  Large penetration of renewable energy sources (RESs) brings huge uncertainty
into the electricity markets. While existing deterministic market clearing
fails to accommodate the uncertainty, the recently proposed stochastic market
clearing struggles to achieve desirable market properties. In this work, we
propose a value-oriented forecasting approach, which tactically determines the
RESs generation that enters the day-ahead market. With such a forecast, the
existing deterministic market clearing framework can be maintained, and the
day-ahead and real-time overall operation cost is reduced. At the training
phase, the forecast model parameters are estimated to minimize expected
day-ahead and real-time overall operation costs, instead of minimizing forecast
errors in a statistical sense. Theoretically, we derive the exact form of the
loss function for training the forecast model that aligns with such a goal. For
market clearing modeled by linear programs, this loss function is a piecewise
linear function. Additionally, we derive the analytical gradient of the loss
function with respect to the forecast, which inspires an efficient training
strategy. A numerical study shows our forecasts can bring significant benefits
of the overall cost reduction to deterministic market clearing, compared to
quality-oriented forecasting approach.
