Evaluating congestion management schemes in liberalized electricity markets using an agent-based simulator

Abstract

In this paper we compare different congestion management schemes in liberalized electricity markets using an agent-based simulator. By modelling market participants as adaptive agents in oligopolistic structures, we consider the possibility of strategic behavior and the existence/exercise of market power. Generation companies submit their bids to the market place in order to maximize their payoffs, where we apply reinforcement learning as behavioral agent model. The market is then cleared taking into account specific congestion management methods, such as locational marginal pricing (LMP), market splitting and flow-based market coupling. We demonstrate the functionality of the simulator using a test network, illustrating that different congestion management methods lead to different market dynamics and/or equilibria. Additionally, we assess the effects on the distribution of the surplus for producers and consumers as well as overall social welfare.