Student Projects

Master's thesis

Ancillary services are essential for power-system security, but their future demand remains uncertain. Increasing shares of variable renewable energy sources, distributed energy resources, and electrified loads may increase forecast errors and system imbalances. Conversely, improved forecasting, shorter scheduling horizons, reserve sharing, and dynamic dimensioning may reduce reserve requirements. The goal of the thesis is to project the frequency-restoration reserve demand in Switzerland to 2035. The project will review international reserve sizing and activation practices, identify historical demand drivers, develop and validate a quantitative model, and apply it to analyze technical and regulatory scenarios.  The project is co-designed with Swissgrid, which will provide technical feedback on the scope, methodology, and results.

Heat waves are no longer only a weather problem for power systems, they are also a cooling problem. Large thermal power plants, including nuclear plants, must reject heat to a river or to the air. In hot and dry periods the river warms, its flow falls, and warmer air degrades cooling performance, forcing plants to reduce output. During the record European heat wave of June 2026, reactors and thermal plants at several sites across Europe were taken offline or run at reduced output because of high river temperatures and cooling-water discharge limits. This Master's thesis will assess the cooling capacity of the Swiss nuclear power plants under future climate conditions, comparing once-through cooling at Beznau with cooling-tower operation at Leibstadt and Gösgen. Climate-model scenarios giving river water and air temperature at each site will be translated into plant-level power loss, and the resulting risk quantified through metrics such as loss hours, expected energy not generated, and event duration. Your application documents should include your CV and transcript (BSc AND MSc). Please send your documents by e-mail to Dr. James Ciyu Qin ().

Extreme weather events such as flooding, heavy snowfall, and storms increasingly disrupt transport networks, and for electric vehicles (EVs) the effects reach well beyond the area directly hit. In a study of Greater London, flooding raised charger utilisation by more than 50% in parts of the network over 10 km from any flooded area. Charging demand does not disappear when stations are lost, it transfers to the chargers that remain, so queues build and mobility slows when it is needed most. This Master's thesis will develop a coupled model of the transport and charging networks that captures the interaction between charging infrastructure, traffic flow, and driver behaviour under extreme weather. Through scenario-based simulation, it will trace how a local loss of charging capacity propagates through the surrounding road network and evaluate the operational measures that contain it, for both evacuation and ordinary commuting. Indicators such as evacuation time, traffic efficiency, and charging availability will support recommendations for strengthening transport resilience. Your application documents should include your CV and transcript (BSc AND MSc). Please send your documents by e-mail to Dr. James Ciyu Qin ().

Wind and solar deliver power through converters rather than rotating machines, which reduces the inertia of the power system. After a disturbance the frequency then falls faster and there is less time to react. Inverter-based resources can be controlled to imitate the missing inertial response, an approach known as virtual inertia (VI), and virtual inertia scheduling determines how much to procure alongside generation dispatch. The remaining obstacle is computational, because frequency dynamics are nonlinear and nonconvex and cannot be embedded in a dispatch problem in closed form. This Master's thesis will address that obstacle with a machine-learning surrogate. Time-domain simulations of inverter-level dynamics will generate frequency-response data across virtual inertia settings, a neural network will be trained to predict the rate of change of frequency and the frequency nadir, and the trained network will then be recast as linear constraints and embedded directly in frequency-constrained dispatch. The outcome is a co-optimisation of generation and virtual inertia that keeps the system secure at a modest cost. Your application documents should include your CV and transcript (BSc AND MSc). Please send your documents by e-mail to Dr. James Ciyu Qin ().

A detailed market dispatch model is necessary to assess the reliability of the European power system and to examine future generation scenarios. Modelling the individual generation technologies is the main challenge, especially for hydropower, where the link between water availability and generation is complex and plant specific. Operational data are not scarce, but their quality is uneven. The ENTSO-E transparency platform publishes hourly generation for hundreds of European units, with missing periods, late revisions, and production types that operators classify differently, so a model fitted to one plant inherits every defect in that plant's record. This Master's thesis will apply time series foundation models, which are pre-trained across collections of series and can forecast series they have never seen. The models are univariate, so each plant is forecast from its own generation history alone. The student will continue pre-training open checkpoints on the European fleet, fine-tune to individual plants, and test whether one shared model forecasts better than models fitted to each plant separately. Your application documents should include your CV and transcript (BSc AND MSc). Please send your documents by e-mail to Dr. James Ciyu Qin ().

