Overview of machine learning and data-driven methods in agent-based modeling of energy markets

Abstract

Local energy markets (LEM) allow prosumers and consumers to trade energy directly between each other and offer flexibility services to the grid. The benefits and challenges related to such markets need to be identified, and agent-based modeling (ABM) is a useful method to conduct simulation experiments with different market structures and clearing mechanisms. Machine learning (ML) and data-driven methods when integrated with ABM show great potential for constructing new distributed, agent-level knowledge. In this paper, we discuss the requirements for coupling ML methods and ABM. We also provide an overview of published literature on the common methods of integration of ML and data-driven methods in ABM of energy markets and discuss how these requirements are commonly addressed.