since the emergence of power market, the target of power generating utilities has mainly switched from cost minimization to revenue maximization. they dispatch their power energy generation units in the uncertain environment of power market. as a result, multi-stage stochastic programming has been applied widely by many power generating agents as a suitable tool for dealing with self-scheduling...

Portfolio and risk management problems of power utilities may be modeled by multistage stochastic programs. These models use a set of scenarios and corresponding probabilities to model the multivariate random data process (electrical load, stream flows to hydro units, and fuel and electricity prices). For most practical problems the optimization problem that contains all possible scenarios is t...

stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on scenario generation model about future environment developments. In the present paper, the scenario model is developed for the case when enough data paths can be generated, but due to solvability of stochastic program the scenario tree has to be constructed. The propos...

I the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree s...

To develop practical and efficient scenario tree reduction methods, we introduce a new methodology which allows for changing node values, and an easy-tocalculate distance function to measure the difference between two scenario trees. Based on minimizing the new distance, we first construct a primitive scenario tree reduction model which also minimizes the Wasserstein distance between the reduce...