Renewable Energy Projects Wait Years in Grid Connection Queues With No Systematic Way to Prioritize Them

More than 2,500 GW of energy projects — renewables, battery storage, and large electricity consumers like data centers — are currently stuck in grid connection queues worldwide, waiting years for approval. Of these, 1,500 GW are in advanced development stages and ready to build. This queue backlog is now the single biggest constraint on adding new clean power capacity globally, larger than any technology or financing barrier. Grid operators lack systematic methods to screen, prioritize, or fast-track viable projects, and the queue processes themselves were designed for an era when a handful of large power plants connected per year, not thousands of distributed projects.

The queued capacity (2,500 GW) is equivalent to five times the amount of solar and wind capacity added globally in 2022. The IEA's "Grid Delay Case" shows that if grid bottlenecks persist, cumulative CO₂ emissions from the power sector would be 58 gigatonnes higher through 2050 — equivalent to four full years of global power sector emissions — with warming likely exceeding 2°C. Meanwhile, grid infrastructure takes 5–15 years to plan, permit, and build, compared to 1–5 years for the renewable projects waiting to connect. This mismatch means the queue will continue growing unless the process itself is redesigned.

Most grid connection processes operate on a first-come, first-served basis, which rewards early application rather than project viability or system value. Speculative projects clog queues — developers submit applications for multiple sites knowing most won't proceed, consuming grid operator review capacity. Some jurisdictions have introduced "ready to build" gates or financial deposits to filter speculative applications, but these are ad hoc and vary widely across markets. Grid-enhancing technologies (dynamic line rating, advanced power flow control, topology optimization) could unlock up to 1,600 GW of additional capacity on existing lines without new construction, but adoption remains minimal because grid operators lack tools to assess where GETs would have the greatest impact and regulatory frameworks don't incentivize their use. The fundamental problem is that connection queue management is treated as an administrative process rather than a system optimization problem.

A computational framework that models the grid connection queue as an optimization problem — matching project characteristics (location, size, generation profile, flexibility) against network capacity constraints and planned upgrades — could dramatically accelerate viable connections. This would require combining power system modeling with queue management data to identify which projects could connect immediately using existing capacity, which would benefit from targeted GET deployment, and which require new infrastructure. Machine learning approaches could predict project viability from application data to filter speculative entries. Standardized "queue analytics" tools that grid operators could adopt across jurisdictions would multiply impact.

A student team could build a prototype queue optimization tool using publicly available data: FERC interconnection queue data (publicly available for U.S. grid operators), synthetic network models (IEEE test cases), and published generation profiles for solar and wind. The team would model a simplified queue prioritization algorithm that scores projects based on grid impact, readiness, and system value, then compare its outcomes against first-come-first-served ordering. Skills in power systems engineering, optimization, and data science would be most relevant. A more design-oriented team could study the queue process from the developer perspective, interviewing renewable energy developers about their queue experience and identifying process redesign opportunities.