity generation capacity is heavily dependent on climatic factors. This dependence is expected to increase in the future as Europe transitions to a carbon-neutral economy. This work is based on a set of simulations carried out with the Dispa-SET power system model, developed by the JRC, using hourly datasets for wind and solar capacity factors, electricity demand and hydropower inflows based on actual meteorological conditions. The simulations reproduce the behaviour of all the European power systems, as they operated in 2016, using a set of 26 different climatic conditions as inputs.Ĭlustering of extremes is critical for hydrological design and risk management and challenges the popular assumption of independence of extremes. We investigate the links between clustering of extremes and long-term persistence, else Hurst-Kolmogorov (HK) dynamics, in the parent process exploring the possibility of inferring the latter from the former.

Otto matic pawear inargi sistam series#

We find that (a) identifiability of persistence from maxima depends foremost on the choice of the threshold for extremes, the skewness and kurtosis of the parent process, and less on sample size and (b) existing indices for inferring dependence from series of extremes are downward biased when applied to non-Gaussian processes. We devise a probabilistic index based on the probability of occurrence of peak-over-threshold events across multiple scales, which can reveal clustering, linking it to the persistence of the parent process. Its application shows that rainfall extremes may exhibit noteworthy departures from independence and consistency with an HK model. Power generation sector worldwide accounts for high water withdrawal and consumption due to the hydropower generation and cooling of thermal power plants. Hence, the operation of the power generation sector is constrained by the availability of the water resources, as well as the addition of constrains on water resources used for other purposes, such as irrigation, flood control, water supply, agriculture, etc. The optimal utilization of water resources between the water and energy sector is defined under the term water-energy (or water-power) nexus.