Comment by lambdadmitry | Hacker News Reader

Comment by lambdadmitry | Hacker News Reader

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Hydroelectric can't ramp up to a large fraction of national demand, for multiple unsurmountable reasons (like, you can't dump the water fast enough for one). Location selection doesn't matter that much as they don't try to optimise cost, they just look at aggregate output.

People living off grid still continue to consume the products of grid-enabled industries, from manufacturing to farming to logistics to infrastructure. Heating/cooling and lighting a cabin is much more trivial than paving a road coming to that cabin, feeding its inhabitants, building solar panels for them, or ensuring dense enough population to make research and manufacturing viable. In fact, we don't need to extrapolate minor part of personal consumption to the whole society, we have aggregate energy consumption numbers.

"Market forces" can't generate electricity on their own. Instead of taking the bottom-up view, fraught with wishful thinking and unstated assumptions, why not take the top down, working from the potential generation capacity under ideal assumptions? That's exactly what the paper I linked did, and the results are… not great for 100% renewable

> can’t ramp up

6% is already a large fraction of grid demand and that’s an average. It’s critical for finding anything that’s even vaguely accurate.

“Why no take the top down” because we want to minimize costs. It doesn’t matter how much over production happens vs how much energy storage happens, what matters is how much each costs.

If you really want to do a high level analysis you need to cover a wide range of major inputs. Hydro flexibility and the costs to increase that flexibility, as in how much can it ramp up and how much can it average over a year. Next is the actual cost of Wind in each location for various turbines and the time of day for production this varies quite a bit and becomes a massive optimization problem. Next is solar production at each location as well as how panels are aimed in various wealthier conditions, again a major optimization problem. Next actual energy output vs demand, Aka actually modeling what happens to both for various weather events. Next is cost of grid storage capacity, as in how much capital needs to sit around. Next is how much to utilize grid storage as in a charge vs discharge cycle, the first vs second are different for each technology and a mix of several is likely. Next grid interconnect costs as in how much does flexibility cost both in dollars and resistive losses. Next optimized Solar and wind capacity vs grid demand as in at each set of seasonal and weather conditions to minimize costs. Finally changes to costs over time or based on deployment.

It’s possible to model all of that, but in turns out that’s what markets do. The bottom up approach isn’t wishful thinking it’s what actually happens as people build A due to A being profitable up to point X when building B is more profitable etc etc.

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