Security assessment of Smart Grids using High-Performance Computing

Background

Modern Smart Grids rely on advanced computational tools to provide valuable information to the network operators. One of the most important tools used to ensure the security of Smart Grids are transient stability simulations. They analyse the behaviour of Smart Grid models in time after a disturbance has occurred (loss of generation, lightning hitting a line, etc.).

To ensure the reliability of electricity grids, these are designed with N+1 redundancy, meaning that they can lose any one component and the system should survive and remain within acceptable operating limits. To ensure this feature, system operators routinely perform a so-called N-1 security assessment: they simulate the loss of any one single component in the Smart Grid and check that all of them lead to a secure operation.

As this procedure is extremely time consuming, cluster computing is frequently used by executing the different simulations in parallel and aggregating their results.

Objectives

In this project, you have to develop a security assessment platform that exploits parallel computing on the university cluster computer ARC to check N-1 conditions in a realistic Smart Grid. You will use the dynamic simulator RAMSES1 to perform the actual simulations and get the results through a Python-based interface. The platform should be implemented in Python or another Linux-based scripting language supported by ARC.

The ARC HPC systems [Source: ARC]

Deliverables

  • A complete literature review including a comparison between different methods currently used for N-1 security assessment in Smart Grids.
  • An N-1 security platform implemented on ARC. It should receive a list of contingencies to be analysed and the security constraints to be satisfied and provide a detailed result.
  • (Bonus) Interface to cloud services to submit and analyse the tasks (exporting to Dropbox, mobile notifications, etc.).
  • All the code developed should be documented and published on GitHub under an MIT License2. The final code (along with all other supplementary files) should be published on Zenodo and the DOI included in the final report3.

Student profile

  • Good programming skills (Python, Linux).

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