|Title||Forecasting real-time locational marginal price: A state space approach|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||Yuting Ji, Jinsub Kim, Robert J Thomas, Lang Tong|
|Conference Name||2013 Asilomar Conference on Signals, Systems and Computers|
|Conference Location||Pacific Grove, CA, USA|
|Keywords||CERTS, locational marginal pricing, reliability and markets, RM13-002|
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.