  Day-ahead scheduling of electricity generation or unit commitment is an
important and challenging optimization problem in power systems. Variability in
net load arising from the increasing penetration of renewable technologies have
motivated study of various classes of stochastic unit commitment models. In
two-stage models, the generation schedule for the entire day is fixed while the
dispatch is adapted to the uncertainty, whereas in multi-stage models the
generation schedule is also allowed to dynamically adapt to the uncertainty
realization. Multi-stage models provide more flexibility in the generation
schedule, however, they require significantly higher computational effort than
two-stage models. To justify this additional computational effort, we provide
theoretical and empirical analyses of the value of multi-stage solution for
risk-averse multi-stage stochastic unit commitment models. The value of
multi-stage solution measures the relative advantage of multi-stage solutions
over their two-stage counterparts. Our results indicate that, for unit
commitment models, value of multi-stage solution increases with the level of
uncertainty and number of periods, and decreases with the degree of risk
aversion of the decision maker.
