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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.

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.

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.

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


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