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Adelaide Power Systems Summer School 2020
Physics-Informed Neural Networks for Power Systems
Transforming Neural Networks to MILP
Databases for Data-Driven Methods on Power Systems
Convex Formulation of AC-OPF for Distribution Grids
Nordic Market Model and DC-OPF formulation with Losses

Adelaide Power Systems Summer School 2020 (APSSS 2020)

Download the assignment material here: Assignment Material

Physics-informed neural networks

  • Code on Github for Physics-Informed Neural Networks for Power Systems: Github Code.
  • Code on Github for Physics-Informed Neural Networks for System Identification: Github Code.
  • Assignment and Code on Physics-Informed Neural Networks: Assignment, Code
The available code is based on the following two papers:
[1] G. S. Misyris, A. Venzke, S. Chatzivasileiadis, Physics-Informed Neural Networks for Power Systems, accepted at IEEE PES General Meeting 2020, Montreal, Canada. [ paper ]
[2] J. Stiasny, G. S. Misyris, S. Chatzivasileiadis, Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics, submitted, 2020. [ paper ]

Transforming Neural Networks to MILP: Capturing previously intractable constraints

The available code is based on the following paper:
[1] A. Venzke, D. T. Viola, J. Mermet-Guyennet, G. S. Misyris, S. Chatzivasileiadis, Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs, submitted, 2020. [ paper ]

Databases for Data-Driven Methods on Power Systems

  • IEEE 14-bus system; N-1 and Small-Signal Stability, Q-limits not enforced, 49'615 points: Database
  • IEEE 14-bus system; N-1 and Small-Signal Stability, Q-limits enforced, 675'367 points: Database
  • NESTA 162-bus system; N-1 and Small-Signal Stability, >500'000 points: Database

The two 14-bus system databases are considered complete. More information about these can be found here: Instructions about IEEE 14-bus databases

The available databases have been based on the following papers:
[1] F. Thams, A. Venzke, R. Eriksson, S. Chatzivasileiadis, Efficient Database Generation for Data-Driven Security Assessment of Power Systems, IEEE Transactions on Power Systems, vol 35, no. 1, pp. 30-41, Jan. 2020. [ paper ]
[2] A. Venzke, D.K. Molzahn, S. Chatzivasileiadis, Efficient Creation of Datasets for Data-Driven Power System Applications, Accepted at the 21st Power System Computation Conference (PSCC), 2020. [ paper ]

Convex Formulation of AC-OPF for Distribution Grids

Nordic Market Model and DC-OPF formulation with Losses