The value of seasonal streamflow forecasts for the hydropower industry
has long been assessed by considering metrics related to hydropower
availability. However, this approach overlooks the role played by
hydropower dams within the power grid, therefore providing a myopic view
of how forecasts could improve the operations of large-scale power
systems. With the aim of understanding how the value of streamflow
forecasts penetrates through the power grid, we developed a
coupled-water energy model that is subject to reservoir inflow forecasts
with different levels of accuracy. We implement the modelling framework
on a real-world case study based on the Cambodian grid, which relies on
hydropower, coal, oil, and imports from neighboring countries. In
particular, we evaluate the performance in terms of metrics selected
from both the reservoir and power systems, including available and
dispatched hydropower, power production costs, CO2 emissions, and
transmission line congestion. Through this framework, we demonstrate
that streamflow forecasts can positively impact the operations of
hydro-dominated power systems, especially during the transition from wet
to dry seasons. Moreover, we show that the value largely varies with the
specific metric of performance at hand as well as the level of
operational integration between water and power systems.