We use welfare analysis to evaluate the efficiency impact of virtual trading by financial participants on wholesale electricity markets. We use a stylized model to determine the optimal bidding strategy under different scenarios of market outcomes for financial participants. A welfare comparison between scenarios with and without optimal bidding shows that the main impacts of virtual trading by financial participants are welfare transfers between consumers and producers, while the impact on total surplus is limited.
JEL Classification: D44 – Auctions; D47 – Market Design; D61 – Allocative Efficiency; Cost-Benefit Analysis; G14 – Information and Market Efficiency; Event Studies; Insider Trading; L94 – Electric Utilities; Q48 – Energy: Government Policy.
CelebiMetinHajosAttilaHanserPhilip Q.2010. “Virtual Bidding: The Good, the Bad and the Ugly.”The Electricity Journal23 (5): 16–25. doi:10.1016/j.tej.2010.04.014.
2.
HadsellLester. 2011. “Inefficiency in Deregulated Wholesale Electricity Markets: The Case of the New England ISO.”Applied Economics43 (5): 515–25. doi:10.1080/00036840802584943.
3.
HadsellLesterShawkyHany A.2007. “One-Day Forward Premiums and the Impact of Virtual Bidding on the New York Wholesale Electricity Market Using Hourly Data.”Journal of Futures Markets27 (11): 1107–25. doi:10.1002/fut.20278.
4.
IsemongerAlan G.2006. “The Benefits and Risks of Virtual Bidding in Multi-Settlement Markets.”The Electricity Journal19 (9): 26–36. doi:10.1016/j.tej.2006.09.010.
5.
JhaAkshayaWolakFrank A.2019. “Can Financial Participants Improve Price Discovery and Efficiency in Multi-Settlement Markets with Trading Costs?” Working Paper. Working Paper Series. National Bureau of Economic Research. doi:10.3386/w25851.
6.
KazempourJalalHobbsBenjamin F.2018a. “Value of Flexible Resources, Virtual Bidding, and Self-Scheduling in Two-Settlement Electricity Markets with Wind Generation—Part I: Principles and Competitive Model.”IEEE Transactions on Power Systems33 (1): 749–59. doi:10.1109/TPWRS.2017.2699687.
7.
KazempourJalalHobbsBenjamin F.2018b. “Value of Flexible Resources, Virtual Bidding, and Self-Scheduling in Two-Settlement Electricity Markets with Wind Generation—Part II: ISO Models and Application.”IEEE Transactions on Power Systems33 (1): 760–70. doi:10.1109/TPWRS.2017.2699688.
8.
LedgerwoodShaun D.PfeifenbergerJohannes P.2013. “Using Virtual Bids to Manipulate the Value of Financial Transmission Rights.”The Electricity Journal26 (9): 9–25. doi:10.1016/j.tej.2013.09.014.
9.
LiHongyanTesfatsionLeigh. 2011. “ISO Net Surplus Collection and Allocation in Wholesale Power Markets under LMP.”IEEE Transactions on Power Systems26 (2): 627–41. doi:10.1109/TPWRS.2010.2059052.
10.
LiRuoyangSvobodaAlva J.OrenShmuel S.2015. “Efficiency Impact of Convergence Bidding in the California Electricity Market.”Journal of Regulatory Economics48 (3): 245–84. doi:10.1007/s11149-015-9281-3.
11.
MatherJonathanBitarEilyanPoollaKameshwar. 2017. “Virtual Bidding: Equilibrium, Learning, and the Wisdom of Crowds.”IFAC-PapersOnLine50 (1): 225–32. doi:10.1016/j.ifacol.2017.08.038.
12.
OpgrandJeffPreckelPaul V.GothamDouglas J.LiuAndrew L.2022. “Price Formation in Auctions for Financial Transmission Rights.”The Energy Journal, 43(3), 33–57. doi:10.5547/01956574.43.3.jopg
13.
ParsonsJohn E.ColbertCathleenLarrieuJeremyMartinTaylorErinMastrangelo. 2015. “Financial Arbitrage and Efficient Dispatch in Wholesale Electricity Markets.”SSRN Electronic Journal. doi:10.2139/ssrn.2574397.
SamuelsonPaul A.1973. “Proof That Properly Discounted Present Values of Assets Vibrate Randomly.”The Bell Journal of Economics and Management Science4 (2): 369–74. doi:10.2307/3003046.
17.
SaraviaCeleste. 2003. “Speculative Trading and Market Performance: The Effect of Arbitrageurs on Efficiency and Market Power in the New York Electricity Market.”https://escholarship.org/uc/item/0mx44472.
