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 associated with multiparametric programs offline, the online computational 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.

%B IEEE Transactions on Power Systems %8 07/2016 %! IEEE Trans. Power Syst. %R 10.1109/TPWRS.2016.2592380 %0 Conference Paper %B 48th Hawaii International Conference on System Sciences (HICSS) %D 2015 %T Probabilistic Forecast of Real-Time LMP via Multiparametric Programming %A Yuting Ji %A Robert J. Thomas %A Lang Tong %K CERTS %K locational marginal pricing %K reliability and markets %K RM13-002 %X The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced. %B 48th Hawaii International Conference on System Sciences (HICSS) %I IEEE %C Kauai, HI %8 01/2015 %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070121&refinements%3D4260156971%26filter%3DAND%28p_IS_Number%3A7069647%29 %R 10.1109/HICSS.2015.306