One of the worst features of blockchain technologies like cryptocurrency and NFTs is their horrific power consumption. While we should squeeze every bit of efficiency out of our electricity usage, most blockchains require computers to repeatedly perform meaningless calculations.
The obvious solution is to base blockchains on useful computations – something we may need to do anyway. Unfortunately, the math involved in a blockchain must have a very specific property: the solution must be difficult to calculate, but easy to verify. Nevertheless, a number of useful calculations have been identified as possible replacements for the calculations currently used in many systems.
A newspaper released this week adds another option to this list. Optimization problems are notoriously expensive in terms of calculations, but the quality of a solution is relatively easy to evaluate. And in this case, the systems being optimized are small energy networks, meaning this approach could partially offset some of a blockchain’s horrific power consumption.
The classic example of the math problem that makes sense in blockchain is the factorization of a large number that is the product of two prime numbers. It is mathematically difficult to identify the two prime numbers, but once you have them it is trivially easy to confirm the outcome of multiplication. And from an energy waste standpoint, it’s useless to do a nontrivial calculation unless you happen to know of a situation where those numbers matter.
Optimization problems are similar. Finding an optimal solution, such as the shortest route spanning multiple cities, involves sampling all possible routes. And the number of possible routes increases dramatically with each additional city added to the route. However, for many optimization problems it is a much simpler calculation to find out if a suggested route is efficient, meaning any solutions are easy to verify.
Most importantly, optimization issues constantly arise in the real world, from how to squeeze the most cartons into a shipping container to assigning tools and technicians to ensure maintenance work is done efficiently. That difference is behind a research team’s efforts to shift blockchains from a proof-of-work (PoW) like factoring a large number to a proof-of-solution (PoSo), where blockchain transactions result in a usable computation. . (If you’re wondering why PoSo ended with that second “o,” drop the letter and think about it for a moment.)
When choosing an optimization problem for their PoSo blockchain, the researchers opted for irony, focusing on the energy supply that other blockchains eat through. They identify various problems in energy distribution where optimization is needed: matching supply and demand, figuring out the most economical combination of generation sources, and so on.
They also argue that blockchain may make more sense as the energy market starts to decentralize a bit, with a growing number of items such as microgrids, rooftop solar, intermittent power sources, and smaller on-grid batteries all decentralizing the sources of on-grid power. . The complexity of managing all that as a single, centralized network increases accordingly, so the researchers suggest that small subnetworks could manage themselves through PoSo-based optimizations.
No more Enrons?
To test their system, they turn to two small power systems. One is the University of Manchester which has some cogeneration facilities, electrical storage and heat storage along with some boilers. Figuring out which of these to trigger under different conditions is an economic optimization problem, but computationally arithmetical enough to calculate a solution in just 220 seconds. Verifying that solution takes a total of one second.
They conducted a similar analysis for a system that provides a combination of electricity, heating and cooling for a district in the city of Suzhou, China. Again, the system managed to quickly generate optimal solutions for resource allocation and was competitive with a centralized management system.
The problem is that the system still needs multiple computers to perform calculations and verification, so it will take more energy than simply performing the optimization on a single system. But the researchers argue that the PoSo blockchain solution offers an important advantage: it is more difficult to game.
Imagine a situation where the central management system administrator wants to give preference to specific generation sources, even (or especially) if they are more expensive than other options. There is essentially nothing that could stop it. In contrast, in a distributed system, all individual nodes will compete to find the best solution. Even if one or two nodes are compromised, others must produce optimized systems and the verification process ensures that one of them is used.
Overall, this seems a bit of a challenge as it’s not clear how often energy prices are manipulated on the kind that this system would protect against. Still, it’s nice to see some concrete ideas about how to use blockchain in situations where the energy demand isn’t terrible, and there are some valuable practical results.
Nature Energy2022. DOI: 10.1038/s41560-022-01027-4 (About DOIs).