Student Projects

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.

Climate change mitigation requires transforming the energy system. However, the boundary conditions for this transformation in the coming decades are highly uncertain, for instance, regarding costs and maturity of key technologies. The thesis computes a plan for the energy transition that considers these uncertainties.

Model-based planning of renewable energy systems faces a trade-off: On the one hand, great detail gives more detailed and reliable results; on the other hand, additional details increase model size, quickly leading to computationally intractable models. Against this background, the thesis investigates which details models must prioritize to keep critical results accurate but remain solvable.

National decarbonization strategies often include hydrogen as part of their future energy mix. Hydrogen is a net-zero energy carrier since it can both be produced and consumed without greenhouse gas emissions. Nonetheless, recent research suggests that leaked hydrogen has an indirect greenhouse gas effect through chemical reactions it causes in the atmosphere. The goal of this semester project or bachelor thesis is to determine to what extent this climate impact of hydrogen must be considered in future modeling studies and to develop policy targets for hydrogen leakage rates.

This master's thesis 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.

Traditional energy system stress prediction relies on computationally intensive optimization models to identify climate-induced stressful events. This project evaluates machine learning (ML) approaches that can predict energy system stress events directly from weather and demand data—without running complex optimization models.
You'll analyze meteorological and energy datasets to create labeled stress event datasets, engineer features from raw climate data to develop renewable generation proxies, and implement multiple ML architectures. The goal is to create a fast, accurate alternative to traditional optimization-based methods that maintains prediction quality suitable for large-scale climate risk assessment.

As renewable energy becomes weather-dependent, energy droughts and extreme climate events create unprecedented system stress, driving up prices and threatening supply security. This project explores demand response—the strategic modification of energy consumption patterns—as a powerful solution to enhance system resilience during climate-induced energy shortages.
You'll implement advanced demand response models in the ZEN-garden framework, incorporating multi-sector interactions across electricity, heating, and cooling systems. Through comprehensive techno-economic analysis, you'll quantify cost savings and resilience benefits compared to traditional solutions like storage and transmission expansion, while conducting regional comparative studies across European countries under various climatic stress conditions.

The European energy transition faces a critical vulnerability: multi-year water droughts that can disrupt hydroelectric generation and constrain thermal power plants for consecutive years. This project investigates how prolonged drought events impact energy system costs, reliability, and transition pathways across European countries.
You'll analyze climate data to define representative drought scenarios, implement them in the ZEN-garden optimization framework, and optimize the European energy transition under extreme water scarcity conditions. The research will reveal which countries are most vulnerable, quantify economic impacts, and identify optimal mitigation strategies combining technology deployment and operational adaptations.

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.

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.

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?

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.

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?

Energy policies are shaping the energy transition. It is important to model the effect of those policies to understand their impact on complex systems such as the power system. In addition, many other factors, key to such an analysis, come into play. How does, for instance, climate variability influence our findings?

The rapid growth of renewable energy sources, such as wind and solar, has significantly reduced the mechanical inertia of modern power systems, leading to increased vulnerability to frequency disturbances. Virtual inertia (VI), implemented via inverter-based controls, can mitigate these stability risks but optimally scheduling VI alongside generation dispatch is challenging due to complex and nonlinear frequency dynamics. This master's thesis aims to develop an innovative end-to-end optimisation framework that integrates neural networks directly into the dispatch optimisation process. By employing neural networks trained on extensive datasets generated from Simulink simulations, the project seeks to achieve accurate and computationally efficient co-optimisation of virtual inertia and generation dispatch. This approach promises to enhance system stability while reducing operational costs and complexity. A Masters thesis is preferred. Semester thesis can also be considered.

This master’s thesis aims to develop a comprehensive model to characterise the behaviours and potential of inverter-based resources (IBRs), including solar PV, wind, fuel cells, and battery storage, in supporting grid modernisation and enhancing energy security. By focusing on their role in providing ancillary services during grid disturbances, the project seeks to leverage IBRs to improve grid stability, energy efficiency, and resilience in a smart grid context. Key tasks include modelling IBR dynamics, integrating these models into broader power system frameworks, and analysing their capabilities to mitigate stability challenges in high renewable penetration scenarios.

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.

Extreme weather events, such as flooding, heavy snowfall, and storms, increasingly disrupt transportation networks, posing significant threats to regional mobility and economic stability. These disruptions propagate through interconnected systems, with widespread consequences on infrastructure and services. The rise of electric vehicles (EVs) presents new challenges, as charging infrastructure may become a critical bottleneck during emergencies, leading to prolonged delays and reduced evacuation efficiency. Developing resilient transport systems is imperative to minimise these impacts and ensure reliable mobility in the face of climate uncertainty.

