Lectures and Courses

TASK DESCRIPTION • Mathematical Modeling: Understand and accurately implement the mathematical models underlying MCDA methods. • Tool Development: Design and develop the MCDA online tool using Python, implementing multiple MCDA methods for preference elicitation. • Data Visualization: Create interactive data visualizations using libraries such as D3.js or Pythonbased tools to effectively present analysis results. • User Interface Design: Develop a user-friendly interface that allows for easy input of data and interpretation of results. • Testing and Validation: Perform thorough testing to ensure the tool's functionality, reliability, and accuracy. • Documentation: Document the development process and prepare user manuals or guides.

We are offering an exciting internship opportunity for a motivated Master's student to contribute to the development of an online tool designed for eliciting user preferences using various MCDA methods. The tool will be programmed in Python and will feature interactive data visualization components to enhance user engagement and comprehension.

Dr. River Huang (river.huang@psi.ch)

The Swiss Energy Strategy 2050 aims to achieve zero net emissions target as of 2050. The four leading Swiss research institutes — Paul Scherrer Institute (PSI), Swiss Federal Laboratories for Materials Science and Technology (EMPA), Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), and Swiss Federal Institute of Aquatic Science and Technology (EAWAG)—are at the forefront of this en-deavour. In the context of the SCENE project, these institutes are collaboratively developing science-based roadmaps that outline the anticipated pathways to attain net-zero emissions before 2040. The tran-sition to net zero requires a multifaceted approach, encompassing technological advancements, con-sumption reductions, and market-based mechanisms for emission compensation and reduction. An es-sential component of this transition is a comprehensive CO2 emission-related cost analysis. This analysis will evaluate the financial implications of shifting energy technologies, reducing consumption, and imple-menting market-based emission compensation and reduction strategies.

The goal of this project is to build on existing work to assess the costs and emissions reduction poten-tials of individual measures at the four RIs. These measures need to be assessed both individually and collectively in the context of a Marginal Abatement Cost Curve. The results will be used to elaborate a Roadmap for each of the institutions, in order to outline a realistic pathway to achieve net zero by 2050. Steps in this project include: • Estimating the costs of emission reduction strategies of waste, paper and water management for four research institutes • Exploring the feasibility and accessing the costs of biomass heating technologies combined with biochar for four research institutes (can be a generally small utility) • Developing a potential small-scale optimization model that covers various technologies to design long-term net-zero roadmaps for four institutes (existed data can be used) • Life cycle assessment of energy efficiency improvement measures/projects such as virtual meet-ing emissions • Visualization of emission reduction pathways and cost implications using interactive/online tools

See above or attached

Buildings account for around 40% of the EU's final energy consumption. To decarbonise its building stock, the EU aims to increase the share of renewable energy in buildings and requires member states to renovate the worst-performing shares of their buildings. Multi-family buildings (MFBs), where approximately half of the EU population lives, pose significant challenges to this transition. Despite their potential for energy savings, MFBs, especially social housing, have lower renovation rates and less adoption of low-emission technologies like photovoltaics and heat pumps. Social housing tenants often have lower incomes and spend a disproportionate share of their earnings on heating. Renovating these buildings is, however, quite complicated. While many residents of multi-family buildings initially support energy-efficient renovations, challenges often arise before, during, and after the construction or transformation process. These include uncertainty about the effectiveness and the duration of construction work, disruptions like dust and restricted access, and a preference for maintaining low rents over improving energy efficiency. Temporary relocations, particularly to container housing, are resisted, and some technologies face ideological scepticism. In this context, the perceived non-monetary costs often outweigh financial considerations, reducing ten-ants' willingness to support renovations, even when long-term benefits are clear.

The complexity of renovating multi-family buildings—encompassing technical challenges (e.g., performance, longevity, feasibility of implementation), economic considerations (e.g., return on investment, initial investment, rent increase), and social implications (e.g., tenant displacement, construction duration, comfort issues like dust, social equity)—highlights the need for a comprehensive, multi-criteria and / or bi-level optimisation approach. This approach integrates these dimensions into the decision-making pro-cess to achieve balanced and effective renovation outcomes for different parties. The objective of this Master thesis is to develop a techno-economic mixed-integer optimisation tool that supports multi-objective and / or bi-level decision-making in energy-efficient renovations. By evaluating various renovation strategies, options, and technologies, the thesis aims to present solutions that balance tenant and landlord acceptance across multiple criteria, ultimately contributing to more sustainable and socially equitable housing practices.

Russell McKenna, rmckenna@ethz.ch

Buildings are responsible for 40 % of the final energy consumption and 36% of the greenhouse gas emissions in the European Union (EU). In order to decarbonize its housing sector, the EU has set ambitious targets to increase energy efficiency and on-site renewable energy production in buildings. The residential sector specifically has seen a strong increase in distributed energy sys-tem investment mainly driven by installations in owner-occupied single-family houses. In multi-family buildings (MFB) on the other hand, which account for 48.6% of the EU’s residential hous-ing stock and are predominantly renter-occupied in many Western European countries, large amounts of building efficiency and renewable energy generation potential remain untapped due to split incentives between landlords and renters, i.e. owners bearing the investments costs and renters paying for the operating costs.

Based on the EU’s revised Renewable Energy Directive II, many member states have put regu-latory frameworks and policy measures into place encouraging renters and owners in MFB to produce, consume and trade energy locally through collective self-consumption communities (CSC). Comparing CSC policies across European countries reveals a strong variation in the un-derlying business models. In particular, the differences in local electricity prices, feed-in tariffs and grid fees for electricity produced in CSC communities could have a large impact on their profitability. These circumstances inevitably raise the question how these different CSC models will impact building energy system (BES) investment decisions in MFB.

REQUIREMENTS • Familiarity with energy systems analysis • Strong mathematical background • Interest conceptual model development

The student will enrich and build on an existing open-source noise propagation model for wind turbines (https://github.com/Maxbeal/noisemodel) with GIS information to better quantify noise propagation and impacts on surrounding populations.

Two parallel objectives: 1. Improve the current noise propagation model (e.g., the inclusion of obstacles, etc.). 2. Create a European map indicating zones where wind turbines cannot be implemented due to noise exposure. More precisely, the student will have to combine several GIS data resources (e.g., OpenStreetMap, JRC) and map layers (buildings, infrastructure, population) to: • Include obstacles (forest, buildings) that may impede noise propagation, • Identify areas and populations potentially affected by the noise emissions of a wind turbine, • Estimate the extent of noise exposure (intensity and duration), to assess the impact of current and future wind turbine installations in Europe. The second objective is to draw a map layer representing exclusion zones based on population exposure to noise to determine the potential and limits for future wind turbine installations in Europe.

Romain Sacchi, romain.sacchi@psi.ch