To achieve ambitious greenhouse gas emission reduction targets in time, the planning of future energy systems needs to accommodate societal preferences, e.g.~low levels of acceptance for transmission expansion or onshore wind turbines, and must also acknowledge the inherent uncertainties of technology cost projections. To date, however, many capacity expansion models lean heavily towards only minimising system cost and only studying few cost projections. Here, we address both criticisms in unison. While taking account of technology cost uncertainties, we apply methods from multi-objective optimisation to explore trade-offs in a fully renewable European electricity system between increasing system cost and extremising the use of individual technologies for generating, storing and transmitting electricity to build robust insights about what actions are viable within given cost ranges.

With rising shares of renewables and the need to properly assess trade-offs between transmission, storage and sectoral integration as balancing options, building a bridge between energy system models and detailed power flow studies becomes increasingly important, but is computationally challenging. We compare approximations for two nonlinear phenomena, power flow and transmission losses, in linear capacity expansion problems that co-optimise investments in generation, storage and transmission infrastructure. We evaluate different flow representations discussing differences in investment decisions, nodal prices, the deviation of optimised flows and losses from simulated AC power flows, and the computational performance.

Social acceptance is a multifaceted consideration when planning future energy systems, yet often challenging to address endogeneously. One key aspect regards the spatial distribution of investments. Here, I evaluate the cost impact and changes in optimal system composition when development of infrastructure is more evenly shared among countries and regions in a fully renewable European power system. I deliberately deviate from the resource-induced cost optimum towards more equitable and self-sufficient solutions in terms of power generation.

Panel session presentation introducing how PyPSA can be used for sector-coupling applications.

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is to use alternatively fueled vehicles (AFV). Heavy-duty vehicles (HDV) emit a large share of GHG emissions in the transport sector and are therefore the subject of growing attention from global regulators. Fuel cell and green hydrogen technologies are a promising option to decarbonize HDVs, as their fast refueling and long vehicle ranges are in line with current logistic operation concepts.

The common linear optimal power flow (LOPF) formulation that underlies most transmission expansion planning (TEP) formulations uses bus voltage angles as auxiliary optimization variables to describe Kirchhoff’s voltage law. As well as introducing a large number of auxiliary variables, the angle-based formulation has the disadvantage that it is not well-suited to considering the connection of multiple disconnected networks, It is, however, possible to circumvent these auxiliary variables and reduce the required number of constraints by expressing Kirchhoff’s voltage law directly in terms of the power flows, based on a cycle decomposition of the network graph.

Rapid increase in the number of electric vehicles will likely deteriorate voltage profiles and overload distribution networks. Controlling the charging schedule of electric vehicles in a coordinated manner provides a potential solution to mitigate the issues and could defer reinforcement of network infrastructure. This work presents a method for robust, cost-minimising, day-ahead scheduling of overnight charging of electric vehicles in low voltage networks in a stochastic environment with minimal real-time adaptation.

Introduction to the PyPSA-Eur snakemake workflow and underlying scripts.

Models for long-term investment planning of the power system typically return a single optimal solution per set of cost assumptions. However, typically there are many near-optimal alternatives that stand out due to other attractive properties like social acceptance. Understanding features that persist across many cost-efficient alternatives enhances policy advice and acknowledges structural model uncertainties. We apply the modeling-to-generate-alternatives (MGA) methodology to systematically explore the near-optimal feasible space of a completely renewable European electricity system model.

Governments across the world are planning to increase the share of renewables in their energy systems. The siting of new wind and solar power plants requires close coordination with grid planning, and hence co-optimization of investment in generation and transmission expansion in spatially and temporally resolved models is an indispensable but complex problem. Particularly considerations of transmission expansion planning (TEP) add to the problem’s complexity. Even if the power flow equations are linearized, the optimization problem is still bilinear and mixed-integer due to the dependence of line expansion on line impedance and a discrete set of line expansion options.

Die Umsetzung der Energie- und Klimaziele der Bundesregierung führen zu einem deutlichen Rückgang des Gasbedarfs. Damit stehen auch im Gasmarkt erhebliche Änderungen und infrastrukturelle Herausforderungen an. Nicht alle Gasverteilnetze werden wirtschaftlich fortbestehen können. Das Fernleitungsnetz wird zukünftig im ähnlichen Umfang wie heute benötigt. Änderungen sind vor allem bei der Auslastung und bei den Importrouten bei zunehmender Integration von strombasiert hergestelltem Methan und angestrebter Diversifizierung der Importländer zu erwarten. Die im Auftrag des Umweltbundesamtes durchgeführte Studie analysiert Szenarien aus der Literatur und führt darauf aufbauend vereinfachte Modellberechnungen für den infrastrukturellen Bedarf und den damit verbundenen Kosten durch.

Scientific consultation of the BMVI on mobility and fuel strategy

PSE@ResearchDayUK, Centre for Process Systems Engineering, Imperial College London, UK

Master Thesis, Sustainable Energy Systems, School of Engineering, The University of Edinburgh, 2017

Bachelor Thesis, School of Economics, Karlsruhe Institute of Technology, 2016