11h30 - 11h55
Risk informed decision making for reservoir operation under flooding conditions
We present the Risk Informed Decision Making Framework and software tool to formally account for risk and uncertainty in hydropower system operations under flooding conditions. The tool provides a robust and comprehensive approach towards identifying alternative operating plans that formally adopt a state-of-the-art risk informed decision making framework. It is solidly grounded in and follows a well-structured planning process, and augments currently used methods by incorporating techniques from probabilistic risk analysis and Multi-criteria Decision Analysis techniques. We present the results of a case study to illustrate the framework and the software system and the numerous advantages over currently used approaches.
11h55 - 12h20
Medium-Term Hydropower Scheduling with Binary State Variables
Hydropower producers rely on stochastic optimization when scheduling their resources over longer periods of time. Considering the complexity of the scheduling problem, it is normally casted as a linear programming problem with only sales of energy. Demand for flexible generation where generators provide back-up capacity is growing. Consequently, it becomes increasingly important to capture parts of the hydropower operational characteristics that are not easily linearized, e.g. unit commitment.
We apply recent improvements in the stochastic dual dynamic programming (SDDP) algorithm to the medium-term hydropower scheduling problem, allowing representation of non-convexities by binary variables.
12h20 - 12h45
Fast Near-Optimal Heuristic for the Short-Term Hydro-Generation Planning Problem
Short-term hydro-generation planning can be efficiently modeled as a mixed integer linear program (MILP). However, for Hydro-Quebec’s production system, the resulting MILP is too large to be solved in reasonable time with commercial solvers. We developed a three-phase approach based on price decomposition that yields quickly near-optimal solutions to large-scale real-world instances. We will present this approach and give numerical illustrations on real instances.