Semester project

This project investigates the adequacy of Switzerland's 2050 power system scenarios from the SWEET PATHFNDR study, with a specific focus on negative net load variations — situations where demand drops or renewable generation surges unexpectedly, complementing existing adequacy research that typically emphasizes positive net load variations (demand spikes or generation shortfalls). Using the flexibility-focused Insufficient Ramping Resource Expectation (IRRE) metric, the student will reproduce and extend an existing adequacy-assessment workflow to correctly handle negative net load variations, apply it to the PATHFNDR scenarios, and organize, visualize, and interpret the resulting large-scale simulation data, bridging energy system optimization and reliability analysis to assess how well future Swiss power systems can accommodate flexibility challenges in both directions.

Master's thesis

This project extends RRE's adequacy assessment framework for the future Swiss power system by incorporating nodal (spatial) detail into the Insufficient Ramping Resource Expectation (IRRE) methodology, which quantifies the probability that available flexible resources are insufficient to accommodate unexpected power imbalances such as renewable forecast errors. Building on scenario outputs from the third SWEET EDGE model intercomparison — which provides consistent 2035 and 2050 Swiss power system pathways across multiple energy system optimization models — the student will enhance the IRRE framework to better capture how flexibility and unexpected generation variations are distributed across the network, then apply this improved methodology to the EDGE scenarios to evaluate their adequacy under different future system configurations. The project sits at the interface of energy system optimization and power system reliability analysis, requiring the student to understand and extend the existing methodology, manage and process scenario data, and critically assess the resulting adequacy indicators.

The Swiss energy landscape is facing an unprecedented transformation, driven by the national commitment to achieve net-zero greenhouse gas emissions by 2050. While several European energy system models address sector-coupled transition pathways at a continental or regional scale, their spatial aggregation overlooks the defining characteristics of the Swiss system: flexible hydropower with large seasonal storage at the crossroads of the European grid, making the country both a battery and a transit hub for its neighbors, while facing a growing structural winter import reliance. In this Master’s thesis, we want to investigate the optimal transition pathway for the Swiss energy system. To this end, we use an existing electricity-only model in the Nexus-e optimization platform and translate it into the sector-coupled transition pathway framework ZEN-garden.

Master's thesis:

Direct air capture (DAC) technologies currently face uptake challenges due to high costs. A major contributor to the high cost of DAC is the sorbent material. Current sorbents are often expensive, energy-intensive to regenerate, and prone to deactivation, limiting the scalability and efficiency of DAC. Therefore, developing more efficient, cost-effective, and durable sorbents is essential. Strontium oxide (SrO) is a promising solid sorbent for DAC, as it rapidly captures CO2 from ambient air, outperforming other solid sorbents such as CaO or MgO. This suggests that SrO is a competitive candidate for DAC and could enable more efficient carbon capture, potentially reducing processing and upscaling costs. However, a comprehensive comparison of DAC sorbents should also account for life-cycle impacts, including energy demand, climate change, resource depletion, and water consumption.

Master's thesis:

The project develops a physically informed machine-learning model to estimate lightning density from atmospheric and climate variables. The model will combine historical data with knowledge of thunderstorm and lightning formation, then link the predicted lightning exposure to transmission-line failure models. The final objective is to assess how vulnerable power transmission systems are to lightning under both current and future climate conditions.

Master's thesis:

Renewable-powered PEM electrolyzers are exposed to intermittent operating conditions that may accelerate degradation and reduce hydrogen supply continuity. This thesis aims to develop a Dynamic Bayesian Network framework to investigate how renewable intermittency propagates through the system and influences hydrogen-supply resilience over time. Representative solar and wind profiles will be used to compare different storage and support strategies. The model will identify the main factors affecting system resilience and provide insights into the design and operation of more robust green hydrogen systems.

Master's thesis:

Hydrogen explosions may evolve from relatively slow deflagrations to highly destructive detonations, posing major safety challenges for industrial plants and emerging hydrogen energy systems. This thesis develops a physics-informed Bayesian modeling framework to analyse hydrogen deflagration-to-detonation transition (DDT) using experimental evidence and reduced-order physical relationships. The model will identify the conditions leading to DDT, estimate its likelihood and run-up distance, and provide an interpretable tool for supporting preliminary explosion risk assessment and safety-oriented layout design in hydrogen facilities.

Energy system optimization models are commonly used to set national energy targets and inform policy decisions. These models, however, are usually based on cost-minimization routines that inadequately incorporate soft factors such as public preferences. Consequently, the models often poorly reflect real-world decision-making and produce inaccurate forecasts. This Master’s thesis will explore novel methods for incorporating public opinion into energy system model formulations. The student will work with public opinion data and RRE’s ZEN-garden optimization model to identify climate-friendly, low-cost, and socially acceptable transition pathways for Switzerland. The ideal candidate has a creative, independent work ethic and foundational knowledge of energy system optimization and Python programming.

In this Master's thesis, we want to investigate the impact of non-CO₂ emissions on the European energy transition. In particular, we want to highlight sectors with high, persistent non-CO2 emissions and outline strategies to reduce them. We can then compare strategies for achieving climate goals in a “CO2-only” energy system versus an “all greenhouse gas” energy system.

 In this Master’s thesis, we aim to understand the optimal use of time-series aggregation in energy system optimization; in particular, under which model conditions the aggregation approaches work well and under which poorly. To this end, we will use several time series aggregation methods already implemented in the linear optimization problem ZEN-garden, developed at RRE. Then, we want to design strongly different energy system models to test the boundaries of time series aggregation and storage-level representation methods. 

As energy systems increasingly rely on weather-dependent renewable sources, their exposure to climate variability and extreme events grows. Conventional planning approaches based on historical data or limited climate model simulations inadequately capture rare but high-impact conditions that threaten security of supply. This thesis develops a physically and statistically consistent climate parametrization to efficiently generate synthetic, energy-relevant weather scenarios, with a focus on system stress conditions. The approach is evaluated in a European energy system model to support climate-resilient planning of energy transition pathways.

Integer variables are a major computational bottleneck of modern energy systems, often rendering traditional Branch-and-Bound methods intractable. This thesis investigates a novel alternative: the "Hyper-Lp-Box ADMM." This decomposition framework accelerates optimization by solving purely continuous problems and iteratively projecting them toward integer feasibility using a unique minimal binary representation. In this project, the student will refine this cutting-edge algorithm, benchmark it against state-of-the-art algorithms, and demonstrate its capabilities on a real-world Swiss grid expansion model. This master's thesis presents an opportunity to push the boundaries of mathematical optimization and address critical scalability challenges in low-carbon energy planning.

It is a common practice to rely on a single cost-minimal solution of an energy system optimization model (ESOM) to inform renewable energy system planning. This is being criticized for neglecting numerous alternative solutions that are slightly more expensive but are preferred for unmodeled social factors. Many approaches of modelling to generate alternatives (MGA) are invented to find alternative design solutions that are within an acceptable cost increasement. However, these approaches are computationally challenging and not applicable on large-scale energy system models. As a result, we aim to deploy a surrogate model based on machine learning methods to generate alternative solutions efficiently. The surrogate model can learn the mapping of the system cost for different designs with only a few samples. Goal of this project is to investigate whether this efficient method can find all near-optimal alternatives completely and correctly. 

This master's thesis will tackle complex self-discharge and non-linear capacity fading effects for long duration storage technologies, especially batteries. This potentially impactful aspect is often underrated in linearized energy system optimization, operating on the large scale. Iterative approaches based on operational maps will be tested on different granularity of space and time aggregations on a regional energy model. The final goal is providing energy modelers with insights into storage fading and evaluating the impact on results and related decision-making process. Emerging technologies will also be included, since the activity is conducted in collaboration with the deep tech start-up Unbound Potential (UP), which is developing promising membrane-less flow batteries. The topic is particularly relevant to properly assess the performance of storage options, which will play an increasingly crucial role as sources of flexibility in the net-zero energy transition.

This project tackles the challenge of uncertainty in energy system expansion planning, where long-term investment decisions are highly sensitive to uncertain inputs such as technology costs, demand trajectories, and policy developments. In this master's thesis, an intrusive Polynomial Chaos Expansion (PCE) approach will be developed and integrated into ZEN-garden, an open-source energy system optimization framework. By analytically propagating uncertainty through PCE, the method aims to improve the reliability and computational efficiency of expansion planning without relying on costly scenario-based sampling. The proposed approach will be validated through case studies, with the goal of enabling more robust and informed decision-making for future low-carbon energy transitions.

