Negative energy prices and artificial intelligence

Since renewable energy has started to become popular, an odd problem has appeared in wholesale energy markets: negative prices.

In other words, energy plants sometimes pay their customers to take energy off their hands. Usually older, less flexible plants that can't shut down without incurring costs are affected.

One solution to this problem is batteries. The idea is to store the energy when it is overabundant, and use it later when it is expensive. This is sometimes called "peak shaving".

Batteries are a great idea, but not the only solution. Another is to simply find an application that is energy hungry and can be run intermittently.

One possible application for soaking up excess energy is desalination. For example, a desert region near an ocean could build solar plants to desalinate water during the day only. The question is whether building a desalination plant that only runs 12 hours a day is worth the savings in energy. 

Another way to make use of energy that might go to waste is using it to power computers that perform analytics. The energy demand of data centers is growing quickly.

One source of energy needs is Bitcoin. Bitcoin mining consumes huge amounts of energy, so it is a great example of a use for negative energy prices. In fact there are already a lot of bitcoin miners in Western China, where solar and wind installations have outstripped grid upgrades. In these areas renewable energy is often curtailed because the grid can't keep up. So the energy is basically free to the miners.

Extremely cheap bitcoin mining arguably undermines the whole concept, but here is a more productive idea: Training artificial intelligence. For example, have a look at this link to gcp leela, a clone of Google Deepmind Alphago zero:

https://github.com/gcp/leela-zero

The entire source code is free, and it's not a lot of code. But that free code is just the learning model, and its based on well known principles. It's probably just as good as Deepmind Alphago Zero when trained, but they figure it would take them 1700 years to train -- unless of course they could harness other resources.

This is partly because they don't have access to Google's specialized TPU hardware. Whatever the reason, training it is going to burn through a lot of energy.

This would be a great application for negatively priced energy. Game playing is more a stunt than a commercial application, but when they are paying you to use the energy, why not? And as time passes, more useful AI apps will need training.

So it gets down to whether the business model of peak shaving with batteries makes more economic sense than banks of custom chips for training neural networks for AI in batches. The advantage of batteries is that you can sell the energy later for more, but it's not terribly efficient, and using it directly is a better idea. Cheap computer hardware and a growing demand for AI may fit this niche very well.

This puts a whole new twist on the idea that big tech companies are investing in renewables. These companies make extensive used of AI, which is trained in batch processes.