Location:Olin 305
When: April 4th at 1:30 p.m.
Title: Virtual Trading in Multi-Settlement Electricity Markets
Abstract: We study the role of virtual trading in electricity markets that consist of day-ahead (DA) markets, bids adjustment (BA) periods, and real time (RT) dispatches. Suppliers and load-serving entities engage in contracts for electricity in the DA market. They utilize the BA period to reconcile any discrepancies between the contracted quantities and the actual deliveries that occur during the real-time dispatch period. This multi-settlement market structure potentially gives rise to market inefficiencies. Virtual trading, designed as a purely financial transaction, was introduced to address these issues by allowing purely financial entities to arbitrage between the day-ahead market and the real-time dispatch without commitment to physical participation. While virtual trading has been widely adopted in practice, theoretical studies of its effects, particularly in markets with renewable energy sources, are limited in the literature. In this paper, we narrow this gap by characterizing the equilibrium behaviors of renewable and conventional suppliers, load serving entities, and virtual traders in a supply function equilibrium model. Our analysis shows that without virtual trading, load serving entities exert market power by underbidding their true demand estimates in the DA market, leading to a DA price lower than the expected RT price. Introducing virtual trading reduces the price gap between the two markets and eventually eliminates it as trading volume increases. Nevertheless, virtual trading additionally incentivizes load serving entities to underbid rather than present truthful demand estimates. Moreover, the inclusion of renewable suppliers also reduces load serving entities’ bidding amounts in the day-ahead market equilibrium. We numerically confirm our findings using data from both the California Independent System Operator Open Access Same-time Information System and the New York Independent System Operator. (joint work with Dan Bienstock, Garud Iyengar, and Bo Yang). Paper can be downloaded at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4641966
Bio: Agostino Capponi is a Professor in the Department of Industrial Engineering and Operations Research at Columbia University, where he is also a member of the Data Science Institute and the founding director of the Columbia Center for Digital Finance and Technology. His current research interests are in financial technology, market microstructure, energy markets, and networks. Agostino’s research has been funded by major agencies, including NSF, DARPA, DOE, IBM, GRI, INET, Ripple, Stellar, and the Ethereum foundation. His research has been recognized with the 2018 NSF CAREER award, and with a JP Morgan AI Research Faculty award. His research has also been covered by various media outlets, including Bloomberg, the Financial Times, Vox, and Politico. Agostino is a fellow of the crypto and blockchain economics research forum, and an academic fellow of Alibaba’s Luohan academy. He serves as an editor of Management Science in the Finance Department, co-editor of Mathematics and Financial Economics, and area editor of Operations Research. He also serves or has served as an associate editor of major journals in his field, including Stochastic Systems, Stochastic Models, the SIAM Journal on Financial Mathematics, Mathematical Finance, and Finance and Stochastic. Agostino is the former Chair of the SIAG/FME Activity Group and of the INFORMS Finance Section, and is currently a member of the Council of the Bachelier Finance Society. Agostino is co-editor of the book Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices, published in 2023 by the Cambridge University press, and often listed as one of the Amazon best sellers in banking and finance.
Zoom link: https://wse.zoom.us/j/94601022340