Student information
MSc thesis topic: Data Driven Utility Route Planner: Maps4Cables
Alliander is a Distribution System Operator (DSO), responsible for a reliable, sustainable and affordable energy network for roughly one third of the Netherlands. Maintaining 93.226 kilometres of electricity cables and 41.755 kilometres of gas pipelines providing access to energy for 5.8 million customer connections. The growing economy, housing development, and general sustainability improvement, is creating an exponentially rising demand for electricity and electricity infrastructure. The shortage of technical personnel and materials complicates the timely expansion of the energy network. This results in increasing waiting time for new customer connections on the energy network. This is referred to as transport scarcity, meaning that the energy network is unable to meet the rising power demand in a given area.
Determining the optimal placement for new energy infrastructure is a complicated and time consuming task. The problem of determining a new cable or pipeline route is referred to as “tracébepaling” at Alliander. It plays a key role in facilitating the energy transition as sub-optimal placement will negatively affect the affordability and reliability of the energy network.
The state of the art (SOTA) of “tracébepaling” has been implemented in a Python script, that is described in a forthcoming journal paper. However, the SOTA has several shortcomings, which include:
- It is not possible to generate multiple cable routes which are different from each other given the same start and end point.
- It is unable to account for maximum cable length.
- The resulting route is often jagged due to usage of a cost raster on a square grid.
- Alignment to existing infrastructure cannot be guaranteed.
- It does not account for potential short cuts such as crossing traffic infrastructure using drillings or passings through existing tunnels.
Objectives
- Explore the possibilities of (human-guided) reinforcement learning in utility route generation. This would be a continuation of research done for a previous MGI thesis.
- Explore the use of multigraphs and/or extended neighbourhoods.
- Demonstrate implementation of the solution on a case study.
Exploring the possibilities of (human-guided) reinforcement learning in utility route generation. - Demonstrate implementation on a case study.
Literature
- Bagli, S., Geneletti, D., Orsi, F. (2011). Routeing of power lines through least-cost path analysis and multicriteria evaluation to minimise environmental impacts, Environmental Impact Assessment Review, 31( 3), 234-239.
- Lin, J., Ma, Z., Gomez, R., Nakamura, K., He, B., & Li, G. (2020). A Review on Interactive Reinforcement Learning From Human Social Feedback. IEEE Access, 8, 120757–120765.
- Wu, J., Huang, Z., Huang, W., & Lv, C. (2021). Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 855–869.
- Zhang, Q., Cheng, C., Fang, Z., Jiang, D., He, B., & Li, G. (2022). Continuous Control for Autonomous Underwater Vehicle Path Following Using Deep Interactive Reinforcement Learning. 2022 International Conference on Machine Learning, Control, and Robotics (MLCR), 19–26.
Requirements
- Dutch proficiency is preferred as much documentation is in Dutch
- Experience with Python
- If the reinforcement option is chosen: Deep Learning (GRS34806 or similar)
- If opting for a graph-based solution: Interest in expanding existing basic knowledge on Graph Theory
Theme(s): Modelling & visualisation