Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning
This provides a tool for system operators to enable online forecasting of market operations with stochastic generation and demand, though it is incremental as it builds on existing dictionary learning methods.
The paper tackled probabilistic forecasting and simulation of electricity markets by proposing an online dictionary learning (ODL) technique that exploits the structure of optimal power flow, resulting in several orders of magnitude improvement in computation time compared to Monte Carlo methods.
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.