  An important revenue stream for electric battery operators is often
arbitraging the hourly price spreads in the day-ahead auction. The optimal
approach to this is challenging if risk is a consideration as this requires the
estimation of density functions. Since the hourly prices are not normal and not
independent, creating spread densities from the difference of separately
estimated price densities is generally intractable. Thus, forecasts of all
intraday hourly spreads were directly specified as an upper triangular matrix
containing densities. The model was a flexible four-parameter distribution used
to produce dynamic parameter estimates conditional upon exogenous factors, most
importantly wind, solar and the day-ahead demand forecasts. These forecasts
supported the optimal daily scheduling of a storage facility, operating on
single and multiple cycles per day. The optimization is innovative in its use
of spread trades rather than hourly prices, which this paper argues, is more
attractive in reducing risk. In contrast to the conventional approach of
trading the daily peak and trough, multiple trades are found to be profitable
and opportunistic depending upon the weather forecasts.
