  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.
