  Energy forecasting is an essential task in power system operations. Operators
usually issue forecasts and leverage them to schedule energy dispatch ahead of
time. However, forecast models are typically developed in a way that overlooks
the operational value of the forecasts. To bridge the gap, we design a
value-oriented point forecasting approach for sequential energy dispatch
problems with renewable energy sources. At the training phase, we align the
loss function with the overall operation cost function, thereby achieving
reduced operation costs. The forecast model parameter estimation is formulated
as a bilevel program. Under mild assumptions, we convert the upper-level
objective into an equivalent form using the dual solutions obtained from the
lower-level operation problems. Additionally, a novel iterative solution
strategy is proposed for the newly formulated bilevel program. Under such an
iterative scheme, we show that the upper-level objective is locally linear
regarding the forecast model output, and can act as the loss function.
Numerical experiments demonstrate that, compared to commonly used statistical
quality-oriented point forecasting methods, forecasts obtained by the proposed
approach result in lower operation costs. Meanwhile, the proposed approach is
more computationally efficient than traditional two-stage stochastic programs.
