Forrest Bagley, whose family-owned solar company has built arrays in Maine, Massachusetts and New York, identified Illinois as a promising market due to generous state incentives and available land. However, the interconnection process—getting approval from utilities to connect new solar arrays to the electric grid—proved to be a major hurdle. An AI tool has emerged to tackle this problem, helping developers like Bagley navigate the complex, time-consuming process more efficiently.
The AI software analyzes grid capacity, utility requirements, and project data to flag potential interconnection issues early, which can shave months off the typical timeline. By streamlining the application and review process, the tool aims to reduce the backlog of projects waiting for utility approval—a bottleneck that has stymied community solar growth across multiple states. Faster connections mean new arrays can start generating low-carbon electricity sooner.
For developers, delays in interconnection are not just frustrating but expensive. Every month a project sits idle adds carrying costs for land leases, equipment, and financing. By shortening the interconnection queue, the AI tool directly improves project economics, enabling smaller developers to compete with larger firms and accelerating the deployment of community solar assets.
Illinois has set ambitious clean energy targets, aiming for 100% carbon-free electricity by 2050 with interim goals for community solar. Streamlining grid connections is critical to achieving those targets. The AI approach could become a model for other states facing similar interconnection logjams as renewable energy capacity expands nationwide.
Critics caution that automation cannot replace human engineering judgment entirely. Interconnection studies require site-specific safety and reliability assessments that AI may oversimplify. If the tool glosses over complex grid conditions, it could lead to system upgrades that aren't discovered until late in the design phase, offsetting early time savings.