Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty,
dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A …

This paper addresses the long-term planning of electric power infrastructures considering
high renewable penetration. To capture the intermittency of these sources, we propose a …

Hydropower producers rely on stochastic optimization when scheduling their resources over
long periods of time. Due to its computational complexity, the optimization problem is …

We address the long-term planning of electric power infrastructure under uncertainty. We
propose a Multistage Stochastic Mixed-integer Programming formulation that optimizes the …

This paper presents strong scenario dominance cuts for effectively solving the multi-stage
stochastic mixed-integer programs (M-SMIPs), specifically focusing on the two most well …

Generation expansion planning for hydro-thermal power systems aims to find optimal
investment decisions among a set of possible power plant projects. For any given …

We propose a new algorithm for solving multistage stochastic mixed integer linear
programming (MILP) problems with complete continuous recourse. In a similar way to cutting …

Mixed integer dynamic approximation scheme (MIDAS) is a new sampling-based algorithm
for solving finite-horizon stochastic dynamic programs with monotonic Bellman functions …

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty,
dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A …

This paper presents two algorithms for solving a medium-term hydro optimization.
Considered are risk-averse operation, provision of spinning reserves as well as short-term …