The ACEWEM framework: An integrated agent-based and statistical modelling laboratory for repeated power auctions

Abstract

An agent-based model for power auction is proposed.A new stochastic decision algorithm is designed.We study the emergence of market power.Unexpected supply or demand shocks lead to emergence of strategic offers.Re-dispatch scheme results in higher market prices compared with the LMP scheme. We propose a novel framework for experimental designs of liberalised wholesale power markets, namely the Agent-based Computational Economics of the Wholesale Electricity Market (ACEWEM) framework. Here, we describe a detailed market simulation whereby the strategies of power generators emerge as a result of a stochastic profit maximisation learning algorithm based upon the GAMLSS (Generalized Additive Models for Location Scale and Shape) statistical framework. The ACEWEM framework, which integrates the agent-based modelling paradigm with formal statistical methods to represent better real-world decision rules, is designed to be the foundation for large custom-purpose experimental studies inspired by computational learning. The paper therefore makes a methodological contribution in the development of an expert model of repeated auctions with capacity and physical constrains. It also makes an applied contribution by providing a more realistic basis for identifying whether high market prices can be ascribed to problems of market structure or exercise of market power.