Open student projects

This project aims to develop a cross-country analytical dataset capturing the degree of dependency on digital technologies within energy systems, and to compare results using multi-criteria decision analysis (MCDA). The research will follow four stages: 1. Data and literature review: Identify relevant digital dependency indicators through a systematic review of energy system digitalisation, digital sovereignty, and energy governance literature. 2. Dataset construction: Select several countries at least (3-5 countries) and build a harmonised dataset describing their digital technology reliance across key energy system domains (generation, grids, markets, system operation, and customer-side technologies). Indicators may include reliance on foreign software providers, cloud infrastructures, data and smart meter penetration. 3. MCDA assessment: Apply a multi-criteria decision analysis framework to evaluate and compare countries’ digital sovereignty profiles. This may include weighting schemes, sensitivity analysis, and scenario-based evaluations. 4. Interpretation and policy recommendation: Analyse cross-country differences, identify main drivers of digital dependency, and discuss strategic implications for energy system planning and policy recommendations.

This thesis addresses the need for structured methods to evaluate digital dependency and assess its implications for energy system resilience and national energy strategies.

For applying, please forward your CV and transcript to Dr. Fabian Heymann, fheymann@ethz.ch

This project aims at developing a literature-based methodology (techno-economic framework) for analysing the effects of AI on energy systems, building on a thorough, structured literature review while extracting key techno-economic characteristics of the documented use of AI in energy systems. The research project will follow a four-stage approach: 1. Literature review: Conduct an extensive review of the intersection between AI and energy system modelling, including use cases in scenario analysis, optimization, and efficiency gains. 2. Development of techno-economic framework: Based on the literature review, the applicant will develop a techno-economic framework that allows to study the interaction and impact of a digital technology such as AI on the various components of energy systems (supply, demand, networks). 3. Modelling: Together with an expert from the Paul Scherrer Institute (PSI), the successful applicant will apply the techno-economic framework to the Swiss TIMES model. This may include data pre-processing, scenario development and identifying key scenario drivers. 4. Result analysis and interpretation: Analysing and comparing the outcomes of STEM (that includes a modelling framework of AI) while comparing it to a baseline scenario (without AI). Comparative analysis and visualization of results and achievable transition pathways. The outcomes will provide highly relevant, timely and quantifiable insights into the potential contributions of AI for decarbonizing energy systems.

In Switzerland, the Swiss TIMES energy system model (STEM) serves as a central reference for evaluating national pathways toward net-zero emissions. In the part of this master thesis, the applicant would be developing a comprehensive framework to study techno-economic effects of digital technologies such AI on energy systems. In the next step, the framework will be integrated into STEM in order to identify and study the effects of AI on energy system transitions in unprecedented detail.

For applying, please forward your CV and transcript to Dr. Fabian Heymann, fheymann@ethz.ch

• Collect indicators: compile quantitative metrics across journals. • Define qualitative criteria: operationalize constructs like methodological rigor, policy relevance, replication culture, editorial transparency. • Design & run the expert survey: ask energy scholars to rank journals they know and elicit criterion importance. • Develop a group MCDA framework: aggregate many experts’ inputs (weights, rankings) into a fair, robust consensus result. • Explain the ranking: apply rule-extraction to state the reasons behind each journal’s position • Visualize & report: build clear dashboards/figures and write a concise study report (potential publication).

We will build a mass-expert, MCDA-based ranking of energy journals. Global experts will rank journals they know; we’ll combine these inputs with bibliometric and qualitative indicators, and use a group decisionmaking framework. For explanation, we’ll employ MCDA tools (with Dominance-based Rough Set Approach (DRSA) as one explanatory module) to derive human-readable reasons behind the rankings.

Dr. River Huang, river.huang@psi.ch