One of the worst features of blockchain technologies like cryptocurrency and NFTs is their terrible energy consumption. If we were to squeeze every ounce of efficiency out of our electricity consumption, most blockchains require computers to repeatedly perform meaningless calculations.
The obvious solution is to base blockchains on useful calculations – something we may need to do anyway. Unfortunately, the mathematics of a blockchain must have a very specific property: the solution must be difficult to calculate, but easy to verify. Nonetheless, a number of useful calculations have been identified as possible replacements for those currently used in many systems.
A paper published this week adds another option to that list. Optimization problems are notoriously computationally intensive, but evaluating the quality of a solution is relatively easy. And in this case, the optimized systems are small energy grids, which means this approach could partially offset some of a blockchain’s terrible energy consumption.
The classic example of a math problem that makes sense in blockchain is factoring a large number that is the product of two prime numbers. It’s computationally difficult to identify the two prime numbers, but once you have them, it’s trivially easy to confirm the result of their multiplication. And from an energy wasting perspective, performing the non-trivial calculation is useless unless you know a situation where those numbers matter.
Optimization problems are similar. In order to find an optimal solution, e.g. B. the shortest route that includes several cities, all possible routes must be tested. And with each additional city added to the itinerary, the number of possible routes increases dramatically. However, for many optimization problems, finding out whether a proposed route is efficient is a much simpler computation, meaning all solutions are easy to check.
Most importantly, in the real world, optimization issues crop up all the time, from how to squeeze the most crates into a shipping container to allocating tools and technicians to ensure maintenance is done efficiently. This difference is behind a research team’s efforts to move blockchains from a proof-of-work (PoW) like factoring a large number to a proof-of-solution (PoSo) where blockchain transactions result in a useful 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 and focused on the energy supply that eats up other blockchains. They identify several power distribution issues that require optimization: matching supply and demand, identifying the most economical combination of generation sources, and so on.
They also argue that blockchain may make more sense as the energy market begins to decentralize a bit, with an increasing number of elements such as microgrids, solar rooftops, intermittent power sources, and smaller grid-tied batteries all decentralizing the sources of grid-tied energy. The complexity of managing all of this as a single, centralized grid grows accordingly, so the researchers suggest that small sub-grids could manage themselves through PoSo-based optimizations.
No more Enrons?
To test their system, they turn to two small energy systems. One of these is the University of Manchester, which owns some combined heat and power plants, electrical storage and heat storage, and some boilers. Figuring out which of these to activate under different circumstances is an economical optimization problem, but computationally manageable enough that a solution could be computed in as little as 220 seconds. This solution takes a total of one second to verify.
They performed a similar analysis for a system that provides a combination of power, 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 requires multiple computers to perform calculations and checks, so it takes more energy than just running the optimization on a single system. However, the researchers argue that the PoSo blockchain solution offers a key benefit: it’s harder to play.
Imagine a situation where the operator of the central management system wants to favor certain generation sources, even (or especially) if they are more expensive than other options. There is basically nothing that could stop it. In contrast, in a distributed system, all individual nodes compete for the best solution. Even if one or two nodes are compromised, other optimized systems should produce, and the verification process will ensure one of them is used.
Overall, this seems a bit far-fetched as it is not clear how often energy price manipulations occur that this system would protect against. Still, it’s nice to see some concrete ideas for using blockchain in situations where the energy demands aren’t terrible, and there are some valuable practical results.
energy of nature2022. DOI: 10.1038/s41560-022-01027-4 (About DOIs).
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