Education

Lead: District heating and cooling (DHC) systems play a key role in the decarbonization of the built environment by enabling efficient, centralized thermal energy supply. At the same time, the increasing penetration of electric heat pumps (HPs) introduces new challenges and opportunities at the interface between electricity and thermal networks. Multi-functional HP systems, capable of operating in coordination with DHC networks, offer enhanced operational flexibility by allowing buildings or energy communities to switch between electric and district-based thermal supply depending on economic, environmental and grid-related conditions. However, effectively exploiting the potential of multi-functional HPs requires consistent modelling and optimization frameworks that capture the coupled operation of electricity, heating and cooling systems. In particular, there is a need for system-level optimization approaches that explicitly consider trade-offs between operating cost, CO₂ emissions and/or electrical grid impact. Such approaches can provide valuable insights for system operators and planners on how multi-functional HPs can support cost-effective and low-carbon operation of district energy systems. Short description This thesis focuses on the modeling and multi-objective optimization of a DHC system equipped with a hybrid electric HP. The main goal is to develop a system-level operational optimization framework that quantifies trade-offs between operating cost, CO₂ emissions and/or electrical demand. The work will compare hybrid HP operation against conventional district-only and electric-only supply strategies, highlighting the benefits and limitations of hybridization under different system conditions. Objectives • Establish a system-level modelling framework for HPs integration in DHC systems. • Evaluate economic, environmental and electrical grid impacts of hybrid operation through multi-objective optimization. • Identify and quantify trade-offs between cost, CO₂ emissions, and electrical demand enabled by hybrid operation. • Assess the benefits of multi-functional HPs relative to conventional district-only and electric-only supply strategies. Tasks The work in this master thesis entails: • Literature review to determine current optimization frameworks addressing the role of district heating networks. • Developing mathematical models of the DHC system and the hybrid HP, including key thermal and electrical constraints. • Implementing (or adapting) an existing optimization model (e.g., mixed integer linear program) for system-level operation of the hybrid HP. • Applying multi-objective optimization techniques to generate and analyse Pareto-optimal operating strategies. • Performing case studies and comparative analyses against baseline supply configurations and interpreting the results.

• Establish a system-level modelling framework for HPs integration in DHC systems. • Evaluate economic, environmental and electrical grid impacts of hybrid operation through multi-objective optimization. • Identify and quantify trade-offs between cost, CO₂ emissions, and electrical demand enabled by hybrid operation. • Assess the benefits of multi-functional HPs relative to conventional district-only and electric-only supply strategies.

To apply, please send a short motivation, your CV, and transcript of records to Dr. Tom Terlouw (tom.terlouw@psi.ch) and Dr. Tarek Alskaif (tarek.alskaif@wur.nl).

• 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