%0 Conference Paper
%B 2013 Asilomar Conference on Signals, Systems and Computers
%D 2013
%T Forecasting real-time locational marginal price: A state space approach
%A Yuting Ji
%A Kim, Jinsub
%A Robert J. Thomas
%A Lang Tong
%K CERTS
%K locational marginal pricing
%K reliability and markets
%K RM13-002
%X The problem of forecasting the real-time locational marginal price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of 6-8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
%B 2013 Asilomar Conference on Signals, Systems and Computers
%I IEEE
%C Pacific Grove, CA, USA
%P 379 - 383
%8 11/2013
%R 10.1109/ACSSC.2013.6810300