Agentic AI in Energy Operations: Transforming Efficiency and Sustainability
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P132Keywords:
Agentic AI, Energy Optimization, Sustainable Operations, Autonomous Systems, Operational EfficiencyAbstract
The world's Energy & Utilities (E&U) industries are being rapidly transformed digitally due to increased complexity in grids, rapid integration of renewables, constraints on the workforce, and increasing mandates to decarbonize. As such, traditional automation based on static rules, and pre-defined operating conditions, will be unable to manage the volatility and resiliency demands of today's energy systems. Agentic AI utilizing autonomous multi-agents capable of perceiving, reasoning, planning and acting together, presents an opportunity to transition to sustainable and self optimizing energy ecosystems. This paper integrates advances in multi-agent reinforcement learning (MARL), deep reinforcement learning (DRL) for microgrid optimization, distributed intelligence in the cloud-edge, and interpretable AI to provide a comprehensive operational framework for the implementation of Agentic AI in energy systems. Utilizing state-of-the art literature (2021–2026) we describe conceptual models which enable autonomous load balancing, renewable dispatch optimization, predictive maintenance and coordination of resilient microgrids during extreme events. Studies indicate that Agentic AI may result in lower OPEX, higher utilization of DER, better carbon performance and faster outage recovery. However, achieving these benefits requires stronger governance, robust cyber security protection and human-in-the-loop design. This paper concludes with a developmental road map, and future research priorities required to scale autonomous intelligence throughout global energy networks.
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