The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.

%B Hawaii International Conference on Systems Science (HICSS) %8 01/2017 %@ 978-0-9981331-0-2 %G eng %U http://aisel.aisnet.org/hicss-50/es/markets/10/ %0 Journal Article %J IEEE Transactions on Power Systems %D 2016 %T Probabilistic Forecasting of Real-Time LMP and Network Congestion %A Yuting Ji %A Robert J. Thomas %A Lang Tong %K RM13-002 %XThe 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