Designing with Information Feedbacks: Forecast Informed Reservoir Sizing
and Operation
Abstract
The value of streamflow forecasts to inform water infrastructure
operations has been extensively studied. Yet, their value in informing
infrastructure design is still unexplored. In this work, we investigate
how dam design is shaped by information feedbacks. We demonstrate how
flexible operating policies informed by streamflow forecasts enable the
design of less costly reservoir relative to alternatives that do not
rely on forecast information. Our approach initially establishes
information bounds by selecting the most informative lead times of
perfect streamflow forecasts to be included in the infrastructure
design. We then analyze the design and operational sensitivities
relative to realistic imperfect streamflow forecasts synthetically
modeled to explicitly represent different biases. We demonstrate our
approach through an ex-post analysis of the Kariba dam in the Zambezi
river basin.
Results show that informing dam design with perfect forecasts enable
attaining the same hydropower production of the existing dam, while
reducing infrastructure size and associated capital costs by 20%. The
use of forecasts with lower skill reduces this gain to approximately
15%. Finally, the adoption of forecast information in the operation of
the existing system facilitate an annual average increase of 60 GWh in
hydropower production. This finding, extrapolated to the new planned
dams in the basin, suggests that consideration of forecast informed
policies could yield power production benefits equal to 75% of the
current annual electricity consumption of the Zambian agricultural
sector. Forecast information feedbacks have a strong potential to become
a valuable asset for the ongoing hydropower expansion in the basin.