CASE STUDY | JUNE, 2024

energy providers and grid operators: manage demand and avoid costly blackouts

Heatwaves pose a significant financial risk to energy markets by driving up demand and spiking electricity prices. This case study showcases how if leveraged, Climavision’s Horizon AI Point Forecast can help navigate crucial scenarios just like the one right before the start of summer 2024, helping energy providers and grid operators manage demand and avoid costly blackouts.

During the month of June, PJM (Pennsylvania-New Jersey-Maryland Interconnection), the Regional Transmission Organization (RTO) coordinating electricity across 13 states in the eastern U.S., faced a major heatwave that tested the resilience of its power grid. High Heat Alerts were issued as temperatures soared beyond seasonal norms, resulting in unprecedented demand and pushing the grid to its limits.

From June 16-22, 65 million customers in regions like Pennsylvania, New Jersey, Maryland, and Ohio experienced highs of 90-100°F. As temperatures climbed, electricity consumption spiked, with demand frequently exceeding 150,000 MW, compared to PJM’s typical summer load of 115,000-130,000 MW. The grid operator struggled to keep pace with demand, risking potential blackouts.

Forecast

Climavision’s Horizon AI Point Forecast system provides clarity in the face of adverse weather conditions. The nodal-level NWP prediction model uses local observational datasets and AI-infused historical data to provide a clearer picture of what’s coming. No matter where you operate, you can access the granular detail you need to stay ahead of the weather.

Climavision’s Horizon AI Point Forecast, though not used at the time, could have provided critical, hyper-local insights into temperature trends during this period. The model forecasted 90+ degree days across PJM’s largest cities for June 17-22.

For example, on June 18th, Chicago’s O’Hare International Airport was forecasted to reach the mid-to-upper 90s, and the actual temperature hit 94°F. By June 22nd, Washington-Dulles (KIAD) was predicted to hit 100°F—and indeed, it did. If energy traders and grid operators had utilized this level of precision, they could have better prepared for the surge in demand and optimized their response.