  Wind integration in power grids is very difficult, essentially because of the
uncertain nature of wind speed. Forecasting errors on output from wind turbines
may have costly consequences. For instance, power might be bought at highest
price to meet the load. On the other hand, in case of surplus, power may be
wasted. Energy storage facility may provide some recourse against the
uncertainty on wind generation. Because of the sequential nature of power
scheduling problems, stochastic dynamic programming is often used as solution
method. However, this scheme is limited to very small networks by the so-called
curse of dimensionality. To face such limitations, several approximate
approaches have been proposed. We analyze the management of a network composed
of conventional power units as well as wind turbines through approximate
dynamic programming. We consider a general power network model with ramping
constraints on the conventional generators. We use generalized linear
programming techniques to linearize the problems. We test the algorithm on
several networks of different sizes and report results about the computation
time. We also carry out comparisons with classical dynamic programming on a
small network. The results show the algorithm seems to offer a fair trade-off
between solution time and accuracy.
