I have a very limited understanding of this field, so the following is probably a bit wrong but hopefully leading in the right direction.
In the US (and maybe some other countries) there is a regulated energy demand & supply prediction market. This is set up at various points in the electric grid, which is connected to many different suppliers of both renewable and non-renwable sources of electricity.
The coal power plants are required to be able to deliver enough electricity so that there is a low chance of power outage. The bound on that chance is defined by regulations; I’m not sure whether it is measured retroactively (say, % of days without power per household) or directly from the prediction market (say, prepare to supply at least 4 std above the mean demand). Prediction is key here, as plants need to prepare in advance (say, 12 hours, not sure) for some technical reason and are committed to some degree of energy generation. The better the prediction, the less they need to generate to be safe from edge cases.
The addition of highly-variable/unpredictable renewable sources of energy to the grid means that they can take some of the load off from coal plants. But again, increasing the predictive accuracy means that the coal plants can generate less excess energy.
I think there are some estimates of exactly how much near-term CO2 emissions are being reduced based on better predictions. I’m not sure how to think about the long-term effects on the industry.
Also, there may be other solutions in the longer term. Say, better energy storage mechanisms may replace the need to have highly accurate predictions, as suppliers may save excess energy with less waste.
As a career, my main guess is that the potential for E2G here is greater than the direct impact one has. That is, these are generally high-paying roles and I suspect that donating, say, 10% of the income to Clean Air Task Force would be much more effective for the energy transition. If that’s the case, it should be compared to other possible jobs with even higher salaries.
However, if you are interested in learning more about the industry in general, I think that working in such a company could be a great way for people with strong quantitative background to do so. Generally, when doing any type of data science you are going to learn a lot on the subject matter, and in this case it may even be more so.
Again, these are rough guesses. I’d be interested if you or anyone else here would find relevant literature on the topic or write some stronger opinions and analyses.
I have a very limited understanding of this field, so the following is probably a bit wrong but hopefully leading in the right direction.
In the US (and maybe some other countries) there is a regulated energy demand & supply prediction market. This is set up at various points in the electric grid, which is connected to many different suppliers of both renewable and non-renwable sources of electricity.
The coal power plants are required to be able to deliver enough electricity so that there is a low chance of power outage. The bound on that chance is defined by regulations; I’m not sure whether it is measured retroactively (say, % of days without power per household) or directly from the prediction market (say, prepare to supply at least 4 std above the mean demand). Prediction is key here, as plants need to prepare in advance (say, 12 hours, not sure) for some technical reason and are committed to some degree of energy generation. The better the prediction, the less they need to generate to be safe from edge cases.
The addition of highly-variable/unpredictable renewable sources of energy to the grid means that they can take some of the load off from coal plants. But again, increasing the predictive accuracy means that the coal plants can generate less excess energy.
I think there are some estimates of exactly how much near-term CO2 emissions are being reduced based on better predictions. I’m not sure how to think about the long-term effects on the industry.
Also, there may be other solutions in the longer term. Say, better energy storage mechanisms may replace the need to have highly accurate predictions, as suppliers may save excess energy with less waste.
As a career, my main guess is that the potential for E2G here is greater than the direct impact one has. That is, these are generally high-paying roles and I suspect that donating, say, 10% of the income to Clean Air Task Force would be much more effective for the energy transition. If that’s the case, it should be compared to other possible jobs with even higher salaries.
However, if you are interested in learning more about the industry in general, I think that working in such a company could be a great way for people with strong quantitative background to do so. Generally, when doing any type of data science you are going to learn a lot on the subject matter, and in this case it may even be more so.
Again, these are rough guesses. I’d be interested if you or anyone else here would find relevant literature on the topic or write some stronger opinions and analyses.