Are you inspired by the challenge of grid congestion and renewable integration? This PhD position focuses on net-aware modeling and optimal operation of multicommodity energy systems within the BACH project, a real-life demonstration project on TU/e campus.
InformationThe BACH (Brainport Approach for a Congestion-free Holland) project addresses the increasing congestion in Dutch electricity distribution grids caused by the rapid growth of renewable energy generation and increased electrification of energy demand. Due to limited grid capacity and long connection queues, customers can no longer get a new connection, or expansion of an existing connection, this holds for both demand and supply. A large share of renewable generation cannot be connected or must be curtailed.
BACH develops a data-driven and proactive approach to congestion mitigation by integrating electricity, heat and gas systems into a multicommodity energy system. The optimal operation of a multicommodity energy system is demonstrated on TU/e campus. The real-life project data and results will be enriched to make them applicable on a large scale. Using distribution grid data, customer profiles and energy transition scenario’s, the project identifies where storage and conversion technologies can be deployed most effectively in the future to relieve grid congestion and increase renewable energy integration.
A central element of BACH is the development of an open-architecture Multicommodity Energy Management System (MC-EMS) and associated impact analysis tools, which together enable grid-aware and market-aware operation of multicommodity energy hubs. The concepts and tools are validated in a real-life mirror location at the TU/e campus and designed for scalability towards regional and national application.
This PhD project focuses on the
modeling and optimal operation of multicommodity energy systems under explicit distribution grid constraints. The research addresses how real grid congestion information can be systematically incorporated into system models and operational decision-making.
The PhD candidate will develop:
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Net-aware models of multicommodity energy systems that explicitly account for distribution grid constraints;
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Optimization-based operational strategies for multicommodity energy systems, from an economic (energy market) and grid perspective, coordinating storage, conversion and flexible demand across multiple energy carriers;
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Framework to assess the effectiveness of net-aware multi-commodity energy systems in a regional context, considering regional energy demand and supply as well as the future effects of the energy transition on changes in demand and supply.
The developed models and strategies will be validated using
real grid data and real operational conditions, with the TU/e campus serving as a full-scale validation environment. The results of this research will contribute to scalable, congestion-aware energy management concepts that support large-scale integration of renewable energy without immediate grid reinforcement.