Student Projects

As we race toward net-zero, we must bridge the gap between transmission and distribution planning. In this thesis, the student will develop computational tools to map Feasible Operating Regions (FORs), quantifying the operational capabilities of future distributed energy resources. These resources include photovoltaic systems, batteries, heat pumps, and electric vehicles, which provide vital services at the transmission/distribution interface. Using high-resolution digital models of the entire Swiss distribution grids and detailed future energy scenarios, this work will directly support flexibility modeling for transmission planning.

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.

This thesis activity, adaptable for a Bachelor's, a long Semester Project, or a Master's level, will focus on the use of Artificial Intelligence (AI) to find resilient solutions for the sustainable energy transition. Among AI methods, statistical Machine Learning (ML) surrogates represent a precious aid when considering complex energy systems under uncertainty, but their practical effectiveness depends on the efficiency of their training process. This project will explore the role of advanced data science techniques in training, thereby supporting a resilient energy transition through surrogate-assisted optimization.

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. 

The NASA and DNV Challenge on Optimization Under Uncertainty focuses on advancing methods for uncertainty quantification and optimization-based design in thepresence of uncertainty.

Complex engineered systems must maintain reliable operation even under rare or extreme events. Designing such safety-critical systems therefore requires explicit treatment of uncertainty. However, quantitative data describing real-world systems are often sparse or prohibitively expensive to obtain, which limits the ability to directly quantify risks or validate models. Consequently, surrogate-based approaches have become essential for quantifying system behavior under incomplete information.

In this project, we aim to deploy the multi-level informed optimization method (MLIO) to solve this challenge. This surrogate-based approach enables scalable uncertainty quantification and efficient exploration of
uncertainty-aware design using only a limited number of simulations. The goal is to investigate whether a more efficient approach can achieve comparable or superior results with limited computational resources. The project is expected to be carried out as a Master’s thesis, with the possibility of adaptation to a Bachelor’s or Semester project for students with a strong background in statistics or uncertainty quantification.

Constructing a detailed market dispatch model is necessary to accurately assess the safety and reliability of the European power system, as well as examine the effects of future generation scenarios. In these efforts, the biggest challenge is modeling the different generation technologies due to their physical properties and lack of available data. This is especially true for hydropower plants, as hydrological data can be difficult to find and is often incomplete. Motivated by this, the RRE is developing data-driven models of the hydropower plants in the European power system. Your application documents should include your CV and transcript of records (with grades). Please send your documents by e-mail to Dr. James Ciyu Qin () or Dr. Andrej Stankovski ().

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.

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.

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.