A comparison of Nash equilibria analysis and agent-based modelling for power markets

In this paper we compare Nash equilibria analysis and agent-based modelling for assessing the market dynamics of network-constrained pool markets. Power suppliers submit their bids to the market place in order to maximize their payoffs, where we apply reinforcement learning as a behavioral agent model. The market clearing mechanism is based on the locational marginal pricing scheme. Simulations are carried out on a benchmark power system. We show how the evolution of the agent-based approach relates to the existence of a unique Nash equilibrium or multiple equilibria in the system. Additionally, the parameter sensitivity of the results is discussed. [All rights reserved Elsevier]

Published in:
International Journal of Electrical Power & Energy Systems, 28, 9, 599-607
Nash equilibria analysis;agent-based modelling;power markets;network-constrained pool markets;reinforcement learning;behavioral agent model;market clearing mechanism;locational marginal pricing scheme;parameter sensitivity;

 Record created 2007-04-04, last modified 2019-03-31

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