Optimal Daily Trading of Battery Operations Using Arbitrage Spreads

TL;DR

This paper develops a dynamic, risk-aware model for optimizing battery arbitrage trading based on hourly spread densities, leveraging exogenous weather factors to improve profitability and reduce risk in energy markets.

Contribution

It introduces a novel approach to battery arbitrage trading using spread densities and exogenous factors, enhancing risk management and operational flexibility.

Findings

Multiple trades can be profitable depending on weather forecasts.

Using spread densities reduces trading risk compared to price-based strategies.

Dynamic parameter estimation improves scheduling accuracy.

Abstract

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.