AI and The Grid: Clearing the Interconnection Backlog

The United States is grappling with an unprecedented backlog of energy projects awaiting connection to the grid. At the same time, the increasing number of electric vehicles, heat pumps, and data centers is driving demand even higher.

Lengthy interconnection queues have become a major bottleneck in efforts to deploy clean energy. We need to clear these to further electrify and decarbonize the way we live and work. 

In response, stakeholders are turning to classic machine learning (ML) and large language models (LLMs) to streamline the interconnection process. From automating tedious application reviews to accelerating complex grid studies, AI-driven tools are being piloted to streamline the process and speed up the integration of new energy assets. 

Why the Gridlock?

The surge in clean energy and dramatically increased electricity demand has overwhelmed the interconnection system, which relies on outdated study rules and manual processes.

Many speculative projects drop out, forcing costly re-studies and further delays. Meanwhile, transmission upgrades can add years to the waiting period.

The current system and processes, built for a simpler and slower era, can’t handle the complexity, volume, and velocity of today’s growing energy demand.

Scale of the Problem

According to Lawrence Berkeley National Laboratory (Berkeley Lab), 2,600 GW of generation and storage capacity is currently awaiting interconnection. This is more than twice the entire existing power plant fleet of 1,250 GW.

Berkeley Lab also reports that clean energy projects account for 95% of the interconnection queue, with average wait times stretching to 4–5 years. In some regions, it can exceed seven years.

These delays threaten clean energy goals, grid reliability, and broader electrification efforts. This makes interconnection one of the greatest rate limiters in the energy transition.

Opportunities for the Application of AI

From ML to cutting-edge LLM, AI-based tools are being used to target and solve various pinch points, including the following.

Application Processing and Queue Review

Problem: In some regions, more than 90% of interconnection applications are deemed "deficient" and contain missing information, incorrect data, or blank fields, resulting in weeks of rework."

Solution: LLMs are being developed to automatically screen application documents, extract relevant text and data, cross-reference this information with regulatory rules, and generate deficiency reports within minutes.

Example 1: The Department of Energy’s AI4IX initiative (AI for Interconnection and Grid Planning Exchange) is funding AI-powered tools to modernize and accelerate the grid interconnection process. The goal is to reduce application cycle times by over 50% through advanced process automation.

Example 2: Google and PJM, the largest grid operator in North America, are partnering to automate the "viability filter" used by PJM's grid planners."

Grid Modeling & Studies

Problem: Initial load-flow studies are crucial for determining whether new energy projects can be safely interconnected. It can be time-consuming, often taking up to six months, as engineers manually integrate data from various sources, such as energy management systems, planning cases, and previous upgrades.

Solution: Surrogate ML models are enabling near-instantaneous approximations of traditional load-flow simulations. Complementary automation systems now ingest EMS and CIM data, resolve discrepancies in naming schemas, and streamline the creation of complete, validated study cases.

Example 1: Amazon Web Services is collaborating with the Southwest Power Pool to automate the generation of load-flow cases. Their goal is to cut the interconnection timelines from multiple years to just a few months.

Example 2: Pearl Street Technologies and MISO have piloted AI-enabled tools to automate key elements of Phase 1 interconnection studies, reducing timelines that traditionally took weeks or months down to as little as one day in some cases.

Permitting and Compliance

Problem: Environmental Impact Statements can be extensive and generate a large volume of public comments. Analysts currently spend months manually clustering and drafting responses to these comments.

Solution: LLMs can streamline environmental reviews and accelerate compliance workflows.

Example: SearchNEPA, part of PNNL’s PolicyAI suite, leverages advanced semantic search to: 

• Analyze past environmental reports.

• Identify patterns.

• Cluster similar public comments.

• Suggest draft responses to help subject matter experts reply more efficiently.

Decision Support & Stakeholder Tools

Problem: Developers must currently search across multiple websites and databases to manually piece together grid maps, project waitlists, and power capacity forecasts. They often deal with messy and inconsistent data along the way.

Solution: Natural language chatbots and dashboards will allow users to ask questions about real-time capacity and other key variables.

Example: Google and PJM have partnered to unify data, automate queue review, and create a “Google Maps for the Grid” that all stakeholders can leverage. A real-time dashboard is being developed to overlay queue status, real-power flows, and upgrade costs on a map interface. 

Cross-Cutting Challenges

As AI tools are introduced into interconnection and grid planning workflows, several cross-cutting challenges must be addressed to ensure adoption, trust, and long-term success across the energy landscape:

• Data Silos & Governance: AI systems rely on consistent, high-quality data, yet critical grid data remains fragmented across platforms. Utilities and grid operators require robust version control, shared taxonomies, and traceability to confidently integrate AI into their planning processes.

• Model Reliability: Surrogate ML output must match the performance of traditional engineering studies, especially under high-renewables conditions where edge cases are common. Developers want consistency, while grid operators demand precision.

• Explainability: Stakeholders won’t support AI systems that they can’t understand. From denied applications to queue forecasts, decisions must be explainable, auditable, and reproducible. This is especially true for tools used in regulatory processes.

• Cybersecurity: As AI touches operational systems, the risk of attack grows. Planners and policymakers need assurance that AI models meet cybersecurity standards (e.g., NERC-CIP) with secure APIs and hardened infrastructure.

• Regulatory Risk: Without FERC or DOE-endorsed frameworks, promising tools may face pushback. Policy clarity around acceptable use, data access, and transparency will accelerate adoption.

A Developing Story 

AI is not a silver bullet, but it is a breakthrough tool for modernizing grid planning and accelerating interconnection. Early pilots are proving that speed and scale are possible.

Still, real-world adoption will require more than just technology. It demands trust, transparency, and policy reforms that give utilities, developers, and regulators the confidence to act.

If we get this right, AI won’t just speed up interconnection; it will help unlock the clean energy future we desire.

About the Author

Bill Stark is dedicated to solving climate challenges and is inspired by those leading the way at the intersection of AI and climate. Through this newsletter, he shares his insights, hoping to celebrate pioneers, spark conversations, and inspire more people to contribute to a sustainable future.

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