Integration of AI in Distributed Energy Resource Management for Enhanced Load Balancing and Grid Stability

Abstract

The landscape of power systems is undergoing a transformative shift with the burgeoning inclusion of Distributed Energy Resources (DERs), which, while beneficial in enhancing the sustainability of electricity supply, introduces complexity in grid management. This paper presents a comprehensive framework leveraging Artificial Intelligence (AI) to orchestrate DER operations, thus achieving optimized load balancing and grid stability. A multiagent system that utilizes machine learning algorithms is proposed, capable of predictive analytics and real-time decision-making. The architecture is underpinned by a robust data layer that assimilates inputs from a myriad of sensors and smart meters, facilitating the dynamic management of DERs. Through the simulation of various scenarios, the system demonstrates significant improvements in load distribution, peak shaving, and voltage regulation. The framework also showcases resilience against fluctuations and anomalies, attributing to the self-learning capability of AI models that continuously refine control strategies. The adaptability of the system is evaluated in the context of grid demand-response initiatives and the integration of intermittent renewable energy sources. Overall, the results indicate a substantial advancement in the operational efficiency of power grids, highlighting the synergy between AI and energy resource management.