Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity
This work addresses the problem of improving profitability and risk management for electricity market participants, such as merchant arbitrage facilities, through more accurate spread forecasting, though it appears incremental as it builds on existing density modeling approaches.
The paper tackles the problem of optimizing daily electricity storage trading by developing dynamic conditional density functions to forecast price spreads between different hours, enabling an optimal day-ahead schedule and bidding strategy for arbitrage. It uses skewed-t representations with latent moments that respond to exogenous factors like weather and demand, selecting the best specification based on Pinball Loss and allowing risk calculation for spread arbitrages.
This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for a merchant arbitrage facility into the day-ahead auctions for wholesale electricity. The four latent moments of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the mean, variance, skewness and kurtosis of the densities to respond hourly to such factors as weather and demand forecasts. The best specification for each spread is selected based on the Pinball Loss function, following the closed form analytical solutions of the cumulative density functions. Those analytical properties also allow the calculation of risk associated with the spread arbitrages. From these spread densities, the optimal daily operation of a battery storage facility is determined.