2026 Event Schedule

As the electric power grid experiences unprecedented demand growth driven by data centers, electric vehicles (EVs), hydrogen production, and crypto mining, effective planning for large load integration has become a critical priority. In particular, the rapid expansion of AI-driven infrastructure is reshaping the demand landscape, introducing loads with high power density, steep ramp rates, large peak-to-average ratios, and complex, variable profiles. These characteristics present unique technical and regulatory challenges for utilities, planners, and grid operators. This course provides participants with a solid foundation in the integration of large and AI-based loads, covering key aspects such as forecasting, interconnection processes, grid impact analysis, and mitigation strategies. 

The course begins with an overview of large load types, topologies, historical demand trends, and evolving business models, emphasizing the accelerating impact of AI workloads. A major focus will be placed on advanced load forecasting methods, including top-down and bottom-up approaches, and the challenges of both short- and long-term projections. Practical utility case studies will illustrate methods for scenario planning, uncertainty management, and resource planning. A dedicated section will address regulatory, permitting, and policy frameworks, including interconnection timelines, environmental reviews, equity concerns, and the contrasting priorities of utilities and developers.

The course then delves into the technical challenges posed by large and AI-driven loads, such as grid stability, voltage regulation, harmonics, and thermal constraints. In response, attendees will explore enabling technologies such as software-defined power control, grid-forming energy storage systems (ESS), supercapacitors and UPS systems, and demand response and load flexibility solutions. Finally, participants will engage with modeling tools and real-world case studies that highlight best practices and lessons learned from the field. 

Participants will explore the following key topics:

• Definitions, typologies, and characteristics of large loads
• Growth drivers and regional demand trends
• AI and high-performance computing as disruptive demand factors
• Forecasting methodologies and data inputs
• Challenges in forecasting emerging loads
• Regulatory, permitting, and policy frameworks
• Technical impacts on grid stability, reliability, and power quality
• Emerging technologies and solutions to mitigate grid impacts
• Case studies from utility and developer perspectives