01582nas a2200205 4500008003900000245007600039210006900115260004200184300001400226520091200240653001001152653003201162653002801194653001301222100001501235700001601250700002301266700001501289856007201304 2013 d00aForecasting real-time locational marginal price: A state space approach0 aForecasting realtime locational marginal price A state space app aPacific Grove, CA, USAbIEEEc11/2013 a379 - 3833 aThe 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.10aCERTS10alocational marginal pricing10areliability and markets10aRM13-0021 aJi, Yuting1 aKim, Jinsub1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/forecasting-real-time-locational