PAI seminar: Riding the AI Wave to Catalyze Power Grid Transformation

Inside the event

PAI seminar on Oct 22 (Thu) 10 - 11 AM by Prof. Junjie Qin from Purdue University on "Riding the AI Wave to Catalyze Power Grid Transformation" at SEC 3.314. Abstract: Hyperscalers are racing to train ever larger models, and the electricity needed to power them is rising just as fast. The critical issue is timing: generation and transmission upgrades often take 5 to 10 years, while data centers can be built in a fraction of that time, making the grid an immediate bottleneck for scaling AI infrastructure. How can we unlock gigawatts of grid capacity on a much faster timeline while keeping electricity affordable for existing customers and long-term planning on track? The first part of this talk focuses on accelerating large load interconnection via flexible connection programs that explicitly use load flexibility to advance energization. Focusing on partially measured distribution grids, where only some loads are metered and communicated in real time, I will discuss why such programs can unlock substantial grid hosting capacity while requiring only infrequent load interventions (e.g., ~1% annual curtailment). I will also show how a flexibility-aware notion of hosting capacity allows utilities to trade off load flexibility, sensing and communication, and asset upgrades. The second part surveys two related lines of work from my group: quantifying and limiting the impact of flexible connection on the wholesale prices paid by existing customers, and modeling the couplings between the grid and flexible load systems such as data centers and electric vehicles. Together, these tools and insights point to practical pathways for mobilizing near-term grid capacity while informing longer-horizon planning, market design, and policy.