This version is available at https://strathprints.strath.ac.uk/33071/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model.
Increasingly, the electric energy transmitted and distributed by national power systems is traded competitively in free markets. Long-term decisions must be made by authorities as to the structure of energy markets and the regulations that govern interactions between participants. It is not practical to experiment with real energy markets and in order to establish the potential effects of making these decisions there are few options but to simulate the markets computationally. This paper proposes that the complexity of power systems and the associated energy markets necessitates an open approach in their modelling and simulation. It presents an open source software package for simulating electric energy markets using the Python programming language. Power systems and their associated constraints are modelled using traditional steady-state analysis techniques. While market participants are represented by reactive agents that learn through reinforcement. The software and all of its dependencies are open and freely available to the scientific community
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