Research Assistant: How Engineering, Economics, and Policy Shape Reliable and Sustainable Electricity

Introduction to the topic

Energy is central to modern life, and electricity powers nearly all daily activities. Yet electricity grids are under increasing pressure from emerging demands such as electric vehicles, data centers powering smartphones and streaming platforms, AI servers, smart homes, and industrial electrification. At the same time, electricity grids are complex socio-technical systems, where changes in one part of the system can ripple across the entire network. Ensuring that electricity is delivered reliably, affordably, and sustainably is therefore extremely complex and requires a deep understanding of how engineering, economic, and regulatory decisions interact.

Project details

This research project invites Research Assistants (RAs) to join an interdisciplinary study of electricity distribution grids that bring together engineering, economics, and public policy and builds on ongoing peer-reviewed research on electricity grids and digitalization.

RAs will help expand a growing open-source complex-system modeling toolbox designed to improve how we model decisions in electricity distribution grids. Using system dynamics and agent-based modeling, implemented through Insight Maker (a web-based, open-source simulation platform), students will examine how technical constraints, economic trade-offs, and policy decisions shape key electricity system outcomes such as reliability, cost, and sustainability. Case studies and data from Europe and North America will ground the analysis in real-world electricity systems.

Throughout the project, RAs will develop both theoretical knowledge and hands-on skills in modeling, analysis, and interdisciplinary problem-solving. Typical research activities include conducting targeted literature reviews, building and testing simulation models, collecting and analyzing open-source electricity datasets and regulatory documents and working with policy case studies and stakeholder interviews.

Together with the mentor, RAs will define a focused research question aligned with their interests, whether oriented toward modeling and simulation, economic analysis, or regulatory and policy design.

Through this, RAs will gain experience in translating results from complex models into relevant insights for policymakers and regulators. They will learn how to communicate their findings through a written research report and a poster-style presentation. The project spans multiple semesters and while students will not participate in every phase, each contribution will meaningfully support the broader research objectives.

This project is ideal for students passionate about the energy field, with backgrounds in engineering, economics, public policy, or computer science, and offers the potential to build a strong interdisciplinary team.

Selected relevant publications:

• Leiva Vilaplana, J. A., Yang, G., Monaco, R., Bergaentzlé, C., Ackom, E., & Morais, H. (2025). Digital versus grid investments in electricity distribution grids: Informed decision-making through system dynamics. Applied Energy, 386, 125536. https://doi.org/10.1016/j.apenergy.2025.125536
• Ringler, P., Keles, D., & Fichtner, W. (2016). Agent-based modelling and simulation of smart electricity grids and markets: A literature review. Renewable and Sustainable Energy Reviews, 57, 205–215. https://doi.org/10.1016/j.rser.2015.12.169
• Koot, M., & Wijnhoven, F. (2021). Usage impact on data center electricity needs: A system dynamics forecasting model. Applied Energy, 291, 116798. https://doi.org/10.1016/j.apenergy.2021.116798

Prerequisites

• Basic familiarity with quantitative analysis from fields such as engineering, economics, public policy, environmental studies, computer science, or related disciplines.
• Basic knowledge of the electricity sector would be helpful.
• Interest in systems thinking or modeling tools (system dynamics or agent-based modeling) is beneficial, though all required tools will be taught during the project.

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