Dynamic Modeling with Empirical Data forHydropower Decision Support

Long-term decision making strategic investment planning under the condition of great uncertainty is of great importance to power utilities such as Manitoba Hydro. However, existing analysis methods lack the definition of clear formal methods to define the framework for these uncertainties nor to coordinate the decision-making methodologies to support the definition of robust investment programme planning. Dynamic modeling with empirical data could be of use to reduce the degree of uncertainties. This project will address the long-term decision making problem that Manitoba Hydro is currently facing, using dynamic modeling, model filtering techniques and practical data fusion. The expected outcome of this project would be a novel but broadly applicable approach to dynamically exploit real data for decision making, execute in-time model intervention simulation, and visualize data in appropriate formats to support decision makers in assessing the options. An implementation of this approach will also be provided, with an eye towards use by senior analysts in Manitoba Hydro to test and evaluate their strategies against issues related to this long-term decision making problem.

Faculty Supervisor:

Nate Osgood

Student:

Partner:

Manitoba Hydro

Discipline:

Engineering

Sector:

Professional, scientific and technical services; Utilities

University:

University of Saskatchewan

Program: