AI for grid operating optimization applies advanced data analytics and machine learning techniques to large volumes of operational, asset and system data to support more accurate forecasting, faster decision making and improved operational efficiency. AI models are increasingly used to enhance short-term load and renewable generation forecasting, support predictive maintenance and anomaly detection in grid assets, and assist operators in managing more dynamic and complex power flows. 

As power systems integrate higher shares of variable renewable energy, electric vehicles and distributed energy resources, grid behavior becomes less predictable and more sensitive to local constraints. Traditional rule-based or deterministic methods often struggle to capture these interactions in real time. AI tools can learn from historical and live datasets to identify patterns, anticipate stress conditions and highlight emerging risks earlier than manual analysis alone, improving situational awareness for system operators. 

In operational practice, AI is most commonly applied to prioritize alerts, improve forecast usability and narrow the set of actions operators must evaluate under time pressure. For asset-intensive systems, AI-based condition monitoring supports a shift from reactive or time-based maintenance toward condition-based approaches, reducing forced outages and improving asset availability. In distribution networks, AI-enabled analytics increasingly support visibility of DER behavior, local congestion risks and load growth driven by electrification and EV charging. Together, these applications help utilities operate existing infrastructure more efficiently while maintaining reliability in increasingly complex system conditions.[1]

Main outcomes of the solution 

• Reliability & Flexibility: Improves grid stability by enhancing forecast accuracy, enabling earlier detection of abnormal conditions and supporting more timely operator response to system variability. 
• Institutional Efficiency & Transparency: Strengthens data-driven decision making by converting large operational datasets into actionable insights, reducing manual workload and improving visibility for operators and planners. 
• Affordability: Supports more efficient use of existing assets by reducing unplanned outages, optimizing maintenance activities and limiting avoidable operational costs. 

Key deployment and scaling factors 

1. Ensure availability of high-quality historical and real-time datasets, including load, renewable output, asset condition and environmental data. 
2. Perform integration of AI models with existing SCADA, EMS and asset management platforms to ensure operational relevance and safe deployment. 
3. Develop robust cybersecurity and data governance frameworks to protect operational data and maintain institutional trust. 
4. Develop workforce capabilities that combine grid engineering expertise with data engineering and AI model development skills. 

Case Study

Camus Energy (2019), USA

Camus Energy has deployed an AI-enabled Distributed Energy Resource Management System (DERMS) designed to support real-time situational awareness and grid-aware coordination of distributed energy resources on distribution networks. The platform integrates data from advanced metering infrastructure, IoT sensors and network topology models with machine learning–based forecasting to provide operators with visibility of load, voltage conditions and DER behavior at feeder and substation level. 

By combining short-term load forecasting with modeled behavior of EV charging, rooftop solar and flexible demand, the system supports identification of emerging congestion risks and local hosting-capacity constraints. Utilities can use this information to assess where managed charging, demand-side flexibility or other operational measures may be applied before technical limits are reached, within established grid-security and operating rules. 

In collaboration with utilities including AES Indiana, the platform has been applied to analyze EV charging impacts and evaluate managed-charging strategies as part of broader distribution planning and operational processes. The case illustrates how AI-enabled analytics and DERMS platforms can support more informed operational decision-making, improve utilization of existing distribution assets and help utilities manage growing DER penetration under accelerating electrification.[2,3]

References:

1. Energy and AI I IEA (April 2025) 
2. The Smartest DER Control, Rooted in Grid Awareness I Camus 
3. Technology Convergence Report 2025 I World Economic Forum (June 2025)