Are we seeing clearly? The need for aligned vision and supporting strategies to deliver net-zero electricity systems

This paper explores the trends, step changes and innovations that could impact the integration of renewable energy into electricity systems, explores interventions that may be required, and identifies key areas for policy makers to consider. A Delphi approach is used to collect, synthesise, and seek consensus across expert viewpoints. Over sixty experts across a range of geographies including the US, Europe, New-Zealand, Australia, Africa, India and China participated. They identified 26 trends, 20 step changes, and 26 innovations that could lead to major shifts in the design, operation, or management of electricity systems. Findings suggest that key challenges are not technological. Instead they are with delivering an aligned vision, supported by institutional structures, to incentivise, facilitate, and de-risk the delivery of a completely different type of energy system. There is a clear role for government and policy to provide a future energy vision and steer on strategic issues to deliver it; to create space for new actors and business models aligned with this vision; and to create an environment where research, development, demonstration and deployment can promote technologies, system integration and business model innovation at a rate commensurate with delivering net-zero electricity system

Wind Power Persistence Characterized by Superstatistics

Mitigating climate change demands a transition towards renewable electricity generation, with wind power being a particularly promising technology. Long periods either of high or of low wind therefore essentially define the necessary amount of storage to balance the power system. While the general statistics of wind velocities have been studied extensively, persistence (waiting) time statistics of wind is far from well understood. Here, we investigate the statistics of both high- and low-wind persistence. We find heavy tails and explain them as a superposition of different wind conditions, requiring q-exponential distributions instead of exponential distributions. Persistent wind conditions are not necessarily caused by stationary atmospheric circulation patterns nor by recurring individual weather types but may emerge as a combination of multiple weather types and circulation patterns. This also leads to Fréchet instead of Gumbel extreme value statistics. Understanding wind persistence statistically and synoptically may help to ensure a reliable and economically feasible future energy system, which uses a high share of wind generation

A comprehensive analysis of flexibility options to integrate high shares of renewable electricity in a European power network

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.In dieser Arbeit wird der Flexibilitätsbedarf eines hochgradig erneuerbaren Elektrizitätsnetzwerks untersucht, das mit den Schwankungen auf Grund von Wind- und Solarenergieerzeugung auf unterschiedlichen Zeitskalen kompatibel sein muss. Des Weiteren wird eine Kostenoptimierungsstudie präsentiert, um eine optimale Verteilung von Infrastruktur, Speichersystemen, Leitungsausbau, sowie die Notwendigkeit der Integration konventioneller Energiekraftwerke auszuloten. Dazu wurde ein Modell entwickelt, das im Laufe der Studie um verschiedene Komponenten erweitert wird, um deren Einfluss auf das Energiesystem zu studieren. Hierzu beginnt das Modell mit der Annahme eines Energiemixes basierend auf den derzeitig integrierten Energieerzeugungsquellen und wird sukzessive um Speichertechnologien, innereuropäische Stromleitungssysteme und schließlich um eine politische und sozioökonomische Flexibilität erweitert, um die Sensitivität des Energiemixes auf diese Erweiterungen zu untersuchen. Zusammenfassend wird in dieser Arbeit gezeigt, dass langsam-flexible Grundlastkraftwerke nur genutzt werden können, solange der Anteil erneuerbarer Energien unter 50% liegt, unabhängig von der Ausdehnung des Leitungsnetzes innerhalb Europas. Des Weiteren wird gezeigt, dass für ein von erneuerbaren Energieerzeugern dominiertes Energiesystem die hochflexiblen konventionellen Energieerzeuger (d.h. Gaskraftwerke) die einzigen notwendigen Backupgeneratoren sind. Die Gesamtbackupkapazität eines hochgradig erneuerbaren Energiesystems kann nur durch ein europaweites Stromnetz signifikant gesenkt werden. Dieses Resultat ist unabhängig von der Komplexität der Kostenannahmen innerhalb des Modells. Speichertechnologien können die benötigte Backupkapazität aus konventionelle Kraftwerken weiter reduzieren. Dies zeigt klar die Wichtigkeit der Integration von Technologien in den Energiemix, die die notwendige Flexibilität besitzen, die durch erneuerbare Energiequellen erzeugten räumlichen und zeitlichen Fluktuationen auszugleichen. Solche Technologien umfassen beispielsweise Energiespeichersysteme, internationale Stromübertragungsleitungen sowie Wasserkraftwerke. Es wird dargelegt, dass ein kostenoptimiertes europäisches Elektrizitätssystem mit beinahe 100% erneuerbarer Energieerzeugung zu einem ähnlichen Kostenaufwand möglich ist wie das bereits heute betriebene System. Allerdings ist dazu ein um das neunfache erhöhter Netzausbau nötig. Eine Beschränkung des Netzausbaus erhöht die Systemkosten um ein Drittel. Ein Kompromissnetzwerk mit dem vierfachen Leitungsvolumen des heutigen Netzes erweist sich allerdings bereits als kosteneffizient. Daraus geht deutlich hervor, dass durch eine Erhöhung des Leitungsvolumens innerhalb Europas eine kostengünstige Einbindung erneuerbarer Energien erreicht werden kann, was unabdingbar ist, um die aktuellen Klimaschutzziele zu erreichen. Diese Studie endet mit einer Sensitivitätsanalyse, die den Einfluss politischer Entscheidungen auf den Ausbau dieses Energiesystems untersucht. Aus dieser Studie geht klar hervor, dass ein hochgradig erneuerbares Energiesystem flexibel genug ist, um mit einem breiten Spektrum politischer und sozio-ökonomischer Entscheidungen kostengünstig zurecht zu kommen

PyPSA: Python for Power System Analysis

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)

Backup flexibility classes in emerging large-scale renewable electricity systems

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

Supplementary Data: Code, Input Data and Model data: PyPSA-Eur: An Open Optimisation Model of the European Transmission System

<p>Supplementary Data (preliminary version)</p> <p>PyPSA-Eur: An Open Optimisation Model of the European Transmission System</p> <p>Authors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown</p> <p>and</p> <p>The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios</p> <p>Authors: J. Hörsch, T. Brown</p> <p>The files in this record contain the scripts to build a <a href="http://pypsa.org/">PyPSA</a> model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>base_network.py creates the initial PyPSA network topology.</p> <p>add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.</p> <p>simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.</p> <p>cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.</p> <p>prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p><strong>Data</strong></p> <p>The input data include:</p> <ul> <li>Electricity sector data</li> <li>Topology derived from the analysis of an extract of the <a href="https://www.entsoe.eu/data/map/">ENTSO-E online map</a> using <a href="https://github.com/bdw/GridKit">GridKit</a> .</li> <li>A cost database with literature sources.</li> </ul> <p> </p