This Master’s thesis project aims to build a comprehensive transport system model that captures the complex interactions between EV infrastructure, traffic flow, and driver behaviour under extreme weather conditions. By integrating scenario-based simulations, the model will assess strategies to mitigate congestion, optimise charging infrastructure, and enhance response capabilities. Performance indicators such as evacuation times, traffic efficiency, and infrastructure reliability will guide data-driven recommendations for system improvements. The project’s outcomes will provide relevant stakeholders with actionable strategies to strengthen transportation resilience, safeguard mobility, and support sustainable infrastructure development.

Household adoption of green technologies, such as rooftop solar photovoltaic (PV) systems, heat pumps (HP), and electric vehicles (EV), plays a pivotal role in achieving net-zero greenhouse gas emissions by 2050. While existing studies have extensively examined factors influencing the adoption of individual technologies, limited research has focused on the co-adoption of multiple clean energy solutions.
This thesis aims to explore the combined influence of demographic, spatial, and housing factors on the adoption of clean energy technologies in Switzerland. The study will collect and analyze data at cantonal, municipal, and building levels, incorporating information on PV, HP, and EV adoption alongside relevant demographic and spatial factors. Regression-based models will be developed to assess the determinants driving the adoption of these technologies, offering insights into patterns of co-adoption and their underlying drivers. The research findings will contribute to a deeper understanding of household adoption behaviors and inform policy strategies for accelerating the clean energy transition in Switzerland.

If interested contact Palucci Matteo at: matteo.palucci@supsi.ch

The electrical grid is part of the critical infrastructure of every country. Its reliable and secure operation ensures resources for economy and society are always available. The energy transition, which is required among others, to reduce carbon emissions and contribute to more sustainable societies, requires integrating large amounts of distributed and renewable energy. While this constitutes a challenge by itself, it also makes operating the power grid increasingly difficult. Power now flows in “all” directions, in an ageing system, originally designed to provide energy from a few large producers hierarchically “top-down” to consumers.
To maintain reliable, cost-efficient operation of evolving grids, the AC optimal power flow problem (AC-OPF) must be solved more frequently. Traditional solvers, using Newton-Raphson methods, struggle with large-scale grids due to computational demands and convergence issues.
Neural power flow solvers address these challenges by shifting computational complexity from operations to training. Our GridFM–v0, leveraging graph neural networks (GNNs), is a foundation model designed as such a solver.
For GridFM–v0, grids are represented as graphs, where electrical buses are nodes with active power, reactive power, voltage magnitude and voltage angle as embeddings, and where transmission lines are the edges.
It is pre-trained using self-supervision, reconstructing masked node features of existing power flow solutions covering diverse grid topologies and load conditions.
A hybrid, physics informed loss function minimize reconstruction errors and ensures physical accuracy. This enables generalization across various scenarios and different grids including system security and optimal power flow tasks.
With the power grid being critical infrastructure, and the current geo-political situation, utilities are either very reluctant and/or legally bound to NOT make their grid data publicly available, which requires large amounts of training data to be produced synthetically. To address the data-challenge for power grid foundation models, the focus of the project is developing a unified tool for generating and accessing existing data from multiple sources. This tool will enhance the practical applicability of advanced models like GridFM–v0, enabling more realistic and actionable insights for modern power systems.

The electrical grid is part of the critical infrastructure of every country. Its reliable and secure operation ensures resources for economy and society are always available. The energy transition, which is required among others, to reduce carbon emissions and contribute to more sustainable societies, requires integrating large amounts of distributed and renewable energy. While this constitutes a challenge by itself, it also makes operating the power grid increasingly difficult. Power now flows in “all” directions, in an ageing system, originally designed to provide energy from a few large producers hierarchically “top-down” to consumers.
To maintain reliable, cost-efficient operation of evolving grids, the AC optimal power flow problem (AC-OPF) must be solved more frequently. Traditional solvers, using Newton-Raphson methods, struggle with large-scale grids due to computational demands and convergence issues.
Neural power flow solvers address these challenges by shifting computational complexity from operations to training. Our GridFM–v0, leveraging graph neural networks (GNNs), is a foundation model designed as such a solver.
For GridFM–v0, grids are represented as graphs, where electrical buses are nodes with active power, reactive power, voltage magnitude and voltage angle as embeddings, and where transmission lines are the edges.
It is pre-trained using self-supervision, reconstructing masked node features of existing power flow solutions covering diverse grid topologies and load conditions.
A hybrid, physics informed loss function minimize reconstruction errors and ensures physical accuracy. This enables generalization across various scenarios and different grids including system security and optimal power flow tasks.Building on this foundation, the focus of this project is to analyze the applicability of foundation models like GridFM–v0 for downstream tasks, including:

1. Optimal Power Flow (OPF): Evaluating GridFM–v0’s ability to improve computational speed and accuracy in solving optimal power flow problems under varying grid scenarios and conditions.
2. Cascading Failure Prediction: Exploring the use of GridFM–v0 to predict cascading failures by modeling how local disturbances propagate through the grid and identifying vulnerable areas.

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.

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.