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.

1 aDeng, Weisi1 aJi, Yuting1 aTong, Lang uhttp://aisel.aisnet.org/hicss-50/es/markets/10/01394nas a2200157 4500008003900000022001400039245007000053210006900123260001200192520089100204653001301095100001501108700002301123700001501146856007501161 2016 d a0885-895000aProbabilistic Forecasting of Real-Time LMP and Network Congestion0 aProbabilistic Forecasting of RealTime LMP and Network Congestion c07/20163 aThe 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.

10aRM13-0021 aJi, Yuting1 aThomas, Robert, J.1 aTong, Lang uhttps://certs.lbl.gov/publications/probabilistic-forecasting-real-time01319nas a2200181 4500008003900000245007600039210006900115260002900184520065000213653001000863653003200873653002800905653001300933100001500946700002300961700001500984856013800999 2015 d00aProbabilistic Forecast of Real-Time LMP via Multiparametric Programming0 aProbabilistic Forecast of RealTime LMP via Multiparametric Progr aKauai, HIbIEEEc01/20153 aThe 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.10aCERTS10alocational marginal pricing10areliability and markets10aRM13-0021 aJi, Yuting1 aThomas, Robert, J.1 aTong, Lang uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070121&refinements%3D4260156971%26filter%3DAND%28p_IS_Number%3A7069647%29