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 ...

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 i...

In the previous post, we tried to estimate the opportunity for storage based arbitrage in a variety of European day-ahead electricity markets. For that we have used a "fast & efficient" storage model inspired by lithium-ion based storage systems  with a round-trip cycle efficiency of over 90% and the ability to charge or discharge the entire storage capacity during each interval - i.e. a 1MWh storage with a 1 MW input/output power resulting in a storage duration of 1 hour. Other storage technologies allow a more independent scaling of the maximum charge/discharge power from the maximum amount of energy the storage can hold, typically at a much lower marginal cost per additional unit of capacity. One such example is pumped hydro storage, which is by far the most common form of large-scale electricity storage today. The input output power rating is determined by the configuration of the turbines, generators and pumps which contribute significantly to the cost of the instal...

As more and more solar and wind generation capacity is integrated into a power grid without sufficient storage , the intra-day electricity market prices tend to fluctuate between near-zero when there is an over-supply of renewable energy and quite expensive when fossil fuel power plants need to be brought online to cover the demand. The fewer hours these plants run and the more expensive the fossil fuel becomes the more the range of this short term fluctuations will increase, unless they can potentially be substituted by a cheaper solution.  Using electricity market data from the ENTSO-E transparency platform , we can determine what would be the maximum profit the operator of a hypothetical storage unit could make by deploying an optimal strategy of buying low and selling high. The storage is assumed to have one unit of power, i.e. in this case it could either charge or discharge one MWh of energy in a given hour. The storage system can have a capacity of n hours and a round-trip ...