Near-optimal solutions can be better than optimal solutions.
H/t to Richard who introduced me to Prof. Tom Brown from the TU Berlin. He’s an energy systems modeler.
He calculates ideal energy systems within financial and physical constraints.
In his article “Near-Optimal Solutions for Unpopular Infrastructure” he argues that by modeling an energy system that costs just 10% more than the cheapest option, you can have a much better outcome:
- Fewer wind turbines sight
- Less solar PV within sight
- Fewer transmission lines
He also built a free tool to play around with different energy system scenarios 👇
This tool calculates the cost of meeting a constant electricity demand from a combination of wind power, solar power and storage for different regions of the world. First choose your location to determine the weather data for the wind and solar generation. Then choose your cost and technology assumptions to find the solution with least cost.