Category: Science

Generating Electricity without Fossil Fuels. Part IV: Comparisons and Policy Recommendations

How should we generate electricity?

Last post, we described a simple model of an economy that uses 100 GW of electricity. The economy was assumed to initially be predominantly fossil fuels. We got order of magnitude estimates for various scenarios of how to transition from fossil fuels to either solar / wind or natural gas.

This post directly compares the results from last post. It concludes with my own opinion of which strategy we should pursue.

For this model, I will state numbers for both the 100 GW model economy and numbers for a 500 GW economy – about the size of the US.

Generating Electricity without Fossil Fuels. Part III: A Simple Model

How should we generate electricity?

Last post, we discussed the various power sources from the perspective of the grid and briefly discussed energy storage. This post will put together the results of Parts I & II in a simple model to test different strategies for moving away from using fossil fuels to generate our electricity.

The simplifications in the model will make the transition away from fossil fuels look easier than it is. But they should be a fair comparison between the different strategies we might use.

Generating Electricity without Fossil Fuels. Part I: Overview of Alternative Power Sources

How should we generate electricity?

Currently, the majority of our electricity comes from fossil fuels, especially coal and natural gas. Burning fossil fuels has given us access to tremendous amounts of energy and has made modern civilization possible. Without them, we would have had trouble feeding ourselves, let alone obtaining our current standard of living.

Unfortunately, burning fossil fuels releases greenhouse gases that warm the global climate. And they will run out eventually. What other sources are available?

Hard Science and Soft Science

The criteria people typically use to distinguish the more reliable ‘hard sciences’ from the less reliable ‘soft sciences’ do not match our understanding of which is which. Instead, I think that the difference lies in the history of the fields. For hard sciences, the science precedes the community. For soft sciences, the community precedes the science. Hard sciences have norms of empiricism. Soft sciences try to import rules of empiricism. If you want to harden a science, it is more important to grow the norms than to establish the rules. One way to accomplish this is to require early career scientists replicate at least one major result in their field as part of their education.

Machine Learning is Mētis-Based Programing

In Seeing Like A State, James C. Scott contrasts formalized systems of knowledge with what he calls mētis. Mētis is “a wide array of practical skills and acquired intelligence in responding to a constantly changing natural and human environment”. Mētis is usually associated with traditional forms of knowledge and formalized systems of knowledge with modernity. Scott resists this comparison because many “traditional” forms of knowledge only look ancient and can quickly adapt in response to new conditions. I agree and wanted to find an example where the reverse is true: where formal knowledge is traditional and mētis is new. I believe that I have found such an example in computer science.

Book Review of A HISTORY OF THE THERMOMETER AND ITS USE IN METEOROLOGY by W. E. Knowles Middleton (1966)

This is the sequel to The History of the Barometer (1965) and prequel to the more ambitious Invention of the Meteorological Instruments (1969). Middleton has a very particular area of expertise and knows it very well. He seems to have examined every thermometer that was produced before the year 1800 which still exists and to have read almost every text that references them, in the original language. Why was I interested in such a particular book? Middleton tells the reason in his Preface: unlike barometers, where almost all the progress since the 1600s has been technical, the history of thermometers is as much about what we think ‘temperature’ is as it is about the device itself. My interest is in how these philosophical questions about temperature were asked and answered.

Noisy Chaos

In case deterministic chaos isn’t enough you, this post adds in something extra: a little bit of randomness. Rather than making things more complicated, this actually makes them smoother. If you’ve read the What is Chaos? series, you know that finding periodic orbits is important to understand chaos. The randomness allows you to determine how many periodic orbits you need to make predictions.

What is Chaos? Part VIII: Periodic Orbit Theory

This is the final post for my explanation of chaos theory to a popular audience. When the motion is chaotic, it is impossible to make long time predictions for a particular trajectory, but it is possible to make long time statistical predictions. I hope to explain the basic ideas of how we can calculate these long time averages for a strange attractor.

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