Market power and efficiency in a computational electricity market with discriminatory double-auction pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm (1995) to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.

[1]  Raymond J. Kopp,et al.  A Shock to the System: Restructuring America's Electricity Industry , 1996 .

[2]  Leigh Tesfatsion,et al.  Concentration and capacity effects on electricity market power , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[3]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[4]  Dhananjay K. Gode,et al.  Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality , 1993, Journal of Political Economy.

[5]  Nicolaas J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses , 1998 .

[6]  N. M. Fehr,et al.  SPOT MARKET COMPETITION IN THE UK ELECTRICITY INDUSTRY , 1993 .

[7]  R. Green,et al.  Competition in the British Electricity Spot Market , 1992, Journal of Political Economy.

[8]  Jan Paul Acton,et al.  Structural Reform in Electric Power: A Framework for Analysis , 1987 .

[9]  Mark A. Satterthwaite,et al.  The Bayesian theory of the k-double auction: Santa Fe Institute Studies in the Sciences of Complexity , 2018 .

[10]  L. Tesfatsion.  Structure, behavior, and market power in an evolutionary labor market with adaptive search☆ , 2001 .

[11]  P. Klemperer.  Auction Theory: A Guide to the Literature , 1999 .

[12]  Desiree Hansson.  A SHOCK TO THE SYSTEM , 1993 .

[13]  C. A. Holt,et al.  5. Industrial Organization: A Survey of Laboratory Research , 1995 .

[14]  P. Klemperer.  What Really Matters in Auction Design , 2001 .

[15]  Derek W. Bunn,et al.  Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market ☆ , 2001 .

[16]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[17]  D. Lane,et al.  Electricity market power: marginal cost and relative capacity effects , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[18]  G. Sheblé.  Computational Auction Mechanisms for Restructured Power Industry Operation , 1999 .

[19]  R. Palmer,et al.  Characterizing effective trading strategies: Insights from a computerized double auction tournament , 1994 .

[20]  Oliver Stehmann,et al.  Privatisation, restructuring, and regulation of network utilities , 2001 .

[21]  Colin Camerer,et al.  Experience‐weighted Attraction Learning in Normal Form Games , 1999 .

[22]  Fernando L. Alvarado,et al.  MARKET POWER: A DYNAMIC DEFINITION , 1998 .

[23]  D. Newbery.  Privatization, Restructuring, and Regulation of Network Utilities , 2000 .

[24]  Edmund Chattoe-Brown.  Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes , 1998 .

[25]  Ian Domowitz,et al.  Automated trade execution systems , 1990 .

[26]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .