Leader: Dr. Amaury Tilmant
Dr. François Anctil (Université Laval)
Dr. Bryan Tolson (University of Waterloo)
Objective: Incorporation of hydrological ensemble forecasts into a large-scale, stochastic, optimization algorithm.
Significance: Traditional stochastic optimization techniques are not computationally amenable methods for solving large-scale reservoir operation problems subject to hydrological ensemble forecasts (H-EPS). Most attempts found in the literature have adopted simplifications and/or case studies that are not representative of the real conditions faced by operators in Canada (e.g., Faber and Stedinger, 2001). Recent advances in the field of stochastic programming offer new opportunities in terms of modelling details, particularly with respect to the size of the system and to the treatment of hydrologic uncertainty (Tilmant et al., 2008; Marques and Tilmant, 2013). We propose a solution strategy based on the construction of a locally accurate approximation of the objective function instead of an exhaustive representation over the entire state-space. This approximate solution strategy is particularly attractive here because the use of frequently updated H-EPS implies that there is no need to explore the entire state space but only the most relevant subset considering the initial status of the system and the forecasts (Meier et al., 2012).
Outcomes: Project 2-4 will propose an optimization framework that can solve the multi-reservoir operation problem using H-EPS. By integrating H-EPS in a stochastic optimization model, reservoir operators will be able to derive dynamic, risk-based, reservoir operation policies. This outcome will help Canadian agencies and hydropower companies responsible for operating large-scale water resource systems to improve their management tools.