This project addresses the increasing complexity faced by Virtual Power Plant (VPP) operators in scheduling their distributed energy resources across multiple electricity markets while navigating the limitations imposed by distribution network constraints. In this master's thesis, a novel grid-constrained market scheduling tool will be developed, leveraging data-driven methods to estimate potential network congestion penalties without requiring explicit knowledge of the grid topology. The research will extend an existing multi-stage stochastic optimization framework for VPP bidding by integrating a trained penalty estimator based on historical data, and the enhanced bidding strategy will be benchmarked against a naive approach to demonstrate its effectiveness for reliable and profitable market participation.

This Master’s thesis project investigates control strategies for distributed energy resources (PVs, BESSs, HPs, and EVs) in integrated large-scale MV-LV grids. It explores how distributed energy resource flexibility can fosted renewables penetration while respecting operational constraints.

The electrification of transport and heating represents a big challenge for the energy transition. However, demand-side flexibility can be applied to these demands to ease the difficult task of matching supply and demand. Can this form of system flexibility aid in building a more reliable future power system?

This project investigates the future electricity demand and flexibility potential of heat pumps (HPs), a key technology for decarbonizing the heating sector by 2050. Using data on HP installations, building thermal characteristics, and historical temperature profiles, the student will develop an algorithm to model HP demand and flexibility. The student will further analyze results to assess regional differences and the potential impact of large-scale HP deployment on the electricity grid.

This semester project explores the economic and ecological trade-offs involved in enhancing the resilience of carbon dioxide (CO₂) transport networks within Carbon Capture, Transport, and Storage (CCTS) systems. As Europe moves toward net-zero emissions, particularly in hard-to-abate sectors like cement and chemicals, resilient CO₂ transport infrastructure becomes essential due to the geographic separation of emission sources and storage sites.

The project focuses on evaluating different resilience strategies—such as implementing backup connections or temporary CO₂ storage during network failures—versus the option of inaction, which involves emitting CO₂ and paying carbon prices during disruptions. A key outcome will be a decision-support model that quantifies costs and emissions over time, helping determine when resilience investments become economically viable. Tasks include system modeling, data collection, trade-off analysis, and the development of a sensitivity analysis tool.

The global shift to low-carbon energy technologies has made it important to understand how countries develop and diversify their energy trade. This semester project applies the Economic Complexity Index (ECI)—originally used to assess export sophistication—to the field of energy and renewable energy trade. It aims to develop an energy-specific complexity index (ECI-energy) using international trade data and evaluate the maturity and evolution of national energy trade portfolios. The analysis will focus on how countries specialize in key technologies such as solar panels, wind turbines, and batteries. A central research question is: How have countries developed their energy industries in terms of economic complexity?

Renewable energy system planning is highly sensitive to a wide range of
uncertainties, including capital costs of emerging renewable
technologies, fuel price volatility, supply and transport capacities,
fluctuation in the demand, and climate variability. Due to the complexity
and interdependence of these factors, the common practice of relying on
a limited set of predefined scenarios falls short of capturing the full
spectrum of uncertainty.
This semester’s project aims to explore a novel approach to addressing
uncertainty in energy planning. A surrogate-assisted multi-level
optimization method will be applied to a complex energy system. This
innovative method enables a comprehensive treatment of deep
uncertainty through a scalable and detailed exploration of how various
technology configurations perform under combinatorial uncertainties.
The goal is to generate actionable insights into cost-effective, sustainable
and robust energy system designs that are resilient to a realistic range of
uncertain futures.

Switzerland’s energy transition depends on key renewable technologies such as solar PV, wind turbines, batteries, and heat pumps. These devices are mostly imported, making their supply chains vulnerable to global disruptions. This project aims to assess the supply risks linked to these technologies by using existing supply chain and energy system models based on the ZEN-garden framework. The student will first understand and refine the connection between the models, then apply them to evaluate risk levels under different policy scenarios. The results will help identify critical supply chain vulnerabilities that could affect Switzerland’s progress toward its energy and climate goals.

