kugelfish

For a hypothetical future power grid where production is dominated by variable renewable sources like PV and wind, the challenge of balancing supply and demand at all times is well know. Using again the ENTSO-E open-source data and Python linear programming solver , we extrapolate the grid level solar and wind forecast data to cover the entire yearly electricity demand while minimizing the sum of the absolute errors between supply and demand for all hours in a year. In order to turn the problem of minimizing the sum of absolute values into a linear programming problem, we use the bounding technique presented in this tutorial . Using for example the data for the German power grid from Spring 2022 to Spring 2023, the optimal generation mix according to the above criteria is about 75% wind and 25% PV (blue line).  With this configuration about 80% of the annual supply & demand of 475 TWh would be matched at the hourly level, leaving a discrepancy of 20% or 107TWh to be equalized someh

While there is no technological silver-bullet to combat climate change, electrification is about as good as it gets - maybe at least a silver spoon to help keep the monsters at bay... Today about 80% of the world-wide energy consumption is still based on fossil fuels. The good news is that as part of a net zero transformation of the energy system, we do not need  to replace all the fossil fuel inputs, but only their useful outputs. According to this Sankey energy flow diagram for the US in 2022 (source: LLNL ), about 20% of the system input is based on low CO2e energy sources. However this would correspond to 65% of the actually useful energy services provided by the system. Some of the most significant sources of loss are from the use of petroleum in the transport sector or from the electricity generation using natural gas or coal.  While generating more electrical power from modern renewable sources like wind and solar reduces the need for gas & coal by at least 2 times, assuming