# Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity

## TL;DR

This paper develops dynamic skewed-t density models for electricity price spreads, enabling optimal storage operation and bidding strategies in day-ahead electricity markets, by incorporating exogenous factors and risk assessment.

## Contribution

It introduces a novel dynamic density modeling approach for electricity spreads that responds to external drivers, improving storage and trading decision-making.

## Key findings

- Dynamic density models accurately forecast hourly spreads.
- Optimal storage schedules improve arbitrage profitability.
- Risk measures derived from densities inform better trading strategies.

## Abstract

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.