PhD position: Decision-Making for Heat Transition Planning

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Testo originale dell'annuncio (in inglese)

PhD Position: Decision-Making for Heat Transition Planning under Data Sharing Constraints Can grid operators and other stakeholders make robust decisions when the data they need cannot always be shared? The transition to sustainable heating is one of the biggest challenges of the energy transition. Increasing electrification through technologies such as heat pumps creates new planning and congestion challenges for electricity distribution grids. At the same time, effective planning depends on access to data from multiple stakeholders - including municipalities, distribution system operators (DSOs), and households. However, such data are often fragmented, privacy-sensitive, or unavailable due to technical, legal and organizational constraints. As a PhD candidate, you will develop novel decision-making methods that enable robust decisions for heat transition planning despite these data-sharing constraints. Rather than assuming full access to data, your research will investigate how planning and operational decisions can be improved when information is incomplete, uncertain, decentralized or privacy-preserved. The project You will join the newly funded NWO (Dutch Research Council) KIC project Sharing the Warmth , an interdisciplinary collaboration between Wageningen University, TU Delft, Hogeschool Utrecht, CWI, Dutch municipalities, DSOs, and other societal/industry partners. The project brings together expertise in energy informatics, optimization, data science, social sciences, and governance to develop trusted data-sharing solutions for the heat transition. Within the project, you will have the opportunity to collaborate with other PhD candidates and a postdoctoral researcher working on complementary topics including energy data spaces, privacy-preserving data-sharing, governance, and citizen participation. You will also have access to real-world use cases and operational datasets through our network of municipalities, DSOs and industrial partners. Your research Your research will investigate how data-sharing constraints shape decision-making for the heat transition in future electricity networks, using flexible electrification technologies with heat pumps as the primary use case. The research will consider not only technical data availability, but also the governance, privacy and stakeholder requirements that determine how data can be shared and used in practice. You will explore questions such as: What data do municipalities, DSOs, and other stakeholders need for heat transition planning, and how do data ownership and governance arrangements shape the opportunities and barriers for data-sharing? How do incomplete, uncertain, or privacy-preserved data influence heat transition planning? How can DSOs and municipalities make robust decisions under realistic data-sharing constraints? How can flexibility from distributed energy resources be coordinated while respecting data-sharing requirements? How can decision-making methods account for varying levels of trust, transparency, and willingness to share data among stakeholders? What decision-making methods are most effective when information is decentralized or only partially observable? Depending on the research direction, you may employ techniques from mathematical modelling, machine learning, uncertainty quantification, distributed decision-making, or other data-driven approaches. Your duties and responsibilities in this 4-year project include: analysing data requirements, data availability, and socio-technical barriers to data-sharing for heat transition planning; investigating how incomplete or constrained data influence planning and operational decision-making; modelling the flexibility of heat pumps and other distributed energy resources for congestion management and heat transition planning; working together with interdisciplinary researchers to translate stakeholder needs and data-sharing requirements into decision-support methods; developing novel decision-making methods