kugelfish

In the previous post , we made some back of the envelope estimation of what an optimal solar, wind & storage based power grid could look like. For the analysis we were using a standard linear programming model using open-data from the ENTSO-E transparency platform as well as a Python based linear programming (LP) framework. For the LP framework we chose PuLP for its beginner friendly fluent and natural sounding model definition. See this tutorial for a more detailed introduction into LP with PuLP or this this  comparison of some popular Python LP frameworks . Understanding and debugging the output of an LP solver is non-obvious for casual users. Hence, being able to translate the mathematical model into what looks like natural python code can help to prevent mistakes during model setup. The PuLP framework can interface with different external solver backends out of which the CBC solver is packaged with the PuLP distribution. As input data, we can get time-series data with hour

As wind and solar are reaching a double-digit share of production in many power grids, the focus is increasingly shifting towards adding storage. Large consumers like Google are pushing their suppliers towards   providing low carbon energy 24/7 all year round . The same goal is also captured well by the catchy new term of " Grüne Grundlast " (green baseload) , despite being slightly misleading as the goal should not be constant production when nobody needs it, but production which matches the load at all times. Using national power grid data from the ENTSO-E transparency platform , some very rough and hand-wavy cost assumptions and a standard  Python LP solver , we can do a back of the envelope estimation of what it would take to satisfy todays electricity needs of some European countries using an extrapolation of their current solar and wind production in combination with a fictitious storage pool.   Linear Programming is a very common optimisation technique used in operati