Why Top Investors Have an Edge in Identifying Renewable Opportunities

This map highlights the increasing precision of energy models, which now forecast power prices at a granular level — down to specific locations — by analyzing the complex interplay of all energy subsectors in a given area. 

Hyper-local energy supply and demand dynamics play a critical role in price formation, a key factor for companies planning data centers. 

Ever wonder why top energy investors, like Bill Gates' Breakthrough Energy, are more confident in certain renewable projects? They've developed and shared a model—similar to the open-source PyPSA—that analyzes any grid location and maps the entire energy stack. 

Here’s how these hyper-local models give investors an edge:

• The model identifies greater demand for renewable power in areas where it detects grid bottlenecks.

• This approach forecasts greater demand for renewable power by modeling not just electricity generation but also modeling energy needs across all industries.
• This includes heating for buildings, agriculture, transportation (including shipping and aviation), and non-energy feedstock for the chemical sector.

• The model captures the value of hydrogen and next-generation liquid fuels, known as Power-to-X fuels, highlighting the broader potential of renewable projects.
• The model’s granular 3-hour resolution captures the variability of renewable energy resources. For instance, it simulates using excess solar power to produce hydrogen, reducing solar curtailment on that part of the grid and making the local solar project appear more economically viable.

• The model simulates practical uses of hydrogen, focusing on aviation fuel and naphtha.
• The model determines the most cost-effective set of activities for the economy, which is why it concludes that using hydrogen for aviation fuel is more economical than converting it back to electricity.
• The model maps how hydrogen is transported from solar and wind-rich production areas to industrial hubs.
• The model identifies potential hydrogen storage sites on the map. Low-cost geological storage locations allow for continuous production in fuel-synthesis plants, which are typically inflexible.
• The model maps areas where it is viable for utilities to convert gas networks into hydrogen pipelines, capturing how retrofitting existing gas pipelines can reduce the overall cost of the hydrogen network.
• The model forecasts the eventual decay of plastics and its impact on CO2 levels, driving the demand for naphtha in the production of eco-plastics.

Data centers will compete with emerging electrofuel plants for access to prime locations on the grid. So, they need to look for opportunities to fill the gaps in the system. In some areas, power is abundant — or even stranded — because transmission constraints prevent it from being exported, making local consumption the most efficient use.