This research project deals with repeated decision problems under
uncertainty which can be formulated as multistage stochastic
programs (MSPs). Without any suitable approximations, such problems
can usually not be solved, neither analytically nor numerically.
Thus, the principal objective of this project is the development and
validation of a universal stage aggregation and scenario generation
scheme which applies to a broad class of MSPs. Error bounds and
convergence properties will be thoroughly investigated. Further
emphasis is put on the formulation of weak regularity conditions
under which the new approximation scheme is applicable. As far as
discretization of time and (probability) space is concerned, the
tradeoff between temporal and spacial resolution will be assessed.
The new approximation method is applied to the valuation of electricity derivatives (e.g. swing options) and some other prototypical real-life decision problems in the energy and finance sectors.
stochastic programming, approximation scheme, aggregation, discretization, eectricity derivatives
|type||applied research project|
|start of project||2005|
|end of project||2006|