NeuraFlux: A scalable and adaptive framework for autonomous data-driven multi-agent power optimization

NeuraFlux is an open-source, adaptive multi-agent reinforcement learning platform designed to optimize energy management in complex, dynamic environments. It addresses key challenges in coordinating distributed energy resources, including scalability limitations, difficulties in managing competing objectives, and lack of real-time adaptability. This paper presents two primary contributions: the theoretical foundations of NeuraFlux and its significance in modern power systems infrastructure and control, along with a novel training algorithm optimized for real-world deployment performance. Through three case studies—energy storage market arbitrage, heating, ventilation, and air conditioning (HVAC) system control, and electric vehicle grid integration—NeuraFlux’s effectiveness in managing intricate, multi-agent, and multi-objective optimization challenges is demonstrated. The modularity and scalability demonstrated in these examples, combined with the framework’s technical robustness for edge deployment, establish NeuraFlux as a powerful and practical tool for deploying advanced control systems in modern power and energy systems. • Scalable Optimization: Multi-agent deep reinforcement learning-based framework for optimizing distributed energy resources, aimed at supporting real-time, data-driven power systems optimization in diverse scenarios. • Modular and Scalable Design: The framework’s architecture is both modular and scalable, integrating with various assets and consumer devices, and featuring a novel training algorithm optimized for real-world performance. • Open Source Contribution: By releasing NeuraFlux as open-source, the research promotes transparency, collaboration, and innovation, enabling diverse applications and advancements in energy optimization contexts.