  The objective-based forecasting considers the asymmetric and non-linear
impacts of forecasting errors on decision objectives, thus improving the
effectiveness of its downstream decision-making process. However, existing
objective-based forecasting methods are risk-neutral and not suitable for tasks
like power system inertia management and unit commitment, of which
decision-makers are usually biased toward risk aversion in practice. To tackle
this problem, this paper proposes a generic risk-aware objective-based
forecasting method. It enables decision-makers to customize their forecasting
with different risk preferences. The equivalence between the proposed method
and optimization under uncertainty (stochastic/robust optimization) is
established for the first time. Case studies are carried out on a Great Britain
2030 power system with system operational data from National Grid. The results
show that the proposed model with deterministic optimization can approximate
the performance of stochastic programming or robust optimization at only a
fraction of their computational cost.
