Reinforcement learning-driven local transactive energy market for distributed energy resources

This journal publication details some crucial development steps for the local energy market mechanism at the heart of ALEX, the autonomous local energy exchange.

The landscape of electricity distribution is undergoing a profound transformation driven by Distributed Energy Resources (DERs). These resources are reshaping consumption patterns and challenging traditional grid operations. While traditional control methods struggle to adapt to the distributed nature of DERs, alternative approaches such as market mechanisms are gaining traction.

In this journal publication, the TREX-Ai team delves into the dynamics of local energy markets (LEMs) as a solution to align end-user interests with grid stakeholders. The key challenge lies in incentivizing active participation and automating behavior to optimize grid performance.

The study introduces ALEX (Autonomous Local Energy Exchange), now TREX-Ai’s core technology, as an automated local energy market. Through meticulous experimentation, the researchers identify essential market properties conducive to incentivizing desired end-user behavior and enabling automation. The goal is to ensure that market incentives align with broader grid objectives even in the face of full automation. This paves the way for a more resilient and adaptive energy ecosystem. 


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