33-, 119-, and 136-bus system data for reinforcement learning-based distribution network reconfiguration

Citation Author(s):
Nastaran
Gholizadeh
University of Alberta
Submitted by:
Nastaran Gholizadeh
Last updated:
Mon, 08/28/2023 - 17:13
DOI:
10.21227/m49t-q808
License:
0
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Abstract 

The 33-, 119-, and 136-bus datasets are commonly used in the field of power systems and electrical engineering to train reinforcement learning-based algorithms for distribution network reconfiguration. Distribution network reconfiguration involves altering the topology of the electrical distribution grid by opening or closing switches to optimize certain objectives, such as minimizing power losses, improving voltage profiles, or enhancing overall system efficiency. This process is essential for maintaining a reliable and cost-effective power distribution system.

This dataset includes the hourly active and reactive power consumption data for the 33-, 119-, and 136-bus test systems, the resistance and reactance of the lines and the network topology information. Additionally, it includes all feasible topologies for these test systems for network reconfiguration.

Instructions: 

This dataset includes the hourly active and reactive power consumption data for the 33-, 119-, and 136-bus test systems, the resistance and reactance of the lines and the network topology information. Additionally, it includes all feasible topologies for these test systems for network reconfiguration.