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This is the future. Using algorithms to optimize usage of renewable energy. Not only will it be lower carbon, it will be cheaper. What's interesting is they describe it working on forecasts (for wind and sun) instead of instantaneous renewable production. I wonder what the rationale for that was? Basing the algorithm on instantaneous information should be more accurate and thus give better savings, but maybe it varied too much to reliably run the loads they want. Imagine when your fridge can do this: freeze extra cold when the sun is shining (or wind is blowing), don't run the compressor when it's not, only run the blower after you open the door to move that extra cold from the freezer, allow a slightly larger temperature range, and of course run as necessary to avoid spoilage. It's not a simple algorithm, it has to handle various timeframes, such as solar being a daily cycle except there's less in winter and can go for a week or more with very little (storm/overcast). Maybe it could also use a bit of "learning" like the Nest thermostats to also optimize predicted usage. I know of one commercial product that sort of does this: the Zappi electric car charger. If you have grid-tied solar, it measures the current being fed back to the grid and adjusts the charging current to match. So if a cloud goes over your house, or you turn on a big appliance, the charger reduces the power to the car by the same amount. This maximizes the use of your own solar energy and minimizes the use of grid energy. https://myenergi.com/product/zappi/ |
I've been posting for years that an effective grid "battery" is internet connected refrigerators, water heaters, A/C, car chargers, etc., that only run when power is cheap, i.e. when solar/wind is providing excess power.
A great deal of our demand for electricity is elastic and shiftable, which will eliminate a huge chunk of the need for grid batteries.
Glad to see this finally gaining some traction!