Load Flow with Uncertain Loading and Generation in Future Smart Grids

The growing amount of renewable and fluctuating energy sources for the production of electrical energy increases the volatility and level of uncertainty in the operation of power systems. Whether it is the growing number of photovoltaic installations harnessing solar energy or large-scale wind farms, these new class of environmentally dependent appliances increase the unpredictability of load situations hitherto known only from consumer behavior. One of the mayor concerns in grid operation under increasing feed-in from unpredictable generation and consumption is the detection of peaks in network strain. In order to limit investments into grid infrastructure to a reasonable level node-specific limitations for power injections are introduced to reduce the probability of such peaks that may pose a threat to a stable operation of the power system. In order to support the ongoing integration of renewable generation into the grid, a trade-off has to be found between investment costs and imposed operational constraints. In order to determine the probability of congestions under these unpredictable conditions, mathematical algorithms are employed that are able to estimate the probability of certain line loading levels from the probabilistic data derived from the appliances’ behavior.