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In this work the flexibility requirements of a highly renewable European electricity network that has to cover fluctuations of wind and solar power generation on different temporal and spatial scales are studied. Cost optimal ways to do so are analysed that include optimal distribution of the infrastructure, large scale transmission, storage, and dispatchable generators. In order to examine these issues, a model of increasing sophistication is built, first considering different flexibility classes of conventional generation, then adding storage, before finally considering transmission to see the effects of each.
To conclude, in this work it was shown that slowly flexible base load generators can only be used in energy systems with renewable shares of less than 50%, independent of the expansion of an interconnecting transmission network within Europe. Furthermore, for a system with a dominant fraction of renewable generation, highly flexible generators are essentially the only necessary class of backup generators. The total backup capacity can only be decreased significantly if interconnecting transmission is allowed, clearly favouring a European-wide energy network. These results are independent of the complexity level of the cost assumptions used for the models. The use of storage technologies allows to reduce the required conventional backup capacity further. This highlights the importance of including additional technologies into the energy system that provide flexibility to balance fluctuations caused by the renewable energy sources. These technologies could for example be advanced energy storage systems, interconnecting transmission in the electricity network, and hydro power plants.
It was demonstrated that a cost optimal European electricity system with almost 100% renewable generation can have total system costs comparable to today's system cost. However, this requires a very large transmission grid expansion to nine times the line volume of the present-day system. Limiting transmission increases the system cost by up to a third, however, a compromise grid with four times today's line volume already locks in most of the cost benefits. Therefore, it is very clear that by increasing the pan-European network connectivity, a cost efficient inclusion of renewable energies can be achieved, which is strongly needed to reach current climate change prevention goals.
It was also shown that a similarly cost efficient, highly renewable European electricity system can be achieved that considers a wide range of additional policy constraints and plausible changes of economic parameters.
Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit).
High shares of intermittent renewable power generation in a European electricity system will require flexible backup power generation on the dominant diurnal, synoptic, and seasonal weather timescales. The same three timescales are already covered by today’s dispatchable electricity generation facilities, which are able to follow the typical load variations on the intra-day, intra-week, and seasonal timescales. This work aims to quantify the changing demand for those three backup flexibility classes in emerging large-scale electricity systems, as they transform from low to high shares of variable renewable power generation. A weather-driven modelling is used, which aggregates eight years of wind and solar power generation data as well as load data over Germany and Europe, and splits the backup system required to cover the residual load into three flexibility classes distinguished by their respective maximum rates of change of power output. This modelling shows that the slowly flexible backup system is dominant at low renewable shares, but its optimized capacity decreases and drops close to zero once the average renewable power generation exceeds 50% of the mean load. The medium flexible backup capacities increase for modest renewable shares, peak at around a 40% renewable share, and then continuously decrease to almost zero once the average renewable power generation becomes larger than 100% of the mean load. The dispatch capacity of the highly flexible backup system becomes dominant for renewable shares beyond 50%, and reach their maximum around a 70% renewable share. For renewable shares above 70% the highly flexible backup capacity in Germany remains at its maximum, whereas it decreases again for Europe. This indicates that for highly renewable large-scale electricity systems the total required backup capacity can only be reduced if countries share their excess generation and backup power.