- While traditional brown energy remains a significant energy source for AI data centers, clean and renewable energy continues to grow in capacity.
- Goldman Sachs believes 40% of new data center capacity will be from renewables.
- Hyperscalers and data centers are adopting a mixed energy portfolio, combining brown, clean, and renewable energy to balance emissions while ensuring 24/7 reliability.
As the artificial intelligence (AI) revolution drives the growth of AI data centers, the topic of energy continues to gain prominence. AI data centers cannot function without energy sources, and how that power is generated seems just as important as how reliable the power is. Energy sources are commonly labeled brown, clean or renewable. Goldman Sachs says AI data center power consumption demand is expected to grow by more than 160% by 2030 from 2023 levels. Let’s take a look at each of these sources and how efficient and reliable they can be for AI data centers.
What is Brown Energy?
For decades, the world has relied heavily on brown energy from fossil fuels like oil, coal and natural gas. These dominant fuels powered the Industrial Revolution and remain the primary sources of electricity for the grid today, with coal and natural gas generating nearly 60%.
- Coal-fired power plants are some of the worst offenders as they release enormous amounts of carbon dioxide and greenhouse gases into the atmosphere. Thermal efficiency (TE) measures how effectively a fuel’s heat is converted into electricity. Coal has some of the lowest TE efficiency, around 33%.
- Natural gas is the top fuel source for powering the electric grid at 43% and is cleaner than coal but still contributes carbon emissions when it’s burned. TE is between 35% to 42% on a simple cycle gas turbine and up to 62% when using combined cycle gas turbines (CCGT), which use the exhaust heat to boil water in a steam turbine, adding the extra 20% to 25% TE.
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The environmental impact of burning fossil fuels has paved the way for the clean energy and decarbonization movement. The combustion process produces carbon emissions, which contribute to air pollution and climate change. As AI data centers consume massive amounts of electricity, they’re also trying to meet clean energy initiatives.
What is Clean Energy?
Clean energy has much less environmental impact, producing low to zero greenhouse gas emissions. It also generates less pollution and leaves a smaller carbon footprint. Some examples of clean energy are:
- Solar power generates electricity using sunlight and photovoltaic panels with TE between 15% to 22%.
- Wind power generates electricity by harnessing kinetic energy from the wind with turbines with mechanical efficiency (ME) between 35% to 50%; since they don’t operate on thermal cycles, there isn’t heat conversion.
- Hydropower generates electricity from water flowing through turbines, again no thermal cycle but the ME is 85% to 95% since the momentum of the water converts to power with almost no loss.
- Geothermal power uses the heat emanating from the earth’s core with a low TE ranging from 10% to 23%
- Nuclear energy generates power through fission, heating water to create steam that drives turbines, with TE averaging between 33% to 37%.
For AI data centers, the most practical clean energy sources come from nuclear, solar and wind power. While the efficiency of hydropower is exceptionally high, it isn’t practical for data centers due to factors like heavy capex to build dams and reservoirs, environmental impacts and geographical limitations. Clean energy enables data centers to lower their carbon footprint and enhance their environmental reputation.
