  The short-term forecasting of real-time locational marginal price (LMP) and
network congestion is considered from a system operator perspective. A new
probabilistic forecasting technique is proposed based on a multiparametric
programming formulation that partitions the uncertainty parameter space into
critical regions from which the conditional probability distribution of the
real-time LMP/congestion is obtained. The proposed method incorporates
load/generation forecast, time varying operation constraints, and contingency
models. By shifting the computation cost associated with multiparametric
programs offline, the online computation cost is significantly reduced. An
online simulation technique by generating critical regions dynamically is also
proposed, which results in several orders of magnitude improvement in the
computational cost over standard Monte Carlo methods.
