Considerations for Distributed Edge Data Centers and Use of Building Loads to Support Large Interconnections

Summary

The rapid growth of AI and machine learning is creating unprecedented electricity demand from distributed edge data centers, straining existing grid infrastructure, causing significant interconnection delays of 1-10 years, and increasing costs for ratepayers. This report proposes a grid-integration framework that combines feeder hosting capacity analysis with building energy efficiency, load flexibility, and waste heat reuse to expand grid headroom. The proposed solutions aim to reduce delays, lower costs, and accelerate AI-ready infrastructure deployment, with the framework currently at the planning stage.

Key Facts

Source Document

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