Short-Term Multi-Stage Stochastic Optimization of Hydropower Reservoirs Under Meteorological Uncertainty

Hydroelectric power systems are characterized by variability and uncertainty in yield and water resources obligations. Market volatility and the growing number of operational constraints for flood control, navigation, environmental obligations and ancillary services (including load balancing requirements for renewable resources) further the need to quantify sources of uncertainty.

This research presents an integrated framework to handle several sources of uncertainty. Main focus is on the meteorological forecast uncertainty based on deterministic and probabilistic Numerical Weather Predictions (NWP), its consistent propagation through load and streamflow forecasts, and the generation of scenario trees with novel multi-dimensional distance metrics. The scenario trees enable us to extend a deterministic optimization setup to a multi-stage stochastic optimization approach as the mathematical formulation of the short-term system management.

The Federal Columbia River Power System (FCRPS), managed by the Bonneville Power Administration, the US Army Corps of Engineers and the Bureau of Reclamation, serves as a large-scale test case for the application of the new framework. We proof the feasibility of the new approach and verify the operational applicability within a real-time environment.