11h00 - 11h25
Multistage Stochastic Optimization Methods Applied to Solve the Medium-Term Operation Planning Problem
The purpose of this work is to present a comparative study about the performance of different multistage stochastic optimization methods applied to the Medium Term Operation Planning problem: Nested Decomposition, a common approach for solving these kinds of problems, and the Progressive Hedging method, particularly promising to solve multistage stochastic.
11h25 - 11h50
Midterm Hydro Generation Scheduling under Inflow Uncertainty Using the Progressive Hedging Algorithm
We propose a new stochastic optimization model to solve Hydro-Québec's midterm generation scheduling problem (MGSP). The aim is to establish weekly generation targets for controllable hydro plants to maximize reservoir energy storage at the end of a 18-24 months planning horizon. Reservoir inflow variability is modeled using a finite scenario tree. Variablehead hydro plants generation functions are modeled as concave piecewise linear functions of reservoir storage and turbined outflow. The MGSP is formulated as a huge multistage stochastic linear program. A Lagrangean relaxation is applied on non-anticipativity constraints of the stochastic program. A scenario decomposition approach is used to solve efficiently the stochastic program. We apply the well-know progressive hedging algorithm. This optimization model is tested on Hydro-Québec large-scale hydro-dominated power system.