Hydro-Power Planning Optimization

In the present master thesis, three different algorithms, for optimizing a medium-term hydro-power planning problem with consideration of stochas-tic values, are presented and compared. The uncertainty arises due to vary-ing water inflows and uncertain evolution of prices. The solution methods yield robust control strategies, which indicate for every time step, e.g. every month in one year, the optimal amount of energy to turbine and pump. A power plant with a single reservoir, that is able to turbine and pump, is considered. The stochastic processes are modelled using samples from esti-mated probability density functions for every time step. The stochastic linear programming, represents the stochastics in a sce-nario tree, consisting of a selection of possible stochastic evolutions with corresponding probabilities, and optimizes it using a standard solver for lin-ear programs. The other two methods stochastic dynamic programming and stochastic dual dynamic programming are based on the principle of optimal