Research Paper

Core Innovation

This paper presents PowerAtlas, an LLM-agent framework that integrates historical data, domain knowledge, and physical grid constraints to co-schedule electricity and computing tasks. It outperforms general-purpose LLM schedules by ensuring feasibility and cost efficiency under real-world grid operating conditions.

Why It Matters

Data centers increasingly act as flexible but volatile grid loads, risking grid violations and unserved computing demand. Coordinated scheduling improves grid reliability and computing task fulfillment, reducing operational costs and enabling scalable integration of AI workloads with power systems.

Potential Customers & Pain Points

• Power utilities – Need to manage volatile data center loads without grid violations
• Data center operators – Need to meet SLAs while minimizing energy costs
• Cloud service providers – Need reliable cost-effective AI workload scheduling under power constraints.

Business Model

Subscription-based SaaS platform for utilities and data center operators with tiered pricing based on scale and features; consulting services for integration and customization.

Competitive Landscape

• Google DeepMind Energy
• AutoGrid
• Siemens EnergyIP

Implementation Challenges

• Integration complexity between power grid operations and computing task management
• Adoption resistance from traditional grid operators
• Dependence on accurate real-time data and forecasting

Validation Strategy

• Pilot deployments with provincial power utilities and data centers
• Benchmarking against existing scheduling methods using ECBench dataset
• Demonstrating cost savings and grid compliance improvements in live environments