What good is engineering research with no practical relevance?
The short answer to your question is that you are vastly overestimating your, and other engineer's, ability to judge what techniques will ever have practical relevance.

I think it was Michael Stonebraker, a Turing award winning computer scientist with no lack of practical impact, who said that the sweet spot for academic applied research are techniques that are about 10 years away from being widely implementable. If you limit yourself to things that you can already do today, you will fail to propose the kind of radically new developments that should, at least in theory, distinguish academic research from other drivers of innovation, such as startup companies or industrial R&amp;D. Incidentally, if the lack of impact your work has right now is distressing you, you should ponder the question whether you would not achieve higher job satisfaction in a startup or industrial lab.

I find your example of self-learning power grids particularly unconvincing. If we rewind time a few years and relate your arguments to research into automated driving, I am sure you will find plenty of people who found this research to be a waste of time. Driving surely is a safety critical field, and automotive is highly regulated. Algorithms for automated driving assistance completely failed to, and to some extent still fail to, address the practical concerns of many stakeholders as well as governmental safety guarantees. And yet here we are. I am not sure if the same will happen to power grids, but it is absolutely plausible that it will.

You may also be interested in reading into TRLs (technology readiness levels), as used for instance by the European Union's framework programmes as well as NASA.



The basic concept here is that academic research is usually best suited to bring ideas from TRL 0 or 1 to 3 or 4. The "Matlab implementations" you complain about may very well just be the laboratory tests that are meant on TRL 3. This is very much in line with the position in the grander scheme of the progress of technology that many large organizations envision for academic research labs.