The global energy transition from fossil fuels to clean energy is reshaping trade dependencies, but the nature of these dependencies differs fundamentally. Fossil fuels (oil, gas, coal) require continuous flow of commodities to meet ongoing demand, while clean energy systems (solar PV, wind, batteries) demand front-loaded stocks of critical minerals, components, and manufacturing capacity during deployment phases. Leveraging trade network and supply chain data, with dynamic material flow analysis and policy-driven scenario analysis, this Master Thesis project will quantify fossil and renewable energy's historical and current trade dependencies, and provide insights for policymaking in net-zero transition.

The European Union has binding CO2 emissions targets for newly registered and sold passenger cars. These targets drive substantial investment in battery electric vehicles, which are currently the most viable option for decarbonizing passenger car transport. Nonetheless, the full effects of the EUs passenger car targets on greenhouse gas emissions remain to be fully understood. EV emissions are often calculated using average grid emission rates, which may significantly underestimate their climate impacts, particularly in regions where fossil fuel generators are the marginal units of electricity production. The goal of this thesis is therefore to investigate the marginal emission impacts and energy investment impacts of BEVs. A regional analysis will be done to identify whether the EUs policies are feasible, fair, and climate-friendly in all regions of the EU. The thesis is a collaboration between (i) the Institute of Vehicle Concepts (FK) at the German Aerospace Center (DLR) and (ii) the Reliability and Risk Engineering Lab (RRE) at ETH Zurich.

In this master’s thesis, a new surrogate-assisted multi-level method for design under uncertainty is tested on a complex energy system. Indeed, sophisticated and large models are needed to capture the dynamic environment of power grids and understand how to make them resilient in the sustainable transition. The enormous complexity of the problem commonly forces oversimplification, just by accounting for a few scenarios among the many realizable socio-tecno-economic conditions. In this activity, an innovative, fast-scaling, yet accurate method able to approximate the variability of design variables and uncertain parameters interactions will be applied to the integrated Nexus-e model for Switzerland. The final goal is to disclose the most resilient investment and operative opportunities in the presence of a realistic variety of combinatorial uncertainties.

The impact of climate change and extreme weather events is crucial to be assessed for the design and operation of climate-resilient energy systems, which involves scenario analyses. This project aims to leverage generative AI for the generation of climate scenarios and extreme events in an efficient manner, relieving the computational burden of solving partial differential equations. This project is a continuation of a previous semester thesis and the incoming student will start with an existing codebase. The student will be expected to improve the existing algorithms, add new ones to the current pool and perform sensitivity analyses. The student will gain exposure to climate data analysis, deep learning and generative AI in the context of energy systems analyses. A Masters thesis is preferred. Semester thesis can also be considered.

The energy transition is driving the deployment of distributed energy resources like photovoltaic panels, electric vehicles, and heat pumps. While beneficial, these technologies challenge power distribution grids (PDGs) that were not designed for such energy inputs. To address this, models of PDGs in Switzerland have been developed to assess the impact of various policies on the country's energy infrastructure. However, the complexity of the national system makes detailed modeling impractical. This thesis focuses on optimizing power system planning in representative PDGs and applying these findings to disaggregate results at a national level.

The rapid adoption of heat pumps (HPs), photovoltaics (PVs), and electric vehicles (EVs) is reshaping low-voltage (LV) distribution networks, posing challenges for Distribution System Operators (DSOs). Uncoordinated HP operations can worsen grid stress and drive costly reinforcements, while coordinated strategies can optimize grid performance and lower costs. This thesis addresses how DSOs can adapt to rising electricity demand by evaluating HP control schemes and pricing strategies to ensure sustainable and efficient grid management.

This internship, which can be extended with a master’s thesis at Swissgrid will tackle challenges at the heart of the energy transition: Current ancillary service markets need to be updated and expanded to accommodate distributed energy resources and address the increasing volatility in the power system due to renewable energy resources such as PV. The tasks will include implementing and testing market simulations, ad-hoc analyses and conceptual developments both within Swissgrid and in collaboration with other companies in the Swiss electricity sector. Ideally, the internship would start in February 2025 for 6-12 months. 

This master's thesis project aims to investigate the costs and benefits of local multi-energy systems in enhancing resilience and thereby providing a secure electricity and heat supply to end-users. To this aim, the in-house developed multi-energy system design optimization model needs to be enhanced such that component failures from multiple failure modes (such as random failures, from hazards such as snowstorms/windstorms) and variable repair time are characterized as uncertainty set and incorporated as a stochastic input to the design problem.