Skip to main content

Please enter a keyword and click the arrow to search the site

Optimal Daily Trading of Battery Operations using Arbitrage Spreads

Journal

Energies

Subject

Management Science and Operations

Authors / Editors

Abramova E;Bunn D W

Publication Year

2021

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.

Keywords

Electricity; Batteries; Spreads; Trading

Available on ECCH

No


Select up to 4 programmes to compare

Select one more to compare
×
subscribe_image_desktop 5949B9BFE33243D782D1C7A17E3345D0

Sign up to receive our latest news and business thinking direct to your inbox

×

Sign up to receive our latest course information and business thinking

Leave your details above if you would like to receive emails containing the latest thought leadership, invitations to events and news about courses that could enhance your career. If you would prefer not to receive our emails, you can still access the case study by clicking the button below. You can opt-out of receiving our emails at any time by visiting: https://london.edu/my-profile-preferences or by unsubscribing through the link provided in our emails. View our Privacy Policy for more information on your rights.