Analyzing Agent Collisions in AI-Aided Energy Management Systems

Abstract

The rapid growth of distributed energy resources (DERs) and autonomous control devices in behind-the-meter (BTM) systems has created a decentralized energy landscape, where artificial intelligence (AI) agents independently manage local objectives. While these AI-driven energy management systems (EMS) offer improved efficiency and flexibility, their uncoordinated operation poses risks to grid stability. Specifically, operational collisions can occur when self-interested agents pursue local optima without regard for system-wide safety, resulting in simultaneous violations of physical grid constraints. For instance, smart EV chargers and microgrid optimizers acting independently may synchronize high-demand actions, causing voltage sags or transformer overloads. This paper presents a systematic framework to characterize and detect agent-induced collisions in multi-agent energy systems. We formalize operational collisions in power grids, introduce metrics to quantify their frequency and severity, and develop an analytical workflow to attribute these events to specific agent policies. A case study with networked microgrids (MGs) demonstrates the framework by comparing independent and shared reward strategies, showing how cooperative incentives can mitigate collision risks. By proactively addressing these safety challenges, our work advances the development of resilient and trustworthy AI-driven energy management for future smart grids.